Development and functional validation of a disulfidoptosis-related gene prognostic model for lung adenocarcinoma based on bioinformatics and experimental validation.

Abstract

Background: Disulfidoptosis is increasingly linked to cancer progression, yet its immunological impacts and prognostic value in lung adenocarcinoma (LUAD) remain poorly understood. This study aims to delineate the predictive significance of disulfidoptosis-related genes (DRGs) in LUAD, their potential as therapeutic targets, and their interaction with the tumor microenvironment.

Methods: We analyzed the expression profiles of 23 DRGs and survival data, performing consensus clustering to identify molecular subtypes. Survival analysis and gene set variation analysis (GSVA) were used to explore cluster differences. Key DRGs were selected for Cox and LASSO regression to develop a prognostic model. Tensin4 (TNS4), a key gene in the model, was further evaluated through immunohistochemistry (IHC) in LUAD and normal tissues and gene knockdown experiments in vitro.

Results: Two clusters were identified, with 225 differentially expressed genes. A six-gene signature was developed, which classified LUAD patients into high- and low-risk groups, showing significant survival differences. The risk score independently predicted LUAD prognosis and correlated with immunotherapy responses. IHC showed elevated TNS4 levels in LUAD tissues, while in vitro TNS4 knockdown reduced both cell proliferation and migration.

Conclusion: This study highlights the role of DRGs in LUAD, with a validated gene signature offering new avenues for targeted therapies, potentially improving LUAD treatment outcomes.