Unveiling Novel Regulatory Mechanisms of BEX1 in Breast, Gastric, and Colorectal Cancer via a Systems Biology Approach: The Roles of lncRNAs COLCA1 and GAS6-AS1 and Their Interactions

Unveiling Novel Regulatory Mechanisms of BEX1 in Breast, Gastric, and Colorectal Cancer via a Systems Biology Approach: The Roles of lncRNAs COLCA1 and GAS6-AS1 and Their Interactions | 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 Unveiling Novel Regulatory Mechanisms of BEX1 in Breast, Gastric, and Colorectal Cancer via a Systems Biology Approach: The Roles of lncRNAs COLCA1 and GAS6-AS1 and Their Interactions Mohammadreza Rezaei, Parnian Salehipour, Mehrnoosh Tavakoli, Maryam Mousavi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5118033/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background : This study aimed to explore novel regulatory networks involving the BEX1 gene and its interaction with non-coding RNAs (ncRNAs) in breast cancer (BC), gastric cancer (GC), and colorectal cancer (CRC). BEX1 has been linked to tumor suppression, but its role in signaling pathways and its interactions with regulatory RNAs in these cancers has not been fully elucidated. Methods : High-throughput microarray datasets (GSE10810, GSE54129, and GSE208099) were analyzed to investigate BEX1 expression in breast cancer, gastric cancer, and colorectal cancer. The expression analysis and survival outcomes for BEX1 and selected lncRNAs were validated using the ENCORI platform. Regulatory interactions of BEX1 with proteins and microRNAs were identified using STRING and miRWalk, respectively, while lncRNA interactions were examined through lncRRIsearch. Final validation of differential expression analysis and biomarker potential was conducted using qRT-PCR, along with ROC analysis to assess diagnostic capability. Results : BEX1, identified as a tumor suppressor with low expression in breast, gastric, and colorectal cancer, demonstrated potential as a diagnostic biomarker, particularly in breast cancer (AUC: 0.8025, p = 0.0011). The lncRNAs COLCA1 and GAS6-AS1 were found to potentially regulate BEX1 expression. BEX1 exhibited significant interactions with two key proteins involved in cancer-related signaling pathways: CALML3 and LMO2. Moreover, BEX1 and these proteins demonstrated competitive interactions with miR-3616-3p, which was found to suppress BEX1 expression by targeting its 3'UTR. COLCA1 and GAS6-AS1 also exhibited dysregulated expression across breast, gastric, and colorectal cancers, suggesting their potential as diagnostic biomarkers. Conclusion : The lncRNAs GAS6-AS1 and COLCA1, alongside miR-3616-3p, may play pivotal roles in regulating cancer-related pathways, including gastric acid secretion, insulin signaling, and homeostasis. These regulatory processes occur through direct and indirect interactions between the non-coding RNAs and BEX1, further highlighting the potential of these molecules as therapeutic targets and diagnostic biomarkers. Bioinformatics Systems Biology long non-coding RNA Bioinformatics Systems Biology Cancer Genetics 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 The Brain-Expressed X-linked (BEX) gene family, particularly BEX1, plays a crucial role in various cellular processes that are linked to cancer development, including breast, gastric, and colorectal cancers. BEX1, along with its homologs, is involved in regulating cell survival, apoptosis, and cell cycle progression, all of which are critical processes in tumorigenesis ( 1 ). In breast cancer, BEX1 is often associated with tumor suppression. Its expression is correlated with the modulation of apoptosis pathways and the inhibition of cell proliferation. Specifically, BEX1 interacts with the p75 neurotrophin receptor (p75NTR) to influence NF-kappaB signaling, a key pathway involved in regulating cell survival and apoptosis. Loss of BEX1 expression has been linked to poor response to therapeutic agents like tamoxifen, highlighting its potential role as a prognostic marker and therapeutic target in breast cancer ( 1 ). In the context of gastric and colorectal cancers, BEX1 and its related proteins are involved in autophagy and apoptosis regulation. For instance, BEX2, a closely related homolog, has been shown to regulate the JNK/c-Jun signaling pathway, which is crucial for cell proliferation and survival in colorectal cancer cells. Knockdown of BEX2 leads to a significant reduction in cancer cell proliferation and tumor growth, demonstrating the importance of BEX family proteins in these malignancies ( 2 ). High-throughput genomics experiments have revolutionized cancer research by enabling the rapid and precise analysis of vast genetic datasets, which is crucial for identifying novel cancer biomarkers. These advanced technologies facilitate the discovery of specific genetic alterations and expression patterns that are essential for early diagnosis, prognosis, and personalized treatment. For instance, integrating high-throughput proteomics with artificial intelligence significantly enhances the identification and validation of cancer biomarkers by analyzing both genomic and protein data ( 3 ). Additionally, high-throughput liquid biopsy methods that detect multiple biomarkers in single extracellular vesicles have improved the accuracy and efficiency of cancer diagnostics ( 4 ). Moreover, targeted next-generation sequencing assays provide comprehensive genomic profiling of solid tumors, offering critical insights into molecular alterations and supporting the development of precision therapies ( 5 ). These high-throughput genomics techniques are indispensable tools in the ongoing quest to discover and validate new cancer biomarkers, ultimately improving patient outcomes through personalized medicine. Long noncoding RNAs (lncRNAs) are emerging as valuable biomarkers for both inflammation and cancer due to several distinct characteristics. First, many lncRNAs exhibit tissue-specific expression, making them ideal for identifying disease origins. Second, their expression levels are often upregulated in response to specific inflammatory or oncogenic signals. Third, lncRNAs released from cells are encapsulated and safeguarded within extracellular vesicles, ensuring their stability. Finally, circulating lncRNAs in the bloodstream can be efficiently detected through various RNA sequencing techniques, enhancing their potential as non-invasive molecular markers for disease ( 6 ). The selection of hub lncRNAs, which are central to these regulatory networks, is particularly important for understanding cancer mechanisms and developing targeted therapies. In papillary thyroid cancer, for example, identifying key lncRNAs has led to the development of prognostic models that predict patient outcomes based on the interactions of these lncRNAs with miRNAs and mRNAs ( 7 ). Similarly, in ovarian cancer, constructing lncRNA-miRNA-mRNA networks has provided insights into novel therapeutic targets and potential biomarkers for early detection ( 8 ). Also, in hepatocellular carcinoma, it is demonstrated that EZH2 mRNA is regulated by different lncRNAs, including lncRNA HAGLR ( 9 ). In GC, CCDC144NL-AS1 and LINC01094 lncRNAs can act as two potential diagnostic biomarkers ( 10 )Understanding the roles of these hub lncRNAs across different cancer types is essential for advancing precision oncology and improving patient outcomes. RNA interaction and network analysis are integral to a systems biology approach, significantly enhancing cancer diagnosis and therapy by providing a comprehensive understanding of the molecular interactions within cancer cells. By mapping the intricate networks of coding and non-coding RNAs, researchers can identify key regulatory RNAs that drive cancer progression and pinpoint potential therapeutic targets. For example, ceRNA networks, which include mRNAs, lncRNAs, and miRNAs, reveal how disruptions in these interactions contribute to tumorigenesis and can highlight biomarkers for early diagnosis and targeted therapy ( 11 ). Additionally, integrating RNA-Seq data with network analysis has been shown to improve the prediction of key cancer drivers and therapeutic responses, providing a more personalized approach to cancer treatment ( 12 ). Furthermore, understanding the connectivity between non-coding RNAs and protein-coding genes enhances the identification of potential intervention points within the cancer regulatory networks, offering new avenues for therapeutic development ( 13 ). These systems biology strategies not only facilitate the discovery of novel biomarkers but also pave the way for innovative treatments that target the complex regulatory networks of cancer cells. In this study, we aimed to find novel regulatory lncRNAs in gastric cancer (GC), breast cancer (BC), and colorectal cancer (CRC) patients. We used several systems biology approaches to find potential correlations and interactions between the expression levels of different coding and non-coding RNAs in the patients. Furthermore, the diagnostic possibility of each hub RNA is evaluated in this study. All in all, we tried to find novel regulatory functions of BEX1 in the development of GC, BC, and CRC to find novel and general functionality for BEX1. 