Predictive value of an eight-mRNA signature in colon adenocarcinoma prognosis

Colorectal cancer is a prevalent malignancy, with colon adenocarcinoma as the most common type. Early diagnosis biomarkers and effective risk stratification are crucial for optimal treatment. In this study, gene expression data from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) were analyzed to identify relevant genes for colon adenocarcinoma. These datasets were standardized and subjected to weighted gene co-expression network analysis and differentially expressed gene analysis. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression analyses were performed to generate a risk profile and identify prognosis-related genes. Receiver operating characteristic (ROC) analysis, Kaplan–Meier (KM) curve, and Cox analyses validated the risk signature. Immune cell infiltration patterns and immunological activities in high- and low-risk groups were assessed using single-sample gene set enrichment analysis (ssGSEA). GSEA was used to investigate the signaling pathways associated with low-risk and high-risk groups, whereas ssGSEA was used to analyze those associated with high-risk groups. A line graph was created to predict the overall survival (OS) of patients. Quantitative real-time polymerase chain reaction confirmed differential gene expression between normal and cancerous colon tissues. The eight genes identified – ACOX1, ATP8B1, CHGA, NAT2, PKIB, SLC39A8, TINAG, and VEGFA – correlated with tumor immunity and clinical outcomes. This eight-gene risk profile can accurately stratify risk and predict OS based on KM curves, ROC analysis, and regression models. GSEA analysis revealed calcium ion metabolism as the top pathway in the GEO dataset. These findings provide a foundation for prognostic evaluation and may guide therapeutic decision-making in colon adenocarcinoma.