Exploring the Roles of lncRNAs MZF1-AS1 and MEG9 in Breast, Gastric, and Colorectal Cancer Development: Regulation of LCN6 in Fatty Acid Signaling Pathways

Exploring the Roles of lncRNAs MZF1-AS1 and MEG9 in Breast, Gastric, and Colorectal Cancer Development: Regulation of LCN6 in Fatty Acid Signaling Pathways | 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 Exploring the Roles of lncRNAs MZF1-AS1 and MEG9 in Breast, Gastric, and Colorectal Cancer Development: Regulation of LCN6 in Fatty Acid Signaling Pathways Mohammad Hassan Zoofaghari, Mohammad Sharif Sharifani, Mahsa Ghandi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4567087/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 The discovery of cancer biomarkers is pivotal for early diagnosis, therapeutic planning, and patient monitoring, offering a molecular insight into tumor characteristics. This study aimed to identify novel diagnostic and prognostic biomarkers for breast cancer (BC), gastric cancer (GC), and colorectal cancer (CRC) using a systems biology approach. Methods High-throughput gene expression analysis was conducted using the limma package in R Studio on datasets GSE134359, GSE54129, and GSE81558. Potential non-coding regulatory factors were identified through RNA and protein interaction analyses. Interaction networks were visualized with Cytoscape. Pathway enrichment analysis (Reactome) and survival analysis (GEPIA2) were utilized to elucidate the regulatory mechanisms of selected RNAs. The findings were validated using qRT-PCR experiments on GC, BC, and CRC samples. Results Bioinformatics analyses revealed significantly low expression of LCN6 in BC, CRC, and GC samples. Interaction analysis showed that lncRNAs MEG9 and MZF1-AS1 physically interact with LCN6 mRNA. Gene expression analysis using ENCORI indicated dysregulation of MEG9 and MZF1-AS1 in BC, CRC, and GC samples. Correlation analysis uncovered novel relationships between the expression of these lncRNAs and mRNA in the three cancer types. ROC analysis suggested that LCN6 and MZF1-AS1 are potential biomarkers for GC and CRC, while MEG9 could serve as a robust diagnostic biomarker for BC, CRC, and GC. Significant positive correlations were observed between MZF1-AS1 and MEG9 with LCN6 in BC samples and between LCN6 and MEG9 in GC samples, but no correlation was found in CRC samples. Conclusion LncRNAs MZF1-AS1 and MEG9 may regulate the expression of LCN6 in the "transport of fatty acid" signaling pathway, potentially influencing the risk of BC, GC, and CRC through this regulatory mechanism. Cancer Biology Bioinformatics lncRNA Systems Biology Microarray qRT-PCR microRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Breast cancer (BC), the most prevalent cancer among women globally; gastric cancer (GC), ranked fifth worldwide with high mortality; and colorectal cancer (CRC), the third most common cancer, represent significant global health challenges ( 1 ). The heterogeneity of these cancers, influenced by genetic, environmental, and lifestyle factors, has spurred advancements in personalized treatments, including targeted therapies and immunotherapies ( 2 ). Despite these advances, disparities in access to care and outcomes persist, underscoring the need for equitable healthcare solutions, early detection, and preventive strategies against modifiable risk factors ( 3 ). Cancer biomarker discovery is pivotal for the early diagnosis, therapeutic stratification, and monitoring of cancer, offering a molecular snapshot of the tumor's phenotype. Advanced technologies in proteomics and genomics have propelled the identification of biomarkers, yet translating these discoveries into clinical practice presents significant challenges due to the complexity of tumor biology and the need for rigorous validation. Notable advancements include the use of protein arrays for high-throughput identification and the exploration of circulating tumor DNA and cells for non-invasive diagnostics, highlighting the potential of biomarkers in personalized medicine and the ongoing quest for specificity and sensitivity in cancer detection ( 4 – 6 ). The intricate interplay between microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) represents a fascinating aspect of post-transcriptional gene regulation, playing pivotal roles in diverse biological processes and diseases, including cancer. miRNAs, short non-coding RNAs of approximately 22 nucleotides, modulate gene expression by binding to complementary sequences on target mRNAs, leading to translational repression or mRNA degradation ( 7 ). Conversely, lncRNAs, exceeding 200 nucleotides in length, engage in complex regulatory activities such as chromatin remodeling, transcriptional control, and acting as competing endogenous RNAs (ceRNAs) or "sponges" for miRNAs, thereby modulating their activity and impact on target mRNAs ( 8 ). The dynamic regulatory network facilitated by miRNA-lncRNA interactions adds a layer of complexity to our understanding of cellular homeostasis, development, and pathology, illustrating the necessity of a systemic approach to unravel the functional nuances of these ncRNAs in physiological and disease contexts ( 9 , 10 ). These interactions are not only crucial for maintaining cellular function but also offer potential targets for therapeutic intervention in diseases such as cancer, highlighting the importance of further exploration into the miRNA-lncRNA axis ( 11 ). In this research, we want to find new potential diagnostic biomarkers for BC, GC, and CRC based on bioinformatics and an experimental approach. One of the other important goals of our investigation is to find the interaction of non-coding RNAs (miRNAs and lncRNAs) with important mRNAs. 2. Materials and Methods 2.1. High throughput data analysis Analysis of high-throughput microarray data was conducted to identify potential novel biomarkers with coding capabilities for GC, BC, and CRC. The microarray datasets utilized for the identification of differentially expressed genes (DEGs) in BC, GC, and CRC were GSE134359, GSE54129, and GSE81558, as detailed in Table 1 . The initial processing of these raw microarray datasets was carried out using the affy package, with normalization conducted through the Robust Multi-array Average (RMA) method provided by the same package. To ensure the quality of the dataset, principal component analysis (PCA), sample correlation analysis, and boxplot visualization of expression data for both normal and cancerous samples were employed. The statistical examination of the expression data from the microarrays was undertaken using the limma package. Both affy and limma packages were acquired from Bioconductor.org. Visualization of the microarray analysis results was achieved using ggplot2 and pheatmap packages, which were sourced from https://cran.r-project.org/ . Furthermore, gene expression analysis validation was conducted through the use of the GEPIA2 ( http://gepia2.cancer-pku.cn/ ) and ENCORI ( https://rnasysu.com/encori/ ) online platforms. Table 1 Characteristics of microarray datasets in this study. Three datasets were used in this study. dataset tumor samples control samples platform disease reference GSE134359 74 12 GPL17586 breast cancer ( 12 ) GSE208099 111 21 GPL570 gastric cancer ( 13 ) GSE81558 42 9 GPL15207 colorectal cancer ( 14 ) 2.2. RNA interaction and enrichment analyses In this research, various online platforms were leveraged to identify emerging non-coding regulatory biomarkers within BC, GC, and CRC specimens. The lncRRIsearch ( 15 ) web application ( http://rtools.cbrc.jp/LncRRIsearch ) facilitated the selection of innovative regulatory lncRNAs. To delineate the protein interaction networks associated with the identified mRNAs, the STRING database ( https://string-db.org/ ) ( 16 ) was utilized. New regulatory miRNAs were discovered by applying miRWalk ( http://mirwalk.umm.uni-heidelberg.de/ ) ( 17 – 19 ). Visualization of the RNA interaction networks was accomplished using the Cytoscape ( 20 , 21 ) software. Enrichment analyses of pathways were conducted utilizing the enrichr ( https://maayanlab.cloud/Enrichr/ ) ( 22 – 24 ) and Reactome ( https://reactome.org/ ) ( 25 – 27 )web databases. Additionally, to elucidate the association between these targets' expression levels and cancer patients' survival rates, a survival analysis was conducted using the GEPIA2 online database ( 28 ). In the survival analysis, three selected RNAs (mRNA and the two lncRNAs) were considered as one signature model for BC, GC, and CRC samples. 