Dual-Biofluid Metabolomics Combined With Machine Learning Enables Noninvasive Diagnosis of Drug Resistance in Benign Prostatic Hyperplasia.

Authors

Shilong Liu, Yanbo Chen, Ziwei Wang, Tong Hu, Wei Chen, Xiaoyu Xu, Xinyan Pang, Yushu Ding, Qi Chen, Meng Gu, Haisong Tan, Chengbang Wang, Xiaohui Liu, Shunxiang Li, Kun Qian, Bin Xu

Published in

Small (Weinheim an der Bergstrasse, Germany). Pages e13614. Sep 25, 2026. Epub Sep 25, 2026.

Abstract

With global population aging, benign prostatic hyperplasia (BPH) prevalence has risen, with one-quarter of patients showing inadequate treatment response or drug resistance (DR) and no reliable noninvasive diagnostic method currently available. Current methods rely on clinical experience, delaying precision management. We developed a nanoparticle-enhanced mass spectrometry platform to analyze serum and urine metabolomics in 224 BPH patients (104 DR, 120 drug-sensitive [DS]). Using gradient-boosted decision trees (GBDT), we integrated dual-biofluid metabolic fingerprints (serum and urine) to distinguish DR/DS subgroups. The platform enabled rapid analysis (<25 s/sample, 1 µL volume) with high reproducibility (CV < 10%). SMF yielded a five-feature panel including five putatively annotated DR-associated metabolites, achieving an AUC of 0.89 for DR detection. UMF analysis yielded 3 features (AUC = 0.81). Critically, combining SMF/UMF data (8-feature panel) enhanced diagnostic performance to AUC = 0.95 (95% CI: 0.91-0.96), outperforming single-biofluid models. This dual-biofluid metabolomic approach provides a noninvasive, rapid method for BPH-DR stratification, integrating systemic (serum) and local (urine) metabolic insights. The platform's scalability and 0.95 AUC highlight its potential for clinical translation, offering a foundation for personalized BPH management and reducing reliance on invasive procedures. Notably, multicenter external validation further supported the model's discriminative performance in an independent cohort.

PMID:
42786996
Bibliographic data and abstract were imported from PubMed on 25 Sep 2026.

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