Identification of potential biomarkers associated with CD4 T cell infiltration in myocardial ischemia-reperfusion injury using bioinformation analysis.

Background: Myocardial ischemia-reperfusion injury (MIRI) is often part of clinical events such as cardiac arrest, resuscitation, and reperfusion after coronary artery occlusion. Recently, more and more studies have shown that the immune microenvironment is an integral part of ischemia-reperfusion injury (IRI), and CD4 T-cell infiltration plays an important role, but there are no relevant molecular targets for clinical diagnosis and treatment.

Methods: The transcriptome data and matched group information were retrieved from the Gene Expression Omnibus (GEO) database. The ImmuCellAI-mouse (Immune Cell Abundance Identifier for mouse) algorithm was used to calculate each symbol's CD4 T cell infiltration score. The time period with the greatest change in the degree of CD4 T cell infiltration [ischemia-reperfusion 6 hours (IR6h)-ischemia-reperfusion 24 hours (IR24h)] was selected for the next analysis. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to screen out CD4 T cell-related genes and from which the gene was screened for the highest correlation with CD4 T cell infiltration. The potential regulatory mechanism of CD4 T cells in MIRI was discussed through various enrichment analysis. Finally, we analyzed the expression and molecular function (MF) of and its related genes in MIRI.

Results: A total of 406 CD4 T cell-related genes were obtained by intersecting the results of WGCNA and differential expression analysis. Functional enrichment analysis indicated that the CD4 T cell-related genes were mainly involved in chemokine signaling pathway and cell cycle. By constructing a protein-protein interaction (PPI) network, a total of 12 hub genes were identified as candidate genes for further analysis. Through the correlation analysis between the 12 candidate genes found in the PPI network and CD4 T cell infiltration fraction, we determined the core gene . Finally, a gene interaction network was constructed to decipher the biological functions of using GeneMANIA.

Conclusions: In this study, RNA sequencing (RNA-Seq) data at different time points after reperfusion were subjected to a series of bioinformatics methods such as PPI network, WGCNA module, etc., and , a pivotal gene associated with CD4 T-cells, was found, which may serve as a new target for diagnosis or treatment.