Identification of Potential Biomarkers for Colorectal Cancer Using Bioinformatics Analysis

Identification of Potential Biomarkers for Colorectal Cancer Using Bioinformatics Analysis | 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 Identification of Potential Biomarkers for Colorectal Cancer Using Bioinformatics Analysis an luo, henglang xu, mingjun xie, ke hu, yiping jiang, jia hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2243522/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 most common malignant tumor of the intestine, and its incidence and mortality rate are at the forefront. Early diagnosis and intervention of CRC is of great significance. however, there is a lack of precise diagnostic biomarkers. We aim to explore potential biomarkers for CRC and provide a new treatment idea for CRC. Methods We first identified differentially expressed genes (DEGs) in 26 colorectal tumor tissue samples and 26 matched non-tumor tissue samples in the GSE25070 dataset. We then performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs using the Database for Annotation Visualization and Integrated Discovery (DAVID). We further constructed protein-protein interaction (PPI) networks of DEGs using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and screened 10 hub genes using Cytoscape software. GO and KEGG enrichment analysis of hub genes was done by the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). Finally, we analyzed the expression levels and survival of hub genes using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Results We obtained 756 DEGS (254 upregulated genes and 502 downregulated genes) from the GSE25070 dataset, and DEGs were mainly enriched in inflammatory response, neutrophil chemotaxis, and cytokine-cytokine receptor. Ten hub genes were identified, including five upregulated genes (VEGFA, IL1B, MMP9, CXCL8, and CCND1) and five downregulated genes (MAPK3, ADH1A, SLC26A3, ADH1C, and UGT1A8). Five upregulated genes were highly expressed in CRC patients, and IL1B and CXCL8 genes were significantly associated with overall survival in colorectal cancer patients, and high expression of IL1B and CXCL8 had a greater survival advantage. Conclusion IL1B and CXCL8 are potential biomarkers for CRC. colorectal cancer bioinformatics analysis differentially expressed genes hub genes survival analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cancer remains the most threatening disease to human beings in the 21st century. Colorectal cancer is one of the most common malignancies of the digestive system, and the Global Cancer Statistics 2020 report( 1 ) shows that colorectal cancer is at the top of the list in terms of incidence and mortality. Early colorectal cancer may not have obvious symptoms, and when most patients present with complaints of hematochezia, it usually indicates that the disease has progressed to intermediate to advanced stages and metastases are present( 2 ). Most colorectal cancers evolve from polyps, especially adenomatous polyps( 3 , 4 ). Unfortunately, many countries and regions do not offer colonoscopy screening as a routine medical examination, leading to this catastrophic event. Currently, serum carcinoembryonic antigen (CEA) is the most commonly used and important diagnostic marker for colorectal cancer( 5 , 6 ), but it lacks specificity and sensitivity. Therefore, there is an urgent need to explore new biomarkers to provide new ideas for the treatment of CRC ( 7 – 9 ). To date, a variety of biomarkers are considered to be associated with survival prognosis in CRC. Zong confirmed that CRC patients with high expression of FOXD1 and Plk2 had a worse prognosis by constructing a Nomogram model( 10 ). It was reported that ATP2A1 is a potential pathogenic factor, and CRC patients with high expression of ATP2A1 have a low survival rate( 11 ). It has also been claimed that CX3CR1 promotes cell proliferation and migration of CRC cells by regulating macrophage polarization( 12 ). However, few studies have explored the interaction of biomarkers in CRC through comprehensive analysis. This study aimed to identify potential biomarkers of CRC by bioinformatics analysis. The dataset GSE25070 was used to screen DEGs between colorectal tumor tissues and matched normal adjacent tissues and to perform GO function and KEGG pathway enrichment analysis on DEGs. The STRING database was then used to construct a PPI network of DEGs, and Cytoscape software was used to identify the hub genes of DEGs. Using the WebGestalt online tool, we perform GO and KEGG pathway enrichment analysis of the hub genes. Finally, we performed expression and survival analysis of hub genes using the GEPIA 2 database. Our study identified two potential biomarkers involved in CRC progression, which could be new therapeutic targets for CRC. Materials And Methods Microarray Data We first downloaded the microarray expression data of GSE25070 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25070 ) from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). The GSE25070 dataset is based on the GPL6883 platform (Illumina HumanRef-8 v3.0 expression beadchip), and it includes 26 colorectal tumor tissue samples and 26 matched adjacent non-tumor tissue samples. We then further processed the GSE25070 dataset in Microsoft Excel. Degs Analysis To further identify DEGs in 26 colorectal tumor tissue samples and 26 matched adjacent non-tumor tissue samples, we used the GEO2R online tool in the GEO database for analysis. Adjusted p-values 1 were set as screening parameters. DEGs with logFC > 0 were identified as upregulated genes and those with logFC < 0 were considered as downregulated genes. Heatmaps of the DEGs were drawn by Heml 1.0.3.7 software. Analysis Of Go Functional And Kegg Pathway Enrichment Using the DAVID online database ( http://david.ncifcrf.gov/tools.jsp ), we analyzed GO and KEGG pathway enrichment analyses for 254 upregulated genes and 504 downregulated genes, respectively. GO analysis included biological processes (BP), cellular components (CC), and molecular functions (MF). False discovery rate (FDR) < 0.05 was statistically significant. Ppi Network Analysis And Identification Of Hub Genes To further explore the interaction relationships between DEGs, we used the STRING database ( https://string-db.org/ ) to construct a protein-protein interaction (PPI) network, and Cytoscape 3.9.0 software was used to visualize the PPI results. Meanwhile, we used the cytoHubba plugin in this software to screen the upregulated and downregulated genes for hub genes separately, and the top five genes in terms of degree value were identified as hub genes. Finally, GO and KEGG enrichment analysis was performed for 10 hub genes using the WebGestalt online tool ( http://www.webgestalt.org/ ), and P < 0.05 was statistically significant. Expression And Survival Analysis Of Hub Genes (Dup: Abstract ?) We used the GEPIA 2 database ( http://gepia2.cancer-pku.cn ) to compare the expression of 10 hub genes between CRC patients and normal patients. We then further explored survival analysis of hub genes, including overall survival and disease-free survival. We divided patients into high and low expression groups based on median hub gene expression and calculated hazards ratios (HR) and their associated 95% confidence intervals (CI) and log-rank test P values. P < 0.05 was considered to be statistical significance. Results Identification of DEGs By analyzing the GEO2R online tool in the GEO database, we obtained 756 DEGs from the GSE25070 dataset, including 254 upregulated genes and 502 downregulated genes (Table 1), and visualized the results of the DEGs as a volcano plot (Fig. 1 a). Heatmaps of DEGs was plotted using Heml 1.0.3.7 software (Fig. 1 b). Functional Enrichment Analysis Of Degs We used the DAVID database to obtain GO functional and KEGG pathway enrichment analyses of DEGs. The GO analysis revealed that 254 upregulated genes were primarily involved in inflammatory response, extracellular matrix organization, and neutrophil chemotaxis biological processes (Fig. 2 a, Table 2), whereas 502 downregulated genes were primarily involved in cadmium ion response, retinol metabolic process, and cellular zinc ion homeostasis (Fig. 2 b, Table 2). For cellular fractions, upregulated genes were mainly enriched in the extracellular space, extracellular region, and extracellular matrix (Fig. 2 a, Table 2), while downregulated genes were enriched in the extracellular exosome, apical plasma membrane, and extracellular space (Fig. 2 b, Table 2). In terms of molecular function, the results showed that upregulated genes were mainly enriched in extracellular matrix structural constituent, chemokine activity, and extracellular matrix structural constituent conferring tensile strength (Fig. 2 a, Table 2), while the downregulated genes were mainly enriched in structural constituent of muscle, steroid dehydrogenase activity, and retinol dehydrogenase activity (Fig. 2 b, Table 2). KEGG pathway enrichment analysis showed that upregulated genes were mainly involved in Rheumatoid arthritis, IL-17 signaling pathway, and Cytokine-cytokine receptor interaction (Fig. 2 c, Table 3), while downregulated genes were mainly involved in Metabolic pathways, Drug metabolism - cytochrome P450, and Bile secretion (Fig. 2 d, Table 3). Analysis Of Ppi Networks And Hub Genes Using the STRING database, we obtained PPI networks for 254 upregulated genes and 502 downregulated genes. The PPI network for the upregulated gene contains 203 nodes and 1258 edges, and the PPI network for the downregulated gene includes 400 nodes and 1146 edges. We visualized the results using Cytoscape 3.9.0 software (Fig. 3 a-b). We also screened 5 hub genes each for upregulated and downregulated genes using the cytoHubba plugin (Fig. 3 c-d), including VEGFA, IL1B, MMP9, CXCL8, CCND1, MAPK3, ADH1A, SLC26A3, ADH1C, and UGT1A8. Specific information about the 10 hub genes is shown in Table 4. We further performed GO and KEGG enrichment analysis on 10 hub genes using WebGestalt online tool. GO enrichment analysis showed that the 10 hub genes were mainly enriched in cytokine-mediated signaling pathway, leukocyte migration, myeloid leukocyte migration, and positive regulation of cell migration for BP (Fig. 4 a, Table 5); and secretory granule, secretory vesicle, and pseudopodium for CC (Fig. 4 b, Table 5); and alcohol dehydrogenase activity, zinc-dependent, alcohol dehydrogenase (NAD) activity, retinol dehydrogenase activity, and molecular function regulator for MF (Fig. 4 c, Table 5). KEGG pathway analysis revealed 10 hub genes mainly involved in bladder cancer, AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, and human cytomegalovirus infection (Fig. 4 d, Table 6). Expression And Survival Analysis Of Hub Genes We performed an analysis using the GEPIA 2 database to further compare the expression of the 10 hub genes between CRC patients and normal patients. The findings revealed that 5 upregulated genes (VEGFA, IL1B, MMP9, CXCL8, and CCND1) were significantly more expressed in colorectal cancer than in normal controls, while 5 downregulated genes (MAPK3, ADH1A, SLC26A3, ADH1C, and UGT1A8) were the inverse (Fig. 5 a). Next we performed survival analysis for the 5 upregulated genes, including overall survival and disease-free survival (Fig. 5 b-c). The results showed that IL1B and CXCL8 genes were significantly associated with OS in colorectal cancer patients (p < 0.05), and Patients with high expression of IL1B and CXCL8 had a greater survival advantage (Fig. 5 b), so we tentatively speculate that IL1B and CXCL8 are potential biomarkers of CRC. Discussion Colorectal cancer is a problem that deserves global attention. The number of diagnosed cases( 13 ) in the United States exceeded 150,000 in 2022, and the incidence of CRC is increasing in China. The number of CRC deaths worldwide( 14 ) is expected to rise significantly by 2035 if early detection services and specialized care are lacking. Here, we obtained 254 upregulated genes and 502 downregulated genes by analyzing the GSE25070 dataset. And these DEGs were analyzed for GO and KEGG pathway enrichment, which revealed that DEGs were mainly enriched in inflammatory response, neutrophil chemotaxis, and cytokine-cytokine receptor interaction pathway. More critically, we screened for hub genes and obtained IL1B and CXCL8 by further analysis of hub genes, demonstrating that they are closely associated with CRC progression and survival prognosis. IL1B is a member of the inflammatory cytokine interleukins( 15 ), and numerous previous studies have shown that inflammation plays a crucial role in the development of tumors( 16 – 19 ). IL1B has been reported to be associated with the development and metastasis of a variety of tumors, including non-small cell lung cancer( 20 ), cervical cancer( 21 ), and breast cancer( 22 ). Although high expression of pro-inflammatory cytokines seems to be detrimental to the survival prognosis of CRC patients, studies have claimed that IL1B gene rs1143623 and rs1143634 polymorphisms are associated with reduced risk and better survival in CRC patients( 23 ). CXCL8 (also known as interleukin-8) is a member of the CXC chemokine family( 24 ), and CXC has been identified as a potential therapeutic target for a variety of cancers as a fundamental regulator of leukocyte directed migration( 25 – 27 ). Numerous studies have demonstrated that CXCL8 is highly expressed in CRC and is closely associated with CRC progression( 28 – 30 ). However, does high expression of CXCL8 predict a poor prognosis for CRC patients? This remains a controversial question, and there are studies( 31 ) confirming a greater survival advantage of high CXCL8 expression in CRC patients, which is consistent with our findings. In addition to inflammatory response, our study showed that DEGs are also mainly enriched in neutrophil chemotaxis and cytokine-cytokine receptor interaction pathway. Tumors orchestrate neutrophil recruitment by releasing neutrophil-associated chemokines( 32 ), and CXCL8, a neutrophil-specific chemokine, is elevated in the CRC tumor microenvironment and promotes angiogenesis, invasion, and metastasis( 33 – 35 ). Numerous studies have reported significant enrichment of the cytokine-cytokine receptor interaction pathway in CRC( 36 – 38 ). It has been shown that cytokine-cytokine receptor interaction was significantly and positively correlated with tumor metastatic areas, and the enrichment score of the cytokine-cytokine receptor interaction pathway decreased after treatment with IP6 in an in situ transplantation model of CRC mice, thus interfering with CRC metastasis( 39 ). In summary, our study suggests that both IL1B and CXCL8 are associated with inflammatory response, neutrophil chemotaxis, and cytokine-cytokine receptor interaction pathway. However, our study has some limitations. The sample size of the GSE25070 dataset is not large enough, and we need experiments to further validate our results. Therefore, our findings should be reserved for further confirmation in future studies. Conclusion In conclusion, based on bioinformatics analysis of DEGs in CRC samples and matched adjacent normal tissue samples, we finally identified 2 hub genes (IL1B and CXCL8). Our study suggests that IL1B and CXCL8 may be potential biomarkers for colorectal cancer. Abbreviations CRC colorectal cancer DEGs differentially expressed genes GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes DAVID Database for Annotation Visualization and Integrated Discovery PPI protein-protein interaction STRING Search Tool for the Retrieval of Interacting Genes/Proteins WebGestalt WEB-based GEne SeT AnaLysis Toolkit GEPIA Gene Expression Profiling Interactive Analysis CEA serum carcinoembryonic antigen GEO Gene Expression Omnibus BP biological processes CC cellular components MF molecular functions. Declarations Authors’ Contributions AL and JH participated in the design of this project. AL and JH analyzed the experimental data. AL drafted the manuscript. KH, HLX, MJX, YPJ, and JH contributed to the revision of the manuscript. All authors read and approved the final manuscript. Acknowledgments Not applicable. Funding This study was supported by the Jiangxi Province Traditional Chinese Medicine Young and Middle-aged Backbone Talents (Third Batch) Training Program (Ganzhong Medicine Kejiaozi [2021] No. 2). Availability of data and materials GSE25070 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25070). Ethics approval and consent to participate This study does not contain any studies with human participants or animals. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Jiangxi University of Traditional Chinese Medicine, Nanchang 330006, Jiangxi, China. 2 Department of Gastroenterology, Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang 33006, Jiangxi, China. ORCID iD Jia Hu https://orcid.org/0000-0003-2387-1557 References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Identification of hub genes associated with neutrophils infiltration in colorectal cancer. J Cell Mol Med. 2021;25(7):3371–80. Qi Y, Qi H, Liu Z, He P, Li B. Bioinformatics Analysis of Key Genes and Pathways in Colorectal Cancer. J Comput Biol. 2019;26(4):364–75. Li J, Wang Y, Wang X, Yang Q. CDK1 and CDC20 overexpression in patients with colorectal cancer are associated with poor prognosis: evidence from integrated bioinformatics analysis. World J Surg Oncol. 