Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation - Wang, Yuzhi; Xu, Yunfei; Liu, Chunyang; Yuan, Chengliang; Zhang, Yi (Frontiers in Immunology, 2023)
Disulfidptosis is a newly identified variant of cell death characterized by disulfide accumulation, which is independent of ATP depletion. Accordingly, the latent influence of disulfidptosis on the prognosis of lung adenocarcinoma (LUAD) patients and the progression of tumors remains poorly understood. In our research, we characterized the transcriptional and genetic alterations of disulfidptosis regulators (DRs) in LUAD, resulting in the identification of two distinct DR clusters. Utilizing the differentially expressed genes (DEGs) derived from these clusters, we integrated 10 machine learning algorithms into 101 combinations, ultimately selecting the "Least Absolute Shrinkage and Selection Operator (LASSO) + Random Survival Forest (RFS)" algorithm to develop a disulfidptosis score (DS) based on the average C-index across different cohorts. Our model effectively stratified LUAD patients into high- and low-DS subgroups, with this latter demonstrating superior overall survival (OS), a reduced mutational landscape, enhanced immune status, and increased sensitivity to immunotherapy. Notably, the predictive accuracy of DS outperformed the published LUAD signature and clinical features. Finally, we validated the DS expression using clinical samples and found that inhibiting KYNU suppressed LUAD cells proliferation, invasiveness, and migration in vitro. In conclusion, the DR-based scoring system that we developed enabled accurate prognostic stratification of LUAD patients and provides important insights into the molecular mechanisms and treatment strategies for LUAD.