Metabolomics-Based Liquid Biopsy for Predicting Clinically Significant Prostate Cancer.
Journal
Cancers
Journal Volume
17
Journal Issue
23
Start Page
Article number 3815
ISSN
2072-6694
Date Issued
2025-11-28
Author(s)
Lin, Yuan-Chi
Chen, Chung-Hsin
Lee, Cheng-Fan
Hsiao, Pei-Wen
Pu, Yeong-Shiau
Abstract
Prostate cancer (PC) remains a major cause of cancer deaths in men. The serum biomarker prostate-specific antigen (PSA) lacks specificity in distinguishing clinically significant PC (sPC) from insignificant PC (isPC), leading to overdiagnosis and overtreatment. Although magnetic resonance imaging (MRI) improves detection, it is expensive, is time-consuming, and may involve inter-reader discrepancies. Recently, metabolomics, which has a high analytical sensitivity and broad molecular-feature coverage, has emerged as a promising tool to risk-stratify PC. This review examined studies of blood and urine metabolomics for sPC biomarker identification. Significant metabolite changes in sPC patients often involved fatty acid metabolism, sphingolipid metabolism, glycolysis, the citric acid cycle, purine/pyrimidine metabolism, and tyrosine/phenylalanine metabolism. Specifically, more than one study reported increased lactate and phenylalanine levels, along with decreased tyrosine, xanthine, and histidine levels, in sPC patients. Several metabolic panels outperformed serum PSA in predicting sPC, particularly when combined with clinical factors. Among these, two urine-based tests may have higher accuracy in predicting sPC than most current commercially available assays. However, direct comparison between studies may be inappropriate due to methodological heterogeneity, the variability in biospecimen types, inconsistent use of digital rectal examinations, and different sPC definitions and predictive endpoints. Most relevant studies were of small sample size or lacked external validation. Despite these challenges, metabolomics-based liquid biopsies show strong potential for improving sPC detection. Future research should focus on protocol standardization, MRI integration, absolute metabolite quantification, and validation in large and independent cohorts to enhance model credibility.
Subjects
Gleason score
National Comprehensive Cancer Network risk grouping
clinically significant prostate cancer
mass spectrometry
metabolomics
nuclear magnetic resonance
SDGs
Publisher
MDPI
Type
review article