Identification of potential crucial genes in atrial fibrillation: a bioinformatic analysis

Identification of Potential Crucial Biomarkers in STEMI Through Integrated Bioinformatic Analysis

BACKGROUND

ST-segment elevation myocardial infarction (STEMI) is one of the leading causes of fatal cardiovascular diseases, which have been the prime cause of mortality worldwide. Diagnosis in the early phase would benefit clinical intervention and prognosis, but the exploration of the biomarkers of STEMI is still lacking.

OBJECTIVES

In this study, we conducted a bioinformatics analysis to identify potential crucial biomarkers in the progress of STEMI.

METHODS

We obtained GSE59867 for STEMI and stable coronary artery disease (SCAD) patients. Differentially expressed genes (DEGs) were screened with the threshold of |log2fold change| > 0.5 and p <0.05. Based on these genes, we conducted enrichment analysis to explore the potential relevance between genes and to screen hub genes. Subsequently, hub genes were analyzed to detect related miRNAs and DAVID to detect transcription factors for further analysis. Finally, GSE62646 was utilized to assess DEGs specificity, with genes demonstrating AUC results exceeding 75%, indicating their potential as candidate biomarkers.

RESULTS

133 DEGs between SCAD and STEMI were obtained. Then, the PPI network of DEGs was constructed using String and Cytoscape, and further analysis determined hub genes and 6 molecular complexes. Functional enrichment analysis of the DEGs suggests that pathways related to inflammation, metabolism, and immunity play a pivotal role in the progression from SCAD to STEMI. Besides, related-miRNAs were predicted, has-miR-124, has-miR-130a/b, and has-miR-301a/b regulated the expression of the largest number of genes. Meanwhile, Transcription factors analysis indicate that EVI1, AML1, GATA1, and PPARG are the most enriched gene. Finally, ROC curves demonstrate that MS4A3, KLRC4, KLRD1, AQP9, and CD14 exhibit both high sensitivity and specificity in predicting STEMI.

CONCLUSIONS

This study revealed that immunity, metabolism, and inflammation are involved in the development of STEMI derived from SCAD, and 6 genes, including MS4A3, KLRC4, KLRD1, AQP9, CD14, and CCR1, could be employed as candidate biomarkers to STEMI.

Integrated analysis and validation of ferroptosis-related genes and immune infiltration in acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) is indeed a significant cause of mortality and morbidity in individuals with coronary heart disease. Ferroptosis, an iron-dependent cell death, is characterized by the accumulation of intracellular lipid peroxides, which is implicated in cardiomyocyte injury. This study aims to identify biomarkers that are indicative of ferroptosis in the context of AMI, and to examine their potential roles in immune infiltration.

METHODS

Firstly, the GSE59867 dataset was used to identify differentially expressed ferroptosis-related genes (DE-FRGs) in AMI. We then performed gene ontology (GO) and functional enrichment analysis on these DE-FRGs. Secondly, we analyzed the GSE76591 dataset and used bioinformatic methods to build ceRNA networks. Thirdly, we identified hub genes in protein-protein interaction (PPI) network. After obtaining the key DE-FRGs through the junction of hub genes with ceRNA and least absolute shrinkage and selection operator (LASSO). ImmucellAI was applied to estimate the immune cell infiltration in each sample and examine the relationship between key DE-FRGs and 24 immunocyte subsets. The diagnostic performance of these genes was further evaluated using the receiver operating characteristic (ROC) curve analysis. Ultimately, we identified an immune-related ceRNA regulatory axis linked to ferroptosis in AMI.

