A 6-Gene Risk Signature Predicts Survival of Glioblastoma Multiforme

Abstract

Background. This study aims to develop novel signatures for glioblastoma multiforme (GBM). Methods. GBM expression profiles from The Cancer Genome Atlas (TCGA) were downloaded and DEGs between tumor and normal samples were identified by differential expression analysis (DEA). A risk signature was developed by applying weighted gene coexpression network analysis (WGCNA) and Cox regression analysis. Patients were divided into high and low risk group, followed by evaluating the performance of the signature via Kaplan-Meier curve analysis. In addition, the prognostic significance of the signature was further validated using an independent validation dataset from Chinese Glioma Genome Atlas (CGGA). DEGs between high and low risk group were subjected to functional annotation. Results. A total of 748 DEGs were identified between primary tumor and normal samples. Following WGCNA and Cox regression analysis, 6 DEGs were identified and used to construct a risk signature. The signature showed high performance in both training and validation dataset. Subsequently, 397 DEGs were identified between high and low risk group. These DEGs were mainly enriched in terms related to calcium signaling, cAMP-mediated signaling, and synaptic transmission. Conclusions. The risk signature may contribute to GBM diagnosis in future clinical practice.

A robust two‐gene signature for glioblastoma survival prediction

“…Recently, five gene‐related signatures have been reported to predict the prognosis of patients with GBM. Therefore, we compared the prognostic value of our two‐gene (GRIA2 and RYR3) signature (hereinafter referred to as GR‐Sig) with that of different gene sets for predicting prognosis of patients with GBM: the 4‐gene (HOXC10, OSMR, SCARA3, and SLC39A10) signature derived from Cao's study 24 (hereinafter referred to as HOSS‐Sig); the 4‐gene (LHX2, MEOX2, SNAI2, and ZNF22) signature derived from Cheng's study 25 (hereinafter referred to as LMSZ‐Sig); the 5‐gene (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) signature derived from Yin's study 26 (hereinafter referred to as PRGIT‐Sig); the 5‐gene (DES, RANBP17, CLEC5A, HOXC11, and POSTN) signature derived from Wang's study 27 (hereinafter referred to as DRCHP‐Sig); the 6‐gene (BPIFB2, HOXA13, LRRC10, NELL1, SDR16C5, and XIRP2) signature derived from Zhao's study 28 (hereinafter referred to as BHLNSX‐Sig).…”

An Immune-Related Gene Pairs Signature for Predicting Survival in Glioblastoma

“…Recently, omics technology and bioinformatics analysis have created an opportunity to identify novel molecular biomarkers and understand potential mechanisms in many tumors. For example, Zhao et al ( 12 ) identified a six-gene risk signature for the outcome prediction of GBM. Several studies have reported immune-related gene pairs (IRGPs) signature in ovarian cancer ( 13 ), liver cancer ( 14 ), and colorectal cancer ( 15 ) and showed well-predictive accuracy.…”

The clinical significance of glutathione peroxidase 2 in glioblastoma multiforme

“…Numerous genes have been validated to participate in the pathogenesis of GBM, and they serve as markers for differentiation and prognosis of GBM [ 3 , 4 ]. For example, overexpression of epidermal growth factor receptor (EGFR) is found in more than 30% of patients with GBM [ 5 ]. GBM tumor cells with EGFR have higher ability of migration and infiltration [ 5 ].…”

“…For example, overexpression of epidermal growth factor receptor (EGFR) is found in more than 30% of patients with GBM [ 5 ]. GBM tumor cells with EGFR have higher ability of migration and infiltration [ 5 ]. And patients with EGFR have a relatively reduced response to therapies, with shorter duration of survival [ 6 ].…”