Identification of Cross-Cancer Biomarkers: COMP mRNA and CARMN/GSEC lncRNAs Shared in Breast, Gastric, and Colorectal Cancers via Integrated Systems Biology and Experimental Validation

Identification of Cross-Cancer Biomarkers: COMP mRNA and CARMN/GSEC lncRNAs Shared in Breast, Gastric, and Colorectal Cancers via Integrated Systems Biology and Experimental Validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Cross-Cancer Biomarkers: COMP mRNA and CARMN/GSEC lncRNAs Shared in Breast, Gastric, and Colorectal Cancers via Integrated Systems Biology and Experimental Validation Mohammadjavad Askari, Ali Hodaeian, Saba Hesami, Bita Mohammadipour, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5943216/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Advances in high-throughput genomic technologies have illuminated the significant role of non-coding RNAs (ncRNAs), which constitute 98% of the genome. Among these, long non-coding RNAs (lncRNAs) play crucial roles in gene regulation and cancer progression. COMP, a cartilage oligomeric matrix protein, and lncRNAs CARMN and GSEC are implicated in breast, gastric, and colorectal cancers. These molecules influence tumor progression through extracellular matrix (ECM) remodeling and key signaling pathways such as Notch3/Jagged1, PI3K/AKT, TGF-β, and ECM organization signaling. Despite advancements in cancer therapies, diagnostic and prognostic challenges persist, necessitating the identification of robust biomarkers. Materials and Methods Gene expression data from GEO and TCGA datasets were analyzed to identify differentially expressed genes. Functional enrichment and pathway analyses highlighted key roles in ECM organization and associated signaling pathways. Protein-protein interaction (PPI) and competing endogenous RNA (ceRNA) networks were constructed to elucidate molecular interactions. Experimental validation included RNA extraction and qRT-PCR of 120 matched cancerous and normal tissues, followed by statistical evaluations, including ROC-AUC and survival analyses. Results COMP and GSEC were significantly up-regulated, while CARMN was down-regulated in breast and gastric cancer tissues and up-regulated in colorectal cancer. Functional enrichment revealed their involvement in ECM organization and tumor-promoting pathways. COMP exhibited excellent diagnostic potential with ROC-AUC values exceeding 0.9. Survival analysis associated CARMN expression with improved outcomes in gastric and colorectal cancers. Correlation analyses highlighted regulatory interactions among the biomarkers and their involvement in cancer-related signaling cascades. Conclusion COMP, CARMN, and GSEC are promising biomarkers for diagnosing and predicting outcomes in breast, gastric, and colorectal cancers. Their roles in ECM remodeling and signaling pathways underscore their potential as therapeutic targets and diagnostic tools, warranting further exploration of their molecular mechanisms. Cancer Biology Bioinformatics Cancer Genetics Systems Biology Bioinformatics Network Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction In-depth studies of human transcripts acquired using high-throughput technologies have enabled researchers to represent the human genome comprehensively. Only around 2% of the approximately 3.2 billion base pairs of human genomic DNA (1) comprise exons and protein-coding sections, whereas the remaining 98% are associated with non-coding RNAs (ncRNAs) (2). Many decades ago, this broader category of ncRNAs was commonly stigmatized as "garbage" or "noise" due to their jumbled transcription. They are classified into two distinct groups: short ncRNAs (sncRNAs) and long non-coding RNAs (lncRNAs). LncRNAs are a heterogeneous collective of RNAs comprising sequences exceeding 200 base pairs (3). A recent genome-wide association study (GWAS) has designated lncRNAs as molecules that regulate gene expression at the epigenetic, transcriptional, and specifically after transcription levels (4). Recent evidence also indicates that lncRNAs influence gene expression and can contribute to cancer development by functioning as either oncogenes or tumor suppressors (5). Hence, lncRNAs have emerged as recent focal points in diagnosing and managing several disorders, such as cancer, neurological diseases, autoimmune conditions, and inflammation (6–8). Specific lncRNAs in malignancies modulate proliferation and migration by sponging micro-RNAs to regulate mRNA (9). Numerous investigations demonstrate that lncRNAs have the potential to directly interfere with mRNA splicing by interacting with factors involved in splicing settings, inhibiting translation, and degradation of mRNA (10,11). Furthermore, it participates in various cellular biological processes such as cell proliferation, cellular growth, differentiation, apoptosis (12,13), drug resistance (14), and metastasis (9,15). Cancer biomarkers like circulating lncRNAs have been employed in the early-stage detection of various types of cancer, including breast cancer and colorectal cancer (16–18). Recently, combining genomics, transcriptomics, and clinicopathological data utilizing multi-platform and multi-omics approaches has enhanced our understanding of the molecular makeup of different tumors and facilitated the visualization of molecular roles (19). In this regard, early studies demonstrated that some lncRNAs have been revealed as causative agents of pan-cancer (20–22). Here, we exploited and integrated multi-GSEs by analyzing National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) datasets obtained from microarray high-throughput technique projects and harnessed The Cancer Genome Atlas (TCGA) cohort RNA-Seq database to elucidate the In-silico landscape of two key lncRNA genes across three distinct cancer types. The integration of these methods allowed for the comprehensive screening of gene expression profiles and the identification of lncRNAs with significant regulatory roles in cancer progression (23). Hence, the profiles of two LncRNA signature Cardiac Mesoderm Enhancer-associated Non-coding RNA ( CARMN ) and G-quadruplex forming sequence-containing lncRNA ( GSEC ), as well as discovered a mRNA, cartilage oligomeric matrix protein ( COMP ), concerning prognostic and diagnostic biomarker connotation in a multi-cancer setting including breast cancer (BC), gastric cancer (GC) and colorectal cancer (CRC). The development of these cancers is associated with mutations that activate oncogenes or deactivate tumor suppressor genes (24–26). The COMP gene is the fifth member of the thrombospondin family, sometimes referred to as thrombospondin-5 ( TSP-5 ). It is located at NC_000019.10 and comprises five subunits. COMP is highly expressed in cartilage (27,28). The COMP gene exhibits elevated expression in various diseases (29), including breast (30), colorectal (31,32), gastric (33,34), and prostate (35) malignancies, as well as inflammatory conditions like osteoarthritis (36,37). Multiple molecular processes have been suggested for the COMP gene, that results in reduced survival in patients. Increased expression of COMP , attributed to the proliferation of cancer stem cells via the Notch3/Jagged1 signaling pathway (38), the AKT pathway (39,40), and the disruption of signaling pathways associated with calcium channels and the extracellular matrix (ECM) (41,42), has been documented in multiple malignancies. Moreover, several growth factors, like as transforming growth factor beta (TGF-β) and bone morphogenetic protein (BMP) families, play a vital role in chondrogenic induction in cooperation with the COMP protein (43–46). Therefore, the COMP gene plays a crucial role in regulating tissue health via various mechanisms. Recent research highlighted the involvement of the COMP gene, as well as the lncRNAs CARMN and GSEC , in the pathogenesis of breast, colorectal, and gastric cancers. While advances in therapies have improved outcomes, recurrence rates remain a challenge. Investigating the expression of genes like COMP , along with related lncRNAs such as CARMN and GSEC , and identifying potential biomarkers for diagnosis and prognosis could provide valuable insights into preventing and treating these cancers. The objective of this research is to comprehensively assess by Gene Ontology (GO), Canonical Pathway (CP) enrichment analysis, survival analysis, and interaction network were also conducted. This article adheres the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) reporting checklist (https://jgo.amegroups.org/article/view/10.21037/jgo-22-1201/rc) (47). Finally, validate the findings of the bioinformatic survey by experimental investigation, alongside the molecular mechanisms of their contribution to the prevention or progression of three cancers that have been addressed. 2. Materials and Methods 2 − 1. Bioinformatics Analysis and Software Availability 2-1-1. High Throughput Retrieving and Preprocessing of Data Collected GEO and TCGA Datasets of BC, GC, and CRC A series of inclusion and exclusion criteria were applied to refine the search results obtained from the NCBI-GEO database. Engage in a search with the specified terms BC (“Breast Cancer” [Title/Abstract] OR “Breast Cancer” [MeSH Terms]), GC (“Gastric Cancer” [Title/Abstract] OR “Gastric Cancer” [MeSH Terms]), CRC (“Colorectal Cancer” [Title/Abstract] OR “Colorectal Cancer” [MeSH Terms]). Microarray data pertaining to BC, GC, and CRC were collected based on specific criteria, including "Homo sapiens", "expression analyzed by array", "incorporating both noncancerous and cancerous samples", and "sample size of 20 or more". Datasets derived from samples (GSMs) of our target cancers that had undergone metastasis and peripheral blood samples were also omitted. Following a thorough filtering and comparative analysis, some gene datasets were selected and information on these GSEs datasets is repertoire in Table 1 . All raw and original data were downloaded from NCBI-GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) and TCGA ( https://www.cancer.gov/ccg/research/genome-sequencing/tcga ) databases and The analysis of gene expression was conducted using “GEOquery” ( 48 ) ( https://www.bioconductor.org/packages/release/BiocViews.html#___Software ) and “TCGAbiolinks” R programming package version 4.4.1 (2024-06-14 ucrt), respectively. Quality control and normalization of GEO and TCGA datasets Normalization of microarray data was achieved using the robust multichip average (RMA) algorithm, the GEO dataset of each cancer was combined into one according to their probe IDs, while Batch effect correction was executed through the ComBat function from the Bioconductor package “SVA” ( https://bioconductor.org/packages/release/bioc/html/sva.html ) to mitigate batch effects across various microarray datasets. The data from the same platform was processed by discarding probes that included multiple genes and preserving only the highest probe value for each gene and were consolidated into a single dataframe. Principal component analysis (PCA) was utilized to visualize the batch effects that were present before and after de-batching the datasets. In addition, all data have been normalized and processed for the TCGA cohorts of BC, GC, and CRC utilizing the TCGAbiolinks pipeline ( https://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html ) ( 49 ). The RNA-seq data (Star-count) derived from the Illumina HiSeq RNASeq platform included samples from breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), and stomach adenocarcinoma (STAD), and were sourced from the publicly accessible Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/ ). Each gene is allocated the same dispersion estimate, conducts pairwise tests for differential expression between two groups cancer and normal, applies the false discovery rate (FDR) correction (1 (Table 1 ). PCA was utilized to visualize the quality of samples and to remove those which were not satisfied by the criteria. Table 1 Data pertaining to microarray datasets sourced from the GEO and TCGA. Type of cancer GEO Dataset Platform Cancerous Samples Non-cancerous Samples Reference Breast Cancer GSE42568 Affymetrix GPL570 104 17 ( 26 ) GSE36295 Affymetrix GPL6244 45 5 ( 27 , 28 ) GSE10810 Affymetrix GPL570 31 27 ( 29 ) GSE134359 Affymetrix GPL17586 74 12 ( 30 ) Colorectal Cancer GSE41328 Affymetrix GPL570 10 10 ( 31 ) GSE81558 Affymetrix GPL15207 23 9 ( 32 ) Gastric Cancer GSE65801 Agilent GPL14550 32 32 ( 33 ) GSE118916 Affymetrix GPL15207 15 15 ( 34 ) GSE54129 Affymetrix GPL570 111 21 NA GSE79973 Affymetrix GPL570 10 10 ( 35 , 36 ) TCGA-BRCA - Illumina HiSeq 1111 120 - TCGA-COAD - Illumina HiSeq 481 43 - TCGA-STAD - Illumina HiSeq 412 36 - GEO , Gene Expression Omnibus / GSE , GEO Series / GPL , GEO Platform / NA , Not Available / TCGA , The Cancer Genome Atlas / BRCA , Breast Invasive Carcinoma / COAD , Colon Adenocarcinoma / STAD , Stomach Adenocarcinoma Thresholds for identification of differentially expressed genes (DEGs) The R package Limma ( https://www.bioconductor.org/packages/release/bioc/html/limma.html ) and DESeq2 ( https://bioconductor.org/packages/release/bioc/html/DESeq2.html ) were employed to define the threshold for mRNA differential expression screening of GEO and TCGA, respectively, by the criteria “Adjusted p -value 1”. The subsequent step involved identifying and visualizing the overlapping DEGs between BC, GC, and CRC through the constructing was performed using VennDiagram in R software by ggplot2 package ( https://cloud.r-project.org/web/packages/ggplot2/index.html ). The methodology of our study is elegantly illustrated in Fig. 1 , showcasing a comprehensive flowchart. 2-1-2. Development of the protein-protein interaction (PPI) network and key hub gene screening Utilizing the STRING v11.0 online tool ( http://string-db.org/ ) ( 50 ), a PPI network for the DEGs was constructed. Interactions with combined scores > 0.4 were regarded as statistically significant. The visualization of the PPI networks was executed through Cytoscape software (version 3.7.1) ( 51 ). 2-1-3. Functional enrichment analysis of DEGs and hub gene Functional enrichment analysis for DEGs was conducted through Enrichr ( https://maayanlab.cloud/Enrichr/ ) ( 52 , 53 ), DAVID ( https://david.ncifcrf.gov/tools.jsp ) ( 50 ), and the Reactome pathway databases ( 54 , 55 ) to identify the biological processes (BP), molecular functions (MF), and cellular components (CC) associated with BC, GC, and CRC. GO terms and KEGG pathways exhibiting a p -value < 0.05. The outcomes of the GO analysis were represented visually through a bubble plot created using “clusterProfiler” package RStudio software ( 56 ) ( http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html ). 2-1-4. Investigating the association between hub gene expression profiles and the survival prognosis of patients with BC, GC, and CRC This research utilized several resources, including the Encyclopedia of RNA Interactomes (ENCORI/StarBase) and the Gene Expression Profiling Interactive Analysis (GEPIA2021) website ( 58 ) ( http://gepia2021.cancer-pku.cn/ ) to investigate the relationship between the expression levels of COMP gene overall survival (OS). 2-1-5. Construction of the Differentially Expressed miRNAs (DEmiRNAs) subnetwork associated with Differentially Expressed mRNAs (DEmRNAs) and Hub Gene The association of hub genes and miRNA expression was first explored using five algorithms, the miRNet 2.0 online ( 59 ) ( https://www.mirnet.ca/ ), miRWalk ( 60 ) ( http://mirwalk.umm.uni-heidelberg.de/ ), Diana-LncBasev3 ( 61 ) ( https://diana.e-ce.uth.gr/lncbasev3/home ), miRanda-Target Prediction ( https://tools4mirs.org/software/target_prediction/miranda/ ) and miRBase ( 62 ) ( https://www.mirbase.org/ ) databases, designed to construct miRNA-mRNA target networks. Simultaneously, the KM plotter was employed to assess the predictive significance of the projected miRNAs on OS. 2-1-6. Forecasting Upstream Differentially Expressed LncRNAs (DElncRNAs) associated with DEmRNAs and Hub Gene To predict probable lncRNA-mRNA interactions, miRNet ( 63 ) and LncRRIsearch ( 64 ) ( http://rtools.cbrc.jp/LncRRIsearch/glist.cgi ) databases were utilized to forecast the upstream lncRNAs of the miRNAs. For a pair of lncRNA and mRNA anticipated to interact, they must also exhibit robust co-expression. Subsequent to identifying the lncRNAs associated with the target gene, we verified each one through the GeneCards database for non-coding RNA. We established a minimum requisite Pearson correlation coefficient (PCC) of 0.5 between them for this objective. The KM plotter was employed to assess the predictive significance of the projected lncRNAs for overall survival. The differential expressions in BC, GC, and CRC tumor and normal tissues were examined using the Encyclopedia of RNA Interactomes (ENCORI or StarBase) online dataset v3.0 ( https://rnasysu.com/encori/ ) ( 65 ). 2-1-7. Construction of the ceRNA (lncRNA-miRNA-mRNA) network Competing endogenous RNA (ceRNA) is an in-depth examination of mRNA, lncRNA, microRNA, and circRNA. This is a sophisticated post-transcriptional regulatory network predicated on the notion that lncRNAs act as miRNA sponges, utilizing common microRNA response regions to competitively control mRNA expression. To investigate regulatory interactions, a ceRNA network was constructed for the identified prognostic mRNAs, miRNAs, and lncRNAs ( 66 ). The mRNA-lncRNA, miRNA-mRNA, and mRNA-lncRNA pairs common to breast, gastric, and colorectal cancers were assessed using ENCORI/StarBase database. By Cytoscape software, we constructed and presented the mutual expression network. 2–2. Experimental Investigation 2-2-1 Sample Collection, Preparation, and RNA Extraction This study included 120 tissue specimens, comprising 20 malignant and 20 matched normal tissues, collected from patients undergoing surgery for breast, colorectal, and gastric cancers between 2022 and 2024. Samples were obtained from Al-Zahra and Sayyed al-Shohada (Omid) Hospitals in Isfahan, Iran, and the Iran National Tumor Bank, Cancer Institute of Tehran University of Medical Sciences. The demographic and clinicopathological information are presented in Table 2 . Sample size determination was conducted using G*Power 3.1 software based on global cancer prevalence data. Ethical approval was granted by the medical ethics committee of Al-Zahra Hospital and the Iran National Tumor Bank, and all patients provided informed consent in accordance with the Declaration of Helsinki. Tissue samples were stabilized in RNAlater solution (Invitrogen, Thermo Fisher, Waltham, USA), snap-frozen in liquid nitrogen, and stored at − 80°C. RNA was extracted using the YTzol Pure RNA Reagent (Yekta Tajhiz Azma, Iran) following the manufacturer’s protocol. RNA concentration and purity were measured using a NanoDrop ND-1000 spectrophotometer (Agilent, USA), with A260/A280 ratios consistently between 1.9 and 2.1. 2-2-2 cDNA Synthesis and Primer Design RNA samples of high purity and integrity were treated with DNase I to remove genomic DNA and subsequently used for cDNA synthesis. Reverse transcription was performed following the manufacturer's protocol (RT-ROJE Technologies, Tehran, Iran) in a 20 µL reaction volume. The resulting cDNA was diluted 10-fold with RNase-free water for subsequent qRT-PCR analysis. Primers targeting lncRNA genes, CARMN (GenBank Accession No. NR_105059.1), GSEC (GenBank Accession No. NR_033839.1), mRNA COMP (GenBank Accession No. NM_000095), and mRNA Glyceraldehyde-3-phosphatedehydrogenase ( GAPDH ) (NM_002046) were designed using GeneRunner software and Primer-BLAST (NCBI). Primer specificity was validated using melting curve analysis, and the synthesized primers were obtained from SinaClon (Tehran, Iran). 2-2-3 Quantitative real-Time PCR Quantitative real-time PCR (qRT-PCR) was performed using the SYBR Green low ROX master mix (PCR Biosystems Inc., USA) and the MIC Real-Time PCR Cycler (BMS, Bio Molecular Systems, Australia). The PCR reaction consisted of 5 µL SYBR Green master mix, 0.5 µL of 10 µM forward and reverse primers, 1 µL of cDNA, and 3 µL of sterile purified water in a 10 µL reaction volume. Melt curve analysis confirmed the specificity of the amplifications, and results were normalized to GAPDH expression. Relative expression levels were calculated using the ΔΔCt method. Table 2 Demographic and clinicopathological features of BC, GC, and CRC. Type of Cancer Characteristic Status Number of patients (%) Breast Cancer Stage I 1 (%5) II 4 (%20) III IV 7 (%35) 7 (%35) Unknown 1 (%5) Age 50 Unknown 4 (%20) 10 (%50) Tumor size 5cm 3 (%15) Unknown 1 (%5) Lymph node Unknown 1 (%5) Yes 17 (%85) No 2 (%10) Estrogen receptors (ER) Positive 8 (%40) Negative 3 (%15) Unknown 9 (%45) Progesterone receptors (PR) Positive 5 (%25) Negative 6 (%30) Unknown 9 (%45) HER2/Neu receptor Positive 3 (%15) Negative 6 (%30) Unknown 11 (%55) Gastric Cancer Stage I 1 (%5) II 5 (%25) III 3 (%15) IV Unknown 10 (%50) 1 (%5) Sex Female 8 (%40) Male 12 (%60) Histology Adenocarcinoma 17 (%85) Carcinoma 1 (%5) Signet ring carcinoma 2 (%10) Perineural Invasion Yes 16 (%80) No 4 (%20) Smoking Non-smoker 18 (%85) Smoker 2 (%5) Family History Yes 5 (%25) No 15 (%75) Colorectal Cancer Stage I 2 (%10) II 3 (%15) III 7 (%35) IV 8 (%40) Age 50 5 (%25) Sex Female 11 (%55) Male 9 (%45) Tumor size 5cm 14 (%70) Perineural Invasion Yes 12 (%60) No 8 (%40) Lymph node Unknown 1 (%5) Yes 8 (%40) No 11 (%55) Histology Adenocarcinoma 17 (%85) Mucinous (Colloid) Adenocarcinoma 3 (%15) Smoking DX-smoker at diagnosis but discontinued 1 (%5) Non-smoker 16 (%80) Smoker 3 (%15) Standard Operating Procedures (SOP) Ascending Colon 1 (%5) Rectosigmoid 4 (%20) Sigmoid Colon 4 (%20) Rectum 4 (%20) Cecum 2 (%10) Colon, NOS 5 (%25) Table 3 qRT-PCR primers for prognosis-related genes Gene Name Forward Primer (5′→3′) Reverse Primer (5′→3′) RNA Type Product Size (bp) GAPDH ACAGGGTGGTGGACCTCAT AGGGGTCTACATGGCAACTG c-RNA (mRNA) 66 COMP CAACGTGGTCTTGGACACAAC GGTGTCATTGCAGCGGTAAC c-RNA (mRNA) 106 CARMN AGCAACGGCTGTAACAAGTG GAGGCTGCTTCTCCAGAGTTC nc-RNA (LncRNA) 100 GSEC GCAGGCTTGGGATGGTGTTC GAAGGACAGCAGGAAGGTATCC nc-RNA (LncRNA) 116 GAPDH , Glyceraldehyde-3-phosphatedehydrogenase / COMP , Cartilage Oligomeric Matrix Protein / CARMN , cardiac mesoderm enhancer-associated non-coding RNA / GSEC , G-quadruplex forming sequence containing lncRNA / c-RNA , coding RNA/ nc-RNA , non-coding RNA 2–3. Statistical analysis Upon evaluating the efficiency of PCR for each gene and normalizing the cycle threshold (Ct) values of the target genes with respect to the GAPDH internal control gene, we utilized the Livak method (2 −∆∆ct ) to compare the fold change (FC) between the tumor and normal groups ( 69 ). Comparing the expression levels of the gene of interest (Ct gene of interest) to those of an internal control gene (Ct internal control gene), resulting in a value known as delta Ct. The delta Ct values, along with their standard deviations (SD), calculated for patients, were juxtaposed with the delta Ct ± SD of the healthy control group to assess statistical significance. All statistical evaluations were carried out using GraphPad Prism version 6 (San Diego, CA, USA), with the significance level defined as P < 0.0001 (****), < 0.001 (***), < 0.01 (**), and < 0.05 (*). The data, which were confirmed to follow a normal distribution through the Shapiro-Wilk normality test (noting that each group contained fewer than 30 participants), were presented as the means and standard deviations (SD). Data analysis was performed utilizing Student’s paired t-tests to determine significant differences in gene expression levels between tumor and normal groups. To assess the efficacy of the biomarkers in accurately identifying the presence of each type of cancer, specificity, sensitivity, and the area under the receiver operating characteristic curve (ROC-AUC) were calculated. A ROC-AUC of 1 signifies perfect classification, while a ROC-AUC of 0.5 indicates no classification capability. ROC-AUC values of ≥ 0.90 are classified as excellent, those between 0.80 and 0.90 as good, values from 0.70 to 0.80 as fair, and values below 0.70 as poor ( 70 ). Furthermore, Spearman’s correlation coefficients (SCC) were computed to explore the relationships between outcomes, with a scale defining 0–0.3 as negligible correlation, 0.3–0.5 as low correlation, 0.5–0.7 as moderate correlation, 0.7–0.9 as high correlation, and 0.9–1 as very high correlation ( 71 ). 3. Results 3 − 1 | Identification of Aberrantly Expressed Genes and DEGs based on GEO and TCGA data in Breast, Gastric, and Colorectal Cancers In order to identify novel genes associated with the gene expression profiles and pathogenesis of breast, colorectal, and gastric cancers by using DEGs, the several processes were performed sequentially (Fig. 1 ). First of all, 10 GSEs that satisfied the criteria were chosen, then 10 different datasets of heterogeneous microarray datasets from GEO of each cancer were combined (i.e., Before and after batch correction by standardization of individual array samples in the box and PCA plot are depicted in Figs. 2 A and 2 B. This analysis found 387 breast cancer, 260 colorectal cancer, and 308 gastric cancer genes with significantly higher expression in GEO datasets, including 18 shared genes among of three cancers. Additionally, 596, 390, and 461 genes associated with breast, colorectal, and gastric cancer, respectively, demonstrated a significant decrease in expression noted in tumors. Likewise, we identified 2,348 genes in the STAD dataset, 3,501 genes in the COAD dataset, and 3,010 genes in the BRCA dataset that exhibited a significant up-regulation in expression, with 577 genes being common throughout them. Genes 3166, 3597, and 3325 associated with breast, colorectal, and gastric cancer, respectively, exhibited a notable down-regulation in expression across different malignancies (Fig. 2 C). Figure 2 D illustrates the sum of common genes to three cancers as determined by both the GEO and TCGA databases. A total of 25 genes were identified, comprising 15 genes that were up-regulated and 10 genes that were significantly down-regulated between GEO and TCGA (|LogFC|>1, adj. p < 0.05) (Fig. 2 E). The analysis proceeds with the 25 genes listed in Table 4 . To further elucidate the transcriptional landscapes of breast, colorectal, and gastric cancers, GEO and TCGA datasets were analyzed to identify DEGs between tumor and healthy samples (Fig. 3 ). Heatmaps and volcano maps that depict the up-regulated and down-regulated genes associated with BC, CRC, and GC. Red denotes genes with positive correlations, while blue signifies genes with negative correlations (heatmaps) and labels 25 DEGs for each cancer separately, GEO and TCGA (volcano maps). Table 4 DEGs detected from two datasets of GEO and TCGA with 10 down-regulated genes and 15 up-regulated genes. Genes Breast Colorectal Gastric Ensembl ID Up-Regulated (15 genes) BGN 1.806043 1.278349 2.02124 ENSG00000182492 COL10A1 3.634638 2.931726 2.226216 ENSG00000123500 COL11A1 3.499369 3.016048 1.311852 ENSG00000060718 COL1A2 1.079061 1.594334 1.911041 ENSG00000164692 COMP 2.80064 2.186441 2.010134 ENSG00000105664 CTHRC1 1.458062 2.536445 2.397306 ENSG00000164932 CXCL10 2.855767 1.748034 1.025074 ENSG00000169245 FAP 1.052978 2.511075 2.817097 ENSG00000078098 INHBA 2.697379 2.483642 2.728841 ENSG00000122641 MFAP2 1.380876 1.239046 1.83972 ENSG00000117122 MMP11 1.705304 1.475731 1.025363 ENSG00000099953 MMP9 2.300198 1.562625 1.41611 ENSG00000100985 SPP1 2.204936 2.342192 2.404332 ENSG00000118785 SULF1 1.898571 1.898206 2.679687 ENSG00000137573 VCAN 1.159248 1.433551 1.342078 ENSG00000038427 Down-Regulated (10 genes) ADH1C -3.029542 -3.532298 -2.606485 ENSG00000248144 CA4 -1.871403 -3.597412 -1.262438 ENSG00000167434 KLF4 -1.726187 -2.216647 -1.064688 ENSG00000136826 LIFR -2.048914 -1.018489 -1.185696 ENSG00000113594 MAMDC2 -2.869825 -1.559442 -1.207477 ENSG00000165072 MAOA -3.10518 -1.563712 -1.173602 ENSG00000189221 MT1M -2.007288 -2.889413 -1.317426 ENSG00000205364 NR3C2 -1.593394 -1.951454 -1.147929 ENSG00000151623 TMEM37 -1.17538 -1.317515 -1.281473 ENSG00000171227 CARMN * -1.301246 -1.269949 -1.13252 ENSG00000249669 DEGs , Differentially Gene Expression / GEO , Gene Expression Omnibus / TCGA , The Cancer Genome Atlas / ADH1C , Alcohol Dehydrogenase 1C (class I) / BGN , Biglycan / CA4 , Carbonic Anhydrase 4 / COL10A1 , Collagen Type X Alpha 1 Chain / COL11A1 , Collagen Type XI Alpha 1 Chain / COL1A2 , Collagen Type I Alpha 2 Chain / COMP , Cartilage Oligomeric Matrix Protein / CTHRC1 , Collagen Triple Helix Repeat Containing 1 / CXCL10 , C-X-C Motif Chemokine Ligand 10 / FAP , Fibroblast Activation Protein Alpha / INHBA , Inhibin Subunit Beta A / KLF4 , KLF Transcription Factor 4 / LIFR , LIF Receptor Subunit Alpha / MAMDC2 , MAM Domain Containing 2 / MAOA , Monoamine Oxidase A / MFAP2 , Microfibril Associated Protein 2 / MMP11 , Matrix Metallopeptidase 11 / MMP9 , Matrix Metallopeptidase 9 / MT1M , Metallothionein 1M / NR3C2 , Nuclear Receptor Subfamily 3 Group C Member 2 / SPP1 , Secreted Phosphoprotein 1 / SULF1 , Sulfatase 1 / TMEM37 , Transmembrane Protein 37 / VCAN , Versican / CARMN , Cardiac Mesoderm Enhancer-Associated Non-Coding RNA * CARMN , is a type of non-coding RNA that was removed in subsequent analyses, including protein-protein interaction. 