Identification of prognostic gene markers for the early diagnosis of colorectal cancer

Identification of prognostic gene markers for the early diagnosis of colorectal cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of prognostic gene markers for the early diagnosis of colorectal cancer kirti sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4657501/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Colorectal Cancer (CRC) is the frequently occurring malignant tumor in colon and rectum with high mortality rate. The signaling pathway involved in CRC and CRC driven genes are largely unknown. Methods To identify the gene signatures which help in early diagnosis of CRC, we downloaded three datasets (GSE24514, GSE8671 and GSE21510) from the Gene Expression Omnibus (GEO) Database. GO and KEGG pathway enrichment analysis were conducted using DAVID database. A protein–protein interaction (PPI) network was constructed using STRING and cytoscape software. These hub genes were verified by survival analysis using GEPIA database. Results A total of 120 DEGs were identified including (75 upregulated genes and 45 downregulated genes). Seven modules were identified from protein –protein interaction network using MCODE plug in tool of cytoscape, only three Modules ( 1 , 2 and 3 ) selected with score ≥ 5 and node ≥ 10. Module 1 contained downregulated genes and Module 2 and 3 contained upregulated genes. Hub genes identified from Module 1 with connectivity score ≥ 16 included CDK1 , CCNB1 , FOXM1 , RRM2 , MAD2L1 , NEK2 , MCM4 and PBK . Out of 8 genes examined, only 3 exhibited significant correlations with overall survival among CRC patients (p > 0.05). MAD2L1 , MCM4 , and PBK demonstrated relatively lower expression levels of these genes were correlated with poor prognosis in CRC patients. Hub genes from Modules 2 and 3 (connectivity score ≥ 6) included MYL9, CNN1, MYH11, MYLK, TAGLN, GUCA2A, GUCA2B, ZG16 and SLC26A3 . Survival analysis indicated that higher expression of MYL9, CNN1 and TAGLN correlated with poor prognosis, while lower expression of ZG16 and SLC26A3 was linked to poorer outcomes in CRC patients (p < 0.05). These eight hub genes, believed to promote tumor activity, are promising candidates for new CRC therapeutic targets. Conclusion Eight hub DEGs ( MAD2L1, MCM4, PBK, MYL9, CNN1, TAGLN, ZG16 and SLC26A3 ) were identified, to be strongly correlated with the overall survival of patients with CRC based on GEO and GEPIA data. These eight genes have the potential as novel and independent prognostic biomarkers for early diagnosis of CRC and forecasting clinical results of CRC patients. Several studies revealed that suppression of these genes inhibits the proliferation of CRC. Cancer Biology Colorectal cancer Differentially expressed genes Microarray Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background Colorectal cancer (CRC) represents a significant epidemiological challenge due to its prevalence as among the most frequently occurring malignancies and its associated elevated mortality rate ( 1 ). As per the World Health Organization (WHO), CRC stands as the second most prominent cause of cancer-related deaths worldwide and the ranked third in terms of the most prevalent cancer overall, comprising 10% of all cancer cases. The occurrence and death rates exhibit notable geographic variations. Eastern Europe had the greatest fatality rates, while Europe, Australia, and New Zealand had the highest incidence rates. In 2040, it is projected that there will be 3.2 million new cases of CRC each year, marking a 63% increase, along with 1.6 million annual deaths, representing a 73% rise ( 2 ). According to Aran et al., 2016 ( 3 ) tumor start, development, and metastasis are all associated with gene mutations, cellular settings, and environmental factors in CRC, making it a polyphase disease ( 3 ). Research investigations have elucidated the participation of a myriad of genes and cellular pathways in the onset and progression of CRC ( 4 ). Unfortunately, distant metastases are found in 20–25% of patients with newly diagnosed CRC, and only a small subset of these patients can undergo curative surgery ( 5 ). More importantly, a majority of CRC patients with resectable tumors will encounter recurrence within two years, impacting around 50% of patients ( 6 ). The five-year relative survival rate for early-stage CRC is approximately 90%. Only 4 out of 10 CRC cases, however, are typically diagnosed at the early stage ( 7 ). Hence, it is imperative to explore potential biomarkers for early-stage CRC to improve patient prognosis. Through various investigations, genes such as AXIN , CTNNB1 , EGFR , JAG-1 , NOTCH-1 , PIK3CA , PTEN , RAF , RAS , SMADs , TGFBR1 and TGFBR2 have been identified as contributors to increased proliferation, invasion, progression, or apoptosis suppression in CRC cells ( 8 ). The status of a patient's biomarkers has grown to be crucial information in the planning of treatment for CRC. Biomarker testing has significantly increased the range of therapy options available to patients, particularly those with metastatic CRC. While the existing fecal occult blood test methods are widely employed in population screening initiatives, they are susceptible to numerous interfering factors that can lead to inaccurate results, including false negatives or false positives, and exhibit low sensitivity rates in the detection of colon polyps ( 9 ). Proteomic methodologies, including 2-dimensional electrophoresis and mass spectrometry, along with genomic techniques like DNA microarray analysis, are frequently utilized for assessing the expression patterns of proteins and genes in cancer cells, body fluids and surrounding tissues ( 10 ). Zhau et al., 2019 ( 11 ) and Kou et al., 2015 ( 12 ) analyzed the GSE4107 dataset comprised 12 samples of patients with CRC and 10 samples of healthy patients considered as normal controls. However, only a small number of samples were included in these two investigations, and it is yet unknown which molecular pathways contribute to CRC carcinogenesis. In this current study, three datasets were obtained GSE24514 ( 13 ), GSE8671 ( 14 ) and GSE21510 ( 15 ) from the Gene expression omnibus (GEO) ( www.ncbi.nih.gov/geo ) database (GPL96 [HG-U133A] Affymetrix Human Genome U133A Array and GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 plus 2.0 Array) in order to identify differentially expressed genes (DEGs) in CRC tissues. Subsequent to this, the biological pathways and function of significant genes were elucidated via gene ontology and pathway enrichment analysis, conducted using the platform ( https://david.ncifcrf.gov/ ). The results of this research provide new perspectives on possible markers for CRC and could enhance comprehension of molecular processes behind CRC growth and advancement. 2. Methods 2.1. Microarray data Three gene expression profiles (GSE24514, GSE8671, and GSE21510) were downloaded from the Gene expression omnibus ( http://www.ncbi.nlm.nih.gov/geo/ ) database. Alhopuro et al., 2012 ( 13 ) contributed GSE24514, which utilized the Affymetrix GPL570 platform. [GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array]. There were 17 CRC samples and 6 normal control samples in the GSE24514 dataset. The Gene Spring software (Silicon Genetics, San Carlos, CA) was employed to identify genes that exhibited significant under expression in MSI cancers in comparison to normal mucosa samples. GSE8671 was composed of 20 CRC samples and 6 normal controls, which was presented by Sabetes et al., 2007 ( 14 ). The analysis was performed according to the Affymetrix GPL570 platform [GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array]. Tsukamoto et al., 2011 ( 15 ) submitted GSE21510, which is based on the Affymetrix GPL570 platform (HG-U133_Plus_2) and includes 11 CRC samples and 5 normal control samples. In this analysis, a Student's t-test was utilized; DEGs were identified using a fold change threshold of ≥ 1.25 and ≤ -1.25. Statistical significance was determined with a threshold of p ≥ 0.05. 2.2 Gene ontology and pathway enrichment analysis of DEGs DEGs were subjected to Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. This analytical approach was utilized to identify DEGs at the biological functional level ( 16 ). Functional genomics annotations were integrated using the database for annotation, visualization, and integrated discovery (DAVID) online tool (version DAVID 2021; https://david.ncifcrf.gov/ ). A statistical significance threshold of p ≥ 0.05 was utilized to indicate the presence of a significant difference. 2.3 Integration of the protein-protein interaction (PPI) network The investigation of putative DEGs connections at the protein level using the Search Tool for the Retrieval of Interacting Genes (STRING; version 12.0; http://string-db.org/cgi/input.pl ) was conducted. Validated trials served as the basis for the PPI networks of DEGs developed by STRING. The significant PPI score was less than 0.4. The software Cytoscape (version 3.10.1; http://www.cytoscape.org/ ) was employed to visualize the PPI network. A statistical significance threshold of p ≥ 0.05 was utilized to indicate the presence of a significant difference. 2.4 Module analysis of the PPI network Module analysis was conducted on the PPI network utilizing the Molecular Complex Detection (MCODE) algorithm within Cytoscape, the parameters were configured as follows: degree cutoff = 2, node score cutoff = 0.2, k-core = 2, and max depth = 100. Modules meeting the criteria of an MCODE score ≥ 5 and comprising at least 10 nodes were considered significant. Following selection, GO functional and KEGG pathway enrichment analysis were conducted on the most significant module. A significance threshold of p ≥ 0.05 was applied. 3. Results 3.1 Identification of DEGs In this research, we selected 48 CRC and 17 non-cancerous colorectal tissues from three datasets GSE24514, GSE8671 and GSE21510 which were analyzed by GEO2R. However, we found 120 DEGs with repeated emergence in these datasets, including 75 upregulated genes (logFC ≥ 1.25) and 45 downregulated genes (logFC ≤ -1.25), as demonstrated in the Venn diagram (Fig. 1, 2). 3.2 GO function and KEGG pathway enrichment analysis of the DEGs To deepen our understanding of the selected DEGs, GO and KEGG pathway analyses were conducted using DAVID. GO analysis revealed that both upregulated and downregulated DEGs were notably abundant in the categories of 'molecular function', 'biological processes', and 'cellular component classification'. Specifically, in the molecular function, upregulated genes were primarily enriched in functions such as 'hormone activity', 'actin binding', and 'guanylate cyclase activator activity' (Table 1, Fig. 3A). Conversely, downregulated DEGs exhibited enrichment in various functions, including 'CXCR chemokine receptor binding', 'chemokine activity', 'protein binding', and 'ATP binding' (Table 2, Fig. 3A). Within the biological processes, upregulated DEGs were prominently associated with 'excretion', 'actomyosin structure organization', 'bicarbonate transport' (Table 1, Fig. 3A), while downregulated DEGs were significantly enriched in biological processes like 'negative regulation of stress-activated MAPK cascade', 'cell division', and 'chemokine-mediated signaling pathway' (Table 2, Fig. 3B). Moreover, GO cellular component analysis indicated that upregulated DEGs were prominently linked with components like 'brush border membrane', 'extracellular exosome', and 'apical plasma membrane' (Table 1, Fig. 3A), whereas downregulated DEGs were enriched in components such as 'basal plasma membrane', 'nucleus', and 'spindle pole'. These findings underscore the significant enrichment of DEGs in functions related to 'binding', 'ion channel', and 'cell cycle' (Table 2, Fig. 3B). 3.3 KEGG pathway analysis KEGG pathway analysis indicated that the upregulated DEGs were prominently linked with pathways such as 'Vascular smooth muscle contraction', 'Regulation of actin cytoskeleton', 'Aldosterone-regulated sodium reabsorption', 'Neuroactive ligand-receptor interaction', and 'Steroid hormone biosynthesis' (Table 3, Fig. 4A). On the other hand, the downregulated DEGs were prominently linked with pathways including 'Epithelial cell signaling in Helicobacter pylori infection', 'Cell cycle', 'Legionellosis', 'Cellular senescence', and 'Rheumatoid arthritis' (Table 4, Fig. 4B). 3.3 PPI network construction and hub gene identification The data from the STRING database were utilized to generate the expression profiles of Differentially Expressed Genes (DEGs) (Fig. 5) After the removal of disconnected and partially linked nodes, a DEG network was constructed. The hub genes selected from modules 1 with connectivity score ≥ 16 were CDK1 (Cyclin-dependent kinase1, CCNB1 (cyclinB1), FOXM1 (Forkhead box protein M1), RRM2 (Ribonucleotide reductase subunit M2), MAD2L1 (Mitotic spindle assembly checkpoint protein MAD2A), NEK2 (NIMA related kinase 2), MCM4 (Minichromosome Maintenance Complex Component 4) and PBK (PDZ binding kinase). The hub genes selected from module 2 and 3 with connectivity score ≥ 6 were MYL9 (Myosin light chain 9), CNN1 (Calponin-1), MYH11 (Myosin-11), MYLK (Myosin light chain kinase), TAGLN ( Transgelin ; Actin cross-linking/gelling protein), GUCA2A ( Guanylate Cyclase Activator 2A ), GUCA2B (Guanylate cyclase Activator 2B), ZG16 (Zymogen granule protein 16) and SLC26A3 (Solute carrier family 26 member 3) (Table 5). 