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Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk

Author

Listed:
  • Yaohua Yang

    (University of Virginia)

  • Yaxin Chen

    (Sichuan University)

  • Shuai Xu

    (Vanderbilt University Medical Center)

  • Xingyi Guo

    (Vanderbilt University Medical Center)

  • Guochong Jia

    (Vanderbilt University Medical Center)

  • Jie Ping

    (Vanderbilt University Medical Center)

  • Xiang Shu

    (Memorial Sloan Kettering Cancer Center)

  • Tianying Zhao

    (Vanderbilt University Medical Center)

  • Fangcheng Yuan

    (Vanderbilt University Medical Center)

  • Gang Wang

    (Sichuan University)

  • Yufang Xie

    (Sichuan University)

  • Hang Ci

    (Sichuan University)

  • Hongmo Liu

    (Sichuan University)

  • Yawen Qi

    (Sichuan University)

  • Yongjun Liu

    (University of Washington Medical Center)

  • Dan Liu

    (Sichuan University)

  • Weimin Li

    (Sichuan University)

  • Fei Ye

    (Vanderbilt University Medical Center)

  • Xiao-Ou Shu

    (Vanderbilt University Medical Center)

  • Wei Zheng

    (Vanderbilt University Medical Center)

  • Li Li

    (University of Virginia)

  • Qiuyin Cai

    (Vanderbilt University Medical Center)

  • Jirong Long

    (Vanderbilt University Medical Center)

Abstract

The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P

Suggested Citation

  • Yaohua Yang & Yaxin Chen & Shuai Xu & Xingyi Guo & Guochong Jia & Jie Ping & Xiang Shu & Tianying Zhao & Fangcheng Yuan & Gang Wang & Yufang Xie & Hang Ci & Hongmo Liu & Yawen Qi & Yongjun Liu & Dan L, 2024. "Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50404-y
    DOI: 10.1038/s41467-024-50404-y
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    References listed on IDEAS

    as
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