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Cancer Epigenetics for Precision Medicine

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Cover of 'Cancer Epigenetics for Precision Medicine'

Table of Contents

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    Book Overview
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    Chapter 1 Early Epigenetic Markers for Precision Medicine
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    Chapter 2 Interplay Between Genetic and Epigenetic Changes in Breast Cancer Subtypes
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    Chapter 3 Role of Microbiome in Carcinogenesis Process and Epigenetic Regulation of Colorectal Cancer
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    Chapter 4 Epigenome-Based Precision Medicine in Lung Cancer
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    Chapter 4 Review on Current Trends of Deep Learning
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    Chapter 5 Epigenetics in Hematological Malignancies
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    Chapter 6 MicroRNAs Role in Prostate Cancer
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    Chapter 7 Effects of Dietary Nutrients on Epigenetic Changes in Cancer
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    Chapter 8 Diet, Microbiome, and Epigenetics in the Era of Precision Medicine
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    Chapter 9 Alcohol-Induced Epigenetic Changes in Cancer
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    Chapter 10 Epigenetic Basis of Circadian Rhythm Disruption in Cancer
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    Chapter 11 Epigenetic Changes of the Immune System with Role in Tumor Development
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    Chapter 12 DNA Methylation as a Biomarker of Aging in Epidemiologic Studies
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    Chapter 13 Challenges and Opportunities in Social Epigenomics and Cancer
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    Chapter 14 Epigenetic and Genetic Regulation of PDCD1 Gene in Cancer Immunology
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    Chapter 15 Methylation and MicroRNA Profiling to Understand Racial Disparities of Prostate Cancer
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    Chapter 16 Analysis of DNA Hypermethylation in Pancreatic Cancer Using Methylation-Specific PCR and Bisulfite Sequencing
  19. Altmetric Badge
    Chapter 17 Pyrosequencing Methylation Analysis
Attention for Chapter 12: DNA Methylation as a Biomarker of Aging in Epidemiologic Studies
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Chapter title
DNA Methylation as a Biomarker of Aging in Epidemiologic Studies
Chapter number 12
Book title
Cancer Epigenetics for Precision Medicine
Published in
Methods in molecular biology, September 2018
DOI 10.1007/978-1-4939-8751-1_12
Pubmed ID
Book ISBNs
978-1-4939-8750-4, 978-1-4939-8751-1
Authors

Unhee Lim, Min-Ae Song

Abstract

Cancer is largely an aging disease. Accelerated biological aging may be the strongest predictor of cancer and other chronic disease risks. In the absence of reliable and quantifiable biomarkers of aging to date, it has long been observed that tumorigenesis shares distinct epigenetic alterations with the aging process. Recently, epigenetic age estimates have been developed based on the availability of genome-wide DNA methylation profiles, by applying in the prediction formula the methylation level at a subset of highly predictive methylation sites, called epigenetic clock. These DNA methylation age estimates have produced remarkably strong correlations with chronological age, with a small deviation and high reproducibility across different age groups and study populations. Moreover, an increasing number of epidemiologic studies have demonstrated an independent association of DNA methylation age or the extent of acceleration with mortality and various aging-related conditions, even after accounting for differences in chronological age and other risk factors. Although epigenetic profiles are known to be tissue-specific, both target tissue- and multiple tissue-derived estimates appear to perform well to capture what is thought to be the cumulative epigenetic drift that represents a multifactorial degenerative process across tissues and organisms. Further refinement of the epigenetic age estimates is anticipated over time to accommodate a better technological coverage of the methylome and a better understanding of the biology underlying predictive regions. Epidemiologic principles will remain critical for the evaluation of research findings involving, for example, different study populations, design, follow-up time, and quality of covariate data. Overall, the epigenetic age estimates are an exciting development with useful implications for biomedical research of healthy aging and disease prevention and control.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Ph. D. Student 8 18%
Student > Master 7 16%
Student > Bachelor 4 9%
Other 2 4%
Other 4 9%
Unknown 11 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 20%
Medicine and Dentistry 8 18%
Agricultural and Biological Sciences 5 11%
Nursing and Health Professions 3 7%
Neuroscience 2 4%
Other 5 11%
Unknown 13 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 September 2018.
All research outputs
#18,648,325
of 23,102,082 outputs
Outputs from Methods in molecular biology
#7,990
of 13,208 outputs
Outputs of similar age
#257,498
of 335,392 outputs
Outputs of similar age from Methods in molecular biology
#158
of 247 outputs
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So far Altmetric has tracked 13,208 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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We're also able to compare this research output to 247 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.