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Myocyte enhancer factor 2D provides a cross-talk between chronic inflammation and lung cancer

Overview of attention for article published in Journal of Translational Medicine, March 2017
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Title
Myocyte enhancer factor 2D provides a cross-talk between chronic inflammation and lung cancer
Published in
Journal of Translational Medicine, March 2017
DOI 10.1186/s12967-017-1168-x
Pubmed ID
Authors

Hai-xing Zhu, Lin Shi, Yong Zhang, Yi-chun Zhu, Chun-xue Bai, Xiang-dong Wang, Jie-bai Zhou

Abstract

Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide. Patients with chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), are exposed to a higher risk of developing lung cancer. Chronic inflammation may play an important role in the lung carcinogenesis among those patients. The present study aimed at identifying candidate biomarker predicting lung cancer risk among patients with chronic respiratory diseases. We applied clinical bioinformatics tools to analyze different gene profile datasets with a special focus on screening the potential biomarker during chronic inflammation-lung cancer transition. Then we adopted an in vitro model based on LPS-challenged A549 cells to validate the biomarker through RNA-sequencing, quantitative real time polymerase chain reaction, and western blot analysis. Bioinformatics analyses of the 16 enrolled GSE datasets from Gene Expression Omnibus online database showed myocyte enhancer factor 2D (MEF2D) level significantly increased in COPD patients coexisting non-small-cell lung carcinoma (NSCLC). Inflammation challenge increased MEF2D expression in NSCLC cell line A549, associated with the severity of inflammation. Extracellular signal-regulated protein kinase inhibition could reverse the up-regulation of MEF2D in inflammation-activated A549. MEF2D played a critical role in NSCLC cell bio-behaviors, including proliferation, differentiation, and movement. Inflammatory conditions led to increased MEF2D expression, which might further contribute to the development of lung cancer through influencing cancer microenvironment and cell bio-behaviors. MEF2D might be a potential biomarker during chronic inflammation-lung cancer transition, predicting the risk of lung cancer among patients with chronic respiratory diseases.

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The data shown below were collected from the profile of 1 X user 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 21%
Researcher 4 17%
Other 2 8%
Professor 2 8%
Student > Bachelor 2 8%
Other 3 13%
Unknown 6 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 33%
Medicine and Dentistry 7 29%
Veterinary Science and Veterinary Medicine 1 4%
Neuroscience 1 4%
Engineering 1 4%
Other 0 0%
Unknown 6 25%
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 25 March 2017.
All research outputs
#15,451,618
of 22,961,203 outputs
Outputs from Journal of Translational Medicine
#2,250
of 4,013 outputs
Outputs of similar age
#194,525
of 309,205 outputs
Outputs of similar age from Journal of Translational Medicine
#44
of 72 outputs
Altmetric has tracked 22,961,203 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,013 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 31st percentile – i.e., 31% 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 309,205 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.