通过综合生物信息学分析和机器学习识别射血分数保留的心力衰竭中与衰老相关的基因特征

Identification of ageing-associated gene signatures in heart failure with preserved ejection fraction by integrated bioinformatics analysis and machine learning.

作者信息

Li Guoxing, Zhou Qingju, Xie Ming, Zhao Boying, Zhang Keyu, Luo Yuan, Kong Lingwen, Gao Diansa, Guo Yongzheng

机构信息

Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.

Cardiovascular Disease Laboratory of Chongqing Medical University, Chongqing 400016, China.

出版信息

Genes Dis. 2024 Dec 3;12(4):101478. doi: 10.1016/j.gendis.2024.101478. eCollection 2025 Jul.

Abstract

The incidence of heart failure with preserved ejection fraction (HFpEF) increases with the ageing of populations. This study aimed to explore ageing-associated gene signatures in HFpEF to develop new diagnostic biomarkers and provide new insights into the underlying mechanisms of HFpEF. Mice were subjected to a high-fat diet combined with L-NG-nitroarginine methyl ester (l-NAME) to induce HFpEF, and next-generation sequencing was performed with HFpEF hearts. Additionally, separate datasets were acquired from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were used to identify ageing-related DEGs. Support vector machine, random forest, and least absolute shrinkage and selection operator algorithms were employed to identify potential diagnostic genes from ageing-related DEGs. The diagnostic value was assessed using a nomogram and receiver operating characteristic curve. The gene and related protein expression were verified by reverse transcription PCR and western blotting. The immune cell infiltration in hearts was analysed using the single-sample gene-set enrichment analysis algorithm. The results showed that the merged HFpEF datasets comprised 103 genes, of which 15 ageing-related DEGs were further screened in. The ageing-related DEGs were primarily associated with immune and metabolism regulation. AGTR1a, NR3C1, and PRKAB1 were selected for nomogram construction and machine learning-based diagnostic value, displaying strong diagnostic potential. Additionally, ageing scores were established based on nine key DEGs, revealing noteworthy differences in immune cell infiltration across HFpEF subtypes. In summary, those results highlight the significance of immune dysfunction in HFpEF. Furthermore, ageing-related DEGs might serve as promising prognostic and predictive biomarkers for HFpEF.

摘要

射血分数保留的心力衰竭(HFpEF)的发病率随着人群老龄化而增加。本研究旨在探索HFpEF中与衰老相关的基因特征,以开发新的诊断生物标志物,并为HFpEF的潜在机制提供新的见解。对小鼠进行高脂饮食联合L-NG-硝基精氨酸甲酯(L-NAME)诱导HFpEF,并对HFpEF心脏进行下一代测序。此外,从基因表达综合数据库(GEO)获取单独的数据集。使用差异表达基因(DEG)来识别与衰老相关的DEG。采用支持向量机、随机森林和最小绝对收缩和选择算子算法从与衰老相关的DEG中识别潜在的诊断基因。使用列线图和受试者工作特征曲线评估诊断价值。通过逆转录PCR和蛋白质印迹法验证基因和相关蛋白的表达。使用单样本基因集富集分析算法分析心脏中的免疫细胞浸润。结果显示,合并后的HFpEF数据集包含103个基因,其中进一步筛选出15个与衰老相关的DEG。与衰老相关的DEG主要与免疫和代谢调节相关。选择AGTR1a、NR3C1和PRKAB1进行列线图构建和基于机器学习的诊断价值评估,显示出强大的诊断潜力。此外,基于9个关键DEG建立了衰老评分,揭示了HFpEF各亚型在免疫细胞浸润方面的显著差异。总之,这些结果突出了免疫功能障碍在HFpEF中的重要性。此外,与衰老相关的DEG可能作为HFpEF有前景的预后和预测生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddc7/12053710/ae81b12feb06/gr1.jpg

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