Karina Dalsgaard Sørensen

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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Lang Wu, University of Hawaii at Manoa
  • ,
  • Yaohua Yang, Vanderbilt University
  • ,
  • Xingyi Guo, Vanderbilt University
  • ,
  • Xiao Ou Shu, Vanderbilt University
  • ,
  • Qiuyin Cai, Vanderbilt University
  • ,
  • Xiang Shu, Vanderbilt University
  • ,
  • Bingshan Li, Vanderbilt University
  • ,
  • Ran Tao, Vanderbilt University
  • ,
  • Chong Wu, Florida State University
  • ,
  • Jason B. Nikas, Genomix Inc.
  • ,
  • Yanfa Sun, University of Hawaii at Manoa, Longyan University
  • ,
  • Jingjing Zhu, University of Hawaii at Manoa
  • ,
  • Monique J. Roobol, Erasmus University Rotterdam
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  • Graham G. Giles, Melbourne School of Population and Global Health, Cancer Council Victoria
  • ,
  • Hermann Brenner, German Cancer Research Center
  • ,
  • Esther M. John, Stanford University
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  • Judith Clements, Queensland University of Technology, Translational Research Institute
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  • Eli Marie Grindedal, University of Oslo
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  • Jong Y. Park, University of South Florida
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  • Janet L. Stanford, Fred Hutchinson Cancer Research Center, University of Washington
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  • Zsofia Kote-Jarai, Royal Marsden NHS Foundation Trust
  • ,
  • Christopher A. Haiman, University of Southern California
  • ,
  • Rosalind A. Eeles, Royal Marsden NHS Foundation Trust
  • ,
  • Wei Zheng, Vanderbilt University
  • ,
  • Jirong Long, Vanderbilt University
  • ,
  • The PRACTICAL consortium
  • ,
  • CRUK Consortium
  • ,
  • BPC3 Consortium
  • ,
  • CAPS Consortium
  • ,
  • PEGASUS Consortium

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.

Original languageEnglish
Article number3905
JournalNature Communications
Volume11
ISSN2041-1723
DOIs
Publication statusPublished - Aug 2020

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