3.4 Module analysis In the PPI networks, significant modules with MCODE scores ≥ 5 and nodes ≥ 10 were identified. Specifically, module 1 had an MCODE score of 15.2 and comprised 114 nodes, module 2 had an MCODE score of 6.333 and contained 19 nodes, while module 3 had an MCODE score of 5 with 10 nodes (Fig. 6). 3.5 Validation of Expression level of Hub Genes We utilized the GEPIA database ( http://gepia.cancer-pku.cn/ ) to perform survival analysis of the identified hub genes in colorectal cancer (CRC). GEPIA is an interactive web-based tool that integrates TCGA and GTEx data, allowing for comprehensive gene expression and survival analysis.To assess the prognostic significance of the hub genes, we conducted both Overall Survival (OS) analysis. Each gene was input into GEPIA, and 'Colorectal Adenocarcinoma' was selected as the cancer type. Patients were divided into high and low expression groups based on the median expression levels. Kaplan-Meier survival plots were generated, and the log-rank test was used to evaluate statistical significance. The findings demonstrated a substantial relationship between the expression levels of MAD2L1, MCM4, PBK, ZG16, SLC26A3, MYL9, CNN1 and TAGLN and the Overall survival among CRC patients was observed, indicating potential variations in expression were linked to a worse prognosis for the patients (p < 0.05). For CRC patients MAD2L1, MCM4, PBK, MYL9, CNN1, TAGLN, ZG16 and SLC26A3 may therefore be utilized as prognostic markers (Fig. 7). The survival analysis also revealed that there was no significant difference (p > 0.05) in overall survival (OS) between the eight additional genes CDK1, CCNB1, FOXM1, RRM2, NEK2, MYH11, MYLK, GUCA2A and GUCA2B with high and low expression (Fig. 8). 4. Discussion As per World Health Organization (WHO), CRC stands as the second prominent cause of cancer-related deaths worldwide and ranked third in terms of most prevalent cancer overall, comprising 10% of all cancer cases. The occurrence and death rates exhibit notable geographic variations. Eastern Europe had the greatest fatality rates, while Europe, Australia, and New Zealand had the highest incidence rates. In 2040, it is projected that there will be 3.2 million new cases of CRC each year, marking a 63% increase, along with 1.6 million annual deaths, representing a 73% rise ( 2 ). The five-year relative survival rate for early-stage CRC is approximately 90%. Only 4 out of 10 CRC cases, however, are typically diagnosed at the early stage ( 7 ) that’s why knowing etiology of CRC etiology is very important. The three gene expression datasets (GSE24514, GSE8671 and GSE21510) from the GEO database were used in this investigation to compare DEGs in CRC with normal tissues. In all, the three datasets revealed 75 up-regulated and 45 down-regulated DEGs. It was mentioned that DEGs, which includes CDK1, CCNB1, FOXM1, RRM2, MAD2L1, NEK2, MCM4, PBK, MYL9, CNN1, MYH11, MYLK , TAGLN, GUCA2A, GUCA2B, ZG16 and SLC26A3 were hub genes in the PPI network analysis. The survival analysis suggested that only eight genes ( MAD2L1, MCM4, PBK, MYL9, CNN1, TAGLN, ZG16 and SLC26A3 ) had a variations in expression were linked to a worse prognosis for the patients. MAD2L1 is one of the important gene that plays an important role in cell proliferation and growth. The gene encoding mitotic arrest defective protein 2 ( MAD2L1 ) is situated on chromosome 4 in humans (chromosome 6 in mice), and it is a crucial part of the mitotic checkpoint complex protein. MAD2L1 is overexpressed in colorectal cancer (CRC), promoting cell proliferation and migration, and is regulated by TEAD4. This overexpression correlates with poor prognosis due to increased chromosomal instability in CRC patients. Xiang et al., 2020 ( 17 ) verified that MAD2L1 knockdown may considerably inhibit the growth of CRC cells by causing apoptosis in the cells and affecting the initiation of the cell cycle. In addition, MAD2L1 may prove to be a novel biomarker for CRC diagnosis and treatment ( 17 ). Several studies have reported higher levels of MCM4 expression in colorectal cancer tissues compared to adjacent normal tissues. This overexpression suggests a potential role for MCM4 in the development or progression of colorectal cancer. Elevated MCM4 expression has been associated with poorer prognosis in colorectal cancer patients. High MCM4 levels have been correlated with advanced tumor stage, lymph node metastasis, and decreased overall survival rates. MCM4 is involved in DNA replication and cell cycle regulation. Its overexpression in colorectal cancer cells may contribute to increased cell proliferation and tumor growth.Some studies suggest a potential link between MCM4 expression and chemoresistance in colorectal cancer. High levels of MCM4 may confer resistance to chemotherapy agents commonly used in the treatment of colorectal cancer, leading to treatment failure and disease recurrence ( 18 ). Several studies have been shown that PBK is involved in promoting cellular proliferation of tumor cells. Due to its involvement in cancer progression, PBK has emerged as a potential therapeutic target in colorectal cancer .Many studies have been done to target the gene PBK , which is thought to be a promising therapeutic target ( 19 ). With the use of OTS514, a particular PBK inhibitor, Koshino et al., 2021 was able to successfully stop the growth of multiple myeloma and CRC cells through down-regulating PHH3, OTS514 not only prevented CRC cells from proliferating, it also caused apoptosis ( 20 ). Myosin light chain 9 ( MYL9 ) is the most extensively researched member of the myosin superfamily and MYL9 is a fibroblast-specific biomarker linked with a poor prognosis in CRC. It has previously been noted that early-stage and recurring CRC tissues exhibit elevated MYL9 expression. MYL9 promotes the proliferation, invasion, migration, and angiogenesis of colorectal cancer cells. Feng et al., 2022 ( 21 ) demostrated that suppression of MYL9 inhibited the cell proliferation, migration and invasion while overexpression exert the opposite effect. This suggests that MYL9 may act as an oncogene in colorectal cancer progression ( 21 ). Calponin 1 ( CNN1 ) is a protein that has been identified as a significant marker in colorectal cancer (CRC). Studies indicate that CNN1 expression in the tumor-associated stromal cells (TASCs) of CRC patients correlates with a worse prognosis. These TASCs, when expressing CNN1 , have been shown to support cancer progression by enhancing tumor cell proliferation, migration, and metastatic potential. The expression of CNN1 , along with another protein TPM2, has been found to outperform other previously reported TASC-associated markers in prognostic significance​ ( 22 ). Weighted gene co-expression network analysis (WGCNA) has identified CNN1 as a hub gene associated with CRC recurrence. Higher levels of CNN1 expression are significantly linked with shorter survival times in CRC patients. This association has been validated in multiple independent datasets and supported by functional enrichment analyses that place CNN1 in critical pathways related to cancer progression ​( 23 ). Given its significant role in CRC progression and recurrence, CNN1 could serve as a target for therapeutic interventions aimed at improving patient outcomes. Targeting CNN1 and related pathways may help develop strategies to inhibit tumor growth and metastasis in CRC patients​. Transgelin ( TAGLN ), also known as SM22-alpha, is a protein involved in actin cytoskeleton organization and smooth muscle contraction Several studies have reported increased TAGLN expression in colorectal cancer tissues compared to adjacent normal tissues. This upregulation has been associated with tumor progression, invasion, and metastasis.A more advanced CRC pathological stage and a worse clinical outcome were linked to increased expression of TAGLN , indicating a potential function for TAGLN in promoting the course of CRC disease ( 24 ). The earlier research showed that TAGLN functions as a tumor suppressor and that inhibiting it is a critical step in the development and transformation of tumors ( 25 ). In the present study patient with high expression of TAGLN has lower Overall survival as compared to moderate – low expression, TAGLN can be considered as an oncogene for early prognosis of CRC. High TAGLN expression levels in colorectal cancer have been associated with poor prognosis, including shorter overall survival and disease-free survival. Therefore, TAGLN expression may serve as a prognostic marker for CRC patients.Inhibition of TAGLN expression or function could potentially suppress tumor invasion and metastasis, offering a novel therapeutic strategy. ZG16 is a gene that has garnered attention for its role in colorectal cancer (CRC). Research indicates that ZG16 expression is significantly downregulated in colorectal cancer tissues compared to normal tissues. This reduced expression has been associated with the progression of CRC and is considered one of the most consistently downregulated genes in these cancerous tissues ( 26 ). Overexpression of ZG16 in CRC cell lines and xenograft models has demonstrated a suppression of tumor growth, which is partly due to its role in activating T-cell mediated immunity. Specifically, ZG16 appears to decrease the expression of inhibitory checkpoint molecules like PD1 and CTLA4 on T cells, thereby enhancing the immune system's ability to target and destroy cancer cells​ ( 27 ). ZG16 might serve as a potential biomarker for CRC. Its downregulation correlates with various molecular and clinicopathological phenotypes of the disease, suggesting it could be useful in both diagnosis and prognosis of colorectal cancer​. SLC26A3 is involved in the transport of chloride and bicarbonate ions, which is essential for maintaining the ionic and pH balance in the colonic epithelium. Proper function of this gene contributes to the normal physiological state of the intestinal lining, inhibiting tumorigenesis. SLC26A3 is predominantly expressed in the colorectum and is downregulated in colorectal cancer (CRC) tissues. High SLC26A3 levels correlate with better prognosis and are inversely related to cancer progression. The STAS domain of SLC26A3 inhibits CRC cell proliferation. Overexpression of SLC26A3 suppresses CRC cell proliferation, invasion, migration, and colony formation, while its knockdown or knockout promotes these behaviors. This suggests that SLC26A3 acts as a tumor suppressor in CRC and could be a biomarker for poor prognosis ( 28 ). 5. Conclusion Eight hub DEGs ( MAD2L1, MCM4, PBK, MYL9, CNN1, TAGLN, ZG16 and SLC26A3 ) were identified, to be strongly correlated with the overall survival of patients with CRC based on GEO and GEPIA data analysis. These eight genes have the potential to be novel, independent prognostic biomarkers for early diagnosis of CRC that can be utilized to forecast the clinical results of CRC patients. Several studies revealed that suppression of these genes, inhibits the proliferation of CRC. List Of Abbreviations CRC Colorectal Cancer GEO Gene Expression Omnibus DEGs differentially expressed genes GO Gene ontology KEGG Kyoto Encyclopedia of Genes and Genomes DAVID Database for annotation, visualization, and integrated discovery DEGs Differentially expressed genes PPI Protein-protein interaction MCODE Molecular Complex Detection STRING Search Tool for the Retrieval of Interacting Genes GEPIA Gene Expression Profiling Interactive Analysis CDK1 Cyclin-dependent kinase1 CCNB1 Cyclin-B1 FOXM1 ForkheadboxproteinM1 RRM2 Ribonucleotide reductase subunit M2 MAD2L1 Mitotic spindle assembly checkpoint protein MAD2A NEK2 NIMA related kinase MCM4 Minichromosome Maintenance Complex Component 4 PBK PDZ binding kinase MYL9 Myosin light chain 9 CNN1 Calponin-1 MYH11 Myosin-11 MYLK Myosin light chain kinase TAGLN Transgelin; Actin cross-linking/gelling protein GUCA2A Guanylate Cyclase Activator 2A GUCA2B Guanylate cyclase Activator 2B ZG16 Zymogen granule protein 16 SLC26A3 Solute carrier family 26 member 3 WGCNA Weighted gene co-expression network analysis TASCs Tumor-associated stromal cells References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Category Term Count p-value Genes FDR GOTERM_BP_DIRECT GO:0007588~excretion 3 0.005239 SCNN1B, GUCA2B, SLC26A3 0.980121 GOTERM_BP_DIRECT GO:0031032~actomyosin structure organization 3 0.006279 CNN1, EPB41L4B, MYH11 0.980121 GOTERM_BP_DIRECT GO:0015701~bicarbonate transport 3 0.006644 SLC26A2, CA4, SLC26A3 0.980121 GOTERM_BP_DIRECT GO:0090675~intermicrovillar adhesion 2 0.006976 CDHR2, CDHR5 0.980121 GOTERM_BP_DIRECT GO:0006958~complement activation, classical pathway 4 0.009778 IGHM, CR2, C7, CLU 1 GOTERM_BP_DIRECT GO:0009408~response to heat 3 0.013227 CXCL12, SST, CRYAB 1 GOTERM_BP_DIRECT GO:0071320~cellular response to cAMP 3 0.018107 AQP8, PCK1, SLC26A3 1 GOTERM_BP_DIRECT GO:1990748~cellular detoxification 2 0.020784 AQP8, ABCG2 1 GOTERM_CC_DIRECT GO:0031526~brush border membrane 7 6.50E-08 PRKCB, AQP8, CDHR2, CA4, CDHR5, SLC26A3, ABCG2 7.78E-06 GOTERM_CC_DIRECT GO:0070062~extracellular exosome 25 1.10E-07 IGHM, SLC26A2, FBLN1, CLU, CA1, C7, CA4, MYH11, PCK1, NXPE4, CR2, PRKCB, GUCA2B, CEACAM1, DES, CXCL12, MEP1A, SCNN1B, CDHR2, PADI2, CDHR5, CRYAB, MS4A1, PLPP1, CES2 7.78E-06 GOTERM_CC_DIRECT GO:0016324~apical plasma membrane 11 9.73E-07 CEACAM1, SLC26A2, SCNN1B, AQP8, KCNMA1, CDHR2, CA4, CDHR5, SLC26A3, PLPP1, ABCG2 4.60E-05 GOTERM_CC_DIRECT GO:0031528~microvillus membrane 4 1.98E-04 CEACAM1, SLC26A2, CDHR2, CDHR5 0.007032 GOTERM_CC_DIRECT GO:0005886~plasma membrane 33 2.82E-04 IGHM, VIPR1, SLC26A2, ADH1B, AQP8, FHL1, BEST2, CHRDL1, MYLK, EPB41L4B, C7, CA4, CHP2, BTNL8, CR2, FNBP1, PRKCB, GCG, ABCA8, CEACAM1, MEP1A, SCNN1B, KCNMA1, CDHR2, MALL, FXYD6, CDHR5, ALPI, SLC26A3, MS4A1, PDE9A, PLPP1, ABCG2 0.008001 GOTERM_CC_DIRECT GO:0005576~extracellular region 17 0.002176 CHGA, DHRS11, EDN3, ENTPD5, GUCA2B, FBLN1, GCG, CLU, CHRDL1, GUCA2A, PYY, CXCL12, C7, SST, PADI2, ALPI, INSL5 0.051492 GOTERM_MF_DIRECT GO:0005179~hormone activity 6 4.58E-05 PYY, EDN3, SST, GCG, GUCA2A, INSL5 0.009076 GOTERM_MF_DIRECT GO:0003779~actin binding 7 0.001219 CNN1, CEACAM1, TAGLN, KCNMA1, LMOD1, TNS1, MYLK 0.120635 GOTERM_MF_DIRECT GO:0030250~guanylate cyclase activator activity 2 0.006944 GUCA2B, GUCA2A 0.45832 GOTERM_MF_DIRECT GO:0005102~receptor binding 6 0.014251 BTNL8, CXCL12, EDN3, HHLA2, GCG, CLU 0.705442 GOTERM_MF_DIRECT GO:0019531~oxalate transmembrane transporter activity 2 0.034249 SLC26A2, SLC26A3 0.762809 GOTERM_MF_DIRECT GO:0005516~calmodulin binding 4 0.036752 CNN1, CEACAM1, MYH11, MYLK 0.762809 GOTERM_MF_DIRECT GO:0008271~secondary active sulfate transmembrane transporter activity 2 0.040959 SLC26A2, SLC26A3 0.762809 GOTERM_MF_DIRECT GO:0072582~17-beta-hydroxysteroid dehydrogenase (NADP+) activity 2 0.044297 DHRS11, HSD17B2 0.762809 GO : gene ontology, DEGs : differentially expressed genes. Table 2: GO analysis of downregulated DEGs associated with colorectal cancer. Category Term Count p-value Genes FDR GOTERM_BP_DIRECT GO:0032873~negative regulation of stress-activated MAPK cascade 3 1.E-04 MYC, PBK, FOXM1 0.04641 GOTERM_BP_DIRECT GO:0051301~cell division 7 1.75E-04 CCNB1, UBE2S, PRC1, CDK1, CKS2, NEK2, MAD2L1 0.04641 GOTERM_BP_DIRECT GO:0070098~chemokine-mediated signaling pathway 4 4.63E-04 CXCL8, CXCL1, CXCL3, CXCL2 0.081728 GOTERM_BP_DIRECT GO:0030593~neutrophil chemotaxis 4 7.62E-04 CXCL8, CXCL1, CXCL3, CXCL2 0.09071 GOTERM_BP_DIRECT GO:0071222~cellular response to lipopolysaccharide 5 8.60E-04 SLC7A5, CXCL8, CXCL1, CXCL3, CXCL2 0.09071 GOTERM_BP_DIRECT GO:0031640~killing of cells of other organism 4 0.001027 CXCL8, CXCL1, CXCL3, CXCL2 0.09071 GOTERM_BP_DIRECT GO:0061844~antimicrobial humoral immune response mediated by antimicrobial peptide 4 0.002151 CXCL8, CXCL1, CXCL3, CXCL2 0.152432 GOTERM_BP_DIRECT GO:0044344~cellular response to fibroblast growth factor stimulus 3 0.002301 CXCL8, MYC, CD44 0.152432 GOTERM_BP_DIRECT GO:0009410~response to xenobiotic stimulus 5 0.002938 TGIF1, CDH3, MYC, CDK1, SORD 0.173021 GOTERM_BP_DIRECT GO:0010629~negative regulation of gene expression 5 0.004856 TGIF1, SLC7A5, CXCL8, MYC, CDK1 0.25736 GOTERM_CC_DIRECT GO:0009925~basal plasma membrane 4 3.73E-04 SLC12A2, SLC7A5, TACSTD2, MET 0.042502 GOTERM_CC_DIRECT GO:0005634~nucleus 23 1.60E-03 TGIF1, GINS1, TEAD4, RRM2, SHMT2, TACSTD2, FOXM1, PUS7, NME1, MMP12, CCNB1, UBE2S, PRC1, MYC, PBK, CDK1, MCM4, NEK2, TRIP13, ECT2, KPNA2, CDKN3, MAD2L1 0.091053 GOTERM_CC_DIRECT GO:0000922~spindle pole 4 2.77E-03 CCNB1, PRC1, NEK2, MAD2L1 0.092609 GOTERM_CC_DIRECT GO:0000307~cyclin-dependent protein kinase holoenzyme complex 3 3.78E-03 CCNB1, CDK1, CKS2 0.092609 GOTERM_CC_DIRECT GO:0097125~cyclin B1-CDK1 complex 2 4.06E-03 CCNB1, CDK1 0.092609 GOTERM_CC_DIRECT GO:0030496~midbody 4 0.006656 PRC1, CDK1, NEK2, ECT2 0.12647 GOTERM_CC_DIRECT GO:0070062~extracellular exosome 11 0.012559 SLC12A2, SLC7A5, PSAT1, SHMT2, TACSTD2, CDK1, SORD, TIMP1, PAICS, CD44, NME1 0.204532 GOTERM_CC_DIRECT GO:0005829~cytosol 19 0.020953 SLC12A2, RRM2, SHMT2, TACSTD2, SORD, PAICS, NME1, SLC7A5, CCNB1, UBE2S, PSAT1, PRC1, CDK1, NEK2, ECT2, KPNA2, CD44, CDKN3, MAD2L1 0.280458 GOTERM_CC_DIRECT GO:0071162~CMG complex 2 0.022141 GINS1, MCM4 0.280458 GOTERM_CC_DIRECT GO:0005654~nucleoplasm 15 0.026631 TGIF1, GINS1, TEAD4, FOXM1, CCNB1, UBE2S, PRC1, MYC, CDK1, MCM4, NEK2, ECT2, KPNA2, LGR5, MAD2L1 0.287143 GOTERM_MF_DIRECT GO:0045236~CXCR chemokine receptor binding 4 4.50E-06 CXCL8, CXCL1, CXCL3, CXCL2 6.62E-04 GOTERM_MF_DIRECT GO:0008009~chemokine activity 4 1.84E-04 CXCL8, CXCL1, CXCL3, CXCL2 0.01351 GOTERM_MF_DIRECT GO:0005515~protein binding 38 0.001423 IFITM3, IFITM1, CXCL8, SHMT2, TACSTD2, CXCL1, TMEM97, FOXM1, CXCL2, CCNB1, MYC, PBK, NEK2, TIMP1, ECT2, KPNA2, TGIF1, TEAD4, SLC12A2, GINS1, RRM2, PAICS, NME1, SLC7A5, BACE2, UBE2S, PSAT1, PRC1, MTHFD2, CKS2, CDK1, MCM4, TRIP13, MET, LGR5, CD44, CDKN3, MAD2L1 0.069735 GOTERM_MF_DIRECT GO:0005524~ATP binding 9 0.018227 UBE2S, PBK, CDK1, MCM4, NEK2, TRIP13, MET, PAICS, NME1 0.669838 GOTERM_MF_DIRECT GO:0019901~protein kinase binding 5 0.027961 SLC12A2, CCNB1, PRC1, CKS2, FOXM1 0.821441 GOTERM_MF_DIRECT GO:0061575~cyclin-dependent protein serine/threonine kinase activator activity 2 0.034845 CCNB1, CKS2 0.821441 GOTERM_MF_DIRECT GO:0001046~core promoter sequence-specific DNA binding 2 0.039116 MMP12, MYC 0.821441 GOTERM_MF_DIRECT GO:0004672~protein kinase activity 4 0.052843 PBK, CDK1, NEK2, MET 0.970989 GOTERM_MF_DIRECT GO:0042802~identical protein binding 8 0.084859 PSAT1, PRC1, SORD, TRIP13, MET, PAICS, MAD2L1, NME1 1 GO : gene ontology, DEGs : differentially expressed genes. Table 3: KEGG pathway analysis of upregulated DEGs associated with colorectal cancer. Term Count p-value Genes FDR hsa04270:Vascular smooth muscle contraction 7 4.90E-05 EDN3, PRKCB, KCNMA1, MYH11, PPP1R12B, MYL9, MYLK 0.007107 hsa04810:Regulation of actin cytoskeleton 6 0.005445 CXCL12, C7, MYH11, PPP1R12B, MYL9, MYLK 0.394783 hsa04960:Aldosterone-regulated sodium reabsorption 3 0.014603 HSD11B2, PRKCB, SCNN1B 0.705814 hsa04080:Neuroactive ligand-receptor interaction 6 0.035272 PYY, VIPR1, EDN3, SST, GCG, INSL5 0.989441 hsa00140:Steroid hormone biosynthesis 3 0.038332 HSD11B2, DHRS11, HSD17B2 0.989441 hsa04921:Oxytocin signaling pathway 4 0.041422 PRKCB, PPP1R12B, MYL9, MYLK 0.989441 hsa04971:Gastric acid secretion 3 0.055302 PRKCB, SST, MYLK 0.989441 hsa04610:Complement and coagulation cascades 3 0.068752 CR2, C7, CLU 0.989441 hsa04911:Insulin secretion 3 0.068752 PRKCB, KCNMA1, GCG 0.989441 hsa04970:Salivary secretion 3 0.078745 PRKCB, KCNMA1, BEST2 0.989441 hsa04510:Focal adhesion 4 0.0807 PRKCB, PPP1R12B, MYL9, MYLK 0.989441 hsa00910:Nitrogen metabolism 2 0.081885 CA1, CA4 0.989441 hsa04972:Pancreatic secretion 3 0.092215 PRKCB, KCNMA1, SLC26A3 1 KEGG : Kyoto Encyclopedia of Genes and Genomes, DEGs : differentially expressed genes. Table 4: KEGG pathway analysis of downregulated DEGs associated with colorectal cancer. Term Count p-value Genes FDR hsa05120:Epithelial cell signaling in Helicobacter pylori infection 5 1.23E-04 CXCL8, CXCL1, CXCL3, CXCL2, MET 0.012263 hsa04110:Cell cycle 6 2.59E-04 CCNB1, MYC, CDK1, MCM4, TRIP13, MAD2L1 0.012957 hsa05134:Legionellosis 4 0.00114 CXCL8, CXCL1, CXCL3, CXCL2 0.038002 hsa04218:Cellular senescence 5 0.002537 CCNB1, CXCL8, MYC, CDK1, FOXM1 0.063425 hsa05323:Rheumatoid arthritis 4 0.004861 CXCL8, CXCL1, CXCL3, CXCL2 0.066358 hsa04657:IL-17 signaling pathway 4 0.005009 CXCL8, CXCL1, CXCL3, CXCL2 0.066358 hsa05167:Kaposi sarcoma-associated herpesvirus infection 5 0.005538 CXCL8, MYC, CXCL1, CXCL3, CXCL2 0.066358 hsa04061:Viral protein interaction with cytokine and cytokine receptor 4 0.005952 CXCL8, CXCL1, CXCL3, CXCL2 0.066358 hsa05146:Amoebiasis 4 0.006288 CXCL8, CXCL1, CXCL3, CXCL2 0.066358 hsa04064:NF-kappa B signaling pathway 4 0.006636 CXCL8, CXCL1, CXCL3, CXCL2 0.066358 hsa04936:Alcoholic liver disease 4 0.015474 CXCL8, CXCL1, CXCL3, CXCL2 0.140676 hsa01240:Biosynthesis of cofactors 4 0.018859 PSAT1, SHMT2, MTHFD2, NME1 0.157159 hsa05230:Central carbon metabolism in cancer 3 0.027765 SLC7A5, MYC, MET 0.208736 hsa04115:p53 signaling pathway 3 0.030769 CCNB1, RRM2, CDK1 0.208736 KEGG : Kyoto Encyclopedia of Genes and Genomes, DEGs : differentially expressed genes. Table 5: Top hub genes selected from module 1, 2 and 3 (by p-value). Gene adj. p-value p-value Log FC Description CDK1 5.30E-03 2.65E-04 -2.53506 Cyclin dependent kinase 1 CCNB1 2.60E-03 4.50E-05 -2.21741 Cyclin B1 FOXM1 4.08E-03 3.71E-04 -1.5344 Forkhead box protein M1 RRM2 3.86E-03 2.07E-04 -1.86578 Ribonucleotide reductase regulatory subunit M2 MAD2L1 3.22E-03 5.91E-05 -2.34892 MAD2 mitotic arrest deficient-like 1 (yeast) NEK2 1.61E-03 1.59E-05 -1.85592 NIMA related kinase 2 MCM4 3.23E-03 2.06E-04 -1.57324 Minichromosome maintenance complex component 4 PBK 1.03E-02 2.97E-04 -2.15241 PDZ binding kinase MYL9 1.13E-02 5.44E-03 2.37522 Myosin light chain 9 CNN1 3.06E-03 1.20E-03 2.976985 Calponin 1 MYH11 7.95E-03 4.10E-03 2.567059 Myosin-11 MYLK 8.33E-03 2.17E-03 2.26391 Myosin light chain kinase GUCA2A 3.71E-04 2.63E-07 5.046581 Guanylate cyclase activator 2A GUCA2B 3.20E-04 1.54E-07 4.896999 Guanylate cyclase activator 2B ZG16 5.65E-03 9.24E-05 4.681086 Zymogen granule protein 16 SLC26A3 5.43E-03 1.85E-04 3.611448 Solute carrier family 26 member 3 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4657501","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320463278,"identity":"d2c00b28-2ae0-4d06-a81f-e0d4a9d57c2e","order_by":0,"name":"kirti sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACCWYgkVDBxsPP3nzwAZDNw0eEFsaGhDN8MpI9x5INQFrYCGphAGphbJOzMbiRoyYBEiGoRbKd9/mDB2xmPEAtbJVfc+xk2BiYHz66gUeLNDO7YUMCTxqP5Jm3x27LbksGOozN2DgHjxY5ZjagXySO8fAdz0u7LbmNGaiFh02asBaD/zwMB3LMiiW31RPWIg3WksDGI3Aix4zx47bDhLVINrMxzkg4wMYDCmRpxm3HediYCfhF4vwxho8//7HZg6Ly489t1SDGw8f4tKAAZh4wSaxyEGD8QYrqUTAKRsEoGDEAADuBQfH/2M7hAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-3013-4236","institution":"","correspondingAuthor":true,"prefix":"","firstName":"kirti","middleName":"","lastName":"sharma","suffix":""}],"badges":[],"createdAt":"2024-06-29 04:44:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4657501/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4657501/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59525580,"identity":"14d864a1-c0ae-415d-b02b-88f395d56fed","added_by":"auto","created_at":"2024-07-02 20:53:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273864,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the upregulated and downregulated DEGs in CRC tissues compared with noncancerous colon-rectum tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003e75 upregulated DEGs were identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e45 downregulated DEGs were identified.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/f063618009d07c879123a181.png"},{"id":59525634,"identity":"453431c9-67bb-46fc-bf56-0ddbd9afa2bb","added_by":"auto","created_at":"2024-07-02 20:53:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":237116,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs are analyzed using a Venn diagram. The GSE21510 dataset is represented by the blue circle, the GSE24514 dataset is represented by the pink circle, and the GSE8671 dataset is represented by the green circle. The point at which the three circles intersects, reflect overlapping DEGs among the three datasets.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/93d9ce9115069a15a871c75e.png"},{"id":59525609,"identity":"e15cb846-543e-4a52-8481-95959eb7df7f","added_by":"auto","created_at":"2024-07-02 20:53:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86713,"visible":true,"origin":"","legend":"\u003cp\u003eGene ontology analysis of differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eGene ontology analysis of \u0026nbsp;upregulated genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eGene ontology analysis of \u0026nbsp;downregulated genes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/9143bf2a60dd5bf24086e2e3.png"},{"id":59525626,"identity":"0343c6a5-efed-44dd-8663-a84708a8dd76","added_by":"auto","created_at":"2024-07-02 20:53:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66845,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway \u0026nbsp;analysis of differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. KEGG pathway analysis of \u0026nbsp;upregulated \u0026nbsp;genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. KEGG pathway analysis of \u0026nbsp;downregulated genes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/7bc60bc83a584b76ce430c62.png"},{"id":59525615,"identity":"6d8edfc7-87c1-48f2-9c0f-c4df5b6c4410","added_by":"auto","created_at":"2024-07-02 20:53:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":373933,"visible":true,"origin":"","legend":"\u003cp\u003eProtein –Protein interaction network of differentially expressed genes representing seven modules networks which have been identified by MCODE. Red nodes representing upregulated genes, Green nodes represent downregulated genes and nodes with yellow border representing seed. The module is a highly complex region of the PPI network.