An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

Lang Wu (), Yaohua Yang, Xingyi Guo, Xiao-Ou Shu, Qiuyin Cai, Xiang Shu, Bingshan Li, Ran Tao, Chong Wu, Jason B. Nikas, Yanfa Sun, Jingjing Zhu, Monique J. Roobol, Graham G. Giles, Hermann Brenner, Esther M. John, Judith Clements, Eli Marie Grindedal, Jong Y. Park, Janet L. Stanford, Zsofia Kote-Jarai, Christopher A. Haiman, Rosalind A. Eeles, Wei Zheng and Jirong Long ()
Additional contact information
Lang Wu: University of Hawaii at Manoa
Yaohua Yang: Vanderbilt University Medical Center
Xingyi Guo: Vanderbilt University Medical Center
Xiao-Ou Shu: Vanderbilt University Medical Center
Qiuyin Cai: Vanderbilt University Medical Center
Xiang Shu: Vanderbilt University Medical Center
Bingshan Li: Vanderbilt University
Ran Tao: Vanderbilt University Medical Center
Chong Wu: Florida State University
Jason B. Nikas: Genomix Inc
Yanfa Sun: University of Hawaii at Manoa
Jingjing Zhu: University of Hawaii at Manoa
Monique J. Roobol: Erasmus University Medical Center
Graham G. Giles: University of Melbourne
Hermann Brenner: German Cancer Research Center (DKFZ)
Esther M. John: Stanford University School of Medicine
Judith Clements: Queensland University of Technology
Eli Marie Grindedal: Oslo University Hospital
Jong Y. Park: Moffitt Cancer Center
Janet L. Stanford: Fred Hutchinson Cancer Research Center
Zsofia Kote-Jarai: The Institute of Cancer Research, and The Royal Marsden NHS Foundation Trust
Christopher A. Haiman: University of Southern California
Rosalind A. Eeles: The Institute of Cancer Research, and The Royal Marsden NHS Foundation Trust
Wei Zheng: Vanderbilt University Medical Center
Jirong Long: Vanderbilt University Medical Center

Nature Communications, 2020, vol. 11, issue 1, 1-11

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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-17673-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17673-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-17673-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().