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Cancer Epidemiology and Prevention$
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Michael Thun, Martha S. Linet, James R. Cerhan, Christopher A. Haiman, and David Schottenfeld

Print publication date: 2017

Print ISBN-13: 9780190238667

Published to Oxford Scholarship Online: December 2017

DOI: 10.1093/oso/9780190238667.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 19 October 2021

Application of Biomarkers in Cancer Epidemiology

Application of Biomarkers in Cancer Epidemiology

(p.77) 6 Application of Biomarkers in Cancer Epidemiology
Cancer Epidemiology and Prevention

Roel Vermeulen

Douglas A. Bell

Dean P. Jones

Montserrat Garcia-Closas

Avrum Spira

Teresa W. Wang

Martyn T. Smith

Qing Lan

Nathaniel Rothman

Oxford University Press

Advancements in OMICs are now enabling investigators to explore comprehensively the biological consequences of exogenous and endogenous exposures by detecting molecular signatures of exposure, early signs of adverse biological effects, preclinical disease, and molecularly defined cancer subtypes. These new technologies have proven invaluable for assembling a comprehensive portrait of human exposure, health, and disease. This includes hypothesis-driven biomarkers, as well as platforms that can agnostically analyze entire biologic processes and “compartments,” including the measurement of small molecules (metabolomics), DNA polymorphisms and rarer inherited variants (genomics), methylation and microRNA (epigenomics), chromosome-wide alterations, mRNA (transcriptomics), proteins (proteomics), and the microbiome (microbiomics). Although the implementation of these technologies in epidemiologic studies has already shown great promise, some challenges of particular importance must be addressed. Non-genetic OMIC markers vary over time due to both random variation and physiologic changes. Therefore, there is an urgent need for cohorts to collect repeat biological samples over time.

Keywords:   biomarkers, biological effects, molecular, OMICs, metabolomics, epigenomics, transcriptomics, cancer, epidemiology

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