Abstract
Myocardial infarction (MI) is the most prevalent coronary heart disease caused by the complex molecular interactions between multiple genes and environment. Here, we aim to identify potential biomarkers for the disease development and for prognosis of MI. We have used gene expression dataset (GSE66360) generated from 51 healthy controls and 49 patients experiencing acute MI and analyzed the differentially expressed genes (DEGs), protein-protein interactions (PPI), gene network-clusters to annotate the candidate pathways relevant to MI pathogenesis. Bioinformatic analysis revealed 810 DEGs. Their functional annotations have captured several MI targeting biological processes and pathways like immune response, inflammation and platelets degranulation. PPI network identify seventeen hub and bottleneck genes, whose involvement in MI was further confirmed by DisGeNET database. OpenTarget Platform reveal unique bottleneck genes as potential target for MI. Our findings identify several potential biomarkers associated with early stage MI providing a new insight into molecular mechanism underlying the disease.
Highlights
Despite the improvement in its management, Myocardial infarction (MI) remains one of the most prevalent and challenging heart diseases due to high morbidity and mortality
Most significantly differentially expressed genes (DEGs) enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways are related to the following functions: Cytokine-cytokine receptor interaction, TNF and NFkB signaling
By constructing the protein-protein interactions (PPI) network using STRING and CytoHubba, seventeen hub and bottleneck genes were found, whose involvement in MI was further confirmed by DisGeNET data
Summary
Despite the improvement in its management, Myocardial infarction (MI) remains one of the most prevalent and challenging heart diseases due to high morbidity and mortality. Mechanisms underlying the changes that occur in immune cells and other signaling pathways during MI remain poorly elucidated. Identification of dysregulated individual genes and signaling pathways is crucial for understanding the pathogenic molecular changes in MI and for developing effective treatments with novel targets. Gene expression microarray studies are commonly used for high throughput biomarker identification. We applied integrated analysis of microarray data collected from MI patients and healthy control to identify and classify differentially expressed genes (DEGs) and to explore the reliable and unique MI-associated molecular signature profile and their key pathways and screened for potential MI target genes based on genetic association studies. Molecular exploration of gene expression changes in MI patients is very crucial not just to understand the molecular basis of disease development and to identify potential therapeutic targets. We aim to identify potential biomarkers for the disease development mechanisms and for prognosis of MI using extensive integrated biological network analysis
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