Urinary Metabolome Study for Monitoring Prostate Cancer Recurrence Following Radical Prostatectomy.

Area of Science:

• Urology
• Oncology
• Analytical Chemistry

Background:

• Prostate cancer (PCa) is a common male malignancy.
• Biochemical recurrence or metastasis affects 20-40% of patients post-radical prostatectomy (RP).
• Early detection of PCa recurrence is crucial for effective patient management.

Purpose of the Study:

• To explore the potential of urinary volatile organic compounds (VOCs) as biomarkers for PCa detection and monitoring.
• To investigate the utility of VOCs in differentiating PCa patients before and after RP, including those with recurrence or metastasis.
• To assess the application of metabolomics and machine learning in analyzing urinary VOCs for PCa diagnosis.

Main Methods:

• Collected 165 urine samples from PCa-positive (pre- and post-RP) and PCa-negative individuals.
• Categorized post-RP cohort into recovered healthy, biochemical recurrence, and recurrent metastasis groups.
• Utilized stir bar sorptive extraction, gas chromatography-mass spectrometry, metabolomics, and machine learning for VOC analysis.

Main Results:

• Identified over 100 potential VOCs capable of differentiating PCa patients.
• Demonstrated the ability of VOCs to distinguish between pre-RP, post-RP, biochemical recurrence, and metastatic PCa.
• Highlighted the potential of machine learning algorithms in analyzing VOC data for PCa stratification.

Conclusions:

• Urinary VOCs show promise as biomarkers for differentiating PCa patients before and after RP.
• These findings offer preliminary insights for developing post-treatment monitoring tools for PCa.
• Further validation in larger, independent cohorts is necessary to confirm reproducibility and translational impact.