Machine learning identified the application of Disulfidoptosis-Related Ferroptosis Genes Guinness in Lung Adenocarcinoma

Disulfidoptosis, a recently recognized form of Regulated Cell Death (RCD), is known to play a crucial role in the development and progression of lung adenocarcinoma (LUAD). This study employed correlation analysis to identify 21 ferroptosis-related genes associated with disulfidoptosis, subsequently utilizing these genes to distinguish between two distinct groups characterized by contrasting prognostic and immune cell infiltration features. A risk model consisting of 17 DFG-related genes (SOD1, AKT1S1, TMPRSS11E, EPHX3, CPS1, PAK1, PSMB1, CDH17, NLRP2, HSD17B14, MRPL16, LACTB2, DEDD2, MCEMP1, LCAL1, VSTM2L, FLNC) was constructed through univariate Cox analysis and Lasso regression analysis. Within the TCGA cohort, the prognostic significance of these risk features was established as independent factors. Furthermore, a nomogram was developed to predict individual overall survival rates at 1, 3, and 5 years; with an AUC value of 0.833 for the first year, indicating its marked clinical utility. Conversely, high-scoring attributed encompass tumors in advanced stages and lower levels of cell differentiation. Furthermore, our observations suggested that patients in the high-risk group may potentially derive benefits from the administration of drugs such as 5-Fluorouracil.In conclusion, DFG holds potential for assessing patient prognosis, immune characteristics, and treatment outcomes. The findings of this study offer novel insights for clinical application and immune-based therapies.