A 6-Gene Risk Signature Predicts Survival of Glioblastoma Multiforme

Background. This study aims to develop novel signatures for glioblastoma multiforme (GBM). Methods. GBM expression profiles from The Cancer Genome Atlas (TCGA) were downloaded and DEGs between tumor and normal samples were identified by differential expression analysis (DEA). A risk signature was developed by applying weighted gene coexpression network analysis (WGCNA) and Cox regression analysis. Patients were divided into high and low risk group, followed by evaluating the performance of the signature via Kaplan-Meier curve analysis. In addition, the prognostic significance of the signature was further validated using an independent validation dataset from Chinese Glioma Genome Atlas (CGGA). DEGs between high and low risk group were subjected to functional annotation. Results. A total of 748 DEGs were identified between primary tumor and normal samples. Following WGCNA and Cox regression analysis, 6 DEGs were identified and used to construct a risk signature. The signature showed high performance in both training and validation dataset. Subsequently, 397 DEGs were identified between high and low risk group. These DEGs were mainly enriched in terms related to calcium signaling, cAMP-mediated signaling, and synaptic transmission. Conclusions. The risk signature may contribute to GBM diagnosis in future clinical practice.