2020;18(1):50. Li C, Liu T, Liu Y, Zhang J, Zuo D. Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer. J Cell Mol Med. 2021;25(12):5811–22. Lan TT, Song Y, Liu XH, Liu CP, Zhao HC, Han YS, et al. IP6 reduces colorectal cancer metastasis by mediating the interaction of gut microbiota with host genes. Front Nutr. 2022;9:979135. Tables Table 1 to 6 are available in the Supplementary Files section. Supplementary Files Table1.xls Table2.xls Table3.xls Table4.xls Table5.xls Table6.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2243522","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":153444216,"identity":"147c44e0-91e5-45b3-af53-27409a4e2194","order_by":0,"name":"an luo","email":"","orcid":"https://orcid.org/0000-0002-4202-7098","institution":"Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"an","middleName":"","lastName":"luo","suffix":""},{"id":153444217,"identity":"f980ead4-e2de-46bb-882c-096351fc2b1d","order_by":1,"name":"henglang 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13:02:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2243522/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2243522/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29410777,"identity":"5f5f2088-6314-43fe-a918-e813881133f2","added_by":"auto","created_at":"2022-11-22 20:37:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91298,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plots and heatmaps of DEGs expression in the GSE25070 dataset.\u003cstrong\u003e a \u003c/strong\u003eVolcano plots, red dots represent upregulated genes, green dots represent downregulated genes, and black dots represent no differentially expressed genes.\u003cstrong\u003e b \u003c/strong\u003eHeatmaps of DEGs\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/9166e6e31a841f921e38ab47.png"},{"id":29410589,"identity":"1f1729b5-5178-4103-9c47-32359fabb219","added_by":"auto","created_at":"2022-11-22 20:29:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48461,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of DEGs. \u003cstrong\u003ea \u003c/strong\u003eGO functional analysis of upregulated genes. \u003cstrong\u003eb\u003c/strong\u003e GO functional analysis of downregulated genes. \u003cstrong\u003ec\u003c/strong\u003e KEGG pathway analysis of upregulated genes.\u003cstrong\u003e d \u003c/strong\u003eKEGG pathway analysis of downregulated genes\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/88e389efe5828d5baf9ebe12.png"},{"id":29410596,"identity":"5b270b97-9970-45da-a09e-2d5f99c61138","added_by":"auto","created_at":"2022-11-22 20:29:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79826,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of PPI Networks and Hub Genes. \u003cstrong\u003ea\u003c/strong\u003e PPI network for upregulated genes. \u003cstrong\u003eb \u003c/strong\u003ePPI network for downregulated genes. \u003cstrong\u003ec\u003c/strong\u003e 5 hub genes for upregulated genes. \u003cstrong\u003ed \u003c/strong\u003e5 hub genes for downregulated genes\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/13cdee93470235e8098032c9.png"},{"id":29410778,"identity":"c3953c43-f1e4-4570-b48e-db7eb0a537cb","added_by":"auto","created_at":"2022-11-22 20:37:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57253,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analysis of 10 hub genes. \u003cstrong\u003ea \u003c/strong\u003eBiological processes. \u003cstrong\u003eb\u003c/strong\u003eCellular components. \u003cstrong\u003ec\u003c/strong\u003e Molecular function. \u003cstrong\u003ed\u003c/strong\u003e KEGG pathways\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/a705160e29db8d1eafd2a981.png"},{"id":29410597,"identity":"81860b18-738a-432b-b18e-cc16a26997ae","added_by":"auto","created_at":"2022-11-22 20:29:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":65878,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and Survival Analysis of Hub Genes. \u003cstrong\u003ea\u003c/strong\u003e The expression of 10 hub genes between CRC patients and normal patients. \u003cstrong\u003eb\u003c/strong\u003e Overall survival analysis of 5 upregulated genes. \u003cstrong\u003ec\u003c/strong\u003e Disease-free survival analysis of 5 upregulated genes\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/e62872e5d33a6a7dd5b43bd1.png"},{"id":29411039,"identity":"653a9c39-9032-484e-8ea2-5d06415ed4bf","added_by":"auto","created_at":"2022-11-22 20:45:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1895590,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/b5ca3f0a-be08-4764-a479-6a547b77d745.pdf"},{"id":29410587,"identity":"f0e2a97f-8151-42d4-9a33-3855855c497b","added_by":"auto","created_at":"2022-11-22 20:29:35","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33792,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xls","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/dc14be9c00466bc83b6bdc27.xls"},{"id":29410588,"identity":"324a5785-ff5e-4495-90ca-3d900c48dc90","added_by":"auto","created_at":"2022-11-22 20:29:35","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31744,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xls","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/6d3f972c2688066af508942f.xls"},{"id":29410779,"identity":"252118e3-a186-4e87-b1a2-1591c703a819","added_by":"auto","created_at":"2022-11-22 20:37:35","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":30208,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.xls","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/3fb02238d4704233e765d1c1.xls"},{"id":29410591,"identity":"e440f10e-3dfc-47e7-8fd3-a009d00441b6","added_by":"auto","created_at":"2022-11-22 20:29:35","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":30208,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.xls","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/dba8737b3252fea168c0463c.xls"},{"id":29411037,"identity":"58aeeaea-a569-444b-800d-0534ea5bf4f3","added_by":"auto","created_at":"2022-11-22 