Exploring metabolic signatures in urine using NMR for improved prognosis of gliomas

Gliomas represent the tumors of the central nervous system that originate from glial cells. Overall survival predictions and treatment regimen selection are based on accurate tumor diagnosis and grading. However, the diagnosis of glioma remains critically dependent on either invasive biopsies or advanced imaging.

This exploratory study aims to assess the diagnostic potential of urine specimens for discriminating gliomas from controls and identify the dysregulated pathways in a North Indian cohort. Urine is an ideal non-invasive candidate, requires no prior preparation, and considerably increases patient compliance.

Urine samples from 50 glioma patients were analysed with ¹H NMR (Nuclear Magnetic Resonance) spectroscopy and compared with those of healthy controls. Statistical analysis was performed in MetaboAnalyst 6.0 to identify significantly perturbed metabolites. Diagnostic performance was assessed using the Receiver Operating Characteristic (ROC) curve, and the Random Forest model was used to evaluate classification accuracy. Pathway enrichment and topology analysis based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database were performed to identify dysregulated pathways.

¹H NMR metabolic analysis of urine samples revealed seven statistically significant (p < 0.05) metabolites namely acetate, pyruvate, creatinine, dimethylamine, glutamine, alanine and carnitine. This panel of metabolites displayed excellent diagnostic capability with an Area Under the Curve of 0.90 as measured by a multivariate ROC curve. The random forest model efficiently differentiated glioma from control samples using significant metabolites. Disruption in the primary energy pathways of the body and in the metabolism of major amino acids was observed in the pathway analysis.

Integration of these urinary signatures into current clinical practice can serve as an additional diagnostic tool and a non-invasive screening method for populations at risk. They can also be monitored in real time, thus aiding in adaptive treatment strategies and therapy assessment.