Mining prognostic marker of glioma based on weighted gene co-expression network analysis

Chunyu Zhang; Liguo YE; Long Wang; Yinqiu Tan; Fanen Yuan; Ye Tao; Qianxue Chen; Daofeng Tian

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Mining prognostic marker of glioma based on weighted gene co-expression network analysis

VernacularTitle:基于加权基因共表达网络分析挖掘脑胶质瘤预后标志
Author: Chunyu ZHANG ; Liguo YE ; Long WANG ; Yinqiu TAN ; Fanen YUAN ; Ye TAO ; Qianxue CHEN ; Daofeng TIAN
From: Journal of Chinese Physician 2021;23(4):529-533
CountryChina
Language:Chinese
Abstract: Objective:To identify effective biomarkers for glioma patients.Methods:The mRNA expression profiles of 464 glioma patients with complete clinical follow-up information were downloaded from the Chinese Glioma Genome Atlas (CGGA). Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules related to World Health Organization (WHO) grading of glioma, and univariate and multivatiate Cox regression analysis were performed to identify gliomas survival-related genes.Results:In weighted gene co-expression analysis, the module Brown was significantly positively correlated with glioma WHO stage ( r=0.55, P<0.05). In univariate analysis, five genes (TAGLN2, IGFBP2, METTL7B, ARAP3, PLAT) that were most significantly associated with clinical prognosis were selected for multivariate survival analysis, and the prognosis model was established to calculate the risk score. The receiver operating characteristic curve (ROC) confirmed that the risk score had high accuracy in predicting the 1-, 3-, 5-year survival rate of glioma patients. The above survival analysis results were verified in the Cancer Genome Atlas (TCGA) database. Conclusions:We use mRNA expression profiles to establish prognostic markers for gliomas to assess the overall survival of patients with glioma.