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/46136c3f7c2e4199a7ede503.png"},{"id":59525608,"identity":"22e658a8-0ea1-41d1-9281-53dce67eee50","added_by":"auto","created_at":"2024-07-02 20:53:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":176523,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant modules identified by MCODE with MCODE score ≥5 and node ≥10.Each node represent the relevant protein (gene). Nodes with yellow border. representing seed, red nodes representing upregulated \u0026nbsp;genes and green nodes representing downregulated genes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/65deac10b5907dea220362ef.png"},{"id":59525610,"identity":"e792a6ad-4e16-4943-8517-6ed154e69ed2","added_by":"auto","created_at":"2024-07-02 20:53:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":310173,"visible":true,"origin":"","legend":"\u003cp\u003eThe validation of prognostic value of 8 hub genes. ((\u003cstrong\u003eA\u003c/strong\u003e–\u003cstrong\u003eH\u003c/strong\u003e): \u003cem\u003eMAD2L1\u003c/em\u003e,\u003cem\u003e MCM4\u003c/em\u003e,\u003cem\u003e PBK\u003c/em\u003e,\u003cem\u003e MYL9\u003c/em\u003e,\u003cem\u003e CNN1\u003c/em\u003e,\u003cem\u003e TAGLN\u003c/em\u003e,\u003cem\u003e ZG16 \u003c/em\u003eand\u003cem\u003e SLC26A3\u003c/em\u003e)\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/4fc205a59ce35f0b954b035c.png"},{"id":59525613,"identity":"947728e0-d875-451d-a28e-5dda0f09ae6d","added_by":"auto","created_at":"2024-07-02 20:53:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":287423,"visible":true,"origin":"","legend":"\u003cp\u003eThe validation of prognostic value of 8 hub genes. ((\u003cstrong\u003eA\u003c/strong\u003e–\u003cstrong\u003eI\u003c/strong\u003e):\u003cem\u003e CDK1\u003c/em\u003e,\u003cem\u003e CCNB1\u003c/em\u003e,\u003cem\u003e FOXM1\u003c/em\u003e,\u003cem\u003e RRM2\u003c/em\u003e,\u003cem\u003e NEK2\u003c/em\u003e,\u003cem\u003e MYH11\u003c/em\u003e,\u003cem\u003e MYLK\u003c/em\u003e,\u003cem\u003e GUCA2A \u003c/em\u003e\u0026nbsp;and \u003cem\u003eGUCA2B\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/6b11c81b5baaefae22197e84.png"},{"id":59525727,"identity":"039141fc-8d0f-4c8f-80e2-d0b2bbcd610f","added_by":"auto","created_at":"2024-07-02 20:53:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2568575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4657501/v1/e2a29553-dffa-4ff7-b68a-4296b9ac3191.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIdentification of prognostic gene markers for the early diagnosis of colorectal cancer\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eColorectal cancer (CRC) represents a significant epidemiological challenge due to its prevalence as among the most frequently occurring malignancies and its associated elevated mortality rate (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). As per the World Health Organization (WHO), CRC stands as the second most prominent cause of cancer-related deaths worldwide and the ranked third in terms of the most prevalent cancer overall, comprising 10% of all cancer cases. The occurrence and death rates exhibit notable geographic variations.\u003c/p\u003e \u003cp\u003eEastern Europe had the greatest fatality rates, while Europe, Australia, and New Zealand had the highest incidence rates. In 2040, it is projected that there will be 3.2\u0026nbsp;million new cases of CRC each year, marking a 63% increase, along with 1.6\u0026nbsp;million annual deaths, representing a 73% rise (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). According to Aran et al., 2016 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) tumor start, development, and metastasis are all associated with gene mutations, cellular settings, and environmental factors in CRC, making it a polyphase disease (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Research investigations have elucidated the participation of a myriad of genes and cellular pathways in the onset and progression of CRC\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Unfortunately, distant metastases are found in 20\u0026ndash;25% of patients with newly diagnosed CRC, and only a small subset of these patients can undergo curative surgery (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). More importantly, a majority of CRC patients with resectable tumors will encounter recurrence within two years, impacting around 50% of patients (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The five-year relative survival rate for early-stage CRC is approximately 90%. Only 4 out of 10 CRC cases, however, are typically diagnosed at the early stage (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Hence, it is imperative to explore potential biomarkers for early-stage CRC to improve patient prognosis. Through various investigations, genes such as \u003cem\u003eAXIN\u003c/em\u003e, \u003cem\u003eCTNNB1\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eJAG-1\u003c/em\u003e, \u003cem\u003eNOTCH-1\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eRAF\u003c/em\u003e, \u003cem\u003eRAS\u003c/em\u003e, \u003cem\u003eSMADs\u003c/em\u003e, \u003cem\u003eTGFBR1\u003c/em\u003e and \u003cem\u003eTGFBR2\u003c/em\u003e have been identified as contributors to increased proliferation, invasion, progression, or apoptosis suppression in CRC cells (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The status of a patient's biomarkers has grown to be crucial information in the planning of treatment for CRC. Biomarker testing has significantly increased the range of therapy options available to patients, particularly those with metastatic CRC.\u003c/p\u003e \u003cp\u003eWhile the existing fecal occult blood test methods are widely employed in population screening initiatives, they are susceptible to numerous interfering factors that can lead to inaccurate results, including false negatives or false positives, and exhibit low sensitivity rates in the detection of colon polyps (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProteomic methodologies, including 2-dimensional electrophoresis and mass spectrometry, along with genomic techniques like DNA microarray analysis, are frequently utilized for assessing the expression patterns of proteins and genes in cancer cells, body fluids and surrounding tissues (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZhau et al., 2019 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and Kou et al., 2015 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) analyzed the GSE4107 dataset comprised 12 samples of patients with CRC and 10 samples of healthy patients considered as normal controls. However, only a small number of samples were included in these two investigations, and it is yet unknown which molecular pathways contribute to CRC carcinogenesis.\u003c/p\u003e \u003cp\u003eIn this current study, three datasets were obtained GSE24514 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), GSE8671 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and GSE21510 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) from the Gene expression omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.ncbi.nih.gov/geo\" target=\"_blank\"\u003ewww.ncbi.nih.gov/geo\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database (GPL96 [HG-U133A] Affymetrix Human Genome U133A Array and GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 plus 2.0 Array) in order to identify differentially expressed genes (DEGs) in CRC tissues. Subsequent to this, the biological pathways and function of significant genes were elucidated via gene ontology and pathway enrichment analysis, conducted using the platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The results of this research provide new perspectives on possible markers for CRC and could enhance comprehension of molecular processes behind CRC growth and advancement.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Microarray data\u003c/h2\u003e \u003cp\u003eThree gene expression profiles (GSE24514, GSE8671, and GSE21510) were downloaded from the Gene expression omnibus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database.\u003c/p\u003e \u003cp\u003eAlhopuro et al., 2012 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) contributed GSE24514, which utilized the Affymetrix GPL570 platform. [GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array]. There were 17 CRC samples and 6 normal control samples in the GSE24514 dataset. The Gene Spring software (Silicon Genetics, San Carlos, CA) was employed to identify genes that exhibited significant under expression in MSI cancers in comparison to normal mucosa samples.\u003c/p\u003e \u003cp\u003eGSE8671 was composed of 20 CRC samples and 6 normal controls, which was presented by Sabetes et al., 2007 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The analysis was performed according to the Affymetrix GPL570 platform [GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array].\u003c/p\u003e \u003cp\u003eTsukamoto et al., 2011 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) submitted GSE21510, which is based on the Affymetrix GPL570 platform (HG-U133_Plus_2) and includes 11 CRC samples and 5 normal control samples. In this analysis, a Student's t-test was utilized; DEGs were identified using a fold change threshold of \u0026ge;\u0026thinsp;1.25 and \u0026le; -1.25. Statistical significance was determined with a threshold of p\u0026thinsp;\u0026ge;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Gene ontology and pathway enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eDEGs were subjected to Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. This analytical approach was utilized to identify DEGs at the biological functional level (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Functional genomics annotations were integrated using the database for annotation, visualization, and integrated discovery (DAVID) online tool (version DAVID 2021; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A statistical significance threshold of p\u0026thinsp;\u0026ge;\u0026thinsp;0.05 was utilized to indicate the presence of a significant difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Integration of the protein-protein interaction (PPI) network\u003c/h2\u003e \u003cp\u003eThe investigation of putative DEGs connections at the protein level using the Search Tool for the Retrieval of Interacting Genes (STRING; version 12.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/cgi/input.pl\u003c/span\u003e\u003cspan address=\"http://string-db.org/cgi/input.pl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was conducted. Validated trials served as the basis for the PPI networks of DEGs developed by STRING. The significant PPI score was less than 0.4. The software Cytoscape (version 3.10.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to visualize the PPI network. A statistical significance threshold of p\u0026thinsp;\u0026ge;\u0026thinsp;0.05 was utilized to indicate the presence of a significant difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Module analysis of the PPI network\u003c/h2\u003e \u003cp\u003eModule analysis was conducted on the PPI network utilizing the Molecular Complex Detection (MCODE) algorithm within Cytoscape, the parameters were configured as follows: degree cutoff\u0026thinsp;=\u0026thinsp;2, node score cutoff\u0026thinsp;=\u0026thinsp;0.2, k-core\u0026thinsp;=\u0026thinsp;2, and max depth\u0026thinsp;=\u0026thinsp;100. Modules meeting the criteria of an MCODE score\u0026thinsp;\u0026ge;\u0026thinsp;5 and comprising at least 10 nodes were considered significant. Following selection, GO functional and KEGG pathway enrichment analysis were conducted on the most significant module. A significance threshold of p\u0026thinsp;\u0026ge;\u0026thinsp;0.05 was applied.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of DEGs\u003c/h2\u003e \u003cp\u003eIn this research, we selected 48 CRC and 17 non-cancerous colorectal tissues from three datasets GSE24514, GSE8671 and GSE21510 which were analyzed by GEO2R. However, we found 120 DEGs with repeated emergence in these datasets, including 75 upregulated genes (logFC\u0026thinsp;\u0026ge;\u0026thinsp;1.25) and 45 downregulated genes (logFC \u0026le; -1.25), as demonstrated in the Venn diagram (Fig.\u0026nbsp;1, 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 GO function and KEGG pathway enrichment analysis of the DEGs\u003c/h2\u003e \u003cp\u003eTo deepen our understanding of the selected DEGs, GO and KEGG pathway analyses were conducted using DAVID. GO analysis revealed that both upregulated and downregulated DEGs were notably abundant in the categories of 'molecular function', 'biological processes', and 'cellular component classification'. Specifically, in the molecular function, upregulated genes were primarily enriched in functions such as 'hormone activity', 'actin binding', and 'guanylate cyclase activator activity' (Table\u0026nbsp;1, Fig.\u0026nbsp;3A). Conversely, downregulated DEGs exhibited enrichment in various functions, including 'CXCR chemokine receptor binding', 'chemokine activity', 'protein binding', and 'ATP binding' (Table\u0026nbsp;2, Fig.\u0026nbsp;3A). Within the biological processes, upregulated DEGs were prominently associated with 'excretion', 'actomyosin structure organization', 'bicarbonate transport' (Table\u0026nbsp;1, Fig.