Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis

The interaction between neutrophils and atrial myocytes in the occurrence and development of atrial fibrillation

BACKGROUND

Atrial fibrillation (AF) is one of the most prevalent sustained cardiac arrhythmias, strongly associated with neutrophils. However, the underlying mechanism remain unclear. This study aims to explore the interaction between neutrophils and atrial myocytes in the pathogenesis of AF.

METHODS

Patch-clamp was employed to record the action potential duration (APD) and ion channels in HL-1 cells. Flow cytometry was used to assess the differentiation of neutrophils. The mRNA and protein levels of CACNA1C, CACNA2D, and CACNB2 in HL-1 cells were detected.

RESULTS

High-frequency electrical stimulation resulted in a shortening of the APD in HL-1 cells. Flow cytometry demonstrated that neutrophils were polarized into N1 phenotype when cultured with stimulated HL-1 cells medium. Compared to control neutrophils conditioned medium (CM), cocultured with TNF-α knockout neutrophils CM prolonged APD and the L-type Ca (2+) channel (LTCC) of HL-1 cells. Additionally, the expression of CACNA2D, CACNB2 and CACNA1C in HL-1 cells were upregulated. Compared with CACNA1C siRNA-transfected HL-1 cells treated with TNF-α siRNA-transfected neutrophils CM, the APD and LTCC of CACNA1C siRNA-transfected HL-1 cells were shortened in control N1 neutrophil CM. The APD and LTCC of control HL-1 cells were also shortened in control N1 neutrophil CM, but prolonged in TNF-α siRNA-transfected neutrophils CM.

CONCLUSION

These findings suggest that neutrophils were polarized into N1 phenotype in AF, TNF-α released from N1 neutrophils contributes to the pathogenesis of AF, via decreasing the APD and LTCC in atrial myocytes through down-regulation of CACNA1C expression.

Integrated bioinformatics analysis identified leucine rich repeat containing 15 and secreted phosphoprotein 1 as hub genes for calcific aortic valve disease and osteoarthritis

Calcific aortic valve disease (CAVD) and osteoarthritis (OA) are common diseases in the ageing population and share similar pathogenesis, especially in inflammation. This study aims to discover potential diagnostic and therapeutic targets in patients with CAVD and OA. Three CAVD datasets and one OA dataset were obtained from the Gene Expression Omnibus database. We used bioinformatics methods to search for key genes and immune infiltration, and established a ceRNA network. Immunohistochemical staining was performed to verify the expression of candidate genes in human and mice aortic valve tissues. Two key genes obtained, leucine rich repeat containing 15 (LRRC15) and secreted phosphoprotein 1 (SPP1), were further screened using machine learning and verified in human and mice aortic valve tissues. Compared to normal tissues, the infiltration of immune cells in CAVD tissues was significantly higher, and the expressions of LRRC15 and SPP1 were positively correlated with immune cells infiltration. Moreover, the ceRNA network showed extensive regulatory interactions based on LRRC15 and SPP1. The authors' findings identified LRRC15 and SPP1 as hub genes in immunological mechanisms during CAVD and OA initiation and progression, as well as potential targets for drug development.

Causal relationship between immune cells and atrial fibrillation: A Mendelian randomization study

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia, with recent research indicating a correlation between immune system characteristics and the development of AF. However, it remains uncertain whether the immunological response is the primary underlying component or a secondary consequence of AF. Initially, we investigated the effect of immune cells on AF by performing forward Mendelian randomization (MR) analyses with immune cells as the exposure variable and their associated genetic variants as instrumental variables. Subsequently, we performed reverse MR analyses with AF as the exposure variable and immune cells as the outcome variable to exclude the interference of reverse causality, to distinguish between primary and secondary effects, and to further elucidate the causal relationship between the immune system and AF. We discovered that membrane proteins on specific immune cells, such as CD25 on memory B cells-which functions as a part of the interleukin-2 receptor-may be risk factors for AF development, with odds ratios of 1.0233 (95% confidence interval: 1.0012-1.0458, P = .0383). In addition, certain immune cell counts, such as the CD4 regulatory T cell Absolute Count, play a protective factor in the development of AF (odds ratio: 0.9513, 95% confidence interval: 0.9165-0.9874; P = .0086). More detailed results are elaborated in the main text. Our MR study has yielded evidence that substantiates a genetically inferred causal association between the immune system and AF. Identifying the risk factors associated with AF is vital to facilitate the development of innovative pharmaceutical treatments.

