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

Journal: Genes & diseases
Published Date: Dec 3, 2024

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.

Authors

  • Guoxing Li
    Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
  • Qingju Zhou
    Department of Health Management Center, Chongqing General Hospital, Chongqing University, Chongqing 400010, China.
  • Ming Xie
  • Boying Zhao
    Department of Cardiothoracic Surgery, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing University, Chongqing 400010, China.
  • Keyu Zhang
    Cardiovascular Disease Laboratory of Chongqing Medical University, Chongqing 400016, China.
  • Yuan Luo
    Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Lingwen Kong
    Institute of Intergrated Traditional Chinese and Western Medicine, Huashan Hospital Fudan University, Shanghai, China.
  • Diansa Gao
    Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
  • Yongzheng Guo
    Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.

Keywords

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