Construction and Analysis of the lncRNA-miRNA-mRNA Network Based on Competing Endogenous RNA in Atrial Fibrillation

Comparative transcriptome of normal and cancer-associated fibroblasts

BACKGROUND

The characteristics of a tumor are largely determined by its interaction with the surrounding micro-environment (TME). TME consists of both cellular and non-cellular components. Cancer-associated fibroblasts (CAFs) are a major component of the TME. They are a source of many secreted factors that influence the survival and progression of tumors as well as their response to drugs. Identification of markers either overexpressed in CAFs or unique to CAFs would pave the way for novel therapeutic strategies that in combination with conventional chemotherapy are likely to have better patient outcome.

METHODS

Fibroblasts have been derived from Benign Prostatic Hyperplasia (BPH) and prostate cancer. RNA from these has been used to perform a transcriptome analysis in order to get a comparative profile of normal and cancer-associated fibroblasts.

RESULTS

The study has identified 818 differentially expressed mRNAs and 17 lincRNAs between normal and cancer-associated fibroblasts. Also, 15 potential lincRNA-miRNA-mRNA combinations have been identified which may be potential biomarkers.

CONCLUSIONS

This study identified differentially expressed markers between normal and cancer-associated fibroblasts that would help in targeted therapy against CAFs/derived factors, in combination with conventional therapy. However, this would in future need more experimental validation.

Integrative experimental validation of concomitant miRNAs and transcription factors with differentially expressed genes in acute myocardial infarction

Identification of concomitant miRNAs and transcription factors (TFs) with differential expression (DEGs) in MI is crucial for understanding holistic gene regulation, identifying key regulators, and precision in biomarker and therapeutic target discovery. We performed a comprehensive analysis using Affymetrix microarray data, advanced bioinformatic tools, and experimental validation to explore potential biomarkers associated with human pathology. The search strategy includes the identification of the GSE83500 dataset, comprising gene expression profiles from aortic wall punch biopsies of MI and non-MI patients, which were used in the present study. The analysis identified nine distinct genes exhibiting DEGs within the realm of MI. miRNA-gene/TF and TF-gene/miRNA regulatory relations were mapped to retrieve interacting hub genes to acquire an MI miRNA-TF co-regulatory network. Furthermore, an animal model of I/R-induced MI confirmed the involved gene based on quantitative RT-PCR and Western blot analysis. The consequences of the bioinformatic tool substantiate the inference regarding the presence of three key hub genes (UBE2N, TMEM106B, and CXADR), a central miRNA (hsa-miR-124-3p), and sixteen TFs. Animal studies support the involvement of predicted genes in the I/R-induced myocardial infarction assessed by RT-PCR and Western blotting. Thus, the final consequences suggest the involvement of promising molecular pathways regulated by TF (p53/NF-κB1), miRNA (hsa-miR-124-3p), and hub gene (UBE2N), which may play a key role in the pathogenesis of MI.

Transcriptional and Translational Regulation of Differentially Expressed Genes in Yucatan Miniswine Brain Tissues following Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of morbidity, disability, and mortality worldwide. Motor and cognitive deficits and emotional disturbances are long-term consequences of TBI. A lack of effective treatment for TBI-induced neural damage, functional impairments, and cognitive deficits makes it challenging in the recovery following TBI. One of the reasons may be the lack of knowledge underlying the complex pathophysiology of TBI and the regulatory factors involved in the cellular and molecular mechanisms of inflammation, neural regeneration, and injury repair. These mechanisms involve a change in the expression of various proteins encoded by genes whose expression is regulated by transcription factors (TFs) at the transcriptional level and microRNA (miRs) at the mRNA level. In this pilot study, we performed the RNA sequencing of injured tissues and non-injured tissues from the brain of Yucatan miniswine and analyzed the sequencing data for differentially expressed genes (DEGs) and the TFs and miRs regulating the expression of DEGs using in-silico analysis. We also compared the effect of the electromagnetic field (EMF) applied to the injured miniswine on the expression profile of various DEGs. The results of this pilot study revealed a few DEGs that were significantly upregulated in the injured brain tissue and the EMF stimulation showed effect on their expression profile.

