No abstract

The proposed analysis considers aspects of both statistical and biological validation of the glycolysis effect on brain gliomas, at both genomic and metabolic level. 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 towards 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, TP53), oncogenes (CDK4, EGFR, 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.

No abstract

The purpose of this study is to identify reliable sets of metabolic markers that provide accurate classification of complex brain tumors and facilitate the process of clinical diagnosis. Several ratios of metabolites are tested alone or in combination with imaging markers. A wrapper feature selection and classification methodology is studied, employing Fisher's criterion for ranking the markers. The set of extracted markers that express statistical significance is further studied in terms of biological behavior with respect to the brain tumor type and grade. The outcome of this study indicates that the proposed method by exploiting the intrinsic properties of data can actually reveal reliable and biologically relevant sets of metabolic markers, which form an important adjunct toward a more accurate type and grade discrimination of complex brain tumors

The aim of this study is to reveal the discriminative potential of energy related metabolites in brain gliomas classification. The proposed analysis considers two aspects, the statistical and biological verification of metabolites' effects. In particular, Magnetic Resonance Spectroscopic Imaging (MRSI) is first employed for the statistical evaluation of metabolites. Five of the identified significant metabolites, namely glucose, pyruvate, lactate, alanine and lipids, are involved in the energy production process, necessary for the survival of the cell. In the second stage of analysis, we consider specific metabolic pathways like glycolysis, lactate fermentation, citric acid cycle and lipogenesis in order to evaluate the role of these metabolites in the energy production process. The results of the proposed double process have shown that these five metabolites are capable to discriminate the three types of gliomas provided. Low grade glioma (GR2) can be discriminated from intermediate grade ones (GR3) with accuracy of 86%. Intermediate grade gliomas versus high grade ones (GR4) with accuracy of 98% and finally low grade versus high grade with accuracy of 100%. Accuracy was extensively evaluated with the area under the ROC (AUROC) global metric.