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Identifying global expression patterns and key regulators in epithelial to mesenchymal transition through multi-study integration

Overview of attention for article published in BMC Cancer, June 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

8 tweeters


18 Dimensions

Readers on

53 Mendeley
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Identifying global expression patterns and key regulators in epithelial to mesenchymal transition through multi-study integration
Published in
BMC Cancer, June 2017
DOI 10.1186/s12885-017-3413-3
Pubmed ID

Princy Parsana, Sarah R. Amend, James Hernandez, Kenneth J. Pienta, Alexis Battle


Epithelial to mesenchymal transition (EMT) is the process by which stationary epithelial cells transdifferentiate to mesenchymal cells with increased motility. EMT is integral in early stages of development and wound healing. Studies have shown that EMT could be a critical early event in tumor metastasis that is involved in acquisition of migratory and invasive properties in multiple carcinomas. In this study, we used 15 published gene expression microarray datasets from Gene Expression Omnibus (GEO) that represent 12 cell lines from 6 cancer types across 95 observations (45 unique samples and 50 replicates) with different modes of induction of EMT or the reverse transition, mesenchymal to epithelial transition (MET). We integrated multiple gene expression datasets while considering study differences, batch effects, and noise in gene expression measurements. A universal differential EMT gene list was obtained by normalizing and correcting the data using four approaches, computing differential expression from each, and identifying a consensus ranking. We confirmed our discovery of novel EMT genes at mRNA and protein levels in an in vitro EMT model of prostate cancer - PC3 epi, EMT and Taxol resistant cell lines. We validate our discovery of C1orf116 as a novel EMT regulator by siRNA knockdown of C1orf116 in PC3 epithelial cells. Among differentially expressed genes, we found known epithelial and mesenchymal marker genes such as CDH1 and ZEB1. Additionally, we discovered genes known in a subset of carcinomas that were unknown in prostate cancer. This included epithelial specific LSR and S100A14 and mesenchymal specific DPYSL3. Furthermore, we also discovered novel EMT genes including a poorly-characterized gene C1orf116. We show that decreased expression of C1orf116 is associated with poor prognosis in lung and prostate cancer patients. We demonstrate that knockdown of C1orf116 expression induced expression of mesenchymal genes in epithelial prostate cancer cell line PC3-epi cells, suggesting it as a candidate driver of the epithelial phenotype. This comprehensive approach of statistical analysis and functional validation identified global expression patterns in EMT and candidate regulatory genes, thereby both extending current knowledge and identifying novel drivers of EMT.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 23%
Student > Bachelor 12 23%
Student > Master 5 9%
Student > Doctoral Student 3 6%
Researcher 3 6%
Other 5 9%
Unknown 13 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 30%
Agricultural and Biological Sciences 9 17%
Computer Science 3 6%
Engineering 2 4%
Medicine and Dentistry 2 4%
Other 5 9%
Unknown 16 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 13 October 2017.
All research outputs
of 13,669,352 outputs
Outputs from BMC Cancer
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Outputs of similar age
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Outputs of similar age from BMC Cancer
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Altmetric has tracked 13,669,352 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,121 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 84% 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 264,149 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them