Network centrality approaches used to uncover and classify most influential nodes with their related miRNAs in cardiovascular diseases

Abstract

Cardiovascular diseases (CVDs) is the diseases of the heart and blood vessels such as hypertension, coronary artery disease, peripheral artery disease, stroke, heart disease congenital, cardiac arrest and heart failure. CVDs is the leading cause of death worldwide, with around 17.9 million fatalities in 2019. In this study we identified hub genes (which could be used as new biomarkers or therapeutic targets in CVD) and pathways associated with CVD in infants based on gene expression profiles. Despite the discovery of a number of potential biomarkers, it is unlikely that a single biomarker can help definitively classify CVD. A total 24 Differentially expressed genes (DEGs) between CVD and normal (controls) infants were identified based on linear modeling of the microarray data using Limma package in GEO2R. A protein-protein interaction (PPI) network (with 222 nodes and 2992 interaction/edges) was constructed using the STRING (available online, at https://string-db.org/). Based on primary measures of centrality, four significant genes Osteoglycin (OGN), Toll-like receptor 3 (TLR3)s, C3 (Complement component 3), and Nicotinamide Phosphoribosyl transferase (NAMPT) were revealed using Cytoscape's plugin (Cytohubba, CytoNCA, Centiscape, Network Analyzer) and IVI graph packages in R. Topological centrality was applied to characterize the biological importance of genes in the network. in order to identify the biological functions and enrichment signaling pathways of DEGs, ToppFun (https://toppgene.cchmc.org/enrichment.jsp) and Funrich (Functional Enrichment analysis tool http://funrich.org/) were used. Further, these hub genes were uploaded to the miRNet database to find their association with microRNAs (A network with 47 nodes and 85 edges). Finally, four core miRNAs, has-miR-210-3p, has-miR-133a-3p, has-miR-129-2-3p, and has-miR-124-3p, were employed in mienturnet for disease ontology, with three key genes in common between two centralities (Degree and Betweenness). Finally, these hub genes were uploaded to the DGIdb4.0 database to find their association with Drugs. The resultant molecular studies found TLR3 interaction with rintatolimod. The goal of this study is to uncover important genes linked to CVD and further investigate their prognostic significance for its early detection and effective therapies.