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In Silico Integrative Approach Revealed Key MicroRNAs and Associated Target Genes in Cardiorenal Syndrome.

Authors :
Ishrat, Romana
Ahmed, Mohd Murshad
Tazyeen, Safia
Alam, Aftab
Farooqui, Anam
Ali, Rafat
Imam, Nikhat
Tamkeen, Naaila
Ali, Shahnawaz
Zubbair Malik, Md
Sultan, Armiya
Source :
Bioinformatics & Biology Insights; 6/30/2021, p1-8, 8p
Publication Year :
2021

Abstract

Cardiorenal syndromes constellate primary dysfunction of either heart or kidney whereby one organ dysfunction leads to the dysfunction of another. The role of several microRNAs (miRNAs) has been implicated in number of diseases, including hypertension, heart failure, and kidney diseases. Wide range of miRNAs has been identified as ideal candidate biomarkers due to their stable expression. Current study was aimed to identify crucial miRNAs and their target genes associated with cardiorenal syndrome and to explore their interaction analysis. Three differentially expressed microRNAs (DEMs), namely, hsa-miR-4476, hsa-miR-345-3p, and hsa-miR-371a-5p, were obtained from GSE89699 and GSE87885 microRNA data sets, using R/GEO2R tools. Furthermore, literature mining resulted in the retrieval of 15 miRNAs from scientific research and review articles. The miRNAs-gene networks were constructed using miRNet (a Web platform of miRNA-centric network visual analytics). CytoHubba (Cytoscape plugin) was adopted to identify the modules and the top-ranked nodes in the network based on Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality. The overlapped miRNAs were further used in pathway enrichment analysis. We found that hsa-miR-21-5p was common in 8 pathways out of the top 10. Based on the degree, 5 miRNAs, namely, hsa-mir-122-5p, hsa-mir-222-3p, hsa-mir-21-5p, hsa-mir-146a-5p, and hsa-mir-29b-3p, are considered as key influencing nodes in a network. We suggest that the identified miRNAs and their target genes may have pathological relevance in cardiorenal syndrome (CRS) and may emerge as potential diagnostic biomarkers. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
11779322
Database :
Complementary Index
Journal :
Bioinformatics & Biology Insights
Publication Type :
Academic Journal
Accession number :
151191366
Full Text :
https://doi.org/10.1177/11779322211027396