Untargeted urine metabolite profiling by mass spectrometry aided by multivariate statistical analysis to predict prostate cancer treatment outcome.

Abstract

Deciphering metabolomic networks has been demonstrated to provide valuable information for diagnosing and monitoring diseases. Herein, we report a technique to monitor untargeted urine metabolites to evaluate prostate cancer aggressiveness and treatment outcome. Direct chemical profiling of urine was achieved by a combined procedure of hyphenating laser diode thermal desorption with atmospheric pressure chemical ionization mass spectrometry (LDTD-APCI-MS). We describe a conceptually new approach to monitoring preoperative urinary metabolic alterations associated with prostate cancer recurrence. By evaluating mass/charge (m/z) ratios and peak intensities of ions detected by mass spectroscopy of urine samples, we revealed that intensities at m/z 313.2740 (±0.0003) and 341.3054 (±0.0006) attributable to monoacylglycerol backbone fragments from glycerides can be statistically correlated to disease progression.