Progress of circRNA/lncRNA-miRNA-mRNA axis in atrial fibrillation

Atrial fibrillation (AF) is a prevalent arrhythmia that requires effective biomarkers and therapeutic targets for clinical management. In recent years, non-coding RNAs (ncRNAs) have emerged as key players in the pathogenesis of AF, particularly through the ceRNA (competitive endogenous RNA) mechanism. By acting as ceRNAs, ncRNAs can competitively bind to miRNAs and modulate the expression of target mRNAs, thereby influencing the biological behavior of AF. The ceRNA axis has shown promise as a diagnostic and prognostic biomarker for AF. This review provides a comprehensive overview of the roles of ncRNAs in the development and progression of AF, highlighting the intricate crosstalk between different ncRNAs in AF pathophysiology. Furthermore, we discuss the potential implications of targeting the circRNA/lncRNA-miRNA-mRNA axis for the diagnosis, prognosis, and therapeutic intervention of AF.

Identification of functional lncRNAs in atrial fibrillation based on RNA sequencing

BACKGROUND

Atrial fibrillation (AF) is one of the most common arrhythmia contributing to serious conditions such as stroke and heart failure. Recent studies demonstrated that long noncoding RNAs (lncRNAs) were related to cardiovascular disease. However, the molecular mechanisms of AF are not fully clear. This study intended to discover lncRNAs that are differentially expressed in AF compared with controls and evaluate the potential functions of these lncRNAs.

METHODS

Ninety-seven patients (49 patients with AF and 48 patients without AF) were included in this study. Among these patients, leucocyte suspensions of 3 AF patients and 3 controls were sent for RNA-seq analysis to select differentially expressed lncRNA and mRNA. Different lncRNA expressions were validated in another samples (46 AF patients and 45 controls). Gene ontology (GO) enrichment analysis was conducted to annotate the function of selected mRNAs. Alternative splicing (AS) analysis was performed and a lncRNA-mRNA network was also constructed. The receiver operating characteristics (ROC) curve was used to evaluate diagnostic values. Logistic regression analysis was utilized to assess the risk or protective factor of AF.

RESULTS

A total of 223 mRNAs and 105 lncRNAs were detected in AF patients compared with controls. Total 4 lncRNAs (LINC01781, AC009509.2, AL662844.3, AL662844.4) associated with AF were picked out for validation in another samples by quantitative real-time PCR (qRT-PCR), detecting that upregulated AC009509.2 and downregulated LINC01781 in AF patients. Multivariate logistic regression analysis illustrated that left atrial diameter (OR 1.201; 95% CI 1.093-1.320; P=0.000) and AC009509.2 (OR 1.732; 95% CI 1.092-2.747; P=0.020) were related to AF respectively. ROC curve showed that AC009509.2, LINC01781 and left atrial diameter (LAD) were predictors of AF. For LINC01781, the area under the curve (AUC) was 0.654 (95% CI 0.541-0.767, P=0.0113). For AC009509.2, the AUC was 0.710 (95% CI 0.599-0.822, P=0.0005). Bioinformatic methods (GO enrichment, AS analysis and lncRNA-mRNA network construction) were performed to reveal the role of lncRNAs.

CONCLUSIONS

This study discussed differentially expressed lncRNA and their potential interaction with mRNA in AF. LncRNA AC009509.2 could be a new potential biomarker for AF prediction.

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.

Construction of atrial fibrillation-related circRNA/lncRNA-miRNA-mRNA regulatory network and analysis of potential biomarkers

BACKGROUND

The specific pathogenesis of atrial fibrillation (AF) remains unclear. In this study, we examined the expression of differential messenger RNAs (mRNAs), circular RNAs (circRNAs), and long-stranded noncoding RNAs (lncRNAs) from human peripheral blood mononuclear cells to initially construct a circRNA/lncRNA-miRNA-mRNA ceRNA regulatory network to explore the pathogenesis of AF and to screen for potential biomarkers.

METHODS

A total of four pairs of AF cases and healthy subjects were selected to detect differentially expressed mRNAs, circRNAs, and lncRNAs in peripheral blood mononuclear cells by microarray analysis. And 20 pairs of peripheral blood from AF patients and healthy subjects were selected for validation of mRNA, circRNA, and lncRNA by quantitative real-time PCR (qRT-PCR).The relevant ceRNA networks were constructed by GO and KEGG and correlation analysis.