RESULTS

Among 56 DE-FRGs identified in AMI, 41 of them were integrated into the construction of competitive endogenous RNA (ceRNA) networks. TLR4 and PIK3CA were identified as key DE-FRGs and PIK3CA was confirmed as a diagnostic biomarker for AMI. Moreover, CD4_native cells, nTreg cells, Th2 cells, Th17 cells, central-memory cells, effector-memory cells, and CD8_T cells had higher infiltrates in AMI samples compared to control samples. In contrast, exhausted cells, iTreg cells, and Tfh cells had lower infiltrates in AMI samples. Spearman analysis confirmed the correlation between 24 immune cells and PIK3CA/TLR4. Ultimately, we constructed an immune-related regulatory axis involving XIST and OIP5-AS1/miR-216a/PIK3CA.

CONCLUSION

Our comprehensive analysis has identified PIK3CA as a robust and promising biomarker for this condition. Moreover, we have also identified an immune-related regulatory axis involving XIST and OIP5-AS1/miR-216a/PIK3CA, which may play a key role in regulating ferroptosis during AMI progression.

Identification and verification of atrial fibrillation hub genes caused by primary mitral regurgitation

In the clinic, atrial fibrillation (AF) is a common arrhythmia. Despite constant innovation in treatments for AF, they remain limited by a lack of knowledge of the underlying mechanism responsible for AF. In this study, we examined the molecular mechanisms associated with primary mitral regurgitation (MR) in AF using several bioinformatics techniques. Limma was used to identify differentially expressed genes (DEGs) associated with AF using microarray data from the GSE115574 dataset. WGCNA was used to identify significant module genes. A functional enrichment analysis for overlapping genes between the DEGs and module genes was done and several AF hub genes were identified from a protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were generated to evaluate the validity of the hub genes. We examined 306 DEGs and 147 were upregulated and 159 were downregulated. WGCNA analysis revealed black and ivory modules that contained genes associated with AF. Functional enrichment analysis revealed various biological process terms related to AF. The AUCs for the 8 hub genes screened by the PPI network analysis were > 0.7, indicating satisfactory diagnostic accuracy. The 8 AF-related hub genes included SYT13, VSNL1, GNAO1, RGS4, RALYL, CPLX1, CHGB, and CPLX3. Our findings provide novel insight into the molecular mechanisms of AF and may lead to the development of new treatments.

Bioinformatic Identification of Potential RNA Alterations on the Atrial Fibrillation Remodeling from Human Pulmonary Veins

Atrial fibrillation (AF) is the most frequent persistent arrhythmia. Many genes have been reported as a genetic background for AF. However, most transcriptome analyses of AF are limited to the atrial samples and have not been evaluated by multiple cardiac regions. In this study, we analyzed the expression levels of protein-coding and long noncoding RNAs (lncRNAs) in six cardiac regions by RNA-seq. Samples were donated from six subjects with or without persistent AF for left atria, left atrial appendages, right atria, sinoatrial nodes, left ventricles, right ventricles, and pulmonary veins (PVs), and additional four right atrial appendages samples were collected from patients undergoing mitral valve replacement. In total, 23 AF samples were compared to 23 non-AF samples. Surprisingly, the most influenced heart region in gene expression by AF was the PV, not the atria. The ion channel-related gene set was significantly enriched upon analysis of these significant genes. In addition, some significant genes are cancer-related lncRNAs in PV in AF. A co-expression network analysis could detect the functional gene clusters. In particular, the cancer-related lncRNA, such as SAMMSON and FOXCUT, belong to the gene network with the cancer-related transcription factor FOXC1. Thus, they may also play an aggravating role in the pathogenesis of AF, similar to carcinogenesis. In the least, this study suggests that (1) RNA alteration is most intense in PVs and (2) post-transcriptional gene regulation by lncRNA may contribute to the progression of AF. Through the screening analysis across the six cardiac regions, the possibility that the PV region can play a role other than paroxysmal triggering in the pathogenesis of AF was demonstrated for the first time. Future research with an increase in the number of PV samples will lead to a novel understanding of the pathophysiology of AF.

