Hub genes Associated with Colorectal Cancer Progression Based on Bioinformatics Analysis

Abstract

Objective: To screen potential genes related to the diagnosis, treatment, and prognosis of colorectal cancer (CRC) based on bioinformatics methods, and explore their mechanisms. Methods: The GSE23878 dataset was downloaded from the gene expression omnibus (GEO) database to screen differential expression genes (DEGs) and module genes highly correlated with CRC clinical phenotype using R language and weighted gene co-expression network analysis (WGCNA), respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed with the Metascape database. The protein-protein interaction networks (PPI) were constructed by the STRING database to identify Hub genes. Gene set enrichment analysis (GSEA) was further employed to clarify the biological processes and signaling pathways in which the Hub genes are involved. Results: A total of 1836 significant DEGs were screened, including 482 upregulated genes and 1354 downregulated genes. WGCNA analysis showed that the cyan module and blue module exhibited the strongest correlation with the clinical phenotype of CRC. KEGG enrichment analysis revealed that these genes were mainly involved in key signaling pathways such as cancer pathways, cell cycle, cell migration, and invasion. GSEA analysis suggested that the E2F transcription factor family was most significantly enriched in related biological processes. Based on the above analytical methods, we ultimately identified 6 genes (AURKB, CDC20, CDCA3, CDCA8, BIRC5, RRM2) closely associated with colorectal cancer, which may play critical roles in its pathogenesis, progression, and prognosis. Conclusion: The 6 Hub genes identified in our study contribute to a deeper understanding of the mechanisms underlying the occurrence and development of CRC, and provide potential targets for the diagnosis, treatment, and prognosis evaluation of CRC, laying the theoretical groundwork for subsequent basic research and clinical translational applications.