Identification of lipid metabolism-related genes in myocardial infarction: implications for diagnosis and therapy
Abstract 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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | Journal of Cardiothoracic Surgery |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13019-025-03525-4 |
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| Summary: | Abstract 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. |
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| ISSN: | 1749-8090 |