CDK5R1, GSE1, HSPG2 and WDFY3 as indirect epigenetic-sensitive genes in atrial fibrillation

BACKGROUND

Although mounting evidence supports that aberrant DNA methylation occurs in the hearts of patients with atrial fibrillation (AF), noninvasive epigenetic characterization of AF has not yet been defined.

METHODS

We investigated DNA methylome changes in peripheral blood CD4+ T cells isolated from 10 patients with AF relative to 11 healthy subjects (HS) who were enrolled in the DIANA clinical trial (NCT04371809) via reduced-representation bisulfite sequencing (RRBS).

RESULTS

An atrial-specific PPI network revealed 18 hub differentially methylated genes (DMGs), wherein ROC curve analysis revealed reasonable diagnostic performance of DNA methylation levels found within CDK5R1 (AUC = 0.76; p = 0.049), HSPG2 (AUC = 0.77; p = 0.038), WDFY3 (AUC = 0.78; p = 0.029), USP49 (AUC = 0.76; p = 0.049), GSE1 (AUC = 0.76; p = 0.049), AIFM1 (AUC = 0.76; p = 0.041), CDK5RAP2 (AUC = 0.81; p = 0.017), COL4A1 (AUC = 0.86; p < 0.001), SEPT8 (AUC = 0.90; p < 0.001), PFDN1 (AUC = 0.90; p < 0.01) and ACOT7 (AUC = 0.78; p = 0.032). Transcriptional profiling of the hub DMGs provided a significant overexpression of PSDM6 (p = 0.004), TFRC (p = 0.01), CDK5R1 (p < 0.001), HSPG2 (p = 0.01), WDFY3 (p < 0.001), USP49 (p = 0.004) and GSE1 (p = 0.021) in AF patients vs HS.

CONCLUSIONS

CDK5R1, GSE1, HSPG2 and WDFY3 resulted the best discriminatory genes both at methylation and gene expression level. Our results provide several candidate diagnostic biomarkers with the potential to advance precision medicine in AF.

Identification of immune infiltration and immune-related biomarkers of periprosthetic joint infection

Background

The immune response associated with periprosthetic joint infection (PJI) is an emerging but relatively unexplored topic. The aim of this study was to investigate immune cell infiltration in periprosthetic tissues and identify potential immune-related biomarkers.

Methods

The GSE7103 dataset from the GEO database was selected as the data source. Differentially expressed genes (DEGs) and significant modular genes in weighted correlation network analysis (WGCNA) were identified. Functional enrichment analysis and transcription factor prediction were performed on the overlapping genes. Next, immune-related genes from the ImmPort database were matched. The protein-protein interaction (PPI) analysis was performed to identify hub genes. CIBERSORTx was used to evaluate the immune cell infiltration pattern. Spearman correlation analysis was used to evaluate the relationship between hub genes and immune cells.

Results

A total of 667 DEGs were identified between PJI and control samples, and 1847 PJI-related module genes were obtained in WGCNA. Enrichment analysis revealed that the common genes were mainly enriched in immune and host defense-related terms. TFEC, SPI1, and TWIST2 were the top three transcription factors. Three hub genes, SDC1, MMP9, and IGF1, were identified in the immune-related PPI network. Higher levels of plasma cells, CD4+ memory resting T cells, follicular helper T cells, resting mast cells, and neutrophils were found in the PJI group, while levels of M0 macrophages were lower. Notably, the expression of all three hub genes correlated with the infiltration levels of seven types of immune cells.

