RNA-Based Biomarkers for Diagnostic Discrimination of Ischemic and Hemorrhagic Stroke: A Systematic Review

TL;DR

This review explores RNA biomarkers in blood that could help distinguish between two types of stroke, ischemic and hemorrhagic, for faster and more accurate diagnosis.

Contribution

The study systematically reviews RNA-based biomarkers for their potential to differentiate between ischemic and hemorrhagic stroke.

Findings

Several RNA biomarkers, including miRNA-124-3p and lncRNA XIST, showed significant differences between ischemic and hemorrhagic stroke.

Methodological heterogeneity across studies limits the ability to perform a meta-analysis.

Machine learning techniques demonstrated potential in clustering RNA biomarkers to distinguish stroke types.

Abstract

Background: Diagnostic discrimination between ischemic stroke (IS) and hemorrhagic stroke (HS) is required for successful intervention with time-critical acute treatments. The available data on blood-based RNA biomarkers and discrimination between IS and HS are limited. This systematic review aimed to examine and summarize the existing literature on potentially useful blood-based RNA biomarkers that may aid in preclinical acute diagnosis. Methods: We systematically reviewed the literature on the ability of blood-based RNA biomarkers to discriminate between IS and HS according to PRISMA guidelines. We searched PubMed, EMBASE, The Cochrane Library, and The Web of Science for eligible randomized controlled trials, observational studies, and case–control studies published in the English language without time limitation. The risk of bias was evaluated using the Newcastle–Ottawa Scale. Results: We included eight studies with a total of 728 patients (436 with IS and 292 with HS) in our review. The study quality was good in five and fair in three investigations. No meta-analysis was performed due to high heterogeneity in methods and study endpoints. Reported biomarkers include miRNA-124-3p, miRNA-16, miRNA-340-5p, lncRNA XIST (X-inactive specific transcript), PFKFB3 mRNA (6-phosphofructo-2-kinase/fructose-2,6-biphosphatase), tRNA derivatives, tRNA fragments, extracellular miRNAs, transcriptome changes, and MCEMP1 gene expression. Assessment techniques varied widely across studies, ranging from RNA sequencing to qPCR, microarray, human transcriptome array, and ELISA. MicroRNA-124-3p, miRNA-340-5p, lncRNA XIST, PFKFB3 mRNA, and MCEMP1 gene expression differed significantly between IS and HS. In one study, principal component analysis and unsupervised learning demonstrated the utility of hierarchical clustering of differentially expressed exons to discriminate between HS and IS. Conclusions: This review demonstrates the utility of single RNA-based targets and clusters that may have diagnostic value in distinguishing IS from HS. However, the current body of evidence is limited by considerable methodological heterogeneity between studies. Registration: This systematic review was prospectively registered on PROSPERO on 21 April 2023 (CRD42023411203).

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