ArticleMolecular biology reports2014
Interaction network analysis revealed biomarkers in myocardial infarction.
Article in Molecular biology reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- TRIM13 prevents cardiomyocyte injury by affecting apoptosis through interacting with CD3D in myocardial infarction.iScience · 2025Article
- B2M is a Biomarker Associated With Immune Infiltration In High Altitude Pulmonary Edema.Combinatorial chemistry & high throughput screening · 2024Article
- Potential mechanisms underlying podophyllotoxin-induced cardiotoxicity in male rats: toxicological evidence chain (TEC) concept.Frontiers in pharmacology · 2024Article
- Constructing differential co-expression network to predict key pathways for myocardial infarction.Experimental and therapeutic medicine · 2019Article
- Analysis of Effect ofFrontiers in pharmacology · 2019Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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