The disulfidptosis-related signature associated with the tumor environment and prognosis of patients with Colon Cancer

Abstract

Abstract Background: Disulfidptosis, a novel form of metabolism-associated regulated cell death (RCD), is a promising target for therapeutic intervention in cancer. However, the molecular subtypes associated with disulfidptosis, as well as the associated metabolomics and immune microenvironment, have not been fully explored in a comprehensive analysis of the prognostic profile of colon cancer. Methods: Based on the differences in the expression of disulfidptosis-related genes (DRGs), patients with colon cancer(COAD) were divided into different subtypes by consensus clustering. Through univariate regression analysis and LASSO-Cox regression analysis of differentially expressed genes (DEGs) among three subtypes, we constructed and validated a DRG risk score to predict the prognosis of patients with COAD, while also identifying three gene subtypes. Analysis of DRG risk score, clinical characteristics, tumor microenvironment (TME), somatic cell mutations, and immunotherapy sensitivity revealed significant correlations between them. Finally, real-time fluorescence quantitative PCR (qRT-PCR) was used to analyze the expression levels of risk model prognostic signature genes in colon cancer specimens. Results: Based on the differences in the expression of disulfidptosis-related genes (DRGs), patients with colon cancer(COAD) were divided into different subtypes by consensus clustering. Through univariate regression analysis and LASSO-Cox regression analysis of differentially expressed genes (DEGs) among three subtypes, we constructed and validated a DRG risk score to predict the prognosis of patients with COAD, while also identifying three gene subtypes. Analysis of DRG risk score, clinical characteristics, tumor microenvironment (TME), somatic cell mutations, and immunotherapy sensitivity revealed significant correlations between them. Finally, real-time fluorescence quantitative PCR (qRT-PCR) was used to analyze the expression levels of risk model prognostic signature genes in colon cancer specimens. Conclusion: We identified 10 disulfide death prognostic signature genes that can help clinicians predict the prognosis of colon cancer patients and provide reference value for targeted therapy.