20:45:35","extension":"xls","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":33792,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.xls","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/107ae839cb243b87deb43dae.xls"},{"id":29410594,"identity":"827fbb64-6373-4738-9eaa-9d0c681b9117","added_by":"auto","created_at":"2022-11-22 20:29:35","extension":"xls","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":30208,"visible":true,"origin":"","legend":"","description":"","filename":"Table6.xls","url":"https://assets-eu.researchsquare.com/files/rs-2243522/v1/1af60952efc9861bfa51769b.xls"}],"financialInterests":"","formattedTitle":"Identification of Potential Biomarkers for Colorectal Cancer Using Bioinformatics Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer remains the most threatening disease to human beings in the 21st century. Colorectal cancer is one of the most common malignancies of the digestive system, and the Global Cancer Statistics 2020 report(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) shows that colorectal cancer is at the top of the list in terms of incidence and mortality. Early colorectal cancer may not have obvious symptoms, and when most patients present with complaints of hematochezia, it usually indicates that the disease has progressed to intermediate to advanced stages and metastases are present(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Most colorectal cancers evolve from polyps, especially adenomatous polyps(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Unfortunately, many countries and regions do not offer colonoscopy screening as a routine medical examination, leading to this catastrophic event. Currently, serum carcinoembryonic antigen (CEA) is the most commonly used and important diagnostic marker for colorectal cancer(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), but it lacks specificity and sensitivity. Therefore, there is an urgent need to explore new biomarkers to provide new ideas for the treatment of CRC (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo date, a variety of biomarkers are considered to be associated with survival prognosis in CRC. Zong confirmed that CRC patients with high expression of FOXD1 and Plk2 had a worse prognosis by constructing a Nomogram model(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). It was reported that ATP2A1 is a potential pathogenic factor, and CRC patients with high expression of ATP2A1 have a low survival rate(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It has also been claimed that CX3CR1 promotes cell proliferation and migration of CRC cells by regulating macrophage polarization(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, few studies have explored the interaction of biomarkers in CRC through comprehensive analysis. This study aimed to identify potential biomarkers of CRC by bioinformatics analysis. The dataset GSE25070 was used to screen DEGs between colorectal tumor tissues and matched normal adjacent tissues and to perform GO function and KEGG pathway enrichment analysis on DEGs. The STRING database was then used to construct a PPI network of DEGs, and Cytoscape software was used to identify the hub genes of DEGs. Using the WebGestalt online tool, we perform GO and KEGG pathway enrichment analysis of the hub genes. Finally, we performed expression and survival analysis of hub genes using the GEPIA 2 database. Our study identified two potential biomarkers involved in CRC progression, which could be new therapeutic targets for CRC.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray Data\u003c/h2\u003e \u003cp\u003eWe first downloaded the microarray expression data of GSE25070 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25070\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25070\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) from the Gene Expression Omnibus (GEO) database (\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). The GSE25070 dataset is based on the GPL6883 platform (Illumina HumanRef-8 v3.0 expression beadchip), and it includes 26 colorectal tumor tissue samples and 26 matched adjacent non-tumor tissue samples. We then further processed the GSE25070 dataset in Microsoft Excel.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDegs Analysis\u003c/h3\u003e\n\u003cp\u003eTo further identify DEGs in 26 colorectal tumor tissue samples and 26 matched adjacent non-tumor tissue samples, we used the GEO2R online tool in the GEO database for analysis. Adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| \u0026gt; 1 were set as screening parameters. DEGs with logFC\u0026thinsp;\u0026gt;\u0026thinsp;0 were identified as upregulated genes and those with logFC\u0026thinsp;\u0026lt;\u0026thinsp;0 were considered as downregulated genes. Heatmaps of the DEGs were drawn by Heml 1.0.3.7 software.\u003c/p\u003e\n\u003ch3\u003eAnalysis Of Go Functional And Kegg Pathway Enrichment\u003c/h3\u003e\n\u003cp\u003eUsing the DAVID online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://david.ncifcrf.gov/tools.jsp\u003c/span\u003e\u003cspan address=\"http://david.ncifcrf.gov/tools.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we analyzed GO and KEGG pathway enrichment analyses for 254 upregulated genes and 504 downregulated genes, respectively. GO analysis included biological processes (BP), cellular components (CC), and molecular functions (MF). False discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was statistically significant.\u003c/p\u003e\n\u003ch3\u003ePpi Network Analysis And Identification Of Hub Genes\u003c/h3\u003e\n\u003cp\u003eTo further explore the interaction relationships between DEGs, we used the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to construct a protein-protein interaction (PPI) network, and Cytoscape 3.9.0 software was used to visualize the PPI results. Meanwhile, we used the cytoHubba plugin in this software to screen the upregulated and downregulated genes for hub genes separately, and the top five genes in terms of degree value were identified as hub genes. Finally, GO and KEGG enrichment analysis was performed for 10 hub genes using the WebGestalt online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.webgestalt.org/\u003c/span\u003e\u003cspan address=\"http://www.webgestalt.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was statistically significant.\u003c/p\u003e\n\u003ch3\u003eExpression And Survival Analysis Of Hub Genes (Dup: Abstract ?)\u003c/h3\u003e\n\u003cp\u003eWe used the GEPIA 2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to compare the expression of 10 hub genes between CRC patients and normal patients. We then further explored survival analysis of hub genes, including overall survival and disease-free survival. We divided patients into high and low expression groups based on median hub gene expression and calculated hazards ratios (HR) and their associated 95% confidence intervals (CI) and log-rank test P values. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eBy analyzing the GEO2R online tool in the GEO database, we obtained 756 DEGs from the GSE25070 dataset, including 254 upregulated genes and 502 downregulated genes (Table\u0026nbsp;1), and visualized the results of the DEGs as a volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Heatmaps of DEGs was plotted using Heml 1.0.3.7 software (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional Enrichment Analysis Of Degs\u003c/h3\u003e\n\u003cp\u003eWe used the DAVID database to obtain GO functional and KEGG pathway enrichment analyses of DEGs. The GO analysis revealed that 254 upregulated genes were primarily involved in inflammatory response, extracellular matrix organization, and neutrophil chemotaxis biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;2), whereas 502 downregulated genes were primarily involved in cadmium ion response, retinol metabolic process, and cellular zinc ion homeostasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;2). For cellular fractions, upregulated genes were mainly enriched in the extracellular space, extracellular region, and extracellular matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;2), while downregulated genes were enriched in the extracellular exosome, apical plasma membrane, and extracellular space (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;2). In terms of molecular function, the results showed that upregulated genes were mainly enriched in extracellular matrix structural constituent, chemokine activity, and extracellular matrix structural constituent conferring tensile strength (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;2), while the downregulated genes were mainly enriched in structural constituent of muscle, steroid dehydrogenase activity, and retinol dehydrogenase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis showed that upregulated genes were mainly involved in Rheumatoid arthritis, IL-17 signaling pathway, and Cytokine-cytokine receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Table\u0026nbsp;3), while downregulated genes were mainly involved in Metabolic pathways, Drug metabolism - cytochrome P450, and Bile secretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAnalysis Of Ppi Networks And Hub Genes\u003c/h3\u003e\n\u003cp\u003eUsing the STRING database, we obtained PPI networks for 254 upregulated genes and 502 downregulated genes. The PPI network for the upregulated gene contains 203 nodes and 1258 edges, and the PPI network for the downregulated gene includes 400 nodes and 1146 edges. We visualized the results using Cytoscape 3.9.0 software (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). We also screened 5 hub genes each for upregulated and downregulated genes using the cytoHubba plugin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d), including VEGFA, IL1B, MMP9, CXCL8, CCND1, MAPK3, ADH1A, SLC26A3, ADH1C, and UGT1A8. Specific information about the 10 hub genes is shown in Table\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eWe further performed GO and KEGG enrichment analysis on 10 hub genes using WebGestalt online tool. GO enrichment analysis showed that the 10 hub genes were mainly enriched in cytokine-mediated signaling pathway, leukocyte migration, myeloid leukocyte migration, and positive regulation of cell migration for BP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Table\u0026nbsp;5); and secretory granule, secretory vesicle, and pseudopodium for CC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, Table\u0026nbsp;5); and alcohol dehydrogenase activity, zinc-dependent, alcohol dehydrogenase (NAD) activity, retinol dehydrogenase activity, and molecular function regulator for MF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, Table\u0026nbsp;5). KEGG pathway analysis revealed 10 hub genes mainly involved in bladder cancer, AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, and human cytomegalovirus infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExpression And Survival Analysis Of Hub Genes\u003c/h3\u003e\n\u003cp\u003eWe performed an analysis using the GEPIA 2 database to further compare the expression of the 10 hub genes between CRC patients and normal patients. The findings revealed that 5 upregulated genes (VEGFA, IL1B, MMP9, CXCL8, and CCND1) were significantly more expressed in colorectal cancer than in normal controls, while 5 downregulated genes (MAPK3, ADH1A, SLC26A3, ADH1C, and