Arce Domingo-Relloso1,2,3, Tianxiao Huan4,5, Karin Haack6, Angela L Riffo-Campos7, Daniel Levy4,5, M Daniele Fallin8,9, Mary Beth Terry10, Ying Zhang11, Dorothy A Rhoades12, Miguel Herreros-Martinez13, Esther Garcia-Esquinas14,15, Shelley A Cole6, Maria Tellez-Plaza16, Ana Navas-Acien17. 1. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA. ad3531@cumc.columbia.edu. 2. Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Melchor Fernandez Almagro Street, 5, Madrid, Spain. ad3531@cumc.columbia.edu. 3. Department of Statistics and Operations Research, University of Valencia, Valencia, Spain. ad3531@cumc.columbia.edu. 4. The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA. 5. The Framingham Heart Study, Framingham, MA, USA. 6. Population Health Program, Texas Biomedical Research Institute, San Antonio, TX, USA. 7. Department of Pathology, Universidad de La Frontera, Temuco, Chile. 8. Department of Mental Health, Johns Hopkins University, Baltimore, MD, USA. 9. Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA. 10. Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA. 11. Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Oklahoma, USA. 12. Department of Medicine, Stephenson Cancer Center, University of Oklahoma Health Sciences, Oklahoma City, OK, USA. 13. Bioinformatics Unit, Institute for Biomedical Research INCLIVA, Valencia, Spain. 14. Universidad Autonoma de Madrid, Madrid, Spain. 15. CIBERESP (CIBER of Epidemiology and Public Health), Madrid, Spain. 16. Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Melchor Fernandez Almagro Street, 5, Madrid, Spain. 17. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA. an2737@cumc.columbia.edu.
Abstract
BACKGROUND: Epigenetic alterations may contribute to early detection of cancer. We evaluated the association of blood DNA methylation with lymphatic-hematopoietic cancers and, for comparison, with solid cancers. We also evaluated the predictive ability of DNA methylation for lymphatic-hematopoietic cancers. METHODS: Blood DNA methylation was measured using the Illumina Infinium methylationEPIC array in 2324 Strong Heart Study participants (41.4% men, mean age 56 years). 788,368 CpG sites were available for differential DNA methylation analysis for lymphatic-hematopoietic, solid and overall cancers using elastic-net and Cox regression models. We conducted replication in an independent population: the Framingham Heart Study. We also analyzed differential variability and conducted bioinformatic analyses to assess for potential biological mechanisms. RESULTS: Over a follow-up of up to 28 years (mean 15), we identified 41 lymphatic-hematopoietic and 394 solid cancer cases. A total of 126 CpGs for lymphatic-hematopoietic cancers, 396 for solid cancers, and 414 for overall cancers were selected as predictors by the elastic-net model. For lymphatic-hematopoietic cancers, the predictive ability (C index) increased from 0.58 to 0.87 when adding these 126 CpGs to the risk factor model in the discovery set. The association was replicated with hazard ratios in the same direction in 28 CpGs in the Framingham Heart Study. When considering the association of variability, rather than mean differences, we found 432 differentially variable regions for lymphatic-hematopoietic cancers. CONCLUSIONS: This study suggests that differential methylation and differential variability in blood DNA methylation are associated with lymphatic-hematopoietic cancer risk. DNA methylation data may contribute to early detection of lymphatic-hematopoietic cancers.
BACKGROUND: Epigenetic alterations may contribute to early detection of cancer. We evaluated the association of blood DNA methylation with lymphatic-hematopoietic cancers and, for comparison, with solid cancers. We also evaluated the predictive ability of DNA methylation for lymphatic-hematopoietic cancers. METHODS: Blood DNA methylation was measured using the Illumina Infinium methylationEPIC array in 2324 Strong Heart Study participants (41.4% men, mean age 56 years). 788,368 CpG sites were available for differential DNA methylation analysis for lymphatic-hematopoietic, solid and overall cancers using elastic-net and Cox regression models. We conducted replication in an independent population: the Framingham Heart Study. We also analyzed differential variability and conducted bioinformatic analyses to assess for potential biological mechanisms. RESULTS: Over a follow-up of up to 28 years (mean 15), we identified 41 lymphatic-hematopoietic and 394 solid cancer cases. A total of 126 CpGs for lymphatic-hematopoietic cancers, 396 for solid cancers, and 414 for overall cancers were selected as predictors by the elastic-net model. For lymphatic-hematopoietic cancers, the predictive ability (C index) increased from 0.58 to 0.87 when adding these 126 CpGs to the risk factor model in the discovery set. The association was replicated with hazard ratios in the same direction in 28 CpGs in the Framingham Heart Study. When considering the association of variability, rather than mean differences, we found 432 differentially variable regions for lymphatic-hematopoietic cancers. CONCLUSIONS: This study suggests that differential methylation and differential variability in blood DNA methylation are associated with lymphatic-hematopoietic cancer risk. DNA methylation data may contribute to early detection of lymphatic-hematopoietic cancers.
Entities:
Keywords:
American Indians; DNA methylation; Epigenetics; Hematopoietic cancers; Lymphatic cancers
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