2.3. Clinical features of human samples for qRT-PCR experiment The Ethics Committee of Al-Zahra Hospital, affiliated with Isfahan University of Medical Sciences, granted approval for all the research protocols involving human specimens utilized in this study, with all participating patients providing their written informed consent. In this case-control investigation, the expression profiles of selected mRNAs and lncRNAs were analyzed in 20 tissue samples from BC, CRC, and GC patients. These were then contrasted with those from 20 adjacent non-tumorous tissue samples for each cancer type. It is noteworthy that none of the subjects had previously undergone chemotherapy or radiation therapy. Before being preserved in RNA Later solution (Invitrogen, USA) for subsequent pathological examination, the tissue specimens were meticulously rinsed with distilled water and then immediately frozen in liquid nitrogen. Detailed clinicopathological characteristics of the human samples are delineated in Tables 2 – 4 . Table 2 Clinical characteristics of BC samples. Variable Status Number % Stage I 0 0 II 6 30 III 12 60 IV 0 0 Unknown 2 10 Age 45 8 40 Unknown 2 10 Tumor size (TS) 5cm 6 30 Unknown 2 10 Menopausal status Yes 18 90 No 2 10 Unknown 0 0 Lymph node Yes 16 80 No 2 10 Unknown 2 10 ER receptor Positive 8 40 Negative 7 35 Unknown 5 25 PR receptor Positive 6 30 Negative 9 45 Unknown 5 25 HER2/neu receptor Positive 10 50 Negative 5 25 Unknown 5 25 Table 2 Clinicopathological characteristics of GC samples. Variable Status Number % Age 50 12 60 Sex Male 18 90 Female 2 10 Tumor Size 5 cm 10 50 Histology Adenocarcinoma 18 90 Mucinous Adenocarcinoma 1 5 Signet Ring Carcinoma 1 5 Perineural Invasion No 6 30 Yes 14 70 Nodal Extension No 16 80 Yes 4 20 TNM Staging I 1 5 II 6 30 IIIA 2 10 IIIB 4 20 IV 7 35 Family History No 14 70 Yes 6 30 Smoking DX-Smoker at Diagnosis but Discontinued 2 10 Ex-Smoker 2 10 Non-Smoker 15 75 smoker 1 5 Table 3 Clinicopathological table of colorectal cancer patients. Variable Status Number % Stage I 2 10 II 3 15 III 7 35 IV 8 40 Unknown 0 0 Age 50 15 75 Unknown 0 0 Tumor size (TS) 5cm 11 55 Unknown 0 0 Lymphatic Invasion Yes 8 40 No 11 55 Unknown 1 5 Perineural Invasion Yes 12 60 No 8 40 Unknown 0 0 Smoking Non-smoker 17 85 Smoker 3 15 Unknown 0 0 Sex Female 11 55 Male 9 45 Unknown 0 0 2.4. Statistical analyses The quantitative real-time polymerase chain reaction (qRT-PCR) experiment's statistical evaluation was conducted using Graph Pad Prism 8 software. Both paired and unpaired t-tests were utilized to determine the significance level. Furthermore, receiver operating characteristic (ROC) analysis was executed to assess the diagnostic potential of the tumor specimens. The area under the curve (AUC) was critically appraised within the ROC outcomes. An AUC value ranging from 0.7 to 0.8 is indicative of an acceptable biomarker; a range from 0.8 to 0.9 signifies a good biomarker, while an AUC value from 0.9 to 1 is reflective of an excellent diagnostic biomarker. 3. Results 3.1. Microarray analysis Microarray analysis was conducted on three high-throughput datasets to identify new diagnostic biomarkers for BC, GC, and CRC. Principal component analysis (PCA) was utilized to assess the quality of the microarray samples. The PCA results for BC (Fig. 1 a), GC (Fig. 1 b), and CRC (Fig. 1 c) indicated that all microarray samples were of adequate quality for subsequent analysis. Differential expression analysis (DEA) was conducted to discover novel, shared potential diagnostic biomarkers for BC, GC, and CRC. The results of this analysis highlighted LCN6 as significantly low expressed in BC (logFC: -4.256, adj. P. Value < 0.0001), GC (logFC: -1.256, adj.P.Value: 0.000228569), and CRC (logFC: -1.756, adj.P.Value < 0.0001) (Fig. 2 ). Additionally, Fig. 3 illustrates the most prominently differentially expressed genes (DEGs) in the tumor samples. 3.2. Protein-protein interaction and pathway analysis The table of proteins with direct and indirect interaction with LCN6 is provided in Table 4 . The k-means clustering method was performed to cluster all nodes in the protein interaction network (Fig. 4 ) into three groups. Enrichr performed pathway enrichment and gene ontology analysis. Based on protein interaction analysis, LCN6 has direct and indirect interaction with following proteins: ADAM7, CST11, LCN10, LCN12, LCN15, LCN6, LCN8, LCN9, LCNL1, RNASE10, SPINT4, TEDDM1, APOD, C8G, LCN1, OBP2A, OBP2B, PTGDS, DEFB119, DEFB128 , and WFDC9 . The mentioned proteins are divided into three different clusters using the K-mean clustering method. The first cluster regulates the “Transport of Fatty Acids signaling pathway” and is involved in the “Positive Regulation of Cilium Movement” biological process. A list of all related signaling pathways correlated to all nodes in the protein interaction network is provided in Table 4 . A diagram of “Fatty acid metabolism” is provided in Fig. 5 . In this diagram, the regulatory role of the LCN family is demonstrated. The family members exhibit variations in their amino acid sequences, yet they exhibit a highly preserved beta-barrel configuration consisting of an eight-stranded anti-parallel beta-sheet. This configuration forms a pocket for ligand-binding, facilitating the binding and transportation of lipids and other small hydrophobic molecules ( 29 ). Lipocalin proteins have been implicated in various biological processes, including immune responses, prostaglandin synthesis, retinoid binding, and interactions with cancer cells. Specifically, Lipocalins 1, 9, 12, and 15 (LCN1, 9, 12, and 15) demonstrate the capacity to transport diverse hydrophobic molecules. Table 4 Clusters of protein interaction network and related signaling pathways. cluster color count name pathway biological process 1 Red 12 ADAM7 Transport Of Fatty Acids Positive Regulation of Cilium Movement CST11 LCN10 LCN12 LCN15 LCN6 LCN8 LCN9 LCNL1 RNASE10 SPINT4 TEDDM1 2 Green 6 APOD Arachidonic acid metabolism Sensory Perception of Chemical Stimulus C8G LCN1 OBP2A OBP2B PTGDS 3 Blue 3 DEFB119 Beta Defensins Antifungal Innate Immune Response DEFB128 WFDC9 3.3. Non-coding interaction analysis To find novel non-coding regulatory RNAs for LCN6, miRNA and lncRNA interaction analysis were utilized. For the miRNA interaction analysis, the following criteria were considered: location of interaction: seed region (3’UTR), and binding probability (score): 1. Using the mentioned conditions, 13 miRNAs were selected as a regulatory network for LCN6. Furthermore, based on lncRNA interaction analysis, lncRNAs MZF1-AS1 and MEG9 are the two novel regulators of LCN6 (Fig. 6 ). Based on correlation analysis (ENCORI), MEG9 has a significant positive correlation with LCN6 expression in BC, GC, and CRC samples (Fig. 7 ). Also, MZF1-AS1 has a significant correlation with LCN6 in BC samples. 3.4. Expression analysis of LCN6, MEG9, and MZF1-AS1 Bioinformatics analysis and experimental validations of computational results showed different results about the expression patterns of LCN6, MEG9, and MZF1-AS1 in BC, GC, and CRC samples. First, RNAseq data analysis by the ENCORI database revealed that LCN6 is significantly low-expressed in BC, GC, and CC samples. Based on the mentioned analysis, MZF1-AS1 is highly expressed in BC, GC, and CC samples. Also, MEG9 is down-regulated in BC samples (Fig. 8 ). Based on the qRT-PCR experiment, there was no significant change in the expression level of LCN6, MEG9, and MZF1-AS1 in BC samples. However, there was a significantly low expression of LCN6 in GC and CRC samples (same as bioinformatics results). MZF1-AS1 and MEG9 also have significantly low expression in GC and CRC samples (apposite to bioinformatics results, Fig. 9 ). Based on survival analysis, there is no significant relation between the survival rate of BC, GC, and CRC patients and the changes in the expression level of LCN6, MEG9, and MZF1-AS1 (Fig. 10 ). For validation of bioinformatics correlation analysis (Fig. 7 ), the same analysis was performed on the qRT-PCR data. Based on the correlation analysis of LCN6 with MEG9 and MZF1-AS1, LCN6 has a significant positive correlation with MEG9 and MZF1-AS1 in BC samples. Also, LCN6 has a significant positive interaction with MEG9 in CRC samples (Fig. 11 ). Based on ROC analysis, LCN6 might be a suitable diagnostic biomarker for CRC and an acceptable biomarker for GC. lncRNA MEG9 was identified as an excellent diagnostic biomarker of CRC and an acceptable biomarker of BC and GC. MZF1-AS1 is a good biomarker of GC and an excellent biomarker of CRC samples (Fig. 12 ). All information about gene expression analysis is provided in Table 5 . Information of correlation analysis is provided in Table 6 . Table 5 Information on gene expression analysis in this study. expression ROC gene disease logFC p- value AUC p- value LCN6 BC 0.5548 0.2305 0.6250 0.1762 GC -3.398 0.0005 0.7875 0.0019 CC -3.836 0.0002 0.8125 0.0007 MZF1-AS1 BC 1.139 0.7562 0.5900 0.3302 GC -1.729 0.0029 0.8179 0.0011 CC -6.150 < 0.0001 0.9875 < 0.0001 MEG9 BC 1.800 0.0532 0.7025 0.0284 GC -2.381 0.0003 0.7675 0.0038 CC -6.426 < 0.0001 0.9775 < 0.0001 Table 6 Information of correlation analysis of lncRNAs with LCN6 mRNA in this study. BC GC CRC r p -value r p -value r p -value MZF1-AS1 0.7218 0.0003 0.2249 0.3403 0.2586 0.271 MEG9 0.7001 0.0006 0.7872 < 