A1 Refereed original research article in a scientific journal
An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk
Authors: Wu L, Yang YH, Guo XY, Shu XO, Cai QY, Shu X, Li BS, Tao R, Wu C, Nikas JB, Sun YF, Zhu JJ, Roobol MJ, Giles GG, Brenner H, John EM, Clements J, Grindedal EM, Park JY, Stanford JL, Kote-Jarai Z, Haiman CA, Eeles RA, Zheng W; PRACTICAL consortium; CRUK Consortium; BPC3 Consortium; CAPS Consortium; PEGASUS Consortium
Publisher: NATURE PUBLISHING GROUP
Publication year: 2020
Journal: Nature Communications
Journal name in source: NATURE COMMUNICATIONS
Journal acronym: NAT COMMUN
Article number: ARTN 3905
Volume: 11
Issue: 1
Number of pages: 11
ISSN: 2041-1723
DOI: https://doi.org/10.1038/s41467-020-17673-9
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/51872573
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. Genome wide association studies have identified multiple loci associated with risk of developing prostate cancer but the functional significance of many of these are unknown. Here, after generating models to predict methylation, the authors identify CpG methylation sites associated with prostate cancer.
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