Prognostic and Immunological Analysis of Disulfidoptosis-Related Ferroptosis Genes in Lung Adenocarcinoma

Disulfidoptosis, a newly identified form of regulated cell death (RCD), significantly influences the progression of lung adenocarcinoma (LUAD). This study identified 340 ferroptosis-related genes strongly correlated with disulfidoptosis through correlation analysis. By intersecting these genes with those from module genes selected via weighted gene co-expression network analysis (WGCNA), 31 genes were found. In the TCGA-LUAD cohort, Kaplan-Meier(KM) survival analysis initially screened these genes, leading to the selection of 6 Disulfidoptosis-Related Ferroptosis (DRF) genes (IL33, SLC2A1, CDCA3, KIF20A, FANCD2, RRM2) through further screening with Random Forest and SVM-RFE. Based on the expression levels of DRF genes, two distinct groups with differing prognostic and immune characteristics were identified. A machine learning-driven signature (MLS) of 24 DRF-related genes was then constructed using the RSF + SuperPC algorithm and validated in the TCGA-LUAD and GSE31210 datasets. Compared with 77 other signatures, MLS demonstrated superior performance in both datasets. A low MLS score was associated with immune activation, higher tumor mutation burden, and better survival probability. Conversely, a high MLS score correlated with poorer prognosis and reduced potential benefit from immune therapy, although treatments like Doramapimod might still offer benefits. The cell cycle pathway was a key factor distinguishing high from low MLS groups. Overall, MLS shows promise for predicting prognosis in LUAD patients and identifying those who might benefit from immune therapy. Additionally, DRF genes have potential clinical value for diagnosing and treating other cancers, as indicated by pan-cancer analysis. q-PCR experiments targeting select DRF genes confirmed their feasibility as diagnostic markers for LUAD.