Metabolomics-Derived Prostate Cancer Biomarkers: Fact or Fiction?

Despite continuing research for precise
probing and grading of
prostate cancer (PC) biomarkers, the indexes lack sensitivity and
specificity. To search for PC biomarkers, we used proton nuclear magnetic
resonance (1H NMR)-derived serum metabolomics. The study
comprises 102 serum samples obtained from low-grade (LG, n = 40) and high-grade (HG, n = 30) PC cases and
healthy controls (HC, n = 32). 1H NMR-derived
serum data were examined using principal component analysis and orthogonal
partial least-squares discriminant analysis. The strength of the model
was verified by internal cross-validation using the same samples divided
into 70% as training and 30% as test data sets. Receiver operating
characteristic (ROC) curve examination was also achieved. Serum metabolomics
reveals that four biomarkers (alanine, pyruvate, glycine, and sarcosine)
were able to accurately (ROC 0.966) differentiate 90.2% of PC cases
with 84.4% sensitivity and 92.9% specificity compared with HC. Similarly,
three biomarkers, alanine, pyruvate, and glycine, were able to precisely
(ROC 0.978) discriminate 92.9% of LG from HG PC with 92.5% sensitivity
and 93.3% specificity. The robustness of these biomarkers was confirmed
by prediction of the test data set with >99% diagnostic precision
for PC determination. These findings demonstrate that 1H NMR-based serum metabolomics is a promising approach for probing
and grading PC.