0.0001 0.3574 0.1219 4. Discussion The "transport of fatty acids" signaling pathway plays a crucial role in the development of several cancers, including BC, GC, and CRC. In breast and colorectal cancers, fatty acid-binding proteins (FABPs) are significantly involved in the modulation of lipid metabolism, which influences cancer cell proliferation, growth, and metastasis. FABP5, in particular, has been highlighted for its role in promoting lipolysis of lipid droplets, enhancing de novo fatty acid synthesis, and activating inflammatory pathways through the nuclear factor-kappa B (NF-κB) signaling in cancer cells, which contributes to the aggressiveness of breast cancer cells ( 30 ). Similarly, in colorectal cancer, the overexpression of fatty acid synthase (FASN) enhances cancer cell proliferation and metastasis by regulating the AMP-activated protein kinase (AMPK)/mechanistic target of rapamycin (mTOR) pathway, suggesting a potential target for therapeutic interventions ( 31 ). The fatty acid translocase CD36 also promotes metastasis in colorectal cancer by enhancing cancer cell proliferation, migration, and invasion, further underscoring the critical role of fatty acid transport in cancer progression ( 32 ). lncRNAs MEG9 (also known as MEG3) and MZF1-AS1 have been identified as critical regulators in the pathogenesis of various cancers, including BC, GC, and CRC. MEG9, primarily functioning as a tumor suppressor, exerts its influence through several distinct mechanisms. In BC, MEG9 has been shown to inhibit cell proliferation and induce apoptosis by enhancing p53 signaling, a key tumor suppressor pathway. This is achieved through MEG9's interaction with p53, stabilizing the protein and enhancing its transcriptional activity, which leads to the suppression of tumor growth and proliferation ( 33 ). In GC, MEG9 has been reported to modulate angiogenesis and epithelial-mesenchymal transition (EMT) by regulating key signaling molecules such as VEGF and TGF-β, contributing to the suppression of cancer metastasis ( 34 ). In CRC, MEG9 affects the tumor microenvironment by altering the expression of immune checkpoint proteins, thereby impacting immune evasion mechanisms and influencing cancer progression ( 35 ). On the other hand, MZF1-AS1 is recognized as an oncogenic lncRNA that significantly contributes to cancer progression by modulating several key pathways. In BC, MZF1-AS1 promotes tumor growth and metastasis by sponging miR-30a-5p, leading to the upregulation of target oncogenes such as VEGF and MMP9, which are crucial for angiogenesis and cell invasion ( 36 ). This interaction enhances the EMT process, facilitating tumor cell migration and invasion. In GC, MZF1-AS1 has been shown to interact with and modulate the JAK/STAT signaling pathway, leading to increased cancer cell proliferation and survival, further underscoring its role in enhancing the oncogenic potential of gastric cancer cells ( 37 ). In CRC, MZF1-AS1 promotes cancer progression by similarly sponging miR-30a-5p, which leads to the derepression of genes involved in cell proliferation and survival, contributing to a more aggressive cancer ( 36 ). The protein Lipocalin 6 (LCN6) has been recently studied in the context of BC, GC, and CRC for its role in cancer progression and potential as a therapeutic target. In breast cancer, LCN6 is implicated in the regulation of the epithelial-mesenchymal transition (EMT), a critical process in cancer metastasis. Research has demonstrated that LCN6 can influence key markers of EMT, including N-cadherin and vimentin, indicating its role in enhancing the invasive and migratory capabilities of BC cells. This interaction suggests that LCN6 could contribute to the aggressive behavior of BC by facilitating the dissemination of cancer cells to distant sites ( 38 ). In colorectal cancer, LCN6 is linked to inflammatory pathways that are known to contribute to cancer development, particularly in the context of chronic inflammation. A study highlighted that LCN6, along with other inflammatory mediators, is upregulated in CRC tissues, particularly in those arising from colitis, a condition that predisposes to CRC. The study further elaborated that LCN6 might function through the IL-6/STAT3/NF-κB signaling pathway, promoting cell survival and potentially facilitating the transition from inflammation to cancer ( 39 ). These findings indicate that LCN6 could serve as a biomarker for CRC progression, particularly in patients with a history of inflammatory bowel disease. These observations across different cancer types underline the diverse roles that LCN6 can play in cancer biology. By influencing key processes such as EMT in breast cancer and mediating inflammation-driven pathways in colorectal cancer, LCN6 emerges as a multifaceted protein with potential implications for therapy and prognosis in various cancers. Further research into LCN6 could provide deeper insights into its mechanisms of action and pave the way for novel therapeutic strategies targeting this protein in a range of malignancies. In previous studies, we found different RNA regulatory networks that might play a significant role in the development of different cancer types. Based on our evaluations, lncRNA NORAD ( 40 ) and LINC00520 ( 41 ) could be novel regulatory factors of VTCN1 and ESR2 in BC development, respectively. Non-coding regulatory networks of THBS2—especially lncRNA BAIAP2–AS1—are a potential diagnostic biomarker of GC ( 42 ). MiR-3679-5p changes the risk of BC through regulation of XBP1 expression level ( 43 ). However, due to the limitations of sample collection and experimental instruments in this experiment and previous investigations, more experimental validations are needed. 5. Conclusion In this study, we performed an integrated systems biology investigation to find novel interactions in BC, CRC, and GC patients. Based on the results, MEG9 and MZF1-AS1 are the two regulators of LCN6 in the mentioned diseases. miR-1247-3p could also regulate the expression level of LCN6. Dysregulation of MEG9, MZF1-AS1, and miR-1247-3p might disturb the regular process of the “Fatty acid transport” signaling pathway and increase the risk of BC, CRC, and GC. Declarations 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. Consent for publication: Informed consent was obtained from all individual participants included in the study. Availability of data and materials: The datasets generated or analyzed during the current study are available in the GEO repository, GSE134359, GSE54129, and GSE81558. Conflicts of interest: The authors declare that they have no competing interests. Financial support and sponsorship: Not applicable. Authors’ contribution: Mohammad Hassan Zoofaghari, Mohammad Sharif Sharifani, Mahsa Ghandi, Sanaz Zare, Shantia Yazdani, Sina Fekri, Ghazaleh Sheikhi Ghahi, and Golnaz Enayat Jazi : 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. Mohammad Hassan Zoofaghari, Mohammad Sharif Sharifani, Mahsa Ghandi, and Sanaz Zare equally contributed to this study as the first authors. 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PLoS Comput Biol [Internet]. 2018 Jan 1 [cited 2021 Oct 31];14(1). https://pubmed.ncbi.nlm.nih.gov/29377902/ C BJLMGV G, P L, A F, The reactome pathway knowledgebase. Nucleic Acids Res [Internet]. 2020 Jan 1 [cited 2021 Jul 29];48(D1):D498–503. https://pubmed.ncbi.nlm.nih.gov/31691815/ Tang Z, Kang B, Li C, Chen T, Zhang Z GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res [Internet]. 2019 Jul 1 [cited 2022 Jun 29];47(W1):W556–60. https://pubmed.ncbi.nlm.nih.gov/31114875/ Flower DR, North ACT, Attwood TK Structure and sequence relationships in the lipocalins and related proteins. Protein Sci [Internet]. 1993 [cited 2024 May 24];2(5):753–61. https://pubmed.ncbi.nlm.nih.gov/7684291/ Senga S, Kobayashi N, Kawaguchi K, Ando A, Fujii H (2018) Fatty acid-binding protein 5 (FABP5) promotes lipolysis of lipid droplets, de novo fatty acid (FA) synthesis and activation of nuclear factor-kappa B (NF-κB) signaling in cancer cells. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 1863(9):1057–1067 Lu T, Sun L, Wang Z, Zhang Y, He Z, Xu C (2021) Fatty acid synthase enhances colorectal cancer cell proliferation and metastasis via regulating AMPK/mTOR pathway. Onco Targets Ther [Internet]. 2019 May 2 [cited 2024 May 24];12:3339–47. https://www.dovepress.com/fatty-acid-synthase-enhances-colorectal-cancer-cell-proliferation-and--peer-reviewed-fulltext-article-OTT Drury JM, Rychahou P, Weiss HL, Zaytseva YY. Abstract 2880: CD36, a fatty acid translocase, promotes metastasis in CRC. Cancer Res [Internet] Jul 1 [cited 2024 May 24];81(13_Supplement):2880–2880. /cancerres/article/81/13_Supplement/2880/668703/Abstract-2880-CD36-a-fatty-acid-translocase Ren G, Han G, Song Z, Zang A, Liu B, Hu L LncRNA MCM3AP-AS1 Downregulates LncRNA MEG3 in Triple Negative Breast Cancer to Inhibit the Proliferation of Cancer Cells. Crit Rev Eukaryot Gene Expr [Internet]. 