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Androgen receptor binding sites enabling genetic prediction of mortality due to prostate cancer in cancer-free subjects

  • Shuji Ito
  • , Xiaoxi Liu
  • , Yuki Ishikawa
  • , David D. Conti
  • , Nao Otomo
  • , Zsofia Kote-Jarai
  • , Hiroyuki Suetsugu
  • , Rosalind A. Eeles
  • , Yoshinao Koike
  • , Keiko Hikino
  • , Soichiro Yoshino
  • , Kohei Tomizuka
  • , Momoko Horikoshi
  • , Kaoru Ito
  • , Yuji Uchio
  • , Yukihide Momozawa
  • , Michiaki Kubo
  • , Akihide Masumoto
  • , Akiko Nagai
  • , Daisuke Obata
  • Hiroki Yamaguchi, Kaori Muto, Kazuhisa Takahashi, Ken Yamaji, Kozo Yoshimori, Masahiko Higashiyama, Nobuaki Sinozaki, Satoshi Asai, Satoshi Nagayama, Shigeo Murayama, Shiro Minami, Takao Suzuki, Takayuki Morisaki, Wataru Obara, Yasuo Takahashi, Yoichi Furukawa, Yoshinori Murakami, Yuji Yamanashi, Yukihiro Koretsune, Yoichiro Kamatani, Koichi Matsuda, Christopher A. Haiman, Shiro Ikegawa, Hidewaki Nakagawa, Chikashi Terao

Research output: Contribution to journalArticlepeer-review

Abstract

Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3, a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P = 4.2 × 10−10). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa.

Original languageEnglish
Article number4863
JournalNature communications
Volume14
Issue number1
DOIs
Publication statusPublished - 12-2023
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Biochemistry,Genetics and Molecular Biology
  • General
  • General Physics and Astronomy

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