Abstract

Stroke is the second-most common cause of death worldwide. The pathophysiology of ischemic stroke (IS) is related to inflammation, atherosclerosis, blood coagulation, and platelet activation. MicroRNAs (miRNAs) play important roles in physiological and pathological processes of neurodegenerative diseases and progression of certain neurological diseases, such as IS. Several different miRNAs, and their target genes, are recognized to be involved in the pathophysiology of IS. The capacity of miRNAs to simultaneously regulate several target genes underlies their unique value as diagnostic and prognostic markers in IS. In this review, we focus on the role of miRNAs as diagnostic and prognostic biomarkers in IS. We discuss the most common and reliable detection methods available and promising tests currently under development. We also present original results from bioinformatic analyses of published results, identifying the ten most significant genes (HMGB1, YWHAZ, PIK3R1, STAT3, MAPK1, CBX5, CAPZB, THBS1, TNFRSF10B, RCOR1) associated with inflammation, blood coagulation, and platelet activation and targeted by miRNAs in IS. Additionally, we created miRNA-gene target interaction networks based on Gene Ontology (GO) information derived from publicly available databases. Among our most interesting findings, miR-19a-3p is the most widely modulated miRNA across all selected ontologies and might be proposed as novel biomarker in IS to be tested in future studies.

Highlights

  • Stroke is the second-most common cause of death worldwide, accounting for almost 6.5 million stroke deaths each year

  • In course of thromboembolic stroke, activated platelets orchestrate a thrombo-inflammatory cascade by promoting thrombus formation and growth, activating leukocytes, and potentiating cerebral endothelium injury. In line with this aim, we focused on miRNAs involved in inflammation, blood coagulation, and platelet activation in our bioinformatics analysis in order to identify to account for all potential pathophysiological mechanisms underlying the development of ischemic stroke (IS)

  • In our miR-17-5p miR-106b-5p. 1 common miRNA observed in inflammatory response and platelet analysis we demonstrated ten the most significant genes targeted by miRNAs associated with blood activation namely, miR-186. 4 common miRNAs observed in blood coagulation and platelet activation coagulation, activation, and inflammation process in IS, namely,platelet miR-15b-5p, miR-15a-5p, miR-16-5p, miR-129-5p

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Summary

Introduction

Stroke is the second-most common cause of death worldwide, accounting for almost 6.5 million stroke deaths each year. It has been shown that some pathological processes of the central nervous system, and that Alzheimer’s disease, multiple sclerosis, and stroke are associated to specific alteration of circulating miRNAs [12] This suggests that circulating miRNAs could be used as clinical biomarkers, to provide a sort of “liquid biopsy” taken from peripheral blood but providing information on pathophysiological processes going on in the brain and underlying stroke [13,14,15]. It is possible to sequence multiple samples in one time by pooling with next-generation-sequencing and possible to construct comprehensive expression profiles for every assessed sample, on the other hand potentially large investment in bioinformatics analysis, time and personnel are required [33,34] Beside these techniques, there are new promising methodologies have been developed to detect miRNAs, for example, sensitive miRNA biosensors such as silicon nanowires, gold nanoparticles, silver nanoclusters, or conducting polymer/carbon nanotube hybrids [35,36,37,38]. In this article, we present an overview of the current knowledge on diagnostic and prognostic value of miRNAs related to IS based on human studies and report the results of a quantitative bioinformatic analysis highlighting the most promising miRNAs for clinical application in IS

Circulating miRNAs and Stroke
PCR-Based Analysis for miRNA Expression
MiRNA Profiling and RNA Sequencing Strategy
Bioinformatics Analysis
Findings
Conclusions
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