Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study

Hsiang Po Huang, Chung Hsin Chen, Kai Hsiung Chang, Ming Shyue Lee, Cheng Fan Lee, Yen Hsiang Chao, Shih Yu Lu, Tzu Fan Wu, Sung Tzu Liang, Chih Yu Lin, Yuan Chi Lin, Shih Ping Liu, Yu Chuan Lu, Chia Tung Shun, William J. Huang, Tzu Ping Lin, Ming Hsuan Ku, Hsiao Jen Chung, Yen Hwa Chang, Chun Hou LiaoChih Chin Yu, Shiu Dong Chung, Yao Chou Tsai, Chia Chang Wu, Kuan Chou Chen, Chen Hsun Ho, Pei Wen Hsiao, Yeong Shiau Pu

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Purpose: 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. Methods: 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 construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, 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). Results: 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 of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary 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 threshold probabilities. Models II and III were more robust and clinically relevant than Model GS. Conclusion: 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.
原文英語
文章編號714
期刊Journal of Translational Medicine
21
發行號1
DOIs
出版狀態已發佈 - 12月 2023

ASJC Scopus subject areas

  • 一般生物化學,遺傳學和分子生物學

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