An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk
Authors
Wu, Lang
Yang, Yaohua
Guo, Xingyi
Shu, Xiao-Ou
Cai, Qiuyin
Shu, Xiang
Tao, Ran
Sun, Yanfa
Zhu, Jingjing
Brenner, Hermann
John, Esther M.
Clements, Judith
Grindedal, Eli Marie
Stanford, Janet L.
Kote-Jarai, Zsofia
Haiman, Christopher A.
Long, Jirong
Henderson, Brian E.
Schumacher, Fredrick R.
Easton, Douglas
Benlloch, Sara
Olama, Ali Amin Al
Muir, Kenneth
Berndt, Sonja I.
Conti, David V.
Wiklund, Fredrik
Chanock, Stephen
Gapstur, Susan M.
Stevens, Victoria L.
Tangen, Catherine M.
Batra, Jyotsna
Gronberg, Henrik
Pashayan, Nora
Schleutker, Johanna
Albanes, Demetrius
Weinstein, Stephanie
Wolk, Alicja
West, Catharine
Mucci, Lorelei
Cancel-Tassin, Géraldine
Koutros, Stella
Sorensen, Karina Dalsgaard
Neal, David E.
Hamdy, Freddie C.
Donovan, Jenny L.
Travis, Ruth C.
Hamilton, Robert J.
Ingles, Sue Ann
Rosenstein, Barry S.
Lu, Yong-Jie
Kibel, Adam S.
Vega, Ana
Kogevinas, Manolis
Penney, Kathryn L.
Cybulski, Cezary
Nordestgaard, Børge G.
Maier, Christiane
Kim, Jeri
Teixeira, Manuel R.
Neuhausen, Susan L.
De Ruyck, Kim
Razack, Azad
Newcomb, Lisa F.
Gamulin, Marija
Kaneva, Radka
Usmani, Nawaid
Claessens, Frank
Townsend, Paul A.
Dominguez, Manuela Gago
Menegaux, Florence
Khaw, Kay-Tee
Cannon-Albright, Lisa
Pandha, Hardev
Thibodeau, Stephen N.
Hunter, David J.
Blot, William J.
Riboli, Elio
Publication Date
2020-08-06Journal Title
Nature Communications
Publisher
Nature Publishing Group UK
Volume
11
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Wu, L., Yang, Y., Guo, X., Shu, X., Cai, Q., Shu, X., Li, B., et al. (2020). An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk. Nature Communications, 11 (1)https://doi.org/10.1038/s41467-020-17673-9
Abstract
Abstract: It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.
Keywords
Article, /631/67/2324, /631/67/68, /631/208, article
Identifiers
s41467-020-17673-9, 17673
External DOI: https://doi.org/10.1038/s41467-020-17673-9
This record's URL: https://www.repository.cam.ac.uk/handle/1810/308851
Rights
Licence:
https://creativecommons.org/licenses/by/4.0/