Identification of a Specific Gene Module for Predicting Prognosis in Glioblastoma Patients - Tang, Xiangjun; Xu, Pengfei; Wang, Bin; Luo, Jie; Fu, Rui; Huang, Kuanming; Dai, Longjun; Lu, Junti; Cao, Gang; Peng, Hao; Zhang, Li; Zhang, Zhaohui; et al. (Frontiers in Oncology, 2019)
Introduction: Glioblastoma (GBM) is the most common and the most malignant variant in the intrinsic glial brain tumors. The poor prognosis of GBM has not significantly improved despite the development of innovative diagnostic methods and new therapies. Therefore, understanding the molecular mechanism of aggressive behavior of GBM and finding appropriate prognostic markers and therapeutic targets may lead to early diagnosis, appropriate therapies, and reliable prognosis Methods: We used weighted gene co-expression network analysis to construct gene co-expression network in 524 glioblastoma samples from the cancer genome atlas (TCGA).Then a riskscore was constructed based on four module genes and patients’ overall survival. The prognostic and predictive accuracy of riskscore were verified in GSE16011 cohort and REMBRANDT cohort. Results: We identified a gene module (green module) related to prognosis. Then, the 4 hub genes were performed multivariate Cox analysis and construct a Cox proportional hazards regression model from 524 glioblastoma patients. The risk score for predicting survival time was calculated with a formula based on the top four genes in the green module: risk score = (0.00889 × EXPCLEC5A) + (0.0681 × EXPFMOD) + (0.1724 × EXP FKBP9) + (0.1557 × EXPLGALS8). The five-year survival rate of the high-risk group was significantly lower than that of the low-risk group. Conclusions: This study demonstrated the potential application of weighted gene co-expression network analysis-based gene prognostic model for predicting survival outcome of glioblastoma patients.