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Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages

Overview of attention for article published in BMC Cancer, December 2017
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages
Published in
BMC Cancer, December 2017
DOI 10.1186/s12885-017-3832-1
Pubmed ID
Authors

Daifeng Wang, John D. Haley, Patricia Thompson

Abstract

The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown. Using comparative network and clustering analysis, we systematically analyzed time-series gene expression data from lung cancer cell lines H358 and A549 that were induced to undergo EMT. We also predicted the putative regulatory networks controlling EMT expression dynamics, especially for the EMT-dynamic genes and related these patterns to patient outcomes using data from TCGA. Example EMT hub regulatory genes were validated using RNAi. We identified several novel genes distinct from the static states of E or M that exhibited temporal expression patterns or 'periods' during the EMT process that were shared in different lung cancer cell lines. For example, cell cycle and metabolic genes were found to be similarly down-regulated where immune-associated genes were up-regulated after middle EMT stages. The presence of EMT-dynamic gene expression patterns supports the presence of differential activation and repression timings at the transcriptional level for various pathways and functions during EMT that are not detected in pure E or M cells. Importantly, the cell line identified EMT-dynamic genes were found to be present in lung cancer patient tissues and associated with patient outcomes. Our study suggests that in vitro identified EMT-dynamic genes capture elements of gene EMT expression dynamics at the patient level. Measurement of EMT dynamic genes, as opposed to E or M only, is potentially useful in future efforts aimed at classifying patient's responses to treatments based on the EMT dynamics in the tissue.

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X Demographics

The data shown below were collected from the profiles of 9 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Researcher 5 18%
Student > Master 3 11%
Other 2 7%
Student > Doctoral Student 1 4%
Other 4 14%
Unknown 7 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 32%
Agricultural and Biological Sciences 4 14%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Mathematics 1 4%
Computer Science 1 4%
Other 3 11%
Unknown 9 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 15 February 2018.
All research outputs
#6,389,918
of 24,271,113 outputs
Outputs from BMC Cancer
#1,572
of 8,620 outputs
Outputs of similar age
#119,991
of 448,231 outputs
Outputs of similar age from BMC Cancer
#43
of 176 outputs
Altmetric has tracked 24,271,113 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 8,620 research outputs from this source. They receive a mean Attention Score of 4.5. 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 448,231 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 176 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.