Establishment and validation of a prognostic scoring model based on disulfidptosis-related long non-coding RNAs in stomach adenocarcinoma

Abstract

Background Stomach adenocarcinoma (STAD), a frequently occurring gastrointestinal tumour, is often detected late and has a poor prognosis. Long non-coding RNAs (lncRNAs) significantly affect tumour development. Recent studies have identified disulfidptosis as a previously unexplained form of cell death. Herein, we aimed to examine the predictive value of disulfidptosis-related lncRNA models for the clinical prognosis and immunotherapy of STAD. Methods STAD-related transcriptomic data were obtained from The Cancer Genome Atlas (TCGA), whereas genes associated with disulfidptosis were identified from previously published papers. A risk prediction model for disulfidptosis-related lncRNAs was developed using the Cox regression and least absolute shrinkage selection algorithm methods. The accuracy of the model was confirmed using calibration curves, and the biological functions were analysed using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). Finally, the tumour mutation burden (TMB) and tumour immune dysfunction and exclusion (TIDE) algorithms were used to screen drugs that are sensitive to STAD. Results The risk prediction models were constructed using seven disulfidptosis-related lncRNAs. The validated results were consistent with the predicted ones, with significant survival differences. When combined with clinical data, the risk scores were used as independent prognostic markers. Based on the tumour mutation load, the high-risk patient group had a poorer survival rate as compared with the low-risk patient group. Further studies were conducted to understand the different groups’ inconsistent responses to immune status; subsequently, relatively sensitive drugs were identified. Conclusions Overall, seven markers of disulfidptosis-related lncRNAs associated with STAD were found to facilitate prognostic prediction, suggesting new ideas for immunotherapy and clinical applications.