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Murine Myocardial Infarction Model using Permanent Ligation of Left Anterior Descending Coronary Artery
Published on: August 16, 2019
Interaction network analysis revealed biomarkers in myocardial infarction
Tong Zhang1, Li-Li Zhao, Zhuo-Ran Zhang
1Department of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street of Nangang District, Harbin, 150001, Heilongjiang Province, China.
Insights
This study identified 246 differentially expressed genes (DEGs) in myocardial infarction (MI) patients, highlighting immune response pathways. Key genes like CD3E and CD4 may serve as novel biomarkers for MI diagnosis and therapy.
Area of Science:
- Genomics
- Immunology
- Cardiovascular Disease Research
Background:
- Myocardial infarction (MI) leads to cardiac cell death due to prolonged ischemia.
- Identifying novel diagnostic and therapeutic targets for MI is crucial.
Purpose of the Study:
- To identify novel diagnostic and therapeutic targets for myocardial infarction (MI).
- To analyze gene expression profiles and construct interaction networks in MI patients.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) datasets (GSE22229, GSE29111) for gene expression analysis.
- Identified differentially expressed genes (DEGs) using a p-value < 0.05 and |logFC| > 1 threshold.
- Constructed gene interaction networks and sub-networks using NetBox, identifying linker genes and calculating gene degrees.
Main Results:
- Identified 246 DEGs in MI patients, significantly enriched in immune response pathways.
- Key genes with high network degrees, including CD3E, CD4, and CD3G, were identified.
- Network modules and DEGs were predominantly associated with immune response pathways.
Conclusions:
- High-degree genes within the identified network may serve as potential biomarkers for MI diagnosis.
- These findings suggest immune response pathways are critical in MI pathogenesis and present therapeutic targets.
Abstract:
Myocardial infarction (MI) is a serious heart disease. The cardiac cells of patients with MI will die due to lack of blood for a long time. In this study, we aimed to find new targets for MI diagnosis and therapy. We downloaded GSE22229 including 12 blood samples from healthy persons and GSE29111 from Gene Expression Omnibus including 36 blood samples from MI patients. Then we identified differentially expressed genes (DEGs) in patients with MI compared to normal controls with p value < 0.05 and |logFC| > 1. Furthermore, interaction network and sub-network of these of these DEGs were constructed by NetBox. Linker genes were screened in the Global Network database. The degree of linker genes were calculated by igraph package in R language. Gene ontology and kyoto encyclopedia of genes and genomes pathway analysis were performed for DEGs and network modules. A total of 246 DEGs were identified in MI, which were enriched in the immune response. In the interaction network, LCK, CD247, CD3D, FYN, HLA-DRA, IL2, CD8A CD3E, CD4, CD3G had high degree, among which CD3E, CD4, CD3G were DEGs while others were linker genes screened from Global Network database. Genes in the sub-network were also enriched in the immune response pathway. The genes with high degree may be biomarkers for MI diagnosis and therapy.
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