A novel disulfidptosis-related lncRNA signature predicts prognosis and therapeutic response in low-grade glioma

Abstract

Abstract Background: Low-grade glioma (LGG) is the most common subtype of glioma, and disulfidptosis is a recently defined form of cell death that plays an important role in the development of several malignant cancers. Long noncoding RNAs (lncRNAs) are key molecules and potential biomarkers for the diagnosis and treatment of various tumors. However, the effects of disulfidptosis-related lncRNAs (DSRLs) on LGG remain unclear. The purpose of this study was to develop a new prognostic DSRLs signature for LGG and investigate its underlying biological mechanisms. Methods: We downloaded LGG RNA sequencing profiles, clinical data, and tumor mutational burden (TMB) data from the Cancer Genome Atlas (TCGA) database. The gene expression profiles of the DSRLs were screened. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox analyseswere performed to build a prognostic model for DSRLs.Patients with LGGs were divided into high- and low-risk subgroups based on their risk median value. The prognostic model was further validated using Cox regression, Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) curves, nomograms, C-index, and principal component analysis (PCA). Moreover, the relationships between the model and gene set enrichment analysis (GSEA), immunological function, immune infiltration, m6A methylation, TMB, and drug sensitivity were investigated. Results: In this study, 19 DSRLs were selected to establish a prognostic model. Survival analysis was performed and time-dependent ROC curves were constructed, and the results confirmed the high accuracy of the model in predicting the prognosis of patients with LGG. Univariate and multivariate Cox regression analyses revealed that the risk score was an independent prognostic factor. Furthermore, we discovered substantial disparities in tumor immune characteristics, m6A methylation, TMB, and drug sensitivity between the high- and low-risk groups. Patients with high-risk LGG tend to respond better to immune checkpoint inhibitor (ICI) therapy; however, patients with low-risk LGG were more sensitive to chemotherapeutic drugs. Conclusion: The prognostic model based on 19 disulfidptosis-related lncRNAs can accurately and effectively predict clinical outcomes in patients with LGG. These may be reliable biomarkers for risk stratification, evaluation of possible immunotherapy, and assessment of chemotherapy sensitivity for LGG.