Shandong University of Traditional Chinese Medicine, Jinan, China.
Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Front Immunol. 2024 Apr 15;15:1367235. doi: 10.3389/fimmu.2024.1367235. eCollection 2024.
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.
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.
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.
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.
面对老年人口的持续增长，了解和对抗与年龄相关的心脏衰退变得更加紧迫，这需要我们揭示新的病理和心脏保护途径。
我们通过 WGCNA 和 CellAge 数据库获得了心力衰竭相关的衰老基因。通过 GO 和 KEGG 富集分析，我们阐明了心力衰竭和衰老相关的生物学功能和信号通路。我们使用了三种机器学习算法：LASSO、RF 和 SVM-RFE，进一步筛选心力衰竭相关的衰老基因，并通过多种机器学习算法进行拟合和验证。我们通过 DSigDB 数据库搜索治疗与年龄相关的心力衰竭的药物。最后，我们使用 CIBERSORT 完成衰老样本的免疫浸润分析。
通过 WGCNA 和 CellAge 数据库，我们获得了心力衰竭中 57 个上调和 195 个下调的衰老相关基因。GO 和 KEGG 富集分析表明，衰老相关基因主要涉及细胞衰老和细胞周期等机制。我们通过机器学习进一步筛选衰老相关基因，得到了 14 个关键基因。我们在测试集和 2 个外部验证集上使用了 15 种机器学习算法模型和 207 种组合进行了验证，最高准确率为 0.911。通过 DSigDB 数据库的筛选，我们认为利莫那班和洛伐他汀具有延缓衰老和保护心脏的潜力。免疫浸润分析的结果表明，衰老心肌中巨噬细胞 M2 和 T 细胞 CD8 之间存在显著差异。
我们通过生物信息学和多种机器学习算法鉴定了心力衰竭的衰老特征基因和潜在治疗药物，为研究与年龄相关的心脏衰退的机制和治疗提供了新的思路。
