Exploring the Role of Disulfidptosis-Related Genes in GBMLGG Prognosis: A Comprehensive Pan-Cancer Study

Abstract

Abstract Background Disulfidptosis, a novel programmed cell death (PCD) driven by disulfide stress, has emerged as a potential player in various cancer dynamics. However, its implications in Glioma (GBMLGG) prognosis and immunotherapy response remain an uncharted territory. Results An extensive analysis on 15 disulfidptosis-associated genes across multiple cancers highlighted their diverse prognostic values. Specifically for GBMLGG, these genes offered unique non-clustered grouping with profound significance in prognosis differentiation. Out of these, seven pivotal genes were pinpointed using a robust machine learning framework encompassing 101 algorithm combinations. Their prognostic reliability was underscored through receiver operating characteristic curves and Kaplan-Meier (KM) analyses, both presenting highly satisfactory outcomes. The culmination of this work led to the formulation of a predictive nomogram. In-depth correlations were established between these target genes and key tumor-immunity factors, with 13 types of immune cells and 19 immune checkpoint genes showcasing significant ties. Single-cell analysis provided further validation to these findings. Conclusions This study underscores the intricate relationship between disulfidptosis-associated genes and GBMLGG prognosis. The derived nomogram, based on meticulously selected target genes through machine learning, exhibited remarkable accuracy across different datasets, offering promising avenues for GBMLGG prognostic strategies. Trial registration: Not applicable