Microprotein Dysregulation in the Serum of Patients with Atrial Fibrillation

The incidence rate of atrial fibrillation (AF) has stayed at a high level in recent years. Despite the intensive efforts to study the pathologic changes of AF, the molecular mechanism of disease development remains unclarified. Microproteins are ribosomally translated gene products from small open reading frames (sORFs) and are found to play crucial biological functions, while remain rare attention and indistinct in AF study. In this work, we recruited 65 AF patients and 65 healthy subjects for microproteomic profiling. By differential analysis and cross-validation between independent datasets, a total of 4 microproteins were identified as significantly different, including 3 annotated ones and 1 novel one. Additionally, we established a diagnostic model with either microproteins or global proteins by machine learning methods and found the model with microproteins achieved comparable and excellent performance as that with global proteins. Our results confirmed the abnormal expression of microproteins in AF and may provide new perspectives on the mechanism study of AF.

Exploring the pathogenesis and immune infiltration in dilated cardiomyopathy complicated with atrial fibrillation by bioinformatics analysis

Background

Atrial fibrillation (AF) is a serious complication of dilated cardiomyopathy (DCM), which increases the risk of thromboembolic events and sudden death in DCM patients. However, the common mechanism of DCM combined with AF remains unclear. This study aims to explore the molecular mechanism and analyze immune infiltration in DCM complicated with AF through comprehensive bioinformatics analysis.

Methods

The gene expression datasets of DCM (GSE141910) and AF (GSE41177 and GSE79768) were obtained from the Gene Expression Omnibus database. Gene enrichment analyses were performed after screening the common differentially expressed genes (DEGs) of DCM and AF. Protein-protein interaction network was constructed in the STRING database and visualized in Cytoscape software, which helped to further screen the central functional modules of DEGs and hub genes. In addition, ImmuCellAI algorithm was performed to estimate immune infiltration patterns, and Spearman correlation was conducted to investigate the correlation between the abundance of multiple immune cells and the expression levels of hub immune-related genes after obtaining hub immune-related genes from the ImmPort database. The hub immune-related genes expression and immune infiltration patterns were additionally verified in the validation datasets (GSE57338, GSE115574, and GSE31821). The diagnostic effectiveness of hub immune-related genes was evaluated through Receiver Operator Characteristic Curve analysis.

Results

A total of 184 common DEGs in DCM and AF were identified for subsequent analyses. The functions of hub genes were significantly associated with immune responses. We identified 7 hub immune-related genes (HLA-DRA, LCK, ITK, CD48, CD247, CD3D, and IL2RG) and a spectrum of immune cell subsets including Monocyte, Neutrophil, and follicular helper T (Tfh) cells were found to be concurrently dysregulated in both DCM and AF. 7 hub immune-related genes were predominantly favorably correlated with Tfh cells and were primarily negatively correlated with Neutrophil infiltrations in DCM and AF. CD48+CD3D were verified to diagnose DCM and AF with excellent sensitivity and specificity, showing favorable diagnostic value.

Conclusions

Our study reveals that immune cells (Tfh cells) disorders caused by hub immune-related genes (CD48 and CD3D) may be the common pathogenesis of DCM combined with AF, which lays a foundation for further immune mechanism research.

Ablation of CXCR4 expression in cardiomyocytes exacerbates isoproterenol‑induced cell death and heart failure

