• DocumentCode
    12463
  • Title

    On the Relevance of Glycolysis Process on Brain Gliomas

  • Author

    Kounelakis, M.G. ; Zervakis, M.E. ; Giakos, G.C. ; Postma, G.J. ; Buydens, L.M.C. ; Kotsiakis, X.

  • Author_Institution
    Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
  • Volume
    17
  • Issue
    1
  • fYear
    2013
  • fDate
    Jan. 2013
  • Firstpage
    128
  • Lastpage
    135
  • Abstract
    The proposed analysis considers aspects of both statistical and biological validation of the glycolysis effect on brain gliomas, at both genomic and metabolic levels. In particular, two independent datasets are analyzed in parallel: one engaging genomic (microarray expression) data and the other metabolomic (magnetic resonance spectroscopy imaging) data. The aim of this study is twofold. First to show that, apart from the already studied genes (markers), other genes such as those involved in the human cell glycolysis significantly contribute in gliomas´ discrimination. Second, to demonstrate how the glycolysis process can open new ways toward the design of patient-specific therapeutic protocols. The results of our analysis demonstrate that the combination of genes participating in the glycolytic process (ALDOA, ALDOC, ENO2, GAPDH, HK2, LDHA, LDHB, MDH1, PDHB, PFKM, PGI, PGK1, PGM1, and PKLR) with the already known tumor suppressors (PTEN, Rb, and TP53), oncogenes (CDK4, EGFR, and PDGF), and HIF-1 enhance the discrimination of low- versus high-grade gliomas, providing high prediction ability in a cross-validated framework. Following these results and supported by the biological effect of glycolytic genes on cancer cells, we address the study of glycolysis for the development of new treatment protocols.
  • Keywords
    biochemistry; biomedical MRI; brain; cancer; cellular biophysics; genetics; genomics; tumours; ALDOA; ALDOC; CDK4; EGFR; ENO2; GAPDH; HIF-1; HK2; LDHA; LDHB; MDH1; PDGF; PDHB; PFKM; PGI; PGK1; PGM1; PKLR; PTEN; Rb; TP53; biological validation; brain glioma; cancer cell; genomic data; glycolysis process; glycolytic gene; glycolytic process; high-grade glioma discrimination; human cell glycolysis; magnetic resonance spectroscopy imaging; metabolic level; metabolomic data; microarray expression; oncogenes; patient-specific therapeutic protocol; statistical validation; tumor suppressor; Accuracy; Bioinformatics; Cancer; Genomics; Metabolomics; Sugar; Support vector machines; Brain gliomas discrimination; genomics; glycolysis; metabolomics; Brain Neoplasms; Cluster Analysis; Computational Biology; Databases, Factual; Gene Expression Profiling; Glioma; Glycolysis; Humans; Magnetic Resonance Spectroscopy; Metabolome; Support Vector Machines;
  • fLanguage
    English
  • Journal_Title
    Biomedical and Health Informatics, IEEE Journal of
  • Publisher
    ieee
  • ISSN
    2168-2194
  • Type

    jour

  • DOI
    10.1109/TITB.2012.2199128
  • Filename
    6199982