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

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 su...

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Vydáno v:Genes & diseases Ročník 12; číslo 4; s. 101478
Hlavní autoři: Li, Guoxing, Zhou, Qingju, Xie, Ming, Zhao, Boying, Zhang, Keyu, Luo, Yuan, Kong, Lingwen, Gao, Diansa, Guo, Yongzheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier B.V 01.07.2025
Chongqing Medical University
KeAi Communications Co., Ltd
Témata:
Ageing
Bioinformatics analysis
Full Length
HFpEF
Immune dysfunction
Machine learning
Bioinformatics analysis
HFpEF
Immune dysfunction
Ageing
Machine learning
ISSN:2352-3042, 2352-4820, 2352-3042
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Shrnutí: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.
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These authors contributed equally to this work.
ISSN:2352-3042
2352-4820
2352-3042
DOI:10.1016/j.gendis.2024.101478