UGT1A8) were the inverse (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eNext we performed survival analysis for the 5 upregulated genes, including overall survival and disease-free survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-c). The results showed that IL1B and CXCL8 genes were significantly associated with OS in colorectal cancer patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Patients with high expression of IL1B and CXCL8 had a greater survival advantage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), so we tentatively speculate that IL1B and CXCL8 are potential biomarkers of CRC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eColorectal cancer is a problem that deserves global attention. The number of diagnosed cases(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) in the United States exceeded 150,000 in 2022, and the incidence of CRC is increasing in China. The number of CRC deaths worldwide(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) is expected to rise significantly by 2035 if early detection services and specialized care are lacking.\u003c/p\u003e \u003cp\u003eHere, we obtained 254 upregulated genes and 502 downregulated genes by analyzing the GSE25070 dataset. And these DEGs were analyzed for GO and KEGG pathway enrichment, which revealed that DEGs were mainly enriched in inflammatory response, neutrophil chemotaxis, and cytokine-cytokine receptor interaction pathway. More critically, we screened for hub genes and obtained IL1B and CXCL8 by further analysis of hub genes, demonstrating that they are closely associated with CRC progression and survival prognosis. IL1B is a member of the inflammatory cytokine interleukins(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and numerous previous studies have shown that inflammation plays a crucial role in the development of tumors(\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). IL1B has been reported to be associated with the development and metastasis of a variety of tumors, including non-small cell lung cancer(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), cervical cancer(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and breast cancer(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Although high expression of pro-inflammatory cytokines seems to be detrimental to the survival prognosis of CRC patients, studies have claimed that IL1B gene rs1143623 and rs1143634 polymorphisms are associated with reduced risk and better survival in CRC patients(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). CXCL8 (also known as interleukin-8) is a member of the CXC chemokine family(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and CXC has been identified as a potential therapeutic target for a variety of cancers as a fundamental regulator of leukocyte directed migration(\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Numerous studies have demonstrated that CXCL8 is highly expressed in CRC and is closely associated with CRC progression(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, does high expression of CXCL8 predict a poor prognosis for CRC patients? This remains a controversial question, and there are studies(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) confirming a greater survival advantage of high CXCL8 expression in CRC patients, which is consistent with our findings.\u003c/p\u003e \u003cp\u003eIn addition to inflammatory response, our study showed that DEGs are also mainly enriched in neutrophil chemotaxis and cytokine-cytokine receptor interaction pathway. Tumors orchestrate neutrophil recruitment by releasing neutrophil-associated chemokines(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and CXCL8, a neutrophil-specific chemokine, is elevated in the CRC tumor microenvironment and promotes angiogenesis, invasion, and metastasis(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Numerous studies have reported significant enrichment of the cytokine-cytokine receptor interaction pathway in CRC(\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). It has been shown that cytokine-cytokine receptor interaction was significantly and positively correlated with tumor metastatic areas, and the enrichment score of the cytokine-cytokine receptor interaction pathway decreased after treatment with IP6 in an in situ transplantation model of CRC mice, thus interfering with CRC metastasis(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In summary, our study suggests that both IL1B and CXCL8 are associated with inflammatory response, neutrophil chemotaxis, and cytokine-cytokine receptor interaction pathway. However, our study has some limitations. The sample size of the GSE25070 dataset is not large enough, and we need experiments to further validate our results. Therefore, our findings should be reserved for further confirmation in future studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, based on bioinformatics analysis of DEGs in CRC samples and matched adjacent normal tissue samples, we finally identified 2 hub genes (IL1B and CXCL8). Our study suggests that IL1B and CXCL8 may be potential biomarkers for colorectal cancer.