\u0026nbsp;3A), while downregulated DEGs were significantly enriched in biological processes like 'negative regulation of stress-activated MAPK cascade', 'cell division', and 'chemokine-mediated signaling pathway' (Table\u0026nbsp;2, Fig.\u0026nbsp;3B). Moreover, GO cellular component analysis indicated that upregulated DEGs were prominently linked with components like 'brush border membrane', 'extracellular exosome', and 'apical plasma membrane' (Table\u0026nbsp;1, Fig.\u0026nbsp;3A), whereas downregulated DEGs were enriched in components such as 'basal plasma membrane', 'nucleus', and 'spindle pole'. These findings underscore the significant enrichment of DEGs in functions related to 'binding', 'ion channel', and 'cell cycle' (Table\u0026nbsp;2, Fig.\u0026nbsp;3B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 KEGG pathway analysis\u003c/h2\u003e \u003cp\u003eKEGG pathway analysis indicated that the upregulated DEGs were prominently linked with pathways such as 'Vascular smooth muscle contraction', 'Regulation of actin cytoskeleton', 'Aldosterone-regulated sodium reabsorption', 'Neuroactive ligand-receptor interaction', and 'Steroid hormone biosynthesis' (Table\u0026nbsp;3, Fig.\u0026nbsp;4A). On the other hand, the downregulated DEGs were prominently linked with pathways including 'Epithelial cell signaling in \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection', 'Cell cycle', 'Legionellosis', 'Cellular senescence', and 'Rheumatoid arthritis' (Table\u0026nbsp;4, Fig.\u0026nbsp;4B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 PPI network construction and hub gene identification\u003c/h2\u003e \u003cp\u003eThe data from the STRING database were utilized to generate the expression profiles of Differentially Expressed Genes (DEGs) (Fig.\u0026nbsp;5) After the removal of disconnected and partially linked nodes, a DEG network was constructed. The hub genes selected from modules 1 with connectivity score\u0026thinsp;\u0026ge;\u0026thinsp;16 were \u003cem\u003eCDK1\u003c/em\u003e (Cyclin-dependent kinase1, \u003cem\u003eCCNB1\u003c/em\u003e (cyclinB1), \u003cem\u003eFOXM1\u003c/em\u003e (Forkhead box protein M1), RRM2 (Ribonucleotide reductase subunit M2), \u003cem\u003eMAD2L1\u003c/em\u003e (Mitotic spindle assembly checkpoint protein MAD2A), \u003cem\u003eNEK2\u003c/em\u003e (NIMA related kinase 2), \u003cem\u003eMCM4\u003c/em\u003e (Minichromosome Maintenance Complex Component 4) and \u003cem\u003ePBK\u003c/em\u003e (PDZ binding kinase). The hub genes selected from module 2 and 3 with connectivity score\u0026thinsp;\u0026ge;\u0026thinsp;6 were \u003cem\u003eMYL9\u003c/em\u003e (Myosin light chain 9), \u003cem\u003eCNN1\u003c/em\u003e(Calponin-1), \u003cem\u003eMYH11\u003c/em\u003e (Myosin-11), MYLK (Myosin light chain kinase), \u003cem\u003eTAGLN\u003c/em\u003e (\u003cem\u003eTransgelin\u003c/em\u003e; Actin cross-linking/gelling protein), \u003cem\u003eGUCA2A\u003c/em\u003e (\u003cem\u003eGuanylate Cyclase Activator 2A\u003c/em\u003e), \u003cem\u003eGUCA2B\u003c/em\u003e (Guanylate cyclase Activator 2B), \u003cem\u003eZG16\u003c/em\u003e (Zymogen granule protein 16) and \u003cem\u003eSLC26A3\u003c/em\u003e (Solute carrier family 26 member 3) (Table\u0026nbsp;5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Module analysis\u003c/h2\u003e \u003cp\u003eIn the PPI networks, significant modules with MCODE scores\u0026thinsp;\u0026ge;\u0026thinsp;5 and nodes\u0026thinsp;\u0026ge;\u0026thinsp;10 were identified. Specifically, module 1 had an MCODE score of 15.2 and comprised 114 nodes, module 2 had an MCODE score of 6.333 and contained 19 nodes, while module 3 had an MCODE score of 5 with 10 nodes (Fig.\u0026nbsp;6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Validation of Expression level of Hub Genes\u003c/h2\u003e \u003cp\u003eWe utilized the GEPIA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to perform survival analysis of the identified hub genes in colorectal cancer (CRC). GEPIA is an interactive web-based tool that integrates TCGA and GTEx data, allowing for comprehensive gene expression and survival analysis.To assess the prognostic significance of the hub genes, we conducted both Overall Survival (OS) analysis. Each gene was input into GEPIA, and 'Colorectal Adenocarcinoma' was selected as the cancer type. Patients were divided into high and low expression groups based on the median expression levels. Kaplan-Meier survival plots were generated, and the log-rank test was used to evaluate statistical significance.\u003c/p\u003e \u003cp\u003eThe findings demonstrated a substantial relationship between the expression levels of \u003cem\u003eMAD2L1, MCM4, PBK, ZG16, SLC26A3, MYL9, CNN1\u003c/em\u003e and \u003cem\u003eTAGLN\u003c/em\u003e and the Overall survival among CRC patients was observed, indicating potential variations in expression were linked to a worse prognosis for the patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For CRC patients \u003cem\u003eMAD2L1, MCM4, PBK, MYL9, CNN1, TAGLN, ZG16\u003c/em\u003e and \u003cem\u003eSLC26A3\u003c/em\u003e may therefore be utilized as prognostic markers (Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eThe survival analysis also revealed that there was no significant difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in overall survival (OS) between the eight additional genes \u003cem\u003eCDK1, CCNB1, FOXM1, RRM2, NEK2, MYH11, MYLK, GUCA2A\u003c/em\u003e and \u003cem\u003eGUCA2B\u003c/em\u003e with high and low expression (Fig.\u0026nbsp;8).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs per World Health Organization (WHO), CRC stands as the second prominent cause of cancer-related deaths worldwide and ranked third in terms of most prevalent cancer overall, comprising 10% of all cancer cases. The occurrence and death rates exhibit notable geographic variations. Eastern Europe had the greatest fatality rates, while Europe, Australia, and New Zealand had the highest incidence rates. In 2040, it is projected that there will be 3.2\u0026nbsp;million new cases of CRC each year, marking a 63% increase, along with 1.6\u0026nbsp;million annual deaths, representing a 73% rise (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The five-year relative survival rate for early-stage CRC is approximately 90%. Only 4 out of 10 CRC cases, however, are typically diagnosed at the early stage (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) that\u0026rsquo;s why knowing etiology of CRC etiology is very important. The three gene expression datasets (GSE24514, GSE8671 and GSE21510) from the GEO database were used in this investigation to compare DEGs in CRC with normal tissues. In all, the three datasets revealed 75 up-regulated and 45 down-regulated DEGs. It was mentioned that DEGs, which includes \u003cem\u003eCDK1, CCNB1, FOXM1, RRM2, MAD2L1, NEK2, MCM4, PBK, MYL9, CNN1, MYH11, MYLK\u003c/em\u003e, \u003cem\u003eTAGLN, GUCA2A, GUCA2B, ZG16\u003c/em\u003e and \u003cem\u003eSLC26A3\u003c/em\u003e were hub genes in the PPI network analysis. The survival analysis suggested that only eight genes (\u003cem\u003eMAD2L1, MCM4, PBK, MYL9, CNN1, TAGLN, ZG16\u003c/em\u003e and \u003cem\u003eSLC26A3\u003c/em\u003e ) had a variations in expression were linked to a worse prognosis for the patients.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMAD2L1\u003c/em\u003e is one of the important gene that plays an important role in cell proliferation and growth. The gene encoding mitotic arrest defective protein 2 (\u003cem\u003eMAD2L1\u003c/em\u003e) is situated on chromosome 4 in humans (chromosome 6 in mice), and it is a crucial part of the mitotic checkpoint complex protein. \u003cem\u003eMAD2L1\u003c/em\u003e is overexpressed in colorectal cancer (CRC), promoting cell proliferation and migration, and is regulated by TEAD4. This overexpression correlates with poor prognosis due to increased chromosomal instability in CRC patients. Xiang et al., 2020 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) verified that \u003cem\u003eMAD2L1\u003c/em\u003e knockdown may considerably inhibit the growth of CRC cells by causing apoptosis in the cells and affecting the initiation of the cell cycle. In addition, \u003cem\u003eMAD2L1\u003c/em\u003e may prove to be a novel biomarker for CRC diagnosis and treatment (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have reported higher levels of \u003cem\u003eMCM4\u003c/em\u003e expression in colorectal cancer tissues compared to adjacent normal tissues. This overexpression suggests a potential role for \u003cem\u003eMCM4\u003c/em\u003e in the development or progression of colorectal cancer. Elevated \u003cem\u003eMCM4\u003c/em\u003e expression has been associated with poorer prognosis in colorectal cancer patients. High \u003cem\u003eMCM4\u003c/em\u003e levels have been correlated with advanced tumor stage, lymph node metastasis, and decreased overall survival rates.\u003cem\u003eMCM4\u003c/em\u003e is involved in DNA replication and cell cycle regulation. Its overexpression in colorectal cancer cells may contribute to increased cell proliferation and tumor growth.Some studies suggest a potential link between \u003cem\u003eMCM4\u003c/em\u003e expression and chemoresistance in colorectal cancer. High levels of \u003cem\u003eMCM4\u003c/em\u003e may confer resistance to chemotherapy agents commonly used in the treatment of colorectal cancer, leading to treatment failure and disease recurrence (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have been shown that \u003cem\u003ePBK\u003c/em\u003e is involved in promoting cellular proliferation of tumor cells. Due to its involvement in cancer progression, PBK has emerged as a potential therapeutic target in colorectal cancer .Many studies have been done to target the gene \u003cem\u003ePBK\u003c/em\u003e, which is thought to be a promising therapeutic target (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). With the use of OTS514, a particular \u003cem\u003ePBK\u003c/em\u003e inhibitor, Koshino et al., 2021 was able to successfully stop the growth of multiple myeloma and CRC cells through down-regulating PHH3, OTS514 not only prevented CRC cells from proliferating, it also caused apoptosis (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMyosin light chain 9 (\u003cem\u003eMYL9\u003c/em\u003e) is the most extensively researched member of the myosin superfamily and \u003cem\u003eMYL9\u003c/em\u003e is a fibroblast-specific biomarker linked with a poor prognosis in CRC. It has previously been noted that early-stage and recurring CRC tissues exhibit elevated \u003cem\u003eMYL9\u003c/em\u003e expression. \u003cem\u003eMYL9\u003c/em\u003e promotes the proliferation, invasion, migration, and angiogenesis of colorectal cancer cells. Feng et al., 2022 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) demostrated that suppression of \u003cem\u003eMYL9\u003c/em\u003e inhibited the cell proliferation, migration and invasion while overexpression exert the opposite effect. This suggests that \u003cem\u003eMYL9\u003c/em\u003e may act as an oncogene in colorectal cancer progression (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCalponin 1 (\u003cem\u003eCNN1\u003c/em\u003e) is a protein that has been identified as a significant marker in colorectal cancer (CRC). Studies indicate that \u003cem\u003eCNN1\u003c/em\u003e expression in the tumor-associated stromal cells (TASCs) of CRC patients correlates with a worse prognosis. These TASCs, when expressing \u003cem\u003eCNN1\u003c/em\u003e, have been shown to support cancer progression by enhancing tumor cell proliferation, migration, and metastatic potential. The expression of \u003cem\u003eCNN1\u003c/em\u003e, along with another protein TPM2, has been found to outperform other previously reported TASC-associated markers in prognostic significance​ (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Weighted gene co-expression network analysis (WGCNA) has identified \u003cem\u003eCNN1\u003c/em\u003e as a hub gene associated with CRC recurrence. Higher levels of \u003cem\u003eCNN1\u003c/em\u003e expression are significantly linked with shorter survival times in CRC patients. This association has been validated in multiple independent datasets and supported by functional enrichment analyses that place \u003cem\u003eCNN1\u003c/em\u003e in critical pathways related to cancer progression ​(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Given its significant role in CRC progression and recurrence, \u003cem\u003eCNN1\u003c/em\u003e could serve as a target for therapeutic interventions aimed at improving patient outcomes. Targeting \u003cem\u003eCNN1\u003c/em\u003e and related pathways may help develop strategies to inhibit tumor growth and metastasis in CRC patients​.