A Risk Classification System With Five-Gene for Survival Prediction of Glioblastoma Patients

Frontiers in Neurology
Yulin WangMinghua Zhuang

Abstract

Objective: Glioblastoma (GBM) is the most common and fatal primary brain tumor in adults. It is necessary to identify novel and effective biomarkers or risk signatures for GBM patients. Methods: Differentially expressed genes (DEGs) between GBM and low-grade glioma (LGG) in TCGA samples were screened out and weight correlation network analysis (WGCNA) was performed to confirm WHO grade-related genes. Five genes were selected via multivariate Cox proportional hazards regression analysis and were used to construct a risk signature. A nomogram composed of the risk signature and clinical characters (age, radiotherapy, and chemotherapy experience) was established to predict 1, 3, 5-year survival rate for GBM patients. Results: One hundred ninety-four DEGs in blue gene module were found to be positively related to WHO grade via WGCNA. Five genes (DES, RANBP17, CLEC5A, HOXC11, POSTN) were selected to construct a risk signature for GBM via R language. This risk signature was identified to independently predict the outcome of GBM patients, as well as stratified by IDH1 status, MGMT promoter status, and radio-chemotherapy. The nomogram was established which combined the risk signature with clinical factors. The results of c-index, ROC cu...Continue Reading

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Citations

Jan 22, 2020·Journal of Cellular Biochemistry·Yuhualei PanYan-Bing Zhu
Feb 18, 2020·Journal of Cellular and Molecular Medicine·Yulin WangMinghua Zhuang
Oct 11, 2020·International Journal of Molecular Sciences·Indre ValiulyteArunas Kazlauskas
Aug 17, 2021·Journal of Oncology·Xisong LiangZhixiong Liu

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Methods Mentioned

BETA
dissection
RNA-seq

Software Mentioned

R
R package WGCNA
R package ggplot2
DAVID
rms
R language
WGCNA
GSEA
R language edgeR
R package caret

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