Table_2_Expression pattern and diagnostic value of ferroptosis-related genes in acute myocardial infarction.xlsx

BackgroundFerroptosis is a form of regulatory cell death (RCD) caused by iron-dependent lipid peroxidation. The role of ferroptosis in the process of acute myocardial infarction (AMI) is still unclear and requires further study. Therefore, it is helpful to identify ferroptosis related genes (FRGs) involved in AMI and explore their expression patterns and molecular mechanisms.
MethodsThe AMI-related microarray datasets GSE66360 and GSE61144 were obtained using the Gene Expression Omnibus (GEO) online database. GO annotation, KEGG pathway enrichment analysis and Protein-protein interaction (PPI) analysis were performed for the common significant differential expression genes (CoDEGs) in these two datasets. The FRGs were obtained from the FerrDb V2 and the differentially expressed FRGs were used to identify potential biomarkers by receiver operating characteristic (ROC) analysis. The expression of these FRGs was verified using external dataset GSE60993 and GSE775. Finally, the expression of these FRGs was further verified in myocardial hypoxia model.
ResultsA total of 131 CoDEGs were identified and these genes were mainly enriched in the pathways of “inflammatory response,” “immune response,” “plasma membrane,” “receptor activity,” “protein homodimerization activity,” “calcium ion binding,” “Phagosome,” “Cytokine-cytokine receptor interaction,” and “Toll-like receptor signaling pathway.” The top 7 hub genes ITGAM, S100A12, S100A9, TLR2, TLR4, TLR8, and TREM1 were identified from the PPI network. 45 and 14 FRGs were identified in GSE66360 and GSE61144, respectively. FRGs ACSL1, ATG7, CAMKK2, GABARAPL1, KDM6B, LAMP2, PANX2, PGD, PTEN, SAT1, STAT3, TLR4, and ZFP36 were significantly differentially expressed in external dataset GSE60993 with AUC ≥ 0.7. Finally, ALOX5, CAMKK2, KDM6B, LAMP2, PTEN, PTGS2, and ULK1 were identified as biomarkers of AMI based on the time-gradient transcriptome dataset of AMI mice and the cellular hypoxia model.
ConclusionIn this study, based on the existing datasets, we identified differentially expressed FRGs in blood samples from patients with AMI and further validated these FRGs in the mouse time-gradient transcriptome dataset of AMI and the cellular hypoxia model. This study explored the expression pattern and molecular mechanism of FRGs in AMI, providing a basis for the accurate diagnosis of AMI and the selection of new therapeutic targets.

背景
铁死亡（Ferroptosis）是一种由铁依赖性脂质过氧化引发的调控性细胞死亡（regulatory cell death, RCD）。目前铁死亡在急性心肌梗死（acute myocardial infarction, AMI）进程中的作用尚未明确，仍需进一步研究。因此，筛选参与急性心肌梗死的铁死亡相关基因（ferroptosis related genes, FRGs）并探究其表达模式与分子机制，具有重要的学术与临床价值。

方法
本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取了与急性心肌梗死相关的微阵列数据集GSE66360与GSE61144。对两个数据集中共有的显著差异表达基因（common significant differential expression genes, CoDEGs）开展基因本体（Gene Ontology, GO）注释、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析以及蛋白质相互作用（Protein-protein interaction, PPI）网络分析。从FerrDb V2数据库中获取铁死亡相关基因，通过受试者工作特征（receiver operating characteristic, ROC）分析筛选差异表达的铁死亡相关基因以识别潜在生物标志物。利用外部数据集GSE60993与GSE775验证上述铁死亡相关基因的表达水平，并进一步在心肌缺氧模型中验证其表达情况。

结果
本研究共筛选得到131个共同显著差异表达基因，这些基因主要富集于“炎症应答”“免疫应答”“质膜”“受体活性”“蛋白质同源二聚化活性”“钙离子结合”“吞噬体”“细胞因子-细胞因子受体相互作用”以及“Toll样受体信号通路”等通路。从蛋白质相互作用网络中筛选得到前7个核心基因：ITGAM、S100A12、S100A9、TLR2、TLR4、TLR8及TREM1。在数据集GSE66360与GSE61144中分别筛选得到45个与14个铁死亡相关基因。在外部数据集GSE60993中，铁死亡相关基因ACSL1、ATG7、CAMKK2、GABARAPL1、KDM6B、LAMP2、PANX2、PGD、PTEN、SAT1、STAT3、TLR4及ZFP36的受试者工作特征曲线下面积（Area Under Curve, AUC）≥0.7，差异具有统计学意义。最后，基于急性心肌梗死小鼠的时间梯度转录组数据集与细胞缺氧模型，最终筛选得到ALOX5、CAMKK2、KDM6B、LAMP2、PTEN、PTGS2及ULK1作为急性心肌梗死的生物标志物。

结论
本研究基于现有公共数据集，筛选得到急性心肌梗死患者血液样本中差异表达的铁死亡相关基因，并在急性心肌梗死小鼠的时间梯度转录组数据集与细胞缺氧模型中对其进行了验证。本研究探明了铁死亡相关基因在急性心肌梗死中的表达模式与分子机制，为急性心肌梗死的精准诊断以及新型治疗靶点的筛选提供了理论依据。