Conclusion

The present study revealed immune infiltration signatures in the periprosthetic tissues of PJI patients. SDC1, MMP9, and IGF1 were potential immune-related biomarkers for PJI.

Identification and analysis of differently expressed transcription factors in aristolochic acid nephropathy

BACKGROUND

Aristolochic acid nephropathy (AAN) is a rapidly progressive interstitial nephropathy caused by Aristolochic acid (AA). AAN is associated with the development of nephropathy and urothelial carcinoma. It is estimated that more than 100 million people worldwide are at risk of developing AAN. However, the underlying mechanisms driving renal deterioration in AAN remain poorly understood, and the treatment options are limited.

METHODS

We obtained GSE27168 and GSE136276 series matrix data from the Gene Expression Omnibus (GEO) related to AAN. Using the R Studio environment, we applied the limma package and WGCNA package to identify co-differently expressed genes (co-DEGs). By GO/KEGG/GSVA analysis, we revealed common biological pathways. Subsequently, co-DEGs were subjected to the String database to construct a protein-protein interaction (PPI) network. The MCC algorithms implemented in the Cytohubba plugin were employed to identify hub genes. The hub genes were cross-referenced with the transcription factor (TF) database to identify hub TFs. Immune infiltration analysis was performed to identify key immune cell groups by utilizing CIBERSORT. The expressions of AAN-associated hub TFs were verified in vivo and in vitro. Finally, siRNA intervention was performed on the two TFs to verify their regulatory effect in AAN.

RESULTS

Our analysis identified 88 co-DEGs through the "limma" and "WGCNA" R packages. A PPI network comprising 53 nodes and 34 edges was constructed with a confidence level >0.4. ATF3 and c-JUN were identified as hub TFs potentially linked to AAN. Additionally, expressions of ATF3 and c-JUN positively correlated with monocytes, basophils, and vessels, and negatively correlated with eosinophils and endothelial cells. We observed a significant increase in protein and mRNA levels of these two hub TFs. Furthermore, it was found that siRNA intervention targeting ATF3, but not c-JUN, alleviated cell damage induced by AA. The knockdown of ATF3 protects against oxidative stress and inflammation in the AAN cell model.

CONCLUSION

This study provides novel insights into the role of ATF3 in AAN. The comprehensive analysis sheds light on the molecular mechanisms and identifies potential biomarkers and drug targets for AAN treatment.

Long Non-coding RNA Involved in the Pathophysiology of Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a prevalent and chronic cardiovascular disorder associated with various pathophysiological alterations, including atrial electrical and structural remodeling, disrupted calcium handling, autonomic nervous system dysfunction, aberrant energy metabolism, and immune dysregulation. Emerging evidence suggests that long non-coding RNAs (lncRNAs) play a significant role in the pathogenesis of AF.

OBJECTIVE

This discussion aims to elucidate the involvement of AF-related lncRNAs, with a specific focus on their role as miRNA sponges that modulate crucial signaling pathways, contributing to the progression of AF. We also address current limitations in AF-related lncRNA research and explore potential future directions in this field. Additionally, we summarize feasible strategies and promising delivery systems for targeting lncRNAs in AF therapy.

CONCLUSION

In conclusion, targeting AF-related lncRNAs holds substantial promise for future investigations and represents a potential therapeutic avenue for managing AF.

Analyses of m6A regulatory genes and subtype classification in atrial fibrillation

Objective

To explore the role of m6A regulatory genes in atrial fibrillation (AF), we classified atrial fibrillation patients into subtypes by two genotyping methods associated with m6A regulatory genes and explored their clinical significance.

Methods

We downloaded datasets from the Gene Expression Omnibus (GEO) database. The m6A regulatory gene expression levels were extracted. We constructed and compared random forest (RF) and support vector machine (SVM) models. Feature genes were selected to develop a nomogram model with the superior model. We identified m6A subtypes based on significantly differentially expressed m6A regulatory genes and identified m6A gene subtypes based on m6A-related differentially expressed genes (DEGs). Comprehensive evaluation of the two m6A modification patterns was performed.

