Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction

Front Public Health. 2022 Jun 9;10:894129. doi: 10.3389/fpubh.2022.894129. eCollection 2022.


Acute myocardial infarction (AMI) has a high mortality. The single-cell RNA sequencing (scRNA-seq) method was used to analyze disease heterogeneity at the single-cell level. From the Gene Expression Omnibus (GEO) database (GSE180678), AMI scRNA-seq were downloaded and preprocessed by the Seurat package. Gene expression data came from GSE182923. Cell cluster analysis was conducted. Cell types were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed on hub genes. Drugs were predicted by protein-protein interaction (PPI) and molecular docking. In total, 7 cell clusters were defined based on the scRNA-seq dataset, and the clusters were labeled as 5 cell types by marker genes. Hematopoietic stem cell types as a differential subgroups were higher in AMI than in healthy tissues. From available databases and PPI analysis, 52 common genets were identified. Based on 52 genes, 5 clusters were obtained using the MCODE algorithm, and genes in these 5 clusters involved in immune and inflammatory pathways were determined. Correlation analysis showed that hematopoietic stem cell types were negatively correlated with ATM, CARM1, and CASP8 but positively correlated with CASP3 and PPARG. This was reversed with immune cells. Molecular docking analysis showed that DB05490 had the lowest docking score with PPARG. We identified 5 hub genes (ATM, CARM1, CASP8, CASP3, and PPARG) involved in AMI progression. Compound DB05490 was a potential inhibitor of PPAG.

Keywords: acute myocardial infarction; cellular subpopulations; molecular docking; network pharmacology; single-cell RNA sequencing; therapeutic genes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Caspase 3 / genetics
  • Computational Biology / methods
  • Databases, Genetic
  • Gene Expression Profiling / methods
  • Humans
  • Molecular Docking Simulation
  • Myocardial Infarction* / genetics
  • Network Pharmacology
  • PPAR gamma / genetics
  • Protein Interaction Maps* / genetics
  • Sequence Analysis, RNA


  • PPAR gamma
  • Caspase 3