↓ Skip to main content

PLOS

A DNA Methylation Network Interaction Measure, and Detection of Network Oncomarkers

Overview of attention for article published in PLOS ONE, January 2014
Altmetric Badge

Mentioned by

twitter
2 X users

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
42 Mendeley
Title
A DNA Methylation Network Interaction Measure, and Detection of Network Oncomarkers
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0084573
Pubmed ID
Authors

Thomas E. Bartlett, Sofia C. Olhede, Alexey Zaikin

Abstract

Epigenetic processes--including DNA methylation--are increasingly seen as having a fundamental role in chronic diseases like cancer. DNA methylation patterns offer a route to develop prognostic measures based directly on DNA measurements, rather than less-stable RNA measurements. A novel DNA methylation-based measure of the co-ordinated interactive behaviour of genes is developed, in a network context. It is shown that this measure reflects well the co-regulatory behaviour linked to gene expression (at the mRNA level) over the same network interactions. This measure, defined for pairs of genes in a single patient/sample, associates with overall survival outcome independent of known prognostic clinical features, in several independent data sets relating to different cancer types. In total, more than half a billion CpGs in over 1600 samples, taken from nine different cancer entities, are analysed. It is found that groups of gene-pair interactions which associate significantly with survival identify statistically significant subnetwork modules. Many of these subnetwork modules are shown to be biologically relevant by strong correlation with pre-defined gene sets, such as immune function, wound healing, mitochondrial function and MAP-kinase signalling. In particular, the wound healing module corresponds to an increase in co-ordinated interactive behaviour between genes for worse prognosis, and the immune module corresponds to a decrease in co-ordinated interactive behaviour between genes for worse prognosis. This measure has great potential for defining DNA-based cancer biomarkers. Such biomarkers could naturally be developed further, by drawing on the rapidly expanding knowledge base of network science.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 5%
United Kingdom 1 2%
Brazil 1 2%
Canada 1 2%
United States 1 2%
Unknown 36 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 40%
Student > Ph. D. Student 11 26%
Professor 4 10%
Student > Doctoral Student 2 5%
Student > Postgraduate 2 5%
Other 5 12%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 29%
Biochemistry, Genetics and Molecular Biology 6 14%
Computer Science 4 10%
Mathematics 4 10%
Nursing and Health Professions 2 5%
Other 10 24%
Unknown 4 10%