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A comparison of gene expression profiles in patients with coronary artery disease, type 2 diabetes, and their coexisting conditions

Overview of attention for article published in Diagnostic Pathology, June 2017
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Title
A comparison of gene expression profiles in patients with coronary artery disease, type 2 diabetes, and their coexisting conditions
Published in
Diagnostic Pathology, June 2017
DOI 10.1186/s13000-017-0630-7
Pubmed ID
Authors

Rui Gong, Menghui Chen, Cuizhao Zhang, Manli Chen, Haibin Li

Abstract

To support a hypothesis that there is an intrinsic interplay between coronary artery disease (CAD) and type 2 diabetes (T2D), we used RNA-seq to identify unique gene expression signatures of CAD, T2D, and coexisting conditions. After transcriptome sequencing, differential expression analysis was performed between each disordered state and normal control group. By comparing gene expression profiles of CAD, T2D, and coexisting conditions, common and specific patterns of each disordered state were displayed. To verify the specific gene expression patterns of CAD or T2D, the gene expression data of GSE23561 was extracted. A strong overlap of 191 genes across CAD, T2D and coexisting conditions, were mainly involved in a viral infectious cycle, anti-apoptosis, endocrine pancreas development, innate immune response, and blood coagulation. In T2D-specific PPI networks involving 64 genes, TCF7L2 (Degree = 169) was identified as a key gene in T2D development, while in CAD-specific PPI networks involving 64 genes, HIF1A (Degree = 124), SMAD1 (Degree = 112) and SKIL (Degree = 94) were identified as key genes in the CAD development. Interestingly, with the provided expression data from GSE23561, the three genes were all up-regulated in CAD, and SMAD1 and SKIL were specifically differentially expressed in CAD, while HIF1A was differentially expressed in both CAD and T2D, but with opposite trends. This study provides some evidences in transcript level to uncover the association of T2D, CAD and coexisting conditions, and may provide novel drug targets and biomarkers for these diseases.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 19%
Student > Master 2 13%
Other 2 13%
Student > Doctoral Student 1 6%
Librarian 1 6%
Other 5 31%
Unknown 2 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 38%
Medicine and Dentistry 3 19%
Agricultural and Biological Sciences 2 13%
Nursing and Health Professions 1 6%
Unknown 4 25%

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 10 August 2017.
All research outputs
#9,273,971
of 12,071,979 outputs
Outputs from Diagnostic Pathology
#415
of 743 outputs
Outputs of similar age
#179,168
of 272,591 outputs
Outputs of similar age from Diagnostic Pathology
#7
of 11 outputs
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So far Altmetric has tracked 743 research outputs from this source. They receive a mean Attention Score of 2.3. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.