Bioinformatics-Driven Identification of Hub Genes as Potential Therapeutic Targets in Colorectal Cancer
Keywords:
Bioinformatics, Colorectal cancer (CRC), Differentially expressed genes, Hub gene, Therapeutic targets
Abstract
This study used bioinformatics to identify potential therapeutic targets for colorectal cancer (CRC). Gene expression data from GEO2R were analyzed to identify differentially expressed genes in CRC. FunRich software was used to create the Venn diagrams. The potential "hub genes" were selected from the STRING database. Functional analyses, including Gene Ontology and pathway enrichment, were performed using Database for Annotation, Visualization, and Integrated Discovery (DAVID). Patient survival data from GEPIA, gene expression related to disease stage and metastatic progression, and genetic changes were examined using the cBioPortal and Human Protein Atlas. Among differentially expressed genes, CXCL8, FOXC1, ICOS, and MCF2 were identified as potential hub genes from 89 upregulated genes which plays a significant role in CRC development. The identified genes may serve as valuable biomarkers for diagnosing CRC and predicting patient outcomes, potentially informing the development of targeted treatments to improve patient survival rates.
References
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2. Pitchumoni CS, Broder A. Epidemiology of colorectal cancer. In: Colorectal neoplasia and the colorectal microbiome. Academic Press; 2020. p. 5-33.
3. Mahajan D. Unmasking the Silent Threat: Colorectal Cancer’s Alarming Surge in India’s Young Adults. Journal of Surgical Specialties and Rural Practice. 2023;4(3):111-113.
4. Alaryani FS, Alrdahe SST. A review of treatment, risk factors, and incidence of colorectal cancer. International Journal of Applied Pharmaceutics. 2022;14:1-6.
5. Ferreira AM, Chodankar SU, Vaz FS, D’souza DB, Kulkarni MS. Risk factors for colorectal cancer in Goa, India: A hospital-based case–control study. Indian Journal of Community Medicine. 2021;46(3):474-478.
6. Jung G, Hernandez-Illan E, Moreira L, Balaguer F, Goel A. Epigenetics of colorectal cancer: biomarker and therapeutic potential. Nature Reviews Gastroenterology & Hepatology. 2020;17(2):111-130.
7. Lucafò M, Curci D, Franzin M, Decorti G, Stocco G. Inflammatory bowel disease and risk of colorectal cancer: An overview from pathophysiology to pharmacological prevention. Frontiers in Pharmacology. 2021;12:772101.
8. Vanden Heuvel JP. Analysis of gene expression. In: PCR Protocols in Molecular Toxicology. CRC Press; 2019. p. 41-98.
9. Fajriyah R. Paper review: An overview on microarray technologies. Bulletin of Applied Mathematics and Mathematics Education. 2021;1(1):21.
10. Gawehn E, Schneider G, Hiss JA. Deep Learning in Drug Discovery. Molecular Informatics. 2016;35(1):3-14. doi:10.1002/minf.201501008..
11. Clough E, Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, et al. NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update. Nucleic Acids Research. 2024;52(D1):D138-D144. doi:10.1093/nar/gkad965.
12. Kadali SSK, Gowlikar R, Fatima SN. The Cancer Genomic Atlas–“TO CONQUER CANCER”. International Journal of Molecular and Immuno Oncology. 2021;6(2):76-81.
13. Li X, Li MW, Zhang YN, Xu HM. Common cancer genetic analysis methods and application study based on TCGA database. Hereditas. 2019;41(3):234-242.
14. Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18-27.
15. Yichao Y, Yongbai L, Hailiang L, Dongbo C, Bo L, Murshed A. COL1A1, COL1A2, CHN1, and FN1 Promote Tumorogenesis and Act as Markers of Diagnosis and Survival in Gastric Cancer Patients. Current Pharmaceutical Biotechnology. 2025. doi:10.2174/0113892010332329241119104430.
16. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Research. 2021;49(D1):D605-D612. doi:10.1093/nar/gkaa1074.
17. Digre A, Lindskog C. The Human Protein Atlas—Spatial localization of the human proteome in health and disease. Protein Science. 2021;30(1):218-233. doi:10.1002/pro.3987.
18. Almokhtar BJ, Elengoe A. Determination of the EGFR Gene Role in Lung Cancer Pathway using STRING and Cytoscape Software. Letters in Applied NanoBioScience. 2024;13(3):131. doi:10.33263/LIANBS133.131.
19. Fu L, Zhao L, Li F, Wen F, Zhang P, Yang X, et al. Pharmacological mechanism of quercetin in the treatment of colorectal cancer by network pharmacology and molecular simulation. Journal of Biomolecular Structure and Dynamics. 2024;42(13):7065-7076. doi:10.1080/07391102.2023.2235589.
20. Sun Z, Xia W, Lyu Y, Song Y, Wang M, Zhang R, et al. Immune-related gene expression signatures in colorectal cancer. Oncology Letters. 2021;22(1):543.
21. Wang C, Zhang L. Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis. Medicine. 2022;101(37):e30619.
22. Atasever S. Identification of potential hub genes as biomarkers for breast, ovarian, and endometrial cancers. Frontiers in Life Sciences and Related Technologies. 2024;5(1):74-82. doi:10.51753/flsrt.1405816.
23. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research. 2023;51(D1):D638-D646.
24. Lan Y, Yang X, Wei Y, Tian Z, Zhang L, Zhou J. Explore key genes and mechanisms involved in colon cancer progression based on bioinformatics analysis. Applied Biochemistry and Biotechnology. 2024;196(9):6253-6268.
25. Horaira MA, Islam MA, Kibria MK, Alam MJ, Kabir SR, Mollah MNH. Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents. BMC Medical Genomics. 2023;16(1):64.
26. Asokan S, Bandapalli OR. CXCL8 signaling in the tumor microenvironment. In: Tumor Microenvironment: The Role of Chemokines–Part B. Academic Press; 2021. p. 25-39.
27. Zhang H, Nie J, Bao Z, Shi Y, Gong J, Li H. FOXC1 promotes EMT and colorectal cancer progression by attracting M2 macrophages via the TGF-β/Smad2/3/snail pathway. Cellular Signalling. 2025;130:111680.
28. Yang J, Liu J, Chen Y, Tang W, Bo K, Sun Y, et al. Investigation of ICOS, CD28 and CD80 polymorphisms with the risk of hepatocellular carcinoma: a case–control study in eastern Chinese population. Bioscience Reports. 2019;39(7):BSR20181824.
29. Zaritsky A, Tseng YY, Rabadán MA, Krishna S, Overholtzer M, Danuser G, et al. Diverse roles of guanine nucleotide exchange factors in regulating collective cell migration. Journal of Cell Biology. 2017;216(6):1543-1556.
Published
2024-08-01
How to Cite
Manivannan Rangasamy, SureshKumar Gopal, Delipkumar Kaliyappan, Dhamotharaprasath R, Moulika G, Kalaiyarasan G, & Jayaprasanth J. (2024). Bioinformatics-Driven Identification of Hub Genes as Potential Therapeutic Targets in Colorectal Cancer. Revista Electronica De Veterinaria, 25(1), 4469-4483. https://doi.org/10.69980/redvet.v25i1.2293
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