Table 1_Identification and validation of key genes associated with cell senescence in acute myocardial infarction.xlsx

IntroductionCellular 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.
MethodsAMI 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).
ResultsScreening 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.
ConclusionIdentified 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.

引言：细胞衰老（Cellular senescence）可引发心力衰竭，但目前尚无关于急性心肌梗死（AMI）中与细胞衰老相关诊断标志物的研究报道。因此，本研究通过生物信息学方法，探讨急性心肌梗死（AMI）中与细胞衰老相关的关键基因作用机制。方法：本研究从基因表达综合数据库（GEO）获取急性心肌梗死相关数据集，并从分子特征数据库（MSigDB）中提取3058个细胞衰老相关基因（CSRGs）。首先通过差异表达分析筛选差异表达基因（DEGs），随后开展加权基因共表达网络分析（WGCNA）以获取关键模块。将细胞衰老相关基因、差异表达基因与关键模块中的基因取交集以获得交集基因，并通过构建蛋白质相互作用（PPI）网络筛选候选基因。之后，采用机器学习方法对候选基因进行分析以识别关键基因，并对单个基因开展富集分析。最后，进行免疫浸润分析、内源竞争RNA（ceRNA）网络与药物-基因网络构建。此外，通过实时定量聚合酶链式反应（qRT-PCR）验证关键基因的表达水平。结果：从914个差异表达基因中筛选得到2个关键基因ATP6V0B与DYNLL1，二者参与肥大细胞活化、胞质核糖体等功能通路。随后发现，急性心肌梗死样本与对照样本中，中性粒细胞、静息记忆性CD4+T细胞以及γδT细胞的浸润水平存在显著差异。皮尔逊相关分析显示，中性粒细胞与ATP6V0B呈最强正相关（相关系数=0.7），而与DYNLL1呈最强负相关（相关系数=-0.6）。内源竞争RNA（ceRNA）网络包含1个信使RNA（messenger RNA，mRNA）（DYNLL1）、1个微小RNA（microRNA，miRNA，hsa-miR-330-5p）以及4个环状RNA（circular RNA，circRNAs）。最终，药物-ATP6V0B网络包含74个节点与73条边，药物-DYNLL1网络包含119个节点与118条边。实时定量聚合酶链式反应（qRT-PCR）结果显示，DYNLL1的表达趋势与生物信息学分析结果一致，且在病例组中显著下调。结论：本研究鉴定并验证了DYNLL1为急性心肌梗死（AMI）中与细胞衰老相关的关键基因，其对急性心肌梗死的诊断与分子靶向治疗具有重要意义。