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Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation

Overview of attention for article published in Genome Medicine, January 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

blogs
2 blogs
twitter
7 tweeters
patent
2 patents
facebook
1 Facebook page

Citations

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135 Dimensions

Readers on

mendeley
112 Mendeley
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Title
Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation
Published in
Genome Medicine, January 2012
DOI 10.1186/gm323
Pubmed ID
Authors

Andrew E Teschendorff, Allison Jones, Heidi Fiegl, Alexandra Sargent, Joanna J Zhuang, Henry C Kitchener, Martin Widschwendter

Abstract

Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation. We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions. We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively). We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application. The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Italy 1 <1%
Unknown 110 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 33%
Researcher 23 21%
Student > Master 10 9%
Student > Bachelor 10 9%
Student > Doctoral Student 6 5%
Other 16 14%
Unknown 10 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 32%
Biochemistry, Genetics and Molecular Biology 28 25%
Medicine and Dentistry 13 12%
Business, Management and Accounting 4 4%
Computer Science 4 4%
Other 13 12%
Unknown 14 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 31 July 2019.
All research outputs
#1,216,236
of 21,634,174 outputs
Outputs from Genome Medicine
#257
of 1,369 outputs
Outputs of similar age
#6,782
of 142,848 outputs
Outputs of similar age from Genome Medicine
#2
of 17 outputs
Altmetric has tracked 21,634,174 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,369 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.4. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 142,848 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.