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Updated: Jan 7, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Evaluating Differential Metabolic Profiles by Prostate Cancer Risk Among Prostate Cancer Patients
Tuo Liu1, Jahnvi Roorkeewal2, Melissa A Furlong1
1Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA.
None:
Background: Currently there are no clinically validated biomarkers recommended for prostate cancer (PCa) risk stratification other than prostate-specific antigen (PSA). Objective: This study aimed to identify urine metabolites that are associated with the presence of high-grade PCa at the time of radical prostatectomy. Methods: Urine samples were collected from patients who underwent radical prostatectomy. High-resolution metabolomics were implemented using liquid chromatography mass spectrometry (LC-MS). To enhance metabolic feature identification, sample extracts were analyzed in two modes, C18 chromatography [reverse-phase (RP)] and hydrophilic interaction chromatography (HILIC). Results: This analysis included a total of 22 patients with PCa (10 high-grade and 12 low-grade) and identified 52 differential metabolites, 40 in RP and 12 in HILIC, at the p-value 0.05 level. Among these, methyl alpha-aspartyl phenylalaninate was most significantly differentiated, while 3-methylbutanoicacid had the largest difference (slope -3.488). In the pathway analysis, the histidine metabolism pathway was significantly enriched (p < 0.05) with an enrichment factor of 3.5. Although not statistically significant, alterations were also observed in the vitamin B12, B7 (biotin), B6, and B3 (niacin) pathways. Conclusions: These findings suggest that urinary metabolites may have the potential to differentiate high-grade from low-grade PCa. Our study also highlights the metabolic reprogramming that occurs as PCa becomes more aggressive and potential differences in dietary patterns.
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