Genome-wide expression profiling of glioblastoma using a large combined cohort

Jing Tang,Pingrong Yang,Junquan He,Yang Zhang,Dian He

doi:10.1038/s41598-018-33323-z

Jing Tang, Pingrong Yang + Show 3 more

Open Access

https://doi.org/10.1038/s41598-018-33323-z

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Abstract

Glioblastomas (GBMs), are the most common intrinsic brain tumors in adults and are almost universally fatal. Despite the progresses made in surgery, chemotherapy, and radiation over the past decades, the prognosis of patients with GBM remained poor and the average survival time of patients suffering from GBM was still short. Discovering robust gene signatures toward better understanding of the complex molecular mechanisms leading to GBM is an important prerequisite to the identification of novel and more effective therapeutic strategies. Herein, a comprehensive study of genome-scale mRNA expression data by combining GBM and normal tissue samples from 48 studies was performed. The 147 robust gene signatures were identified to be significantly differential expression between GBM and normal samples, among which 100 (68%) genes were reported to be closely associated with GBM in previous publications. Moreover, function annotation analysis based on these 147 robust DEGs showed certain deregulated gene expression programs (e.g., cell cycle, immune response and p53 signaling pathway) were associated with GBM development, and PPI network analysis revealed three novel hub genes (RFC4, ZWINT and TYMS) play important role in GBM development. Furthermore, survival analysis based on the TCGA GBM data demonstrated 38 robust DEGs significantly affect the prognosis of GBM in OS (p < 0.05). These findings provided new insights into molecular mechanisms underlying GBM and suggested the 38 robust DEGs could be potential targets for the diagnosis and treatment.

Highlights

Glioblastomas (GBMs) are the most common and highly aggressive malignant brain tumors[1,2]
The most comprehensive set of genome-scale mRNA expression data was constructed by combining GBM and normal samples from multiple studies
Based on large-scale gene expression data of GBMs, we have identified 147 robust differential expression genes, which showed the underlying gene expression level differences between NC and GBMs samples

Summary

Introduction

Glioblastomas (GBMs) are the most common and highly aggressive malignant brain tumors[1,2]. Understanding the molecular mechanism of GBMs is an important prerequisite for discovering a novel and effective therapeutic strategy[5,6,7,8]. The number of normal samples is inadequate for directly identifying the robust differential expression genes associated with GBM. Functional analysis base on these robust gene sets was performed and certain deregulated gene expression programs (e.g. cell cycle, immune response, p53 signaling pathway) are identified in glioblastoma process. The identified robust genes may facilitate the understanding of glioblastoma’s etiology and the discovery of novel hub genes, transcriptional factors and two microRNA driving GBM tumorgenicity would have therapeutic implications

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