Bibliographic Details
| Title: |
Androgen receptor binding sites enabling genetic prediction of mortality due to prostate cancer in cancer-free subjects |
| Authors: |
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, The BioBank Japan Project, Yoichiro Kamatani, Koichi Matsuda, Christopher A. Haiman, Shiro Ikegawa, Hidewaki Nakagawa, Chikashi Terao |
| Source: |
Nature Communications, Vol 14, Iss 1, Pp 1-10 (2023) |
| Publisher Information: |
Nature Portfolio, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Science |
| Subject Terms: |
Science |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2041-1723 |
| Relation: |
https://doaj.org/toc/2041-1723 |
| DOI: |
10.1038/s41467-023-39858-8 |
| Access URL: |
https://doaj.org/article/ee30aa5cf88e456e9ab064b3dd8991f6 |
| Accession Number: |
edsdoj.30aa5cf88e456e9ab064b3dd8991f6 |
| Database: |
Directory of Open Access Journals |
