Journal
JOURNAL OF PROTEOME RESEARCH
Volume 18, Issue 3, Pages 1316-1327Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.8b00926
Keywords
prostate cancer; biochemical recurrence; metabolomics; lipidomics; liquid chromatography mass spectrometry; nuclear magnetic resonance spectroscopy
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Funding
- Vasser-Wooley endowed chair
- Evans County CARES foundation
- Georgia Research Alliance
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Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an omics or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients (n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.
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