\u003c/p\u003e \u003cp\u003eTransgelin (\u003cem\u003eTAGLN\u003c/em\u003e), also known as SM22-alpha, is a protein involved in actin cytoskeleton organization and smooth muscle contraction Several studies have reported increased \u003cem\u003eTAGLN\u003c/em\u003e expression in colorectal cancer tissues compared to adjacent normal tissues. This upregulation has been associated with tumor progression, invasion, and metastasis.A more advanced CRC pathological stage and a worse clinical outcome were linked to increased expression of \u003cem\u003eTAGLN\u003c/em\u003e, indicating a potential function for \u003cem\u003eTAGLN\u003c/em\u003e in promoting the course of CRC disease (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The earlier research showed that \u003cem\u003eTAGLN\u003c/em\u003e functions as a tumor suppressor and that inhibiting it is a critical step in the development and transformation of tumors (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In the present study patient with high expression of \u003cem\u003eTAGLN\u003c/em\u003e has lower Overall survival as compared to moderate \u0026ndash; low expression, \u003cem\u003eTAGLN\u003c/em\u003e can be considered as an oncogene for early prognosis of CRC. High \u003cem\u003eTAGLN\u003c/em\u003e expression levels in colorectal cancer have been associated with poor prognosis, including shorter overall survival and disease-free survival. Therefore, \u003cem\u003eTAGLN\u003c/em\u003e expression may serve as a prognostic marker for CRC patients.Inhibition of \u003cem\u003eTAGLN\u003c/em\u003e expression or function could potentially suppress tumor invasion and metastasis, offering a novel therapeutic strategy.\u003c/p\u003e \u003cp\u003e \u003cem\u003eZG16\u003c/em\u003e is a gene that has garnered attention for its role in colorectal cancer (CRC). Research indicates that \u003cem\u003eZG16\u003c/em\u003e expression is significantly downregulated in colorectal cancer tissues compared to normal tissues. This reduced expression has been associated with the progression of CRC and is considered one of the most consistently downregulated genes in these cancerous tissues (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Overexpression of \u003cem\u003eZG16\u003c/em\u003e in CRC cell lines and xenograft models has demonstrated a suppression of tumor growth, which is partly due to its role in activating T-cell mediated immunity. Specifically, \u003cem\u003eZG16\u003c/em\u003e appears to decrease the expression of inhibitory checkpoint molecules like PD1 and CTLA4 on T cells, thereby enhancing the immune system's ability to target and destroy cancer cells​ (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). \u003cem\u003eZG16\u003c/em\u003e might serve as a potential biomarker for CRC. Its downregulation correlates with various molecular and clinicopathological phenotypes of the disease, suggesting it could be useful in both diagnosis and prognosis of colorectal cancer​.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSLC26A3\u003c/em\u003e is involved in the transport of chloride and bicarbonate ions, which is essential for maintaining the ionic and pH balance in the colonic epithelium. Proper function of this gene contributes to the normal physiological state of the intestinal lining, inhibiting tumorigenesis.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSLC26A3\u003c/em\u003e is predominantly expressed in the colorectum and is downregulated in colorectal cancer (CRC) tissues. High \u003cem\u003eSLC26A3\u003c/em\u003e levels correlate with better prognosis and are inversely related to cancer progression. The STAS domain of \u003cem\u003eSLC26A3\u003c/em\u003e inhibits CRC cell proliferation. Overexpression of \u003cem\u003eSLC26A3\u003c/em\u003e suppresses CRC cell proliferation, invasion, migration, and colony formation, while its knockdown or knockout promotes these behaviors. This suggests that \u003cem\u003eSLC26A3\u003c/em\u003e acts as a tumor suppressor in CRC and could be a biomarker for poor prognosis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eEight hub DEGs (\u003cem\u003eMAD2L1, MCM4, PBK, MYL9, CNN1, TAGLN, ZG16\u003c/em\u003e and \u003cem\u003eSLC26A3\u003c/em\u003e) were identified, to be strongly correlated with the overall survival of patients with CRC based on GEO and GEPIA data analysis. These eight genes have the potential to be novel, independent prognostic biomarkers for early diagnosis of CRC that can be utilized to forecast the clinical results of CRC patients. Several studies revealed that suppression of these genes, inhibits the proliferation of CRC.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColorectal Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAVID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDatabase for annotation, visualization, and integrated discovery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein-protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCODE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular Complex Detection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTRING\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSearch Tool for the Retrieval of Interacting Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEPIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Profiling Interactive Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCDK1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCyclin-dependent kinase1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCCNB1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCyclin-B1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eFOXM1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForkheadboxproteinM1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRM2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleotide reductase subunit M2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMAD2L1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitotic spindle assembly checkpoint protein MAD2A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eNEK2\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNIMA related kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMCM4\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinichromosome Maintenance Complex Component 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePBK\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePDZ binding kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMYL9\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyosin light chain 9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCNN1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eCalponin-1\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMYH11\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyosin-11\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMYLK\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyosin light chain kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eTAGLN\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransgelin; Actin cross-linking/gelling protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eGUCA2A\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGuanylate Cyclase Activator \u003cem\u003e2A\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eGUCA2B\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGuanylate cyclase Activator \u003cem\u003e2B\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eZG16\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZymogen granule protein 16\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eSLC26A3\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSolute carrier family 26 member 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted gene co-expression network analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTASCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor-associated stromal cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Oncogenesis. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41389-023-00488-w\u003c/span\u003e\u003cspan address=\"10.1038/s41389-023-00488-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e GO analysis of upregulated genes associated with colorectal cancer.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;FDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0007588~excretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.005239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSCNN1B, GUCA2B, SLC26A3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.980121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0031032~actomyosin structure organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.006279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCNN1, EPB41L4B, MYH11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.980121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0015701~bicarbonate transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.006644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC26A2, CA4, SLC26A3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.980121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0090675~intermicrovillar adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.006976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCDHR2, CDHR5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.980121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0006958~complement activation, classical pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.009778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIGHM, CR2, C7, CLU\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0009408~response to heat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.013227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL12, SST, CRYAB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0071320~cellular response to cAMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.018107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAQP8, PCK1, SLC26A3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:1990748~cellular detoxification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.020784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAQP8, ABCG2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0031526~brush border membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e6.50E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRKCB, AQP8, CDHR2, CA4, CDHR5, SLC26A3, ABCG2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e7.78E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0070062~extracellular exosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e1.10E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIGHM, SLC26A2, FBLN1, CLU, CA1, C7, CA4, MYH11, PCK1, NXPE4, CR2, PRKCB, GUCA2B, CEACAM1, DES, CXCL12, MEP1A, SCNN1B, CDHR2, PADI2, CDHR5, CRYAB, MS4A1, PLPP1, CES2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e7.78E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0016324~apical plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e9.73E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCEACAM1, SLC26A2, SCNN1B, AQP8, KCNMA1, CDHR2, CA4, CDHR5, SLC26A3, PLPP1, ABCG2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e4.60E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0031528~microvillus membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e1.98E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCEACAM1, SLC26A2, CDHR2, CDHR5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.007032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005886~plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e2.82E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIGHM, VIPR1, SLC26A2, ADH1B, AQP8, FHL1, BEST2, CHRDL1, MYLK, EPB41L4B, C7, CA4, CHP2, BTNL8, CR2, FNBP1, PRKCB, GCG, ABCA8, CEACAM1, MEP1A, SCNN1B, KCNMA1, CDHR2, MALL, FXYD6, CDHR5, ALPI, SLC26A3, MS4A1, PDE9A, PLPP1, ABCG2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.008001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005576~extracellular region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.002176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCHGA, DHRS11, EDN3, ENTPD5, GUCA2B, FBLN1, GCG, CLU, CHRDL1, GUCA2A, PYY, CXCL12, C7, SST, PADI2, ALPI, INSL5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.051492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005179~hormone activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e4.58E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePYY, EDN3, SST, GCG, GUCA2A, INSL5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.009076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0003779~actin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.001219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCNN1, CEACAM1, TAGLN, KCNMA1, LMOD1, TNS1, MYLK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.120635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0030250~guanylate cyclase activator activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.006944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGUCA2B, GUCA2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.45832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005102~receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.014251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBTNL8, CXCL12, EDN3, HHLA2, GCG, CLU\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.705442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0019531~oxalate transmembrane transporter activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.034249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC26A2, SLC26A3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.762809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005516~calmodulin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.036752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCNN1, CEACAM1, MYH11, MYLK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.762809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0008271~secondary active sulfate transmembrane transporter activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.040959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC26A2, SLC26A3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.762809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.389312977099237%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.068702290076335%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0072582~17-beta-hydroxysteroid dehydrogenase (NADP+) activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"top\"\u003e\n \u003cp\u003e0.044297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.97709923664122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eDHRS11, HSD17B2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.076335877862595%\" valign=\"top\"\u003e\n \u003cp\u003e0.762809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e: gene ontology, \u003cstrong\u003eDEGs\u003c/strong\u003e: differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e\u0026nbsp; GO analysis of downregulated DEGs associated with colorectal cancer.