Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma.

Wen Yin,Haixia Li,Zhaoping Wu,Yudong Cao,Guihua Tang,Quanwei Zhou,Xianyong Fu,Xingjun Jiang

doi:10.3389/fgene.2019.00419

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.

Highlights

Glioblastoma multiforme (GBM) is the most common and aggressive primary central nervous system malignant tumor with high morbidity and mortality
By using Cox regression, we developed a five-gene signature based risk score to demonstrate the association between gene expression and the prognosis of GBM
In The Cancer Genome Atlas (TCGA) dataset, every overlapped differentially expressed genes (DEGs) was evaluated by univariate Cox regression survival analysis

Summary

Introduction

Glioblastoma multiforme (GBM) is the most common and aggressive primary central nervous system malignant tumor with high morbidity and mortality. Some molecular markers including MGMT (O6-methylguanine DNA methyltransferase), IDH (isocitrate dehydrogenase), EGFR (epidermal growth factor receptor), and PTEN (phosphatase and tensin homolog) have been routinely tested in GBM patients clinically (van den Bent et al, 2017; Binabaj et al, 2018). These molecular signatures have contributed to personalized therapeutic approaches and targeted anti-GBM therapies (Huang et al, 2017; Szopa et al, 2017). Considering the poor prognosis of GBM, novel molecular biomarkers and new therapeutic strategies are still urgently required to elucidate the mechanisms of GBM or increase overall patient survival

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