RESULTS

The results showed that compared with healthy subjects, there were 813 differentially expressed mRNAs (DEmRNAs) in peripheral blood monocytes of AF, including 445 upregulated genes and 368 downregulated genes, 120 differentially expressed circRNAs (DEcircRNAs), including 65 upregulated and 55 downregulated, 912 differentially expressed lncRNAs (DElncRNAs), including 531 upregulated and 381 downregulated lncRNAs. GO and KEGG analysis of DERNA revealed the biological processes and pathways involved in AF. Based on microarray data and predicted miRNAs, a ceRNA network containing 34 mRNAs, 212 circRNAs, 108 lncRNAs, and 38 miRNAs was constructed.

CONCLUSION

We revealed a novel ceRNA network in AF and showed that downregulated XIST, circRNA_2773, and CADM1 were negatively correlated with miR-486-5p expression and had a potential targeting relationship with miR-486-5p.

Dominant Myocardial Fibrosis and Complex Immune Microenvironment Jointly Shape the Pathogenesis of Arrhythmogenic Right Ventricular Cardiomyopathy

Background

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable life-threatening myocardial disease characterized by ventricular arrhythmias and sudden cardiac death. Few studies used RNA-sequencing (RNA-seq) technology to analyze gene expression profiles, hub genes, dominant pathogenic processes, immune microenvironment in ARVC. This study aimed to explore these questions via integrated bioinformatics analysis.

Methods

RNA-sequencing datasets of GSE107475, GSE107311, GSE107156, and GSE107125 were obtained from the Gene Expression Omnibus database, including right and left ventricular myocardium from ARVC patients and normal controls. Weighted gene co-expression network analysis identified the ARVC hub modules and genes. Functional enrichment and protein-protein interaction analysis were performed by Metascape and STRING. Single-sample gene-set enrichment analysis (ssGSEA) was applied to assess immune cell infiltration. Transcription regulator (TF) analysis was performed by TRRUST.

Results

Three ARVC hub modules with 25 hub genes were identified. Functional enrichment analysis of the hub genes indicated that myocardial fibrosis was the dominant pathogenic process. Higher myocardial fibrosis activity existed in ARVC than in normal controls. A complex immune microenvironment was discovered that type 2 T helper cell, type 1 T helper cell, regulatory T cell, plasmacytoid dendritic cell, neutrophil, mast cell, central memory CD4 T cell, macrophage, CD56dim natural killer cell, myeloid-derived suppressor cell, memory B cell, natural killer T cell, and activated CD8 T cell were highly infiltrated in ARVC myocardium. The immune-related hub module was enriched in immune processes and inflammatory disease pathways, with hub genes including CD74, HLA-DRA, ITGAM, CTSS, CYBB, and IRF8. A positive linear correlation existed between immune cell infiltration and fibrosis activity in ARVC. NFKB1 and RELA were the shared TFs of ARVC hub genes and immune-related hub module genes, indicating the critical role of NFκB signaling in both mechanisms. Finally, the potential lncRNA-miRNA-mRNA interaction network for ARVC hub genes was constructed.

Conclusion

Myocardial fibrosis is the dominant pathogenic process in end-stage ARVC patients. A complex immune microenvironment exists in the diseased myocardium of ARVC, in which T cell subsets are the primary category. A tight relationship exists between myocardial fibrosis activity and immune cell infiltration. NFκB signaling pathway possibly contributes to both mechanisms.

Exosomal LncRNAs and hepatocellular Carcinoma: From basic research to clinical practice

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with poor prognosis. The incidences of HCC and HCC-related deaths have increased over the last several decades. However, the treatment options for advanced HCC are very limited. Long noncoding RNAs (lncRNAs) wrapped in exosomes can change the expression of their target genes in recipient cells, thereby regulating the behavior of recipient cells. Increasing evidence has demonstrated that there is a correlation between the activation of exosomal lncRNAs and the development of HCC. In this review article, we highlighted the functions of exosomal lncRNAs in the development of HCC, showing that exosomal lncRNAs play a vital role in the growth and progression of HCC and are targets for HCC.