Background: Although ischemic stroke exhibits a high prevalence in the elderly population, the involved genes and pathways are poorly understood. In this study, we proposed to identify differentially expressed genes (DEGs) and constructed a circular RAN (circRNA)/long noncoding RNA (lncRNA)/microRNA (miRNA)-mRNA network associated with the pathogenesis of ischemic stroke by using bioinformatics analysis.
Methods: We constructed a rat model of middle cerebral artery occlusion (MCAO) and conducted total RNA and microRNA sequencing in brain specimens from MCAO and normal rats. Transcriptome-wide expression patterns were analyzed and DEGs were defined by applying Ballgown and a cut of log2-transformed fold-change (log2FC) ≥ 1 (or ≤ -1) with a P value < 0.05. We exploited Pearson correlation analysis to determine the association between the circRNA/lncRNA/mRNA network and miRNAs (P < 0.05 and corr ≤ -0.6), and the competing endogenous RNAs (ceRNA) interaction network was visualized with Cytoscape software and separated into subnetworks using the Molecular Complex Detection (MCODE) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were implemented for the pathway analysis of DEGs.
Results: Upregulated DEGs were significantly enhanced in positive regulation of cell migration, response to wounding, blood vessel morphogenesis, inflammatory response, and cell activation; Downregulated DEGs were associated with control of the modulation of chemical synaptic transmission, synapse organization, regulation of membrane potential, and regulation of ion transport. KEGG-pathway analysis showed that DEG-enhanced pathways were associated with the pathways of TNF signaling pathway, Fluid shear stress and atherosclerosis, NF-kappa B signaling pathway, Lipid and atherosclerosis, Human cytomegalovirus infection, Osteoclast differentiation, Chemokine signaling pathway, IL-17 signaling pathway, Viral protein interaction with cytokine and cytokine receptor, and Cytokine-cytokine receptor interaction. We uncovered several novel lncRNAs (lnc_00231, lnc_002239, lnc_004172; and a novel_circ0001704), five miRNAs (miR-200b-3p, miR-223-3p, miR-200c-3p, miR-3084a-3p, and miR-664-2-5p), and the top-10 mRNAs (upregulated mRNAs were Pdgfa, Il1b, Gdf15, Fosl1, and Cxcl2; downregulated mRNAs were Prkar2b, Olfm3, Lrrc73, Tmem38a, and Dlgap3) that were involved in ischemic stroke.
Conclusions: Through bioinformatic network analysis, we identified the underlying molecular mechanisms and key central genes that may contribute to an inflammatory response after cerebral infarction.
Keywords: Ischemic stroke; differentially expressed genes; gene ontology.
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