ORIGINAL RESEARCH article
Front. Immunol.
Sec. Inflammation
This article is part of the Research TopicExploring Cardiovascular and Cerebrovascular Diseases Interaction with Inflammation: Biomarkers, Drug Targets, and Personalized Treatments through Multi-omics Data Integration, Volume IIView all 7 articles
Identification of immune-relatedgenes in diagnosing hypercholesterolemia with myocardial infarction through bioinformatics analysis
Provisionally accepted
- 1Department of Cardiology, The First Affiliated Hospital of Xiamen University, Xiamen, China
- 2Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cell Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
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Background: Increasing evidence suggests that familial hypercholesterolemia (FHC) exacerbates myocardial infarction (MI). This study aimed to identify possible candidate biomarkers for patients with FHC and MI. Methods: The data were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using Limma, while module genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA) in GSE48060. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, protein-protein interaction (PPI) network and CIBERSORT methods were performed to explore the intersection genes. A receiver operating characteristic (ROC) curve were employed to evaluate the diagnostic effectiveness, with validation conducted using datasets GSE61144 and RT-qPCR. Results: The FHC datasets included 656 DEGs, while there were 956 DEGs and 90 module genes in MI datasets. There were 49 overlapping DEGs between FHC and MI, which were associated with immune functions. Additionally, immune infiltration analysis revealed disturbances in immune cell populations. There were 13 candiate hub genes were screen after PPI network analysis. MCEMP1 were identified as the real hub genes after the intersection of the candiate hub genes and module genes in FHC and MI. ROC curve analysis indicated high diagnostic ability of MCEMP1 to detect MI in GSE61144 datasets. In addition, RT-qPCR was used to detect MCEMP1 expression in ApoE-/- mice, and the results were consistent with the bioinformatics analysis. Conclusion: MCEMP1 were identified and provided new insights into the diagnosis
Keywords: Hypercholesterolemia, Myocardial Infarction, Immune infiltration, diagnosis, WGCNA
Received: 29 Sep 2025; Accepted: 12 Dec 2025.
Copyright: © 2025 Wu, Peng, Yu, Lin, Zhang, Wu and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Caisheng Wu
Qiang Xie
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