ArticleBMC cardiovascular disorders2020
Identification of candidate genes in ischemic cardiomyopathy by gene expression omnibus database.
Article in BMC cardiovascular disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 35 citations in OpenAlex.
- Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy.BMC medical informatics and decision making · 2026Article
- Sarcomeric remodelling in human heart failure unraveled by single molecule long read sequencing.EMBO molecular medicine · 2026Article
- Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery.Scientific reports · 2025Article
- Identification of novel ferroptosis-related biomarkers associated with the oxidative stress pathways in ischemic cardiomyopathy.International journal of cardiology. Heart & vasculature · 2025Article
- Expression of PIM1/ASK1 Molecular Pathway Related Genes in Ischemic Cardiomyopathy.Cardiovascular & hematological disorders drug targets · 2025Article
- Protein glycosylation in cardiovascular health and disease.Nature reviews. Cardiology · 2024Review
- Differences in T cell-associated serum markers between ischemic cardiomyopathy and dilated cardiomyopathy.Journal of thoracic disease · 2024Article
- Screening and identification of hub genes for ischemic cardiomyopathy and construction and validation of a clinical prognosis model using bioinformatics analysis.Journal of thoracic disease · 2024Article
- Single-cell RNA sequencing reveals the transcriptional heterogeneity of Tbx18-positive cardiac cells during heart development.Functional & integrative genomics · 2024Article
- Role of Angiopoietic Coronary Endothelial Dysfunction in the Pathogenesis of Ischemic Cardiomyopathy.Biomedicines · 2023Article
- Exploring novel biomarkers in dilated cardiomyopathy‑induced heart failure by integrated analysis andExperimental and therapeutic medicine · 2023Article
- Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure.Molecular medicine (Cambridge, Mass.) · 2022Article
- CBL knockdown protects cardiomyocytes against hypoxia-reoxygenation injury by downregulating GRB2 expression.Experimental and therapeutic medicine · 2022Article
- Dysfunctional network of hub genes in hypertrophic cardiomyopathy patients.American journal of translational research · 2022Article
- Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy.Frontiers in cardiovascular medicine · 2022Article
- Integrated Strategies of Diverse Feature Selection Methods Identify Aging-Based Reliable Gene Signatures for Ischemic Cardiomyopathy.Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIschemic cardiomyopathy (ICM) is one of the most usual causes of death worldwide. This study aimed to find the candidate gene for ICM.
methodsWe studied differentially expressed genes (DEGs) in ICM compared to healthy control. According to these DEGs, we carried out the functional annotation, protein-protein interaction (PPI) network and transcriptional regulatory network constructions. The expression of selected candidate genes were confirmed using a published dataset and Quantitative real time polymerase chain reaction (qRT-PCR).
resultsFrom three Gene Expression Omnibus (GEO) datasets, we acquired 1081 DEGs (578 up-regulated and 503 down-regulated genes) between ICM and healthy control. The functional annotation analysis revealed that cardiac muscle contraction, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in ICM. SNRPB, BLM, RRS1, CDK2, BCL6, BCL2L1, FKBP5, IPO7, TUBB4B and ATP1A1 were considered the hub proteins. PALLD, THBS4, ATP1A1, NFASC, FKBP5, ECM2 and BCL2L1 were top six transcription factors (TFs) with the most downstream genes. The expression of 6 DEGs (MYH6, THBS4, BCL6, BLM, IPO7 and SERPINA3) were consistent with our integration analysis and GSE116250 validation results.
conclusionsThe candidate DEGs and TFs may be related to the ICM process. This study provided novel perspective for understanding mechanism and exploiting new therapeutic means for ICM.
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