2021 [cited 2024 May 24];31(4):81–7. https://pubmed.ncbi.nlm.nih.gov/34587438/ Wang Y, Xie T, Liu H, Yu X. LncRNA HLA-F-AS1 Enhances the Migration, Invasion and Apoptosis of Glioblastoma Cells by Targeting lncRNA MEG3. Cancer Manag Res [Internet]. 2021 Dec 11 [cited 2024 May 24];13:9139–45. https://www.dovepress.com/lncrna-hla-f-as1-enhances-the-migration-invasion-and-apoptosis-of-glio-peer-reviewed-fulltext-article-CMAR Al-Rugeebah A, Alanazi M, Parine NR MEG3: an Oncogenic Long Non-coding RNA in Different Cancers. Pathology & Oncology Research 2019 25:3 [Internet]. 2019 Feb 21 [cited 2024 May 24];25(3):859–74. https://link.springer.com/article/ 10.1007/s12253-019-00614-3 Li J, Zhao LM, Zhang C, Li M, Gao B, Hu XH The lncRNA FEZF1-AS1 Promotes the Progression of Colorectal Cancer Through Regulating OTX1 and Targeting miR-30a-5p. Oncol Res. 2020;28(1):51–63. Zhou Y, Xu S, Xia H, Gao Z, Huang R, Tang E Long noncoding RNA FEZF1-AS1 in human cancers. Clinica Chimica Acta. 2019;497:20–6. Kurozumi S, Alsaeed S, Orah N, Miligy IM, Joseph C, Aljohani A Clinicopathological significance of lipocalin 2 nuclear expression in invasive breast cancer. Breast Cancer Res Treat [Internet]. 2020 Feb 1 [cited 2024 May 24];179(3):557–64. https://link.springer.com/article/10.1007/s10549-019-05488-2 Kim SL, Shin MW, Seo SY, Kim SW Lipocalin 2 potentially contributes to tumorigenesis from colitis via IL-6/STAT3/NF-κB signaling pathway. Biosci Rep [Internet]. 2022 May 1 [cited 2024 May 24];42(5). https://pubmed.ncbi.nlm.nih.gov/35470375/ Rezvani Sichani A, Dadkhah P, Tabandeh T, Kaviani Dehkordi N, Rezaei M, Rahimirad S Molecular insight into the expression level of an immunosuppression gene, VTCN1, and its regulatory factors in breast cancer patients and non-cancerous samples with a higher level of IgE. 2023 Feb 8 [cited 2024 Mar 2]; https://www.researchsquare.com Ezzati E, Mosadeshi S, Akbarinia A, Horriat S, Rezaei M, Azadeh M LINC00520 promotes breast cancer development by low expression as a tumor suppressor and prognostic biomarker by regulating the ESR2 expression level: integrated systems biology bioinformatics and experimental analyses. 2022 Aug 10 [cited 2022 Aug 18]; https://www.researchsquare.com Barani A, Beikverdi K, Mashhadi B, Parsapour N, Rezaei M, Javid P Transcription analysis of the THBS2 gene through regulation by potential non-coding diagnostic biomarkers and oncogenes of gastric cancer in the ECM receptor interaction signaling pathway: integrated systems biology and experimental investigation. 2022 Nov 23 [cited 2023 May 13]; https://www.researchsquare.com Shirdeli SZ, Hashemi SA, Hashemi GS, Khalilian L, Ferdowsian S, Mostaghimi Y LINC1521 and miR-3679-5p modulate cellular response to chemical stress in breast cancer patients through regulation of XBP1 expression as a potential diagnostic biomarker. Res Sq [Internet]. 2023 Aug 11 [cited 2023 Nov 20]; https://www.researchsquare.com Additional Declarations The authors declare no competing interests. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4567087","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313371427,"identity":"86744234-af3e-4e0f-855b-28f639b641e3","order_by":0,"name":"Mohammad Hassan Zoofaghari","email":"","orcid":"https://orcid.org/0009-0006-1001-1813","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Hassan","lastName":"Zoofaghari","suffix":""},{"id":313371529,"identity":"01411703-611f-4eb5-9269-c0ce0435afc8","order_by":1,"name":"Mohammad Sharif Sharifani","email":"","orcid":"https://orcid.org/0009-0004-2308-0098","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Sharif","lastName":"Sharifani","suffix":""},{"id":313371635,"identity":"fb168520-a963-4905-8657-40bd94eb9475","order_by":2,"name":"Mahsa Ghandi","email":"","orcid":"https://orcid.org/0009-0006-6835-4712","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mahsa","middleName":"","lastName":"Ghandi","suffix":""},{"id":313372946,"identity":"91d0ceb9-d6cc-4617-9998-25f37a96f6fe","order_by":3,"name":"Sanaz Zare","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Sanaz","middleName":"","lastName":"Zare","suffix":""},{"id":313373049,"identity":"a59cdd3a-c969-4588-999f-a9622422eaeb","order_by":4,"name":"Shantia Yazdani","email":"","orcid":"https://orcid.org/0009-0007-1803-3213","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Shantia","middleName":"","lastName":"Yazdani","suffix":""},{"id":313373263,"identity":"4d7ef8f0-e0b4-4af5-bd63-04288f090184","order_by":5,"name":"Sina Fekri","email":"","orcid":"https://orcid.org/0009-0007-0338-6999","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Sina","middleName":"","lastName":"Fekri","suffix":""},{"id":313373538,"identity":"eec55303-04d6-4474-92eb-133c10d8511f","order_by":6,"name":"Ghazaleh Sheikhi Ghahi","email":"","orcid":"https://orcid.org/0009-0004-7270-8486","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Ghazaleh","middleName":"Sheikhi","lastName":"Ghahi","suffix":""},{"id":313373704,"identity":"c3696807-c084-42db-a4f5-078dd8ea5291","order_by":7,"name":"Golnaz Enayat Jazi","email":"","orcid":"https://orcid.org/0009-0007-7828-8816","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Golnaz","middleName":"Enayat","lastName":"Jazi","suffix":""},{"id":313373952,"identity":"74d21148-7144-4699-b4a2-084cc5c8f00f","order_by":8,"name":"Sayedeh Zahra Shirdeli","email":"","orcid":"https://orcid.org/0000-0002-5662-1805","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Sayedeh","middleName":"Zahra","lastName":"Shirdeli","suffix":""},{"id":313373953,"identity":"f2d0ca40-a320-48ba-855e-56eb4624b385","order_by":9,"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":313374247,"identity":"ba19f41a-fee2-4b28-b36c-9757bc61dfd2","order_by":10,"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":"2024-06-12 03:04:15","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-4567087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4567087/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58313990,"identity":"cb1c3a5b-b3f0-4545-8be3-8ca941220234","added_by":"auto","created_at":"2024-06-13 20:40:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90868,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) of tumor and control samples in breast cancer (BC, a), gastric cancer (GC, b), and colorectal cancer (CRC, c) datasets. The red color indicates tumor samples, and the blue color indicates normal samples. The samples are separated properly, and the quality of the samples is suitable enough for downstream analyses.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/e121972153fc96d66f97324f.png"},{"id":58313989,"identity":"0660a2ca-c7a3-4885-b496-a45a59ee3df3","added_by":"auto","created_at":"2024-06-13 20:40:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276611,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot illustrating the DEGs in samples of BC (GSE13459), GC (GSE208099), and CRC (GSE44076). In this plot, up-regulated genes are marked in red, while those expressed at lower levels are indicated in blue. The gene \u003cem\u003eLCN6\u003c/em\u003e is highlighted on the plot as a black dot. In the BC dataset, there were 468 up-regulated and 816 low-expressed RNAs. For the GC dataset, there were 54 high-expressed and 103 low-expressed RNAs. For the CRC dataset, there were 31 up-regulated and 81 down-regulated RNAs.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/058c06f9c48932f353b6ee94.png"},{"id":58314403,"identity":"6a697dfc-3cd1-4a5b-97b2-10ab98858cb7","added_by":"auto","created_at":"2024-06-13 20:48:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":673040,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of top DEGs at BC (a), GC (b), and CRC (c).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/a13f9e757ebbe242de3eef19.png"},{"id":58313993,"identity":"47971ae8-d64e-43f8-9c0e-0f71e63132a3","added_by":"auto","created_at":"2024-06-13 20:40:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":327451,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction analysis of \u003cem\u003eLCN6\u003c/em\u003e. Three clusters of proteins interact directly and indirectly with LCN6. Each cluster might regulate a specific signaling pathway.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/322df22dba8c3879bdb41631.png"},{"id":58313995,"identity":"2d2cf5a7-809f-4fe3-a380-119f8261deb8","added_by":"auto","created_at":"2024-06-13 20:40:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":236899,"visible":true,"origin":"","legend":"\u003cp\u003e“Transport of fatty acid” signaling pathway.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/176bb116a41b6c57e4712516.png"},{"id":58313991,"identity":"0cb8d82d-f0a9-40a5-b024-cb18b20127a8","added_by":"auto","created_at":"2024-06-13 20:40:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":171043,"visible":true,"origin":"","legend":"\u003cp\u003eRNA interaction network correlated to LCN6. Yellow nodes indicate related miRNAs and LCN6. Red nodes indicate lncRNAs. The green node indicates the miRNA with stronger interaction with LCN6 (miR-1247-3p).