Metabolic alterations, crucial indicators of glioma development, can be used for detection of glioma before the appearance of fatal phenotype. We have compared the circulating metabolic fingerprints of glioma (n=26) and healthy controls (n=16) to identify a panel of biomarkers for detection of glioma. HRMAS-NMR spectra was obtained from two study groups and data was analysed by ML as well as chemometric methods (PCA and PLSDA). A panel of 38 metabolites was identified by three ML algorithms (logistics regression, extra tree classifier, & random forest), Wilcoxon test (p<0.05), and PLSDA (VIP score>1) which can serve as diagnostic biomarker of glioma.
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