↓ Skip to main content

Copy number alterations detected by whole-exome and whole-genome sequencing of esophageal adenocarcinoma

Overview of attention for article published in Human Genomics, September 2015
Altmetric Badge

Mentioned by

twitter
2 X users

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
51 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Copy number alterations detected by whole-exome and whole-genome sequencing of esophageal adenocarcinoma
Published in
Human Genomics, September 2015
DOI 10.1186/s40246-015-0044-0
Pubmed ID
Authors

Xiaoyu Wang, Xiaohong Li, Yichen Cheng, Xin Sun, Xibin Sun, Steve Self, Charles Kooperberg, James Y. Dai

Abstract

Esophageal adenocarcinoma (EA) is among the leading causes of cancer mortality, especially in developed countries. A high level of somatic copy number alterations (CNAs) accumulates over the decades in the progression from Barrett's esophagus, the precursor lesion, to EA. Accurate identification of somatic CNAs is essential to understand cancer development. Many studies have been conducted for the detection of CNA in EA using microarrays. Next-generation sequencing (NGS) technologies are believed to have advantages in sensitivity and accuracy to detect CNA, yet no NGS-based CNA detection in EA has been reported. In this study, we analyzed whole-exome (WES) and whole-genome sequencing (WGS) data for detecting CNA from a published large-scale genomic study of EA. Two specific comparisons were conducted. First, the recurrent CNAs based on WGS and WES data from 145 EA samples were compared to those found in five previous microarray-based studies. We found that the majority of the previously identified regions were also detected in this study. Interestingly, some novel amplifications and deletions were discovered using the NGS data. In particular, SKI and PRKCZ detected in a deletion region are involved in transforming growth factor-β pathway, suggesting the potential utility of novel biomarkers for EA. Second, we compared CNAs detected in WGS and WES data from the same 15 EA samples. No large-scale CNA was identified statistically more frequently by WES or WGS, while more focal-scale CNAs were detected by WGS than by WES. Our results suggest that NGS can replace microarrays to detect CNA in EA. WGS is superior to WES in that it can offer finer resolution for the detection, though if the interest is on recurrent CNAs, WES can be preferable to WGS for its cost-effectiveness.

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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 50 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 22%
Student > Master 8 16%
Student > Ph. D. Student 7 14%
Student > Doctoral Student 6 12%
Student > Bachelor 5 10%
Other 8 16%
Unknown 6 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 33%
Medicine and Dentistry 9 18%
Agricultural and Biological Sciences 9 18%
Computer Science 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 6 12%
Unknown 6 12%
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 19 November 2015.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from Human Genomics
#438
of 564 outputs
Outputs of similar age
#192,454
of 281,201 outputs
Outputs of similar age from Human Genomics
#9
of 10 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 564 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 281,201 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one.