Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

Abstract

Abstract Background Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma. Method Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients. Results We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21 + MED10 + PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC = 0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n = 156). Conclusion We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.

Keywords

• GBM
• WGCNA
• TCGA
• Cox proportional hazards regression module