Results

The data of 107 samples from three datasets, GSE115574, GSE14975 and GSE41177, were acquired from the GEO database for training models, comprising 65 AF samples and 42 sinus rhythm (SR) samples. The data of 26 samples from dataset GSE79768 comprising 14 AF samples and 12 SR samples were acquired from the GEO database for external validation. The expression levels of 23 regulatory genes of m6A were extracted. There were correlations among the m6A readers, erasers, and writers. Five feature m6A regulatory genes, ZC3H13, YTHDF1, HNRNPA2B1, IGFBP2, and IGFBP3, were determined (p < 0.05) to establish a nomogram model that can predict the incidence of atrial fibrillation with the RF model. We identified two m6A subtypes based on the five significant m6A regulatory genes (p < 0.05). Cluster B had a lower immune infiltration of immature dendritic cells than cluster A (p < 0.05). On the basis of six m6A-related DEGs between m6A subtypes (p < 0.05), two m6A gene subtypes were identified. Both cluster A and gene cluster A scored higher than the other clusters in terms of m6A score computed by principal component analysis (PCA) algorithms (p < 0.05). The m6A subtypes and m6A gene subtypes were highly consistent.

Conclusion

The m6A regulatory genes play non-negligible roles in atrial fibrillation. A nomogram model developed by five feature m6A regulatory genes could be used to predict the incidence of atrial fibrillation. Two m6A modification patterns were identified and evaluated comprehensively, which may provide insights into the classification of atrial fibrillation patients and guide treatment.

Establishment of Risk Model and Analysis of Immunoinfiltration Based on Mitophagy-Related Associated Genes in Atrial Fibrillation

Objective

Atrial fibrillation (AF) is a common tachyarrhythmia whose pathogenesis remains elusive. In the present study, we aimed to investigate the pathological mechanism of mitophagy and immunoinfiltration in AF.

Methods

First, we identified differentially expressed mitophagy-related genes (DEMRGs) based on the GSE79768 and GSE115574 datasets, subjecting them to functional enrichment analysis. STRING, TRRUST, miRNet, miRwalk, and Cytoscape were used to explore the potential regulatory roles of downstream signaling pathways. Subsequently, the random forest method was used to construct the AF risk model, and the DEMRGs most correlated with AF risk were determined by combining the Gini index. ssGSEA algorithm, NMF algorithm, and unsupervised clustering were used to subdivide AF molecular types. We then studied the characteristics of mitophagy- and immune infiltration-related genes in AF. Ultimately, we detected the expression of key genes in canine atrial tissues and HL-1 cells by immunofluorescence and Western blot.

Results

Mitophagy and immune infiltration were significantly enriched and activated in AF samples. Thirty-seven DEMRGs were screened, of which MAPK1, VDAC1, MAPK14, and MTERF3 were most associated with AF risk. The risk model based on these could identify patients at a high risk of AF. The infiltration of immunocells such as mast cells and neutrophils was significantly different among AF types. Finally, expression verification indicated that the expression trend of four key genes in canine atrial muscle tissue and HL-1 cells was consistent.

Conclusion

We found that mitophagy may participate in AF progression through immune activation. In addition, the AF risk prediction model composed of VDAC1, MAPK1, MAPK14, and MTERF3 has a good AF prediction performance, which provides new ideas for the study of AF pathogenesis and potential therapeutic targets.