CXCR4 is a seven‑transmembrane‑spanning Gi‑coupled receptor for the SDF‑1 chemokine and plays a critical role in cardiovascular development and post‑injury repair. However, the specific role of CXCR4 in cardiomyocytes is incompletely understood. It was hypothesized that CXCR4 activation in cardiomyocytes antagonizes β‑adrenoceptor/Gs signaling‑induced cardiac dysfunction. Cardiomyocyte‑specific CXCR4 knockout (CXCR4‑^CMKO) mice were generated by crossing CXCR4^fl/fl and MHC‑Cre^+/‑ mice. Their cardiac structure and function in the basal state are equivalent to that of the control MHC‑Cre^+/‑ littermates until at least 4 months old. However, following continuous subcutaneous administration of isoproterenol (Iso) via an osmotic mini‑pump, the ventricular myocardial contractility, dilation, cardiomyocyte apoptosis, and interstitial fibrosis are worse in CXCR4‑^CMKO mice than in MHC‑Cre^+/‑ littermates. In the cultured H9C2 cardiomyocytes, SDF‑1 treatment markedly attenuated Iso‑induced apoptosis and reduction in phospho‑Akt, and this protective effect was lost by knockdown of CXCR4 or by co‑treatment with Gi inhibitors. In conclusion, CXCR4 promotes cardiomyocyte survival and heart function during β‑adrenergic stress.

Screening and Bioinformatics Analysis of Crucial Gene of Heart Failure and Atrial Fibrillation Based on GEO Database

Background and objectives: In clinical practice, we observed that the prognoses of patients with heart failure and atrial fibrillation were worse than those of patients with only heart failure or atrial fibrillation. The study aims to get a better understanding of the common pathogenesis of the two diseases and find new therapeutic targets. Materials and Methods: We downloaded heart failure datasets and atrial fibrillation datasets from the gene expression omnibus database. The common DEGs (differentially expressed genes) in heart failure and atrial fibrillation were identified by a series of bioinformatics methods. To better understand the functions and possible pathways of DEGs, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Results: We identified 22 up-regulated genes and 14 down-regulated genes in two datasets of heart failure and 475 up-regulated and 110 down-regulated genes in atrial fibrillation datasets. In addition, two co-upregulated (FRZB, SFRP4) and three co-downregulated genes (ENTPPL, AQP4, C1orf105) were identified. GO enrichment results showed that these common differentially expressed genes were mainly concentrated in the signal regulation of the Wnt pathway. Conclusions: We found five crucial genes in heart failure and atrial fibrillation, which may be potential therapeutic targets for patients with heart failure and atrial fibrillation.

CMYA5 is a novel interaction partner of FHL2 in cardiac myocytes

Four-and-a-half LIM domains protein 2 (FHL2) is an anti-hypertrophic adaptor protein that regulates cardiac myocyte signalling and function. Herein, we identified cardiomyopathy-associated 5 (CMYA5) as a novel FHL2 interaction partner in cardiac myocytes. In vitro pull-down assays demonstrated interaction between FHL2 and the N- and C-terminal regions of CMYA5. The interaction was verified in adult cardiac myocytes by proximity ligation assays. Immunofluorescence and confocal microscopy demonstrated co-localisation in the same subcellular compartment. The binding interface between FHL2 and CMYA5 was mapped by peptide arrays. Exposure of neonatal rat ventricular myocytes to a CMYA5 peptide covering one of the FHL2 interaction sites led to an increase in cell area at baseline, but a blunted response to chronic phenylephrine treatment. In contrast to wild-type hearts, loss or reduced FHL2 expression in Fhl2-targeted knockout mouse hearts or in a humanised mouse model of hypertrophic cardiomyopathy led to redistribution of CMYA5 into the perinuclear and intercalated disc region. Taken together, our results indicate a direct interaction of the two adaptor proteins FHL2 and CMYA5 in cardiac myocytes, which might impact subcellular compartmentation of CMYA5.

Integrative identification of immune-related key genes in atrial fibrillation using weighted gene coexpression network analysis and machine learning

Background

The immune system significantly participates in the pathologic process of atrial fibrillation (AF). However, the molecular mechanisms underlying this participation are not completely explained. The current research aimed to identify critical genes and immune cells that participate in the pathologic process of AF.

Methods

CIBERSORT was utilized to reveal the immune cell infiltration pattern in AF patients. Meanwhile, weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules that were significantly correlated with AF. The characteristic genes correlated with AF were identified by the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithm.

Results

In comparison to sinus rhythm (SR) individuals, we observed that fewer activated mast cells and regulatory T cells (Tregs), as well as more gamma delta T cells, resting mast cells, and M2 macrophages, were infiltrated in AF patients. Three significant modules (pink, red, and magenta) were identified to be significantly associated with AF. Gene enrichment analysis showed that all 717 genes were associated with immunity- or inflammation-related pathways and biological processes. Four hub genes (GALNT16, HTR2B, BEX2, and RAB8A) were revealed to be significantly correlated with AF by the SVM-RFE algorithm and LASSO logistic regression. qRT–PCR results suggested that compared to the SR subjects, AF patients exhibited significantly reduced BEX2 and GALNT16 expression, as well as dramatically elevated HTR2B expression. The AUC measurement showed that the diagnostic efficiency of BEX2, HTR2B, and GALNT16 in the training set was 0.836, 0.883, and 0.893, respectively, and 0.858, 0.861, and 0.915, respectively, in the validation set.

Conclusions

Three novel genes, BEX2, HTR2B, and GALNT16, were identified by WGCNA combined with machine learning, which provides potential new therapeutic targets for the early diagnosis and prevention of AF.

Tandem Mass Tagging Based Identification of Proteome Signatures for Reductive Stress Cardiomyopathy

Nuclear factor erythroid 2-related factor 2 (NRF2), a redox sensor, is vital for cellular redox homeostasis. We reported that transgenic mice expressing constitutively active Nrf2 (CaNrf2-TG) exhibit reductive stress (RS). In this study, we identified novel protein signature for RS-induced cardiomyopathy using Tandem Mass Tag (TMT) proteomic analysis in heart tissues of TG (CaNrf2-TG) mice at 6–7 months of age. A total of 1,105 proteins were extracted from 22,544 spectra. About 560 proteins were differentially expressed in TG vs. NTg hearts, indicating a global impact of RS on the myocardial proteome. Over 32 proteins were significantly altered in response to RS -20 were upregulated and 12 were downregulated in the hearts of TG vs. NTg mice, suggesting that these proteins could be putative signatures of RS. Scaffold analysis revealed a clear distinction between TG vs. NTg hearts. The majority of the differentially expressed proteins (DEPs) that were significantly altered in RS mice were found to be involved in stress related pathways such as antioxidants, NADPH, protein quality control, etc. Interestingly, proteins that were involved in mitochondrial respiration, lipophagy and cardiac rhythm were dramatically decreased in TG hearts. Of note, we identified the glutathione family of proteins as the significantly changed subset of the proteome in TG heart. Surprisingly, our comparative analysis of NGS based transcriptome and TMT-proteome indicated that ~50% of the altered proteins in TG myocardium was found to be negatively correlated with their transcript levels. In association with the altered proteome the TG mice displayed pathological cardiac remodeling.

FAM201A, a long noncoding RNA potentially associated with atrial fibrillation identified by ceRNA network analyses and WGCNA

Background

Being the most common arrhythmia in clinic, atrial fibrillation (AF) causes various comorbidities to patients such as heart failure and stroke. LncRNAs were reported involved in pathogenesis of AF, yet, little is known about AF-associated lncRNAs. The present study aims to explore lncRNAs associated with AF susceptibility based on competing endogenous RNA (ceRNA) network analysis and weighted gene co-expression network analysis (WGCNA).

Methods

GSE41177 and GSE79768 datasets were obtained from the Gene Expression Omnibus (GEO) database. Competing endogenous RNA (ceRNA) network analysis was performed using GSE41177. Differentially expressed lncRNAs (DElncRNAs), mRNAs (DEmRNAs) between AF patients and patients with sinus rhythm (SR) were identified from GSE41177 using R software. Then, the ceRNA network was constructed based on DElncRNAs, the predicted target miRNAs and DEmRNAs. Weighted gene co-expression network analysis (WGCNA) was performed using GSE79768 to validate the AF-related lncRNAs identified from GSE41177. LncRNA modules and crucial lncRNAs relevant to AF and were identified.

