Microarray Data Analysis of Molecular Mechanism Associated with Stroke Progression
- 83 Downloads
This study aimed to explore the molecular mechanism of stroke and provide a new target in the clinical management. The miRNA dataset GSE97532 (3 blood samples from middle cerebral artery occlusion (MCAO) and 3 from sham operation) and mRNA dataset GSE97533 (3 blood samples from MCAO and 3 from sham operation) were obtained from GEO database. Differentially expressed mRNA (DEGs) and miRNAs (DEMIRs) were screened out between MCAO and sham operation groups. Then, DEMIR–DEG interactions were explored and visualized using Cytoscape software. Moreover, the enrichment analysis was performed on these DEMIRs and DEGs. Furthermore, protein–protein interaction (PPI) network was constructed. Finally, the DEG-target transcription factors (TFs) were investigated using the WebGestal software. The current bioinformatics analysis revealed 38 DEMIRs and 546 DEGs between MCAO and sham operation groups. The DEMIR–DEG analysis revealed 370 relations, such as miR-107-5p-Furin. The top 10 up- and downregulated DEMIRs were mainly enriched in pathways like cAMP signaling pathway. The PPI network analysis revealed 2 modules. The target DEGs of the 10 up- and downregulated DEMIRs in 2 modules were mainly assembled in functions like ATP binding and pathway including ABC transporters. Furthermore, the DEG–TF network analysis identified 5 outstanding TFs including androgen receptor (AR). miR107-5p might take part in the progression of stroke via inhibiting the expression of Furin. TFs like AR might be used as a novel gene therapy target for stroke. Furthermore, cAMP signaling pathway and ATP binding function might be a novel breakthrough for stroke treatment.
KeywordsStroke Differentially expressed mRNA Differentially expressed miRNA Function and pathway analysis Transcription factors
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife Sciences 4:1–18Google Scholar
- Bandettini WP, Kellman P, Mancini C, Booker O, Vasu S, Leung SW, Wilson JR, Shanbhag SM, Chen MY, Arai AE (2012) MultiContrast delayed enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson 14:83CrossRefGoogle Scholar
- Bao J, Zhou S, Pan S, Zhang Y (2018) Molecular mechanism exploration of ischemic stroke by integrating mRNA and miRNA expression profiles. Clin Lab 64:559–568Google Scholar
- Berardini TZ, Li D, Jaiswal P 2009. The Gene Ontology in 2010: extensions and refinements.Google Scholar
- Chan DK, Cordato D, O'Rourke F, et al (2012). Comprehensive stroke units: a review of comparative evidence and experience. Int J StrokeGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95:14863–14868Google Scholar
- Karin M (1990) Too many transcription factors: positive and negative interactions. The New Biologist 2:126–131Google Scholar
- Rao VLR, Bowen KK, Dhodda VK et al (2010) Gene expression analysis of spontaneously hypertensive rat cerebral cortex following transient focal cerebral ischemia. J Neurochem 83:1072–1086Google Scholar
- Rickhag M, Wieloch T, Gidö G et al (2010) Comprehensive regional and temporal gene expression profiling of the rat brain during the first 24 h after experimental stroke identifies dynamic ischemia-induced gene expression patterns, and reveals a biphasic activation of genes in surviving tissue. J Neurochem 96:14–29CrossRefGoogle Scholar
- Rigoutsos I, Miranda K, Huynh T (2007). rna22: a unified computational framework for discovering miRNA precursors, localizing mature miRNAs, identifying 3′ UTR target-islands, and determining the targets of mature-miRNAs. Ibm CorporationGoogle Scholar
- Szklarczyk D, Franceschini A, Wyder S, et al (2014). STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res, gku1003Google Scholar
- Tadevosyan KTG (2015). Variations in immediate-early genes encoding c-Fos, c-Jun and IER5 transcription factors are associated with ischemic stroke. Advancements in Genetic EngineeringGoogle Scholar
- Tanaka K, Fukuuchi Y, Nogawa S et al (1999) Alteration of cAMP-mediated signal transduction in cerebral ischemia—binding activity of PKA and phosphorylation of CREB. Rinshō Shinkeigaku 39:1298Google Scholar
- Tiedt S, Prestel M, Malik R, et al (2017). Seq identifies circulating miR-125a-5p, miR-125b-5p and miR-143-3p as potential biomarkers for acute ischemic stroke. Circ Res, 121, CIRCRESAHA.117.311572Google Scholar
- Zhang ZL, Wu WC, Liu JQ et al (2014) Screening of differentially expressed genes related to ischemic stroke and functional analysis with DNA microarray. Eur Rev Med Pharmacol Sci 18:1181Google Scholar