Bioinformatics-based screening of genes associated with dilated cardiomyopathy

Area of Science:

• Genomics
• Bioinformatics
• Cardiovascular Research

Background:

• Dilated cardiomyopathy (DCM) diagnosis and treatment are challenging due to a lack of biomarkers.
• Identifying reliable biomarkers is crucial for improving clinical management of DCM.

Purpose of the Study:

• To identify reliable biomarker genes for dilated cardiomyopathy (DCM) using bioinformatics approaches.
• To enhance the clinical management of DCM through biomarker discovery.

Main Methods:

• Downloaded and analyzed three DCM gene expression datasets (GSE120895, GSE42955, GSE3586) from the Gene Expression Omnibus (GEO).
• Utilized differential gene expression analysis, weighted gene coexpression network analysis (WGCNA), machine learning algorithms, and Protein-protein interaction (PPI) networks.
• Screened for differentially expressed genes and core genes within relevant coexpression modules.

Main Results:

• WGCNA identified the turquoise module as highly relevant to DCM.
• Machine learning and PPI network analyses identified 8 and 10 core genes, respectively.
• The HSPA8 gene was consistently identified as a core gene by both machine learning and PPI screening.

Conclusions:

• HSPA8 was identified as a core gene strongly associated with dilated cardiomyopathy (DCM).
• HSPA8 represents a potential diagnostic and therapeutic target for DCM.
• Further investigation of HSPA8 is warranted for clinical applications in DCM.