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Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers

Overview of attention for article published in Genome Medicine, May 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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1 news outlet
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7 X users

Citations

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83 Dimensions

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73 Mendeley
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Title
Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers
Published in
Genome Medicine, May 2018
DOI 10.1186/s13073-018-0548-z
Pubmed ID
Authors

Jinming Cheng, Dongkai Wei, Yuan Ji, Lingli Chen, Liguang Yang, Guang Li, Leilei Wu, Ting Hou, Lu Xie, Guohui Ding, Hong Li, Yixue Li

Abstract

Hepatocellular carcinoma (HCC) is the one of the most common cancers and lethal diseases in the world. DNA methylation alteration is frequently observed in HCC and may play important roles in carcinogenesis and diagnosis. Using the TCGA HCC dataset, we classified HCC patients into different methylation subtypes, identified differentially methylated and expressed genes, and analyzed cis- and trans-regulation of DNA methylation and gene expression. To find potential diagnostic biomarkers for HCC, we screened HCC-specific CpGs by comparing the methylation profiles of 375 samples from HCC patients, 50 normal liver samples, 184 normal blood samples, and 3780 samples from patients with other cancers. A logistic regression model was constructed to distinguish HCC patients from normal controls. Model performance was evaluated using three independent datasets (including 327 HCC samples and 122 normal samples) and ten newly collected biopsies. We identified a group of patients with a CpG island methylator phenotype (CIMP) and found that the overall survival of CIMP patients was poorer than that of non-CIMP patients. Our analyses showed that the cis-regulation of DNA methylation and gene expression was dominated by the negative correlation, while the trans-regulation was more complex. More importantly, we identified six HCC-specific hypermethylated sites as potential diagnostic biomarkers. The combination of six sites achieved ~ 92% sensitivity in predicting HCC, ~ 98% specificity in excluding normal livers, and ~ 98% specificity in excluding other cancers. Compared with previously published methylation markers, our markers are the only ones that can distinguish HCC from other cancers. Overall, our study systematically describes the DNA methylation characteristics of HCC and provides promising biomarkers for the diagnosis of HCC.

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The data shown below were collected from the profiles of 7 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 22%
Researcher 10 14%
Student > Doctoral Student 4 5%
Student > Master 3 4%
Other 2 3%
Other 12 16%
Unknown 26 36%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 22%
Agricultural and Biological Sciences 12 16%
Medicine and Dentistry 9 12%
Computer Science 4 5%
Nursing and Health Professions 3 4%
Other 6 8%
Unknown 23 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 July 2018.
All research outputs
#2,384,194
of 23,963,877 outputs
Outputs from Genome Medicine
#549
of 1,477 outputs
Outputs of similar age
#50,908
of 334,299 outputs
Outputs of similar age from Genome Medicine
#10
of 24 outputs
Altmetric has tracked 23,963,877 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,477 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.4. This one has gotten more attention than average, scoring higher than 62% 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 334,299 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.