Comprehensive Analysis of ceRNA Regulation Network Involved in the Development of Coronary Artery Disease

Exploring the Regulatory Landscape of Dementia: Insights from Non-Coding RNAs

Dementia, a multifaceted neurological syndrome characterized by cognitive decline, poses significant challenges to daily functioning. The main causes of dementia, including Alzheimer's disease (AD), frontotemporal dementia (FTD), Lewy body dementia (LBD), and vascular dementia (VD), have different symptoms and etiologies. Genetic regulators, specifically non-coding RNAs (ncRNAs) such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are known to play important roles in dementia pathogenesis. MiRNAs, small non-coding RNAs, regulate gene expression by binding to the 3' untranslated regions of target messenger RNAs (mRNAs), while lncRNAs and circRNAs act as molecular sponges for miRNAs, thereby regulating gene expression. The emerging concept of competing endogenous RNA (ceRNA) interactions, involving lncRNAs and circRNAs as competitors for miRNA binding, has gained attention as potential biomarkers and therapeutic targets in dementia-related disorders. This review explores the regulatory roles of ncRNAs, particularly miRNAs, and the intricate dynamics of ceRNA interactions, providing insights into dementia pathogenesis and potential therapeutic avenues.

Characterization of lncRNA-associated ceRNA network to uncover novel potential biomarkers in coronary artery disease

The purpose of this study was to construct a competitive endogenous RNA (ceRNA) network related to long non-coding RNA (lncRNAs) via the bioinformatics analysis, reveal the pathogenesis of coronary heart disease (CAD) and develop new biomarkers for CAD. The gene expression datasets of peripheral blood of CAD were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed mRNAs, miRNAs and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) were identified. Subsequently, a ceRNA network involving lncRNAs, miRNAs, and mRNAs was built. Moreover, DElncRNAs in the cytoplasm were screened and a DElncRNA-associated ceRNA network was established. In total, 1860 DEmRNAs, 393 DElncRNAs and 20 DEmiRNAs were filtrated in patients with CAD compared with normal controls. Functional analysis suggested that DEmRNAs significantly enriched in CAD-related pathways, such as PI3K-Akt signaling pathways and MAPK signaling pathway. The ceRNA network contained 12 DEmiRNAs, 30 DElncRNAs and 537 DEmRNAs. Afterwards, the cytoplasm ceRNA network was consisted of 537 DEmRNAs, 12 DEmiRNAs and 12 DElncRNAs. Such as, up-regulated LncRNA-HOX transcript antisense RNA (HOTAIR) was interacted with down-regulated has-miR-326 and has-miR-1. The successful construction of lncRNA-associated ceRNA network is helpful to better clarify the pathogenesis of CAD and provide potential peripheral blood biomarkers for CAD.

Comprehensive Analysis of circRNA-miRNA-mRNA Regulatory Network and Novel Potential Biomarkers in Acute Myocardial Infarction

Background

Circular RNA (circRNA) plays an important role in the regulation of gene expression and the occurrence of human diseases. However, studies on the role of circRNA in acute myocardial infarction (AMI) are limited. This study was performed to explore novel circRNA-related regulatory networks in AMI, aiming to better understand the molecular mechanism of circRNAs involvement in AMI and provide basis for further scientific research and clinical decision-making.

Methods

The AMI-related microarray datasets GSE160717 (circRNA), GSE31568 (miRNA), GSE61741 (miRNA), and GSE24519 (mRNA) were obtained from the Gene Expression Omnibus (GEO) database. After differential expression analysis, the regulatory relationships between these DERNAs were identified by online databases circBank, circInteractome, miRDB, miRWalk, Targetscan, and then two circRNA-miRNA-mRNA regulatory networks were constructed. Differentially expressed genes (DEGs) in this network were selected followed by enrichment analysis and protein–protein interaction (PPI) analysis. Hub genes were identified using Cytohubba plug-in of Cytoscape software. Hub genes and hub gene-related miRNAs were used for receiver operating characteristic curve (ROC) analysis to identify potential biomarkers. The relative expression levels of these biomarkers were further assessed by GSE31568 (miRNA) and GSE66360 (mRNA). Finally, on the basis of the above analysis, myocardial hypoxia model was constructed to verify the expression of Hub genes and related circRNAs.

Results

A total of 83 DEcircRNAs, 109 CoDEmiRNAs and 1204 DEGs were significantly differentially expressed in these datasets. The up-regulated circRNAs and down-regulated circRNAs were used to construct a circRNA-miRNA-mRNA regulatory network respectively. These circRNA-related DEGs were mainly enriched in the terms of “FOXO signaling pathway,” “T cell receptor signaling pathway,” “MAPK signaling pathway,” “Insulin resistance,” “cAMP signaling pathway,” and “mTOR signaling pathway.” The top 10 hub genes ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A, CACNA1E, HDAC2, SRSF1, ANK2, and HNRNPA2B1 were identified from the PPI network. Hub genes GPM6A, SRSF1, ANK2 and hub gene-related circRNAs hsa_circ_0023461, hsa_circ_0004561, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276, and hsa_circ_0045519 were identified as potential biomarkers in AMI.

