Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up

Autor: Heather Johnson, Jinan Guo, Xuhui Zhang, Heqiu Zhang, Athanasios Simoulis, Alan H. B. Wu, Taolin Xia, Fei Li, Wanlong Tan, Allan Johnson, Nishtman Dizeyi, Per-Anders Abrahamsson, Lukas Kenner, Xiaoyan Feng, Chang Zou, Kefeng Xiao, Jenny L. Persson, Lingwu Chen
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: BMC Medicine, Vol 18, Iss 1, Pp 1-14 (2020)
Druh dokumentu: article
ISSN: 1741-7015
DOI: 10.1186/s12916-020-01834-0
Popis: Abstract Background Heterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies. Methods Using a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy. Results The 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963–0.929) in the retrospective cohort (n = 614), AUC of 0.901 (0.929–0.873) in the prospective cohort (n = 396), and AUC of 0.936 (0.956–0.916) in the large combination cohort (n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors (p
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