Comprehensive bioinformatic analysis reveals prognostic significance and functional insights of candidate gene expression in colorectal cancer

The purpose of this study was to investigate biomarkers associated with poor clinical outcomes in colorectal cancer (CRC) by utilizing comprehensive datasets from the gene expression omnibus (GEO) databases GSE41258, GSE39582, and GSE44861. We initially identified differentially expressed genes (DEGs) and applied weighted gene co-expression network analysis (WGCNA) to the GSE41258 dataset to reveal key gene modules associated with CRC. Enrichment analyses were conducted to gain insights into the underlying biology of CRC, particularly focusing on pathways linked to the identified gene modules. Our analysis unveiled a distinct module strongly correlated with CRC carcinogenesis, with significant pathways related to extracellular matrix organization and vasculature development. Furthermore, we identified nine candidate genes (CDH11, COL1A1, COL1A2, COL5A1, COL5A2, FAP, SPARC, SULF1, and THY1) as potential crosstalk genes across various datasets. Notably, eight of these candidate genes exhibited a significant correlation with poor overall survival (OS) and recurrence-free survival (RFS) in CRC patients, suggesting their potential as prognostic biomarkers. Experimental validation using short hairpin RNA (shRNA)-mediated knockdown in HCT116 cells demonstrated that silencing of these candidate genes significantly impaired cancer cell proliferation, providing biological evidence supporting their functional roles in CRC progression. Our integrative approach offers a comprehensive understanding of the molecular landscape of CRC and identifies promising biomarkers for further exploration and validation.