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Comprehensive analysis of the lncRNA-miRNA-mRNA ceRNA network in aortic dissection
1Department of Cardiovascular Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Insights
This study identifies differentially expressed long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in aortic dissection (AD). A competing endogenous RNA (ceRNA) network was constructed to offer new insights into AD diagnosis and treatment.
Area of Science:
- Cardiovascular Biology
- Molecular Biology
- Genomics
Background:
- Aortic dissection (AD) is a life-threatening cardiovascular disease.
- Long noncoding RNAs (lncRNAs) play roles in disease by acting as competing endogenous RNAs (ceRNAs) that regulate microRNA (miRNA) and messenger RNA (mRNA) levels.
Purpose of the Study:
- To construct and analyze the lncRNA-miRNA-mRNA ceRNA network in aortic dissection.
- To identify potential diagnostic markers and therapeutic targets for AD.
Main Methods:
- Downloaded and analyzed Gene Expression Omnibus (GEO) data (GSE52093) for AD and normal samples.
- Identified differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs).
- Constructed the ceRNA network using bioinformatics databases (miRcode, miRDB, miRTarBase, TargetScan) and visualized it with Cytoscape.
Main Results:
- Identified 13 differentially expressed lncRNAs and 472 differentially expressed mRNAs in AD samples.
- Constructed a ceRNA network involving 7 lncRNAs, 18 miRNAs, and 48 mRNAs.
Conclusions:
- A lncRNA-miRNA-mRNA ceRNA network in AD was successfully constructed using bioinformatics approaches.
- This network may offer novel perspectives for AD diagnosis and the development of potential therapeutic strategies.
Introduction:
Aortic dissection (AD) is a critical cardiovascular system disease caused by blood in the aortic cavity penetrating into the middle layer of the aortic wall through the intimal rift and extending along the long axis of the aorta. Studies have shown that long noncoding RNAs (lncRNAs) can bind to microRNAs (miRNAs) as competing endogenous RNAs (ceRNAs) and further affect the levels of target mRNAs. The purpose of this study was to construct and analyse the lncRNA-miRNA-mRNA ceRNA network in AD.
Material And Methods:
We first downloaded the AD expression data of five normal samples and seven AD samples from the Gene Expression Omnibus (GEO) database (GSE52093) and identified differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEmRNAs were performed using the DAVID and KOBAS online databases. The ceRNA network was constructed by using the miRcode, miRDB, miRTarBase, and TargetScan databases and visualised by Cytoscape v3.6.1. In addition, a protein-protein interaction (PPI) network of DEmRNAs was also constructed.
Results:
In total, 13 lncRNAs and 472 mRNAs were identified as significantly differentially expressed between the AD and normal samples (|logFC| > 1.0, false discovery rate (FDR) < 0.05). In addition, seven lncRNAs, 18 miRNAs, and 48 mRNAs contributed to the construction of the ceRNA network.
Conclusions:
We confirmed the DElncRNAs and DEmRNAs in AD and constructed a lncRNA-miRNA-mRNA ceRNA network by using bioinformatics methods, which may provide a novel perspective for the diagnosis of, and potential therapeutic targets for, AD.