2. Materials and Methods 2.1. Gene Expression Analyses First, to evaluate the expression level of BEX1 in high-throughput experiments, the following microarray datasets were analyzed for BC, GC, and CRC, respectively: GSE10810 ( 14 ), GSE54129, and GSE208099 ( 15 ). The significance level of microarray analyses is set on adj. P. Value 1 was considered as the dysregulation threshold. Using the GEOquery ( 16 ) package, the raw gene expression matrix of all datasets was downloaded, and the limma ( 17 ) package performed the statistical analyses for finding the differentially expressed genes (DEGs) in all datasets. Visualization of the plots was performed using the ggplot2 ( 18 ) package. The expression level of BEX1 in BC, GC, and CRC was evaluated using ENCORI ( 19 ) (comparing to control samples) for the validation of microarray analysis. Survival analyses were conducted using ENCORI to evaluate the relation of expression changes in selected mRNA and lncRNAs with the survival rate of cancer patients. 2.2. RNA interaction and functional enrichment analysis In this study, several online platforms were employed to identify emerging non-coding regulatory biomarkers in BC, GC, and CRC samples. The lncRRIsearch ( 20 ) web application ( http://rtools.cbrc.jp/LncRRIsearch ) was used to select novel regulatory lncRNAs. To map the protein interaction networks associated with the identified mRNA, the STRING ( 21 ) database ( https://string-db.org/ ) was utilized. Novel regulatory miRNAs were identified using the miRWalk ( 22 , 23 )platform ( http://mirwalk.umm.uni-heidelberg.de/ ). MicroRNAs with the following criteria were selected for the network: binding probability (score): 1, interaction in the seed region (3’UTR), and ordered from lower binding energy to higher. RNA interaction networks were visualized through the Cytoscape ( 24 , 25 ) software. Pathway enrichment analysis was performed using the Reactome ( 26 – 28 ) ( https://reactome.org/ ) web database. 2.3. qRT-PCR sample preparation The Ethics Committee of Al-Zahra Hospital, under the jurisdiction of Isfahan University of Medical Sciences, provided ethical approval for all research protocols involving human specimens used in this study. Written informed consent was obtained from all participating patients. In this case-control study, the expression profiles of selected mRNAs and lncRNAs were examined in 20 tissue samples from patients with BC, CRC, and GC. These were compared with 20 adjacent non-tumorous tissue samples for each cancer type. Importantly, none of the subjects had previously received chemotherapy or radiation therapy. The detailed clinicopathological characteristics of the human samples are presented in Tables 1 – 3 . Table 1 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. RNA extraction, cDNA synthesis, and qRT-PCR programs RNA extraction from both tumor and normal tissues was carried out using TRIZOL reagent (Invitrogen, Carlsbad, CA, USA). Following extraction, cDNA synthesis was performed with the TaKaRa cDNA synthesis kit (TaKaRa, Tokyo, Japan), following the manufacturer’s protocol. Real-time PCR was subsequently conducted using Sybergreen (Amplicon Company, Denmark) on a MIC Real-Time PCR machine (Australia). The PCR reactions included an initial denaturation at 95°C for 15 minutes, followed by 40 cycles of 95°C for 15 seconds, 60°C for 20 seconds, and 72°C for 20 seconds. Primers were designed using Oligo 7 software and synthesized by TAQ Copenhagen Company (Denmark), and their sequences are detailed in Table 4 . Table 4 The table of primer sequences. gene RNA type Primer (5' -> 3') Forward/Reverse Tm GC Product length BEX1 mRNA TCGGGAGAAGGAGGAGACTAC F 59.79 57.14 84 TCCATGCTGAGACTGTTTACTG R 58.07 45.45 GAS6-AS1 lncRNA TGGCTGCATTCGTTGACATCTG F 61.76 50 125 CTGGTCCTCGTTTCCTCGTAAC R 60.67 54.55 COLCA1 lncRNA GGTGCAACTGGGTCTGAAAG F 59.05 55 77 GCCTGCTTCACGGTGATATTC R 59.4 52.38 2.5. Statistical analysis The statistical analysis for the qRT-PCR experiment was performed using GraphPad Prism 8 software. Both paired and unpaired t-tests were employed to determine the significance levels. Additionally, receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic potential of the tumor specimens. The area under the curve (AUC) was carefully examined in the ROC results. An AUC value between 0.7 and 0.8 indicates an acceptable biomarker, a value between 0.8 and 0.9 denotes a good biomarker, and an AUC value between 0.9 and 1 represents an excellent diagnostic biomarker. Pearson correlation was performed for an initial validation of lncRNA-mRNA interaction. A correlation (r) higher than 0.7 was considered a strong correlation. 3. Results 3.1. Low expression of BEX1 in BC, GC, and CRC and correlation with the survival rate Principal component analysis (PCA) was performed to assess the quality of microarray samples (Fig. 1 ). Separation of tumor samples from control samples in PCA plots shows the suitable quality of microarray samples in BC (Fig. 1 a, GC (Fig. 1 b), and CRC (Fig. 1 c) datasets. In the microarray datasets, BEX1 has significant low expression in BC (logFC: -3.23432, adj. P. Value < 0.0001), GC (logFC: -2.56432, adj. P. Value < 0.0001), and CRC (logFC: -1.724, adj. P. Value < 0.0001). Volcano plot shows the up and down-regulated DEGs in BC (Fig. 2 a), GC (Fig. 2 b), and CRC (Fig. 2 c). ENCORI expression analysis also validated microarray analysis results (Fig. 3 ). Survival analysis shows no significant correlation between the expression of BEX1 and the survival rate of patients. However, there was a slight and non-significant correlation between the low expression of BEX1 and the higher death rate of BC and GC patients (Fig. 4 ). 3.2. BEX1 indirectly modulates cancer-related signaling pathways Figure 5 shows the protein interaction network of BEX1, which led the study to demonstrate related signaling pathways that are regulated by this regulatory pathway. Based on the protein interaction network, the BEX1 protein is involved in 4 signaling pathways by interacting with other regulatory proteins. Through the regulation of CALML3-6, BEX1 regulates the following signaling pathways: Gastric Acid Secretion (Red), Insulin signaling pathway (Blue), and Pathways in Cancer (Green). Also, BEX1 regulates Transcriptional Misregulation in Cancer (Yellow) signaling pathway through interaction with LDB1 and LMO2. Direct pathway analysis of BEX1 through the Reactome database revealed the potential role of BEX1 in Hemostasis (Fig. 6 ). Direct interaction of BEX1 with PICK1 regulates the binding of PICK1 to TSPAN7 at the homeostasis signaling pathway. Specifically, this binding is part of the “cell surface interaction at the vascular wall.” 3.3. Non-coding interactions with BEX1 To find novel potential regulatory non-coding RNAs affecting the homeostasis signaling pathways in cancer development, miRNA and lncRNA interaction analysis was performed with respect to the central protein-coding gene in this study (BEX1). Among all potential lncRNAs that have interaction with BEX1, two lncRNAs with significant dysregulation in BC, GC, and CRC were selected: GAS6-AS1 and COLCA1 (Fig. 7 ). Survival analysis revealed no significant correlation between GAS6-AS1 and CLCA1. However, due to the p -value of survival analysis for GAS6-AS1 in STAD and COAD and COLCA1 in BRCA, performing the same analysis with a higher sample size or in different populations might result in novel information about the correlation of mentioned dysregulated lncRNAs with the survival rate of BC, GC, and CRC (Fig. 8 ). miRNA interaction analysis revealed the potential role of miR-3616-3p in the regulation of BEX1. Among all miRNAs that have interaction with BEX1, 17 miRNAs had the criteria described in Materials and Methods (Table 5 ). Among those 17 miRNAs, miR-3616-3p has the strongest interaction with the 3’UTR region of mRNA BEX1 (energy: -25 kcal/mol). Furthermore, miR-3616-3p interacts with CALML3 and LMO2, two important nodes in the protein interaction networks (Fig. 5 ). Figure 9 illustrates the interaction network in this study. Table 5 miRNA-BEX interaction analysis revealed that miR-3616-3p has the strongest interaction with BEX mRNA in the 3’UTR (seed) region. miRNA symbol score energy (kcal/mol) seed position miR-3616-3p BEX1 1 -25 1 3UTR miR-6792-5p BEX1 1 -23 1 3UTR miR-6880-5p BEX1 1 -22.7 1 3UTR miR-4697-5p BEX1 1 -22.2 1 3UTR miR-10401-5p BEX1 1 -21.7 1 3UTR miR-3141 BEX1 1 -21.3 1 3UTR miR-6131 BEX1 1 -20.8 1 3UTR miR-492 BEX1 1 -20.7 1 3UTR miR-2278 BEX1 1 -20.7 1 3UTR miR-4713-3p BEX1 1 -20 1 3UTR miR-711 BEX1 1 -19.4 1 3UTR miR-15b-5p BEX1 1 -18.7 1 3UTR miR-1973 BEX1 1 -18.4 1 3UTR miR-4443 BEX1 1 -18.3 1 3UTR miR-4708-3p BEX1 1 -16.3 1 3UTR miR-300 BEX1 1 -16.2 1 3UTR miR-4300 BEX1 1 -16.2 1 3UTR 3.4. qRT-PCR experiment To assess the biomarker potential of BEX1, GAS6-AS1, and COLCA1 in BC, GC, and CRC samples, a qRT-PCR experiment was conducted. Initially, differential expression analysis was performed on the qRT-PCR data to validate the gene expression results from bioinformatics analyses. The experiment revealed that BEX1 exhibited low expression in both BC and GC samples. GAS6-AS1 showed low expression in BC samples but high expression in GC and CRC samples. Similarly, COLCA1 was found to be expressed at low levels in GC samples, while its expression was elevated in CRC samples (Fig. 10 ). Additionally, BEX1 in BC (AUC: 0.8025, p-value: 0.0011) and