MicroRNA Expression Profiles Identify Biomarker for Differentiating the Embolic Stroke from Thrombotic Stroke

• Main Authors: Lai-Te Chen, Chen-Yang Jiang
• Format: Article
• Language: English
• Published: Hindawi Limited 2018-01-01
• Series: BioMed Research International
• Online Access: http://dx.doi.org/10.1155/2018/4514178
• ISSN: 2314-6133; 2314-6141

In order to identify potential biomarkers that distinguish the embolic stroke (ES) from thrombotic stroke (TS), a profile of microRNA expression was analyzed. The GSE60319 expression profile was downloaded from the Gene Expression Omnibus (GEO) database. The GEO2R was applied to screen for differentially expressed microRNAs (DEmiRNAs) between the embolic stroke group and thrombotic stroke group. The miRWalk was utilized to predict the target genes of DEmiRNAs. Genes associated with embolic stroke were downloaded from the Comparative Toxicogenomics Database. Cross reference of target genes to disease related genes was conducted to construct the DEmiRNA-gene network. The protein-protein interaction (PPI) network of overlapping genes was evaluated by STRING, using the MCODE and CytoHubba plugin of Cytoscape to identify the modules and hub genes. The enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) in modules was performed. There were 30 microRNAs in total identified as DEmiRNAs between embolic stroke and thrombotic stroke groups, of which 8 were upregulated and 22 were downregulated. Among these differentially expressed miRNAs, miR-15a-5p, miR-17-5p, miR-19b-3p, and miR-20a-5p were significantly associated with an ES to TS. Using the miRWalk 3.0 online tool, target genes regulated by DEmiRNAs were predicted. In addition, disease related genes were predicted and compared with target genes of DEmiRNAs. 166 overlapped genes regulated by miR-15a-5p, miR-17-5p, miR-19b-3p, and miR-20a-5p were identified, suggesting their association with diseases that contributed to ES, mainly including atrial fibrillation, mitral valve stenosis, myocardial infarction, and aortic dissection. Therefore, miR-15a-5p, miR-17-5p, miR-19b-3p, and miR-20a-5p were promising candidate biomarkers for differentiating an ES from TS. The PPI network demonstrated that miR-15a-5p, miR-17-5p, miR-19b-3p, and miR-20a-5p were associated with an ES by mainly regulating “CCND1, E2F2, E2F3, ITCH, UBE4A, UBE3C, RBL2, FBXO31, EIF2C4, and EIF2C1”. Furthermore, miR-15a-5p and miR-17-5p may function through “cell cycle, prostate cancer, and small cell lung cancer” while miR-19b-3p and miR-20a-5p function through “insulin resistance, hepatitis B, and viral carcinogenesis” and “vasopressin-regulated water reabsorption”, respectively. However, these results were approached in the manner of bioinformatics analysis; therefore, further verification is required.