Disulfidptosis-based molecular clustering and prognostic signatures predict patient survival and the immune landscape in patients with colon cancer.

INTRODUCTION: Disulfidptosis is a unique type of programmed cell death that is distinct from previously known forms of cell death, such as pyroptosis, apoptosis, and necroptosis. Researchers have studied the significance of many forms of cell death in various diseases, particularly malignant tumors, in great detail in recent years. Therefore, how disulfidptosis affects colon cancer and how it functions in the immune system are unknown. METHODS: Disulfidptosis-related gene (DRG) expression information was obtained from the TCGA-COAD cohort. Patients were categorized into two DRG groups using consensus cluster analysis, and the disulfidptosis-related differentially expressed genes (DRDEGs) were subsequently identified by differential analysis of the two clusters. Univariate Cox regression analysis of the DRDEGs was used to identify prognosis-related DEGs (PRDEGs). The screened PRDEGs were then subjected to LASSO-Cox regression analysis to determine the prognostic model on the basis of ten genes. Immunohistochemistry was used to verify the expression and prognostic value of marker genes. RESULTS: In the two DRG clusters, the characteristics of the tumor microenvironment (TME) significantly differed by the TME scores and infiltration levels of 23 human immune cell subpopulations. Prognostically meaningful risk scores were found, with a greater chance of mortality (p = 4.4e-7) for patients in the high-risk category. Furthermore, notable differences in TME scores, immune cell infiltration, and immune checkpoint expression were detected among the risk categories. The ROC curves revealed that the nomogram's 1-, 2-, and 3-year AUCs were 0.75, 0.76, and 0.77, respectively, demonstrating the superior predictive capacity of the nomogram. Immunohistochemistry revealed that patients with high FABP4 and low ADAM8 and FSTL3 expressions had a better prognosis. CONCLUSION: The prognostic features based on 10 PRDEGs performed well in predicting survival, TME status, and response to immunity in COAD patients, helping provide personalized immunotherapy strategies for patients.