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/6767da9599ffcf14fa81f989.png"},{"id":58315448,"identity":"e141f9c2-b93c-4733-a9dd-8df56f0ef883","added_by":"auto","created_at":"2024-06-13 20:56:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":835893,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of LCN6 with correlated lncRNAs (MEG9 and MZF1-AS1). MEG9 has a significant positive correlation with LCN6 in BC, CRC, and GC. MZF1-AS1 has a significant correlation with LCN6 in BC samples. There was no significant correlation between MZF1-AS1 and LCN6 in CRC and GC samples.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/8284112960bdf266903e20e3.png"},{"id":58313998,"identity":"0e8e4d32-6709-416b-90af-d56b707c1c8f","added_by":"auto","created_at":"2024-06-13 20:40:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":351307,"visible":true,"origin":"","legend":"\u003cp\u003egene expression analysis using ENCORI.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/7892fe9faab2367e8bac77b7.png"},{"id":58314406,"identity":"31190775-a363-43fc-b585-22a1f115dee0","added_by":"auto","created_at":"2024-06-13 20:48:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":157673,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of gene expression analyses using qRT-PCR experiment in BC (a), GC (b), and CRC (c) samples, compared to control.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/f934a2acf8509d05ef82741d.png"},{"id":58314405,"identity":"922263a1-0557-45f0-b61f-c58a7ae4ea35","added_by":"auto","created_at":"2024-06-13 20:48:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":65838,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis revealed no significant correlation between LCN6, MEG9, and MZF1-AS1 expression levels in BC, CRC, and GC samples.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/06100bbc23c301c98955d3a7.png"},{"id":58313997,"identity":"6f1b4abd-f8cd-4474-8ae9-c2b596bf4d85","added_by":"auto","created_at":"2024-06-13 20:40:20","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":205662,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis revealed novel potential BC (a), GC (b), and CRC (c) diagnostic biomarkers.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/76c77e3b0d22ed0040495101.png"},{"id":58314000,"identity":"14d88fd2-685d-4dc7-ae7d-f436586645a1","added_by":"auto","created_at":"2024-06-13 20:40:20","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":99706,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between mRNA and lncRNAs in BC (a), GC (b), and CRC (c).\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/e128f3cb320db21671567726.png"},{"id":58316026,"identity":"87c7ab30-b68c-4059-9822-4da79a93b4bc","added_by":"auto","created_at":"2024-06-13 21:04:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4275654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4567087/v1/3b5e9491-ac94-4883-a218-4443f91e1a3e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExploring the Roles of lncRNAs MZF1-AS1 and MEG9 in Breast, Gastric, and Colorectal Cancer Development: Regulation of LCN6 in Fatty Acid Signaling Pathways\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer (BC), the most prevalent cancer among women globally; gastric cancer (GC), ranked fifth worldwide with high mortality; and colorectal cancer (CRC), the third most common cancer, represent significant global health challenges (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The heterogeneity of these cancers, influenced by genetic, environmental, and lifestyle factors, has spurred advancements in personalized treatments, including targeted therapies and immunotherapies (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite these advances, disparities in access to care and outcomes persist, underscoring the need for equitable healthcare solutions, early detection, and preventive strategies against modifiable risk factors (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCancer biomarker discovery is pivotal for the early diagnosis, therapeutic stratification, and monitoring of cancer, offering a molecular snapshot of the tumor's phenotype. Advanced technologies in proteomics and genomics have propelled the identification of biomarkers, yet translating these discoveries into clinical practice presents significant challenges due to the complexity of tumor biology and the need for rigorous validation. Notable advancements include the use of protein arrays for high-throughput identification and the exploration of circulating tumor DNA and cells for non-invasive diagnostics, highlighting the potential of biomarkers in personalized medicine and the ongoing quest for specificity and sensitivity in cancer detection (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe intricate interplay between microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) represents a fascinating aspect of post-transcriptional gene regulation, playing pivotal roles in diverse biological processes and diseases, including cancer. miRNAs, short non-coding RNAs of approximately 22 nucleotides, modulate gene expression by binding to complementary sequences on target mRNAs, leading to translational repression or mRNA degradation (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Conversely, lncRNAs, exceeding 200 nucleotides in length, engage in complex regulatory activities such as chromatin remodeling, transcriptional control, and acting as competing endogenous RNAs (ceRNAs) or \"sponges\" for miRNAs, thereby modulating their activity and impact on target mRNAs (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The dynamic regulatory network facilitated by miRNA-lncRNA interactions adds a layer of complexity to our understanding of cellular homeostasis, development, and pathology, illustrating the necessity of a systemic approach to unravel the functional nuances of these ncRNAs in physiological and disease contexts (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). These interactions are not only crucial for maintaining cellular function but also offer potential targets for therapeutic intervention in diseases such as cancer, highlighting the importance of further exploration into the miRNA-lncRNA axis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this research, we want to find new potential diagnostic biomarkers for BC, GC, and CRC based on bioinformatics and an experimental approach. One of the other important goals of our investigation is to find the interaction of non-coding RNAs (miRNAs and lncRNAs) with important mRNAs.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. High throughput data analysis\u003c/h2\u003e \u003cp\u003eAnalysis of high-throughput microarray data was conducted to identify potential novel biomarkers with coding capabilities for GC, BC, and CRC. The microarray datasets utilized for the identification of differentially expressed genes (DEGs) in BC, GC, and CRC were GSE134359, GSE54129, and GSE81558, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The initial processing of these raw microarray datasets was carried out using the affy package, with normalization conducted through the Robust Multi-array Average (RMA) method provided by the same package. To ensure the quality of the dataset, principal component analysis (PCA), sample correlation analysis, and boxplot visualization of expression data for both normal and cancerous samples were employed. The statistical examination of the expression data from the microarrays was undertaken using the limma package. Both affy and limma packages were acquired from Bioconductor.org. Visualization of the microarray analysis results was achieved using ggplot2 and pheatmap packages, which were sourced from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Furthermore, gene expression analysis validation was conducted through the use of the GEPIA2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ENCORI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) online platforms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of microarray datasets in this study. Three datasets were used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003edataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etumor samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrol samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eplatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE134359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPL17586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE208099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003egastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE81558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPL15207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecolorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. RNA interaction and enrichment