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This study develops and validates urine-based tests to predict clinically significant prostate cancer (sPC) in men at risk. Urine samples from 928 participants were profiled using GC/Q-TOF MS to construct four predictive models, achieving strong AUC performance and improved accuracy with added clinical risk factors, with validation in an independent cohort.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/prediction-of-clinically-significant-prostate-cancer-through-urine-metabolomic-signatures-a-large-scale-validated-study/385213/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/prediction-of-clinically-significant-prostate-cancer-through-urine-metabolomic-signatures-a-large-scale-validated-study/385213.png","ImageObject",300,407,{"name":92,"@type":93},"Grenda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address for prostate cancer prediction?","Question",{"text":112,"@type":113},"It addresses the lack of accurate pre-biopsy markers for identifying potentially lethal prostate cancer.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were predictive models built in this study?",{"text":117,"@type":113},"Urine samples from 928 men were metabolomically profiled using GC/Q-TOF MS, and four models were constructed to discriminate PC, and to predict clinically significant PC among specific risk groups and Gleason sum thresholds.",{"name":119,"@type":110,"acceptedAnswer":120},"What improvement did adding clinical factors bring to the models?",{"text":121,"@type":113},"Adding five clinical risk factors significantly improved the AUCs of the models, with higher diagnostic performance across multiple model types.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},385213,1790626174,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},7971474921005,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Huang et al. Journal of Translational Medicine (2023) 21:714  \n[https://doi.org/10.1186/s12967-023-04424-9](https://doi.org/10.1186/s12967-023-04424-9)  \nJournal of Translational Medicine  \n RESEARCH Open Access  \nPrediction of clinically significant prostate  \ncancer through urine metabolomic signatures: A large-scale validated study  \nHsiang‑Po Huang 1†, Chung‑Hsin Chen2†, Kai‑Hsiung Chang3, Ming‑Shyue Lee4, Cheng‑Fan Lee4, Yen‑Hsiang Chao2, Shih‑Yu Lu2, Tzu‑Fan Wu2, Sung‑Tzu Liang2, Chih‑Yu Lin5, Yuan Chi Lin6, Shih‑Ping Liu2, Yu‑Chuan Lu2,7, Chia‑Tung Shun8, William J. Huang9, Tzu‑Ping Lin9, Ming‑Hsuan Ku9, Hsiao‑Jen Chung9, Yen‑Hwa Chang9, Chun‑Hou Liao 10,11, Chih‑Chin Yu12, Shiu‑Dong Chung13, Yao‑Chou Tsai14,  \nChia‑Chang Wu15,16,17, Kuan‑Chou Chen 16,17,18, Chen‑Hsun Ho 11,19, Pei‑Wen Hsiao5* and Yeong‑Shiau Pu2*  \nAbstract  \nPurpose Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk.  \nMethods Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time‑of‑flight mass spectrophotometry (GC/Q‑TOF MS) metabolomic profiling to con‑ struct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respec‑ tively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC) .  \nResults In Models I, II, III, and GS, the best AUCs (0 . 94, 0 . 85, 0 . 82, and 0. 80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate‑specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs ofthe models (0 . 95, 0 . 92, 0 . 92, and 0. 87, respectively) . At 90% sensitivity, 48%, 47%, 50%, and 36% of unnec‑ essary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325) . Decision curve analysis showed a significant clinical net benefit with each combined model at low thresh‑ old probabilities. Models II and III were more robust and clinically relevant than Model GS.  \nConclusion This urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.  \nKeywords Prediction, Aggressive, Liquid biopsy, Active surveillance, Diagnosis  \n†Hsiang‑Po Huang and Chung‑Hsin Chen are Co‑first authors.  \n*Correspondence:  \nPei‑Wen Hsiao [pwhsiao@gate.sinica.edu.tw](pwhsiao@gate.sinica.edu.tw)[ ](pwhsiao@gate.sinica.edu.tw)Yeong‑Shiau Pu[yspu@ntu.edu.tw](yspu@ntu.edu.tw)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://crea","cbCaitl9qusspnQH","https://ap.wps.com/l/cbCaitl9qusspnQH","pdf",2264033,13,"English","# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What problem does the study address for prostate cancer prediction?\",\"answer\":\"It addresses the lack of accurate pre-biopsy markers for identifying potentially lethal prostate cancer.\"},{\"question\":\"How were predictive models built in this study?\",\"answer\":\"Urine samples from 928 men were metabolomically profiled using GC/Q-TOF MS, and four models were constructed to discriminate PC, and to predict clinically significant PC among specific risk groups and Gleason sum thresholds.\"},{\"question\":\"What improvement did adding clinical factors bring to the models?\",\"answer\":\"Adding five clinical risk factors significantly improved the AUCs of the models, with higher diagnostic performance across multiple model types.\"}]","Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study | PDF",1790267391,33]