Results

In summary, 18 DElncRNAs and 350 DEmRNAs were found between AF patients and SR patients. A total of 5 lncRNAs, 10 miRNAs, and 21 mRNAs were contained in the final ceRNA network. Taking into consideration both the ceRNA theory and inference scores from the comparative toxicogenomics database (CTD) database, the ceRNA axis FAM201A-miR-33a-3p-RAC3 was identified as mostly relevant to AF susceptibility. FAM201A (Gene significance, GS = − 0.62; Module membership, MM = 0.75) was also proved in the blue module, which was identified most highly relevant with AF by WGCNA.

Conclusions

These results demonstrated that decreased expression of FAM201A might be associated with susceptibility of AF. Working as the ceRNA to regulate RAC3 might be one function of FAM201A in AF susceptibility, which requires further exploration in future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01232-w.

Are Interactions between Epicardial Adipose Tissue, Cardiac Fibroblasts and Cardiac Myocytes Instrumental in Atrial Fibrosis and Atrial Fibrillation?

Atrial fibrillation is very common among the elderly and/or obese. While myocardial fibrosis is associated with atrial fibrillation, the exact mechanisms within atrial myocytes and surrounding non-myocytes are not fully understood. This review considers the potential roles of myocardial fibroblasts and myofibroblasts in fibrosis and modulating myocyte electrophysiology through electrotonic interactions. Coupling with (myo)fibroblasts in vitro and in silico prolonged myocyte action potential duration and caused resting depolarization; an optogenetic study has verified in vivo that fibroblasts depolarized when coupled myocytes produced action potentials. This review also introduces another non-myocyte which may modulate both myocardial (myo)fibroblasts and myocytes: epicardial adipose tissue. Epicardial adipocytes are in intimate contact with myocytes and (myo)fibroblasts and may infiltrate the myocardium. Adipocytes secrete numerous adipokines which modulate (myo)fibroblast and myocyte physiology. These adipokines are protective in healthy hearts, preventing inflammation and fibrosis. However, adipokines secreted from adipocytes may switch to pro-inflammatory and pro-fibrotic, associated with reactive oxygen species generation. Pro-fibrotic adipokines stimulate myofibroblast differentiation, causing pronounced fibrosis in the epicardial adipose tissue and the myocardium. Adipose tissue also influences myocyte electrophysiology, via the adipokines and/or through electrotonic interactions. Deeper understanding of the interactions between myocytes and non-myocytes is important to understand and manage atrial fibrillation.

CXCL12/CXCR4 axis as a key mediator in atrial fibrillation via bioinformatics analysis and functional identification

Atrial fibrillation (AF) is an increasingly prevalent arrhythmia with significant health and socioeconomic impact. The underlying mechanism of AF is still not well understood. In this study, we sought to identify hub genes involved in AF, and explored their functions and underlying mechanisms based on bioinformatics analysis. Five microarray datasets in GEO were used to identify the differentially expressed genes (DEGs) by Robust Rank Aggregation (RRA), and hub genes were screened out using protein-protein interaction (PPI) network. AF model was established using a mixture of acetylcholine and calcium chloride (Ach-CaCl2) by tail vein injection. We totally got 35 robust DEGs that mainly involve in extracellular matrix formation, leukocyte transendothelial migration, and chemokine signaling pathway. Among these DEGs, we identified three hub genes involved in AF, of which CXCL12/CXCR4 axis significantly upregulated in AF patients stands out as one of the most potent targets for AF prevention, and its effect on AF pathogenesis and underlying mechanisms were investigated in vivo subsequently with the specific CXCR4 antagonist AMD3100 (6 mg/kg). Our results demonstrated an elevated transcription and translation of CXCL12/CXCR4 axis in AF patients and mice, accompanied with the anabatic atrial inflammation and fibrosis, thereby providing the substrate for AF maintenance. Blocking its signaling via AMD3100 administration in AF model mice reduced AF inducibility and duration, partly ascribed to decreased atrial inflammation and structural remodeling. Mechanistically, these effects were achieved by reducing the recruitment of CD3+ T lymphocytes and F4/80+ macrophages, and suppressing the hyperactivation of ERK1/2 and AKT/mTOR signaling in atria of AF model mice. In conclusion, this study provides new evidence that antagonizing CXCR4 prevents the development of AF, and suggests that CXCL12/CXCR4 axis may be a potential therapeutic target for AF.