analyses\u003c/h2\u003e \u003cp\u003eIn this research, various online platforms were leveraged to identify emerging non-coding regulatory biomarkers within BC, GC, and CRC specimens. The lncRRIsearch (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) web application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rtools.cbrc.jp/LncRRIsearch\u003c/span\u003e\u003cspan address=\"http://rtools.cbrc.jp/LncRRIsearch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) facilitated the selection of innovative regulatory lncRNAs. To delineate the protein interaction networks associated with the identified mRNAs, the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) was utilized. New regulatory miRNAs were discovered by applying miRWalk (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Visualization of the RNA interaction networks was accomplished using the Cytoscape (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) software. Enrichment analyses of pathways were conducted utilizing the enrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and Reactome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003cspan address=\"https://reactome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)web databases. Additionally, to elucidate the association between these targets' expression levels and cancer patients' survival rates, a survival analysis was conducted using the GEPIA2 online database (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In the survival analysis, three selected RNAs (mRNA and the two lncRNAs) were considered as one signature model for BC, GC, and CRC samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Clinical features of human samples for qRT-PCR experiment\u003c/h2\u003e \u003cp\u003eThe Ethics Committee of Al-Zahra Hospital, affiliated with Isfahan University of Medical Sciences, granted approval for all the research protocols involving human specimens utilized in this study, with all participating patients providing their written informed consent. In this case-control investigation, the expression profiles of selected mRNAs and lncRNAs were analyzed in 20 tissue samples from BC, CRC, and GC patients. These were then contrasted with those from 20 adjacent non-tumorous tissue samples for each cancer type. It is noteworthy that none of the subjects had previously undergone chemotherapy or radiation therapy. Before being preserved in RNA Later solution (Invitrogen, USA) for subsequent pathological examination, the tissue specimens were meticulously rinsed with distilled water and then immediately frozen in liquid nitrogen. Detailed clinicopathological characteristics of the human samples are delineated in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of BC samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor size (TS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLymph node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eER receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePR receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHER2/neu receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics of GC samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTumor Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMucinous Adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignet Ring Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerineural Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNodal Extension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTNM Staging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDX-Smoker at Diagnosis but Discontinued\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx-Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological table of colorectal cancer patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor size (TS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLymphatic Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePerineural Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analyses\u003c/h2\u003e \u003cp\u003eThe quantitative real-time polymerase chain reaction (qRT-PCR) experiment's statistical evaluation was conducted using Graph Pad Prism 8 software. Both paired and unpaired t-tests were utilized to determine the significance level. Furthermore, receiver operating characteristic (ROC) analysis was executed to assess the diagnostic potential of the tumor specimens. The area under the curve (AUC) was critically appraised within the ROC outcomes. An AUC value ranging from 0.7 to 0.8 is indicative of an acceptable biomarker; a range from 0.8 to 0.9 signifies a good biomarker, while an AUC value from 0.9 to 1 is reflective of an excellent diagnostic biomarker.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Microarray analysis\u003c/h2\u003e \u003cp\u003eMicroarray analysis was conducted on three high-throughput datasets to identify new diagnostic biomarkers for BC, GC, and CRC. Principal component analysis (PCA) was utilized to assess the quality of the microarray samples. The PCA results for BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), GC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), and CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) indicated that all microarray samples were of adequate quality for subsequent analysis. Differential expression analysis (DEA) was conducted to discover novel, shared potential diagnostic biomarkers for BC, GC, and CRC. The results of this analysis highlighted LCN6 as significantly low expressed in BC (logFC: -4.256, adj. P. Value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), GC (logFC: -1.256, adj.P.Value: 0.000228569), and CRC (logFC: -1.756, adj.P.Value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the most prominently differentially expressed genes (DEGs) in the tumor samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Protein-protein interaction and pathway analysis\u003c/h2\u003e \u003cp\u003eThe table of proteins with direct and indirect interaction with \u003cem\u003eLCN6\u003c/em\u003e is provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The k-means clustering method was performed to cluster all nodes in the protein interaction network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) into three groups. Enrichr performed pathway enrichment and gene ontology analysis. Based on protein interaction analysis, \u003cem\u003eLCN6\u003c/em\u003e has direct and indirect interaction with following proteins: \u003cem\u003eADAM7, CST11, LCN10, LCN12, LCN15, LCN6, LCN8, LCN9, LCNL1, RNASE10, SPINT4, TEDDM1, APOD, C8G, LCN1, OBP2A, OBP2B, PTGDS, DEFB119, DEFB128\u003c/em\u003e, and \u003cem\u003eWFDC9\u003c/em\u003e. The mentioned proteins are divided into three different clusters using the K-mean clustering method. The first cluster regulates the \u0026ldquo;Transport of Fatty Acids signaling pathway\u0026rdquo; and is involved in the \u0026ldquo;Positive Regulation of Cilium Movement\u0026rdquo; biological process. A list of all related signaling pathways correlated to all nodes in the protein interaction network is provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. A diagram of \u0026ldquo;Fatty acid metabolism\u0026rdquo; is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In this diagram, the regulatory role of the LCN family is demonstrated. The family members exhibit variations in their amino acid sequences, yet they exhibit a highly preserved beta-barrel configuration consisting of an eight-stranded anti-parallel beta-sheet. This configuration forms a pocket for ligand-binding, facilitating the binding and transportation of lipids and other small hydrophobic molecules (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Lipocalin proteins have been implicated in various biological processes, including immune responses, prostaglandin synthesis, retinoid binding, and interactions with cancer cells. Specifically, Lipocalins 1, 9, 12, and 15 (LCN1, 9, 12, and 15) demonstrate the capacity to transport diverse hydrophobic molecules.