Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma. (28th August 2021)

• Record Type: Journal Article
• Authors: He, Jimin; Zeng, Chun; Long, Yong
• Other Names: Cordeiro Maria N. D.S. Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2021(2021)
• Publication Date: 2021-08-28
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2021/2191709
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67 ; P < 0.001 ) and its genes (n = 1092 ) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001 ). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.