Single-Cell Sequencing Revealed Pivotal Genes Related to Prognosis of Myocardial Infarction Patients

Comput Math Methods Med. 2022 Mar 12:2022:6534126. doi: 10.1155/2022/6534126. eCollection 2022.

Abstract

Objectives: Myocardial infarction (MI) is a common cardiovascular disease. Histopathology is a main molecular characteristic of MI, but often, differences between various cell subsets have been neglected. Under this premise, MI-related molecular biomarkers were screened using single-cell sequencing.

Methods: This work examined immune cell abundance in normal and MI samples from GSE109048 and determined differences in the activated mast cells and activated CD4 memory T cells, resting mast cells. Weighted gene coexpression network analysis (WGCNA) demonstrated that activated CD4 memory T cells were the most closely related to the turquoise module, and 10 hub genes were screened. Single-cell sequencing data (scRNA-seq) of MI were examined. We used t-distributed stochastic neighbor embedding (t-SNE) for cell clustering.

Results: We obtained 8 cell subpopulations, each of which had different marker genes. 7 out of the 10 hub genes were detected by single-cell sequencing analysis. The expression quantity and proportion of the 7 genes were different in 8 cell clusters.

Conclusion: In general, our study revealed the immune characteristics and determined 7 prognostic markers for MI at the single-cell level, providing a new understanding of the molecular characteristics and mechanism of MI.

Publication types

  • Retracted Publication

MeSH terms

  • CD4-Positive T-Lymphocytes / immunology
  • Chemokines / genetics
  • Computational Biology
  • Gene Expression Profiling
  • Gene Ontology
  • Gene Regulatory Networks*
  • Genetic Markers* / immunology
  • Humans
  • Immunologic Memory / genetics
  • Mast Cells / immunology
  • Myocardial Infarction / genetics*
  • Myocardial Infarction / immunology*
  • Prognosis
  • RNA-Seq / methods
  • RNA-Seq / statistics & numerical data
  • Single-Cell Analysis / methods*
  • Single-Cell Analysis / statistics & numerical data
  • Stochastic Processes

Substances

  • Chemokines
  • Genetic Markers