GAS6-AS1 (AUC: 0.8375, p-value: 0.0003) as well as COLCA1 (AUC: 0.8150, p-value: 0.0007) in CRC are identified as promising diagnostic biomarkers (Fig. 11 ). Pearson correlation analysis demonstrated a strong correlation between the lncRNAs GAS6-AS1 and COLCA1 with BEX1 in GC samples. A strong correlation between GAS6-AS1 and BEX1 was also observed in CRC samples (Fig. 12 ). Statistical parameters of the qRT-PCR experiment are provided in Table 6 . Detail of correlation analysis is provided in Table 7 . Table 6 Details of the gene expression analysis conducted in this study. expression ROC gene disease logFC p- value AUC p- value BEX1 BC -2.516 0.0407 0.7350 0.0110 GC -3.075 0.0079 0.7450 0.0080 CC -1.571 0.1100 0.6375 0.1368 COLCA1 BC -1.673 0.0844 0.5900 0.1441 GC -2.960 0.0361 0.7650 0.0041 CC 2.949 0.0016 0.8150 0.0007 GAS6-AS1 BC -3.286 0.0307 0.7250 0.0149 GC 3.525 0.0136 0.7650 0.0041 CC 3.796 0.0010 0.8375 0.0003 Table 7 Details of the correlation analysis between lncRNAs and BEX1 mRNA in this study. BC GC CRC r p -value r p -value r p -value COLCA1 0.6638 0.0014 0.8382 < 0.0001 0.6244 0.0033 GAS6-AS1 0.6894 0.0008 0.9379 < 0.0001 0.7296 0.0003 4. Discussion In this study, we sought to identify novel non-coding RNA interactions involving the mRNA BEX1 and explore the regulatory roles of these interactions in the progression of breast, gastric, and colorectal cancers. As a tumor suppressor with low expression, BEX1 interacts with the lncRNAs GAS6-AS1 and COLCA1 and is further suppressed by the miRNA miR-3616-3p. This miRNA also regulates the expression of CALML3 and LMO2, two proteins that have significant interactions with BEX1. Additionally, through its interaction with PICK1, BEX1 indirectly influences the homeostasis signaling pathway. Given its involvement in "cell surface interactions at the vascular wall," reduced expression of BEX1 could contribute to a more aggressive tumor phenotype by disrupting normal vascular responses, ultimately promoting tumor growth and metastasis. Therefore, the downregulation of BEX1 in these cancers suggests its critical role in modulating interactions within the tumor microenvironment, particularly within the vascular niche, which may inform future therapeutic approaches aimed at restoring its function ( 29 ). Homeostasis is crucial in maintaining the stability of biological systems, especially in the vascular wall, where it regulates processes such as cell growth, immune response, and endothelial integrity. In the context of cancer, disruptions to homeostatic mechanisms can lead to an altered tumor microenvironment that supports cancer progression. The vascular wall is a critical site where cancer cells interact with endothelial cells and bypass immune surveillance, a process that is highly dependent on maintaining homeostatic balance. When this balance is disturbed, the endothelial barrier becomes compromised, allowing cancer cells to enter the bloodstream and metastasize to distant organs. This highlights the importance of vascular homeostasis in controlling the spread of cancer cells and the potential for therapeutic interventions aimed at restoring this balance ( 30 , 31 ). Cell surface interactions at the vascular wall are also key to cancer development and progression. Tumor cells exploit these interactions to invade and migrate through the vascular endothelium. Molecules such as integrins and selectins play significant roles in this process, facilitating the adhesion of cancer cells to the vascular wall. Once adhered, cancer cells can cross the endothelial barrier and disseminate throughout the body, leading to metastasis. Targeting these cell surface interactions offers a promising strategy for cancer therapies, as blocking the adhesion of cancer cells to the vascular wall could prevent their spread and improve patient outcomes ( 32 ). This approach not only addresses the local impact of tumor growth but also the systemic spread of cancer, making it a crucial area of research ( 33 ). LncRNA COLCA1 has emerged as a key regulator in colorectal cancer (CRC) development, significantly influencing immune responses within the tumor microenvironment. COLCA1 is particularly associated with genetic variants, such as rs3802842, which has been linked to increased CRC susceptibility ( 34 )​. Studies show that COLCA1 co-localizes with eosinophils, macrophages, and other immune cells, suggesting a role in modulating the immune response against tumor growth. This lncRNA is thought to function by affecting the expression of miRNAs, particularly miR-371a-5p, which plays a pivotal role in inflammation and immune regulation ( 35 )​. By suppressing the tumor-suppressive activities of miR-371a-5p, COLCA1 likely contributes to immune evasion and tumor proliferation ( 36 )​. Further evidence points to COLCA1's involvement in key signaling pathways associated with cancer progression, such as the Wnt/β-catenin and mTORC1 pathways ( 34 )​. Through these interactions, COLCA1 influences cell proliferation, angiogenesis, and immune cell infiltration, which are crucial for both tumor growth and metastasis. COLCA1's presence in eosinophilic granules within immune cells, along with its interaction with proteins such as major basic protein (MBP) and eosinophil peroxidase (EPO), highlights its potential role in the immune modulation of cancer ( 35 ). This complex interplay between genetic variants, immune response, and cancer signaling pathways suggests that COLCA1 may serve as a therapeutic target for CRC and other cancers where it is dysregulated ( 34 , 35 ). GAS6-AS1 (Growth Arrest-Specific 6 Antisense 1) plays a key role in various cancers by acting as a competitive endogenous RNA (ceRNA), influencing oncogenic processes. For instance, in lung adenocarcinoma (LUAD), GAS6-AS1 functions by sponging miR-24-3p, a microRNA involved in regulating cancer cell proliferation and invasion. Through this interaction, GAS6-AS1 inhibits the activity of miR-24-3p, thereby upregulating its target gene GTPase IMAP Family Member 6 (GIMAP6), which promotes cancer progression ( 37 )​​. Moreover, GAS6-AS1 is expressed mainly in the cytoplasm of LUAD cells and serves as a vital player in regulating cellular proliferation, migration, and apoptosis ( 37 ). Studies have shown that downregulation of GAS6-AS1 is associated with poor prognosis in LUAD, while its overexpression leads to reduced cell proliferation and invasion, making it a potential diagnostic biomarker and therapeutic target in LUAD​. In hepatocellular carcinoma (HCC), GAS6-AS1 has been shown to sponge miR-585, thereby releasing the oncogene EIF5A2 from inhibition, which in turn enhances tumor growth and metastasis ( 38 ). This pathway highlights GAS6-AS1's oncogenic potential in driving cancer progression through its regulation of the PI3K/AKT signaling pathway ( 39 ). Additionally, in gastric cancer, GAS6-AS1 promotes cell proliferation and invasion by interacting with AXL signaling, a pathway that is crucial in several cancers due to its role in epithelial-to-mesenchymal transition (EMT) and metastasis ​ ( 39 ). Collectively, these studies suggest that GAS6-AS1 serves as a pivotal regulatory molecule in cancer development, interacting with both proteins and miRNAs to influence key signaling pathways, and it holds promise as a therapeutic target across various malignancies​​. In this study, we sought to identify novel regulatory networks involved in the development of BC, GC, and CRC. Our previous research focused on uncovering new regulatory lncRNAs and miRNAs across various cancer types. For instance, we explored the potential regulatory roles of IGF1 ( 40 )and XBP1 ( 41 )in BC, as well as UHRF1 ( 42 )and MEG9 ( 43 ) in BC, GC, and CRC. However, further investigation is required to achieve more precise validation of RNA interactions and to assess the differential expression of these RNAs under varying pathological conditions. 5. Conclusion Based on this investigation, lncRNAs COLCA1 and GAS6-AS1 might regulate cancer-related signaling pathways through the regulation of BEX1 mRNA. Furthermore, the mentioned lncRNAs can be considered as the two potential cancer diagnostic biomarkers (specifically in BC, GC, and CRC). miR-3616-3p is also one of the hub nodes in the interaction network, suppressing the expression level of BEX1. 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, GSE10810, GSE54129, and GSE208099. Conflicts of interest: The authors declare that they have no competing interests. Financial support and sponsorship: Not applicable. Authors’ contribution: Mohammadreza Rezaei, Parnian Salehipour, Mehrnoosh Tavakoli, Maryam Mousavi, Shima Asgari, Dorsan Vatani, Seyedeh Saba Hosseinipouya, Younes Poudineh : Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization; Mohammad Rezaei and Seyedeh Zahra Shirdeli: Writing – Review & Editing, Conceptualization, Methodology, Validation, Supervision; Reza Ghelich: Writing – Original Draft; Mansoureh Azadeh: Writing – Review & Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Mohammadreza Rezaei, Parnian Salehipour, Mehrnoosh Tavakoli, and Maryam Mousav equally contributed to this study as the first authors. Shima Asgari, Dorsan Vatani, Seyedeh Saba Hosseinipouya, and Younes Poudineh equally contributed to this study as second authors. Mohammad Rezaei and Seyedeh Zahra Shirdeli equally contributed to this study as supervisors and third authors. References Kazi JU, Kabir NN, Rönnstrand L (2015) Brain-Expressed X-linked (BEX) proteins in human cancers. