4.7 Article

Molecular Mechanisms Underlying the Progression of Aortic Valve Stenosis: Bioinformatic Analysis of Signal Pathways and Hub Genes

Journal

Publisher

MDPI
DOI: 10.3390/ijms24097964

Keywords

expression profiling; adhesion molecules; inflammation; endothelial cells; signaling

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This study investigated the molecular and cellular mechanisms underlying stenotic calcification of the aortic valve. Gene expression analysis of human aortic stenosis and normal aortic valve tissue identified differentially expressed genes (DEGs) associated with valve calcification and disease progression. Protein-protein interaction analysis revealed hub genes that could serve as potential biomarkers and therapeutic targets for aortic stenosis.
The calcification of the aortic valve causes increased leaflet stiffness and leads to the development and progression of stenotic aortic valve disease. However, the molecular and cellular mechanisms underlying stenotic calcification remain poorly understood. Herein, we examined the gene expression associated with valve calcification and the progression of calcific aortic valve stenosis. We downloaded two publicly available gene expression profiles (GSE83453 and GSE51472) from NCBI-Gene Expression Omnibus database for the combined analysis of samples from human aortic stenosis and normal aortic valve tissue. After identifying the differentially expressed genes (DEGs) using the GEO2R online tool, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We also analyzed the protein-protein interactions (PPIs) of the DEGs using the NetworkAnalyst online tool. We identified 4603 upregulated and 6272 downregulated DEGs, which were enriched in the positive regulation of cell adhesion, leukocyte-mediated immunity, response to hormones, cytokine signaling in the immune system, lymphocyte activation, and growth hormone receptor signaling. PPI network analysis identified 10 hub genes: VCAM1, FHL2, RUNX1, TNFSF10, PLAU, SPOCK1, CD74, SIPA1L2, TRIB1, and CXCL12. Through bioinformatic analysis, we identified potential biomarkers and therapeutic targets for aortic stenosis, providing a theoretical basis for future studies.

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