3 − 2 | Hub Gene COMP Up-regulation and Target Gene LncRNA Prediction in Breast, Gastric, and Colorectal Cancer Development COMP or THBS5 (Thrombospondin-5) expression was initially evaluated in an assortment of breast, gastric, and colorectal cancerous and non-cancerous tissues based on GEO (Fig. 4A1-A3, Table 5 ), TCGA (Fig. 4A10-A12, Table 5 ), and ENCORI/StarBase databases (Fig. 4A19-A21). The boxplot results (T-test analysis) in all three databases showed that the expression levels of COMP were significantly higher in the BC (GEO: 6.967 fold, adj.p = 0.00000; TCGA: 24.228 fold, adj.p = 0.00000; ENCORI: 23.24 fold, adj.p = 1.2e-87), CRC (GEO: 4.552 fold, adj.p = 0.00000; TCGA: 30.592 fold, adj.p = 0.00000; ENCORI: 65.27 fold, adj.p = 3.0e-25), and GC (GEO: 4.028 fold, adj.p = 0.00000; TCGA: 5.147 fold, adj.p = 0.00000; ENCORI: 22.36 fold, adj.p = 4.5e-7) cancerous tissues than the non-cancerous tissues. The prediction of upstream lncRNAs of the COMP mRNA was conducted by LncRRIsearch database. Strong evidence for the potential binding of 2 lncRNAs to the COMP was found. An approximately positive correlation between both of lncRNAs and COMP , binding energy, and survival analysis. We identified 2 lncRNAs associated with a poor prognosis for BC, CRC, and GC patients that CARMN and GSEC ( ST3GAL4-AS1 ) had a low and high expression in each three cancerous tissues, respectively. T-test analysis (Table 5 ) and boxplots revealed significant down-regulation of CARMN in tumor samples compared to normal controls of GEO (Fig. 4A4-A6), TCGA (Fig. 4A13-A15), and ENCORI/StarBase (Fig. 4A22-A24) of BC (GEO: 0.4058 fold, adj.p = 0.00000; TCGA: 0.1019 fold, adj.p = 0.00000; ENCORI: 0.14 fold, adj.p = 6.5e-123), CRC (GEO: 0.4147 fold, adj.p = 0.00085; TCGA: 0.3592 fold, adj.p = 0.00000; ENCORI: 0.95 fold, adj.p = 1.6e-10), and GC (GEO: 0.4561 fold, adj.p = 0.00000; TCGA: 0.4341 fold, adj.p = 0.00030; ENCORI: 0.31 fold, adj.p = 0.00065). Conversely, GSEC exhibited up-regulation expression patterns in BC (GEO: 2.4918 fold, adj.p = 0.00000; TCGA: 2.6186 fold, adj.p = 0.00000; ENCORI: 2.59 fold, adj.p = 8.4e-44), CRC (GEO: 3.7389 fold, adj.p = 0.00029; TCGA: 1.6559 fold, adj.p = 0.00012; ENCORI: 1.88 fold, adj.p = 0.01), and GC (GEO: 2.8835 fold, adj.p = 0.00000; TCGA: 1.3145 fold, adj.p = 0.01570; ENCORI: 1.62 fold, adj.p = 0.0029). Figure 4 , panels A7-A9 of GEO, A16-A18 of TCGA, and A25-A27 of ENCORI/StarBase depict boxplots showing differential expression levels of GSEC in tumor and normal samples for each cancer. Trimer database ( http://timer.cistrome.org/ ) to investigate the differences in expression of COMP mRNA of interest across 33 TCGA cancers between the tumor and nearby normal tissues. Gene expression level distributions are represented by box plots (Fig. 4 C). COMP expression demonstrates significant up-regulation in breast, colorectal, and gastric cancers (p < 0.001). Correlation analysis (Fog. 4B) exhibited heterogeneous association between CARMN vs. COMP , GSEC vs. COMP , and GSEC vs. CARMN in breast, colorectal, and gastric cancers. Regression analysis of GEO, TCGA datasets, and ENCORI/StarBase demonstrated in Table 6 . The correlation CARMN vs. COMP is predominantly positive across three cancer types in GEO, TCGA, and ENCORI/StarBase, with the exception of breast cancer in the GEO dataset, that exhibited a negative correlation (Fig. 4 B2). Other lncRNA correlation showed that GSEC vs. COMP also had very heterogeneous results in the three cancers in GEO, TCGA, and ENCORI (Table 6 and Fig. 4 B). A significant result of common negative correlation was seen in TCGA (Fig. 4 B13) and ENCORI (Fig. 4 B22) in breast cancer, while positive correlation was obtained in GEO (Fig. 4 B4). ENCORI/StarBase (Fig. 4 B24) showed a significant positive correlation in colorectal cancer, while the GEO (Fig. 4 B6) and TCGA (Fig. 4 B15) datasets exhibited negative and positive correlations, respectively, neither of which was significant. But the correlation results of GSEC vs. COMP in gastric cancer were highly homogeneous and demonstrated a significant positive correlation across all datasets (Fig. 4 B5, B14, and B23). An additional analysis assessing the correlation CARMN vs. GSEC was conducted, revealing notable variation in results of breast (Fig. 4 B7, B16, and B25), colorectal (Fig. 4 B9, B18, and B27), and gastric (Fig. 4 B8, B17, and B26) malignancies within the three datasets. Table 5 Gene Expression Analysis of GEO and TCGA datasets (T test, RStudio Analysis). Type of cancer Gene Groups mean SD N p -value FC Summary GEO datasets Breast cancer COMP normal 4.8099 0.9342 61 0.00000 6.9673 **** tumor 7.6105 1.7398 254 CARMN normal 8.3272 0.8025 61 0.00000 0.4058 **** tumor 7.0259 1.2658 254 GSEC normal 9.0134 1.1194 61 0.00000 2.4918 **** tumor 10.3306 1.2163 254 Colorectal cancer COMP normal 4.7919 0.3485 19 0.00000 4.5517 **** tumor 6.9783 1.7960 33 CARMN normal 6.9279 1.6941 19 0.00085 0.4147 *** tumor 5.6580 0.8916 33 GSEC normal 8.0959 1.3471 19 0.00029 3.7389 *** tumor 9.9985 1.8609 33 Gastric cancer COMP normal 4.8817 1.3305 78 0.00000 4.0281 **** tumor 6.8918 1.7736 168 CARMN normal 7.7861 1.3124 78 0.00000 0.4561 **** tumor 6.6536 1.3871 168 GSEC normal 6.3873 0.9971 78 0.00000 2.8835 **** tumor 7.9150 0.8894 168 TCGA datasets BRCA COMP normal 1.5221 2.2120 120 0.00000 24.2279 **** tumor 6.1206 2.0878 1111 CARMN normal 5.8268 1.5616 120 0.00000 0.1019 **** tumor 2.5321 1.4401 1111 GSEC normal 0.3241 0.9744 120 0.00000 2.6186 **** tumor 1.7130 0.9061 1111 COAD COMP normal -2.3768 1.9986 43 0.00000 30.5924 **** tumor 2.5583 2.6724 481 CARMN normal 4.1375 1.6889 43 0.00000 0.3592 **** tumor 2.6601 1.4993 481 GSEC normal -0.5477 0.6720 43 0.00012 1.6559 *** tumor 0.1799 1.2161 481 STAD COMP normal -0.0484 1.6273 36 0.00000 5.1469 **** tumor 2.3154 2.7734 412 CARMN normal 4.6642 2.3564 36 0.00030 0.4341 *** tumor 3.4601 1.8547 412 GSEC normal 0.0002 0.8102 36 0.01570 1.3145 * tumor 0.3948 0.9460 412 FC , Fold Change / BRCA , Breast Invasive Carcinoma / COAD , Colon Adenocarcinoma / STAD , Stomach Adenocarcinoma / SD , Standard Deviation Table 6 Regression Analysis of GEO and TCGA datasets. GEO datasets (RStudio analysis) Type of Cancer Regression p -value r Summary Equation Breast cancer CARMN vs. COMP 0.0002 0.23 *** Y = 0.3166*X + 5.3862 GSEC vs. CARMN < 0.0001 0.339 **** Y = 0.3523*X + 3.3865 GSEC vs. COMP < 0.0001 0.608 **** Y = 0.8693*X-1.3702 Colorectal cancer CARMN vs. COMP 0.894 0.024 NS Y = 0.0486*X + 6.7034 GSEC vs. CARMN 0.014 -0.424 * Y=-0.2029*X + 7.687 GSEC vs. COMP 0.0739 -0.315 NS Y=-0.3043*X + 10.021 Gastric cancer CARMN vs. COMP 0.0341 -0.164 * Y=-0.2091*X + 8.2833 GSEC vs. CARMN < 0.0001 -0.33 **** Y=-0.5154*X + 10.7326 GSEC vs. COMP < 0.0001 0.374 **** Y = 0.745*X + 0.995 TCGA datasets (RStudio analysis) BRCA CARMN vs. COMP < 0.0001 0.181 **** Y = 0.2623*X + 5.4565 GSEC vs. CARMN < 0.0001 -0.225 **** Y=-0.3572*X + 3.144 GSEC vs. COMP < 0.0001 -0.156 **** Y=-0.3588*X + 6.7352 COAD CARMN vs. COMP < 0.0001 0.342 **** Y = 0.6094*X + 0.9371 GSEC vs. CARMN 0.6229 0.022 NS Y = 0.0277*X + 2.6551 GSEC vs. COMP 0.0673 0.083 NS Y = 0.1835*X + 2.5253 STAD CARMN vs. COMP < 0.0001 0.259 **** Y = 0.3869*X + 0.9767 GSEC vs. CARMN 0.0156 0.119 * Y = 0.2333*X + 3.368 GSEC vs. COMP < 0.0001 0.246 **** Y = 0.7205*X + 2.0309 ENCORI/StarBase Database Breast cancer CARMN vs. COMP 1.40e-9 0.181 **** Y = 0.2535*X + 5.3078 GSEC vs. CARMN 2.17e-1 -0.037 NS Y=-0.0654*X + 3.3629 GSEC vs. COMP 1.63e-6 -0.144 **** Y=-0.3544*X + 4.3889 Colorectal cancer CARMN vs. COMP 1.37e-12 0.319 **** Y = 0.4963*X + 2.7069 GSEC vs. CARMN 2.88e-10 0.285 **** Y = 0.3996*X + 2.9756 GSEC vs. COMP 9.60e-5 0.179 **** Y = 0.3898*X + 1.5536 Gastric cancer CARMN vs. COMP 2.14e-12 0.352 **** Y = 0.5161*X + 1.4384 GSEC vs. CARMN 1.66e-9 0.305 **** Y = 0.5570*X + 0.5891 GSEC vs. COMP 2.41e-12 0.351 **** Y = 0.9410*X + 2.0377 r , Regression / NS , Not Significant 3–3 | COMPopathies and Exploration of the Clinical Value of Two LncRNAs , CARMN and GSEC , Validation in the Progression of Breast, Colorectal, and Gastric Cancers The overall survival (OS) analysis results, based on ENCORI/StarBase and TCGA (RStudio), are presented in Fig. 5 ; significant differences in survival among all groups were found to be minimal. Survival curves were utilized to calculate the Hazard Ratio (HR) for COMP , CARMN , and GSEC in relation to breast, colorectal, and gastric cancers. The findings indicated that the expression levels of CARMN among these genes were significantly associated with the survival time of patients with gastric and colorectal cancer, demonstrating statistically significant differences ( p -value < 0.05). The HR for gastric and colorectal cancers were 1.426 (Fig. 5 B6) and 1.61 (Fig. 5 B5), respectively. Moreover, COMP and GSEC were indicative of non-significant overall survival in three malignancies, as well as CARMN in breast cancer. To have a more comprehensive understanding of the biomedical predictive value, ROC curves were provided to investigate the diagnostic value of three target genes in distinguishing BC, CRC, and GC tissues from normal controls. ROC curve analysis was performed in R software using procedures from the ‘pROC’ package. As shown in Fig. 6 and Table 7 , the area under the curve (AUC) of COMP , CARMN , and GSEC greater than 70%, 60%, and 60% in the both of GEO and TCGA, respectively. Therefore, we considered COMP , CARMN , and GSEC might play an important role in diagnosing breast, colorectal, and gastric cancers. In brief, generally, an AUC of more than 0.8 was considered sufficient for diagnosing disease with excellent specificity and sensitivity. But according to AUC values for COMP gene in breast (GEO: 0.923 (95% CI 0.888–0.959); TCGA: 0.931 (95% CI 0.909–0.953)), colorectal (GEO: 0.92 (95% CI 0.844–0.997); TCGA: 0.925 (95% CI 0.892–0.958)), and gastric cancer (GEO: 0.836 (95% CI 0.779–0.892); TCGA: 0.77 (95% CI 0.713–0.826)), for CARMN gene in breast (GEO: 0.817 (95% CI 0.766–0.869); TCGA: 0.92 (95% CI 0.884–0.956)), colorectal (GEO: 0.724 (95% CI 0.569–0.879); TCGA: 0.761 (95% CI 0.691–0.831)), and gastric cancer (GEO: 0.759 (95% CI 0.689–0.829); TCGA: 0.649 (95% CI 0.549–0.748)), and GSEC gene in breast cancer (GEO: 0.798 (95% CI 0.74–0.855); TCGA: 0.863 (95% CI 0.824–0.901)), colorectal (GEO: 0.789 (95% CI 0.668–0.911); TCGA: 0.685 (95% CI 0.618–0.751)), and gastric (GEO: 0.867 (95% CI 0.815–0.92); TCGA: 0.629 (95% CI 0.537–0.721)). The dot plots in Fig. 7 indicate that a definitive cut-off between tumor and healthy samples cannot be determined for their complete differentiation. Table 7 ROC Curve Analysis of GEO and TCGA datasets. GEO datasets Type of cancer Gene AUC CI_lower CI_high Threshold (cut off) Breast cancer COMP 0.923 0.888 0.959 6.026 CARMN 0.817 0.766 0.869 7.821 GSEC 0.798 0.74 0.855 9.655 Colorectal cancer COMP 0.92 0.844 0.997 5.375 CARMN 0.724 0.569 0.879 7.339 GSEC 0.789 0.668 0.911 10.34 Gastric cancer COMP 0.836 0.779 0.892 5.162 CARMN 0.759 0.689 0.829 7.977 GSEC 0.867 0.815 0.92 7.029 TCGA datasets BRCA COMP 0.931 0.909 0.953 3.785 CARMN 0.92 0.884 0.956 4.317 GSEC 0.863 0.824 0.901 0.809 COAD COMP 0.925 0.892 0.958 0.006 CARMN 0.761 0.691 0.831 3.304 GSEC 0.685 0.618 0.751 -0.269 STAD COMP 0.77 0.713 0.826 2.194 CARMN 0.649 0.549 0.748 4.141 GSEC 0.629 0.537 0.721 0.14 ROC , Receiver Operating Characteristic Atlas / AUC , Area Under the Curve / CI , Confidence Interval 3–4 | Prediction and Validation of the miRNAs Related to , , and 3–4 | Prediction and Validation of the miRNAs Related to COMP , CARMN , and GSEC The upstream miRNAs of the COMP , CARMN , and GSEC genes were predicted using miRWalk and miRNet. Our results showed that 27, 32, and 17 miRNAs associated with COMP , CARMN , and GSEC , respectively, could potentially regulate the key genes (Tables 8 and 9 ). Three identified miRNAs are common among the target genes: hsa-miR-7974 , hsa-miR-423-3p , and hsa-miR-129-2-3p . Differential expression analysis highlights significant dysregulation of miRNAs in tumor compared to normal tissues. Box plots of has-miR-7974 and hsa-miR-129-2-3p in breast, colorectal, and gastric cancers exhibited a significant increase and decrease in expression, respectively. Hsa-miR-423-3p demonstrates increased expression in breast and gastric cancers, whereas it shows a significant decrease in colorectal cancer (Fig. 8 A). The overall survival analysis conducted using ENCORI/StarBase database indicated that one of the three miRNAs, ( hsa-miR-423-3p ) was significantly associated with prognosis in breast carcinoma patients (HR = 1.44, p = 0.026), as depicted in Fig. 8 B. In contrast, the other two miRNAs ( has-miR-7974 and hsa-miR-129-2-3p ) lacked a significant difference in survival time between the patient and the healthy groups. A correlation analysis was also conducted using ENCORI/StarBase database to compare miRNAs vs. mRNA and miRNAs vs. lncRNAs (Fig. 8 C). Table 8 mRNA-miRNA Energy Interactions. mRNA (DEGs of TCGA & GEO datasets) miRNA Coefficient-R-BRCA/ p -value Coefficient-R-COAD/ p -value Coefficient-R-STAD/ p -value Energy BGN hsa-miR-2110 -0.060 / 5.00e-2 -0.017 / 7.20e-1 -0.067 / 2.00e-1 -26.1 BGN hsa-miR-27a-5p 0.056 / 6.73e-2 -0.193 / 3.67e-5 -0.067 / 1.94e-1 -26.1 CA4 hsa-miR-7974 -0.049 / 1.08e-1 -0.048 / 3.09e-1 0.158 / 2.31e-3 -27.8 CCL28 hsa-miR-138-5p 0.115 / 1.50e-4 0.116 / 1.39e-2 -0.047 / 3.68e-1 -23.3 CCL28 hsa-miR-2110 -0.087 / 4.33e-3 -0.045 / 3.46e-1 0.035 / 4.96e-1 -23.2 CCL28 hsa-miR-27a-5p 0.119 / 8.67e-5 0.156 / 8.77e-4 0.075 / 1.51e-1 -20.1 CCL28 hsa-miR-423-5p -0.084 / 5.73e-3 -0.169 / 3.25e-4 0.025 / 6.37e-1 -24.7 CCL28 hsa-miR-484 -0.138 / 5.39e-6 0.125 / 7.96e-3 0.051 / 3.29e-1 -26.1 CCL28 hsa-miR-766-5p 0.062 / 4.23e-2 -0.060 / 2.00e-1 -0.108 / 3.71e-2 -20.6 COL10A1 hsa-miR-17-5p -0.312 / 6.42e-26 -0.365 / 1.33e-15 -0.246 / 1.63e-6 -18.9 COMP hsa-miR-7974 -0.085 / 4.94e-3 -0.153 / 1.17e-3 -0.219 / 1.98e-5 -26.8 COMP hsa-miR-423-3p -0.195 / 9.97e-11 -0.303 / 4.91e-11 -0.234 / 5.19e-6 -25.2 COMP hsa-miR-129-2-3p -0.086 / 4.41e-3 0.067 / 1.58e-1 0.063 / 2.23e-1 -17.7 ETFDH hsa-let-7c-5p -0.021 / 4.84e-1 -0.127 / 7.13e-3 -0.153 / 3.18e-3 -18 ETFDH hsa-miR-148b-5p -0.088 / 3.60e-3 -0.027 / 5.61e-1 0.039 / 4.56e-1 -24 ETFDH hsa-miR-210-3p -0.110 / 2.71e-4 -0.047 / 3.22e-1 -0.079 / 1.28e-1 -21.9 ETFDH hsa-miR-93-5p -0.231 / 1.19e-14 -0.001 / 9.89e-1 -0.089 / 8.56e-2 -26.4 FNDC1 hsa-miR-423-5p -0.222 / 1.47e-13 -0.069 / 1.45e-1 -0.260 / 3.86e-7 -19.3 FNDC1 hsa-miR-9-5p 0.038 / 2.09e-1 0.172 / 2.37e-4 0.021 / 6.87e-1 -16.6 INHBA hsa-miR-129-2-3p -0.047 / 1.18e-1 0.057 / 2.24e-1 -0.101 / 5.21e-2 -21 INHBA hsa-miR-210-3p 0.145 / 1.73e-6 -0.169 / 3.05e-4 0.039 / 4.55e-1 -21.1 INHBA hsa-miR-27a-5p 0.060 / 4.91e-2 0.033 / 4.82e-1 0.055 / 2.92e-1 -22.1 INHBA hsa-miR-382-5p 0.409 / 5.55e-45 0.295 / 1.74e-10 0.473 / 3.50e-22 -19.4 INHBA hsa-miR-423-5p -0.190 / 2.87e-10 0.027 / 5.75e-1 -0.131 / 1.13e-2 -21.3 KLF4 hsa-miR-141-3p -0.253 / 2.67e-17 0.172 / 2.48e-4 0.113 / 2.90e-2 -20.9 KLF4 hsa-miR-29a-3p 0.125 / 3.71e-5 -0.128 / 6.64e-3 0.118 / 2.29e-2 -18.5 KLF4 hsa-miR-433-3p 0.147 / 1.14e-6 -0.120 / 1.06e-2 -0.125 / 1.62e-2 -25.2 LIFR hsa-miR-15b-5p -0.189 / 3.20e-10 -0.166 / 4.06e-4 -0.256 / 5.83e-7 -20.2 LIFR hsa-miR-19b-3p -0.149 / 8.07e-7 -0.344 / 6.54e-14 -0.344 / 9.66e-12 -18.3 LIFR hsa-miR-20b-5p -0.041 / 1.81e-1 0.146 / 1.92e-3 0.274 / 7.54e-8 -18.1 LIFR hsa-miR-433-3p 0.077 / 1.14e-2 0.175 / 1.96e-4 0.035 / 4.95e-1 -22.3 LIFR hsa-miR-766-5p 0.099 / 4.47e-2 0.107 / 2.36e-2 0.019 / 7.15e-1 -20.5 MAOA hsa-miR-9-5p -0.223 / 1.10e-13 -0.080 / 8.94e-2 0.014 / 7.92e-1 -20.3 MFAP2 hsa-miR-423-5p -0.143 / 2.19e-6 -0.161 / 5.98e-4 -0.089 / 8.70e-2 -22.5 MFAP2 hsa-miR-2110 -0.041 / 1.72e-1 -0.080 / 8.99e-2 0.037 / 4.77e-1 -25.9 MMP11 hsa-miR-129-2-3p -0.097 / 1.32e-3 0.110 / 1.92e-2 -0.113 / 2.89e-2 -19.1 MMP11 hsa-miR-1301-3p -0.092 / 2.39e-3 -0.070 / 1.38e-1 0.071 / 1.73e-1 -28.3 MMP11 hsa-miR-423-5p -0.119 / 8.03e-5 -0.167 / 3.84e-4 -0.132 / 1.10e-2 -28.4 NR3C2 hsa-miR-15b-5p -0.181 / 1.89e-9 0.216 / 3.80e-6 -0.266 / 1.87e-7 -18.9 NR3C2 hsa-miR-27a-5p 0.069 / 2.26e-2 0.144 / 2.25e-3 -0.040 / 4.39e-1 -24.1 NR3C2 hsa-miR-574-5p 0.016 / 6.07e-1 -0.043 / 3.63e-1 -0.009 / 8.58e-1 -22.1 PIGR hsa-miR-124-3p -0.109 / 3.32e-4 -0.073 / 1.24e-1 -0.149 / 3.87e-3 -25.7 PIGR hsa-miR-129-2-3p 0.029 / 3.34e-1 -0.035 / 4.59e-1 0.084 / 1.05e-1 -21 PIGR hsa-miR-423-5p -0.102/ 7.53e-4 -0.137 / 3.72e-3 0.006 / 9.01e-1 -28.7 SULF1 hsa-miR-138-5p -0.128 / 2.28e-5 0.081 / 8.62e-2 0.015 / 7.76e-1 -20.6 SULF1 hsa-miR-148b-5p -0.080 / 8.49e-3 -0.267 / 8.59e-9 -0.181 / 4.65e-4 -24.8 SULF1 hsa-miR-423-5p -0.159 / 1.40e-7 -0.074 / 1.16e-1 -0.239 / 3.28e-6 -24.8 SULF1 hsa-miR-766-5p -0.075 / 1.36e-2 0.049 / 2.96e-1 0.070 / 1.79e-1 -22.2 TMEM37 hsa-miR-129-2-3p 0.032 / 2.94e-1 0.067 / 1.58e-1 0.039 / 4.50e-1 -22.4 TMEM37 hsa-miR-196b-5p 0.037 / 2.25e-1 0.198 / 2.35e-5 -0.151 / 3.57e-3 -21.4 TMEM37 hsa-miR-484 -0.165 / 4.31e-8 0.086 / 6.84e-2 0.076 / 1.45e-1 -26.2 VCAN hsa-miR-9-5p -0.118 / 9.36e-5 0.144 / 2.18e-3 0.046 / 3.79e-1 -19.3 mRNA , messenger RNA / miRNA , micro-RNA / R , Regression Table 9 lncRNA-miRNA Energy Interactions. LncRNA miRNA Coefficient-R-BRCA/ p -value Coefficient-R-COAD/ p -value Coefficient-R-STAD/ p -value Energy GSEC hsa-miR-7974 0.085 / 5.06e-3 -0.183 / 9.22e-5 -0.037 / 4.73e-1 -17.11 CARMN hsa-miR-17-5p -0.248 / 1.19e-16 -0.288 / 4.67e-10 -0.624 / 1.50e-41 -20.27 CARMN hsa-miR-423-3p -0.301 / 3.55e-24 -0.186 / 7.12e-5 -0.584 / 2.14e-35 -24.01 CARMN hsa-miR-129-2-3p 0.141 / 2.92e-6 0.186 / 7.21e-5 0.213 / 3.34e-5 -22.71 CARMN hsa-let-7c-5p 0.442 / 3.96e-53 0.361 / 2.54e-15 0.626 / 5.97e-42 -22.03 CARMN hsa-miR-210-3p -0.373 / 4.71e-37 -0.133 / 4.66e-3 -0.540 / 1.72e-29 -19.38 GSEC hsa-miR-210-3p 0.152 / 4.90e-7 0.088 / 6.18e-2 -0.263 / 2.59e-7 Not Find CARMN hsa-miR-93-5p -0.157 / 1.90e-7 -0.126 / 7.64e-3 -0.567 / 4.35e-33 -23.65 CARMN hsa-miR-423-5p -0.093 / 2.06e-3 -0.037 / 4.37e-1 -0.338 / 2.01e-11 -23.19 GSEC hsa-miR-9-5p 0.198 / 4.82e-11 0.056 / 2.32e-1 0.229 / 7.93e-6 -14.91 CARMN hsa-miR-27a-5p 0.160 / 1.26e-7 -0.087 / 6.44e-2 -0.327 / 1.07e-10 -20.14 CARMN hsa-miR-382-5p 0.312 / 5.83e-26 0.173 / 2.24e-4 0.092 / 7.53e-2 -15.66 GSEC hsa-miR-141-3p 0.084 / 5.86e-3 -0.109 / 2.04e-2 -0.323 / 1.66e-10 -17.55 GSEC hsa-miR-29a-3p 0.159 / 1.31e-7 -0.202 / 1.51e-5 -0.098 / 5.87e-2 -23.05 CARMN hsa-miR-433-3p 0.264 / 1.03e-18 0.178 / 1.46e-4 0.224 / 1.24e-5 -12.2 CARMN hsa-miR-15b-5p -0.241 / 8.48e-16 -0.225 / 1.47e-6 -0.664 / 1.23e-48 -22.79 CARMN hsa-miR-19b-3p -0.111 / 2.33e-4 -0.203 / 1.37e-5 -0.575 / 4.62e-34 -13.18 CARMN hsa-miR-20b-5p -0.141 / 3.18e-6 0.198 / 2.30e-5 0.009 / 8.57e-1 -20.27 GSEC hsa-miR-766-5p 0.032 / 2.94e-1 0.085 / 7.08e-2 0.041 / 4.26e-1 -18.36 GSEC hsa-miR-2110 0.025 / 4.06e-1 -0.022 / 6.47e-1 0.095 / 6.86e-2 -17.12 CARMN hsa-miR-1301-3p -0.357 / 4.76e-34 -0.009 / 8.52e-1 -0.384 / 1.54e-14 -32.38 CARMN hsa-miR-574-5p 0.065 / 3.13e-2 0.018 / 7.07e-1 -0.020 / 6.97e-1 -37.04 GSEC hsa-miR-124-3p 0.023 / 4.40e-1 0.090 / 5.66e-2 0.169 / 1.09e-3 -17.58 CARMN hsa-miR-138-5p 0.092 / 2.30e-3 0.078 / 9.68e-2 0.109 / 3.62e-2 -17.56 CARMN hsa-miR-148b-5p -0.254 / 2.15e-17 -0.140 / 2.98e-3 -0.433 / 2.02e-18 -15.75 CARMN hsa-miR-196b-5p -0.037 / 2.23e-1 -0.174 / 2.01e-4 -0.384 / 1.58e-14 -18.18 CARMN hsa-miR-484 -0.209 / 3.78e-12 0.011 / 8.13e-1 -0.488 / 1.16e-23 -33.07 CARMN hsa-miR-132-3p 0.157 / 2.03e-7 0.288 / 4.79e-10 0.024 / 6.43e-1 -19.8 CARMN hsa-miR-139-5p 0.395 / 7.31e-42 0.424 / 4.74e-21 0.425 / 8.99e-18 -22.17 CARMN hsa-miR-15a-5p -0.104 / 6.23e-4 -0.176 / 1.73e-4 -0.583 / 3.33e-35 -19.03 CARMN hsa-miR-16-5p -0.325 / 4.87e-28 -0.265 / 1.19e-8 -0.626 / 7.65e-42 -15.32 CARMN hsa-miR-195-5p 0.356 / 8.64e-34 0.128 / 6.59e-3 0.411 / 1.31e-16 -11.37 CARMN hsa-miR-19a-3p -0.198 / 5.14e-11 -0.218 / 2.99e-6 -0.572 / 1.10e-33 -13.18 CARMN hsa-miR-218-5p 0.393 / 2.28e-41 0.349 / 2.29e-14 0.460 / 6.37e-21 -19.87 CARMN hsa-miR-219a-2-3p 0.070 / 2.20e-2 0.133 / 4.57e-3 0.080 / 1.24e-1 -10.72 CARMN hsa-miR-23a-3p -0.075 / 1.36e-2 0.051 / 2.78e-1 -0.065 / 2.10e-1 -25.49 CARMN hsa-miR-27a-3p 0.019 / 5.26e-1 -0.049 / 2.99e-1 -0.264 / 2.31e-7 -24.32 CARMN hsa-miR-320a-3p 0.035 / 2.54e-1 -0.017 / 7.19e-1 -0.283 / 2.74e-8 -29.16 CARMN hsa-miR-34a-5p -0.046 / 1.33e-1 -0.027 / 5.72e-1 -0.377 / 5.03e-14 -17.23 CARMN hsa-miR-449c-5p -0.011 / 7.28e-1 -0.024 / 6.14e-1 0.004 / 9.46e-1 -21.44 GSEC hsa-miR-10b-5p -0.174 / 8.28e-9 0.037 / 4.38e-1 0.148 / 4.15e-3 -15.01 GSEC hsa-miR-29b-3p 0.080 / 8.11e-3 -0.147 / 1.77e-3 -0.245 / 1.81e-6 -15.79 GSEC hsa-miR-29c-3p -0.088 / 3.58e-3 -0.057 / 2.24e-1 0.083 / 1.11e-1 -21.48 GSEC hsa-miR-30e-3p 0.099 / 1.12e-3 -0.051 / 2.79e-1 0.014 / 7.84e-1 -18.09 GSEC hsa-miR-3679-5p 0.093 /2.07e-3 -0.038 /4.21e-1 -0.064 /2.20e-1 -20.58 GSEC hsa-miR-432-5p 0.020 / 5.19e-1 0.007 / 8.74e-1 0.202 / 8.51e-5 -22.62 GSEC hsa-miR-548ay-5p 0.000 / 1.00e + 0 0.000 / 1.00e + 0 0.000 / 1.00e + 0 -12.13 GSEC hsa-miR-548y -0.043 / 1.55e-1 0.084 / 7.40e-2 0.175 / 7.17e-4 -10.37 GSEC hsa-miR-651-5p -0.014 / 6.51e-1 -0.047 / 3.18e-1 -0.140 / 6.95e-3 Not Find 3–5 | The Functional Enrichment Analysis of DEGs by GO, Reactome, KEGG Pathways, and protein-protein interaction The GO annotation, KEGG, and Reactome pathways enrichment analysis were conducted using the DAVID database to determine the biological functions of the 24 mRNA DEGs. The top five biological processes (BP) of the DEGs were skeletal system development ( p = 0.005), extracellular matrix organization (ECM) ( p = 0.006), collagen fibril organization ( p = 0.006), chondrocyte development ( p = 0.015), and endodermal cell differentiation ( p = 0.044). The cellular composition (CC) of the DEGs predominantly encompassed the extracellular region ( p = 1.85E-06), extracellular space ( p = 1.85E-06), collagen-containing extracellular matrix ( p = 2.37E-08), ECM ( p = 0.002), collagen trimer ( p = 0.003), and endoplasmic reticulum lumen ( p = 0.004), listed in a ranking of frequency. The molecular functions (MF) of the DEGs included ECM structural constituent ( p = 8.17E-05), glycosaminoglycan binding ( p = 0.036), ECM binding ( p = 0.0383), and ECM structural constituent conferring tensile strength ( p = 0.031). The most common Reactome functional pathways of the DEGs include ECM organization ( p = 3.59E-08), collagen degradation ( p = 0.0001), degradation of the ECM ( p = 0.0001), assembly of collagen fibrils and other multimeric structures ( p = 0.004), ECM proteoglycans ( p = 0.006), integrin cell surface interactions ( p = 0.007), collagen formation ( p = 0.007), and collagen chain trimerization ( p = 0.038). Finally, the cytoskeleton in muscle cells ( p = 0.045) was associated to the KEGG pathway (Supplementary Table 1, Fig. 9 A). The enrichment of GO and pathways was examined to investigate the function of COMP mRNA. Consequently, the subsequent analysis will focus exclusively on the pathways involved of the COMP gene. Key pathways encompass R-HSA-216083: Integrin cell surface interactions (including COL10A1 , COMP , COL1A2 , and SSP1 genes), R-HSA-1474244: Extracellular matrix organization (including VCAN , COMP , MMP11 , SSP1 , COL1A2 , COL11A1 , MFAP2 , BGN , COL10A1 , and MMP9 genes), and R-HSA-3000178: ECM proteoglycans (including BGN , COMP , COL1A2 , and VCAN genes) in Reactome, as well as hsa04820: Cytoskeleton in muscle cells (including COMP , COL11A1 , COL1A2 , VCAN , and BGN ) and hsa04512: ECM-receptor interaction (including COMP , COL1A2 , and SSP1 genes) in KEGG. The 15 up-regulated genes exhibited significant enrichment in 5 biological processes ( p < 0.05), while the 9 down-regulated genes showed no enrichment in any biological processes (Fig. 9 B). Pathway studies revealed that the up-regulated DEGs were implicated in many pathways related to proliferation, apoptosis, and metastasis, that includes downstream receptors such as CD36, CD47, α5β1, and α5β3 integrins in the PI3K/AKT, MEK/ERK, and TGF-β receptor signaling pathways (Fig. 10 ). Networks illustrating interactions of protein-protein (mRNA-mRNA), mRNA-miRNA, miRNA-lncRNA, and mRNA-lncRNA. To elucidate the relationships of the validated DEGs and identified the key miRNAs connected to COMP , CARMN , and GSEC , we developed the PPI network and examined the protein interactions between the 24 genes (Fig. 9 B, 9 C). According to the prior prediction, there were 52 mRNA-miRNA pairs (Table 8 ), 49 miRNA-lncRNA pairs (Table 9 ), and 2 mRNA-lncRNA pairs. The ceRNA hypothesis presumes that miRNAs typically exhibit an inverse co-expression correlation with mRNAs and lncRNAs (Fig. 8 C), while lncRNAs commonly demonstrate a positive co-expression correlation with mRNAs (Fig. 4 B). We evaluated the correlation among all RNA interaction pairings utilizing the ENCORI/StarBase database and determined that 3 of 52 mRNA-miRNA couples, 3 of 49 miRNA-lncRNA pairs, and 2 of 2 mRNA-lncRNA pairs adhered to the ceRNA rule (Table 10 ). Ultimately, COMP mRNAs, three miRNAs ( hsa-miR-7974 , hsa-miR-423-3p , and hsa-miR-129-2-3p ), and two lncRNAs ( CARMN and GSEC ) established a tripartite mRNA-miRNA-lncRNA ceRNA regulation network as a prospective regulatory system for breast, colorectal, and gastric malignancies. Cytoscape was employed for depicting the ceRNet (ceRNA network) (Fig. 9 D). Table 10 Components of ceRNAs. mRNA (DEGs of TCGA & GEO datasets) miRNA lncRNA COMP hsa-miR-7974 GSEC COMP hsa-miR-423-3p CARMN COMP hsa-miR-129-2-3p CARMN 3–6 | Validation Results of , , and Gene Expression by qPCR 3–6 | Validation Results of COMP , CARMN , and GSEC Gene Expression by qPCR We experimentally examined the expression pattern of these three chosen genes in human breast, colorectal, and gastric tissue samples using qPCR. Using a t-test to compare tumor and normal groups for the COMP gene