Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms

Area of Science:

• Cardiovascular Research
• Gerontology
• Bioinformatics

Background:

• Growing elderly population necessitates understanding age-related cardiac decline.
• Identifying novel pathological and cardioprotective pathways is crucial for combating cardiac aging.

Purpose of the Study:

• To identify aging-related genes associated with heart failure.
• To elucidate the biological functions and signaling pathways involved in aging and heart failure.
• To discover potential therapeutic drugs for age-related heart failure.

Main Methods:

• Weighted gene co-expression network analysis (WGCNA) and CellAge database for gene identification.
• Gene Ontology (GO) and KEGG pathway enrichment analysis for functional insights.
• Machine learning algorithms (LASSO, RF, SVM-RFE) for gene screening and validation; DSigDB for drug discovery; CIBERSORT for immune infiltration analysis.

Main Results:

• Identified 57 up-regulated and 195 down-regulated aging-related genes in heart failure.
• Aging genes are primarily involved in cellular senescence and cell cycle.
• 14 key aging genes were identified and validated with high accuracy (0.911); Rimonabant and lovastatin show potential for treating age-related heart failure.
• Significant differences in Macrophages M2 and T cells CD8 were observed in aging myocardium.

Conclusions:

• Identified aging signature genes and potential therapeutic drugs for heart failure using bioinformatics and machine learning.
• Provides novel strategies for investigating the mechanisms and treatment of age-related cardiac decline.