Identification of signatures associated with microsatellite instability and immune characteristics to predict the prognostic risk of colon cancer.

Background: Microsatellite instability (MSI) significantly impacts treatment response and outcomes in colon cancer; however, its underlying molecular mechanisms remain unclear. This study aimed to identify prognostic biomarkers by comparing MSI and microsatellite stability (MSS).

Methods: Data from the GSE39582 dataset downloaded from the Gene Expression Omnibus database were analyzed for differentially expressed genes (DEGs) and immune cell infiltration between MSI and MSS. Then, weighted gene co-expression network analysis (WGCNA) was utilized to identify the key modules, and the modules related to immune infiltration phenotypes were considered as the immune-related gene modules, followed by enrichment analysis of immune-related module genes. Prognostic signatures were derived using Cox regression, and their correlation with immune features and clinical features was assessed, followed by a nomogram construction.

Results: A total of 857 DEGs and 14 differential immune cell infiltration between MSI and MSS were obtained. Then, WGCNA identified two immune-related modules comprising 356 genes, namely MEturquoise and MEbrown. Eight signature genes were identified, namely , , , , , , , and , followed by prognostic model construction. Both training and validation cohorts revealed that these eight signature genes have prognostic value, and the prognostic model showed superior predictive performance for colon cancer prognosis and distinguished the clinical characteristics of colon cancer patients. Notably, among the signature genes correlated significantly with immune infiltration, human leukocyte antigen expression, and immune pathway enrichment. Finally, the constructed nomogram model could significantly predict the prognosis of colorectal cancer.

Conclusion: This study identifies eight prognostic signature genes associated with MSI and immune infiltration in colon cancer, suggesting their potential for predicting prognostic risk.