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1856(2):226–233 Hu Y, Xiao Q, Chen H, He J, Tan Y, Liu Y et al (2017) BEX2 promotes tumor proliferation in colorectal cancer. Int J Biol Sci [Internet]. [cited 2024 Sep 15];13(3):286–94. http://www.ijbs.com286 Xiao Q, Zhang F, Xu L, Yue L, Kon OL, Zhu Y et al (2021) High-throughput proteomics and AI for cancer biomarker discovery. 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Sci Adv [Internet]. 2021 Apr 21 [cited 2024 Sep 15];7(17):143–64. https://www.science.org/doi/ 10.1126/sciadv.abe0143 Peltekova VD, Lemire M, Qazi AM, Zaidi SHE, Trinh QM, Bielecki R et al (2014) Identification of genes expressed by immune cells of the colon that are regulated by colorectal cancer-associated variants. Int J Cancer 134(10):2330–2341 Song N, Kim K, Shin A, Park JW, Chang HJ, Shi J et al (2018) Colorectal cancer susceptibility loci and influence on survival. Genes Chromosomes Cancer 57(12):630–637 Yin R, Song B, Wang J, Shao C, Xu Y, Jiang H (2022) Genome-Wide Association and Transcriptome-Wide Association Studies Identify Novel Susceptibility Genes Contributing to Colorectal Cancer. J Immunol Res. ;2022 Wang Y, Ma M, Li C, Yang Y, Wang M (2021) GAS6-AS1 Overexpression Increases GIMAP6 Expression and Inhibits Lung Adenocarcinoma Progression by Sponging miR-24-3p. Front Oncol. ;11 Ghafouri-Fard S, Khoshbakht T, Taheri M, Mokhtari M (2021) A review on the role of GAS6 and GAS6-AS1 in the carcinogenesis. Pathology Research and Practice, vol 226. Elsevier GmbH Chen Q, Zhou L, Ma D, Hou J, Lin Y, Wu J et al (2022) LncRNA GAS6-AS1 facilitates tumorigenesis and metastasis of colorectal cancer by regulating TRIM14 through miR-370-3p/miR-1296-5p and FUS. J Transl Med. ;20(1) Rezaei M, Masoudi Marghmaleki R, Sanati Boroujeni F, Shahriari A, Omidghaemi S, Azadeh M (2023) LNC01089-LINC00963/miR-1244-5p/IGF1 ceRNA axis might regulate FOXO signaling pathway in breast cancer patients: a biomarker discovery investigation Shirdeli SZ, Hashemi SA, Ferdowsian S, Mostaghimi Y, Rezaei M, Azadeh M (2023) 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. ; https://doi.org/10.21203/rs.3.rs-3252674/v1 Dayani D, Sharifi S, Mohammadi SS, Ghafourzadeh M, Bahrami S, Azaripour M et al (2024) RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation. ; https://doi.org/10.21203/rs.3.rs-4271471/v1 Zoofaghari MH, Sharif Sharifani M, Ghandi M, Zare S, Yazdani S, Fekri S et al (2024) Exploring the Roles of lncRNAs MZF1-AS1 and MEG9 in Breast, Gastric, and Colorectal Cancer Development: Regulation of LCN6 in Fatty Acid Signaling Pathways Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-5118033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":357833626,"identity":"5fa8ba23-7490-44df-8514-01b45c475d76","order_by":0,"name":"Mohammadreza Rezaei","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mohammadreza","middleName":"","lastName":"Rezaei","suffix":""},{"id":357833627,"identity":"c8104993-767b-4724-be12-0df7ea336dfc","order_by":1,"name":"Parnian Salehipour","email":"","orcid":"","institution":"Zist Fanavari 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Iran","correspondingAuthor":true,"prefix":"","firstName":"Mansoureh","middleName":"","lastName":"Azadeh","suffix":""}],"badges":[],"createdAt":"2024-09-19 15:15:07","currentVersionCode":2,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5118033/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-5118033/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65945597,"identity":"909ebd8e-406e-4803-bb5f-7c87a66e6f62","added_by":"auto","created_at":"2024-10-04 17:34:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60036,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) of BC (a), GC (b), and CRC (c) datasets shows suitable quality of microarray samples.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/7975c3ba870e1b5c87b390f5.png"},{"id":65945211,"identity":"4e84575c-5be5-4b43-9d33-c7ed784c9ebb","added_by":"auto","created_at":"2024-10-04 17:26:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":333922,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot showing the differentially expressed genes (DEGs) in BC (a), GC (b), and CRC (c) microarray tumor samples compared to control. The black points in each plot show BEX1 as the down-regulated mRNA in BC, GC, and CRC samples.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/fa7d707a8ba309ae10a77489.png"},{"id":65946024,"identity":"a41a77f1-961c-4418-a380-e3cfe59ae70f","added_by":"auto","created_at":"2024-10-04 17:42:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165800,"visible":true,"origin":"","legend":"\u003cp\u003eENCORI expression analysis validated the down-regulation of BEX1 in BC, GC, and CRC samples compared to control samples.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/119b6b664cb1006a649a78bd.png"},{"id":65945596,"identity":"79f5e909-c7a5-4549-9729-b6f29acbf50a","added_by":"auto","created_at":"2024-10-04 17:34:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":164186,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of BEX1 in BC, GC, and CRC samples. Low expression of BEX1 might slightly decrease the survival rate of BC and GC patients (not significant result).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/9407fadaa6a6879317810150.png"},{"id":65945214,"identity":"2e76bab9-68aa-4be7-a2cd-c411bde9d86b","added_by":"auto","created_at":"2024-10-04 17:26:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":172270,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction network of BEX1. BEX1 indirectly regulates Gastric Acid Secretion (Red), Insulin signaling pathway (Blue), and Pathways in Cancer (Green), and Transcriptional Misregulation in Cancer (Yellow).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/6547882a40ebfe1d4f022ae4.png"},{"id":65945223,"identity":"11bc3d78-8f8b-4164-893f-4f8c43533df3","added_by":"auto","created_at":"2024-10-04 17:26:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":214059,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral overview of the “cell surface interaction at the vascular wall” mechanism, an important biological process of the homeostasis signaling pathway. The binding of PICK1 to TSPAN7 in the cell membrane could be affected by interaction with the BEX1 protein.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/6c065d1d2a51e785a24f5dd9.png"},{"id":65945217,"identity":"1da36b1e-9b52-4aba-b15e-9a82963c2044","added_by":"auto","created_at":"2024-10-04 17:26:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":300468,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression analysis of lncRNAs GAS6-AS1 and COLCA1 in BC, GC, and CRC. Based on ENCORI analysis, GAS6-AS1 has up-regulation in BC and low expression in GC and CRC. Also, COLCA1 has low expression in three cancer types.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/ce03d183ad81eef487b10b45.png"},{"id":65945212,"identity":"0636a686-9700-4b30-959a-64c29c545abb","added_by":"auto","created_at":"2024-10-04 17:26:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":190565,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of GAS6-AS1 and COLCA1 in BC, GC, and CRC.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/28891760adbf2a141384cab3.png"},{"id":65945222,"identity":"dbce5125-83c2-408c-890c-4a2070fcbecb","added_by":"auto","created_at":"2024-10-04 17:26:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":286172,"visible":true,"origin":"","legend":"\u003cp\u003eThe interaction network of BEX1. Green nodes represent signaling pathways associated with each protein, while blue nodes indicate protein-coding genes, including BEX1. Red nodes correspond to miRNAs, and purple nodes represent lncRNAs. The yellow diamond highlights miR-3616-3p, identified as the key regulatory miRNA within this network. Notably, BEX1 interacts both directly and indirectly (via ceRNA interaction) with two lncRNAs, COLCA1 and GAS6-AS1. The ceRNA interaction between BEX1 and GAS6-AS1 is mediated through their competition for miR-423-5p, while the interaction between BEX1 and COLCA1 is established via their association with miR-15b-5p and miR-30e-3p.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/151cde19c54cb1ea441d9d3d.png"},{"id":65945600,"identity":"fc4eacd8-04e1-48e3-a4f7-fb28756b2628","added_by":"auto","created_at":"2024-10-04 17:34:18","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":159562,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression analysis of BEX1, in BC (a), GC (b), and CRC (c) samples.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/1c71027214f73967fc3fdfbf.png"},{"id":65945220,"identity":"0baebfc3-8d50-4fc9-a744-ab0e8bdb071b","added_by":"auto","created_at":"2024-10-04 17:26:18","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":380869,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis in BC (a), GC (b), and CRC (c) samples.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/6bcea92e061a9243750885f7.png"},{"id":65945599,"identity":"952d3f3d-31d1-4ab4-bf8d-b1cce698120a","added_by":"auto","created_at":"2024-10-04 17:34:18","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":127946,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation analysis of lncRNAs with BEX1 in BC (a), GC (b), and CRC (c) samples.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/36a15ba754f0a341fd59cfd6.png"},{"id":65946289,"identity":"2f3bedf3-c608-4eac-b6fc-ff6d214ee1bd","added_by":"auto","created_at":"2024-10-04 17:50:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3522135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5118033/v2/4ff0a5c0-1755-4cc4-8ff8-61dbc0658277.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Unveiling Novel Regulatory Mechanisms of BEX1 in Breast, Gastric, and Colorectal Cancer via a Systems Biology Approach: The Roles of lncRNAs COLCA1 and GAS6-AS1 and Their Interactions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Brain-Expressed X-linked (BEX) gene family, particularly BEX1, plays a crucial role in various cellular processes that are linked to cancer development, including breast, gastric, and colorectal cancers. BEX1, along with its homologs, is involved in regulating cell survival, apoptosis, and cell cycle progression, all of which are critical processes in tumorigenesis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn breast cancer, BEX1 is often associated with tumor suppression. Its expression is correlated with the modulation of apoptosis pathways and the inhibition of cell proliferation. Specifically, BEX1 interacts with the p75 neurotrophin receptor (p75NTR) to influence NF-kappaB signaling, a key pathway involved in regulating cell survival and apoptosis. Loss of BEX1 expression has been linked to poor response to therapeutic agents like tamoxifen, highlighting its potential role as a prognostic marker and therapeutic target in breast cancer (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In the context of gastric and colorectal cancers, BEX1 and its related proteins are involved in autophagy and apoptosis regulation. For instance, BEX2, a closely related homolog, has been shown to regulate the JNK/c-Jun signaling pathway, which is crucial for cell proliferation and survival in colorectal cancer cells. Knockdown of BEX2 leads to a significant reduction in cancer cell proliferation and tumor growth, demonstrating the importance of BEX family proteins in these malignancies (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh-throughput genomics experiments have revolutionized cancer research by enabling the rapid and precise analysis of vast genetic datasets, which is crucial for identifying novel cancer biomarkers. These advanced technologies facilitate the discovery of specific genetic alterations and expression patterns that are essential for early diagnosis, prognosis, and personalized treatment. For instance, integrating high-throughput proteomics with artificial intelligence significantly enhances the identification and validation of cancer biomarkers by analyzing both genomic and protein data (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Additionally, high-throughput liquid biopsy methods that detect multiple biomarkers in single extracellular vesicles have improved the accuracy and efficiency of cancer diagnostics (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Moreover, targeted next-generation sequencing assays provide comprehensive genomic profiling of solid tumors, offering critical insights into molecular alterations and supporting the development of precision therapies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These high-throughput genomics techniques are indispensable tools in the ongoing quest to discover and validate new cancer biomarkers, ultimately improving patient outcomes through personalized medicine.\u003c/p\u003e \u003cp\u003eLong noncoding RNAs (lncRNAs) are emerging as valuable biomarkers for both inflammation and cancer due to several distinct characteristics. First, many lncRNAs exhibit tissue-specific expression, making them ideal for identifying disease origins. Second, their expression levels are often upregulated in response to specific inflammatory or oncogenic signals. Third, lncRNAs released from cells are encapsulated and safeguarded within extracellular vesicles, ensuring their stability. Finally, circulating lncRNAs in the bloodstream can be efficiently detected through various RNA sequencing techniques, enhancing their potential as non-invasive molecular markers for disease (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The selection of hub lncRNAs, which are central to these regulatory networks, is particularly important for understanding cancer mechanisms and developing targeted therapies. In papillary thyroid cancer, for example, identifying key lncRNAs has led to the development of prognostic models that predict patient outcomes based on the interactions of these lncRNAs with miRNAs and mRNAs (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Similarly, in ovarian cancer, constructing lncRNA-miRNA-mRNA networks has provided insights into novel therapeutic targets and potential biomarkers for early detection (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Also, in hepatocellular carcinoma, it is demonstrated that EZH2 mRNA is regulated by different lncRNAs, including lncRNA HAGLR (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In GC, CCDC144NL-AS1 and LINC01094 lncRNAs can act as two potential diagnostic biomarkers (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)Understanding the roles of these hub lncRNAs across different cancer types is essential for advancing precision oncology and improving patient outcomes.\u003c/p\u003e \u003cp\u003eRNA interaction and network analysis are integral to a systems biology approach, significantly enhancing cancer diagnosis and therapy by providing a comprehensive understanding of the molecular interactions within cancer cells. By mapping the intricate networks of coding and non-coding RNAs, researchers can identify key regulatory RNAs that drive cancer progression and pinpoint potential therapeutic targets. For example, ceRNA networks, which include mRNAs, lncRNAs, and miRNAs, reveal how disruptions in these interactions contribute to tumorigenesis and can highlight biomarkers for early diagnosis and targeted therapy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Additionally, integrating RNA-Seq data with network analysis has been shown to improve the prediction of key cancer drivers and therapeutic responses, providing a more personalized approach to cancer treatment (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Furthermore, understanding the connectivity between non-coding RNAs and protein-coding genes enhances the identification of potential intervention points within the cancer regulatory networks, offering new avenues for therapeutic development (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These systems biology strategies not only facilitate the discovery of novel biomarkers but also pave the way for innovative treatments that target the complex regulatory networks of cancer cells.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to find novel regulatory lncRNAs in gastric cancer (GC), breast cancer (BC), and colorectal cancer (CRC) patients. We used several systems biology approaches to find potential correlations and interactions between the expression levels of different coding and non-coding RNAs in the patients. Furthermore, the diagnostic possibility of each hub RNA is evaluated in this study. All in all, we tried to find novel regulatory functions of BEX1 in the development of GC, BC, and CRC to find novel and general functionality for BEX1.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Gene Expression Analyses\u003c/h2\u003e \u003cp\u003eFirst, to evaluate the expression level of BEX1 in high-throughput experiments, the following microarray datasets were analyzed for BC, GC, and CRC, respectively: GSE10810 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), GSE54129, and GSE208099 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The significance level of microarray analyses is set on adj. P. Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and considering the significance criteria, |logFC| \u0026gt; 1 was considered as the dysregulation threshold. Using the GEOquery (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) package, the raw gene expression matrix of all datasets was downloaded, and the limma (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) package performed the statistical analyses for finding the differentially expressed genes (DEGs) in all datasets. Visualization of the plots was performed using the ggplot2 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) package. The expression level of BEX1 in BC, GC, and CRC was evaluated using ENCORI (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) (comparing to control samples) for the validation of microarray analysis. Survival analyses were conducted using ENCORI to evaluate the relation of expression changes in selected mRNA and lncRNAs with the survival rate of cancer patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. RNA interaction and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eIn this study, several online platforms were employed to identify emerging non-coding regulatory biomarkers in BC, GC, and CRC samples. The lncRRIsearch (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\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) was used to select novel regulatory lncRNAs. To map the protein interaction networks associated with the identified mRNA, the STRING (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) 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) was utilized. Novel regulatory miRNAs were identified using the miRWalk (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)platform (\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). MicroRNAs with the following criteria were selected for the network: binding probability (score): 1, interaction in the seed region (3\u0026rsquo;UTR), and ordered from lower binding energy to higher. RNA interaction networks were visualized through the Cytoscape (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) software. Pathway enrichment analysis was performed using the Reactome (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (\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) web database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. qRT-PCR sample preparation\u003c/h2\u003e \u003cp\u003eThe Ethics Committee of Al-Zahra Hospital, under the jurisdiction of Isfahan University of Medical Sciences, provided ethical approval for all research protocols involving human specimens used in this study. Written informed consent was obtained from all participating patients. In this case-control study, the expression profiles of selected mRNAs and lncRNAs were examined in 20 tissue samples from patients with BC, CRC, and GC. These were compared with 20 adjacent non-tumorous tissue samples for each cancer type. Importantly, none of the subjects had previously received chemotherapy or radiation therapy. The detailed clinicopathological characteristics of the human samples are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\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=\"Tab2\" 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=\"Tab3\" 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. RNA extraction, cDNA synthesis, and qRT-PCR programs\u003c/h2\u003e \u003cp\u003eRNA extraction from both tumor and normal tissues was carried out using TRIZOL reagent (Invitrogen, Carlsbad, CA, USA). Following extraction, cDNA synthesis was performed with the TaKaRa cDNA synthesis kit (TaKaRa, Tokyo, Japan), following the manufacturer\u0026rsquo;s protocol. Real-time PCR was subsequently conducted using Sybergreen (Amplicon Company, Denmark) on a MIC Real-Time PCR machine (Australia). The PCR reactions included an initial denaturation at 95\u0026deg;C for 15 minutes, followed by 40 cycles of 95\u0026deg;C for 15 seconds, 60\u0026deg;C for 20 seconds, and 72\u0026deg;C for 20 seconds. Primers were designed using Oligo 7 software and synthesized by TAQ Copenhagen Company (Denmark), and their sequences are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe table of primer sequences.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer (5' -\u0026gt; 3')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForward/Reverse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProduct length\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\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCGGGAGAAGGAGGAGACTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCATGCTGAGACTGTTTACTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAS6-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGCTGCATTCGTTGACATCTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTGGTCCTCGTTTCCTCGTAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOLCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGTGCAACTGGGTCTGAAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCCTGCTTCACGGTGATATTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.38\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=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis for the qRT-PCR experiment was performed using GraphPad Prism 8 software. Both paired and unpaired t-tests were employed to determine the significance levels. Additionally, receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic potential of the tumor specimens. The area under the curve (AUC) was carefully examined in the ROC results. An AUC value between 0.7 and 0.8 indicates an acceptable biomarker, a value between 0.8 and 0.9 denotes a good biomarker, and an AUC value between 0.9 and 1 represents an excellent diagnostic biomarker. Pearson correlation was performed for an initial validation of lncRNA-mRNA interaction. A correlation (r) higher than 0.7 was considered a strong correlation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Low expression of BEX1 in BC, GC, and CRC and correlation with the survival rate\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was performed to assess the quality of microarray samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Separation of tumor samples from control samples in PCA plots shows the suitable quality of microarray samples in 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) datasets. In the microarray datasets, BEX1 has significant low expression in BC (logFC: -3.23432, adj. P. Value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), GC (logFC: -2.56432, adj. P. Value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and CRC (logFC: -1.724, adj. P. Value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Volcano plot shows the up and down-regulated DEGs in BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), GC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), and CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). ENCORI expression analysis also validated microarray analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Survival analysis shows no significant correlation between the expression of BEX1 and the survival rate of patients. However, there was a slight and non-significant correlation between the low expression of BEX1 and the higher death rate of BC and GC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. BEX1 indirectly modulates cancer-related signaling pathways\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the protein interaction network of BEX1, which led the study to demonstrate related signaling pathways that are regulated by this regulatory pathway. Based on the protein interaction network, the BEX1 protein is involved in 4 signaling pathways by interacting with other regulatory proteins. Through the regulation of CALML3-6, BEX1 regulates the following signaling pathways: Gastric Acid Secretion (Red), Insulin signaling pathway (Blue), and Pathways in Cancer (Green). Also, BEX1 regulates Transcriptional Misregulation in Cancer (Yellow) signaling pathway through interaction with LDB1 and LMO2.\u003c/p\u003e \u003cp\u003eDirect pathway analysis of BEX1 through the Reactome database revealed the potential role of BEX1 in Hemostasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Direct interaction of BEX1 with PICK1 regulates the binding of PICK1 to TSPAN7 at the homeostasis signaling pathway. Specifically, this binding is part of the \u0026ldquo;cell surface interaction at the vascular wall.\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Non-coding interactions with BEX1\u003c/h2\u003e \u003cp\u003eTo find novel potential regulatory non-coding RNAs affecting the homeostasis signaling pathways in cancer development, miRNA and lncRNA interaction analysis was performed with respect to the central protein-coding gene in this study (BEX1). Among all potential lncRNAs that have interaction with BEX1, two lncRNAs with significant dysregulation in BC, GC, and CRC were selected: GAS6-AS1 and COLCA1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Survival analysis revealed no significant correlation between GAS6-AS1 and CLCA1. However, due to the \u003cem\u003ep\u003c/em\u003e-value of survival analysis for GAS6-AS1 in STAD and COAD and COLCA1 in BRCA, performing the same analysis with a higher sample size or in different populations might result in novel information about the correlation of mentioned dysregulated lncRNAs with the survival rate of BC, GC, and CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003emiRNA interaction analysis revealed the potential role of miR-3616-3p in the regulation of BEX1. Among all miRNAs that have interaction with BEX1, 17 miRNAs had the criteria described in Materials and Methods (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among those 17 miRNAs, miR-3616-3p has the strongest interaction with the 3\u0026rsquo;UTR region of mRNA BEX1 (energy: -25 kcal/mol). Furthermore, miR-3616-3p interacts with CALML3 and LMO2, two important nodes in the protein interaction networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates the interaction network in this study.\u003c/p\u003e \u003cp\u003e \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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emiRNA-BEX interaction analysis revealed that miR-3616-3p has the strongest interaction with BEX mRNA in the 3\u0026rsquo;UTR (seed) region.\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=\"char\" char=\".