Construction of a prognostic model for lung adenocarcinoma based on disulfidptosis-related lncRNAs

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, and it has a high incidence and poor prognosis. Disulfidptosis is a novel form of death induced by disulfide stress caused by excessive intracellular cystine accumulation under glucose starvation conditions. This study investigated the significance of disulfidptosis-related long non-coding RNAs (DRlncRNAs) in the risk assessment and prognosis prediction of LUAD.

RNA sequencing data and clinical information of LUAD patients were obtained from The Cancer Genome Atlas database. Differentially expressed genes associated with disulfidptosis were screened using univariate Cox regression analysis. A prognostic model was constructed using the least absolute shrinkage and selection operator and the Cox regression analysis to classify patients into high- and low-risk groups. Time-dependent receiver operating characteristic, C-index, and Kaplan-Meier curves were plotted and compared to evaluate the predictive ability of the prognostic model. Functional gene set enrichment analysis (GSEA) and single-sample GSEA were used to explore the characteristics of enrichment pathways, immune-related functions, and treatment response in the high- and low-risk groups.

A risk prognostic model was constructed consisting of eight DRlncRNAs (ATXN1-AS1, AC018645.3, AC096733.2, AL049836.1, LINC01711, AF131215.5, AC027288.1, and AL606489.1). Univariate and multifactorial Cox analyses showed that the model was a prognostic factor independent of multiple clinicopathologic parameters.

The developed 8-lncRNA prognostic model serves as a valid biomarker for predicting LUAD prognosis and provides potential therapeutic insights. Targeting DRlncRNAs may contribute to improved prognosis and guide future therapeutic strategies.