Potential Target Genes in the Development of Atrial Fibrillation: A Comprehensive Bioinformatics Analysis

Advances in the study of S100A9 in cardiovascular diseases

Cardiovascular disease (CVD) is a group of diseases that primarily affect the heart or blood vessels, with high disability and mortality rates, posing a serious threat to human health. The causative factors, pathogenesis, and characteristics of common CVD differ, but they all involve common pathological processes such as inflammation, oxidative stress, and fibrosis. S100A9 belongs to the S100 family of calcium-binding proteins, which are mainly secreted by myeloid cells and bind to the Toll-like receptor 4 and receptor for advanced glycation end products and is involved in regulating pathological processes such as inflammatory response, fibrosis, vascular calcification, and endothelial barrier function in CVD. The latest research has found that S100A9 is a key biomarker for diagnosing and predicting various CVD. Therefore, this article reviews the latest research progress on the diagnostic and predictive, and therapeutic value of S100A9 in inflammatory-related CVD such as atherosclerosis, myocardial infarction, and arterial aneurysm and summarizes its molecular mechanisms in the progression of CVD, aiming to explore new predictive methods and to identify potential intervention targets for CVD in clinical practice.

Identification of microRNAs as diagnostic biomarkers for atrial fibrillation: a systematic review and meta-analysis

Background

Genetic factors contribute to the AF pathophysiology by altering the structural and functional properties of proteins involved in different cellular activities. MicroRNAs (miRNAs), which take part in structural and electrical remodeling during the AF evolution, are important genetic elements that must be considered. The aim of study is to determine correlation between the expression of miRNAs and the development of AF, as well as to explain any potential importance of genetic factors in the AF diagnosis.

Methods and Results

Online scientific databases, including Cochrane, ProQuest, PubMed, and Web of Science were used to conduct the literature search. The keywords were associated with or characterized the relationship between miRNAs and AF. The pooled sensitivity and specificity statistical parameters were analyzed using a random-effects model. The miRNAs had a combined sensitivity and specificity of 0.80 (95% CI = 0.70-0.87) and 0.75 (95% CI = 0.64-0.83) for the diagnosis of AF, respectively. The area under the SROC was 0.84 (95% CI = 0.81-0.87). The DOR was 11.80 (95% CI = 6.79-20.50). This study also revealed that miRNAs had a pooled PLR of 3.16 (95% CI = 2.24-4.45) and NLR of 0.27 (95% CI = 0.18-0.39) for the diagnosis of AF. The miR-425-5p demonstrated the highest sensitivity (0.96, 95% CI, 0.89-0.99).

Conclusion

The meta-analysis revealed substantial connection between miRNA expression dysregulation and AF, supporting the potential diagnostic role of miRNAs. The miR-425-5p has potential role as a biomarker for AF.

The Four Key Genes Participated in and Maintained Atrial Fibrillation Process via Reprogramming Lipid Metabolism in AF Patients

Atrial fibrillation (AF) is always in high incidence in the population, which can lead to serious complications. The structural and electrical remodeling of atrial muscle induced by inflammatory reaction or oxidative stress was considered as the major mechanism of AF. The treatment effect is not ideal based on current mechanisms. Recent studies demonstrated that lipid metabolism disorder of atrial muscle played an important role in the occurrence of AF. What key genes are involved is unclear. The purpose of the present study was to explore the lipid metabolism mechanism of AF. With the GEO database and the genomics of AF patients, metabolic related pathways and the key genes were analyzed. At the same time, the rat model of cecal ligation and puncture (CLP) was used to confirm the results. GSE 31821 and GSE 41177 were used as data sources, and the merged differentially expressed genes (DEGs) analysis showed that a total of 272 DEGs were found. GO annotation, KEGG, and gene set enrichment analysis (GSEA) showed that the fatty acid metabolism and the lipid biosynthetic process were involved in AF. Cholesterol biosynthesis, arachidonic acid metabolism, and the lipid droplet pathway were obviously increased in AF. Further analysis showed that four key genes, including ITGB1, HSP90AA1, CCND1, and HSPA8 participated in pathogenesis of AF regulating lipid biosynthesis. In CLP rats, metabolic profiling in the heart showed that the pyrimidine metabolism, the biosynthesis of unsaturated fatty acid metabolism, arginine and proline metabolism, and the fatty acid biosynthesis were involved. The four key genes were confirmed increased in the heart of CLP rats (p < 0.05 or 0.01). The results suggest that the lipid metabolism disorder participates in the occurrence of AF. ITGB1, HSP90AA1, CCND1, and HSPA8 are the key genes involved in the regulation of lipid biosynthesis.

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.