Identification of potential therapeutic targets associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis.

Colorectal cancer (CRC) is the most common malignancy of digestive system with significant mortality rate. CRC patients with comparable clinical symptoms or at similar stages of the disease have different outcomes. This underlying clinical result is almost inevitably due to genetic heterogeneity. Therefore, the current study aimed to highlight gene signatures during CRC and unveil their potential mechanisms through bioinformatic analysis. The gene expression profiles (GSE28000, GSE33113, GSE44861, and GSE37182) were downloaded from the Gene Expression Omnibus database, and the differential expressed genes (DEGs) were identified in normal tissues and tumor tissue samples of CRC patients. In total, 8931 DEGs were identified in CRC, including 411 up-regulated genes and 166 down-regulated genes. Further, a protein-protein interaction network was constructed and the highly related genes were clustered using the Molecular Complex Detection algorithm (MCODE) to retrieve the core interaction in different genes' crosstalk. The screened hub genes were subjected to functional enrichment analysis. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell division, cell cycle, and cell proliferation; the down-regulated DEGs were significantly enriched in BP, including cellular homeostasis, detoxification, defense response, intracellular signaling cascade. Additionally, KEGG pathway analysis displayed the up-regulated DEGs were enriched in the cell cycle, TNF signaling, chemokine signaling pathway, while the down-regulated DEGs were enriched in NF-kB signaling, mineral reabsorption. Furthermore, the overall survival and expression levels of hub genes were detected by the UALCAN database and were further validated using Human Protein Atlas database. Taken together the identified DEGs (MT2A, CCNB1, DLGAP5, CCNA2, CXCL2, and RACGAP1) enhance our understanding of the molecular pathways that underpin CRC pathogenesis and could be exploited as molecular targets and diagnostic biomarkers for CRC therapy.