Abstract
Atrial fibrillation (AF)/paroxysmal AF (PAF) is the main cause of cardiogenic embolism. In recent years, the progression from paroxysmal AF to persistent AF has attracted more and more attention. However, the molecular mechanism of the progression of AF is unclear. In this study, we performed RNA sequencing for normal samples, paroxysmal AF and persistent AF samples to identify differentially expressed gene (DEG) and explore the roles of these DEGs in AF. Totally, 272 differently expressed mRNAs (DEmRNAs) and 286 differentially expressed lncRNAs (DElncRNAs) were identified in paroxysmal AF compared to normal samples; 324 DEmRNAs and 258 DElncRNAs were found in persistent atrial fibrillation compared with normal samples; and 520 DEmRNAs and 414 DElncRNAs were identified in persistent AF compared to paroxysmal AF samples. Interestingly, among the DEGs, approximately 50% were coding genes and around 50% were non-coding RNAs, suggesting that lncRNAs may also have a crucial role in the progression of AF. Bioinformatics analysis demonstrated that these DEGs were significantly related to regulating multiple AF associated pathways, such as the regulation of vascular endothelial growth factor production and binding to the CXCR chemokine receptor. Furthermore, weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub RNAs and lncRNAs to determine their potential associations with AF. Five hub modules were identified in the progression of AF, including blue, brown, gray, turquoise and yellow modules. Interestingly, blue module and turquoise module were significantly negatively and positively correlated to the progression of AF respectively, indicating that they may have a more important role in the AF. Moreover, the hub protein-protein interaction (PPI) networks and lncRNA–mRNA regulatory network were constructed. Bioinformatics analysis on the hub PPI network in turquoise was involved in regulating immune response related signaling, such as leukocyte chemotaxis, macrophage activation, and positive regulation of α-β T cell activation. Our findings could clarify the underlying molecular changes associated fibrillation, and provide a useful resource for identifying AF marker.
Highlights
MATERIALS AND METHODSAtrial fibrillation (AF) is a common tachyarrhythmia, which had been the main cause of cardiogenic embolic infarction (AbdulRahim et al, 2015; Kelley, 2015)
We identified differentially expressed gene (DEG) in the progression of AF with the R package Limma. 558 genes were identified to be differently expressed in paroxysmal AF compared to control samples (Figures 1A,B); 582 genes were identified to be differently expressed in persistent AF compared to control samples (Figures 1C,D); and 934 genes were identified to be differently expressed in persistent AF compared to paroxysmal AF tissues (Figures 1E,F)
324 differently expressed mRNAs (DEmRNAs) were found in persistent atrial fibrillation compared with normal samples, including 219 upregulated and 105 downregulated mRNAs (Figure 2B)
Summary
Atrial fibrillation (AF) is a common tachyarrhythmia, which had been the main cause of cardiogenic embolic infarction (AbdulRahim et al, 2015; Kelley, 2015). LncRNA has been confirmed to have a crucial role in a variety of cell functions, including epigenetic regulation, transcription regulation, etc., and has been potential biomarkers for disease diagnosis and treatment (Cao, 2014; Gu and Chen, 2020) In human cells, it has been identified more than 100,000 lncRNAs (Statello et al, 2021), which play an important role in the cardiovascular system. Weighted gene co-expression network analysis (WGCNA) is used to cluster highly related genes to further understand the hub modules and disease types/clinical phenotypes (Langfelder and Horvath, 2008). We performed RNA sequencing to identify differently expressed mRNAs (DEmRNAs) and lncRNAs in normal samples, paroxysmal AF, and persistent AF. We use bioinformatics methods, such as WGCNA, and PPI network analysis, to identify the hub lncRNAs and mRNAs in AF. The adjacency was used to calculate the topological overlap matrix (TOM)
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