A novel prognostic risk model construction and immune landscape analysis of gastric cancer based on disulfidptosis-related lncRNAs

Abstract

Abstract Background: Disulfidptosis is a novel form of cell death that induces disulfide stress leading to cell death. Therefore, this may be a new direction for future cancer therapy. More and more studies have shown that long non-coding RNAs (lncRNAs) can regulate gastric cancer-related biological processes. At present, there is no research on disulfidptosis-related lncRNAs (DRLs) in GC. Methods: The Cancer Genome Atlas (TCGA) was used to retrieve information about RNA sequencing data, clinical data, and genetic mutation data of GC patients. RNA sequencing data, clinical data, and genomic mutation data of GC patients were downloaded from the Cancer Genome Atlas. First, TCGA data are randomly assigned to the training set and the validation set. Then, a predictive risk model was built in the training set using univariate and multivariate Cox regression models, as well as the least absolute shrinkage and selection operator (LASSO). The predictive value of the model was verified by receiver operating characteristic (ROC) curves and the concordance index (C-index) in the validation set and the entire set. The univariate and multivariate Cox regression analysis, nomogram, and correlation analysis of clinicopathological characteristics were used to confirm the clinical utility of the prognostic risk model. Finally, we further used tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) scores to evaluate the effectiveness of immunotherapy and analyze the sensitivity of related drugs. Results: Six prognostically-related DRLs (TNFRSF10A-AS1, LINC02829, LINC00460, AL139147.1, IGFL2-AS1, and AC104123.1) were used to construct the model. The overall survival (OS) of the high-risk group and the low-risk group was statistically significant, according to the Kaplan-Meier survival graph. The ROC and C-index show that the model has the good predictive ability. Risk score was revealed to be an independent prognostic factor by univariate and multivariate Cox regression analysis. The prognostic risk model was negatively correlated with TMB. According to the results of TIDE, immunotherapy has a better therapeutic effect on the high-risk group. In addition, the prognostic risk model of GC was significantly correlated with drug sensitivity. Conclusions: In summary, this study identified six DRLs as predictors of GC prognosis, which may be a potential biomarker for predicting drug sensitivity and immunotherapy efficacy of GC in the future.