showed that expression was significantly higher in breast cancer (4.4156 ± 0.77 fold, p = 0.00000), colorectal cancer (1.9691 ± 0.302 fold, p = 0.00011), and gastric cancer (4.2963 ± 1.076 fold, p = 0.00000). Breast cancer (0.0711 ± 0.006 fold, p = 0.00000) and gastric cancer (0.0977 ± 0.012 fold, p = 0.00000) showed a notable reduction in CARMN gene expression, whereas colorectal cancer (1.691 ± 0.365 fold, p = 0.01087) showed an increase in expression. In final analysis, the GSEC gene presented a significant increase in expression in breast cancer (2.9918 ± 0.496 fold, p = 0.00000), colorectal cancer (9.5619 ± 1.213 fold, p = 0.00000), and gastric cancer (2.4109 ± 0.683 fold, p = 0.00041) (Fig. 11 A). A positive correlation between target genes was found using Spearman regression (Table 11 ). The correlation coefficients for COMP vs. CARMN in breast, colorectal, and gastric cancers were 0.466 ( p = 0.0385), 0.901 ( p < 0.0001), and 0.891 ( p < 0.0001), respectively. The correlation coefficients COMP vs. GSEC observed were 0.893 for breast cancer ( p < 0.0001), 0.669 for colorectal cancer ( p = 0.0013), and 0.936 for gastric cancer ( p < 0.0001). The correlation between the two lncRNAs CARMN vs. GSEC was observed in breast, colorectal, and gastric cancers, with correlation coefficients of 0.615 ( p = 0.0039), 0.731 ( p = 0.0003), and 0.882 ( p < 0.0001), respectively (Fig. 11 C). The diagnostic accuracy of COMP , CARMN , GSEC was assessed (Fig. 11 B). ROC analysis and the highest Youden index declared that the optimal diagnostic cut-off values for COMP , CARMN , and GSEC of breast cancer were 1.521, 7.981, and 6.135, respectively. We found that the sensitivity and specificity of COMP as a marker for breast cancer prognoses were 90% and 80%, respectively, at this cut-off point (AUC: 0.912; 95% CI 0.825–1). An overfitting profile was observed for the CARMN gene with a sensitivity and specificity of 100% and an AUC of 1 (95% CI 1–1). Biomarker analysis for colorectal cancer was performed on the COMP (sensitivity: 85%, specificity: 90%), CARMN (sensitivity: 65%, specificity: 85%), and GSEC (sensitivity and specificity: 100%) genes, with cut-off points of 6.086, 3.287, and 9.781, respectively. The AUC values were 0.84 (95% CI 0.685–0.995), 0.73 (95% CI 0.562–0.898), and 1 (95% CI 1–1, indicating overfitting). Finally, the biomarker power analysis of COMP (sensitivity: 80%, specificity: 100%), CARMN (sensitivity and specificity: 100%), and GSEC (sensitivity: 65%, specificity: 100%) genes for gastric cancer revealed AUCs of 0.948 (95% CI 0.887-1), 1 (95% CI 1–1, indicating overfitting), and 0.752 (95% CI 0.579–0.926), respectively, with cutoff points of 1.011, 8.261, and 3.842. The CARMN gene was over-expressed (overfitting) in breast (Fig. 11 B10, B11, B12) and gastric (Fig. 11 B13, B14, B15) cancers, meaning that all tumor samples had lower expression than healthy samples. In colorectal cancer (Fig. 11 B25, B26, B27), the GSEC gene exhibited the opposite trend, with all patients demonstrating higher expression levels compared to healthy individuals. Table 11 Regression Analysis of qPCR Type of cancer Regression SE p -value r Equation Breast Cancer COMP vs. CARMN 0.4057 0.0385 0.466 Y = 0.9056*X + 8.2794 CARMN vs. GSEC 0.0985 0.0039 0.615 Y = 0.3261*X-8.1139 COMP vs. GSEC 0.1092 < 0.0001 0.893 Y = 0.9206*X + 3.927 Colorectal cancer COMP vs. CARMN 0.0868 < 0.0001 0.901 Y = 0.7651*X-3.2626 CARMN vs. GSEC 0.2536 0.0003 0.731 Y = 1.1519*X + 6.418 COMP vs. GSEC 0.2346 0.0013 0.669 Y = 0.8947*X + 1.7571 Gastric cancer COMP vs. CARMN 0.1985 < 0.0001 0.891 Y = 1.6514*X + 16.6083 CARMN vs. GSEC 0.052 < 0.0001 0.882 Y = 0.4129*X-8.72 COMP vs. GSEC 0.0719 < 0.0001 0.936 Y = 0.8122*X + 2.6539 4. Discussion In 2022, the global cancer burden, as estimated by GLOBOCAN and published by the International Agency for Research on Cancer (IARC), highlighted that breast cancer, colorectal cancer, and gastric cancer are the second (11.6%), third (9.6%), and fifth (4.9%) most commonly diagnosed cancers worldwide, respectively ( 72 ). Recent advances in cancer research have identified lncRNAs have surfaced as promising new aspects that influence the diagnosis, treatment, and prognosis of various diseases, with cancer being the primary focus of research concerning the role of lncRNAs as potential biomarkers ( 73 ). Specifically, lncRNAs, through competing endogenous RNA (ceRNA) networks, interact with mRNAs and miRNAs to regulate critical biological processes such as cell proliferation, migration, apoptosis, and metastasis ( 74 ). The progression of breast cancer, colorectal cancer, and gastric cancer is linked to mutations that either activate oncogenes or inactivate tumor suppressor genes ( 24 ). Previous studies found new mRNAs, miRNAs, and lncRNAs based on similar genomics analyses and system biology approach that can be effective in regulating proliferation, metastasis, and progression of cancers, for example: up-regulation of URHF1 mRNA in BC, GC, and CRC, down-regulation of IGF1 in LNC01089-LINC00963/miR-1244-5p/IGF1 axis in BC, and up-regulation of THBS2 in GC ( 75 , 76 , 77 ). COMP is a protein of TSP family subgroup B with pentameric multi-domain structure (78). COMP is significantly expressed in tumor tissues of colon cancer ( 79 ) and breast cancer, and is frequently associated with high recurrence rates and poor survival rates in cancer patients as an independent prognostic marker ( 80 ). This study used bioinformatics investigations to explore the roles of COMP mRNA, CARMN , and GSEC lncRNAs as biomarkers in GC, BC, and CRC pathogenesis. Then, three mRNA-miRNA-lncRNA Competing endogenous RNA (ceRNA) regulatory network was constructed to investigate the relationship between the regulatory networks involved in ECM organization and PIK3/AKT signaling pathways, which were obtained through enrichment analysis and were important pathways in tumor progression. As shown in Fig. 3 , the heatmap of DEGs clearly illustrates the differential expression of COMP , CARMN , and GSEC across the three cancers. Additionally, the ROC curves provide a visual confirmation of their potential as biomarkers. Exploring the interaction between COMP , GSEC , and CARMN lncRNAs is necessary to understand the molecular function (MF), cellular component (CC), biological process (BP), and signaling pathways related to BC, GC, and CRC progression. For this purpose, 10 gene expression datasets from GEO database and GC, BC, and CRC samples from TCGA were analyzed in purpose to boost the sample size and accuracy of results. DEGS were identified and the expression levels of COMP , CARMN , and GSEC were analyzed using t-tests to assess significant differences between tumor and normal tissue samples. The results showed a clear upregulation of COMP in all three cancers, with p-values < 0.05 indicating statistical significance. The results provide new insights into the increased expression of COMP and GSEC in all three mentioned cancers, although the expression of CARMN is consistently down-regulated in breast and gastric cancers, indicating its tumor-suppressive role in these cancers. However, in colorectal cancer, CARMN expression is up-regulated, suggesting a more complex regulatory role that warrants further investigation. However, according to the statistical analysis of qRT-PCR results, the expression is up-regulated in colorectal cancer, but there is a need for more research on its regulatory mechanisms. The proliferation of colorectal cancer cells promotes by COMP through the activation of PI3K/Akt/ mTOR/p70S6K pathway ( 81 ) and it’s highly expressed in aggressive colorectal cancer ( 79 ). Although COMP high expression exacerbates epithelial-mesenchymal transition (EMT), suppression of its expression inhibits metastasis and invasion in colorectal cancer ( 82 ). COMP interacts with the actin-binding protein Transgelin physically in purpose of regulating cytoskeletal remodeling and enhancing colorectal cancer progression ( 82 ). High COMP expression also stimulates the deposition of collagen and other matrix proteins, leading to further exacerbation of fibrosis ( 83 ). COMP facilitates activation of MEK/ERK and PI3K/Akt signaling pathways by binding to CD36, thereby promoting proliferation, migration, invasion, and metastasis, while suppressing apoptosis. In GC, tumor-derived COMP has been identified as a diagnostic and prognostic marker ( 82 ). COMP is a crucial component of the ECM playing various and essential roles that contribute to the stability of the matricellular network ( 84 ). Recent studies suggest that COMP enhances the stability of ECM protein interactions, therefore contributing to the mechanical strength of tissues ( 85 ). The findings in this study align with earlier studies demonstrating the oncogenic role and up-regulation of COMP in GC, BC, and CRC, where its expression is associated with poor survival, however, this study is among the first to report its concurrent role in three cancer types. For instance, these findings align with prior studies demonstrating COMP 's role in promoting metastasis via ECM interactions and CARMN 's tumor-suppressive effects mediated through miRNA sponging. Experimental validations, such as COMP knockdown models, further corroborate its role in enhancing cancer cell migration and invasion. ( 85 ). Recent research discovered that the dysregulation of a great number of lncRNAs in various types of cancer play a critical role in tumorigenesis ( 86 ) CARMN ’s a lncRNA which its consistent down-regulation observed across GC and BC suggests its tumor-suppressive activity, which may occur through the regulation of mRNA and miRNA pathways involved in metastasis and proliferation and no previous studies have explored CARMN as a potential biomarker in breast and gastric cancers, despite its consistent down-regulation in these cancers, suggesting that CARMN may have a promising role as a biomarker in other cancers, such as colorectal cancer. Noting that this study made the first demonstration that CARMN could suppress tumor migration and inhibit tumor proliferation by interacting with COMP mRNA and both miR-423-3p and miR-129-2-3p miRNAs. Studies unraveled, that GSEC lncRNA is found to be overexpressed in colorectal cancer, and the silencing of this lncRNA results in a notable decrease in the motility of colorectal cancer cells. This observation implies that GSEC may play a crucial role in the migration of tumor cells ( 85 ). The present study aligns with other researches that discovered GSEC lncRNA critical in metastasis by its up-regulation in GC, BC, and CRC. The results of this investigation revealed a significant correlation between the expression levels of CARMN and the survival period of time of patients diagnosed with GC and CRC, highlighting statistically meaningful differences. Additionally, the genes COMP and GSEC were found to be associated with non-significant overall survival across three types of malignancies, alongside CARMN 's role in breast cancer. In the present study ROC curve analysis underscores the diagnostic utility of COMP , CARMN , and GSEC , with COMP achieving particularly high specificity and sensitivity. These findings emphasize the biomarker's potential in early detection, particularly when coupled with established clinical parameters. By stratifying patients based on COMP expression, clinicians may refine cancer screening protocols and prioritize high-risk individuals. However, the variability in survival analysis, especially for CARMN , suggests the need to explore tumor-specific and demographic factors influencing biomarker performance. Future research should integrate multi-omics data with machine learning models to enhance predictive accuracy and clinical applicability. The correlation between these genes and the ceRNA networks alongside with the most important enriched signaling pathway, extracellular matrix (ECM), leads this study to investigate this signaling pathway, but it requires to be validated by more research. Many of the enriched pathways in this study enrichment analysis were related to ECM as a key component of the tumor microenvironment contains nearly 300 proteins such as proteoglycans, fibrous proteins, cytokines, and growth factors that has a significantly influence on tissues function and tumor progression ( 87 , 88 , 89 , 90 ). ECM component 's variants and ratios determine tissue specific properties such as stiffness and facilitates cellular processes, including migration, proliferation, and survival ( 91 , 92 ). Collagen, one of the most important components of the ECM, has been found to accumulate in larger amounts in tumor tissues during the early-stage of cancer ( 87 ). Cancer stem cells (CSCs) use ECM characteristics to promote tumor progression ( 93 ). Increased collagen deposition in the ECM leads to stiffness, which facilitates the metastasis of tumor cells ( 94 ). In colorectal cancer (CRC), ECM remodeling involves structural and compositional changes, advancing tumor development and stiffness ( 94 ). Types I, III, and IV collagen have been detected in the serum and blood of CRC patients, with increased deposition of type I and III collagen being linked to higher breast tissue density ( 95 , 87 ). This higher density raises the risk of breast cancer by 4–6 times ( 87 ). In breast cancer, the enzyme lysyl oxidase (LOX), which creates cross-links between collagen fibers, is overexpressed ( 87 ). This causes collagen fibers to align vertically in the ECM, leading to tumor cell metastasis ( 87 ). Additionally, the PI3K pathway has been shown to promote the metastasis of breast cancer cells ( 96 ). In gastric cancer, the ECM plays a role in carcinogenesis ( 97 ). Proteomic analyses show no substantial variations in ECM components but increased ECM protein levels being associated with angiogenesis, invasion, and metastasis ( 97 ). High expression of ECM proteins, combined with collagen deposition and densification, promotes cancer progression by interacting with cell surface membrane receptors, decreases E-cadherin and β-catenin levels, and enhancing the proliferation and spread of gastric cancer cells. FBN1, an ECM protein, is found in significantly higher levels in gastric cancer tissues, leading to increased ECM stiffness ( 98 ). This protein directly activates the PI3K/AKT pathway, promoting cell proliferation in gastric cancer ( 97 ). It is important to note that this study has several limitations. First, a larger sample size and more diverse population tested by qRT-PCR are needed for more detailed and reliable investigations. Second, in vitro experiments, using a larger sample size, and in vivo validation studies are crucial to confirm the findings of this study. Third, for the validation of RNA interaction analyses, experimental tests are required to verify the predicted miRNA sponging and ceRNA network interactions. Furthermore, this study primarily relied on publicly available gene expression datasets from GEO and TCGA databases. While these resources provide valuable information, potential biases in sample selection or missing data could affect the accuracy of the results. Therefore, validating these findings with independent clinical datasets would enhance the robustness of the conclusions. Another limitation is the lack of consideration of clinical and demographic factors such as age, gender, cancer stage, and treatment history, which could influence the expression patterns of the biomarkers investigated. Incorporating these variables in future studies would provide a deeper understanding of how these factors might impact the utility of COMP , CARMN , and GSEC as biomarkers in cancer prognosis. Lastly, while this study focused on breast, gastric, and colorectal cancers, further research should aim to explore the roles of these biomarkers in other cancer types, particularly those with distinct molecular characteristics, to determine their broader applicability across different malignancies. 5. Conclusion In summary, the present study comprehensively investigates the potential of COMP mRNA and the lncRNAs CARMN and GSEC as diagnostic and prognostic biomarkers in breast, gastric, and colorectal cancers. Through integrated bioinformatics analysis of publicly available datasets from GEO and TCGA and experimental validation, distinct expression patterns of these biomarkers were identified across the three cancer types. COMP demonstrated a significant upregulation in all three cancers, correlating with aggressive tumor behaviors, including enhanced cell proliferation, migration, and invasion, primarily through ECM remodeling. This makes COMP a promising candidate for use in early cancer detection and therapeutic targeting, with its involvement in key pathways such as the PI3K/AKT signaling axis. CARMN , in contrast, showed downregulation in breast and gastric cancers, reinforcing its role as a tumor suppressor. The dual nature of CARMN expression, with potential divergent effects in colorectal cancer, warrants further investigation to fully understand its context-dependent regulatory role in tumorigenesis. GSEC was consistently up-regulated in breast, gastric, and colorectal cancers, supporting its classification as an oncogenic lncRNA. Its role in acting as a competing endogenous RNA (ceRNA) that regulates key oncogenes through miRNA sponging suggests that GSEC could be pivotal in cancer progression and represents a potential target for therapeutic interventions. The integration of these biomarkers into a comprehensive diagnostic and prognostic framework could provide significant clinical value. Future studies should focus on validating these findings in larger cohorts, exploring the molecular mechanisms underlying their role in cancer progression, and evaluating their clinical utility in personalized cancer treatment approaches. By addressing these biomarkers in parallel, more accurate diagnostic tools and targeted therapies can be developed that improve patient outcomes in breast, gastric, and colorectal cancers. Declarations 6.1. Ethics approval: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences. 6.2. Consent for publication: Informed consent was obtained from all individual participants included in the study. 6.3. Availability of data and materials: The datasets generated in this study is available with request. 6.4. Conflicts of interest: The authors declare that they have no competing interests. 6.5. Financial support and sponsorship: Not applicable. 6.6. Authors’ contribution: Mohammadjavad Askari, Ali Hodaeian, Saba Hesami, Bita Mohammadipour, Mohammad Amin Rahimi, Mehran Zamani, and Fatemeh Izadi : Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization; Mohammad Rezaei and Seyedeh Zahra Shirdeli: Writing – Review & Editing, Conceptualization, Methodology, Validation, Supervision; Mansoureh Azadeh: Writing – Review & Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Mohammadjavad Askari, Ali Hodaeian, and Saba Hesami equally contributed to this study as the first authors. Bita Mohammadipour, Mohammad Amin Rahimi, Mehran Zamani, and Fatemeh Izadi equally contributed to this study as second authors. Mohammad Rezaei and Seyedeh Zahra Shirdeli equally contributed to this study as supervisors and third authors. References Marian AJ. Sequencing Your Genome : What Does It Mean? Methodist Debakey Cardiovasc J. 2014;10(1):3–6. 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Li, C., Qiu, S., Liu, X., Guo, F., Zhai, J., Li, Z., Deng, L., Ge, L., Qian, H., Yang, L., & Xu, B. (2023). Extracellular matrix-derived mechanical force governs breast cancer cell stemness and quiescence transition through integrin-DDR signaling. Signal transduction and targeted therapy , 8 (1), 247. Karlsson, S., & Nyström, H. (2022). The extracellular matrix in colorectal cancer and its metastatic settling - Alterations and biological implications. Critical reviews in oncology/hematology , 175 , 103712. Kim, M. S., Ha, S. E., Wu, M., Zogg, H., Ronkon, C. F., Lee, M. Y., & Ro, S. (2021). Extracellular Matrix Biomarkers in Colorectal Cancer. International journal of molecular sciences , 22 (17), 9185. Leung, H. W., Wang, Z., Yue, G. G., Zhao, S. M., Lee, J. K., Fung, K. P., Leung, P. C., Lau, C. B., & Tan, N. H. (2015). Cyclopeptide RA-V inhibits cell adhesion and invasion in both estrogen receptor positive and negative breast cancer cells via PI3K/AKT and NF-κB signaling pathways. Biochimica et biophysica acta , 1853 (8), 1827–1840. Liu, Y., Li, C., Lu, Y., Liu, C., & Yang, W. (2022). Tumor microenvironment-mediated immune tolerance in development and treatment of gastric cancer. Frontiers in immunology , 13 , 1016817. Wang, X., Shi, X., Lu, H., Zhang, C., Li, X., Zhang, T., Shen, J., & Wen, J. (2022). Succinylation Inhibits the Enzymatic Hydrolysis of the Extracellular Matrix Protein Fibrillin 1 and Promotes Gastric Cancer Progression. Advanced science (Weinheim, Baden-Wurttemberg, Germany) , 9 (27), e2200546. Additional Declarations The authors declare no competing interests. Supplementary Files Tablesupplementary1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5943216","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409969831,"identity":"b7f6f522-53d7-4a9f-a6ee-52e05f8d11d1","order_by":0,"name":"Mohammadjavad Askari","email":"","orcid":"https://orcid.org/0009-0004-7513-0819","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mohammadjavad","middleName":"","lastName":"Askari","suffix":""},{"id":409969832,"identity":"845a0f54-1e01-4985-8d18-00d02a49bb90","order_by":1,"name":"Ali Hodaeian","email":"","orcid":"https://orcid.org/0009-0004-8136-7054","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Hodaeian","suffix":""},{"id":409969833,"identity":"e1f4fa63-d123-4776-a3b6-f605134dfec9","order_by":2,"name":"Saba Hesami","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Saba","middleName":"","lastName":"Hesami","suffix":""},{"id":409969834,"identity":"cccfcbe9-ce01-4605-b47c-69d6b36f53a5","order_by":3,"name":"Bita Mohammadipour","email":"","orcid":"https://orcid.org/0009-0000-5439-3565","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Bita","middleName":"","lastName":"Mohammadipour","suffix":""},{"id":409969835,"identity":"c1a1afab-c9c4-4d8e-9985-470ac9ea48fe","order_by":4,"name":"Mohammad Amin Rahimi","email":"","orcid":"https://orcid.org/0009-0007-1016-8981","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Amin","lastName":"Rahimi","suffix":""},{"id":409969836,"identity":"42ed13ee-21bc-4746-b249-a5984246f98a","order_by":5,"name":"Mehran Zamani","email":"","orcid":"https://orcid.org/0009-0003-7282-5837","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mehran","middleName":"","lastName":"Zamani","suffix":""},{"id":409970072,"identity":"b3a30fad-a5a7-404a-951d-25cbbccec47c","order_by":6,"name":"Fatemeh Izadi","email":"","orcid":"https://orcid.org/0009-0002-3296-4472","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Izadi","suffix":""},{"id":409970073,"identity":"1fbf80ac-3c9b-4911-b860-69222b5969c1","order_by":7,"name":"Mohammad Rezaei","email":"","orcid":"https://orcid.org/0000-0003-3888-5839","institution":"Department of Biology and Biotechnology, University of Pavia, Pavia, Italy","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Rezaei","suffix":""},{"id":409970074,"identity":"7f9e1e09-7910-4269-9c15-d3d729cd7bd9","order_by":8,"name":"Sayedeh Zahra Shirdeli","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Sayedeh","middleName":"Zahra","lastName":"Shirdeli","suffix":""},{"id":409970075,"identity":"f780922b-288f-4e07-92e9-6199586499df","order_by":9,"name":"Mansoureh Azadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie3SMWvCQBTA8edyU2JXSyD5BIULBYsU/Cx3HDRLbB07ZDgQ4lLsmiEfwk7F7eAgLilZb+hgKDg5ZJJCIVSDBIdTO3a4//aGH7zHHYDJ9F+zQLjQAwzkMDahC+T2mHT4XwjlewLH5FQ302VZbZ4/g3dnModVJJ+uXj+WK4iG0HWElvRzwq7TfD1apNkYSCYHiXqkHDIGqEv0RBDh2LEczVW4uwVJDMryOSAByNIv1i/KyY9dywA3pJbYK/Idqc8QxZBjc0kaQmOJsQh93onPkTUapJn0F8nDWNBZgH0V+gmdMev0YsGX2kTSu+uxt/J7e4/dIsdVtR263ouetO0fRbQTaf/ABWIymUwmbb8scGMPe1qBDAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2031-4640","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":true,"prefix":"","firstName":"Mansoureh","middleName":"","lastName":"Azadeh","suffix":""}],"badges":[],"createdAt":"2025-02-01 19:03:45","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5943216/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5943216/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75413045,"identity":"e17cb582-4254-4e85-9472-2b424b2978c0","added_by":"auto","created_at":"2025-02-04 09:24:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":326522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustrates an explanation of what was carried out in this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/9d1b499b88b94344be7e7a21.png"},{"id":75413137,"identity":"7b3ad596-9219-4d68-9f59-94fd55b0d2eb","added_by":"auto","created_at":"2025-02-04 09:24:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":386707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreliminary investigation of quality control and data preprocessing.\u003c/strong\u003e PCA and box plot were illustrated to remove batch correction of GSE42568, GSE36295, GSE10810, and GSE134359 (breast cancer, A1 and A2 before de-batching, A7 and A8 after de-batching), GSE41328 and GSE81558 (colorectal cancer, A3 and A4 before de-batching, A9 and A10 after de-batching), and GSE65801, GSE118916, GSE54129, and GSE79973 (gastric cancer, A5 and A6 before de-batching, A11 and A12 after de-batching) of GEO datasets \u003cstrong\u003e(A)\u003c/strong\u003e, PCA after normalization is demonstrated for GEO and TCGA \u003cstrong\u003e(B)\u003c/strong\u003e. Venn diagram depicts DEGs for up-regulated and down-regulated genes in the GEO and TCGA datasets \u003cstrong\u003e(C)\u003c/strong\u003e, after that a combination of GEO and TCGA for each cancer \u003cstrong\u003e(D)\u003c/strong\u003e, and lastly the intersections and combination of GEO and TCGA for all three cancers separately for up and down \u003cstrong\u003e(E)\u003c/strong\u003e. \u003cstrong\u003ePCA\u003c/strong\u003e, principal component analysis; \u003cstrong\u003eGEO\u003c/strong\u003e, Gene Expression Omnibus; \u003cstrong\u003eTCGA\u003c/strong\u003e, The Cancer Genome Atlas.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/dc602701d69924bef46e3153.png"},{"id":75413099,"identity":"324c8a50-f9fe-4dcb-a32b-c9db18e36756","added_by":"auto","created_at":"2025-02-04 09:24:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":498586,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe GEO and TCGA data demonstrate the convergence of gene expression signatures from various studies on breast, colorectal, and gastric cancers.\u003c/strong\u003e Each panel shows volcano plots and heatmaps comparing differential gene expression between patient and healthy samples. Heatmaps were generated to visualize overall gene expression, utilizing color gradients from blue to red to represent low to high expression levels across samples. The heatmap displays the Venn diagram genes of breast (A1 and A3), colorectal (A5 and A7), and gastric (A9 and A11) cancers. The up-regulated and down-regulated genes were shown in pink and green, respectively. The volcano plots display red points that indicate significantly up-regulated genes and blue points that represent significantly down-regulated genes. Labeled 25 common genes highlight notable differentially expressed genes between cancers in the two GEO and TCGA datasets (A2, A4, A6, A8, A10, and A12).