Reviewing Atrial Fibrillation Pathophysiology from a Network Medicine Perspective: The Relevance of Structural Remodeling, Inflammation, and the Immune System

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The numerous gaps concerning the knowledge of its mechanism make improving clinical management difficult. As omics technologies allow more comprehensive insight into biology and disease at a molecular level, bioinformatics encompasses valuable tools for studying systems biology, as well as combining and modeling multi-omics data and networks. Network medicine is a subarea of network biology where disease traits are considered perturbations within the interactome. With this approach, potential disease drivers can be revealed, and the effect of drugs, novel or repurposed, used alone or in combination, may be studied. Thus, this work aims to review AF pathology from a network medicine perspective, helping researchers to comprehend the disease more deeply. Essential concepts involved in network medicine are highlighted, and specific research applying network medicine to study AF is discussed. Additionally, data integration through literature mining and bioinformatics tools, with network building, is exemplified. Together, all of the data show the substantial role of structural remodeling, the immune system, and inflammation in this disease etiology. Despite this, there are still gaps to be filled about AF.

Identification and validation of key genes associated with atrial fibrillation in the elderly

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia and significantly increases the risk of stroke and heart failure (HF), contributing to a higher mortality rate. Increasing age is a major risk factor for AF; however, the mechanisms of how aging contributes to the occurrence and progression of AF remain unclear. This study conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes and determine their potential associations with aging-related AF.

Materials and methods

WGCNA was performed using the AF dataset GSE2240 obtained from the Gene Expression Omnibus, which contained data from atrial myocardium in cardiac patients with permanent AF or sinus rhythm (SR). Hub genes were identified in clinical samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were also performed.

Results

Green and pink were the most critical modules associated with AF, from which nine hub genes, PTGDS, COLQ, ASTN2, VASH1, RCAN1, AMIGO2, RBP1, MFAP4, and ALDH1A1, were hypothesized to play key roles in the AF pathophysiology in elderly and seven of them have high diagnostic value. Functional enrichment analysis demonstrated that the green module was associated with the calcium, cyclic adenosine monophosphate (cAMP), and peroxisome proliferator-activated receptors (PPAR) signaling pathways, and the pink module may be associated with the transforming growth factor beta (TGF-β) signaling pathway in myocardial fibrosis.

Conclusion

We identified nine genes that may play crucial roles in the pathophysiological mechanism of aging-related AF, among which six genes were associated with AF for the first time. This study provided novel insights into the impact of aging on the occurrence and progression of AF, and identified biomarkers and potential therapeutic targets for AF.

Identifying Liver Metastasis-Related Genes Through a Coexpression Network to Construct a 5-Gene Model for Predicting Pancreatic Ductal Adenocarcinoma Patient Prognosis

OBJECTIVES

This study aimed to develop a liver metastasis-related gene prognostic index (LMPI) for pancreatic ductal adenocarcinoma prognosis and therapy.

METHODS

The Cancer Genome Atlas data set was used to identify liver metastasis-related hub genes via weighted gene coexpression network analysis. The core genes were identified to construct an LMPI by using the Cox regression method. An immune cell abundance identifier was applied to determine the immune cell abundance.

RESULTS

A total of 78 hub liver metastasis-related genes in the black module were significantly enriched in complement and coagulation cascades, fat digestion and absorption, and the PPAR signaling pathway. Then, an LMPI was constructed on the basis of the 5 prognostic genes (MOGAT3, ASGR1, TRPM8, SGSM1, and LOC101927851). Patients with higher LMPI scores had poor overall survival, more co-occurring or mutually exclusive pairs of driver gene mutations, and less benefit from immunotherapy than patients with lower LMPI scores. In addition, a high correlation was also found between LMPI scores and immune infiltration, such as CD4 naive, CD8 T, cytotoxic T, T helper 2, follicular helper T, and natural killer cells.

CONCLUSIONS

The core genes of the LMPI developed may be independent factors for predicting prognosis, immune characteristics, and immunotherapy efficacy in pancreatic ductal adenocarcinoma.

Causal association of peripheral immune cell counts and atrial fibrillation: A Mendelian randomization study

Background

Atrial fibrillation (AF) is the most common and persistent form of arrhythmia. Recently, increasing evidence has shown a link between immune responses and atrial fibrillation. However, whether the immune response is a cause or consequence of AF remains unknown. We aimed to determine whether genetically predicted peripheral immunity might have a causal effect on AF.

