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

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...

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Bibliographic Details
Published in:Scientific reports 2018-10, Vol.8 (1), p.15104-12, Article 15104
Main Authors: Tang, Jing, He, Dian, Yang, Pingrong, He, Junquan, Zhang, Yang
Format: Article
Language:English
Subjects:
38/39
38/61
631/1647/48
631/337/2019
Brain cancer
Brain tumors
Cell cycle
Chemotherapy
Gene expression
Genomes
Glioblastoma
Humanities and Social Sciences
Immune response
Medical prognosis
Molecular modelling
multidisciplinary
p53 Protein
Patients
Prognosis
Science
Science (multidisciplinary)
Signal transduction
Surgery
Survival analysis
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Summary: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 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-33323-z