Comprehensive analysis of the ceRNA network in coronary artery disease.
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ABSTRACT:
SUBMITTER: Bian W
PROVIDER: S-EPMC8688464 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCoronary artery disease (CAD) is one of the most common causes of sudden death with high morbidity in recent years. This paper is aimed at exploring the early peripheral blood biomarkers of sudden death and providing a new perspective for clinical diagnosis and forensic pathology identification by integrated bioinformatics analysis.MethodsTwo microarray expression profiling datasets (GSE113079 and GSE31568) were downloaded from the Gene Expression Omnibus (GEO) database, and we identified differentially expressed lncRNAs, miRNAs, and mRNAs in CAD. Gene Ontology (GO) and KEGG pathway analyses of DEmRNAs were executed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified. Finally, we constructed a competitive endogenous RNA (ceRNA) regulation network among lncRNAs, miRNAs, and mRNAs. Also, the 5 miRNAs of the ceRNA network were verified by RT-PCR.ResultsIn total, 86 DElncRNAs, 148 DEmiRNAs, and 294 DEmRNAs were dysregulated in CAD. We received 12 GO terms and 5 pathways of DEmRNAs. 31 nodes and 78 edges were revealed in the PPI network. The top 10 genes calculated by degree method were identified as hub genes. Moreover, there were a total of 26 DElncRNAs, 5 DEmiRNAs, and 13 DEmRNAs in the ceRNA regulation network. We validated the 5 miRNAs of the ceRNA network by RT-PCR, which were consistent with the results of the microarray.ConclusionsIn this paper, a CAD-specific ceRNA network was successfully constructed, contributing to the understanding of the relationship among lncRNAs, miRNAs, and mRNAs. We identified potential peripheral blood biomarkers in CAD and provided novel insights into the development and progress of CAD.
| S-EPMC7822659 | biostudies-literature
Project description: Not available
| S-EPMC10681391 | biostudies-literature
Project description:ObjectiveERCC4 is one of the most significant molecules of Nucleotide Excision Repair (NER), which has been researched due to its high expression in colorectal cancer (CRC). This study aimed to find out the ceRNA (competitive endogenous RNA) network of ERCC4 in CRC.Methods and materialsPan cancer mRNA expression of ERCC4 was evaluated using TCGA database. The protein expression of ERCC4 was evaluated based on the Human Protein Atlas (HPA). We screened DElncRNAs and DEmiRNAs in two groups of ERCC4 high and ERCC4 low expression in CRC. Then a lncRNA-miRNA-ERCC4 regulatory network was constructed based on DElncRNAs and DEmiRNAs using Starbase database and visualized by Cytoscape software. Kaplan-Meier analysis was performed to evaluate the prognostic value of the ceRNA network. Further, RT-PCR was performed to validate the expression of the representative molecules in the ceRNA network in CRC and normal tissues. The relationship between drug sensitivity and these molecules were also evaluated using RNAactDrug database.ResultsERCC4 was overexpressed in a variety of tumors at mRNA levels, including CRC. High expression of ERCC4 was also observed on protein level in CRC. A total of 1,885 DElncRNAs and 68 DEmiRNAs were identified from CRC samples in ERCC4 high and ERCC4 low expression groups. Predicted by the Starbase database, we got interacting miRNAs and lncRNAs of ERCC4 from the DEmiRNAs and DElncRNAs, and a lncRNA-miRNA-ERCC4 regulatory network was constructed. Kaplan-Meier survival curves results showed that miR-200c-3p (hazard ratio [HR] = 0.62, P = 0.032), MALAT1 (HR = 1.54, P = 0.016), and AC005520.2 (hazard ratio [HR] = 1.75, P = 0.002) were significantly associated with the prognosis of CRC. After validation by RT-PCR, we found that ERCC4 and MALAT1 were up-regulated in CRC compared with normal tissues, while miR-200c-3p was down-regulated. A strong negative correlation was observed between MALAT1 and miR-200c-3p. Drug sensitivity analysis showed that ERCC4, miR-200c and MALAT1 were all associated with Cisplatin.ConclusionWe constructed a ceRNA network of ERCC4 in CRC, of which the MALAT1-miR-200c-3p-ERCC4 axis may be involved in the development, prognosis and chemotherapy sensitivity of CRC. These findings might provide novel clues and insights on the molecular mechanisms of ERCC4 and NER pathway in CRC.
| S-EPMC8679902 | biostudies-literature
Project description:Coronary artery disease (CAD) is a leading cause of death worldwide. Recent genome-wide association studies have identified more than one hundred susceptibility loci associated with CAD. However, the underlying mechanism of these genetic loci to CAD susceptibility is still largely unknown. We performed a tissue-specific network analysis of CAD using the summary statistics from one of the largest genome-wide association studies. Variant-level associations were summarized into gene-level associations, and a CAD-related interaction network was built using experimentally validated gene interactions and gene coexpression in coronary artery. The network contained 102 genes, of which 53 were significantly associated with CAD. Pathway enrichment analysis revealed that many genes in the network were involved in the regulation of peripheral arteries. In summary, we performed a tissue-specific network analysis and found abnormalities in the peripheral arteries might be an important pathway underlying the pathogenesis of CAD. Future functional characterization might further validate our findings and identify potential therapeutic targets for CAD.