Identification of Pivotal MicroRNAs and Target Genes Associated with Persistent Atrial Fibrillation Based on Bioinformatics Analysis

Atrial fibrillation (AF) is one of the most common supraventricular arrhythmias worldwide. However, the specific molecular mechanism underlying AF remains unclear. Our study is aimed at identifying pivotal microRNAs (miRNAs) and targeting genes associated with persistent AF (pAF) using bioinformatics analysis. Three gene expression array datasets (GSE31821, GSE41177, and GSE79768) and an miRNA expression array dataset (GSE68475) associated with pAF were downloaded. Differentially expressed genes (DEGs) were identified using the LIMMA package, and differentially expressed miRNAs (DEMs) were screened from GSE68475. Target genes for DEMs were predicted using the miRTarBase database, and intersections between these target genes and DEGs were selected for further analysis, including the generation of protein–protein interaction (PPI) network, miRNA–transcription factor–target regulatory network, and drug–gene network. A total of 264 DEGs and 40 DEMs were identified between the pAF and control groups. Functional and pathway enrichment analyses of up- and downregulated DEGs were performed. The common genes (CGs) were primarily enriched in the phosphoinositide 3-kinase- (PI3K-) protein kinase B (Akt) signaling pathway, negative regulation of cell division, and response to hypoxia. The PPI network, miRNA–transcription factor–target regulatory network, and drug–gene network were constructed using Cytoscape. The present study revealed several novel miRNAs and genes involved in pAF. We speculated that miR-4298, miR-3125, miR-4306, and miR-671-5p could represent significant miRNAs that act on the target gene superoxide dismutase 2 (SOD2) during the development of pAF and may serve as essential biomarkers for pAF diagnosis and treatment. Moreover, MYC might function in pAF pathogenesis through the PI3K–Akt signaling pathway.

Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a complex disease and affects millions of people around the world. The biological mechanisms that are involved with AF are complex and still need to be fully elucidated. Therefore, we performed a meta-analysis of transcriptome data related to AF to explore these mechanisms aiming at more sensitive and reliable results.

METHODS

Ten public transcriptomic datasets were downloaded, analyzed for quality control, and individually pre-processed. Differential expression analysis was carried out for each dataset, and the results were meta-analytically aggregated using the rth ordered p value method. We analyzed the final list of differentially expressed genes through network analysis, namely topological and modularity analysis, and functional enrichment analysis.

RESULTS

The meta-analysis of transcriptomes resulted in 1197 differentially expressed genes, whose protein-protein interaction network presented 39 hubs-bottlenecks and four main identified functional modules. These modules were enriched for 39, 20, 64, and 10 biological pathways involved with the pathophysiology of AF, especially with the disease's structural and electrical remodeling processes. The stress of the endoplasmic reticulum, protein catabolism, oxidative stress, and inflammation are some of the enriched processes. Among hub-bottlenecks genes, which are highly connected and probably have a key role in regulating these processes, HSPA5, ANK2, CTNNB1, and MAPK1 were identified.

CONCLUSION

Our approach based on transcriptome meta-analysis revealed a set of key genes that demonstrated consistent overall changes in expression patterns associated with AF despite data heterogeneity related, among others, to type of tissue. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets.