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.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.Topol. centrality was applied to characterize the biol. importance of genes in the network. in order to identify the biol. functions and enrichment signaling pathways of DEGs, ToppFun and Funrich (Functional Enrichment anal. tool 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 ontol., 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 mol. 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.