Conclusion

In this study, the potential circRNAs associated with AMI were identified and two circRNA-miRNA-mRNA regulatory networks were constructed. This study explored the mechanism of circRNA involvement in AMI and provided new clues for the selection of new diagnostic markers and therapeutic targets for AMI.

Construction and validation of molecular subtypes of coronary artery disease based on ferroptosis-related genes

Background

This study aims to construct a reliable diagnostic model for coronary artery disease (CAD) patients and explore its potential mechanism by consensus molecular subtypes of ferroptosis-related genes.

Methods

GSE12288 and GSE20680 were downloaded from Gene Expression Omnibus database. CAD patients were divided into different molecular subtypes according to the expression level of ferroptosis-related genes. Then, the distribution of differentially expressed genes, functional annotations and immune infiltration cells between the two subtypes were compared. Finally, a prognostic model of ferroptosis-related genes in CAD was constructed and verified.

Results

Two different molecular subtypes of CAD were obtained according to the expression level of ferroptosis-related genes. Then, a total of 1944 differentially expressed genes (DEGs) were found, among which, 236 genes were up-regulated and 1708 genes were down-regulated. In addition, 43 DEGs were ferroptosis-related genes. Functional enrichment analysis showed that these DEGs between two subtypes of CAD were mainly enriched in immune-related pathways and processes, such as T cell receptor, mTOR, NOD-like receptor and Toll-like receptor signaling pathways. We also found that 21 immune cells were significantly changed between two subtypes of CAD. The LASSO method was performed to identify and construct the 16 ferroptosis-related genes-based diagnostic signature. Diagnostic efficiency of diagnostic signature measured by AUC in the training set and validation cohort was 0.971 and 0.899, respectively.

Conclusions

This study contributes to a more comprehensive understanding of the mechanism of ferroptosis-related genes in CAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02719-1.

Screening for Regulatory Network of miRNA–Inflammation, Oxidative Stress and Prognosis-Related mRNA in Acute Myocardial Infarction: An in silico and Validation Study

Background

Acute myocardial infarction (AMI), which commonly leads to heart failure, is among the leading causes of mortality worldwide. The aim of this study was to find potential regulatory network for miRNA-inflammation, oxidative stress and prognosis-related mRNA to uncover molecular mechanisms of AMI.

Methods

The expression profiles of miRNA and mRNA in the blood samples from AMI patients were downloaded from the Gene Expression Omnibus (GEO) dataset for differential expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to further identify important mRNAs. The negatively regulatory network construction of miRNA–inflammation, oxidative stress and prognosis-related mRNAs was performed, followed by protein–protein interaction (PPI) and functional analysis of mRNAs.

Results

A total of three pairs of negatively regulatory network of miRNA–inflammation and prognosis-related mRNAs (hsa-miR-636/hsa-miR-491-3p/hsa-miR-188-5p/hsa-miR-188-3p-AQP9, hsa-miR-518a-3p-C5AR1 and hsa-miR-509-3-5p/hsa-miR-127-5p-PLAUR), two pairs of negatively regulatory network of miRNA–oxidative stress and prognosis-related mRNAs (hsa-miR-604-TLR4 and hsa-miR-139-5p-CXCL1) and three pairs of negatively regulatory network of miRNA-inflammation, oxidative stress and prognosis-related mRNA (hsa-miR-634/hsa-miR-591-TLR2, hsa-miR-938-NFKBIA and hsa-miR-520h/hsa-miR-450b-3p-ADM) were identified. In the KEGG analysis, some signaling pathways were identified, such as complement and coagulation cascades, pathogenic Escherichia coli infection, chemokine signaling pathway and cytokine–cytokine receptor interaction and Toll-like receptor signaling pathway.

Conclusion

Identified negatively regulatory network of miRNA-inflammation/oxidative stress and prognosis-related mRNA may be involved in the process of AMI. Those inflammation/oxidative stress and prognosis-related mRNAs may be diagnostic and prognostic biomarkers for AMI.

The Integrative Analysis of Competitive Endogenous RNA Regulatory Networks in Coronary Artery Disease

Background: Coronary artery disease (CAD) is the leading cause of cardiovascular death. The competitive endogenous RNAs (ceRNAs) hypothesis is a new theory that explains the relationship between lncRNAs and miRNAs. The mechanism of ceRNAs in the pathological process of CAD has not been fully elucidated. The objective of this study was to explore the ceRNA mechanism in CAD using the integrative bioinformatics analysis and provide new research ideas for the occurrence and development of CAD.

Methods: The GSE113079 dataset was downloaded, and differentially expressed lncRNAs (DElncRNAs) and genes (DEGs) were identified using the limma package in the R language. Weighted gene correlation network analysis (WGCNA) was performed on DElncRNAs and DEGs to explore lncRNAs and genes associated with CAD. Functional enrichment analysis was performed on hub genes in the significant module identified via WGCNA. Four online databases, including TargetScan, miRDB, miRTarBase, and Starbase, combined with an online tool, miRWalk, were used to construct ceRNA regulatory networks.

