Utilization of Capillary Electrophoresis Mass Spectrometry in the Development of a Metabolomic Signature for Prostate Cancer
Andrew Gusev, BA1, Leo L. Cheng, PhD1, Alex Buko, PhD2, Takushi Oga, PhD2, Adam S. Feldman, MD MPH1.
1Massachusetts General Hospital, Boston, MA, USA, 2Human Metabolome Technologies, Boston, MA, USA.
Background
Prostate cancer (PCa) pathogenesis is influenced by alterations in cellular metabolism. Metabolomics measures these biochemical changes to create global tissue metabolite profiles. Urinary studies are noninvasive and can potentially identify biomarkers for PCa. We used Capillary Electrophoresis Mass Spectrometry (CE-MS) to analyze urine from men undergoing prostate biopsy for suspicion of PCa to investigate their metabolomic profiles.
Methods
150 urine specimens were prospectively collected from men undergoing prostate biopsy. After histopathologic evaluation of all biopsy cores was completed, 40 urine samples were selected for metabolomic investigation. 20 samples were taken from men with entirely benign prostate biopsies, and 20 from men with biopsy-proven PCa. An analysis of charged metabolites by CE-MS was performed as described (J Proteome Res. 2:488; 2003). Urinary metabolites were extracted from 100 μL urine by mixing with methanol containing 20 μM of internal standards. CE-MS experiments were performed with the Agilent CE system. Screening of potential biomarkers was performed with statistical protocols and pathway analyses. Metabolites with levels below the detection limit in all samples were excluded. Relative abundances of metabolites were normalized to levels of creatinine.
Results
CE-MS analysis produced thousands of features in the combined anionic and cationic modes. A volcano plot comparing p values against fold change identified 60 metabolites that were statistically different between urine samples of men with PCa and those of men without PCa. Pathway analysis of these using MetaboAnalyst (Metabolites 9:57; 2019) showed high activity in ceramide, short chain fatty acid (SCFA), branched chain amino acid, serine, threonine, and tryptophan metabolism. Figure 1 demonstrates the pathway analysis with fold change represented by color and vertical scale, and number of significant metabolites with size. Figure 2 graphically contrasts metabolomic profiles.
Conclusions
CE-MS analysis identified several metabolic pathways that were upregulated in urine of men with PCa. These metabolites are involved in steroid, aromatic, microorganism and SCFA processes and warrant targeted studies which are underway in our lab. If validated, they have potential to serve as non-invasive biomarkers for PCa diagnosis and therapeutics. 
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