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0032873~negative regulation of stress-activated MAPK cascade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e1.E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYC, PBK, FOXM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.04641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0051301~cell division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e1.75E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1, UBE2S, PRC1, CDK1, CKS2, NEK2, MAD2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.04641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0070098~chemokine-mediated signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4.63E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.081728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0030593~neutrophil chemotaxis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e7.62E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.09071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0071222~cellular response to lipopolysaccharide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e8.60E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC7A5, CXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.09071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0031640~killing of cells of other organism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.001027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.09071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0061844~antimicrobial humoral immune response mediated by antimicrobial peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.002151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.152432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0044344~cellular response to fibroblast growth factor stimulus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.002301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, MYC, CD44\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.152432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0009410~response to xenobiotic stimulus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.002938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTGIF1, CDH3, MYC, CDK1, SORD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.173021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_BP_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0010629~negative regulation of gene expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.004856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTGIF1, SLC7A5, CXCL8, MYC, CDK1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.25736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0009925~basal plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3.73E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC12A2, SLC7A5, TACSTD2, MET\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.042502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005634~nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e1.60E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTGIF1, GINS1, TEAD4, RRM2, SHMT2, TACSTD2, FOXM1, PUS7, NME1, MMP12, CCNB1, UBE2S, PRC1, MYC, PBK, CDK1, MCM4, NEK2, TRIP13, ECT2, KPNA2, CDKN3, MAD2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.091053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0000922~spindle pole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e2.77E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1, PRC1, NEK2, MAD2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.092609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0000307~cyclin-dependent protein kinase holoenzyme complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e3.78E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1, CDK1, CKS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.092609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0097125~cyclin B1-CDK1 complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4.06E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1, CDK1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.092609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0030496~midbody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.006656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRC1, CDK1, NEK2, ECT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.12647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0070062~extracellular exosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.012559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC12A2, SLC7A5, PSAT1, SHMT2, TACSTD2, CDK1, SORD, TIMP1, PAICS, CD44, NME1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.204532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005829~cytosol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.020953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC12A2, RRM2, SHMT2, TACSTD2, SORD, PAICS, NME1, SLC7A5, CCNB1, UBE2S, PSAT1, PRC1, CDK1, NEK2, ECT2, KPNA2, CD44, CDKN3, MAD2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.280458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0071162~CMG complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.022141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGINS1, MCM4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.280458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_CC_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005654~nucleoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.026631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTGIF1, GINS1, TEAD4, FOXM1, CCNB1, UBE2S, PRC1, MYC, CDK1, MCM4, NEK2, ECT2, KPNA2, LGR5, MAD2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.287143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0045236~CXCR chemokine receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4.50E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e6.62E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0008009~chemokine activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e1.84E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.01351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005515~protein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.001423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIFITM3, IFITM1, CXCL8, SHMT2, TACSTD2, CXCL1, TMEM97, FOXM1, CXCL2, CCNB1, MYC, PBK, NEK2, TIMP1, ECT2, KPNA2, TGIF1, TEAD4, SLC12A2, GINS1, RRM2, PAICS, NME1, SLC7A5, BACE2, UBE2S, PSAT1, PRC1, MTHFD2, CKS2, CDK1, MCM4, TRIP13, MET, LGR5, CD44, CDKN3, MAD2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.069735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0005524~ATP binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.018227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eUBE2S, PBK, CDK1, MCM4, NEK2, TRIP13, MET, PAICS, NME1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.669838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0019901~protein kinase binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.027961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC12A2, CCNB1, PRC1, CKS2, FOXM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.821441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0061575~cyclin-dependent protein serine/threonine kinase activator activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.034845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1, CKS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.821441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0001046~core promoter sequence-specific DNA binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.039116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMMP12, MYC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.821441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0004672~protein kinase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.052843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePBK, CDK1, NEK2, MET\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.970989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGOTERM_MF_DIRECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003eGO:0042802~identical protein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.084859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePSAT1, PRC1, SORD, TRIP13, MET, PAICS, MAD2L1, NME1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e: gene ontology, \u003cstrong\u003eDEGs\u003c/strong\u003e: differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e KEGG pathway analysis of upregulated DEGs associated with colorectal cancer.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;FDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04270:Vascular smooth muscle contraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e4.90E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eEDN3, PRKCB, KCNMA1, MYH11, PPP1R12B, MYL9, MYLK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.007107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04810:Regulation of actin cytoskeleton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.005445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL12, C7, MYH11, PPP1R12B, MYL9, MYLK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.394783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04960:Aldosterone-regulated sodium reabsorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.014603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHSD11B2, PRKCB, SCNN1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.705814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04080:Neuroactive ligand-receptor interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.035272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePYY, VIPR1, EDN3, SST, GCG, INSL5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa00140:Steroid hormone biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.038332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHSD11B2, DHRS11, HSD17B2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04921:Oxytocin signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.041422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRKCB, PPP1R12B, MYL9, MYLK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04971:Gastric acid secretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.055302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRKCB, SST, MYLK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04610:Complement and coagulation cascades\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.068752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCR2, C7, CLU\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04911:Insulin secretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.068752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRKCB, KCNMA1, GCG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04970:Salivary secretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.078745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRKCB, KCNMA1, BEST2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04510:Focal adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.0807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRKCB, PPP1R12B, MYL9, MYLK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa00910:Nitrogen metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.081885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCA1, CA4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e0.989441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.11793214862682%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04972:Pancreatic secretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.754442649434572%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.693053311793214%\" valign=\"top\"\u003e\n \u003cp\u003e0.092215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.77221324717286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRKCB, KCNMA1, SLC26A3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG\u003c/strong\u003e: Kyoto Encyclopedia of Genes and Genomes, \u003cstrong\u003eDEGs\u003c/strong\u003e: differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e KEGG pathway analysis of downregulated DEGs associated with colorectal cancer.