Results: DEGs were clustered into ten co-expression modules with different colors using WGCNA. The brown module was identified as the key module with the highest correlation coefficient. 188 hub genes were identified in the brown module for functional enrichment analysis. DElncRNAs were clustered into sixteen modules, including seven modules related to CAD with the correlation coefficient more than 0.5. Three ceRNA networks were identified, including OIP5-AS1-miR-204-5p/miR-211-5p-SMOC1, OIP5-AS1-miR-92b-3p-DKK3, and OIP5-AS1-miR-25-3p-TMEM184B.

Conclusion: Three ceRNA regulatory networks identified in this study may play crucial roles in the occurrence and development of CAD, which provide novel insights into the ceRNA mechanism in CAD.

Detection of an anti-angina therapeutic module in the effective population treated by a multi-target drug Danhong injection: a randomized trial

It’s a challenge for detecting the therapeutic targets of a polypharmacological drug from variations in the responsed networks in the differentiated populations with complex diseases, as stable coronary heart disease. Here, in an adaptive, 31-center, randomized, double-blind trial involving 920 patients with moderate symptomatic stable angina treated by 14-day Danhong injection(DHI), a kind of polypharmacological drug with high quality control, or placebo (0.9% saline), with 76-day following-up, we firstly confirmed that DHI could increase the proportion of patients with clinically significant changes on angina-frequency assessed by Seattle Angina Questionnaire (ΔSAQ-AF ≥ 20) (12.78% at Day 30, 95% confidence interval [CI] 5.86–19.71%, P = 0.0003, 13.82% at Day 60, 95% CI 6.82–20.82%, P = 0.0001 and 8.95% at Day 90, 95% CI 2.06–15.85%, P = 0.01). We also found that there were no significant differences in new-onset major vascular events (P = 0.8502) and serious adverse events (P = 0.9105) between DHI and placebo. After performing the RNA sequencing in 62 selected patients, we developed a systemic modular approach to identify differentially expressed modules (DEMs) of DHI with the Z_summary value less than 0 compared with the control group, calculated by weighted gene co-expression network analysis (WGCNA), and sketched out the basic framework on a modular map with 25 functional modules targeted by DHI. Furthermore, the effective therapeutic module (ETM), defined as the highest correlation value with the phenotype alteration (ΔSAQ-AF, the change in SAQ-AF at Day 30 from baseline) calculated by WGCNA, was identified in the population with the best effect (ΔSAQ-AF ≥ 40), which is related to anticoagulation and regulation of cholesterol metabolism. We assessed the modular flexibility of this ETM using the global topological D value based on Euclidean distance, which is correlated with phenotype alteration (r²: 0.8204, P = 0.019) by linear regression. Our study identified the anti-angina therapeutic module in the effective population treated by the multi-target drug. Modular methods facilitate the discovery of network pharmacological mechanisms and the advancement of precision medicine. (ClinicalTrials.gov identifier: NCT01681316).

Bioinformatics Analysis of ceRNA Network Related to Polycystic Ovarian Syndrome

Introduction

Polycystic ovary syndrome (PCOS) is caused by the hormonal environment in utero, abnormal metabolism, and genetics, and it is common in women of childbearing age. A large number of studies have reported that lncRNA is important to the biological process of cancer and can be used as a potential prognostic biomarker. Thus, we studied lncRNAs' roles in PCOS in this article.

Methods

We obtained mRNAs', miRNAs', and lncRNAs' expression profiles in PCOS specimens and normal specimens from the National Biotechnology Information Gene Expression Comprehensive Center database. The EdgeR software package is used to distinguish the differentially expressed lncRNAs, miRNAs, and mRNAs. Functional enrichment analysis was carried out by the clusterProfiler R Package, and the lncRNA-miRNA-mRNA interaction ceRNA network was built in Cytoscape plug-in BiNGO and Database for Annotation, Visualization, and Integration Discovery (DAVID), respectively.

Results

We distinguished differentially expressed RNAs, including 1087 lncRNAs, 14 miRNAs, and 566 mRNAs in PCOS. Among them, 410 lncRNAs, 11 miRNAs, and 185 mRNAs were contained in the ceRNA regulatory network. The outcomes from Gene Ontology (GO) analysis showed that the differentially expressed mRNAs (DEMs) were mainly enriched in response to the maternal process involved in female pregnancy, morphogenesis of embryonic epithelium, and the intracellular steroid hormone receptor signaling pathway. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis data showed that DEMs were primarily enriched in pathways related to the TGF-β signaling pathway, Type I diabetes mellitus, and glycolysis/gluconeogenesis. In addition, we chose NONHSAT123397, ENST00000564619, and NONHSAT077997 as key lncRNAs due to their high bearing on PCOS.

Conclusion

ceRNA networks play an important role in PCOS. The research indicated that specific lncRNAs were related to PCOS development. NONHSAT123397, ENST00000564619, and NONHSAT077997 could be regarded as potential diagnostic mechanisms and biomarkers for PCOS. This discovery might provide more effective and more novel insights into the mechanisms of PCOS worthy of further exploration.