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Using integrative bioinformatics approaches and machine‑learning strategies to Identify potential signatures for atrial fibrillation

ABSTRACT: Atrial fibrillation (AF) is the most common tachyarrhythmia and seriously affects human health. Key targets of AF bioinformatics analysis can help to better understand the pathogenesis of AF and develop therapeutic targets. The left atrial appendage tissue of 20 patients with AF and 10 patients with sinus rhythm were collected for sequencing, and the expression data of the atrial tissue were obtained. Based on this, 2578 differentially expressed genes were obtained through differential analysis. Different express genes (DEGs) were functionally enriched on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), mainly focusing on neuroactive ligand-receptor interactions, neuronal cell body pathways, regulation of neurogenesis, and neuronal death，regulation of neuronal death, etc. Secondly, 14 significant module genes were obtained by analyzing the weighted gene co-expression network of DEGs. Next, LASSO and SVM analyzes were performed on the differential genes, and the results were in good agreement with the calibration curve of the nomogram model for predicting AF constructed by the weighted gene co-expression network key genes. The significant module genes obtained by the area under the ROC curve (AUC) analysis were analyzed. Through crossover, two key disease characteristic genes related to AF, HOXA2 and RND2, were screened out. RND2 was selected for further research, and qPCR verified the expression of RND2 in sinus rhythm patients and AF patients. Patients with sinus rhythm were significantly higher than those in AF patients. Our study shows that RND2 can be used as a new target for the diagnosis and treatment of AF.

ORGANISM(S): Homo sapiens

PROVIDER: GSE282504 | GEO | 2025/01/29

REPOSITORIES: GEO

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Project description:Note this data set has identical data files: Files GSM40994.txt and GSM40995.txt. GSE2240 contains two different experimental subsets:; 1) Comparison of atrial and ventricular gene expression (atrial tissue of patients with sinus rhythm vs. human left ventricular non-failing myocardium); The purpose of our investigation was to identify the transcriptional basis for ultrastructural and functional specialization of human atria and ventricles. Using exploratory microarray analysis (Affymetrix U133A+B), we detected 11,740 transcripts expressed in human heart, representing the most comprehensive report of the human myocardial transcriptome to date. Variation in gene expression between atria and ventricles accounted for the largest differences in this data set, as 3.300 and 2.974 transcripts showed higher expression in atria and ventricles, respectively. Functional classification based on Gene Ontology identified chamber-specific patterns of gene expression and provided molecular insights into the regional specialization of cardiomyocytes, correlating important functional pathways to transcriptional activity: Ventricular myocytes preferentially express genes satisfying contractile and energetic requirements, while atrial myocytes exhibit specific transcriptional activities related to neurohumoral function. In addition, several pro-fibrotic and apoptotic pathways were concentrated in atrial myocardium, substantiating the higher susceptibility of atria to programmed cell death and extracellular matrix remodelling observed in human and experimental animal models of heart failure. Differences in transcriptional profiles of atrial and ventricular myocardium thus provide molecular insights into myocardial cell diversity and distinct region-specific adaptations to physiological and pathophysiological conditions (Barth AS et al., Eur J Physiol, 2005). 2) Comparison of atrial gene expression in patients with permanent atrial fibrillation and sinus rhythm. Atrial fibrillation is associated with increased expression of ventricular myosin isoforms in atrial myocardium, regarded as part of a dedifferentiation process. Whether re-expression of ventricular isoforms in atrial fibrillation is restricted to transcripts encoding for contractile proteins is unknown. Therefore, this study compares atrial mRNA expression in patients with permanent atrial fibrillation to atrial mRNA expression of patients with sinus rhythm as well as to ventricular gene expression using Affymetrix U133 arrays. In atrial myocardium, we identified 1.434 genes deregulated in atrial fibrillation, the majority of which, including key elements of calcium-dependent signaling pathways, displayed down-regulation. Functional classification based on Gene Ontology provided the specific gene sets of the interdependent processes of structural, contractile and electrophysiological remodeling. In addition, we demonstrate for the first time a prominent up-regulation of transcripts involved in metabolic activities, suggesting an adaptive response to an increased metabolic demand in fibrillating atrial myocardium. Ventricular-predominant genes were five times more likely to be up-regulated in atrial fibrillation (174 genes up-regulated, 35 genes down-regulated), while atrial-specific transcripts were predominantly down-regulated (56 genes up-regulated, 564 genes down-regulated). Overall, in atrial myocardium, functional classes of genes characteristic of ventricular myocardium were found to be up-regulated (e.g. metabolic processes) while functional classes predominantly expressed in atrial myocardium were down-regulated in atrial fibrillation (e.g. signal transduction and cell communication). Therefore, dedifferentiation with adoption of a ventricular-like signature is a general feature of the fibrillating atrium, uncovering the transcriptional response pattern in pmAF (Barth AS et al., Circ Res, 2005).

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Using integrative bioinformatics approaches and machine‑learning strategies to Identify potential signatures for atrial fibrillation

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