Exploring potential genes and pathways related to calcific aortic valve disease

Area of Science:

• Cardiovascular Biology
• Genomics
• Bioinformatics

Background:

• Calcific aortic valve disease (CAVD) is the most common valvular heart disease.
• The underlying pathological mechanisms of CAVD remain incompletely understood.
• Current treatments for CAVD cannot halt or reverse disease progression.

Purpose of the Study:

• To identify potential biomarkers and molecular pathways associated with CAVD using bioinformatics analysis.
• To uncover novel therapeutic targets for the management of CAVD.

Main Methods:

• Differential gene expression analysis was performed on four public microarray datasets (GSE12644, GSE51472, GSE77287, GSE83453).
• Gene Ontology and KEGG pathway enrichment analyses were conducted.
• Protein-protein interaction and microRNA-target interaction networks were constructed to identify hub genes and regulatory miRNAs.

Main Results:

• 327 differentially expressed genes (DEGs) were identified between CAVD and control samples.
• Enrichment analysis revealed significant involvement in pathways such as PI3K-AKT signaling, ECM-receptor interaction, and cytokine-cytokine receptor interaction.
• Nineteen hub genes (e.g., CXCL8, CXCL12, VCAM1) and several microRNAs (e.g., miR-141, miR-34a) were identified as potential key players in CAVD pathogenesis.

Conclusions:

• This study highlights novel molecular biomarkers and pathways implicated in CAVD.
• The identified genes and miRNAs represent promising targets for future diagnostic and therapeutic strategies for CAVD.