Disulfidptosis-related long non-coding RNAs predict prognosis and indicate therapeutic response in non-small cell lung carcinoma

Abstract Objectives Disulfidptosis is a novel form of cell death, whose modulation in tumor cells may present a promising therapeutic strategy for cancer treatment. However, the role of disulfidptosis-related long non-coding RNAs (lncRNAs) in non-small cell lung carcinoma (NSCLC) remains poorly elucidated. This study aims to investigate the prognostic significance of disulfidptosis-related lncRNAs (DRLs) and reveal their relationship to the immune microenvironment of NSCLC. Methods DRLs were identified through co-expression analysis of NSCLC transcriptomic data obtained from the Genomic Data Commons (GDC) data portal. The DRLs prognostic signature (DRLPS) was established using the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Samples were separated into high-DS and low-DS groups based on the median disulfidptosis score (DS) of DRLPS. Integrated analyses were then implemented to unveil the association between DRLs and NSCLC microenvironment. These involved the evaluation of functional enrichments, immune cell infiltrations, genetic alterations, and drug sensitivity. Results A prognostic signature was developed based on six prognostic DRLs, which are AL606489.1, LINC00857, AP003555.1, AP000695.1, AC113346.1, and LINC01615. The Kaplan–Meier survival curves demonstrated the significant association between DRLPS and NSCLC prognosis. The functional enrichment assessment revealed the pivotal involvement of DRLs in immune regulation and metabolism in NSCLC. The low-DS and high-DS subgroups of NSCLC patients exhibited distinct differences in terms of immune infiltration and tumor mutation burden. The potential to predict immunotherapy benefit and drug sensitivity in NSCLC treatments was observed in DRLPS. Conclusions In this study, disulfidptosis-related lncRNAs were identified and their roles in NSCLC were revealed. A novel prognostic signature with the potential to predict drug response in NSCLC treatment was developed.