\" 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\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003escore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eenergy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eseed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eposition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-3616-3p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-6792-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-6880-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-4697-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-10401-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-3141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-6131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-2278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-4713-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-15b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-1973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-4443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-4708-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-4300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3UTR\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. qRT-PCR experiment\u003c/h2\u003e \u003cp\u003eTo assess the biomarker potential of BEX1, GAS6-AS1, and COLCA1 in BC, GC, and CRC samples, a qRT-PCR experiment was conducted. Initially, differential expression analysis was performed on the qRT-PCR data to validate the gene expression results from bioinformatics analyses. The experiment revealed that BEX1 exhibited low expression in both BC and GC samples. GAS6-AS1 showed low expression in BC samples but high expression in GC and CRC samples. Similarly, COLCA1 was found to be expressed at low levels in GC samples, while its expression was elevated in CRC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Additionally, BEX1 in BC (AUC: 0.8025, p-value: 0.0011) and GAS6-AS1 (AUC: 0.8375, p-value: 0.0003) as well as COLCA1 (AUC: 0.8150, p-value: 0.0007) in CRC are identified as promising diagnostic biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Pearson correlation analysis demonstrated a strong correlation between the lncRNAs GAS6-AS1 and COLCA1 with BEX1 in GC samples. A strong correlation between GAS6-AS1 and BEX1 was also observed in CRC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Statistical parameters of the qRT-PCR experiment are provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Detail of correlation analysis is provided in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of the gene expression analysis conducted 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\u003eBEX1\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\u003e-2.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0110\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-3.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0080\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-1.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCOLCA1\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\u003e-1.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0844\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\u003e0.1441\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-2.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0041\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\u003e2.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8150\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\u003eGAS6-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\u003e-3.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0149\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\u003e3.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0041\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\u003e3.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\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 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of the correlation analysis between lncRNAs and BEX1 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\u003eCOLCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8382\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\u003e0.6244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAS6-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9379\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\u003e0.7296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0003\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\u003eIn this study, we sought to identify novel non-coding RNA interactions involving the mRNA BEX1 and explore the regulatory roles of these interactions in the progression of breast, gastric, and colorectal cancers. As a tumor suppressor with low expression, BEX1 interacts with the lncRNAs GAS6-AS1 and COLCA1 and is further suppressed by the miRNA miR-3616-3p. This miRNA also regulates the expression of CALML3 and LMO2, two proteins that have significant interactions with BEX1. Additionally, through its interaction with PICK1, BEX1 indirectly influences the homeostasis signaling pathway. Given its involvement in \"cell surface interactions at the vascular wall,\" reduced expression of BEX1 could contribute to a more aggressive tumor phenotype by disrupting normal vascular responses, ultimately promoting tumor growth and metastasis. Therefore, the downregulation of BEX1 in these cancers suggests its critical role in modulating interactions within the tumor microenvironment, particularly within the vascular niche, which may inform future therapeutic approaches aimed at restoring its function (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHomeostasis is crucial in maintaining the stability of biological systems, especially in the vascular wall, where it regulates processes such as cell growth, immune response, and endothelial integrity. In the context of cancer, disruptions to homeostatic mechanisms can lead to an altered tumor microenvironment that supports cancer progression. The vascular wall is a critical site where cancer cells interact with endothelial cells and bypass immune surveillance, a process that is highly dependent on maintaining homeostatic balance. When this balance is disturbed, the endothelial barrier becomes compromised, allowing cancer cells to enter the bloodstream and metastasize to distant organs. This highlights the importance of vascular homeostasis in controlling the spread of cancer cells and the potential for therapeutic interventions aimed at restoring this balance (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCell surface interactions at the vascular wall are also key to cancer development and progression. Tumor cells exploit these interactions to invade and migrate through the vascular endothelium. Molecules such as integrins and selectins play significant roles in this process, facilitating the adhesion of cancer cells to the vascular wall. Once adhered, cancer cells can cross the endothelial barrier and disseminate throughout the body, leading to metastasis. Targeting these cell surface interactions offers a promising strategy for cancer therapies, as blocking the adhesion of cancer cells to the vascular wall could prevent their spread and improve patient outcomes (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This approach not only addresses the local impact of tumor growth but also the systemic spread of cancer, making it a crucial area of research (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLncRNA COLCA1 has emerged as a key regulator in colorectal cancer (CRC) development, significantly influencing immune responses within the tumor microenvironment. COLCA1 is particularly associated with genetic variants, such as rs3802842, which has been linked to increased CRC susceptibility (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)​. Studies show that COLCA1 co-localizes with eosinophils, macrophages, and other immune cells, suggesting a role in modulating the immune response against tumor growth. This lncRNA is thought to function by affecting the expression of miRNAs, particularly miR-371a-5p, which plays a pivotal role in inflammation and immune regulation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)​. By suppressing the tumor-suppressive activities of miR-371a-5p, COLCA1 likely contributes to immune evasion and tumor proliferation (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)​.\u003c/p\u003e \u003cp\u003eFurther evidence points to COLCA1's involvement in key signaling pathways associated with cancer progression, such as the Wnt/β-catenin and mTORC1 pathways (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)​. Through these interactions, COLCA1 influences cell proliferation, angiogenesis, and immune cell infiltration, which are crucial for both tumor growth and metastasis. COLCA1's presence in eosinophilic granules within immune cells, along with its interaction with proteins such as major basic protein (MBP) and eosinophil peroxidase (EPO), highlights its potential role in the immune modulation of cancer (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This complex interplay between genetic variants, immune response, and cancer signaling pathways suggests that COLCA1 may serve as a therapeutic target for CRC and other cancers where it is dysregulated (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGAS6-AS1 (Growth Arrest-Specific 6 Antisense 1) plays a key role in various cancers by acting as a competitive endogenous RNA (ceRNA), influencing oncogenic processes. For instance, in lung adenocarcinoma (LUAD), GAS6-AS1 functions by sponging miR-24-3p, a microRNA involved in regulating cancer cell proliferation and invasion. Through this interaction, GAS6-AS1 inhibits the activity of miR-24-3p, thereby upregulating its target gene GTPase IMAP Family Member 6 (GIMAP6), which promotes cancer progression (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)​​. Moreover, GAS6-AS1 is expressed mainly in the cytoplasm of LUAD cells and serves as a vital player in regulating cellular proliferation, migration, and apoptosis (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Studies have shown that downregulation of GAS6-AS1 is associated with poor prognosis in LUAD, while its overexpression leads to reduced cell proliferation and invasion, making it a potential diagnostic biomarker and therapeutic target in LUAD​.