Developing a predictive model for evaluating the efficacy of disulfidptosis-related biomarkers in colon adenocarcinoma

Article ID: 4743

Keywords: Disulfidptosis, Colon Adenocarcinoma, Molecular subtypes, Prognostic Markers, Tumor microenvironment

Abstract

Background: This study investigated colon adenocarcinoma (COAD), one of the most common types of cancers worldwide. In recent years, a novel cell death pathway, hydrogen sulfide poisoning, has been identified and targeting disulfide reductase has emerged as a new strategy for cancer treatment. However, the predictive potential of disulfidptosis-related genes (DRGs) in COAD and their characteristics in the tumor immune microenvironment (TIME) remain to be further elucidated. Methods: This study obtained DRGs transcriptome and mutation data of COAD samples were obtained from the Tissue Cancer Genome Atlas (TCGA) database. Pearson and differential expression correlation analysis were used to identify COAD-related DRGs and a risk prognosis model for DRGs was constructed using the univariate least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. Enrichment analysis was then conducted to explore the potential biological functions and signal transduction of differentially expressed genes associated with the model. The reliability of the model was validated through various statistical analyses such as survival analysis, receiver operating characteristic (ROC) curves, calibration plots, and bar graphs. The relationship between the prognostic model, immune microenvironment, and drug sensitivity was examined. Finally, specimens from COAD patients were extracted from the human protein atlas (HPA) database and our Hospital and compared with normal tissues to verify the expression level of DRGs. Results: We successfully established a risk prognostic model containing 6 DRGs (RPA2, TIMP1, WDR1, POLR3K, KTI12, and RTKN). This model performed well in predicting the overall survival of patients with COAD. Validation of this model through Cox analysis and clinical indicators showed considerable potential for predicting the prognosis of patients with COAD. Furthermore, there was a significant correlation between the DRGs prognostic model and tumor microenvironment (TME), immune infiltrating cells, and drug sensitivity (p < 0.05). HPA and experimental results verified that the expression levels of RPA2, TIMP1, POLR3K, KTI12, and RTKN in COAD tumors were higher than those in normal tissues, while the expression levels of WDR1 were opposite (p < 0.01). Conclusion: This study constructed a risk model and identified 6 DRGs as potential molecular therapeutic targets for COAD. The prognosis and immune therapeutic response of patients with COAD are related to DRGs, and targeted therapy for DRGs may provide a new research direction for the diagnosis and treatment of COAD.