↓ Skip to main content

Investigating MicroRNA and transcription factor co-regulatory networks in colorectal cancer

Overview of attention for article published in BMC Bioinformatics, September 2017
Altmetric Badge

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
38 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Investigating MicroRNA and transcription factor co-regulatory networks in colorectal cancer
Published in
BMC Bioinformatics, September 2017
DOI 10.1186/s12859-017-1796-4
Pubmed ID
Authors

Hao Wang, Jiamao Luo, Chun Liu, Huilin Niu, Jing Wang, Qi Liu, Zhongming Zhao, Hua Xu, Yanqing Ding, Jingchun Sun, Qingling Zhang

Abstract

Colorectal cancer (CRC) is one of the most common malignancies worldwide with poor prognosis. Studies have showed that abnormal microRNA (miRNA) expression can affect CRC pathogenesis and development through targeting critical genes in cellular system. However, it is unclear about which miRNAs play central roles in CRC's pathogenesis and how they interact with transcription factors (TFs) to regulate the cancer-related genes. To address this issue, we systematically explored the major regulation motifs, namely feed-forward loops (FFLs), that consist of miRNAs, TFs and CRC-related genes through the construction of a miRNA-TF regulatory network in CRC. First, we compiled CRC-related miRNAs, CRC-related genes, and human TFs from multiple data sources. Second, we identified 13,123 3-node FFLs including 25 miRNA-FFLs, 13,005 TF-FFLs and 93 composite-FFLs, and merged the 3-node FFLs to construct a CRC-related regulatory network. The network consists of three types of regulatory subnetworks (SNWs): miRNA-SNW, TF-SNW, and composite-SNW. To enhance the accuracy of the network, the results were filtered by using The Cancer Genome Atlas (TCGA) expression data in CRC, whereby we generated a core regulatory network consisting of 58 significant FFLs. We then applied a hub identification strategy to the significant FFLs and found 5 significant components, including two miRNAs (hsa-miR-25 and hsa-miR-31), two genes (ADAMTSL3 and AXIN1) and one TF (BRCA1). The follow up prognosis analysis indicated all of the 5 significant components having good prediction of overall survival of CRC patients. In summary, we generated a CRC-specific miRNA-TF regulatory network, which is helpful to understand the complex CRC regulatory mechanisms and guide clinical treatment. The discovered 5 regulators might have critical roles in CRC pathogenesis and warrant future investigation.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 24%
Researcher 9 24%
Student > Ph. D. Student 7 18%
Student > Postgraduate 4 11%
Student > Doctoral Student 2 5%
Other 3 8%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 24%
Biochemistry, Genetics and Molecular Biology 8 21%
Medicine and Dentistry 6 16%
Mathematics 2 5%
Nursing and Health Professions 2 5%
Other 3 8%
Unknown 8 21%

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 14 December 2017.
All research outputs
#9,427,137
of 12,298,022 outputs
Outputs from BMC Bioinformatics
#3,518
of 4,474 outputs
Outputs of similar age
#178,195
of 268,476 outputs
Outputs of similar age from BMC Bioinformatics
#77
of 102 outputs
Altmetric has tracked 12,298,022 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,474 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% 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 268,476 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.