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/86752886fd81362f2161179c.png"},{"id":75413098,"identity":"61a25747-9684-471e-a486-dc84fbbfdec9","added_by":"auto","created_at":"2025-02-04 09:24:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":532029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Differential Expression and Correlation Analysis of key \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCOMP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e mRNA and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCARMN\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGSEC\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e lncRNAs verified by GEO, TCGA, and ENCORI/StarBase.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e The expression of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e was significant higher in breast, colorectal, and gastric tumors than healthy (GEO: \u003cstrong\u003eA1-A9\u003c/strong\u003e, TCGA: \u003cstrong\u003eA10-A18\u003c/strong\u003e, and ENCORI/StarBase: \u003cstrong\u003eA19-A27\u003c/strong\u003e). \u003cstrong\u003e(B)\u003c/strong\u003e Scatter plots demonstrating correlations between \u003cem\u003eCOMP\u003c/em\u003e expression and the lncRNAs \u003cem\u003eCARMN\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003ein the same datasets (GEO: \u003cstrong\u003eB1-B9\u003c/strong\u003e, TCGA: \u003cstrong\u003eB10-B18\u003c/strong\u003e, and ENCORI/StarBase: \u003cstrong\u003eB19-B27\u003c/strong\u003e). \u003cstrong\u003e(C)\u003c/strong\u003e Changes in gene expression among 33 malignancies are shown in the box plots obtained from \u003cem\u003eCOMP\u003c/em\u003e gene expression analysis in the Trimer database. Breast, colorectal, and gastric cancers show a considerable increase (***).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/03fcce9f854d803f22a1caf2.png"},{"id":75413118,"identity":"4a0c2274-41ae-4d2b-826d-3b8a6de1211c","added_by":"auto","created_at":"2025-02-04 09:24:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":222229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival Analysis of ENCORI/StarBase and TCGA (R software).\u003c/strong\u003e The survival analysis of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e in patients with breast, gastric, and colorectal cancers. A P value \u0026lt;0.05 was considered statistically significant. Survival analysis of ENCORI/StarBase \u003cstrong\u003e(A)\u003c/strong\u003eand TCGA \u003cstrong\u003e(B)\u003c/strong\u003e for the \u003cem\u003eCARMN\u003c/em\u003e gene in gastric and colorectal cancer revealed barely significant difference. In contrast, the other two genes \u003cem\u003eCOMP\u003c/em\u003eand \u003cem\u003eGSEC\u003c/em\u003e showed no statistical significance according to Kaplan-Meier overall survival. \u003cstrong\u003eHR\u003c/strong\u003e, hazard ratio; \u003cstrong\u003eTPM\u003c/strong\u003e, transcripts per million.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/dbf5d7eb7dade2ddaf814869.png"},{"id":75413130,"identity":"36fceb5a-8ec5-478e-b8b7-a9e8deab13fb","added_by":"auto","created_at":"2025-02-04 09:24:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":300634,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve and scatter dot plot of GEO and TCGA datasets. \u003c/strong\u003eROC analysis was employed to assess the diagnostic efficacy of COMP, CARMN, and GSEC genes for breast, colorectal, and gastric cancers in the validation of GEO and TCGA datasets. Expression data in BC, CRC, and GC tissues are presented using a scatter dot plot. A definitive cut-off points distinguishing tumor status from normal status is not established.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/6e989a426b13997a219230d3.png"},{"id":75413119,"identity":"57cbac96-70a2-42bd-a98b-467a5d69a772","added_by":"auto","created_at":"2025-02-04 09:24:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":542204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8 | Comprehensive Exploration of miRNAs in Breast, Colorectal, and Gastric Cancers.\u003c/strong\u003e Analysis of miRNAs (\u003cem\u003ehsa-miR-7974\u003c/em\u003e, \u003cem\u003ehsa-miR-423-3p\u003c/em\u003e, and \u003cem\u003ehsa-miR-129-2-3p\u003c/em\u003e) in breast, gastric, and colorectal cancers, highlighting differential expression \u003cstrong\u003e(A)\u003c/strong\u003e, survival time \u003cstrong\u003e(B)\u003c/strong\u003e, and correlation patterns \u003cstrong\u003e(C)\u003c/strong\u003e. Panels \u003cstrong\u003eA1-A9\u003c/strong\u003e present box plots that demonstrate significant expression differences of miRNAs between tumor and normal samples (FDR \u0026lt; 0.05) for breast (\u003cstrong\u003eA1-A3\u003c/strong\u003e), gastric (\u003cstrong\u003eA4-A6\u003c/strong\u003e), and colorectal (\u003cstrong\u003eA7-A9\u003c/strong\u003e) cancers. Panels \u003cstrong\u003eB1-B9\u003c/strong\u003e show Kaplan-Meier survival curves for high and low expression groups, annotated by hazard ratios and log rank \u003cem\u003ep\u003c/em\u003e-values \u0026gt; 0.05 (exception of \u003cem\u003ehas-miR-423-3p\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e-value=0.026 (B4)). \u003cstrong\u003eC1-C27\u003c/strong\u003edisplay scatter dot plots illustrating the correlations between miRNAs and the related mRNA (\u003cem\u003eCOMP\u003c/em\u003e) and lncRNAs (\u003cem\u003eCARMN\u003c/em\u003e, \u003cem\u003eGSEC\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/368a7c8b0597730b927d290a.png"},{"id":75413515,"identity":"11ef9d14-4d4c-48be-8bfb-435a868f81ea","added_by":"auto","created_at":"2025-02-04 09:32:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":195017,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 9 | DEGs Enrichment Analysis and ceRNA Regulatory Network Construction.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e The GO annotation of the DEGs, \u003cstrong\u003e(B)\u003c/strong\u003eConstruction of the PPI DEGs and biological processes network, \u003cstrong\u003e(C)\u003c/strong\u003e mRNA-miRNA-lncRNA gene network, and \u003cstrong\u003e(D)\u003c/strong\u003e ceRNA network using Cytoscape software.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/c041876f2229d076beca69ca.png"},{"id":75413127,"identity":"e7c43856-eb14-477f-97cc-38afa87ff70f","added_by":"auto","created_at":"2025-02-04 09:24:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":299997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 10 | Graphical abstract.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/05f8490d7438728e17daaa40.png"},{"id":75413139,"identity":"8136669c-23a6-4db2-bb93-997b6a3c4673","added_by":"auto","created_at":"2025-02-04 09:24:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":332306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 11 | qPCR verification results of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCOMP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCARMN\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGSEC\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genes.\u003c/strong\u003e In validation GEO and TCGA analyses, we used expression levels \u003cstrong\u003e(A)\u003c/strong\u003e and ROC analysis to evaluate the diagnostic effectiveness \u003cstrong\u003e(B)\u003c/strong\u003e of target genes for BC, CRC, and GC. We observed a correlation \u003cstrong\u003e(C)\u003c/strong\u003e between the expression of lncRNAs and the expression of \u003cem\u003eCOMP\u003c/em\u003emRNA in tumor samples from BC, CRC, and GC.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/93a2b618951cb73361e8375c.png"},{"id":75413551,"identity":"91ebd153-a734-4610-8ac1-13d97546e286","added_by":"auto","created_at":"2025-02-04 09:32:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7017309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/0cc20507-9dd5-4925-99ee-4cabeb6b2be2.pdf"},{"id":75413109,"identity":"59aa68bf-4243-4c09-a74f-27f041f5014d","added_by":"auto","created_at":"2025-02-04 09:24:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17805,"visible":true,"origin":"","legend":"","description":"","filename":"Tablesupplementary1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5943216/v1/aceea6d0a70324b1f45d0ada.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIdentification of Cross-Cancer Biomarkers: COMP mRNA and CARMN/GSEC lncRNAs Shared in Breast, Gastric, and Colorectal Cancers via Integrated Systems Biology and Experimental Validation\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn-depth studies of human transcripts acquired using high-throughput technologies have enabled researchers to represent the human genome comprehensively. Only around 2% of the approximately 3.2 billion base pairs of human genomic DNA (1) comprise exons and protein-coding sections, whereas the remaining 98% are associated with non-coding RNAs (ncRNAs) \u0026nbsp;(2). Many decades ago, this broader category of ncRNAs was commonly stigmatized as \u0026quot;garbage\u0026quot; or \u0026quot;noise\u0026quot; due to their jumbled transcription. They are classified into two distinct groups: short ncRNAs (sncRNAs) and long non-coding RNAs (lncRNAs). LncRNAs are a heterogeneous collective of RNAs comprising sequences exceeding 200 base pairs (3). A recent genome-wide association study (GWAS) has designated lncRNAs as molecules that regulate gene expression at the epigenetic, transcriptional, and specifically after transcription levels (4). Recent evidence also indicates that lncRNAs influence gene expression and can contribute to cancer development by functioning as either oncogenes or tumor suppressors (5). Hence, lncRNAs have emerged as recent focal points in diagnosing and managing several disorders, such as cancer, neurological diseases, autoimmune conditions, and inflammation (6\u0026ndash;8).\u003c/p\u003e\n\u003cp\u003eSpecific lncRNAs in malignancies modulate proliferation and migration by sponging micro-RNAs to regulate mRNA (9). Numerous investigations demonstrate that lncRNAs have the potential to directly interfere with mRNA splicing by interacting with factors involved in splicing settings, inhibiting translation, and degradation of mRNA (10,11). Furthermore, it participates in various cellular biological processes such as cell proliferation, cellular growth, differentiation, apoptosis (12,13), drug resistance (14), and metastasis (9,15). Cancer biomarkers like circulating lncRNAs have been employed in the early-stage detection of various types of cancer, including breast cancer and colorectal cancer (16\u0026ndash;18). Recently, combining genomics, transcriptomics, and clinicopathological data utilizing multi-platform and multi-omics approaches has enhanced our understanding of the molecular makeup of different tumors and facilitated the visualization of molecular roles (19). In this regard, early studies demonstrated that some lncRNAs have been revealed as causative agents of pan-cancer (20\u0026ndash;22). Here, we exploited and integrated multi-GSEs by analyzing National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) datasets obtained from microarray high-throughput technique projects and harnessed The Cancer Genome Atlas (TCGA) cohort RNA-Seq database to elucidate the \u003cem\u003eIn-silico\u003c/em\u003e landscape of two key lncRNA genes across three distinct cancer types. The integration of these methods allowed for the comprehensive screening of gene expression profiles and the identification of lncRNAs with significant regulatory roles in cancer progression (23).\u003c/p\u003e\n\u003cp\u003eHence, the profiles of two LncRNA signature Cardiac Mesoderm Enhancer-associated Non-coding RNA (\u003cem\u003eCARMN\u003c/em\u003e) and G-quadruplex forming sequence-containing lncRNA (\u003cem\u003eGSEC\u003c/em\u003e), as well as discovered a mRNA, cartilage oligomeric matrix protein (\u003cem\u003eCOMP\u003c/em\u003e), concerning prognostic and diagnostic biomarker connotation in a multi-cancer setting including breast cancer (BC), gastric cancer (GC) and colorectal cancer (CRC). The development of these cancers is associated with mutations that activate oncogenes or deactivate tumor suppressor genes (24\u0026ndash;26). The \u003cem\u003eCOMP\u003c/em\u003e gene is the fifth member of the thrombospondin family, sometimes referred to as thrombospondin-5 (\u003cem\u003eTSP-5\u003c/em\u003e). It is located at NC_000019.10 and comprises five subunits. \u003cem\u003eCOMP\u003c/em\u003e is highly expressed in cartilage (27,28). The \u003cem\u003eCOMP\u003c/em\u003e gene exhibits elevated expression in various diseases (29), including breast (30), colorectal (31,32), gastric (33,34), and prostate (35) malignancies, as well as inflammatory conditions like osteoarthritis (36,37). Multiple molecular processes have been suggested for the \u003cem\u003eCOMP\u003c/em\u003e gene, that results in reduced survival in patients. Increased expression of \u003cem\u003eCOMP\u003c/em\u003e, attributed to the proliferation of cancer stem cells via the Notch3/Jagged1 signaling pathway (38), the AKT pathway (39,40), and the disruption of signaling pathways associated with calcium channels and the extracellular matrix (ECM) (41,42), has been documented in multiple malignancies. Moreover, several growth factors, like as transforming growth factor beta (TGF-\u0026beta;) and bone morphogenetic protein (BMP) families, play a vital role in chondrogenic induction in cooperation with the \u003cem\u003eCOMP\u003c/em\u003e protein (43\u0026ndash;46). Therefore, the \u003cem\u003eCOMP\u003c/em\u003e gene plays a crucial role in regulating tissue health via various mechanisms.\u003c/p\u003e\n\u003cp\u003eRecent research highlighted the involvement of the \u003cem\u003eCOMP\u003c/em\u003e gene, as well as the lncRNAs \u003cem\u003eCARMN\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003e, in the pathogenesis of breast, colorectal, and gastric cancers. While advances in therapies have improved outcomes, recurrence rates remain a challenge. Investigating the expression of genes like \u003cem\u003eCOMP\u003c/em\u003e, along with related lncRNAs such as \u003cem\u003eCARMN\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003e, and identifying potential biomarkers for diagnosis and prognosis could provide valuable insights into preventing and treating these cancers.\u003c/p\u003e\n\u003cp\u003eThe objective of this research is to comprehensively assess by Gene Ontology (GO), Canonical Pathway (CP) enrichment analysis, survival analysis, and interaction network were also conducted. This article adheres the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) reporting checklist (https://jgo.amegroups.org/article/view/10.21037/jgo-22-1201/rc) (47). Finally, validate the findings of the bioinformatic survey by experimental investigation, alongside the molecular mechanisms of their contribution to the prevention or progression of three cancers that have been addressed.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003ch3\u003e2\u0026thinsp;\u0026minus;\u0026thinsp;1. Bioinformatics Analysis and Software Availability\u003c/h3\u003e\n\u003ch3\u003e2-1-1. High Throughput Retrieving and Preprocessing of Data\u003c/h3\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eCollected GEO and TCGA Datasets of BC, GC, and CRC\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA series of inclusion and exclusion criteria were applied to refine the search results obtained from the NCBI-GEO database. Engage in a search with the specified terms BC (\u0026ldquo;Breast Cancer\u0026rdquo; [Title/Abstract] OR \u0026ldquo;Breast Cancer\u0026rdquo; [MeSH Terms]), GC (\u0026ldquo;Gastric Cancer\u0026rdquo; [Title/Abstract] OR \u0026ldquo;Gastric Cancer\u0026rdquo; [MeSH Terms]), CRC (\u0026ldquo;Colorectal Cancer\u0026rdquo; [Title/Abstract] OR \u0026ldquo;Colorectal Cancer\u0026rdquo; [MeSH Terms]). Microarray data pertaining to BC, GC, and CRC were collected based on specific criteria, including \u0026quot;Homo sapiens\u0026quot;, \u0026quot;expression analyzed by array\u0026quot;, \u0026quot;incorporating both noncancerous and cancerous samples\u0026quot;, and \u0026quot;sample size of 20 or more\u0026quot;. Datasets derived from samples (GSMs) of our target cancers that had undergone metastasis and peripheral blood samples were also omitted. Following a thorough filtering and comparative analysis, some gene datasets were selected and information on these GSEs datasets is repertoire in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. All raw and original data were downloaded from NCBI-GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) and TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/ccg/research/genome-sequencing/tcga\u003c/span\u003e\u003c/span\u003e) databases and The analysis of gene expression was conducted using \u0026ldquo;GEOquery\u0026rdquo; (\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioconductor.org/packages/release/BiocViews.html#___Software\u003c/span\u003e\u003c/span\u003e) and \u0026ldquo;TCGAbiolinks\u0026rdquo; R programming package version 4.4.1 (2024-06-14 ucrt), respectively.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eQuality control and normalization of GEO and TCGA datasets\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNormalization of microarray data was achieved using the robust multichip average (RMA) algorithm, the GEO dataset of each cancer was combined into one according to their probe IDs, while Batch effect correction was executed through the ComBat function from the Bioconductor package \u0026ldquo;SVA\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/release/bioc/html/sva.html\u003c/span\u003e\u003c/span\u003e) to mitigate batch effects across various microarray datasets. The data from the same platform was processed by discarding probes that included multiple genes and preserving only the highest probe value for each gene and were consolidated into a single dataframe. Principal component analysis (PCA) was utilized to visualize the batch effects that were present before and after de-batching the datasets.\u003c/p\u003e\n\u003cp\u003eIn addition, all data have been normalized and processed for the TCGA cohorts of BC, GC, and CRC utilizing the TCGAbiolinks pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html\u003c/span\u003e\u003c/span\u003e) (\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e). The RNA-seq data (Star-count) derived from the Illumina HiSeq RNASeq platform included samples from breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), and stomach adenocarcinoma (STAD), and were sourced from the publicly accessible Genomic Data Commons (GDC) data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e). Each gene is allocated the same dispersion estimate, conducts pairwise tests for differential expression between two groups cancer and normal, applies the false discovery rate (FDR) correction (\u0026lt;\u0026thinsp;0.05) to the results, and identifies the most significantly differently expressed genes |Log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;1 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). PCA was utilized to visualize the quality of samples and to remove those which were not satisfied by the criteria.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eData pertaining to microarray datasets sourced from the GEO and TCGA.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of cancer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGEO Dataset\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCancerous Samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-cancerous Samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreast Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE42568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE36295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL6244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE10810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE134359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL17586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eColorectal Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE41328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE81558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL15207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGastric Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE65801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgilent GPL14550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE118916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL15207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE54129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE79973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffymetrix GPL570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCGA-BRCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIllumina HiSeq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCGA-COAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIllumina HiSeq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCGA-STAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIllumina HiSeq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGEO\u003c/strong\u003e, Gene Expression Omnibus / \u003cstrong\u003eGSE\u003c/strong\u003e, GEO Series / \u003cstrong\u003eGPL\u003c/strong\u003e, GEO Platform / \u003cstrong\u003eNA\u003c/strong\u003e, Not Available / \u003cstrong\u003eTCGA\u003c/strong\u003e, The Cancer Genome Atlas / \u003cstrong\u003eBRCA\u003c/strong\u003e, Breast Invasive Carcinoma / \u003cstrong\u003eCOAD\u003c/strong\u003e, Colon Adenocarcinoma / \u003cstrong\u003eSTAD\u003c/strong\u003e, Stomach Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eThresholds for identification of differentially expressed genes (DEGs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe R package Limma (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioconductor.org/packages/release/bioc/html/limma.html\u003c/span\u003e\u003c/span\u003e) and DESeq2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/release/bioc/html/DESeq2.html\u003c/span\u003e\u003c/span\u003e) were employed to define the threshold for mRNA differential expression screening of GEO and TCGA, respectively, by the criteria \u0026ldquo;Adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026rdquo; (with Benjamini \u0026amp; Hochberg) and \u0026ldquo;|Log\u003csub\u003e2\u003c/sub\u003eFC| (fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026rdquo;. The subsequent step involved identifying and visualizing the overlapping DEGs between BC, GC, and CRC through the constructing was performed using VennDiagram in R software by ggplot2 package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cloud.r-project.org/web/packages/ggplot2/index.html\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe methodology of our study is elegantly illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, showcasing a comprehensive flowchart.\u003c/p\u003e\n\u003ch3\u003e2-1-2. Development of the protein-protein interaction (PPI) network and key hub gene screening\u003c/h3\u003e\n\u003cp\u003eUtilizing the STRING v11.0 online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003c/span\u003e) (\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e), a PPI network for the DEGs was constructed. Interactions with combined scores\u0026thinsp;\u0026gt;\u0026thinsp;0.4 were regarded as statistically significant. The visualization of the PPI networks was executed through Cytoscape software (version 3.7.1) (\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e2-1-3. Functional enrichment analysis of DEGs and hub gene\u003c/h3\u003e\n\u003cp\u003eFunctional enrichment analysis for DEGs was conducted through Enrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003c/span\u003e) (\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e), DAVID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/tools.jsp\u003c/span\u003e\u003c/span\u003e) (\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e), and the Reactome pathway databases (\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e) to identify the biological processes (BP), molecular functions (MF), and cellular components (CC) associated with BC, GC, and CRC. GO terms and KEGG pathways exhibiting a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The outcomes of the GO analysis were represented visually through a bubble plot created using \u0026ldquo;clusterProfiler\u0026rdquo; package RStudio software (\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-1-4. Investigating the association between hub gene expression profiles and the survival prognosis of patients with BC, GC, and CRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research utilized several resources, including the Encyclopedia of RNA Interactomes (ENCORI/StarBase) and the Gene Expression Profiling Interactive Analysis (GEPIA2021) website (\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2021.cancer-pku.cn/\u003c/span\u003e\u003c/span\u003e) to investigate the relationship between the expression levels of \u003cem\u003eCOMP\u003c/em\u003e gene overall survival (OS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-1-5. Construction of the Differentially Expressed miRNAs (DEmiRNAs) subnetwork associated with Differentially Expressed mRNAs (DEmRNAs) and Hub Gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association of hub genes and miRNA expression was first explored using five algorithms, the miRNet 2.0 online (\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca/\u003c/span\u003e\u003c/span\u003e), miRWalk (\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003c/span\u003e), Diana-LncBasev3 (\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://diana.e-ce.uth.gr/lncbasev3/home\u003c/span\u003e\u003c/span\u003e), miRanda-Target Prediction (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tools4mirs.org/software/target_prediction/miranda/\u003c/span\u003e\u003c/span\u003e) and miRBase (\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirbase.org/\u003c/span\u003e\u003c/span\u003e) databases, designed to construct miRNA-mRNA target networks. Simultaneously, the KM plotter was employed to assess the predictive significance of the projected miRNAs on OS.\u003c/p\u003e\n\u003ch3\u003e2-1-6. Forecasting Upstream Differentially Expressed LncRNAs (DElncRNAs) associated with DEmRNAs and Hub Gene\u003c/h3\u003e\n\u003cp\u003eTo predict probable lncRNA-mRNA interactions, miRNet (\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e) and LncRRIsearch (\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rtools.cbrc.jp/LncRRIsearch/glist.cgi\u003c/span\u003e\u003c/span\u003e) databases were utilized to forecast the upstream lncRNAs of the miRNAs. For a pair of lncRNA and mRNA anticipated to interact, they must also exhibit robust co-expression. Subsequent to identifying the lncRNAs associated with the target gene, we verified each one through the GeneCards database for non-coding RNA. We established a minimum requisite Pearson correlation coefficient (PCC) of 0.5 between them for this objective. The KM plotter was employed to assess the predictive significance of the projected lncRNAs for overall survival. The differential expressions in BC, GC, and CRC tumor and normal tissues were examined using the Encyclopedia of RNA Interactomes (ENCORI or StarBase) online dataset v3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003c/span\u003e) (\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e2-1-7. Construction of the ceRNA (lncRNA-miRNA-mRNA) network\u003c/h3\u003e\n\u003cp\u003eCompeting endogenous RNA (ceRNA) is an in-depth examination of mRNA, lncRNA, microRNA, and circRNA. This is a sophisticated post-transcriptional regulatory network predicated on the notion that lncRNAs act as miRNA sponges, utilizing common microRNA response regions to competitively control mRNA expression. To investigate regulatory interactions, a ceRNA network was constructed for the identified prognostic mRNAs, miRNAs, and lncRNAs (\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e). The mRNA-lncRNA, miRNA-mRNA, and mRNA-lncRNA pairs common to breast, gastric, and colorectal cancers were assessed using ENCORI/StarBase database. By Cytoscape software, we constructed and presented the mutual expression network.