Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma.

Abstract

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor with an unsatisfactory prognosis. This study aims to identify the expression patterns of disulfidptosis-related genes (DRGs), develop a prognostic model, and predict immunological profiles. First, we identified differentially expressed DRGs in TCGA-KIRC cohort and analyzed their mutational profiles, methylation levels, and interaction networks. Subsequently, we identified disulfidptosis-associated molecular subtypes and investigated their prognostic and immunological characteristics. Simultaneously, a disulfidptosis-related prognostic signature (DRPS) was developed using a two-stage stacking framework consisting of 5 machine learning models. The effect of DRPS on immune cell infiltration levels was explored using seven different algorithms, and the status and function of T cells for distinct risk-score groups were evaluated based on T cell exhaustion and dysfunction scores. Additionally, the study also examined differences in clinical characteristics and therapy efficacy between high- and low-risk groups. We found two disulfidptosis-associated clusters, one of which had a poor prognosis and was linked to high immune cell infiltration but impaired T cell function. DRPS showed excellent predictive performance in all four cohorts and could accurately identified disulfidptosis-related molecular subtypes. The DRPS-based risk score was positively associated with poor prognosis, malignant pathological features, high immune cell infiltration levels, and T cell exhaustion or dysfunction, and better respond to immunotherapy and targeted therapy.Additionally, we have identified a close association between ISG20 and disulfidptosis as well as tumor immunity. Our study identified distinct disulfidptosis-related subtypes in ccRCC patients, and constructed the highly accurate and robust DRPS based on an ensemble learning framework, which has critical reference value in clinical decision-making and individualized treatment.And this workalso revealedISG20 exhibits promising potential as a therapeutic target for ccRCC.