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dc.contributor.authorDomingo-Relloso, Arce 
dc.contributor.authorHuan, Tianxiao
dc.contributor.authorHaack, Karin
dc.contributor.authorRiffo-Campos, Angela L
dc.contributor.authorLevy, Daniel
dc.contributor.authorFallin, M Daniele
dc.contributor.authorTerry, Mary Beth
dc.contributor.authorZhang, Ying
dc.contributor.authorRhoades, Dorothy A
dc.contributor.authorHerreros-Martinez, Miguel
dc.contributor.authorGarcía-Esquinas, Esther 
dc.contributor.authorCole, Shelley A
dc.contributor.authorTellez-Plaza, Maria 
dc.contributor.authorNavas-Acien, Ana
dc.date.accessioned2021-04-16T16:52:21Z
dc.date.available2021-04-16T16:52:21Z
dc.date.issued2021-02-25
dc.identifier.citationClin Epigenetics . 2021 Feb 25;13(1):43.es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/12681
dc.description.abstractEpigenetic 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. 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. 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. 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.es_ES
dc.description.sponsorshipThis work was supported by grants from the National Heart, Lung, and Blood Institute (NHLBI) (Contract Numbers 75N92019D00027, 75N92019D00028, 75N92019D00029 and 75N92019D00030) and previous Grants (R01HL090863, R01HL109315, R01HL109301, R01HL109284, R01HL109282, and R01HL109319 and Cooperative Agreements: U01HL41642, U01HL41652, U01HL41654, U01HL65520 and U01HL65521); by the National Institute of Environmental Health Sciences (Grant Numbers R01ES021367, R01ES025216, P42ES010349, P30ES009089); by the Chilean CONICYT/FONDECYT-POSTDOCTORADO Nº3180486 and by a fellowship from “la Caixa” Foundation (ID 100010434) (fellowship code “LCF/BQ/DR19/11740016”). The funders had no role in the planning, conducting, analysis, interpretation, or writing of this study. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health (United States) or the National Health Institute Carlos III (Spain).es_ES
dc.language.isoenges_ES
dc.publisherBioMed Central (BMC) es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAmerican Indianses_ES
dc.subjectDNA methylationes_ES
dc.subjectEpigeneticses_ES
dc.subjectHematopoietic cancerses_ES
dc.subjectLymphatic cancerses_ES
dc.titleDNA methylation and cancer incidence: lymphatic-hematopoietic versus solid cancers in the Strong Heart Study.es_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID33632303es_ES
dc.format.volume13es_ES
dc.format.number1es_ES
dc.format.page43es_ES
dc.identifier.doi10.1186/s13148-021-01030-8es_ES
dc.contributor.funderNIH - National Heart, Lung, and Blood Institute (NHLBI) (Estados Unidos) 
dc.contributor.funderNIH - National Institute of Environmental Health Sciences (NIEHS) (Estados Unidos) 
dc.contributor.funderFundación La Caixa 
dc.contributor.funderFondo Nacional de Desarrollo Científico y Tecnológico (Chile) 
dc.description.peerreviewedes_ES
dc.identifier.e-issn1868-7083es_ES
dc.relation.publisherversionhttps://doi.org/10.1186/s13148-021-01030-8es_ES
dc.identifier.journalClinical epigeneticses_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.rights.accessRightsopen accesses_ES


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Atribución 4.0 Internacional
Este Item está sujeto a una licencia Creative Commons: Atribución 4.0 Internacional