AUTHOR=Yin Wen , Tang Guihua , Zhou Quanwei , Cao Yudong , Li Haixia , Fu Xianyong , Wu Zhaoping , Jiang Xingjun TITLE=Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma JOURNAL=Frontiers in Genetics VOLUME=10 YEAR=2019 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00419 DOI=10.3389/fgene.2019.00419 ISSN=1664-8021 ABSTRACT=

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 5 years 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.cancer-pku.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.