Methods

First, we performed Mendelian randomization (MR) analyses using genetic variants strongly associated with neutrophil, eosinophil, basophil, lymphocyte, and monocyte cell counts as instrumental variables (IVs). Lymphocyte counts were then subjected to further subgroup analysis. The effect of immune cell counts on AF risk was measured using summary statistics from genome-wide association studies (GWAS).

Results

Two-sample MR analysis revealed that a higher neutrophil count, basophil count and lymphocyte count had a causal effect on AF [Odds ratio (OR), 1.06, 95% confidence interval (CI), 1.01-1.10, P = 0.0070; OR, 1.10; 95% CI, 1.04-1.17; P = 0.0015; OR, 0.96; 95% CI, 0.93-0.99; P = 0.0359]. In addition, in our further analysis, genetically predicted increases in CD4 + T-cell counts were also associated with an increased risk of AF (OR, 1.04; 95% CI, 1.0-.09; P = 0.0493).

Conclusion

Our MR analysis provided evidence of a genetically predicted causal relationship between higher peripheral immune cell counts and AF. Subgroup analysis revealed the key role of peripheral lymphocytes in AF, especially the causal relationship between CD4 + T cell count and AF. These findings are beneficial for future exploration of the mechanism of AF.

Single-cell sequencing combined with machine learning reveals the mechanism of interaction between epilepsy and stress cardiomyopathy

Background

Epilepsy is a disorder that can manifest as abnormalities in neurological or physical function. Stress cardiomyopathy is closely associated with neurological stimulation. However, the mechanisms underlying the interrelationship between epilepsy and stress cardiomyopathy are unclear. This paper aims to explore the genetic features and potential molecular mechanisms shared in epilepsy and stress cardiomyopathy.

Methods

By analyzing the epilepsy dataset and stress cardiomyopathy dataset separately, the intersection of the two disease co-expressed differential genes is obtained, the co-expressed differential genes reveal the biological functions, the network is constructed, and the core modules are identified to reveal the interaction mechanism, the co-expressed genes with diagnostic validity are screened by machine learning algorithms, and the co-expressed genes are validated in parallel on the epilepsy single-cell data and the stress cardiomyopathy rat model.

Results

Epilepsy causes stress cardiomyopathy, and its key pathways are Complement and coagulation cascades, HIF-1 signaling pathway, its key co-expressed genes include SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3. The key immune cell subpopulations localized by single-cell data are the T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup.

Conclusion

We believe epilepsy causing stress cardiomyopathy results from a multi-gene, multi-pathway combination. We identified the core co-expressed genes (SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3) and the pathways that function in them (Complement and coagulation cascades, HIF-1 signaling pathway, JAK-STAT signaling pathway), and finally localized their key cellular subgroups (T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup). Also, combining cell subpopulations with hypercoagulability as well as sympathetic excitation further narrowed the cell subpopulations of related functions.

Cytotoxic CD8+ T Cells Are Involved in the Thrombo-Inflammatory Response during First-Diagnosed Atrial Fibrillation

BACKGROUND

Atrial myopathy and atrial fibrillation (AF) accompany thrombo-inflammation. This facilitates disease progression and promotes major adverse cardiovascular events (MACEs). Thrombin receptor (protease-activated receptor 1, PAR1) signalling is central in mediating thrombo-inflammation. We hypothesised that PAR1 signalling links coagulation and inflammation through cytotoxic CD8⁺ T lymphocytes in patients presenting with first-diagnosed AF (FDAF).

METHODS

A total of 210 patients were studied. We included data and blood samples from patients presenting with FDAF (n = 160), cardiac tissue from patients with paroxysmal AF (n = 32) and 20 controls.

RESULTS

During early AF, a pro-inflammatory and cytotoxic subset of T lymphocytes (CD8⁺) circulated more frequently when compared to patients with chronic cardiovascular disease but without AF, accompanied by elevated plasma levels of CD8⁺ effector molecules, which corresponded to biomarkers of adverse cardiac remodelling and atrial dysfunction. Activation of tissue factor (TF) and PAR1 was associated with pro-inflammatory and cytotoxic effector functions. PAR1-related CD8⁺ cell activation was more frequent in FDAF patients that experienced a MACE.

