Identification and validation of key genes associated with cell senescence in acute myocardial infarction

Introduction Cellular senescence can cause heart failure. However, studies on diagnostic markers related to cellular senescence in acute myocardial infarction (AMI) have not been reported. Therefore, this study explores the mechanism of key genes related to cellular senescence in acute myocardial infarction (AMI) through a bioinformatics approach. Methods AMI related datasets were obtained from gene expression omnibus (GEO) database, and 3,058 cellular senescence related genes (CSRGs) were extracted from Molecular Signatures Database (MSigDB). First, differentially expressed genes (DEGs) were screened by differential expression analysis,and then Weighted gene co—expression network analysis (WGCNA) was performed to obtained the key module. CSRGs, DEGs and genes in key module were intersected to acquire intersected genes, and candidate genes were also screened out by constructing a protein protein interaction (PPI) network.Afterwards, candidate genes were then subjected to a machine learning approach to identify key genes and enrichment analyses were performed on individual genes Finally, immuno-infiltrative analysis, competing-endogenous RNA (ceRNA) and drug-gene networks construction were conducted. Besides, the expression of key genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Results Screening for two key genes (ATP6V0B and DYNLL1) from 914 DEGs, and they were involved in functional pathways such as ‘mast cell activation’, ‘cytosolic ribosome’. Thereafter, we found infiltration of neutrophils, CD4 T cells memory resting and T cells gamma delta was notably different between AMI and control samples. Pearson correlation analysis suggested that the neutrophils had highest positive correlation with ATP6V0B (Cor = 0.7), while had highest negative correlation with DYNLL1 (Cor = −0.6). The ceRNA network had one mRNA (DYNLL1), one miRNA (hsa-miR-330-5p) and four circRNAs. Eventually, drug—ATP6V0B network had 74 nodes and 73 edges, drug—DYNLL1 network had 119 nodes and 118 edges. qRT-PCR suggested that the expression trend of DYNLL1 was consistent with the result of bioinformatic analysis. Notably, DYNLL1 was significantly down-regulated in the case group. Conclusion Identified and validated DYNLL1 as a key gene related to cellular senescence in AMI, which is of great significance for the diagnosis and molecular targeted therapy of AMI.