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClusters of protein interaction network and related signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecolor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ename\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebiological process\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADAM7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eTransport Of Fatty Acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003ePositive Regulation of Cilium Movement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCST11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCN10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCN12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCN15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCN6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCN8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCN9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCNL1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRNASE10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPINT4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTEDDM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAPOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eArachidonic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSensory Perception of Chemical Stimulus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC8G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCN1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOBP2A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOBP2B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTGDS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDEFB119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBeta Defensins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAntifungal Innate Immune Response\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDEFB128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWFDC9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Non-coding interaction analysis\u003c/h2\u003e \u003cp\u003eTo find novel non-coding regulatory RNAs for LCN6, miRNA and lncRNA interaction analysis were utilized. For the miRNA interaction analysis, the following criteria were considered: location of interaction: seed region (3\u0026rsquo;UTR), and binding probability (score): 1. Using the mentioned conditions, 13 miRNAs were selected as a regulatory network for LCN6. Furthermore, based on lncRNA interaction analysis, lncRNAs MZF1-AS1 and MEG9 are the two novel regulators of LCN6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Based on correlation analysis (ENCORI), MEG9 has a significant positive correlation with LCN6 expression in BC, GC, and CRC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Also, MZF1-AS1 has a significant correlation with LCN6 in BC samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Expression analysis of LCN6, MEG9, and MZF1-AS1\u003c/h2\u003e \u003cp\u003eBioinformatics analysis and experimental validations of computational results showed different results about the expression patterns of LCN6, MEG9, and MZF1-AS1 in BC, GC, and CRC samples. First, RNAseq data analysis by the ENCORI database revealed that LCN6 is significantly low-expressed in BC, GC, and CC samples. Based on the mentioned analysis, MZF1-AS1 is highly expressed in BC, GC, and CC samples. Also, MEG9 is down-regulated in BC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Based on the qRT-PCR experiment, there was no significant change in the expression level of LCN6, MEG9, and MZF1-AS1 in BC samples. However, there was a significantly low expression of LCN6 in GC and CRC samples (same as bioinformatics results). MZF1-AS1 and MEG9 also have significantly low expression in GC and CRC samples (apposite to bioinformatics results, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Based on survival analysis, there is no significant relation between the survival rate of BC, GC, and CRC patients and the changes in the expression level of LCN6, MEG9, and MZF1-AS1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). For validation of bioinformatics correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), the same analysis was performed on the qRT-PCR data. Based on the correlation analysis of LCN6 with MEG9 and MZF1-AS1, LCN6 has a significant positive correlation with MEG9 and MZF1-AS1 in BC samples. Also, LCN6 has a significant positive interaction with MEG9 in CRC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Based on ROC analysis, LCN6 might be a suitable diagnostic biomarker for CRC and an acceptable biomarker for GC. lncRNA MEG9 was identified as an excellent diagnostic biomarker of CRC and an acceptable biomarker of BC and GC. MZF1-AS1 is a good biomarker of GC and an excellent biomarker of CRC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). All information about gene expression analysis is provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Information of correlation analysis is provided in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation on gene expression analysis in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eexpression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eROC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLCN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMZF1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMEG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation of correlation analysis of lncRNAs with LCN6 mRNA in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCRC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMZF1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe \"transport of fatty acids\" signaling pathway plays a crucial role in the development of several cancers, including BC, GC, and CRC. In breast and colorectal cancers, fatty acid-binding proteins (FABPs) are significantly involved in the modulation of lipid metabolism, which influences cancer cell proliferation, growth, and metastasis. FABP5, in particular, has been highlighted for its role in promoting lipolysis of lipid droplets, enhancing de novo fatty acid synthesis, and activating inflammatory pathways through the nuclear factor-kappa B (NF-κB) signaling in cancer cells, which contributes to the aggressiveness of breast cancer cells (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Similarly, in colorectal cancer, the overexpression of fatty acid synthase (FASN) enhances cancer cell proliferation and metastasis by regulating the AMP-activated protein kinase (AMPK)/mechanistic target of rapamycin (mTOR) pathway, suggesting a potential target for therapeutic interventions (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The fatty acid translocase CD36 also promotes metastasis in colorectal cancer by enhancing cancer cell proliferation, migration, and invasion, further underscoring the critical role of fatty acid transport in cancer progression (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003elncRNAs MEG9 (also known as MEG3) and MZF1-AS1 have been identified as critical regulators in the pathogenesis of various cancers, including BC, GC, and CRC. MEG9, primarily functioning as a tumor suppressor, exerts its influence through several distinct mechanisms. In BC, MEG9 has been shown to inhibit cell proliferation and induce apoptosis by enhancing p53 signaling, a key tumor suppressor pathway. This is achieved through MEG9's interaction with p53, stabilizing the protein and enhancing its transcriptional activity, which leads to the suppression of tumor growth and proliferation (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In GC, MEG9 has been reported to modulate angiogenesis and epithelial-mesenchymal transition (EMT) by regulating key signaling molecules such as VEGF and TGF-β, contributing to the suppression of cancer metastasis (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In CRC, MEG9 affects the tumor microenvironment by altering the expression of immune checkpoint proteins, thereby impacting immune evasion mechanisms and influencing cancer progression (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, MZF1-AS1 is recognized as an oncogenic lncRNA that significantly contributes to cancer progression by modulating several key pathways. In BC, MZF1-AS1 promotes tumor growth and metastasis by sponging miR-30a-5p, leading to the upregulation of target oncogenes such as VEGF and MMP9, which are crucial for angiogenesis and cell invasion (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This interaction enhances the EMT process, facilitating tumor cell migration and invasion. In GC, MZF1-AS1 has been shown to interact with and modulate the JAK/STAT signaling pathway, leading to increased cancer cell proliferation and survival, further underscoring its role in enhancing the oncogenic potential of gastric cancer cells (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In CRC, MZF1-AS1 promotes cancer progression by similarly sponging miR-30a-5p, which leads to the derepression of genes involved in cell proliferation and survival, contributing to a more aggressive cancer (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe protein Lipocalin 6 (LCN6) has been recently studied in the context of BC, GC, and CRC for its role in cancer progression and potential as a therapeutic target. In breast cancer, LCN6 is implicated in the regulation of the epithelial-mesenchymal transition (EMT), a critical process in cancer metastasis. Research has demonstrated that LCN6 can influence key markers of EMT, including N-cadherin and vimentin, indicating its role in enhancing the invasive and migratory capabilities of BC cells. This interaction suggests that LCN6 could contribute to the aggressive behavior of BC by facilitating the dissemination of cancer cells to distant sites (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn colorectal cancer, LCN6 is linked to inflammatory pathways that are known to contribute to cancer development, particularly in the context of chronic inflammation. A study highlighted that LCN6, along with other inflammatory mediators, is upregulated in CRC tissues, particularly in those arising from colitis, a condition that predisposes to CRC. The study further elaborated that LCN6 might function through the IL-6/STAT3/NF-κB signaling pathway, promoting cell survival and potentially facilitating the transition from inflammation to cancer (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). These findings indicate that LCN6 could serve as a biomarker for CRC progression, particularly in patients with a history of inflammatory bowel disease.