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An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells

Overview of attention for article published in BMC Genomics, January 2012
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2 tweeters

Citations

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20 Dimensions

Readers on

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41 Mendeley
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1 CiteULike
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Title
An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells
Published in
BMC Genomics, January 2012
DOI 10.1186/1471-2164-13-732
Pubmed ID
Authors

Seungyoon Nam, Xinghua Long, ChangHyuk Kwon, Sun Kim, Kenneth P Nephew

Abstract

A major goal of the field of systems biology is to translate genome-wide profiling data (e.g., mRNAs, miRNAs) into interpretable functional networks. However, employing a systems biology approach to better understand the complexities underlying drug resistance phenotypes in cancer continues to represent a significant challenge to the field. Previously, we derived two drug-resistant breast cancer sublines (tamoxifen- and fulvestrant-resistant cell lines) from the MCF7 breast cancer cell line and performed genome-wide mRNA and microRNA profiling to identify differential molecular pathways underlying acquired resistance to these important antiestrogens. In the current study, to further define molecular characteristics of acquired antiestrogen resistance we constructed an "integrative network". We combined joint miRNA-mRNA expression profiles, cancer contexts, miRNA-target mRNA relationships, and miRNA upstream regulators. In particular, to reduce the probability of false positive connections in the network, experimentally validated, rather than prediction-oriented, databases were utilized to obtain connectivity. Also, to improve biological interpretation, cancer contexts were incorporated into the network connectivity.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Denmark 1 2%
China 1 2%
Brazil 1 2%
Unknown 36 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 32%
Researcher 10 24%
Student > Master 6 15%
Student > Bachelor 3 7%
Professor > Associate Professor 2 5%
Other 3 7%
Unknown 4 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 46%
Medicine and Dentistry 10 24%
Biochemistry, Genetics and Molecular Biology 5 12%
Computer Science 3 7%
Economics, Econometrics and Finance 1 2%
Other 0 0%
Unknown 3 7%

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 28 December 2012.
All research outputs
#9,508,964
of 12,373,620 outputs
Outputs from BMC Genomics
#5,259
of 7,313 outputs
Outputs of similar age
#173,637
of 262,682 outputs
Outputs of similar age from BMC Genomics
#404
of 609 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,313 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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We're also able to compare this research output to 609 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.