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Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass

Chung-Hsin Chen1, Hsiang-Po Huang2, Kai-Hsiung Chang3

  • 1Department of Urology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.

The World Journal of Men'S Health
|June 12, 2024
PubMed
Summary

A new non-invasive urine test accurately predicts clinically significant prostate cancer (sPC) in at-risk men. This urinary metabolomic profile analysis helps avoid unnecessary biopsies, improving early detection of prostate cancer.

Keywords:
BiomarkersBiopsyMetabolomicsProstatic neoplasms

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Area of Science:

  • Urology
  • Metabolomics
  • Biomarker Discovery

Background:

  • Clinically significant prostate cancer (sPC) detection prior to biopsy is challenging due to a lack of reliable biomarkers.
  • Current diagnostic methods for prostate cancer can be invasive and may lead to overdiagnosis.

Purpose of the Study:

  • To develop a non-invasive urine test for predicting sPC in at-risk men.
  • To utilize urinary metabolomic profiles for early and accurate prostate cancer detection.

Main Methods:

  • Liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling of urine samples from 934 at-risk subjects and 268 treatment-naïve prostate cancer patients.
  • Construction and validation of four predictive models (Model I, II, III, and Gleason score model) using logistic regression to differentiate PC and predict sPC.
  • Analysis using both C18 and hydrophilic interaction liquid chromatography (HILIC) columns.

Main Results:

  • Metabolomic panels achieved high predictive performance (AUC 0.77-0.91) in training and validation cohorts.
  • Combining metabolomic panels with clinical factors (PSA, age, family history, etc.) significantly improved prediction accuracy (AUC 0.88-0.91).
  • The models successfully reduced unnecessary biopsies by 33-41% at 90% sensitivity in the validation cohort.

Conclusions:

  • Urinary metabolomic profiles, when combined with clinical factors, offer an accurate, non-invasive method for predicting sPC before biopsy.
  • This approach has the potential to significantly decrease the number of unnecessary prostate biopsies.
  • The findings support the clinical utility of urine-based metabolomics for prostate cancer risk assessment.