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Title

Computational study of ‘HUB’ microRNA in human cardiac diseases

 

Authors

Remya Krishnan1, Achuthsankar S. Nair1, Pawan K. Dhar*,1,2

 

Affiliation

1Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, Kerala – 695 581;

2School of Biotechnology, Jawaharlal Nehru University, New Delhi 110067

 

Email

Dr. Pawan K. Dhar – E-mail: pawan.dhar@mail.jnu.ac.in; Phone: +911126738887; *Corresponding author

 

Article Type

Hypothesis

 

Date

Received July 29, 2017; Revised January 1, 2017; Accepted January 5, 2017; Published January 24, 2017

 

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs ~22 nucleotides long that do not encode for proteins but have been reported to influence gene expression in normal and abnormal health conditions. Though a large body of scientific literature on miRNAs exists, their network level profile linking molecules with their corresponding phenotypes, is less explored. Here, we studied a network of 191 human miRNAs reported to play a role in 30 human cardiac diseases. Our aim was to study miRNA network properties like hubness and preferred associations, using data mining, network graph theory and statistical analysis. A total of 16 miRNAs were found to have a disease node connectivity of >5 edges (i.e., they were linked to more than 5 diseases) and were considered hubs in the miRNA cardiac disease network. Alternatively, when diseases were considered as hubs, >10 of miRNAs showed up on each ‘disease hub node’. Of all the miRNAs associated with diseases, 19 miRNAs (19/24= 79.1% of upregulated events) were found to be upregulated in atherosclerosis. The data suggest micro RNAs as early stage biological markers in cardiac conditions with potential towards microRNA
based therapeutics.

 

Keywords

disease network, dys-regulation, miRNAs, statistical analysis

 

Citation

Krishnan et al. Bioinformation 13(1): 17-20 (2017)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.