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bioRxiv · 10.1101/2023.12.05.570061

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

Yu, Y.·Li, Y.
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Abstract

BackgroundIn 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. MethodsWe 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. Finally, we searched for drugs to treat age-related heart failure through the DSigDB database. ResultsWe 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. ConclusionsWe 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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Yu, Y., Li, Y.. 2023-12-07. Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms. https://doi.org/10.1101/2023.12.05.570061

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