Abstract
Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.
Highlights
Malignant brain tumors such as glioma and glioblastoma multiforme (GBM), arising from the glial cells of the central nervous system (CNS), are among the most lethal forms of human cancers
Untargeted metabolic profiling was performed by HRMAS-NMR using plasma samples (n = 42) obtained from low-grade (LGG, n = 9) and high-grade glioma (HGG, n = 17)
Out of the 93 initially registered LGG and high-grade glioma patients (HGG) cases, only those patients were included in the study who were confirmed by histopathological analysis of tumor tissue and were graded according to the World Health Organization (WHO) classification of brain tumors
Summary
Malignant brain tumors such as glioma and glioblastoma multiforme (GBM), arising from the glial cells of the central nervous system (CNS), are among the most lethal forms of human cancers. Their aggressive nature, infiltrating growth, and a two-fold blood–brain and blood–brain–tumor barrier (unlike most other cancers) make them both difficult to diagnose early and challenging to treat [1,2,3]. Our understanding of the intricate molecular networks and/or their crosstalks that initiate the successive, yet aggressive series of proliferative events in gliomas is very limited, there is increasing evidence suggesting that a complex interplay of chromosomal alterations (gene-gene fusions), genetic aberrations (point mutations), and epigenetic modifications (methylations) contributes to the tumor biogenesis [3,9,10]. Several research groups have demonstrated that, in order to perform (i) harvesting of energy and replenishing the ‘nutrient (glucose) sink’ for a continuous, unchecked cellular proliferation;
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