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Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms
Yiding Yu1, Lin Wang1, Wangjun Hou1
1Shandong University of Traditional Chinese Medicine, Jinan, China.
Insights
This study identifies key aging genes and potential drugs for heart failure, offering new insights into age-related cardiac decline and its treatment. It highlights cellular senescence and cell cycle pathways.
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.
Background:
In the face of continued growth in the elderly population, the need to understand and combat age-related cardiac decline becomes even more urgent, requiring us to uncover new pathological and cardioprotective pathways.
Methods:
We obtained the aging-related genes of heart failure through WGCNA and CellAge database. We elucidated the biological functions and signaling pathways involved in heart failure and aging through GO and KEGG enrichment analysis. We used three machine learning algorithms: LASSO, RF and SVM-RFE to further screen the aging-related genes of heart failure, and fitted and verified them through a variety of machine learning algorithms. We searched for drugs to treat age-related heart failure through the DSigDB database. Finally, We use CIBERSORT to complete immune infiltration analysis of aging samples.
Results:
We obtained 57 up-regulated and 195 down-regulated aging-related genes in heart failure through WGCNA and CellAge databases. GO and KEGG enrichment analysis showed that aging-related genes are mainly involved in mechanisms such as Cellular senescence and Cell cycle. We further screened aging-related genes through machine learning and obtained 14 key genes. We verified the results on the test set and 2 external validation sets using 15 machine learning algorithm models and 207 combinations, and the highest accuracy was 0.911. Through screening of the DSigDB database, we believe that rimonabant and lovastatin have the potential to delay aging and protect the heart. The results of immune infiltration analysis showed that there were significant differences between Macrophages M2 and T cells CD8 in aging myocardium.
Conclusion:
We identified aging signature genes and potential therapeutic drugs for heart failure through bioinformatics and multiple machine learning algorithms, providing new ideas for studying the mechanism and treatment of age-related cardiac decline.
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