The Bioinformatical Identification of Potential Biomarkers in Heart Failure Diagnosis and Treatment

Area of Science:

• Cardiovascular Biology
• Molecular Genetics
• Bioinformatics

Background:

• Heart failure (HF) is a complex condition characterized by impaired cardiac systolic and diastolic function.
• Understanding the underlying molecular mechanisms of HF is crucial for developing effective treatments.

Purpose of the Study:

• To investigate the molecular mechanisms contributing to heart failure (HF).
• To identify potential diagnostic and therapeutic biomarkers for HF.

Main Methods:

• Differential gene expression analysis using the GSE5406 database.
• Gene Ontology (GO), KEGG pathway, and Protein-Protein Interaction (PPI) network analyses were performed on differentially expressed genes (DEGs).
• Cell counting Kit-8 (CCK-8) assays were used to assess the functional impact of hub genes on cell proliferation.

Main Results:

• Identification of 377 upregulated and 461 downregulated DEGs, enriched in pathways related to extracellular matrix organization and gap junctions.
• Seven hub genes (COL1A1, UBB, COL3A1, HSP90AA1, MYC, STAT3, MAPK1) were identified through PPI network analysis.
• Knockdown of STAT3 promoted H9C2 cell proliferation, while UBB knockdown inhibited it, suggesting their roles in cardiac cell function.

Conclusions:

• The identified hub genes (COL1A1, UBB, COL3A1, HSP90AA1, MYC, STAT3, MAPK1) are implicated in promoting heart failure (HF) progression.
• These genes represent potential novel biomarkers for HF diagnosis and therapeutic targets.