Original Research ARTICLE
Expression profile analysis identifies a novel five-gene signature to improve prognosis prediction of glioblastoma
- 1Xiangya Hospital, Central South University, China
- 2Department of Clinical Laboratory, Hunan Provincial People’s Hospital, China
- 3Central South University, China
Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12–15 months, and the five-year survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival‐related genes were found among these DEGs (p < 0.05). Functional enrichment analysis was performed based on these survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3, IGFBP2 and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five-gene signature was constructed. Based on this risk score system, patients in the high‐risk group had significantly poorer survival results than those in the low‐risk group. Moreover, with the assistance of GEPIA (http://gepia.cancerpku.cn), all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co-expression network of the five genes was constructed based on weighted gene co-expression network analysis (WGCNA). Finally, this five-gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.
Keywords: Glioblastoma, Differentially expressed genes, Gene signature, prognosis, TCGA, GEO
Received: 20 Dec 2018;
Accepted: 17 Apr 2019.
Edited by:Monica Bianchini, University of Siena, Italy
Reviewed by:Sen Peng, Translational Genomics Research Institute, United States
Nitish K. Mishra, University of Nebraska Medical Center, United States
Max Shpak, St David's Medical Center, United States
Copyright: © 2019 Yin, Tang, Zhou, Cao, Li, Fu, Wu and Jiang. 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: Prof. Xingjun Jiang, Central South University, Changsha, China, email@example.com