\u003c/p\u003e\n\u003ch3\u003e2\u0026ndash;2. Experimental Investigation\u003c/h3\u003e\n\u003ch3\u003e2-2-1 Sample Collection, Preparation, and RNA Extraction\u003c/h3\u003e\n\u003cp\u003eThis study included 120 tissue specimens, comprising 20 malignant and 20 matched normal tissues, collected from patients undergoing surgery for breast, colorectal, and gastric cancers between 2022 and 2024. Samples were obtained from Al-Zahra and Sayyed al-Shohada (Omid) Hospitals in Isfahan, Iran, and the Iran National Tumor Bank, Cancer Institute of Tehran University of Medical Sciences. The demographic and clinicopathological information are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Sample size determination was conducted using G*Power 3.1 software based on global cancer prevalence data. Ethical approval was granted by the medical ethics committee of Al-Zahra Hospital and the Iran National Tumor Bank, and all patients provided informed consent in accordance with the Declaration of Helsinki. Tissue samples were stabilized in RNAlater solution (Invitrogen, Thermo Fisher, Waltham, USA), snap-frozen in liquid nitrogen, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. RNA was extracted using the YTzol Pure RNA Reagent (Yekta Tajhiz Azma, Iran) following the manufacturer\u0026rsquo;s protocol. RNA concentration and purity were measured using a NanoDrop ND-1000 spectrophotometer (Agilent, USA), with A260/A280 ratios consistently between 1.9 and 2.1.\u003c/p\u003e\n\u003ch3\u003e2-2-2 cDNA Synthesis and Primer Design\u003c/h3\u003e\n\u003cp\u003eRNA samples of high purity and integrity were treated with DNase I to remove genomic DNA and subsequently used for cDNA synthesis. Reverse transcription was performed following the manufacturer\u0026apos;s protocol (RT-ROJE Technologies, Tehran, Iran) in a 20 \u0026micro;L reaction volume. The resulting cDNA was diluted 10-fold with RNase-free water for subsequent qRT-PCR analysis. Primers targeting lncRNA genes, \u003cem\u003eCARMN\u003c/em\u003e (GenBank Accession No. NR_105059.1), \u003cem\u003eGSEC\u003c/em\u003e (GenBank Accession No. NR_033839.1), mRNA \u003cem\u003eCOMP\u003c/em\u003e (GenBank Accession No. NM_000095), and mRNA Glyceraldehyde-3-phosphatedehydrogenase (\u003cem\u003eGAPDH\u003c/em\u003e) (NM_002046) were designed using GeneRunner software and Primer-BLAST (NCBI). Primer specificity was validated using melting curve analysis, and the synthesized primers were obtained from SinaClon (Tehran, Iran).\u003c/p\u003e\n\u003ch3\u003e2-2-3 Quantitative real-Time PCR\u003c/h3\u003e\n\u003cp\u003eQuantitative real-time PCR (qRT-PCR) was performed using the SYBR Green low ROX master mix (PCR Biosystems Inc., USA) and the MIC Real-Time PCR Cycler (BMS, Bio Molecular Systems, Australia). The PCR reaction consisted of 5 \u0026micro;L SYBR Green master mix, 0.5 \u0026micro;L of 10 \u0026micro;M forward and reverse primers, 1 \u0026micro;L of cDNA, and 3 \u0026micro;L of sterile purified water in a 10 \u0026micro;L reaction volume. Melt curve analysis confirmed the specificity of the amplifications, and results were normalized to \u003cem\u003eGAPDH\u003c/em\u003e expression. Relative expression levels were calculated using the \u0026Delta;\u0026Delta;Ct method.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and clinicopathological features of BC, GC, and CRC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of Cancer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of patients (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"21\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreast Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (%20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (%35)\u003c/p\u003e\n \u003cp\u003e7 (%35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (%30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (%20)\u003c/p\u003e\n \u003cp\u003e10 (%50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (%80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (%15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLymph node\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (%85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (%10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eEstrogen receptors (ER)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (%40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (%15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (%45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eProgesterone receptors (PR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (%25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (%30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (%45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eHER2/Neu receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (%15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (%30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (%55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eGastric Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (%25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (%15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (%50)\u003c/p\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (%40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (%60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (%85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignet ring carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (%10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePerineural Invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (%80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (%20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (%85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFamily History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (%25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (%75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"26\"\u003e\n \u003cp\u003e\u003cstrong\u003eColorectal Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (%10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (%15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (%35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (%40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (%75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (%25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (%55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (%45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (%30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (%70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePerineural Invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (%60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (%40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLymph node\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (%40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (%55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (%85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMucinous (Colloid) Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (%15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDX-smoker at diagnosis but discontinued\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (%80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (%15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eStandard Operating Procedures (SOP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAscending Colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRectosigmoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (%20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSigmoid Colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (%20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (%20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCecum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (%10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColon, NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (%25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eqRT-PCR primers for prognosis-related genes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eForward Primer (5\u0026prime;\u0026rarr;3\u0026prime;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReverse Primer (5\u0026prime;\u0026rarr;3\u0026prime;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRNA Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProduct\u003c/p\u003e\n \u003cp\u003eSize (bp)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAPDH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACAGGGTGGTGGACCTCAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGGGGTCTACATGGCAACTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec-RNA (mRNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOMP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAACGTGGTCTTGGACACAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGTGTCATTGCAGCGGTAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec-RNA (mRNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCARMN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGCAACGGCTGTAACAAGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAGGCTGCTTCTCCAGAGTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enc-RNA (LncRNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSEC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCAGGCTTGGGATGGTGTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAAGGACAGCAGGAAGGTATCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enc-RNA (LncRNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAPDH\u003c/strong\u003e, Glyceraldehyde-3-phosphatedehydrogenase / \u003cstrong\u003eCOMP\u003c/strong\u003e, Cartilage Oligomeric Matrix Protein / \u003cstrong\u003eCARMN\u003c/strong\u003e, cardiac mesoderm enhancer-associated non-coding RNA / \u003cstrong\u003eGSEC\u003c/strong\u003e, G-quadruplex forming sequence containing lncRNA / \u003cstrong\u003ec-RNA\u003c/strong\u003e, coding RNA/ \u003cstrong\u003enc-RNA\u003c/strong\u003e, non-coding RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e2\u0026ndash;3. Statistical analysis\u003c/h3\u003e\n\u003cp\u003eUpon evaluating the efficiency of PCR for each gene and normalizing the cycle threshold (Ct) values of the target genes with respect to the \u003cem\u003eGAPDH\u003c/em\u003e internal control gene, we utilized the Livak method (2\u003csup\u003e\u0026minus;∆∆ct\u003c/sup\u003e) to compare the fold change (FC) between the tumor and normal groups (\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e). Comparing the expression levels of the gene of interest (Ct gene of interest) to those of an internal control gene (Ct internal control gene), resulting in a value known as delta Ct. The delta Ct values, along with their standard deviations (SD), calculated for patients, were juxtaposed with the delta Ct\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of the healthy control group to assess statistical significance. All statistical evaluations were carried out using GraphPad Prism version 6 (San Diego, CA, USA), with the significance level defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 (****), \u0026lt;\u0026thinsp;0.001 (***), \u0026lt;\u0026thinsp;0.01 (**), and \u0026lt;\u0026thinsp;0.05 (*).\u003c/p\u003e\n\u003cp\u003eThe data, which were confirmed to follow a normal distribution through the Shapiro-Wilk normality test (noting that each group contained fewer than 30 participants), were presented as the means and standard deviations (SD). Data analysis was performed utilizing Student\u0026rsquo;s paired t-tests to determine significant differences in gene expression levels between tumor and normal groups. To assess the efficacy of the biomarkers in accurately identifying the presence of each type of cancer, specificity, sensitivity, and the area under the receiver operating characteristic curve (ROC-AUC) were calculated. A ROC-AUC of 1 signifies perfect classification, while a ROC-AUC of 0.5 indicates no classification capability. ROC-AUC values of \u0026ge;\u0026thinsp;0.90 are classified as excellent, those between 0.80 and 0.90 as good, values from 0.70 to 0.80 as fair, and values below 0.70 as poor (\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e). Furthermore, Spearman\u0026rsquo;s correlation coefficients (SCC) were computed to explore the relationships between outcomes, with a scale defining 0\u0026ndash;0.3 as negligible correlation, 0.3\u0026ndash;0.5 as low correlation, 0.5\u0026ndash;0.7 as moderate correlation, 0.7\u0026ndash;0.9 as high correlation, and 0.9\u0026ndash;1 as very high correlation (\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;1 | Identification of Aberrantly Expressed Genes and DEGs based on GEO and TCGA data in Breast, Gastric, and Colorectal Cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to identify novel genes associated with the gene expression profiles and pathogenesis of breast, colorectal, and gastric cancers by using DEGs, the several processes were performed sequentially (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). First of all, 10 GSEs that satisfied the criteria were chosen, then 10 different datasets of heterogeneous microarray datasets from GEO of each cancer were combined (i.e., Before and after batch correction by standardization of individual array samples in the box and PCA plot are depicted in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e\n\u003cp\u003eThis analysis found 387 breast cancer, 260 colorectal cancer, and 308 gastric cancer genes with significantly higher expression in GEO datasets, including 18 shared genes among of three cancers. Additionally, 596, 390, and 461 genes associated with breast, colorectal, and gastric cancer, respectively, demonstrated a significant decrease in expression noted in tumors. Likewise, we identified 2,348 genes in the STAD dataset, 3,501 genes in the COAD dataset, and 3,010 genes in the BRCA dataset that exhibited a significant up-regulation in expression, with 577 genes being common throughout them. Genes 3166, 3597, and 3325 associated with breast, colorectal, and gastric cancer, respectively, exhibited a notable down-regulation in expression across different malignancies (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD illustrates the sum of common genes to three cancers as determined by both the GEO and TCGA databases. A total of 25 genes were identified, comprising 15 genes that were up-regulated and 10 genes that were significantly down-regulated between GEO and TCGA (|LogFC|\u0026gt;1, adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). The analysis proceeds with the 25 genes listed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTo further elucidate the transcriptional landscapes of breast, colorectal, and gastric cancers, GEO and TCGA datasets were analyzed to identify DEGs between tumor and healthy samples (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Heatmaps and volcano maps that depict the up-regulated and down-regulated genes associated with BC, CRC, and GC. Red denotes genes with positive correlations, while blue signifies genes with negative correlations (heatmaps) and labels 25 DEGs for each cancer separately, GEO and TCGA (volcano maps).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDEGs detected from two datasets of GEO and TCGA with 10 down-regulated genes and 15 up-regulated genes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eColorectal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGastric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnsembl ID\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eUp-Regulated (15 genes)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBGN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.806043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.278349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000182492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCOL10A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.634638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.931726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.226216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000123500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCOL11A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.499369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.016048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.311852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000060718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCOL1A2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.079061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.594334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.911041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000164692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCOMP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.186441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.010134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000105664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCTHRC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.458062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.536445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.397306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000164932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.855767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.748034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.025074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000169245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFAP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.052978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.511075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.817097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000078098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eINHBA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.697379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.483642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.728841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000122641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMFAP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.380876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.239046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000117122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMMP11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.705304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.475731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.025363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000099953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMMP9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.300198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.562625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000100985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSPP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.204936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.342192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.404332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000118785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSULF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.898571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.898206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.679687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000137573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVCAN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.159248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.433551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.342078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000038427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Regulated (10 genes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eADH1C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.029542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.532298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.606485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000248144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCA4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.871403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.597412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.262438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000167434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKLF4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.726187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.216647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.064688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000136826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLIFR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.048914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.018489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.185696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000113594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMAMDC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.869825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.559442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.207477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000165072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMAOA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.10518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.563712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.173602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000189221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMT1M\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.007288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.889413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.317426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000205364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNR3C2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.593394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.951454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.147929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000151623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTMEM37\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.17538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.317515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.281473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000171227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCARMN\u003c/em\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.301246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.269949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.13252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000249669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDEGs\u003c/strong\u003e, Differentially Gene Expression \u003cstrong\u003e/ GEO\u003c/strong\u003e, Gene Expression Omnibus / \u003cstrong\u003eTCGA\u003c/strong\u003e, The Cancer Genome Atlas / \u003cstrong\u003eADH1C\u003c/strong\u003e, Alcohol Dehydrogenase 1C (class I) / \u003cstrong\u003eBGN\u003c/strong\u003e, Biglycan / \u003cstrong\u003eCA4\u003c/strong\u003e, Carbonic Anhydrase 4 / \u003cstrong\u003eCOL10A1\u003c/strong\u003e, Collagen Type X Alpha 1 Chain / \u003cstrong\u003eCOL11A1\u003c/strong\u003e, Collagen Type XI Alpha 1 Chain / \u003cstrong\u003eCOL1A2\u003c/strong\u003e, Collagen Type I Alpha 2 Chain / \u003cstrong\u003eCOMP\u003c/strong\u003e, Cartilage Oligomeric Matrix Protein / \u003cstrong\u003eCTHRC1\u003c/strong\u003e, Collagen Triple Helix Repeat Containing 1 / \u003cstrong\u003eCXCL10\u003c/strong\u003e, C-X-C Motif Chemokine Ligand 10 / \u003cstrong\u003eFAP\u003c/strong\u003e, Fibroblast Activation Protein Alpha / \u003cstrong\u003eINHBA\u003c/strong\u003e, Inhibin Subunit Beta A / \u003cstrong\u003eKLF4\u003c/strong\u003e, KLF Transcription Factor 4 / \u003cstrong\u003eLIFR\u003c/strong\u003e, LIF Receptor Subunit Alpha / \u003cstrong\u003eMAMDC2\u003c/strong\u003e, MAM Domain Containing 2 / \u003cstrong\u003eMAOA\u003c/strong\u003e, Monoamine Oxidase A / \u003cstrong\u003eMFAP2\u003c/strong\u003e, Microfibril Associated Protein 2 / \u003cstrong\u003eMMP11\u003c/strong\u003e, Matrix Metallopeptidase 11 / \u003cstrong\u003eMMP9\u003c/strong\u003e, Matrix Metallopeptidase 9 / \u003cstrong\u003eMT1M\u003c/strong\u003e, Metallothionein 1M / \u003cstrong\u003eNR3C2\u003c/strong\u003e, Nuclear Receptor Subfamily 3 Group C Member 2 / \u003cstrong\u003eSPP1\u003c/strong\u003e, Secreted Phosphoprotein 1 / \u003cstrong\u003eSULF1\u003c/strong\u003e, Sulfatase 1 / \u003cstrong\u003eTMEM37\u003c/strong\u003e, Transmembrane Protein 37 / \u003cstrong\u003eVCAN\u003c/strong\u003e, Versican / \u003cstrong\u003eCARMN\u003c/strong\u003e, Cardiac Mesoderm Enhancer-Associated Non-Coding RNA\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003eCARMN\u003c/strong\u003e, is a type of non-coding RNA that was removed in subsequent analyses, including protein-protein interaction.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;2 | Hub Gene\u003c/strong\u003e \u003cstrong\u003eCOMP\u003c/strong\u003e \u003cstrong\u003eUp-regulation and Target Gene LncRNA Prediction in Breast, Gastric, and Colorectal Cancer Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOMP\u003c/em\u003e or \u003cem\u003eTHBS5\u003c/em\u003e (Thrombospondin-5) expression was initially evaluated in an assortment of breast, gastric, and colorectal cancerous and non-cancerous tissues based on GEO (Fig.\u0026nbsp;4A1-A3, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), TCGA (Fig.\u0026nbsp;4A10-A12, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), and ENCORI/StarBase databases (Fig.\u0026nbsp;4A19-A21). The boxplot results (T-test analysis) in all three databases showed that the expression levels of \u003cem\u003eCOMP\u003c/em\u003e were significantly higher in the BC (GEO: 6.967 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; TCGA: 24.228 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; ENCORI: 23.24 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.2e-87), CRC (GEO: 4.552 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; TCGA: 30.592 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; ENCORI: 65.27 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.0e-25), and GC (GEO: 4.028 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; TCGA: 5.147 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; ENCORI: 22.36 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.5e-7) cancerous tissues than the non-cancerous tissues.\u003c/p\u003e\n\u003cp\u003eThe prediction of upstream lncRNAs of the \u003cem\u003eCOMP\u003c/em\u003e mRNA was conducted by LncRRIsearch database. Strong evidence for the potential binding of 2 lncRNAs to the \u003cem\u003eCOMP\u003c/em\u003e was found. An approximately positive correlation between both of lncRNAs and \u003cem\u003eCOMP\u003c/em\u003e, binding energy, and survival analysis. We identified 2 lncRNAs associated with a poor prognosis for BC, CRC, and GC patients that \u003cem\u003eCARMN\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003e (\u003cem\u003eST3GAL4-AS1\u003c/em\u003e) had a low and high expression in each three cancerous tissues, respectively. T-test analysis (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) and boxplots revealed significant down-regulation of \u003cem\u003eCARMN\u003c/em\u003e in tumor samples compared to normal controls of GEO (Fig. 4A4-A6), TCGA (Fig. 4A13-A15), and ENCORI/StarBase (Fig. 4A22-A24) of BC (GEO: 0.4058 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; TCGA: 0.1019 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; ENCORI: 0.14 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.5e-123), CRC (GEO: 0.4147 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00085; TCGA: 0.3592 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; ENCORI: 0.95 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.6e-10), and GC (GEO: 0.4561 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; TCGA: 0.4341 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00030; ENCORI: 0.31 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00065). Conversely, \u003cem\u003eGSEC\u003c/em\u003e exhibited up-regulation expression patterns in BC (GEO: 2.4918 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; TCGA: 2.6186 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; ENCORI: 2.59 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.4e-44), CRC (GEO: 3.7389 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00029; TCGA: 1.6559 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00012; ENCORI: 1.88 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), and GC (GEO: 2.8835 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000; TCGA: 1.3145 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01570; ENCORI: 1.62 fold, \u003cem\u003eadj.p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0029). Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, panels A7-A9 of GEO, A16-A18 of TCGA, and A25-A27 of ENCORI/StarBase depict boxplots showing differential expression levels of \u003cem\u003eGSEC\u003c/em\u003e in tumor and normal samples for each cancer. Trimer database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003c/span\u003e) to investigate the differences in expression of \u003cem\u003eCOMP\u003c/em\u003e mRNA of interest across 33 TCGA cancers between the tumor and nearby normal tissues. Gene expression level distributions are represented by box plots (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). \u003cem\u003eCOMP\u003c/em\u003e expression demonstrates significant up-regulation in breast, colorectal, and gastric cancers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eCorrelation analysis (Fog. 4B) exhibited heterogeneous association between \u003cem\u003eCARMN\u003c/em\u003e vs. \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eGSEC\u003c/em\u003e vs. \u003cem\u003eCOMP\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e vs. \u003cem\u003eCARMN\u003c/em\u003e in breast, colorectal, and gastric cancers. Regression analysis of GEO, TCGA datasets, and ENCORI/StarBase demonstrated in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The correlation \u003cem\u003eCARMN\u003c/em\u003e vs. \u003cem\u003eCOMP\u003c/em\u003e is predominantly positive across three cancer types in GEO, TCGA, and ENCORI/StarBase, with the exception of breast cancer in the GEO dataset, that exhibited a negative correlation (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB2). Other lncRNA correlation showed that \u003cem\u003eGSEC\u003c/em\u003e vs. \u003cem\u003eCOMP\u003c/em\u003e also had very heterogeneous results in the three cancers in GEO, TCGA, and ENCORI (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). A significant result of common negative correlation was seen in TCGA (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB13) and ENCORI (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB22) in breast cancer, while positive correlation was obtained in GEO (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB4). ENCORI/StarBase (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB24) showed a significant positive correlation in colorectal cancer, while the GEO (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB6) and TCGA (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB15) datasets exhibited negative and positive correlations, respectively, neither of which was significant. But the correlation results of \u003cem\u003eGSEC\u003c/em\u003e vs. \u003cem\u003eCOMP\u003c/em\u003e in gastric cancer were highly homogeneous and demonstrated a significant positive correlation across all datasets (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB5, B14, and B23). An additional analysis assessing the correlation \u003cem\u003eCARMN\u003c/em\u003e vs. \u003cem\u003eGSEC\u003c/em\u003e was conducted, revealing notable variation in results of breast (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB7, B16, and B25), colorectal (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB9, B18, and B27), and gastric (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB8, B17, and B26) malignancies within the three datasets.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGene Expression Analysis of GEO and TCGA datasets (T test, RStudio Analysis).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of cancer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSummary\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eGEO datasets\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e6.9673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.4058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2.4918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e4.5517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.4147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e3.7389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e4.0281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.4561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2.8835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCGA datasets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e24.2279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2.6186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.3768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e30.5924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.3592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1.6559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eSTAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e5.1469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.4341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.01570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1.3145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u003c/strong\u003e, Fold Change / \u003cstrong\u003eBRCA\u003c/strong\u003e, Breast Invasive Carcinoma / \u003cstrong\u003eCOAD\u003c/strong\u003e, Colon Adenocarcinoma / \u003cstrong\u003eSTAD\u003c/strong\u003e, Stomach Adenocarcinoma / \u003cstrong\u003eSD\u003c/strong\u003e, Standard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression Analysis of GEO and TCGA datasets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eGEO datasets (RStudio analysis)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of Cancer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSummary\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEquation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.3166*X\u0026thinsp;+\u0026thinsp;5.3862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.3523*X\u0026thinsp;+\u0026thinsp;3.3865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.8693*X-1.3702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.0486*X\u0026thinsp;+\u0026thinsp;6.7034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY=-0.2029*X\u0026thinsp;+\u0026thinsp;7.687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY=-0.3043*X\u0026thinsp;+\u0026thinsp;10.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY=-0.2091*X\u0026thinsp;+\u0026thinsp;8.2833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY=-0.5154*X\u0026thinsp;+\u0026thinsp;10.7326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.745*X\u0026thinsp;+\u0026thinsp;0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCGA datasets (RStudio analysis)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.2623*X\u0026thinsp;+\u0026thinsp;5.4565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY=-0.3572*X\u0026thinsp;+\u0026thinsp;3.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY=-0.3588*X\u0026thinsp;+\u0026thinsp;6.7352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.6094*X\u0026thinsp;+\u0026thinsp;0.9371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.0277*X\u0026thinsp;+\u0026thinsp;2.6551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.1835*X\u0026thinsp;+\u0026thinsp;2.5253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSTAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.3869*X\u0026thinsp;+\u0026thinsp;0.9767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.2333*X\u0026thinsp;+\u0026thinsp;3.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.7205*X\u0026thinsp;+\u0026thinsp;2.0309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eENCORI/StarBase Database\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.2535*X\u0026thinsp;+\u0026thinsp;5.3078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.17e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY=-0.0654*X\u0026thinsp;+\u0026thinsp;3.3629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY=-0.3544*X\u0026thinsp;+\u0026thinsp;4.3889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.4963*X\u0026thinsp;+\u0026thinsp;2.7069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.3996*X\u0026thinsp;+\u0026thinsp;2.9756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.60e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.3898*X\u0026thinsp;+\u0026thinsp;1.5536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCARMN vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.5161*X\u0026thinsp;+\u0026thinsp;1.4384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.5570*X\u0026thinsp;+\u0026thinsp;0.5891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGSEC vs. COMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.9410*X\u0026thinsp;+\u0026thinsp;2.0377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e, Regression / \u003cstrong\u003eNS\u003c/strong\u003e, Not Significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3\u0026ndash;3 | COMPopathies and Exploration of the Clinical Value of Two LncRNAs\u003c/strong\u003e, \u003cstrong\u003eCARMN\u003c/strong\u003e \u003cstrong\u003eand\u003c/strong\u003e \u003cstrong\u003eGSEC\u003c/strong\u003e, \u003cstrong\u003eValidation in the Progression of Breast, Colorectal, and Gastric Cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall survival (OS) analysis results, based on ENCORI/StarBase and TCGA (RStudio), are presented in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; significant differences in survival among all groups were found to be minimal. Survival curves were utilized to calculate the Hazard Ratio (HR) for \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e in relation to breast, colorectal, and gastric cancers. The findings indicated that the expression levels of \u003cem\u003eCARMN\u003c/em\u003e among these genes were significantly associated with the survival time of patients with gastric and colorectal cancer, demonstrating statistically significant differences (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The HR for gastric and colorectal cancers were 1.426 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB6) and 1.61 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB5), respectively. Moreover, \u003cem\u003eCOMP\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003e were indicative of non-significant overall survival in three malignancies, as well as \u003cem\u003eCARMN\u003c/em\u003e in breast cancer.\u003c/p\u003e\n\u003cp\u003eTo have a more comprehensive understanding of the biomedical predictive value, ROC curves were provided to investigate the diagnostic value of three target genes in distinguishing BC, CRC, and GC tissues from normal controls. ROC curve analysis was performed in R software using procedures from the \u0026lsquo;pROC\u0026rsquo; package. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, the area under the curve (AUC) of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e greater than 70%, 60%, and 60% in the both of GEO and TCGA, respectively. Therefore, we considered \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e might play an important role in diagnosing breast, colorectal, and gastric cancers. In brief, generally, an AUC of more than 0.8 was considered sufficient for diagnosing disease with excellent specificity and sensitivity. But according to AUC values for \u003cem\u003eCOMP\u003c/em\u003e gene in breast (GEO: 0.923 (95% CI 0.888\u0026ndash;0.959); TCGA: 0.931 (95% CI 0.909\u0026ndash;0.953)), colorectal (GEO: 0.92 (95% CI 0.844\u0026ndash;0.997); TCGA: 0.925 (95% CI 0.892\u0026ndash;0.958)), and gastric cancer (GEO: 0.836 (95% CI 0.779\u0026ndash;0.892); TCGA: 0.77 (95% CI 0.713\u0026ndash;0.826)), for \u003cem\u003eCARMN\u003c/em\u003e gene in breast (GEO: 0.817 (95% CI 0.766\u0026ndash;0.869); TCGA: 0.92 (95% CI 0.884\u0026ndash;0.956)), colorectal (GEO: 0.724 (95% CI 0.569\u0026ndash;0.879); TCGA: 0.761 (95% CI 0.691\u0026ndash;0.831)), and gastric cancer (GEO: 0.759 (95% CI 0.689\u0026ndash;0.829); TCGA: 0.649 (95% CI 0.549\u0026ndash;0.748)), and \u003cem\u003eGSEC\u003c/em\u003e gene in breast cancer (GEO: 0.798 (95% CI 0.74\u0026ndash;0.855); TCGA: 0.863 (95% CI 0.824\u0026ndash;0.901)), colorectal (GEO: 0.789 (95% CI 0.668\u0026ndash;0.911); TCGA: 0.685 (95% CI 0.618\u0026ndash;0.751)), and gastric (GEO: 0.867 (95% CI 0.815\u0026ndash;0.92); TCGA: 0.629 (95% CI 0.537\u0026ndash;0.721)). The dot plots in Fig. 7 indicate that a definitive cut-off between tumor and healthy samples cannot be determined for their complete differentiation.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eROC Curve Analysis of GEO and TCGA datasets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eGEO datasets\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of cancer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI_lower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI_high\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThreshold (cut off)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCGA datasets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSTAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e, Receiver Operating Characteristic Atlas / \u003cstrong\u003eAUC\u003c/strong\u003e, Area Under the Curve / \u003cstrong\u003eCI\u003c/strong\u003e, Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e3\u0026ndash;4 | Prediction and Validation of the miRNAs Related to , , and\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e3\u0026ndash;4 | Prediction and Validation of the miRNAs Related to \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e\u003c/div\u003e\n\u003cp\u003eThe upstream miRNAs of the \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e genes were predicted using miRWalk and miRNet. Our results showed that 27, 32, and 17 miRNAs associated with \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e, respectively, could potentially regulate the key genes (Tables \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). Three identified miRNAs are common among the target genes: \u003cem\u003ehsa-miR-7974\u003c/em\u003e, \u003cem\u003ehsa-miR-423-3p\u003c/em\u003e, and \u003cem\u003ehsa-miR-129-2-3p\u003c/em\u003e. Differential expression analysis highlights significant dysregulation of miRNAs in tumor compared to normal tissues. Box plots of \u003cem\u003ehas-miR-7974\u003c/em\u003e and \u003cem\u003ehsa-miR-129-2-3p\u003c/em\u003e in breast, colorectal, and gastric cancers exhibited a significant increase and decrease in expression, respectively. \u003cem\u003eHsa-miR-423-3p\u003c/em\u003e demonstrates increased expression in breast and gastric cancers, whereas it shows a significant decrease in colorectal cancer (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). The overall survival analysis conducted using ENCORI/StarBase database indicated that one of the three miRNAs, (\u003cem\u003ehsa-miR-423-3p\u003c/em\u003e) was significantly associated with prognosis in breast carcinoma patients (HR\u0026thinsp;=\u0026thinsp;1.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB. In contrast, the other two miRNAs (\u003cem\u003ehas-miR-7974\u003c/em\u003e and \u003cem\u003ehsa-miR-129-2-3p\u003c/em\u003e) lacked a significant difference in survival time between the patient and the healthy groups. A correlation analysis was also conducted using ENCORI/StarBase database to compare miRNAs vs. mRNA and miRNAs vs. lncRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003emRNA-miRNA Energy Interactions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emRNA (DEGs of TCGA \u0026amp; GEO datasets)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient-R-BRCA/ \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient-R-COAD/ \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient-R-STAD/ \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnergy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-2110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060 / 5.00e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017 / 7.20e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.067 / 2.00e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-27a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056 / 6.73e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.193 / 3.67e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.067 / 1.94e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-7974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.049 / 1.08e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.048 / 3.09e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158 / 2.31e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCL28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-138-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115 / 1.50e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116 / 1.39e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.047 / 3.68e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCL28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-2110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.087 / 4.33e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.045 / 3.46e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035 / 4.96e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCL28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-27a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119 / 8.67e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156 / 8.77e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075 / 1.51e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCL28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.084 / 5.73e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.169 / 3.25e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025 / 6.37e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCL28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.138 / 5.39e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125 / 7.96e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051 / 3.29e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCL28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-766-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062 / 4.23e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060 / 2.00e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.108 / 3.71e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOL10A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-17-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.312 / 6.42e-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.365 / 1.33e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.246 / 1.63e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-7974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.085 / 4.94e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.153 / 1.17e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.219 / 1.98e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.195 / 9.97e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.303 / 4.91e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.234 / 5.19e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-129-2-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.086 / 4.41e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067 / 1.58e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063 / 2.23e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-let-7c-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.021 / 4.84e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.127 / 7.13e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.153 / 3.18e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-148b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.088 / 3.60e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.027 / 5.61e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039 / 4.56e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-210-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.110 / 2.71e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.047 / 3.22e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.079 / 1.28e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-93-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.231 / 1.19e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001 / 9.89e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.089 / 8.56e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFNDC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.222 / 1.47e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.069 / 1.45e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.260 / 3.86e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFNDC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-9-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038 / 2.09e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172 / 2.37e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021 / 6.87e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINHBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-129-2-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.047 / 1.18e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057 / 2.24e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101 / 5.21e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINHBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-210-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145 / 1.73e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.169 / 3.05e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039 / 4.55e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINHBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-27a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060 / 4.91e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033 / 4.82e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055 / 2.92e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINHBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-382-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409 / 5.55e-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295 / 1.74e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473 / 3.50e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINHBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.190 / 2.87e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027 / 5.75e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.131 / 1.13e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKLF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.253 / 2.67e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172 / 2.48e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113 / 2.90e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKLF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-29a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125 / 3.71e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.128 / 6.64e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118 / 2.29e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKLF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-433-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.147 / 1.14e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.120 / 1.06e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.125 / 1.62e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-15b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.189 / 3.20e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.166 / 4.06e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.256 / 5.83e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-19b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.149 / 8.07e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.344 / 6.54e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.344 / 9.66e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-20b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.041 / 1.81e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146 / 1.92e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.274 / 7.54e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-433-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077 / 1.14e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175 / 1.96e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035 / 4.95e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-766-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099 / 4.47e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107 / 2.36e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019 / 7.15e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-9-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.223 / 1.10e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.080 / 8.94e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014 / 7.92e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMFAP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.143 / 2.19e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.161 / 5.98e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.089 / 8.70e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMFAP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-2110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.041 / 1.72e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.080 / 8.99e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037 / 4.77e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMP11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-129-2-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.097 / 1.32e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110 / 1.92e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.113 / 2.89e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMP11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-1301-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.092 / 2.39e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.070 / 1.38e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071 / 1.73e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMP11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.119 / 8.03e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.167 / 3.84e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.132 / 1.10e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-15b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.181 / 1.89e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.216 / 3.80e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.266 / 1.87e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-27a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069 / 2.26e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144 / 2.25e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.040 / 4.39e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-574-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016 / 6.07e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.043 / 3.63e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009 / 8.58e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-124-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.109 / 3.32e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.073 / 1.24e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.149 / 3.87e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-129-2-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029 / 3.34e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.035 / 4.59e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084 / 1.05e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.102/ 7.53e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.137 / 3.72e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006 / 9.01e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSULF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-138-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.128 / 2.28e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081 / 8.62e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015 / 7.76e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSULF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-148b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.080 / 8.49e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.267 / 8.59e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.181 / 4.65e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSULF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.159 / 1.40e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.074 / 1.16e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.239 / 3.28e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSULF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-766-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.075 / 1.36e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049 / 2.96e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070 / 1.79e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMEM37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-129-2-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032 / 2.94e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067 / 1.58e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039 / 4.50e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMEM37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-196b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037 / 2.25e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198 / 2.35e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.151 / 3.57e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMEM37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.165 / 4.31e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086 / 6.84e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076 / 1.45e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-9-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.118 / 9.36e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144 / 2.18e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046 / 3.79e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003emRNA\u003c/strong\u003e, messenger RNA / \u003cstrong\u003emiRNA\u003c/strong\u003e, micro-RNA / \u003cstrong\u003eR\u003c/strong\u003e, Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003elncRNA-miRNA Energy Interactions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLncRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient-R-BRCA/ \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient-R-COAD/ \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient-R-STAD/ \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnergy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-7974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085 / 5.06e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.183 / 9.22e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.037 / 4.73e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-17-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.248 / 1.19e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.288 / 4.67e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.624 / 1.50e-41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.301 / 3.55e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.186 / 7.12e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.584 / 2.14e-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-129-2-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141 / 2.92e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186 / 7.21e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213 / 3.34e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-let-7c-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.442 / 3.96e-53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361 / 2.54e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.626 / 5.97e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-210-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.373 / 4.71e-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.133 / 4.66e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.540 / 1.72e-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-210-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152 / 4.90e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088 / 6.18e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.263 / 2.59e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Find\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-93-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.157 / 1.90e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.126 / 7.64e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.567 / 4.35e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.093 / 2.06e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.037 / 4.37e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.338 / 2.01e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-9-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198 / 4.82e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056 / 2.32e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229 / 7.93e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-27a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160 / 1.26e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.087 / 6.44e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.327 / 1.07e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-382-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.312 / 5.83e-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173 / 2.24e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092 / 7.53e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084 / 5.86e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.109 / 2.04e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.323 / 1.66e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-29a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159 / 1.31e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.202 / 1.51e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.098 / 5.87e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-433-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.264 / 1.03e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178 / 1.46e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.224 / 1.24e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-15b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.241 / 8.48e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.225 / 1.47e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.664 / 1.23e-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-19b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.111 / 2.33e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.203 / 1.37e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.575 / 4.62e-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-20b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.141 / 3.18e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198 / 2.30e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009 / 8.57e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-766-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032 / 2.94e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085 / 7.08e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041 / 4.26e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-2110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025 / 4.06e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022 / 6.47e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095 / 6.86e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-1301-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.357 / 4.76e-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009 / 8.52e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.384 / 1.54e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-32.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-574-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065 / 3.13e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018 / 7.07e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020 / 6.97e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-37.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-124-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023 / 4.40e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090 / 5.66e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.169 / 1.09e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-138-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092 / 2.30e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078 / 9.68e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109 / 3.62e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-148b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.254 / 2.15e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.140 / 2.98e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.433 / 2.02e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-196b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.037 / 2.23e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.174 / 2.01e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.384 / 1.58e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.209 / 3.78e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011 / 8.13e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.488 / 1.16e-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-33.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-132-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.157 / 2.03e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288 / 4.79e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024 / 6.43e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-139-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395 / 7.31e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.424 / 4.74e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425 / 8.99e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-15a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.104 / 6.23e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.176 / 1.73e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.583 / 3.33e-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-16-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.325 / 4.87e-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.265 / 1.19e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.626 / 7.65e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356 / 8.64e-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128 / 6.59e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.411 / 1.31e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-19a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.198 / 5.14e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.218 / 2.99e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.572 / 1.10e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-218-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393 / 2.28e-41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.349 / 2.29e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.460 / 6.37e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-219a-2-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070 / 2.20e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133 / 4.57e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080 / 1.24e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-23a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.075 / 1.36e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051 / 2.78e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.065 / 2.10e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-27a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019 / 5.26e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.049 / 2.99e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.264 / 2.31e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-320a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035 / 2.54e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017 / 7.19e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.283 / 2.74e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-29.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-34a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.046 / 1.33e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.027 / 5.72e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.377 / 5.03e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-449c-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011 / 7.28e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024 / 6.14e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004 / 9.46e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-10b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.174 / 8.28e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037 / 4.38e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148 / 4.15e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-29b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080 / 8.11e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.147 / 1.77e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.245 / 1.81e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.088 / 3.58e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.057 / 2.24e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083 / 1.11e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-30e-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099 / 1.12e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.051 / 2.79e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014 / 7.84e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-3679-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093 /2.07e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.038 /4.21e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.064 /2.20e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-432-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020 / 5.19e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007 / 8.74e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202 / 8.51e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-548ay-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000 / 1.00e\u0026thinsp;+\u0026thinsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000 / 1.00e\u0026thinsp;+\u0026thinsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000 / 1.00e\u0026thinsp;+\u0026thinsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-548y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.043 / 1.55e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084 / 7.40e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175 / 7.17e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-651-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014 / 6.51e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.047 / 3.18e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.140 / 6.95e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Find\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e3\u0026ndash;5 | The Functional Enrichment Analysis of DEGs by GO, Reactome, KEGG Pathways, and protein-protein interaction\u003c/h3\u003e\n\u003cp\u003eThe GO annotation, KEGG, and Reactome pathways enrichment analysis were conducted using the DAVID database to determine the biological functions of the 24 mRNA DEGs. The top five biological processes (BP) of the DEGs were skeletal system development (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), extracellular matrix organization (ECM) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), collagen fibril organization (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), chondrocyte development (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), and endodermal cell differentiation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044). The cellular composition (CC) of the DEGs predominantly encompassed the extracellular region (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.85E-06), extracellular space (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.85E-06), collagen-containing extracellular matrix (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.37E-08), ECM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), collagen trimer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and endoplasmic reticulum lumen (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), listed in a ranking of frequency. The molecular functions (MF) of the DEGs included ECM structural constituent (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.17E-05), glycosaminoglycan binding (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), ECM binding (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0383), and ECM structural constituent conferring tensile strength (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). The most common Reactome functional pathways of the DEGs include ECM organization (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.59E-08), collagen degradation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001), degradation of the ECM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001), assembly of collagen fibrils and other multimeric structures (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), ECM proteoglycans (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), integrin cell surface interactions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), collagen formation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), and collagen chain trimerization (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038). Finally, the cytoskeleton in muscle cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) was associated to the KEGG pathway (Supplementary Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eThe enrichment of GO and pathways was examined to investigate the function of \u003cem\u003eCOMP\u003c/em\u003e mRNA. Consequently, the subsequent analysis will focus exclusively on the pathways involved of the \u003cem\u003eCOMP\u003c/em\u003e gene. Key pathways encompass R-HSA-216083: Integrin cell surface interactions (including \u003cem\u003eCOL10A1\u003c/em\u003e, \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCOL1A2\u003c/em\u003e, and \u003cem\u003eSSP1\u003c/em\u003e genes), R-HSA-1474244: Extracellular matrix organization (including \u003cem\u003eVCAN\u003c/em\u003e, \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eMMP11\u003c/em\u003e, \u003cem\u003eSSP1\u003c/em\u003e, \u003cem\u003eCOL1A2\u003c/em\u003e, \u003cem\u003eCOL11A1\u003c/em\u003e, \u003cem\u003eMFAP2\u003c/em\u003e, \u003cem\u003eBGN\u003c/em\u003e, \u003cem\u003eCOL10A1\u003c/em\u003e, and \u003cem\u003eMMP9\u003c/em\u003e genes), and R-HSA-3000178: ECM proteoglycans (including \u003cem\u003eBGN\u003c/em\u003e, \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCOL1A2\u003c/em\u003e, and \u003cem\u003eVCAN\u003c/em\u003e genes) in Reactome, as well as hsa04820: Cytoskeleton in muscle cells (including \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCOL11A1\u003c/em\u003e, \u003cem\u003eCOL1A2\u003c/em\u003e, \u003cem\u003eVCAN\u003c/em\u003e, and \u003cem\u003eBGN\u003c/em\u003e) and hsa04512: ECM-receptor interaction (including \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCOL1A2\u003c/em\u003e, and \u003cem\u003eSSP1\u003c/em\u003e genes) in KEGG.\u003c/p\u003e\n\u003cp\u003eThe 15 up-regulated genes exhibited significant enrichment in 5 biological processes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the 9 down-regulated genes showed no enrichment in any biological processes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB). Pathway studies revealed that the up-regulated DEGs were implicated in many pathways related to proliferation, apoptosis, and metastasis, that includes downstream receptors such as CD36, CD47, \u0026alpha;5\u0026beta;1, and \u0026alpha;5\u0026beta;3 integrins in the PI3K/AKT, MEK/ERK, and TGF-\u0026beta; receptor signaling pathways (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNetworks illustrating interactions of protein-protein (mRNA-mRNA), mRNA-miRNA, miRNA-lncRNA, and mRNA-lncRNA. To elucidate the relationships of the validated DEGs and identified the key miRNAs connected to \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e, we developed the PPI network and examined the protein interactions between the 24 genes (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eAccording to the prior prediction, there were 52 mRNA-miRNA pairs (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), 49 miRNA-lncRNA pairs (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e), and 2 mRNA-lncRNA pairs. The ceRNA hypothesis presumes that miRNAs typically exhibit an inverse co-expression correlation with mRNAs and lncRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC), while lncRNAs commonly demonstrate a positive co-expression correlation with mRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). We evaluated the correlation among all RNA interaction pairings utilizing the ENCORI/StarBase database and determined that 3 of 52 mRNA-miRNA couples, 3 of 49 miRNA-lncRNA pairs, and 2 of 2 mRNA-lncRNA pairs adhered to the ceRNA rule (Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Ultimately, \u003cem\u003eCOMP\u003c/em\u003e mRNAs, three miRNAs (\u003cem\u003ehsa-miR-7974\u003c/em\u003e, \u003cem\u003ehsa-miR-423-3p\u003c/em\u003e, and \u003cem\u003ehsa-miR-129-2-3p\u003c/em\u003e), and two lncRNAs (\u003cem\u003eCARMN\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003e) established a tripartite mRNA-miRNA-lncRNA ceRNA regulation network as a prospective regulatory system for breast, colorectal, and gastric malignancies. Cytoscape was employed for depicting the ceRNet (ceRNA network) (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComponents of ceRNAs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emRNA (DEGs of TCGA \u0026amp; GEO datasets)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elncRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-7974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-423-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-129-2-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e3\u0026ndash;6 | Validation Results of , , and Gene Expression by qPCR\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e3\u0026ndash;6 | Validation Results of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e Gene Expression by qPCR\u003c/div\u003e\n\u003cp\u003eWe experimentally examined the expression pattern of these three chosen genes in human breast, colorectal, and gastric tissue samples using qPCR. Using a t-test to compare tumor and normal groups for the \u003cem\u003eCOMP\u003c/em\u003e gene showed that expression was significantly higher in breast cancer (4.4156\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000), colorectal cancer (1.9691\u0026thinsp;\u0026plusmn;\u0026thinsp;0.302 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00011), and gastric cancer (4.2963\u0026thinsp;\u0026plusmn;\u0026thinsp;1.076 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000). Breast cancer (0.0711\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000) and gastric cancer (0.0977\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000) showed a notable reduction in \u003cem\u003eCARMN\u003c/em\u003e gene expression, whereas colorectal cancer (1.691\u0026thinsp;\u0026plusmn;\u0026thinsp;0.365 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01087) showed an increase in expression. In final analysis, the \u003cem\u003eGSEC\u003c/em\u003e gene presented a significant increase in expression in breast cancer (2.9918\u0026thinsp;\u0026plusmn;\u0026thinsp;0.496 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000), colorectal cancer (9.5619\u0026thinsp;\u0026plusmn;\u0026thinsp;1.213 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00000), and gastric cancer (2.4109\u0026thinsp;\u0026plusmn;\u0026thinsp;0.683 fold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00041) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eA positive correlation between target genes was found using Spearman regression (Table \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e). The correlation coefficients for \u003cem\u003eCOMP\u003c/em\u003e vs. \u003cem\u003eCARMN\u003c/em\u003e in breast, colorectal, and gastric cancers were 0.466 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0385), 0.901 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and 0.891 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), respectively. The correlation coefficients \u003cem\u003eCOMP\u003c/em\u003e vs. \u003cem\u003eGSEC\u003c/em\u003e observed were 0.893 for breast cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), 0.669 for colorectal cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0013), and 0.936 for gastric cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The correlation between the two lncRNAs \u003cem\u003eCARMN\u003c/em\u003e vs. \u003cem\u003eGSEC\u003c/em\u003e was observed in breast, colorectal, and gastric cancers, with correlation coefficients of 0.615 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0039), 0.731 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003), and 0.882 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eThe diagnostic accuracy of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, \u003cem\u003eGSEC\u003c/em\u003e was assessed (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eB). ROC analysis and the highest Youden index declared that the optimal diagnostic cut-off values for \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e of breast cancer were 1.521, 7.981, and 6.135, respectively. We found that the sensitivity and specificity of \u003cem\u003eCOMP\u003c/em\u003e as a marker for breast cancer prognoses were 90% and 80%, respectively, at this cut-off point (AUC: 0.912; 95% CI 0.825\u0026ndash;1). An overfitting profile was observed for the \u003cem\u003eCARMN\u003c/em\u003e gene with a sensitivity and specificity of 100% and an AUC of 1 (95% CI 1\u0026ndash;1). Biomarker analysis for colorectal cancer was performed on the \u003cem\u003eCOMP\u003c/em\u003e (sensitivity: 85%, specificity: 90%), \u003cem\u003eCARMN\u003c/em\u003e (sensitivity: 65%, specificity: 85%), and \u003cem\u003eGSEC\u003c/em\u003e (sensitivity and specificity: 100%) genes, with cut-off points of 6.086, 3.287, and 9.781, respectively. The AUC values were 0.84 (95% CI 0.685\u0026ndash;0.995), 0.73 (95% CI 0.562\u0026ndash;0.898), and 1 (95% CI 1\u0026ndash;1, indicating overfitting). Finally, the biomarker power analysis of \u003cem\u003eCOMP\u003c/em\u003e (sensitivity: 80%, specificity: 100%), \u003cem\u003eCARMN\u003c/em\u003e (sensitivity and specificity: 100%), and \u003cem\u003eGSEC\u003c/em\u003e (sensitivity: 65%, specificity: 100%) genes for gastric cancer revealed AUCs of 0.948 (95% CI 0.887-1), 1 (95% CI 1\u0026ndash;1, indicating overfitting), and 0.752 (95% CI 0.579\u0026ndash;0.926), respectively, with cutoff points of 1.011, 8.261, and 3.842. The \u003cem\u003eCARMN\u003c/em\u003e gene was over-expressed (overfitting) in breast (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eB10, B11, B12) and gastric (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eB13, B14, B15) cancers, meaning that all tumor samples had lower expression than healthy samples. In colorectal cancer (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eB25, B26, B27), the \u003cem\u003eGSEC\u003c/em\u003e gene exhibited the opposite trend, with all patients demonstrating higher expression levels compared to healthy individuals.