| S-EPMC6068195 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks ("modules"). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene-protein interactions directly affected by genetic variance in CAD risk loci.
| S-EPMC5821758 | biostudies-literature
Project description:Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ∼185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.
| S-EPMC4589895 | biostudies-literature
Project description:Patients with multi-vessel coronary artery disease (MV-CAD) often exhibit poor clinical outcomes compared to single-vessel coronary artery disease (SV-CAD). Increasing evidence suggests that microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) may play a critical role in cardiovascular diseases, however their impact on various vessel lesions, is largely unknown. Here, six patients were selected for the SV-CAD group and 13 for the MV-CAD group by the number of diseased coronary branches. Total RNAs were extracted from peripheral blood of the patients, and the expression profiling was conducted by RNA sequencing (RNASeq). Differentially expressed mRNAs, miRNAs and lncRNAs were identified with p<0.05 and |log2 FC|>1. The GO and KEGG pathway enrichment analysis indicated that differentially expressed mRNAs were mainly enriched in Wnt, Ras and ErbB signaling pathways; miRNAs in mTOR and ErbB signaling pathways; lncRNAs in ErbB signaling pathway, fatty acid metabolism and platelet activation. In addition, we established the ceRNA network and revealed that lncRNA ENST00000501122 may competitively combine with miR-6724-5, regulating the expression of its target mRNAs. Lastly, the most significantly differentially expressed miRNAs, let-7b-5p, let-7e-5p and miR-190a-5p, were selected to evaluate their effects on lipid metabolism and inflammation in cell lines. Overall, our study provides a comprehensive analysis of the transcriptome between MV-CAD and SV-CAD, which will not only help us better understand the mechanisms of different CADs, but also provide a theoretical basis for the prevention and treatment of cardiovascular disease.
2021-12-12 | GSE190627 | GEO
Project description:Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.
| S-EPMC3988072 | biostudies-literature
Project description:The identification of classic risk factors for coronary artery disease unveiled pathophysiologic mechanisms of atherosclerosis. Among them, inflammation as a systemic process measurable in peripheral blood plays a central role in plaque progression. However, other mechanisms of plaque progression remain to be fully understood. Therefore, this study sought to further investigate systemic correlates of plaque progression by global gene expression in peripheral blood. Microrarray gene expression analysis revealed 93 genes differentially expressed between the groups, of which 23 genes have no known function. Among the remaining 70 genes, 10 (14%) were identified to be associated with progenitor and pluripotent cells whereas only 3 genes (4%) had been associated with atherosclerosis. A risk prediction gene signature was developed by a multivariable statistical approach model integrating comprehensive laboratory and clinical patient data. This signature identified plaque progression with 81% sensitivity and 80% specificity (AUC: 0.86) for new patients, as estimated by resampling techniques. Array results were validated by qPCR for the genes ankyrin-2 (ANK2) and glutathione S-transferase theta 1 (GSTT1). In conclusion, patients with pogressive coronary artery disease despite good risk factor control exhibit particular gene expression patterns in peripheral blood. Understanding the functional implications of the observed changes might help to design new approaches to control atherosclerosis progression. From a large database of 45,727 coronary angiograms, peripheral blood was drawn from two patient groups with good risk factor control, but different clinical evolution: First, 16 patients with significant lesion progression leading to repeated coronary interventions and second, 16 patients with angiographically documented stable courses.
2012-11-30 | E-GEOD-34822 | biostudies-arrayexpress
Project description:Predictive assessment of the risk of developing cardiovascular diseases is usually provided by computational approaches centred on Cox models. The complex interdependence structure underlying clinical data patterns can limit the performance of Cox analysis and complicate the interpretation of results, thus calling for complementary and integrative methods. Prognostic models are proposed for studying the risk associated with patients with known or suspected coronary artery disease (CAD) undergoing vasodilator stress echocardiography, an established technique for CAD detection and prognostication. In order to complement standard Cox models, network inference is considered a possible solution to quantify the complex relationships between heterogeneous data categories. In particular, a mutual information network is designed to explore the paths linking patient-associated variables to endpoint events, to reveal prognostic factors and to identify the best possible predictors of death. Data from a prospective, multicentre, observational study are available from a previous study, based on 4313 patients (2532 men; 64±11 years) with known (n=1547) or suspected (n=2766) CAD, who underwent high-dose dipyridamole (0.84?mg?kg(-1) over 6?min) stress echocardiography with coronary flow reserve (CFR) evaluation of left anterior descending (LAD) artery by Doppler. The overall mortality was the only endpoint analysed by Cox models. The estimated connectivity between clinical variables assigns a complementary value to the proposed network approach in relation to the established Cox model, for instance revealing connectivity paths. Depending on the use of multiple metrics, the constraints of regression analysis in measuring the association strength among clinical variables can be relaxed, and identification of communities and prognostic paths can be provided. On the basis of evidence from various model comparisons, we show in this CAD study that there may be characteristic factors involved in prognostic stratification whose complexity suggests an exploration beyond the analysis provided by the still fundamental Cox approach.
| S-EPMC4448804 | biostudies-other