Urinary Metabolomic Profiling of Patients with Glioblastoma Multiforme
- *Corresponding Author:
- Kevin Camphausen
Radiation Oncology Branch
National Cancer Institute
National Institutes of Health
10 Center Drive B2-3561
Bethesda, MD 20892, USA
E-mail: [email protected]
Received Date: February 11, 2013; Accepted Date: February 23, 2013; Published Date: February 26, 2013
Citation: Tandle AT, Shankavaram U, Brown MV, Ho J, Graves C, et al. (2013) Urinary Metabolomic Profiling of Patients with Glioblastoma Multiforme. J Proteomics Bioinform S6:003. doi: 10.4172/jpb.S6-003
Copyright: © 2013 Tandle AT, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
With advances in mass spectrometry, gas- and liquid chromatography, it is now feasible to analyze a variety of biofluids for metabolomic changes in a variety of diseases. Recent studies have shown that unique alterations in major metabolic pathways may be present. In this study, the urinary metabolic signature of patients diagnosed with Glioblastoma Multiforme (GBM) was characterized. Metabolomic analysis identified 368 compounds, with forty-six having a significant difference between the samples from patients with GBM, compared to samples from healthy controls. Random forest analysis separated samples from patients with GBM and healthy controls with a 77% accuracy. Using matched urine samples from patients with GBM undergoing chemoirradiation, comparing their sample before irradiation (pre-RT) and after irradiation (post-RT), several N-acetylated compounds were identified as providing the greatest level of distinction between the pre- and post-RT samples. An accumulation of TCA cycle intermediates indicating changes in the mitochondrial oxidative processes was also observed. In summary, our findings identified 46 compounds that differentiated healthy controls from patients with GBM. These may be useful as diagnostic biomarker candidates and highlight the metabolites associated with the pathophysiology of GBM.