Abstract
PURPOSE: This study aimed to elucidate the predictive role of an oxidative stress-related genes (OSRGs) model in colon cancer. MATERIALS AND METHODS: First, OSRGs that were differentially expressed between tumor and normal tissues were identified using The Cancer Genome Atlas (TCGA)-(Colorectal Adenocarcinoma) COAD dataset. Then, Lasso COX regression was performed to develop an optimal prognostic model patients were stratified into high- and low-risk groups based on the expression patterns of these genes. The model's validity was confirmed through Kaplan-Meier survival curves and receiver operating characteristic curve (ROC) analysis. Additionally, enrichment analyses were performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to uncover underlying mechanisms. RESULTS: A totally of 115 differentially expressed OSRGs were identified within the TCGA cohort, with 17 significantly linked to overall survival. These 17 genes were used to formulate a prognostic model that differentiated patients into distinct risk groups, with the high-risk group demonstrating a notably inferior overall survival rate. The risk score, when integrated with clinical and pathological data, emerged as an independent prognostic indicator of colon cancer. Further analyses revealed that the disparity in prognostic outcomes between risk groups could be attributed to the reactive oxygen species pathway and the p53 signaling pathway. CONCLUSION: A new prediction model was established based on OSRGs. CYP19A1, NOL3 and UCN were found to be highly expressed in tumor tissues and substantial clinical predictive significance. These findings offer new insights into the role of oxidative stress in colon cancer.