Wang Qiang, Wu Xian, Yu Bo
Department of Emergency, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China.
J Cardiothorac Surg. 2025 Jul 9;20(1):289. doi: 10.1186/s13019-025-03525-4.
Myocardial infarction(MI), a severe and often fatal cardiovascular condition, strongly contributes to global mortality and morbidity. Lipids are critical underlying factors in cardiovascular disease. They influence inflammatory responses and modulate leukocyte, vascular cell and cardiac cell functions, affecting the vasculature and heart. We aimed to identify novel biomarkers and therapeutic targets for MI that are linked to lipid metabolism.
Endothelial cell transcriptomes from MI patients and controls were downloaded from the Gene Expression Omnibus (GEO) database. Lipid metabolism genes were obtained from the Molecular Signatures Database (MSigDB). First, we employed the "limma" package to identify differentially expressed genes (DEGs). Moreover, we utilized weighted gene coexpression network analysis (WGCNA) to explore the module genes involved in MI. By intersecting the DEGs, module genes, and lipid metabolism genes, we pinpointed the differentially expressed lipid metabolism genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and protein‒protein interaction (PPI) analyses were subsequently conducted. Cytoscape with MCODE was adopted to identify biomarkers, and receiver operating characteristic (ROC) curve analysis was applied to gauge the discriminatory power of these genes in distinguishing MI patients from controls. Regulatory network analysis involving microRNAs and transcription factors was performed for biomarkers.
Overall, 1760 DEGs, comprising 862 upregulated and 898 downregulated DEGs, were identified. By overlapping the module genes and lipid metabolism-related genes, 73 lipid metabolism-related genes were identified. GO analysis highlighted the most significantly enriched terms, including fatty acid metabolic process, regulation of lipid metabolism, and glycerolipid metabolic process. KEGG analysis revealed that these genes were enriched in pathways such as adipocytokine signalling, arachidonic acid metabolism, and cholesterol metabolism. We constructed a PPI network from the 73 identified lipid metabolism-related genes, highlighting 5 biomarkers (MBOAT2, ABHD5, DGAT2, LCLAT1 and PLPPR2). The expression of the 5 biomarkers significantly differed between the MI patients and the controls (P < 0.05). The area under the ROC curve (AUC) of all the biomarkers was greater than 0.7.
MBOAT2, ABHD5, DGAT2, LCLAT1 and PLPPR2 were identified as biomarkers of MI, providing new ideas for diagnostic and therapeutic approaches.
心肌梗死（MI）是一种严重且往往致命的心血管疾病，对全球死亡率和发病率有重大影响。脂质是心血管疾病的关键潜在因素。它们影响炎症反应，调节白细胞、血管细胞和心脏细胞功能，进而影响血管系统和心脏。我们旨在识别与脂质代谢相关的心肌梗死新生物标志物和治疗靶点。
从基因表达综合数据库（GEO）下载心肌梗死患者和对照组的内皮细胞转录组。脂质代谢基因从分子特征数据库（MSigDB）获取。首先，我们使用“limma”软件包识别差异表达基因（DEG）。此外，我们利用加权基因共表达网络分析（WGCNA）探索参与心肌梗死的模块基因。通过将差异表达基因、模块基因和脂质代谢基因进行交叉分析，我们确定了差异表达的脂质代谢基因。随后进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析以及蛋白质-蛋白质相互作用（PPI）分析。采用带有MCODE的Cytoscape软件识别生物标志物，并应用受试者工作特征（ROC）曲线分析来评估这些基因区分心肌梗死患者和对照组的鉴别能力。对生物标志物进行涉及微小RNA和转录因子的调控网络分析。
总体而言，共识别出1760个差异表达基因，其中862个上调，898个下调。通过将模块基因与脂质代谢相关基因进行重叠分析，确定了73个脂质代谢相关基因。GO分析突出了最显著富集的术语，包括脂肪酸代谢过程、脂质代谢调节和甘油酯代谢过程。KEGG分析表明，这些基因在脂肪细胞因子信号传导、花生四烯酸代谢和胆固醇代谢等途径中富集。我们从73个已识别的脂质代谢相关基因构建了一个PPI网络，突出了5个生物标志物（MBOAT2、ABHD5、DGAT2、LCLAT1和PLPPR2）。这5个生物标志物在心肌梗死患者和对照组之间的表达存在显著差异（P < 0.05）。所有生物标志物的ROC曲线下面积（AUC）均大于0.7。
MBOAT2、ABHD5、DGAT2、LCLAT1和PLPPR2被确定为心肌梗死的生物标志物，为诊断和治疗方法提供了新思路。
