Research Paper Volume 12, Issue 21 pp 22122—22138

Weighted gene correlation network analysis identifies microenvironment-related genes signature as prognostic candidate for Grade II/III glioma

Yong Li1, *, , Gang Deng1, *, , Huikai Zhang1, , Yangzhi Qi1, , Lun Gao1, , Yinqiu Tan1, , Ping Hu1, , Yixuan Wang1, , Baohui Liu1, *, , Qianxue Chen1, *, ,

  • 1 Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuchang, Wuhan 430060, Hubei, P.R. China
* Equal contribution

Received: April 8, 2020       Accepted: September 4, 2020       Published: November 7, 2020
How to Cite

Copyright: © 2020 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Glioma is the most common malignant tumor in the central nervous system. Evidence shows that clinical efficacy of immunotherapy is closely related to the tumor microenvironment. This study aims to establish a microenvironment-related genes (MRGs) model to predict the prognosis of patients with Grade II/III gliomas. Gene expression profile and clinical data of 459 patients with Grade II/III gliomas were extracted from The Cancer Genome Atlas. Then according to the immune/stromal scores generated by the ESTIMATE algorithm, the patients were scored one by one. Weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network to identify potential biomarkers for predicting the prognosis of patients. When adjusting clinical features including age, histology, grading, IDH status, we found that these features were independently associated with survival. The predicted value of the prognostic model was then verified in 440 samples in CGGA part B dataset and 182 samples in CGGA part C dataset by univariate and multivariate cox analysis. The clinical samples of 10 patients further confirmed our signature. Our findings suggested the eight-MRGs signature identified in this study are valuable prognostic predictors for patients with Grade II/III glioma.


GBM: glioblastoma multiforme; OS: overall survival; WGCNA: weighted gene co-expression network analysis; TME: tumor microenvironment; TCGA: the Cancer Genome Atlas; CGGA: Chinese Glioma Genome Atlas; FPKM: Fragments per kilobase of exon per million reads mapped; HRs: hazard ratios; TOM: topological overlap measure; LASSO: least absolute shrinkage and selection operator; ROC: receiver operating characteristic; GO: Gene Ontology; BP: biological processes; CC: cellular components; MF: molecular functions; KEGG: Kyoto Encyclopedia of Genes and Genomes.