Abstract #3192

HRMAS-NMR and Machine learning assisted untargeted Serum Metabolomics identified a panel of circulating biomarkers for detection of glioma.

SAFIA FIRDOUS^1,2, Zubair Nawaz³, Leo Ling Cheng⁴, and Saima Sadaf²

¹Faculty of Rehabilitation and Allied Health Sciences, Riphah International University, Lahore, Pakistan, ²School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan, ³Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan, ⁴Radiopahtological Unit, Massachusetts General Hospital, Boston, MA, United States

Synopsis

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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