\u003c/p\u003e \u003cp\u003eIn hepatocellular carcinoma (HCC), GAS6-AS1 has been shown to sponge miR-585, thereby releasing the oncogene EIF5A2 from inhibition, which in turn enhances tumor growth and metastasis (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This pathway highlights GAS6-AS1's oncogenic potential in driving cancer progression through its regulation of the PI3K/AKT signaling pathway (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Additionally, in gastric cancer, GAS6-AS1 promotes cell proliferation and invasion by interacting with AXL signaling, a pathway that is crucial in several cancers due to its role in epithelial-to-mesenchymal transition (EMT) and metastasis ​ (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Collectively, these studies suggest that GAS6-AS1 serves as a pivotal regulatory molecule in cancer development, interacting with both proteins and miRNAs to influence key signaling pathways, and it holds promise as a therapeutic target across various malignancies​​.\u003c/p\u003e \u003cp\u003eIn this study, we sought to identify novel regulatory networks involved in the development of BC, GC, and CRC. Our previous research focused on uncovering new regulatory lncRNAs and miRNAs across various cancer types. For instance, we explored the potential regulatory roles of IGF1 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)and XBP1 (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)in BC, as well as UHRF1 (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)and MEG9 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) in BC, GC, and CRC. However, further investigation is required to achieve more precise validation of RNA interactions and to assess the differential expression of these RNAs under varying pathological conditions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eBased on this investigation, lncRNAs COLCA1 and GAS6-AS1 might regulate cancer-related signaling pathways through the regulation of BEX1 mRNA. Furthermore, the mentioned lncRNAs can be considered as the two potential cancer diagnostic biomarkers (specifically in BC, GC, and CRC). miR-3616-3p is also one of the hub nodes in the interaction network, suppressing the expression level of BEX1.\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\u003e\u0026nbsp;Consent 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;GSE10810, GSE54129, and GSE208099.\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\u003eMohammadreza Rezaei, Parnian Salehipour, Mehrnoosh Tavakoli, Maryam Mousavi, Shima Asgari, Dorsan Vatani, Seyedeh Saba Hosseinipouya, Younes Poudineh\u003c/strong\u003e: Software, 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\u003eReza Ghelich:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Original Draft; \u003cstrong\u003eMansoureh Azadeh:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Mohammadreza Rezaei, Parnian Salehipour, Mehrnoosh Tavakoli, and Maryam Mousav\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eequally contributed to this study as the first authors. Shima Asgari, Dorsan Vatani, Seyedeh Saba Hosseinipouya, and Younes Poudineh 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\u003eKazi JU, Kabir NN, R\u0026ouml;nnstrand L (2015) Brain-Expressed X-linked (BEX) proteins in human cancers. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1856(2):226\u0026ndash;233\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Xiao Q, Chen H, He J, Tan Y, Liu Y et al (2017) BEX2 promotes tumor proliferation in colorectal cancer. Int J Biol Sci [Internet]. 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J Transl Med. ;20(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezaei M, Masoudi Marghmaleki R, Sanati Boroujeni F, Shahriari A, Omidghaemi S, Azadeh M (2023) LNC01089-LINC00963/miR-1244-5p/IGF1 ceRNA axis might regulate FOXO signaling pathway in breast cancer patients: a biomarker discovery investigation\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirdeli SZ, Hashemi SA, Ferdowsian S, Mostaghimi Y, Rezaei M, Azadeh M (2023) 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. ; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-3252674/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-3252674/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDayani D, Sharifi S, Mohammadi SS, Ghafourzadeh M, Bahrami S, Azaripour M et al (2024) RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation. ; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-4271471/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-4271471/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZoofaghari MH, Sharif Sharifani M, Ghandi M, Zare S, Yazdani S, Fekri S et al (2024) Exploring the Roles of lncRNAs MZF1-AS1 and MEG9 in Breast, Gastric, and Colorectal Cancer Development: Regulation of LCN6 in Fatty Acid Signaling Pathways\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":"long non-coding RNA, Bioinformatics, Systems Biology, Cancer Genetics","lastPublishedDoi":"10.21203/rs.3.rs-5118033/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5118033/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: This study aimed to explore novel regulatory networks involving the BEX1 gene and its interaction with non-coding RNAs (ncRNAs) in breast cancer (BC), gastric cancer (GC), and colorectal cancer (CRC). BEX1 has been linked to tumor suppression, but its role in signaling pathways and its interactions with regulatory RNAs in these cancers has not been fully elucidated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: High-throughput microarray datasets (GSE10810, GSE54129, and GSE208099) were analyzed to investigate BEX1 expression in breast cancer, gastric cancer, and colorectal cancer. The expression analysis and survival outcomes for BEX1 and selected lncRNAs were validated using the ENCORI platform. Regulatory interactions of BEX1 with proteins and microRNAs were identified using STRING and miRWalk, respectively, while lncRNA interactions were examined through lncRRIsearch. Final validation of differential expression analysis and biomarker potential was conducted using qRT-PCR, along with ROC analysis to assess diagnostic capability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: BEX1, identified as a tumor suppressor with low expression in breast, gastric, and colorectal cancer, demonstrated potential as a diagnostic biomarker, particularly in breast cancer (AUC: 0.8025, p = 0.0011). The lncRNAs COLCA1 and GAS6-AS1 were found to potentially regulate BEX1 expression. BEX1 exhibited significant interactions with two key proteins involved in cancer-related signaling pathways: CALML3 and LMO2. Moreover, BEX1 and these proteins demonstrated competitive interactions with miR-3616-3p, which was found to suppress BEX1 expression by targeting its 3'UTR. COLCA1 and GAS6-AS1 also exhibited dysregulated expression across breast, gastric, and colorectal cancers, suggesting their potential as diagnostic biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The lncRNAs GAS6-AS1 and COLCA1, alongside miR-3616-3p, may play pivotal roles in regulating cancer-related pathways, including gastric acid secretion, insulin signaling, and homeostasis. These regulatory processes occur through direct and indirect interactions between the non-coding RNAs and BEX1, further highlighting the potential of these molecules as therapeutic targets and diagnostic biomarkers.\u003c/p\u003e","manuscriptTitle":"Unveiling Novel Regulatory Mechanisms of BEX1 in Breast, Gastric, and Colorectal Cancer via a Systems Biology Approach: The Roles of lncRNAs COLCA1 and GAS6-AS1 and Their Interactions","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-10-04 17:26:13","doi":"10.21203/rs.3.rs-5118033/v2","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}},{"code":1,"date":"2024-09-23 09:44:28","doi":"10.21203/rs.3.rs-5118033/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":"October 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38064603,"name":"Bioinformatics"},{"id":38064604,"name":"Systems Biology"}],"tags":[],"updatedAt":"2024-09-23T09:44:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-04 17:26:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-5118033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5118033","identity":"rs-5118033","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}