Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Journal: Briefings in bioinformatics
Published Date: Sep 2, 2021

Abstract

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

Authors

  • Md Habibur Rahman
    1Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.
  • Humayan Kabir Rana
    Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh.
  • Silong Peng
  • Xiyuan Hu
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Julian M W Quinn
    Bone Biology Divisions, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.
  • Mohammad Ali Moni
    Bone Biology Divisions, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; The University of Sydney, School of Medical Sciences, Faculty of Medicine & Health, NSW 2006, Australia. Electronic address: mohammad.moni@sydney.edu.au.

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