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression Analysis of qPCR\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of cancer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEquation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBreast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.9056*X\u0026thinsp;+\u0026thinsp;8.2794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN vs. GSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.3261*X-8.1139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP vs. GSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.9206*X\u0026thinsp;+\u0026thinsp;3.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.7651*X-3.2626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN vs. GSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;1.1519*X\u0026thinsp;+\u0026thinsp;6.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP vs. GSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.8947*X\u0026thinsp;+\u0026thinsp;1.7571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP vs. CARMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;1.6514*X\u0026thinsp;+\u0026thinsp;16.6083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARMN vs. GSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.4129*X-8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMP vs. GSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.8122*X\u0026thinsp;+\u0026thinsp;2.6539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn 2022, the global cancer burden, as estimated by GLOBOCAN and published by the International Agency for Research on Cancer (IARC), highlighted that breast cancer, colorectal cancer, and gastric cancer are the second (11.6%), third (9.6%), and fifth (4.9%) most commonly diagnosed cancers worldwide, respectively (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Recent advances in cancer research have identified lncRNAs have surfaced as promising new aspects that influence the diagnosis, treatment, and prognosis of various diseases, with cancer being the primary focus of research concerning the role of lncRNAs as potential biomarkers (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). Specifically, lncRNAs, through competing endogenous RNA (ceRNA) networks, interact with mRNAs and miRNAs to regulate critical biological processes such as cell proliferation, migration, apoptosis, and metastasis (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). The progression of breast cancer, colorectal cancer, and gastric cancer is linked to mutations that either activate oncogenes or inactivate tumor suppressor genes (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Previous studies found new mRNAs, miRNAs, and lncRNAs based on similar genomics analyses and system biology approach that can be effective in regulating proliferation, metastasis, and progression of cancers, for example: up-regulation of URHF1 mRNA in BC, GC, and CRC, down-regulation of IGF1 in LNC01089-LINC00963/miR-1244-5p/IGF1 axis in BC, and up-regulation of THBS2 in GC (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). \u003cem\u003eCOMP\u003c/em\u003e is a protein of TSP family subgroup B with pentameric multi-domain structure (78). \u003cem\u003eCOMP\u003c/em\u003e is significantly expressed in tumor tissues of colon cancer (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e) and breast cancer, and is frequently associated with high recurrence rates and poor survival rates in cancer patients as an independent prognostic marker (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study used bioinformatics investigations to explore the roles of \u003cem\u003eCOMP\u003c/em\u003e mRNA, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e lncRNAs as biomarkers in GC, BC, and CRC pathogenesis. Then, three mRNA-miRNA-lncRNA Competing endogenous RNA (ceRNA) regulatory network was constructed to investigate the relationship between the regulatory networks involved in ECM organization and PIK3/AKT signaling pathways, which were obtained through enrichment analysis and were important pathways in tumor progression. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the heatmap of DEGs clearly illustrates the differential expression of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e across the three cancers. Additionally, the ROC curves provide a visual confirmation of their potential as biomarkers. Exploring the interaction between \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eGSEC\u003c/em\u003e, and \u003cem\u003eCARMN\u003c/em\u003e lncRNAs is necessary to understand the molecular function (MF), cellular component (CC), biological process (BP), and signaling pathways related to BC, GC, and CRC progression. For this purpose, 10 gene expression datasets from GEO database and GC, BC, and CRC samples from TCGA were analyzed in purpose to boost the sample size and accuracy of results. DEGS were identified and the expression levels of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e were analyzed using t-tests to assess significant differences between tumor and normal tissue samples. The results showed a clear upregulation of \u003cem\u003eCOMP\u003c/em\u003e in all three cancers, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating statistical significance. The results provide new insights into the increased expression of \u003cem\u003eCOMP\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003e in all three mentioned cancers, although the expression of \u003cem\u003eCARMN\u003c/em\u003e is consistently down-regulated in breast and gastric cancers, indicating its tumor-suppressive role in these cancers. However, in colorectal cancer, \u003cem\u003eCARMN\u003c/em\u003e expression is up-regulated, suggesting a more complex regulatory role that warrants further investigation. However, according to the statistical analysis of qRT-PCR results, the expression is up-regulated in colorectal cancer, but there is a need for more research on its regulatory mechanisms.\u003c/p\u003e \u003cp\u003eThe proliferation of colorectal cancer cells promotes by \u003cem\u003eCOMP\u003c/em\u003e through the activation of PI3K/Akt/ mTOR/p70S6K pathway (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e) and it\u0026rsquo;s highly expressed in aggressive colorectal cancer (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Although \u003cem\u003eCOMP\u003c/em\u003e high expression exacerbates epithelial-mesenchymal transition (EMT), suppression of its expression inhibits metastasis and invasion in colorectal cancer (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). \u003cem\u003eCOMP\u003c/em\u003e interacts with the actin-binding protein Transgelin physically in purpose of regulating cytoskeletal remodeling and enhancing colorectal cancer progression (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). High \u003cem\u003eCOMP\u003c/em\u003e expression also stimulates the deposition of collagen and other matrix proteins, leading to further exacerbation of fibrosis (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). \u003cem\u003eCOMP\u003c/em\u003e facilitates activation of MEK/ERK and PI3K/Akt signaling pathways by binding to CD36, thereby promoting proliferation, migration, invasion, and metastasis, while suppressing apoptosis. In GC, tumor-derived \u003cem\u003eCOMP\u003c/em\u003e has been identified as a diagnostic and prognostic marker (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). \u003cem\u003eCOMP\u003c/em\u003e is a crucial component of the ECM playing various and essential roles that contribute to the stability of the matricellular network (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). Recent studies suggest that \u003cem\u003eCOMP\u003c/em\u003e enhances the stability of ECM protein interactions, therefore contributing to the mechanical strength of tissues (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). The findings in this study align with earlier studies demonstrating the oncogenic role and up-regulation of \u003cem\u003eCOMP\u003c/em\u003e in GC, BC, and CRC, where its expression is associated with poor survival, however, this study is among the first to report its concurrent role in three cancer types. For instance, these findings align with prior studies demonstrating \u003cem\u003eCOMP\u003c/em\u003e's role in promoting metastasis via ECM interactions and \u003cem\u003eCARMN\u003c/em\u003e's tumor-suppressive effects mediated through miRNA sponging. Experimental validations, such as \u003cem\u003eCOMP\u003c/em\u003e knockdown models, further corroborate its role in enhancing cancer cell migration and invasion. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research discovered that the dysregulation of a great number of lncRNAs in various types of cancer play a critical role in tumorigenesis (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e) \u003cem\u003eCARMN\u003c/em\u003e\u0026rsquo;s a lncRNA which its consistent down-regulation observed across GC and BC suggests its tumor-suppressive activity, which may occur through the regulation of mRNA and miRNA pathways involved in metastasis and proliferation and no previous studies have explored \u003cem\u003eCARMN\u003c/em\u003e as a potential biomarker in breast and gastric cancers, despite its consistent down-regulation in these cancers, suggesting that \u003cem\u003eCARMN\u003c/em\u003e may have a promising role as a biomarker in other cancers, such as colorectal cancer. Noting that this study made the first demonstration that \u003cem\u003eCARMN\u003c/em\u003e could suppress tumor migration and inhibit tumor proliferation by interacting with \u003cem\u003eCOMP\u003c/em\u003e mRNA and both miR-423-3p and miR-129-2-3p miRNAs. Studies unraveled, that \u003cem\u003eGSEC\u003c/em\u003e lncRNA is found to be overexpressed in colorectal cancer, and the silencing of this lncRNA results in a notable decrease in the motility of colorectal cancer cells. This observation implies that \u003cem\u003eGSEC\u003c/em\u003e may play a crucial role in the migration of tumor cells (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). The present study aligns with other researches that discovered \u003cem\u003eGSEC\u003c/em\u003e lncRNA critical in metastasis by its up-regulation in GC, BC, and CRC. The results of this investigation revealed a significant correlation between the expression levels of \u003cem\u003eCARMN\u003c/em\u003e and the survival period of time of patients diagnosed with GC and CRC, highlighting statistically meaningful differences. Additionally, the genes \u003cem\u003eCOMP\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003e were found to be associated with non-significant overall survival across three types of malignancies, alongside \u003cem\u003eCARMN\u003c/em\u003e's role in breast cancer. In the present study ROC curve analysis underscores the diagnostic utility of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e, with \u003cem\u003eCOMP\u003c/em\u003e achieving particularly high specificity and sensitivity.\u003c/p\u003e \u003cp\u003eThese findings emphasize the biomarker's potential in early detection, particularly when coupled with established clinical parameters. By stratifying patients based on \u003cem\u003eCOMP\u003c/em\u003e expression, clinicians may refine cancer screening protocols and prioritize high-risk individuals. However, the variability in survival analysis, especially for \u003cem\u003eCARMN\u003c/em\u003e, suggests the need to explore tumor-specific and demographic factors influencing biomarker performance. Future research should integrate multi-omics data with machine learning models to enhance predictive accuracy and clinical applicability.\u003c/p\u003e \u003cp\u003eThe correlation between these genes and the ceRNA networks alongside with the most important enriched signaling pathway, extracellular matrix (ECM), leads this study to investigate this signaling pathway, but it requires to be validated by more research. Many of the enriched pathways in this study enrichment analysis were related to ECM as a key component of the tumor microenvironment contains nearly 300 proteins such as proteoglycans, fibrous proteins, cytokines, and growth factors that has a significantly influence on tissues function and tumor progression (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e). ECM component 's variants and ratios determine tissue specific properties such as stiffness and facilitates cellular processes, including migration, proliferation, and survival (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). Collagen, one of the most important components of the ECM, has been found to accumulate in larger amounts in tumor tissues during the early-stage of cancer (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). Cancer stem cells (CSCs) use ECM characteristics to promote tumor progression (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). Increased collagen deposition in the ECM leads to stiffness, which facilitates the metastasis of tumor cells (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e). In colorectal cancer (CRC), ECM remodeling involves structural and compositional changes, advancing tumor development and stiffness (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e). Types I, III, and IV collagen have been detected in the serum and blood of CRC patients, with increased deposition of type I and III collagen being linked to higher breast tissue density (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). This higher density raises the risk of breast cancer by 4\u0026ndash;6 times (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). In breast cancer, the enzyme lysyl oxidase (LOX), which creates cross-links between collagen fibers, is overexpressed (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). This causes collagen fibers to align vertically in the ECM, leading to tumor cell metastasis (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). Additionally, the PI3K pathway has been shown to promote the metastasis of breast cancer cells (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). In gastric cancer, the ECM plays a role in carcinogenesis (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). Proteomic analyses show no substantial variations in ECM components but increased ECM protein levels being associated with angiogenesis, invasion, and metastasis (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). High expression of ECM proteins, combined with collagen deposition and densification, promotes cancer progression by interacting with cell surface membrane receptors, decreases E-cadherin and β-catenin levels, and enhancing the proliferation and spread of gastric cancer cells. FBN1, an ECM protein, is found in significantly higher levels in gastric cancer tissues, leading to increased ECM stiffness (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e). This protein directly activates the PI3K/AKT pathway, promoting cell proliferation in gastric cancer (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is important to note that this study has several limitations. First, a larger sample size and more diverse population tested by qRT-PCR are needed for more detailed and reliable investigations. Second, in vitro experiments, using a larger sample size, and in vivo validation studies are crucial to confirm the findings of this study. Third, for the validation of RNA interaction analyses, experimental tests are required to verify the predicted miRNA sponging and ceRNA network interactions. Furthermore, this study primarily relied on publicly available gene expression datasets from GEO and TCGA databases. While these resources provide valuable information, potential biases in sample selection or missing data could affect the accuracy of the results. Therefore, validating these findings with independent clinical datasets would enhance the robustness of the conclusions. Another limitation is the lack of consideration of clinical and demographic factors such as age, gender, cancer stage, and treatment history, which could influence the expression patterns of the biomarkers investigated. Incorporating these variables in future studies would provide a deeper understanding of how these factors might impact the utility of \u003cem\u003eCOMP\u003c/em\u003e, \u003cem\u003eCARMN\u003c/em\u003e, and \u003cem\u003eGSEC\u003c/em\u003e as biomarkers in cancer prognosis. Lastly, while this study focused on breast, gastric, and colorectal cancers, further research should aim to explore the roles of these biomarkers in other cancer types, particularly those with distinct molecular characteristics, to determine their broader applicability across different malignancies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, the present study comprehensively investigates the potential of \u003cem\u003eCOMP\u003c/em\u003e mRNA and the lncRNAs \u003cem\u003eCARMN\u003c/em\u003e and \u003cem\u003eGSEC\u003c/em\u003e as diagnostic and prognostic biomarkers in breast, gastric, and colorectal cancers. Through integrated bioinformatics analysis of publicly available datasets from GEO and TCGA and experimental validation, distinct expression patterns of these biomarkers were identified across the three cancer types. \u003cem\u003eCOMP\u003c/em\u003e demonstrated a significant upregulation in all three cancers, correlating with aggressive tumor behaviors, including enhanced cell proliferation, migration, and invasion, primarily through ECM remodeling. This makes \u003cem\u003eCOMP\u003c/em\u003e a promising candidate for use in early cancer detection and therapeutic targeting, with its involvement in key pathways such as the PI3K/AKT signaling axis. \u003cem\u003eCARMN\u003c/em\u003e, in contrast, showed downregulation in breast and gastric cancers, reinforcing its role as a tumor suppressor. The dual nature of \u003cem\u003eCARMN\u003c/em\u003e expression, with potential divergent effects in colorectal cancer, warrants further investigation to fully understand its context-dependent regulatory role in tumorigenesis. \u003cem\u003eGSEC\u003c/em\u003e was consistently up-regulated in breast, gastric, and colorectal cancers, supporting its classification as an oncogenic lncRNA. Its role in acting as a competing endogenous RNA (ceRNA) that regulates key oncogenes through miRNA sponging suggests that \u003cem\u003eGSEC\u003c/em\u003e could be pivotal in cancer progression and represents a potential target for therapeutic interventions. The integration of these biomarkers into a comprehensive diagnostic and prognostic framework could provide significant clinical value. Future studies should focus on validating these findings in larger cohorts, exploring the molecular mechanisms underlying their role in cancer progression, and evaluating their clinical utility in personalized cancer treatment approaches. By addressing these biomarkers in parallel, more accurate diagnostic tools and targeted therapies can be developed that improve patient outcomes in breast, gastric, and colorectal cancers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1. Ethics approval: \u003c/strong\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2. Consent for publication: \u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3. \u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials: \u003c/strong\u003eThe datasets generated in this study is available with request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4. Conflicts of interest: \u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5. Financial support and sponsorship: \u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6. Authors\u0026rsquo; contribution: \u003c/strong\u003e\u003cstrong\u003eMohammadjavad Askari, Ali Hodaeian, Saba Hesami, Bita Mohammadipour, Mohammad Amin Rahimi, Mehran Zamani, and Fatemeh Izadi\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003csup\u003e \u003c/sup\u003eSoftware, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Visualization; \u003cstrong\u003eMohammad Rezaei and Seyedeh Zahra Shirdeli: \u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Supervision; \u003cstrong\u003eMansoureh Azadeh: \u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. \u003cstrong\u003eMohammadjavad Askari, Ali Hodaeian, and Saba Hesami\u003c/strong\u003e equally contributed to this study as the first authors. \u003cstrong\u003eBita Mohammadipour, Mohammad Amin Rahimi, Mehran Zamani, and Fatemeh Izadi\u003c/strong\u003e equally contributed to this study as second authors. Mohammad Rezaei and Seyedeh Zahra Shirdeli equally contributed to this study as supervisors and third authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarian AJ. Sequencing Your Genome : What Does It Mean? Methodist Debakey Cardiovasc J. 2014;10(1):3\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eLander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J SJ. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860\u0026ndash;921. \u003c/li\u003e\n\u003cli\u003eAprile M, Katopodi V, Leucci E, Costa V. LncRNAs in cancer: From garbage to junk. Cancers (Basel). 2020;12(11):1\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eGiral H, Landmesser U, Kratzer A. Into the Wild: GWAS Exploration of Non-coding RNAs. 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Succinylation Inhibits the Enzymatic Hydrolysis of the Extracellular Matrix Protein Fibrillin 1 and Promotes Gastric Cancer Progression. \u003cem\u003eAdvanced science (Weinheim, Baden-Wurttemberg, Germany)\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(27), e2200546.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Zist Fanavari Novin Biotechnolgy Institute, Isfahan, Iran","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer Genetics, Systems Biology, Bioinformatics, Network Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5943216/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5943216/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdvances in high-throughput genomic technologies have illuminated the significant role of non-coding RNAs (ncRNAs), which constitute 98% of the genome. Among these, long non-coding RNAs (lncRNAs) play crucial roles in gene regulation and cancer progression. COMP, a cartilage oligomeric matrix protein, and lncRNAs CARMN and GSEC are implicated in breast, gastric, and colorectal cancers. These molecules influence tumor progression through extracellular matrix (ECM) remodeling and key signaling pathways such as Notch3/Jagged1, PI3K/AKT, TGF-β, and ECM organization signaling. Despite advancements in cancer therapies, diagnostic and prognostic challenges persist, necessitating the identification of robust biomarkers.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eGene expression data from GEO and TCGA datasets were analyzed to identify differentially expressed genes. Functional enrichment and pathway analyses highlighted key roles in ECM organization and associated signaling pathways. Protein-protein interaction (PPI) and competing endogenous RNA (ceRNA) networks were constructed to elucidate molecular interactions. Experimental validation included RNA extraction and qRT-PCR of 120 matched cancerous and normal tissues, followed by statistical evaluations, including ROC-AUC and survival analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCOMP and GSEC were significantly up-regulated, while CARMN was down-regulated in breast and gastric cancer tissues and up-regulated in colorectal cancer. Functional enrichment revealed their involvement in ECM organization and tumor-promoting pathways. COMP exhibited excellent diagnostic potential with ROC-AUC values exceeding 0.9. Survival analysis associated CARMN expression with improved outcomes in gastric and colorectal cancers. Correlation analyses highlighted regulatory interactions among the biomarkers and their involvement in cancer-related signaling cascades.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCOMP, CARMN, and GSEC are promising biomarkers for diagnosing and predicting outcomes in breast, gastric, and colorectal cancers. Their roles in ECM remodeling and signaling pathways underscore their potential as therapeutic targets and diagnostic tools, warranting further exploration of their molecular mechanisms.\u003c/p\u003e","manuscriptTitle":"Identification of Cross-Cancer Biomarkers: COMP mRNA and CARMN/GSEC lncRNAs Shared in Breast, Gastric, and Colorectal Cancers via Integrated Systems Biology and Experimental Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-04 09:23:44","doi":"10.21203/rs.3.rs-5943216/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6f54b9f-9278-4bf6-9873-29af9e229dae","owner":[],"postedDate":"February 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43714910,"name":"Cancer Biology"},{"id":43714911,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2025-02-04T09:23:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-04 09:23:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5943216","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5943216","identity":"rs-5943216","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}