The role of the glycolysis-related genes in brain gliomas treatment design

IFMBE Proceedings

Volumn 37, Issue , 2011, Pages 377-380

  Kounelakis, M G ^a   Zervakis, M E ^a   Giakos, G C ^b   Kotsiakis, X ^c  

^a TECHNICAL UNIVERSITY OF CRETE   (Greece)

^b The University of Akron   (United States)

^c Chania General Hospital   (Greece)

Author keywords

Brain glioma treatment; Brain gliomas discrimination; Genomics; Glycolysis

Indexed keywords

DATA SETS; FEATURE SELECTION AND CLASSIFICATION; FILTER CRITERIA; GENETIC ALTERATIONS; GENOMICS; GLYCOLYSIS; MICROARRAY EXPRESSIONS; TREATMENT DESIGN;

FEATURE EXTRACTION; GENE THERAPY; PATHOLOGY; SUPPORT VECTOR MACHINES;

TUMORS;

EID: 80455132337     PISSN: 16800737     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-642-23508-5_98     Document Type: Conference Paper

References (17)

1. Shiraishi T, Tabuchi K (2003) Genetic alterations of human brain tumors as molecular prognostic factors. Neuropathology 23:95-108.
2. Furnari F. B et al. (2007) Malignant astrocytic glioma: genetics, biology, and paths to treatment. Gene Dev 21:2683-2710.
3. Ohgaki H, Kleihues P (2009) Genetic alterations and signaling pathways in the evolution of gliomas. Cancer Sci 100:2235-2241.
4. Warburg O (1956) On the origin of cancer cells. Science 123:309-314.
5. Robnik-Sikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53:23-69.
6. Wang Y, Makedon F (2004) Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes for Cancer Classification Using Microarray Data. CSB Proc., IEEE Computational Systems Bioinformatics Conference, Stanford, USA, 2004, pp 497-498.
7. Abe S (2005) Support Vector Machines for Pattern Classification Springer-Verlag, London.
8. Kounelakis M. G et al. (2009) Revealing the metabolic profile of brain tumours for diagnosis purposes. EMBS Proc., 31st Annual International IEEE EMBS Conference, Minnesota, USA, 2009, pp 35-38.
9. Pedersen P. L (2007) Warburg, me and Hexokinase 2: Multiple discoveries of key molecular events underlying one of cancers' most common phenotypes, the "Warburg Effect", i.e., elevated glycolysis in the presence of oxygen. J Bioenerg Biomembr 39:211-222.
10. Tennant D. A et al. (2009) Metabolic transformation in cancer. Carcinogenesis 30:1269-1280.
11. Zhao S et al. (2009) Glioma-Derived Mutations in IDH1 Dominantly Inhibit IDH1 Catalytic Activity and Induce HIF-1a. Science 324:261-265.
12. Semenza G. L et al. (1996) Hypoxia response elements in the aldolase A, enolase 1, and lactate dehydrogenase A gene promoters contain essential binding sites for hypoxia-inducible factor 1. J Biol Chem 20:32529-32537.
13. T. Yanagawa, T. Funasaka, S. Tsutsumi, H. Watanabe and A. Raz, "Novel roles of the autocrine motility factor/phosphoglucose isomerase in tumor malignancy", Endocrine Related Cancer, vol. 11, pp. 749-759, December 2004.
14. Levine A. J, Puzio-Kuter A. M (2010) The Control of the Metabolic Switch in Cancers by Oncogenes and Tumor Suppressor Genes. Science 330:1340-1344.
15. Pelicano H et al. (2006) Glycolysis inhibition for anticancer treatment. Oncogene 25:4633-4646.
16. Kim J. W et al. (2006) HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab 3:177-185.
17. Lee S. M et al. (2009) A nucleocytoplasmic malate dehydrogenase regulates p53 transcriptional activity in response to metabolic stress. Cell Death Differ 16:738-748.