CONCLUSIONS

In patients with FDAF, the TF-factor Xa-factor IIa-axis contributes to thrombo-inflammation via PAR1 in CD8⁺ T cells. Intervening in this cascade might be a promising synergistic approach to reducing disease progression and the vascular complications of AF.

Immune remodeling and atrial fibrillation

Atrial fibrillation (AF) is a highly prevalent arrhythmia that causes high morbidity and mortality. However, the underlying mechanism of AF has not been fully elucidated. Recent research has suggested that, during AF, the immune system changes considerably and interacts with the environment and cells involved in the initiation and maintenance of AF. This may provide a new direction for research and therapeutic strategies for AF. In this review, we elaborate the concept of immune remodeling based on available data in AF. Then, we highlight the complex relationships between immune remodeling and atrial electrical, structural and neural remodeling while also pointing out some research gaps in these field. Finally, we discuss several potential immunomodulatory treatments for AF. Although the heterogeneity of existing evidence makes it ambiguous to extrapolate immunomodulatory treatments for AF into the clinical practice, immune remodeling is still an evolving concept in AF pathophysiology and further studies within this field are likely to provide effective therapies for AF.

Identification of Atrial Fibrillation-Related lncRNA Based on Bioinformatic Analysis

BACKGROUND

Atrial fibrillation (AF) is the most common arrhythmia in the world. Long noncoding RNA (lncRNA) has been found to play an important role in cardiovascular diseases including heart failure, myocardial infarction, and atherosclerosis. However, the role of lncRNA in AF has rarely been studied. The purpose of this study is to identify the expression profile of lncRNA in AF patients, explore the function of lncRNA in AF, and provide a potential scientific basis for the treatment of AF in the future.

METHODS

The lncRNA and mRNA expression profiles were obtained from the atrial appendage samples of GSE31821, GSE411774, GSE79768, and GSE115574 in the Gene Expression Omnibus (GEO) database. Functional analysis was performed via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). The "CIBERSORT" R kit was used to analyze 22 immune cell infiltrates in AF and sinus rhythm (SR) patients. The "CORRPLOT" R package was used to analyze the immune correlation between lncRNA and immune cells.

RESULTS

A total of 6 differentially expressed lncRNAs and 45 differentially expressed mRNAs were identified in the AF and SR groups. GO, KEGG, and GSVA results showed that abnormally expressed lncRNAs were involved in signaling pathways related to the atrium, including the Toll-like receptor signaling pathway and calcium signaling pathway. Immune cell infiltration analysis revealed that native B cells, follicular helper T cells, and resting dendritic cells may be involved in the AF process. In addition, LINC00844 was negatively correlated with resting dendritic cells.

CONCLUSION

The expression profile of lncRNA in AF patients was different from that in normal controls. The physiological functions of these differentially expressed lncRNAs may be related to the pathogenesis of AF, which provide a scientific basis for the prognosis and treatment of patients with AF.

Targeting the microRNA-34a as a Novel Therapeutic Strategy for Cardiovascular Diseases

Cardiovascular diseases (CVDs) are still the main cause of morbidity and mortality worldwide and include a group of disorders varying from vasculature, myocardium, arrhythmias and cardiac development. MicroRNAs (miRs) are endogenous non-coding RNAs with 18–23 nucleotides that regulate gene expression. The miR-34 family, including miR-34a/b/c, plays a vital role in the regulation of myocardial physiology and pathophysiological processes. Recently, miR-34a has been implicated in cardiovascular fibrosis, dysfunction and related cardiovascular disorders as an essential regulator. Interestingly, there is a pivotal link among miR-34a, cardiovascular fibrosis, and Smad4/TGF-β1 signaling. Notably, both loss-of-function and gain-of-function approaches identified the critical roles of miR-34a in cardiovascular apoptosis, autophagy, inflammation, senescence and remodeling by modulating multifunctional signaling pathways. In this article, we focus on the current understanding of miR-34a in biogenesis, its biological effects and its implications for cardiac pathologies including myocardial infarction, heart failure, ischaemia reperfusion injury, cardiomyopathy, atherosclerosis, hypertension and atrial fibrillation. Thus, further understanding of the effects of miR-34a on cardiovascular diseases will aid the development of effective interventions. Targeting for miR-34a has emerged as a potential therapeutic target for cardiovascular dysfunction and related diseases.

Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis

Introduction

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, but the molecular mechanisms underlying AF are not known. We aimed to identify the pivotal genes and pathways involved in AF pathogenesis because they could become potential biomarkers and therapeutic targets of AF.

Methods

The microarray datasets of GSE31821 and GSE41177 were downloaded from the Gene Expression Omnibus database. After combining the two datasets, differentially expressed genes (DEGs) were screened by the Limma package. MicroRNAs (miRNAs) confirmed experimentally to have an interaction with AF were screened through the miRTarBase database. Target genes of miRNAs were predicted using the miRNet database, and the intersection between DEGs and target genes of miRNAs, which were defined as common genes (CGs), were analyzed. Functional and pathway-enrichment analyses of DEGs and CGs were performed using the databases DAVID and KOBAS. Protein-protein interaction (PPI) network, miRNA- messenger(m) RNA network, and drug-gene network was visualized. Finally, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the expression of hub genes in the miRNA-mRNA network.

Results

Thirty-three CGs were acquired from the intersection of 65 DEGs from the integrated dataset and 9777 target genes of miRNAs. Fifteen "hub" genes were selected from the PPI network, and the miRNA-mRNA network, including 82 miRNAs and 9 target mRNAs, was constructed. Furthermore, with the validation by RT-qPCR, macrophage migration inhibitory factor (MIF), MYC proto-oncogene, bHLH transcription factor (MYC), inhibitor of differentiation 1 (ID1), and C-X-C Motif Chemokine Receptor 4 (CXCR4) were upregulated and superoxide Dismutase 2 (SOD2) was downregulated in patients with AF compared with healthy controls. We also found MIF, MYC, and ID1 were enriched in the transforming growth factor (TGF)-β and Hippo signaling pathway.

Conclusion

We identified several pivotal genes and pathways involved in AF pathogenesis. MIF, MYC, and ID1 might participate in AF progression through the TGF-β and Hippo signaling pathways. Our study provided new insights into the mechanisms of action of AF.

Pyroptosis-Related Gene Signature Is a Novel Prognostic Biomarker for Sarcoma Patients

Sarcoma is a rare and an extremely aggressive form of cancer that originates from mesenchymal cells. Pyroptosis exerts a dual effect on tumours by inhibiting tumour cell proliferation while creating a microenvironment suitable for tumour cell development and proliferation. However, the significance of pyroptosis-related gene (PRG) expression in sarcoma has not yet been evaluated. Here, we conduct a retrospective analysis to examine PRG expression in 256 sarcoma samples from The Cancer Genome Atlas database. We identified the PRGs that had a significant correlation with overall patient survival in sarcoma by performing a univariate Cox regression analysis. Subsequently, we conducted a LASSO regression analysis and created a risk model for a six-PRG signature. As indicated from the Kaplan–Meier analysis, this signature revealed a significant difference between high- and low-risk sarcoma patients. A receiver operating characteristic curve analysis confirmed that this signature could predict overall patient survival in sarcoma patients with high sensitivity and specificity. Gene ontology annotation and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses revealed that five independent PRGs were closely associated with increased immune activity. Moreover, we also deciphered that increased number of immune cells infiltrated the tumour microenvironment in sarcoma. In brief, the PRG signature can effectively act as novel prognostic biomarker for sarcoma patients and is associated with the tumour immune microenvironment.