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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.