American Association for Cancer Research
10780432ccr182849-sup-207987_2_supp_5287751_pl57h9.docx (14.74 kB)

Supplementary text from Large-scale Circulating microRNA Profiling for the Liquid Biopsy of Prostate Cancer

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journal contribution
posted on 2023-03-31, 20:33 authored by Fumihiko Urabe, Juntaro Matsuzaki, Yusuke Yamamoto, Takahiro Kimura, Tomohiko Hara, Makiko Ichikawa, Satoko Takizawa, Yoshiaki Aoki, Shumpei Niida, Hiromi Sakamoto, Ken Kato, Shin Egawa, Hiroyuki Fujimoto, Takahiro Ochiya

Supplementary text


Japan Agency for Medical Research and Development



The high false-positive rate of prostate-specific antigen (PSA) may lead to unnecessary prostate biopsies. Therefore, the United States Preventive Services Task Force recommends that decisions regarding PSA-based screening of prostate cancer should be made with caution in men ages 55–69 years, and that men ≥70 years should not undergo PSA screening. Here, we investigated the potential of serum miRNAs as an accurate diagnostic method in patients with suspected prostate cancer. Serum samples of 809 patients with prostate cancer, 241 negative prostate biopsies, and 500 patients with other cancer types were obtained from the National Cancer Center, Japan. Forty-one healthy control samples were obtained from two other hospitals in Japan. Comprehensive microarray analysis was performed for all samples. Samples were divided into three sets. Candidate miRNAs for prostate cancer detection were identified in the discovery set (n = 123). A diagnostic model was constructed using combinations of candidate miRNAs in the training set (n = 484). The performance of the diagnostic model was evaluated in the validation set (n = 484). In the discovery set, 18 candidate miRNAs were identified. A robust diagnostic model was constructed using the combination of two miRNAs (miR-17-3p and miR-1185-2-3p) in the training set. High diagnostic performance with a sensitivity of 90% and a specificity of 90% was achieved in the validation set regardless of the Gleason score and clinical tumor–node–metastasis stage. The model developed in this study may help improve the diagnosis of prostate cancer and reduce the number of unnecessary prostate biopsies.

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