Table_3_Identification of PFKFB2 as a key gene for the transition from acute to old myocardial infarction in peripheral blood.XLSX

ObjectiveThis study aims to analyze the gene expression profile of peripheral blood in different stages of myocardial infarction (MI) by transcriptome sequencing, and to study the gene expression characteristics of peripheral blood after MI.MethodsDifferentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify genes and modules associated with old myocardial infarction (OMI). Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation were applied to analyze the potential functions of genes. Hub genes were identified by Random Forest Classifier. CIBERSORT was used to provide an estimate of the abundance of 22 immune cells in peripheral blood. Quantitative polymerase chain reaction (qPCR) was used to detect gene expression levels in clinical samples. The cellular components (CC) of peripheral blood were counted by an automatic hematology analyzer.ResultsThrough differential gene analysis and co-expression network analysis, 11 candidate genes were obtained. A random forest classifier identified 10 hub genes. Immune cell distribution of peripheral blood was found that T cell CD4 memory resting, NK cells resting, Dendritic cells activated, Mast cells resting, Monocytes and Neutrophils were correlated with OMI. Spearman correlation analysis found that PFKFB2 is related to the above immune cells. Low expression of PFKFB2 in peripheral blood of OMI was detected in clinical samples, and the relationship between PFKFB2 and peripheral blood immune cell counts was analyzed, which showed monocytes were associated with PFKFB2 in our study.ConclusionPFKFB2 was low expressed in OMI, and related to the distribution of immune cells. PFKFB2 may play a key role in reflecting the transition from AMI to OMI, and predicting the distribution of immune cells, which provided a new perspective for improving myocardial fibrosis and adverse remodeling.

本研究旨在通过转录组测序分析心肌梗死（MI）不同阶段的周围血液基因表达谱，并研究MI后周围血液的基因表达特征。方法上，运用差异表达基因（DEGs）和加权基因共表达网络分析（WGCNA）识别与陈旧性心肌梗死（OMI）相关的基因和模块。通过基因本体（GO）功能注释和京都基因与基因组百科全书（KEGG）通路注释对基因的潜在功能进行分析。采用随机森林分类器鉴定枢纽基因。CIBERSORT算法用于估计周围血液中22种免疫细胞的丰度。定量聚合酶链反应（qPCR）用于检测临床样本中的基因表达水平。自动血液分析仪用于计数周围血液的细胞成分（CC）。结果显示，通过差异基因分析和共表达网络分析，获得11个候选基因。随机森林分类器鉴定出10个枢纽基因。研究发现，T细胞CD4记忆静息状态、自然杀伤细胞静息状态、树突状细胞活化、肥大细胞静息状态、单核细胞和中性粒细胞与OMI相关。Spearman相关性分析发现，PFKFB2与上述免疫细胞相关。在临床样本中检测到OMI周围血液中PFKFB2的低表达，并分析了PFKFB2与周围血液免疫细胞计数之间的关系，研究表明单核细胞与PFKFB2在本研究中相关。结论表明，PFKFB2在OMI中表达降低，与免疫细胞的分布相关。PFKFB2可能在反映急性心肌梗死（AMI）向OMI转变、预测免疫细胞分布方面发挥关键作用，为改善心肌纤维化和不良重塑提供了新的视角。