{"title":"心肌梗死中脂质代谢相关基因的鉴定:对诊断和治疗的意义。","authors":"Qiang Wang, Xian Wu, Bo Yu","doi":"10.1186/s13019-025-03525-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>MBOAT2, ABHD5, DGAT2, LCLAT1 and PLPPR2 were identified as biomarkers of MI, providing new ideas for diagnostic and therapeutic approaches.</p>","PeriodicalId":15201,"journal":{"name":"Journal of Cardiothoracic Surgery","volume":"20 1","pages":"289"},"PeriodicalIF":1.5000,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12239505/pdf/","citationCount":"0","resultStr":"{\"title\":\"Identification of lipid metabolism-related genes in myocardial infarction: implications for diagnosis and therapy.\",\"authors\":\"Qiang Wang, Xian Wu, Bo Yu\",\"doi\":\"10.1186/s13019-025-03525-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>MBOAT2, ABHD5, DGAT2, LCLAT1 and PLPPR2 were identified as biomarkers of MI, providing new ideas for diagnostic and therapeutic approaches.</p>\",\"PeriodicalId\":15201,\"journal\":{\"name\":\"Journal of Cardiothoracic Surgery\",\"volume\":\"20 1\",\"pages\":\"289\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2025-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12239505/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cardiothoracic Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13019-025-03525-4\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cardiothoracic Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13019-025-03525-4","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
背景:心肌梗死(MI)是一种严重且往往致命的心血管疾病,是全球死亡率和发病率的重要因素。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,影响脉管系统和心脏。我们旨在确定与脂质代谢相关的新型心肌梗死生物标志物和治疗靶点。材料和方法:从Gene Expression Omnibus (GEO)数据库中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)中获得。首先,我们使用“limma”包来鉴定差异表达基因(deg)。此外,我们利用加权基因共表达网络分析(WGCNA)来探索与心肌梗死相关的模块基因。通过交叉deg、模块基因和脂质代谢基因,我们确定了脂质代谢基因的差异表达。随后进行了基因本体(GO)和京都基因与基因组百科全书(KEGG)富集和蛋白质相互作用(PPI)分析。采用MCODE细胞图识别生物标志物,并应用受试者工作特征(ROC)曲线分析来衡量这些基因在区分心肌梗死患者和对照组中的区分能力。对生物标志物进行了涉及microrna和转录因子的调控网络分析。结果:总共鉴定出1760个DEGs,包括862个上调DEGs和898个下调DEGs。通过模块基因与脂质代谢相关基因重叠,鉴定出73个脂质代谢相关基因。氧化石墨烯分析强调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脂代谢过程。KEGG分析显示,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个已确定的脂质代谢相关基因构建了PPI网络,突出了5个生物标志物(MBOAT2, ABHD5, DGAT2, LCLAT1和PLPPR2)。结论:MBOAT2、ABHD5、DGAT2、LCLAT1和PLPPR2可作为心肌梗死的生物标志物,为心肌梗死的诊断和治疗提供了新的思路。
Identification of lipid metabolism-related genes in myocardial infarction: implications for diagnosis and therapy.
Background: 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.
Materials and methods: 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.
Results: 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.
Conclusion: MBOAT2, ABHD5, DGAT2, LCLAT1 and PLPPR2 were identified as biomarkers of MI, providing new ideas for diagnostic and therapeutic approaches.
期刊介绍:
Journal of Cardiothoracic Surgery is an open access journal that encompasses all aspects of research in the field of Cardiology, and Cardiothoracic and Vascular Surgery. The journal publishes original scientific research documenting clinical and experimental advances in cardiac, vascular and thoracic surgery, and related fields.
Topics of interest include surgical techniques, survival rates, surgical complications and their outcomes; along with basic sciences, pediatric conditions, transplantations and clinical trials.
Journal of Cardiothoracic Surgery is of interest to cardiothoracic and vascular surgeons, cardiothoracic anaesthesiologists, cardiologists, chest physicians, and allied health professionals.