\u003c/p\u003e"},{"header":"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\"\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\"\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\"\u003eSTRING\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSearch Tool for the Retrieval of Interacting Genes/Proteins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWebGestalt\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWEB-based GEne SeT AnaLysis Toolkit\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\"\u003eCEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eserum carcinoembryonic antigen\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\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebiological processes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecellular components\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emolecular functions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAL and JH participated in the design of this project. AL and JH analyzed the experimental data. AL drafted the manuscript. KH, HLX, MJX, YPJ, and JH contributed to the revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Jiangxi Province Traditional Chinese Medicine Young and Middle-aged Backbone Talents (Third Batch) Training Program (Ganzhong Medicine Kejiaozi [2021] No. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSE25070 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25070).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not contain any studies with human participants or animals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eJiangxi University of Traditional Chinese Medicine, Nanchang 330006, Jiangxi, China. \u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDepartment of Gastroenterology, Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang 33006, Jiangxi, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID iD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJia Hu https://orcid.org/0000-0003-2387-1557\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Tumour cell apoptosis modulates the colorectal cancer immune microenvironment via interleukin-8-dependent neutrophil recruitment. Cell Death Dis. 2022;13(2):113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePennel KA, Quinn JA, Nixon C, Inthagard J, van Wyk HC, Chang D, et al. CXCL8 expression is associated with advanced stage, right sidedness, and distinct histological features of colorectal cancer. J Pathol Clin Res. 2022;8(6):509\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu H, Cai T, Zhang S, Yan X, Zhou L, He Z, et al. Identification of hub genes associated with neutrophils infiltration in colorectal cancer. J Cell Mol Med. 2021;25(7):3371\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi Y, Qi H, Liu Z, He P, Li B. Bioinformatics Analysis of Key Genes and Pathways in Colorectal Cancer. J Comput Biol. 2019;26(4):364\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Wang Y, Wang X, Yang Q. CDK1 and CDC20 overexpression in patients with colorectal cancer are associated with poor prognosis: evidence from integrated bioinformatics analysis. World J Surg Oncol. 2020;18(1):50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Liu T, Liu Y, Zhang J, Zuo D. Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer. J Cell Mol Med. 2021;25(12):5811\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan TT, Song Y, Liu XH, Liu CP, Zhao HC, Han YS, et al. IP6 reduces colorectal cancer metastasis by mediating the interaction of gut microbiota with host genes. Front Nutr. 2022;9:979135.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"colorectal cancer, bioinformatics analysis, differentially expressed genes, hub genes, survival analysis","lastPublishedDoi":"10.21203/rs.3.rs-2243522/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2243522/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 most common malignant tumor of the intestine, and its incidence and mortality rate are at the forefront. Early diagnosis and intervention of CRC is of great significance. however, there is a lack of precise diagnostic biomarkers. We aim to explore potential biomarkers for CRC and provide a new treatment idea for CRC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe first identified differentially expressed genes (DEGs) in 26 colorectal tumor tissue samples and 26 matched non-tumor tissue samples in the GSE25070 dataset. We then performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs using the Database for Annotation Visualization and Integrated Discovery (DAVID). We further constructed protein-protein interaction (PPI) networks of DEGs using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and screened 10 hub genes using Cytoscape software. GO and KEGG enrichment analysis of hub genes was done by the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). Finally, we analyzed the expression levels and survival of hub genes using the Gene Expression Profiling Interactive Analysis (GEPIA) database.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe obtained 756 DEGS (254 upregulated genes and 502 downregulated genes) from the GSE25070 dataset, and DEGs were mainly enriched in inflammatory response, neutrophil chemotaxis, and cytokine-cytokine receptor. Ten hub genes were identified, including five upregulated genes (VEGFA, IL1B, MMP9, CXCL8, and CCND1) and five downregulated genes (MAPK3, ADH1A, SLC26A3, ADH1C, and UGT1A8). Five upregulated genes were highly expressed in CRC patients, and IL1B and CXCL8 genes were significantly associated with overall survival in colorectal cancer patients, and high expression of IL1B and CXCL8 had a greater survival advantage.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIL1B and CXCL8 are potential biomarkers for CRC.\u003c/p\u003e","manuscriptTitle":"Identification of Potential Biomarkers for Colorectal Cancer Using Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-22 20:29:30","doi":"10.21203/rs.3.rs-2243522/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":"02c39e52-2b03-4e8e-8ece-4d2d15db7ff5","owner":[],"postedDate":"November 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-22T20:29:33+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-22 20:29:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2243522","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2243522","identity":"rs-2243522","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}