Development and Validation of a Novel Prognostic Model for Glioma Based on Immune-Related Genes

Abstract

Background: Glioma is the most common primary tumor of the central nervous system with a poor survival rate. There has been evidence showing that the immune microenvironment influences glioma patient outcomes. Therefore, we established a novel immune-related gene prognosis model to explore the relationship between immunity and glioma prognosis.Methods: The immune scores of samples in the TCGA and CGGA databases were analyzed by ESTIMATE, and then immune-related central genes were identified by weighted gene co-expression network analysis (WGCNA). Seven immune-associated genes were selected to develop a risk signature based on regression with the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate Cox regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and Kaplan-Meier analysis were used to evaluate the model. Gene set enrichment analysis (GSEA) was performed to analyze the molecular characteristics. Immunological profiles were analyzed by CIBERSORT and xCell. The expression levels of COL1A2 in high and low gliomas were analyzed by RT-QPCR and Western blot techniques. MTS, EDU and TRANSWELL methods were used to detect the effects of COL1A2 on glioma cell viability, proliferative capacity, and invasive ability.Results: A valuable prognostic model of immune-related genes(IRGPM) was developed. IRGPM is an independent prognostic factor. Clinical characteristics, molecular function, tumor mutation burden, and immune profiles differed significantly between high- and low-risk groups. COL1A2 expression was higher in high-grade gliomas than in low-grade gliomas and promoted proliferation and invasion of glioma cells.Conclusions: IRGPM is a good independent prognostic factor that is closely associated with glioma progression.