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

Autor:	Chen CH; Department of Urology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan., Huang HP; Graduate Institute of Medical Genomics and Proteomics, College of Medicine, National Taiwan University, Taipei, Taiwan., Chang KH; Institute of Cellular and System Medicine, National Health Research Institutes, Miaoli, Taiwan., Lee MS; Department of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan., Lee CF; Department of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan., Lin CY; Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan., Lin YC; Department of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan., Huang WJ; Department of Urology, Taipei Veterans General Hospital, National Yang Ming Chiao Tung University, Taipei, Taiwan., Liao CH; Division of Urology, Department of Surgery, Cardinal Tien Hospital, New Taipei City, Taiwan.; School of Medicine, College of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan., Yu CC; Division of Urology, Department of Surgery, Taipei Tzu Chi Hospital and The Buddhist Tzu Chi Medical Foundation, College of Medicine, Tzu Chi University, Hualien, Taiwan., Chung SD; Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei City, Taiwan.; Department of Nursing, College of Healthcare & Management, Asia Eastern University of Science and Technology, New Taipei City, Taiwan., Tsai YC; Division of Urology, Department of Medicine, Taipei Tzu Chi Hospital, New Taipei City, Taiwan., Wu CC; Department of Urology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.; Department of Urology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.; TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan., Ho CH; School of Medicine, College of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan.; Division of Urology, Department of Surgery, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan., Hsiao PW; Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan. pwhsiao@gate.sinica.edu.tw., Pu YS; Department of Urology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan. yspu@ntu.edu.tw.
Jazyk:	angličtina
Zdroj:	The world journal of men's health [World J Mens Health] 2024 May 22. Date of Electronic Publication: 2024 May 22.
DOI:	10.5534/wjmh.230344
Abstrakt:	Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88-0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions: Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy. Competing Interests: The authors have nothing to disclose. (Copyright © 2024 Korean Society for Sexual Medicine and Andrology.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu