{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang R"],"funding":["National Natural Science Foundation of China"],"pagination":["2629"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9454849"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(17)"],"pubmed_abstract":["Atrial fibrillation (AF) is a form of sustained cardiac arrhythmia and microRNAs (miRs) play crucial roles in the pathophysiology of AF. To identify novel miR-mRNA pairs, we performed RNA-seq from atrial biopsies of persistent AF patients and non-AF patients with normal sinus rhythm (SR). Differentially expressed miRs (11 down and 9 up) and mRNAs (95 up and 82 down) were identified and hierarchically clustered in a heat map. Subsequently, GO, KEGG, and GSEA analyses were run to identify deregulated pathways. Then, miR targets were predicted in the miRDB database, and a regulatory network of negatively correlated miR-mRNA pairs was constructed using Cytoscape. To select potential candidate genes from GSEA analysis, the top-50 enriched genes in GSEA were overlaid with predicted targets of di"],"journal":["Cells"],"pubmed_title":["Integrated Analysis of the microRNA-mRNA Network Predicts Potential Regulators of Atrial Fibrillation in Humans."],"pmcid":["PMC9454849"],"funding_grant_id":["81970282"],"pubmed_authors":["Huang S","Wang R","Chen J","Chen M","Han W","Wang X","Wang Y","Zhong J","Bektik E","Meng X","Sakon P"],"additional_accession":[]},"is_claimable":false,"name":"Integrated Analysis of the microRNA-mRNA Network Predicts Potential Regulators of Atrial Fibrillation in Humans.","description":"Atrial fibrillation (AF) is a form of sustained cardiac arrhythmia and microRNAs (miRs) play crucial roles in the pathophysiology of AF. To identify novel miR-mRNA pairs, we performed RNA-seq from atrial biopsies of persistent AF patients and non-AF patients with normal sinus rhythm (SR). Differentially expressed miRs (11 down and 9 up) and mRNAs (95 up and 82 down) were identified and hierarchically clustered in a heat map. Subsequently, GO, KEGG, and GSEA analyses were run to identify deregulated pathways. Then, miR targets were predicted in the miRDB database, and a regulatory network of negatively correlated miR-mRNA pairs was constructed using Cytoscape. To select potential candidate genes from GSEA analysis, the top-50 enriched genes in GSEA were overlaid with predicted targets of di","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Aug","modification":"2025-04-04T03:24:32.611Z","creation":"2025-04-04T03:24:32.611Z"},"accession":"S-EPMC9454849","cross_references":{"pubmed":["36078037"],"doi":["10.3390/cells11172629"]}}