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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Oncol. | doi: 10.3389/fonc.2019.00812

Identification of a specific gene module for predicting prognosis in glioblastoma patients

 Xiangjun Tang1,  Pengfei Xu1, Bing Wang2, Jie Luo2, Rui Fu2, Kuanming Huang2, Longjun Dai2, Junti Lu2, Gang Gao2, Hao Peng2, Li Zhang2* and  Qianxue Chen1*
  • 1Renmin Hospital, Faculty of Medical Sciences, Wuhan University, China
  • 2Taihe Hospital, Hubei University of Medicine, China

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.

Keywords: Glioblastoma, WGCNA, Prognostic model, Cox proportional hazards regression module, nomogram

Received: 19 Apr 2019; Accepted: 08 Aug 2019.

Copyright: © 2019 Tang, Xu, Wang, Luo, Fu, Huang, Dai, Lu, Gao, Peng, Zhang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dr. Li Zhang, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei Province, China,
Prof. Qianxue Chen, Renmin Hospital, Faculty of Medical Sciences, Wuhan University, Wuhan, China,