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

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) relat

SUBMITTER: Xu P 

PROVIDER: S-EPMC6211550 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature