ArticleFrontiers in neurology2022
Bioinformatics analysis to identify potential biomarkers and therapeutic targets for ST-segment-elevation myocardial infarction-related ischemic stroke.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Systematic Bioinformatic Analysis of the miRNA Pathway in Inborn Errors of Amino Acid Metabolism Disorders.Molecular syndromology · 2026Article
- Network-based analysis identifies shared mechanisms between ischemic stroke and myocardial infarction and therapeutic ingredients of Buyang Huanwu Decoction.Frontiers in genetics · 2026Article
- Shared genetic features inference among hypoxia-ischemia diseases in the presence of heterogenous omics data based on a novel risk assessment method.Frontiers in genetics · 2025Article
- Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial Infarction.Scientific reports · 2024Article
- Identification of immune-related biomarkers co-occurring in acute ischemic stroke and acute myocardial infarction.Frontiers in neurology · 2023Article
- Identification and validation of senescence-related genes in circulating endothelial cells of patients with acute myocardial infarction.Frontiers in cardiovascular medicine · 2022Article
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Abstract
Background: Acute myocardial infarction (AMI) is one of the major causes of mortality and disability worldwide, and ischemic stroke (IS) is a serious complication after AMI. In particular, patients with ST-segment-elevation myocardial infarction (STEMI) are more susceptible to IS. However, the interrelationship between the two disease mechanisms is not clear. Using bioinformatics tools, we investigated genes commonly expressed in patients with STEMI and IS to explore the relationship between these diseases, with the aim of uncovering the underlying biomarkers and therapeutic targets for STEMI-associated IS. Methods: Differentially expressed genes (DEGs) related to STEMI and IS were identified through bioinformatics analysis of the Gene Expression Omnibus (GEO) datasets GSE60993 and GSE16561, respectively. Thereafter, we assessed protein-protein interaction networks, gene ontology term annotations, and pathway enrichment for DEGs using various prediction and network analysis methods. The predicted miRNAs targeting the co-expressed STEMI- and IS-related DEGs were also evaluated. Results: We identified 210 and 29 DEGs in GSE60993 and GSE16561, respectively. CD8A, TLR2, TLR4, S100A12, and TREM1 were associated with STEMI, while the hubgenes, IL7R, CCR7, FCGR3B, CD79A, and ITK were implicated in IS. In addition, binding of the transcripts of the co-expressed DEGs MMP9, ARG1, CA4, CRISPLD2, S100A12, and GZMK to their corresponding predicted miRNAs, especially miR-654-5p, may be associated with STEMI-related IS. Conclusions: STEMI and IS are related and MMP9, ARG1, CA4, CRISPLD2, S100A12, and GZMK genes may be underlying biomarkers involved in STEMI-related IS.
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