Unraveling disulfidptosis for prognostic modeling and personalized treatment strategies in lung adenocarcinoma.

Aim: To construct and identify a prognostic and therapeutic signature based on disulfidptosis-related genes in lung adenocarcinoma.

Methods: Bioinformatic analysis was performed to assess the differential expression of disulfidptosis-related genes between cancerous and control samples from The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) database. Survival analysis, immune cell infiltration assessment, and examination of oncogenic pathways were performed to uncover potential clinical implications of disulfidptosis gene expression. Differential gene expression analysis between subtypes facilitated the development of a prognostic model using a combination of genes associated with survival. A nomogram was further created using independent clinical and molecular factors.

Results: We identified the significant upregulation of ten disulfidptosis-related genes and delineated two distinct subtypes, C1 and C2. Subtype C2 was associated with prolonged survival. Then, prognostic modeling utilizing six genes (TXNRD1, CPS1, S100P, SCGB3A1, CYP24A1, NAPSA) demonstrated predictive power in both training and validation datasets. The nomogram, incorporating the risk model with clinical features, provided a reliable tool for predicting one-year (AUC 0.77), three-year (AUC 0.75), and five-year (AUC 0.78) survival rates. Additionally, chemotherapy sensitivity analysis highlighted significant resistance in the high-risk group, primarily associated with subtype C1.

Conclusion: Our study reveals distinct LUAD subtypes, offers a robust prognostic model, and underscores clinical implications for personalized therapy based on disulfidptosis-related genes expression profiles.