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Regulatory network of circRNA–miRNA–mRNA contributes to the histological classification and disease progression in gastric cancer

Overview of attention for article published in Journal of Translational Medicine, August 2018
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Title
Regulatory network of circRNA–miRNA–mRNA contributes to the histological classification and disease progression in gastric cancer
Published in
Journal of Translational Medicine, August 2018
DOI 10.1186/s12967-018-1582-8
Pubmed ID
Authors

Jia Cheng, Huiqin Zhuo, Mao Xu, Linpei Wang, Hao Xu, Jigui Peng, Jingjing Hou, Lingyun Lin, Jianchun Cai

Abstract

Little has been known about the role of non-coding RNA regulatory network in the patterns of growth and invasiveness of gastric cancer (GC) development. MicroRNAs (miRNAs) microarray was used to screen differential miRNA expression profiles in Ming's classification. The significant differential expressions of representative miRNAs and their interacting circular RNA (circRNA) were confirmed in GC cell line and 63 pairs of GC samples. Then, a circRNA/miRNA network was constructed by bioinformatics approaches to identify molecular pathways. Finally, we explored the clinical value of the common targets in the pathway by using receiver operating characteristic curve and survival analysis. Significantly differential expressed miRNAs were found in two pathological types of GC. Both of miR-124 and miR-29b were consistently down-regulated in GC. CircHIPK3 could play a negative regulatory role on miR-124/miR-29b expression and associated with T stage and Ming's classification in GC. The bioinformatics analyses showed that targets expression of circHIPK3-miR-124/miR-29b axes in cancer-related pathways was able to predict the status of GC and associated with individual survival time. The targets of circHIPK3-miR-124/miR-29b axes involved in the progression of GC. CircHIPK3 could take part in the proliferation process of GC cell and may be potential biomarker in histological classification of GC.

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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 > Master 5 21%
Student > Bachelor 4 17%
Researcher 3 13%
Student > Ph. D. Student 2 8%
Lecturer 1 4%
Other 1 4%
Unknown 8 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 21%
Pharmacology, Toxicology and Pharmaceutical Science 2 8%
Agricultural and Biological Sciences 2 8%
Medicine and Dentistry 2 8%
Engineering 2 8%
Other 3 13%
Unknown 8 33%
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 03 August 2018.
All research outputs
#20,529,173
of 23,098,660 outputs
Outputs from Journal of Translational Medicine
#3,360
of 4,054 outputs
Outputs of similar age
#288,972
of 331,122 outputs
Outputs of similar age from Journal of Translational Medicine
#64
of 88 outputs
Altmetric has tracked 23,098,660 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,054 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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 88 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.