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; FDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa05120:Epithelial cell signaling in Helicobacter pylori infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e1.23E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2, MET\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.012263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04110:Cell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e2.59E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1, MYC, CDK1, MCM4, TRIP13, MAD2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.012957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa05134:Legionellosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.00114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.038002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04218:Cellular senescence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.002537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1, CXCL8, MYC, CDK1, FOXM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.063425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa05323:Rheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.004861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.066358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04657:IL-17 signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.005009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.066358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa05167:Kaposi sarcoma-associated herpesvirus infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.005538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, MYC, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.066358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04061:Viral protein interaction with cytokine and cytokine receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.005952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.066358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa05146:Amoebiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.006288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.066358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04064:NF-kappa B signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.006636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.066358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04936:Alcoholic liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.015474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCXCL8, CXCL1, CXCL3, CXCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.140676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa01240:Biosynthesis of cofactors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.018859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePSAT1, SHMT2, MTHFD2, NME1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.157159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa05230:Central carbon metabolism in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.027765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC7A5, MYC, MET\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.208736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.55342902711324%\" valign=\"top\"\u003e\n \u003cp\u003ehsa04115:p53 signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.61244019138756%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.030769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.69537480063796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1, RRM2, CDK1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.569377990430622%\" valign=\"top\"\u003e\n \u003cp\u003e0.208736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG\u003c/strong\u003e: Kyoto Encyclopedia of Genes and Genomes, \u003cstrong\u003eDEGs\u003c/strong\u003e: differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e\u0026nbsp; Top hub genes selected from module 1, 2 and 3 (by p-value).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eadj. p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Log FC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCDK1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.30E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.65E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.53506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCyclin dependent kinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCCNB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.60E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.50E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.21741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCyclin B1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFOXM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.08E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.71E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.5344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eForkhead box protein M1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRRM2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.86E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.07E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.86578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRibonucleotide reductase regulatory subunit M2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMAD2L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.22E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.91E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.34892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMAD2 mitotic arrest deficient-like 1 (yeast)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNEK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.61E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.59E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.85592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIMA related kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMCM4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.23E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.06E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.57324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinichromosome maintenance complex component 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePBK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.97E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.15241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePDZ binding kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYL9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.13E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.44E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.37522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMyosin light chain 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCNN1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.06E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.976985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalponin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYH11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;7.95E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.10E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.567059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMyosin-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYLK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.33E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.17E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.26391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMyosin light chain kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGUCA2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.71E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.63E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.046581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGuanylate cyclase activator 2A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGUCA2B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.20E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.54E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.896999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGuanylate cyclase activator 2B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eZG16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.65E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.24E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.681086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZymogen granule protein 16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC26A3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.43E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.85E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.611448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSolute carrier family 26 member 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Colorectal cancer, Differentially expressed genes, Microarray, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4657501/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4657501/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eColorectal Cancer (CRC) is the frequently occurring malignant tumor in colon and rectum with high mortality rate. The signaling pathway involved in CRC and CRC driven genes are largely unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo identify the gene signatures which help in early diagnosis of CRC, we downloaded three datasets (GSE24514, GSE8671 and GSE21510) from the Gene Expression Omnibus (GEO) Database. GO and KEGG pathway enrichment analysis were conducted using DAVID database. A protein\u0026ndash;protein interaction (PPI) network was constructed using STRING and cytoscape software. These hub genes were verified by survival analysis using GEPIA database.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 120 DEGs were identified including (75 upregulated genes and 45 downregulated genes). Seven modules were identified from protein \u0026ndash;protein interaction network using MCODE plug in tool of cytoscape, only three Modules (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) selected with score\u0026thinsp;\u0026ge;\u0026thinsp;5 and node\u0026thinsp;\u0026ge;\u0026thinsp;10. Module 1 contained downregulated genes and Module 2 and 3 contained upregulated genes. Hub genes identified from Module 1 with connectivity score\u0026thinsp;\u0026ge;\u0026thinsp;16 included \u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003eFOXM1\u003c/em\u003e, \u003cem\u003eRRM2\u003c/em\u003e, \u003cem\u003eMAD2L1\u003c/em\u003e, \u003cem\u003eNEK2\u003c/em\u003e, \u003cem\u003eMCM4\u003c/em\u003e and \u003cem\u003ePBK\u003c/em\u003e. Out of 8 genes examined, only 3 exhibited significant correlations with overall survival among CRC patients (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). \u003cem\u003eMAD2L1\u003c/em\u003e, \u003cem\u003eMCM4\u003c/em\u003e, and \u003cem\u003ePBK\u003c/em\u003e demonstrated relatively lower expression levels of these genes were correlated with poor prognosis in CRC patients. Hub genes from Modules 2 and 3 (connectivity score\u0026thinsp;\u0026ge;\u0026thinsp;6) included \u003cem\u003eMYL9, CNN1, MYH11, MYLK, TAGLN, GUCA2A, GUCA2B, ZG16\u003c/em\u003e and \u003cem\u003eSLC26A3\u003c/em\u003e. Survival analysis indicated that higher expression of \u003cem\u003eMYL9, CNN1\u003c/em\u003e and \u003cem\u003eTAGLN\u003c/em\u003e correlated with poor prognosis, while lower expression of \u003cem\u003eZG16\u003c/em\u003e and \u003cem\u003eSLC26A3\u003c/em\u003e was linked to poorer outcomes in CRC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These eight hub genes, believed to promote tumor activity, are promising candidates for new CRC therapeutic targets.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEight hub DEGs (\u003cem\u003eMAD2L1, MCM4, PBK, MYL9, CNN1, TAGLN, ZG16\u003c/em\u003e and \u003cem\u003eSLC26A3\u003c/em\u003e) were identified, to be strongly correlated with the overall survival of patients with CRC based on GEO and GEPIA data. These eight genes have the potential as novel and independent prognostic biomarkers for early diagnosis of CRC and forecasting clinical results of CRC patients. Several studies revealed that suppression of these genes inhibits the proliferation of CRC.\u003c/p\u003e","manuscriptTitle":"Identification of prognostic gene markers for the early diagnosis of colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 20:53:13","doi":"10.21203/rs.3.rs-4657501/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":"c1319872-d6e6-4d05-b2c8-2acb713c9c20","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33886863,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2024-07-02T20:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 20:53:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4657501","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4657501","identity":"rs-4657501","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}