\u003c/p\u003e \u003cp\u003eThese observations across different cancer types underline the diverse roles that LCN6 can play in cancer biology. By influencing key processes such as EMT in breast cancer and mediating inflammation-driven pathways in colorectal cancer, LCN6 emerges as a multifaceted protein with potential implications for therapy and prognosis in various cancers. Further research into LCN6 could provide deeper insights into its mechanisms of action and pave the way for novel therapeutic strategies targeting this protein in a range of malignancies.\u003c/p\u003e \u003cp\u003eIn previous studies, we found different RNA regulatory networks that might play a significant role in the development of different cancer types. Based on our evaluations, lncRNA NORAD (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and LINC00520 (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) could be novel regulatory factors of VTCN1 and ESR2 in BC development, respectively. Non-coding regulatory networks of THBS2\u0026mdash;especially lncRNA BAIAP2\u0026ndash;AS1\u0026mdash;are a potential diagnostic biomarker of GC (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). MiR-3679-5p changes the risk of BC through regulation of XBP1 expression level (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). However, due to the limitations of sample collection and experimental instruments in this experiment and previous investigations, more experimental validations are needed.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we performed an integrated systems biology investigation to find novel interactions in BC, CRC, and GC patients. Based on the results, MEG9 and MZF1-AS1 are the two regulators of LCN6 in the mentioned diseases. miR-1247-3p could also regulate the expression level of LCN6. Dysregulation of MEG9, MZF1-AS1, and miR-1247-3p might disturb the regular process of the \u0026ldquo;Fatty acid transport\u0026rdquo; signaling pathway and increase the risk of BC, CRC, and GC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\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\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets generated or analyzed during the current study are available in the GEO repository,\u0026nbsp;GSE134359, GSE54129, and GSE81558.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support and sponsorship:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMohammad Hassan Zoofaghari, Mohammad Sharif Sharifani, Mahsa Ghandi, Sanaz Zare, Shantia Yazdani, Sina Fekri, Ghazaleh Sheikhi Ghahi, \u0026nbsp;and Golnaz Enayat Jazi\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eSoftware, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Visualization; \u003cstrong\u003eMohammad Rezaei and Seyedeh Zahra Shirdeli:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Supervision; \u003cstrong\u003eMansoureh Azadeh:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Mohammad Hassan Zoofaghari, Mohammad Sharif Sharifani, Mahsa Ghandi, and Sanaz Zare equally contributed to this study as the first authors. Shantia Yazdani, Sina Fekri, Ghazaleh Sheikhi Ghahi, and Golnaz Enayat Jazi 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\u003cli\u003e\u003cspan\u003eHarbeck N, Penault-Llorca F, Cortes J, Gnant M, Houssami N, Poortmans P et al Breast cancer. 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Biosci Rep [Internet]. 2022 May 1 [cited 2024 May 24];42(5). https://pubmed.ncbi.nlm.nih.gov/35470375/\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezvani Sichani A, Dadkhah P, Tabandeh T, Kaviani Dehkordi N, Rezaei M, Rahimirad S Molecular insight into the expression level of an immunosuppression gene, VTCN1, and its regulatory factors in breast cancer patients and non-cancerous samples with a higher level of IgE. 2023 Feb 8 [cited 2024 Mar 2]; https://www.researchsquare.com\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEzzati E, Mosadeshi S, Akbarinia A, Horriat S, Rezaei M, Azadeh M LINC00520 promotes breast cancer development by low expression as a tumor suppressor and prognostic biomarker by regulating the ESR2 expression level: integrated systems biology bioinformatics and experimental analyses. 2022 Aug 10 [cited 2022 Aug 18]; https://www.researchsquare.com\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarani A, Beikverdi K, Mashhadi B, Parsapour N, Rezaei M, Javid P Transcription analysis of the THBS2 gene through regulation by potential non-coding diagnostic biomarkers and oncogenes of gastric cancer in the ECM receptor interaction signaling pathway: integrated systems biology and experimental investigation. 2022 Nov 23 [cited 2023 May 13]; https://www.researchsquare.com\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirdeli SZ, Hashemi SA, Hashemi GS, Khalilian L, Ferdowsian S, Mostaghimi Y LINC1521 and miR-3679-5p modulate cellular response to chemical stress in breast cancer patients through regulation of XBP1 expression as a potential diagnostic biomarker. Res Sq [Internet]. 2023 Aug 11 [cited 2023 Nov 20]; https://www.researchsquare.com\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Zist Fanavari Novin Biotechnology 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":"lncRNA, Systems Biology, Microarray, qRT-PCR, microRNA","lastPublishedDoi":"10.21203/rs.3.rs-4567087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4567087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe discovery of cancer biomarkers is pivotal for early diagnosis, therapeutic planning, and patient monitoring, offering a molecular insight into tumor characteristics. This study aimed to identify novel diagnostic and prognostic biomarkers for breast cancer (BC), gastric cancer (GC), and colorectal cancer (CRC) using a systems biology approach.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eHigh-throughput gene expression analysis was conducted using the limma package in R Studio on datasets GSE134359, GSE54129, and GSE81558. Potential non-coding regulatory factors were identified through RNA and protein interaction analyses. Interaction networks were visualized with Cytoscape. Pathway enrichment analysis (Reactome) and survival analysis (GEPIA2) were utilized to elucidate the regulatory mechanisms of selected RNAs. The findings were validated using qRT-PCR experiments on GC, BC, and CRC samples.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBioinformatics analyses revealed significantly low expression of LCN6 in BC, CRC, and GC samples. Interaction analysis showed that lncRNAs MEG9 and MZF1-AS1 physically interact with LCN6 mRNA. Gene expression analysis using ENCORI indicated dysregulation of MEG9 and MZF1-AS1 in BC, CRC, and GC samples. Correlation analysis uncovered novel relationships between the expression of these lncRNAs and mRNA in the three cancer types. ROC analysis suggested that LCN6 and MZF1-AS1 are potential biomarkers for GC and CRC, while MEG9 could serve as a robust diagnostic biomarker for BC, CRC, and GC. Significant positive correlations were observed between MZF1-AS1 and MEG9 with LCN6 in BC samples and between LCN6 and MEG9 in GC samples, but no correlation was found in CRC samples.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLncRNAs MZF1-AS1 and MEG9 may regulate the expression of LCN6 in the \"transport of fatty acid\" signaling pathway, potentially influencing the risk of BC, GC, and CRC through this regulatory mechanism.\u003c/p\u003e","manuscriptTitle":"Exploring the Roles of lncRNAs MZF1-AS1 and MEG9 in Breast, Gastric, and Colorectal Cancer Development: Regulation of LCN6 in Fatty Acid Signaling Pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 20:40:15","doi":"10.21203/rs.3.rs-4567087/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":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33131645,"name":"Cancer Biology"},{"id":33131646,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2024-06-13T20:40:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 20:40:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4567087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4567087","identity":"rs-4567087","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}