Circulating miRNA profiles and the risk of hemorrhagic transformation after thrombolytic treatment of acute ischemic stroke: a pilot study

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Circulating miRNA profiles and the risk of hemorrhagic transformation after thrombolytic treatment of acute ischemic stroke: a pilot study

Marcin Stańczak^1,2Adam Wyszomirski³Paulina Słonimska⁴Barbara Kołodziej³Bartosz Jabłoński^1,2Anna Stanisławska-Sachadyn^5,6Bartosz Karaszewski^1,2,3*

• ¹Department of Adult Neurology, Faculty of Medicine, Medical University of Gdańsk, Gdańsk, Poland
• ²Department of Adult Neurology, University Clinical Center, Gdańsk, Poland
• ³Brain Diseases Centre, Medical University of Gdańsk, Gdańsk, Poland
• ⁴Laboratory for Regenerative Biotechnology, Department of Biotechnology and Microbiology, Gdańsk University of Technology, Gdańsk, Poland
• ⁵Department of Biotechnology and Microbiology, Gdańsk University of Technology, Gdańsk, Poland
• ⁶BioTechMed Center, Gdańsk University of Technology, Gdańsk, Poland

Background: Hemorrhagic transformation (HT) in acute ischemic stroke is likely to occur in patients treated with intravenous thrombolysis (IVT) and may lead to neurological deterioration and symptomatic intracranial hemorrhage (sICH). Despite the complex inclusion and exclusion criteria for IVT and some useful tools to stratify HT risk, sICH still occurs in approximately 6% of patients because some of the risk factors for this complication remain unknown.

Objective: This study aimed to explore whether there are any differences in circulating microRNA (miRNA) profiles between patients who develop HT after thrombolysis and those who do not.

Methods: Using qPCR, we quantified the expression of 84 miRNAs in plasma samples collected prior to thrombolytic treatment from 10 individuals who eventually developed HT and 10 patients who did not. For miRNAs that were downregulated (fold change (FC) <0.67) or upregulated (FC >1.5) with p < 0.10, we investigated the tissue specificity and performed KEGG pathway annotation using bioinformatics tools. Owing to the small patient sample size, instead of multivariate analysis with all major known HT risk factors, we matched the results with the admission NIHSS scores only.

Results: We observed trends towards downregulation of miR-1-3p, miR-133a-3p, miR-133b and miR-376c-3p, and upregulation of miR-7-5p, miR-17-3p, and miR-296-5p. Previously, the upregulated miR-7-5p was found to be highly expressed in the brain, whereas miR-1, miR-133a-3p and miR-133b appeared to be specific to the muscles and myocardium.

Conclusion: miRNA profiles tend to differ between patients who develop HT and those who do not, suggesting that miRNA profiling, likely in association with other omics approaches, may increase the current power of tools predicting thrombolysis-associated sICH in acute ischemic stroke patients. This study represents a free hypothesis-approach pilot study as a continuation from our previous work. Herein, we showed that applying mathematical analyses to extract information from raw big data may result in the identification of new pathophysiological pathways and may complete standard design works.

1 Introduction

Ischemic stroke was found to have an incidence of 7.6 million individuals worldwide in 2019, resulting in 63.48 million disability-adjusted life years (DALYs) and 3.29 million deaths. Ischemic stroke is a devastating neurological condition characterized by brain tissue damage caused by sudden obstruction of blood flow in the cerebral arteries (1, 2). Treatment in the acute phase aims to restore blood flow through intravenous thrombolysis and mechanical thrombectomy. The former method, which is used in up to 25% of patients, involves the administration of tissue-type plasminogen activator (rtPA), which promotes the formation of plasmin, a proteolytic enzyme. Plasmin breaks the crosslinks between fibrin molecules, leading to thrombus dissolution and restoration of blood flow (3, 4).

Hemorrhagic transformation (HT), which involves the extravasation of blood across a disrupted blood–brain barrier into the brain parenchyma, is one of the most common complications of ischemic stroke (5). According to the European Cooperative Acute Stroke Study (ECASS), HT can be categorized based on its intensity and radiological features into small petechial hemorrhagic infarction (HI1), confluent petechial hemorrhagic infarction (HI2), small parenchymal hemorrhage (PH1) (<30% infarct, mild mass effect), and large parenchymal hemorrhage (PH2, >30% infarct, marked mass effect) (6). Depending on its severity, HT may remain asymptomatic; however, if it is sufficiently large to exert a mass effect on brain tissue outside the infarct, it may cause neurological deterioration (7). Autopsy studies revealed hemorrhagic transformations in 18–42% of patients with acute ischemic stroke, and clinical assessment indicated symptomatic intracerebral hemorrhage after intravenous thrombolysis in approximately 6% of patients (8, 9).

Several studies aim at pinpointing reliable predictors of hemorrhagic transformation. The established clinical risk factors include baseline National Institutes of Health Stroke Scale (NIHSS) score, systolic and diastolic blood pressure, atrial fibrillation, antiplatelets use, age, and time from onset to treatment and hyperglycemia among others (10, 11). Radiological determinants of increased risk of hemorrhagic transformation include a large infarct size, early ischemic changes visible on computed tomography (CT), and absent or poor collaterals (10, 12). Among identified blood biomarkers, matrix metalloproteinase-9 (MMP-9), ferritin, and cellular fibronectin (c-Fn), as well as the neutrophil-to-lymphocyte ratio (NLR) and high-density lipoprotein (HDL), have been extensively studied across multiple experiments (13, 14).

Recent advances in artificial intelligence (AI) and omics have fostered their application in the search for novel HT biomarkers and predictive models. Machine learning methods have been used to develop predictive models based on clinical data and laboratory test results (15). In our previous study, we explored a hypothesis-free approach using MS proteomic data to identify new biomarkers (16). In that study, 15 proteins detected in the blood collected prior to rtPA treatment were unique to patients who developed HT.

MicroRNAs (miRNAs) are small non-coding RNA molecules composed of approximately 22 nucleotides that are known for their regulatory roles in various biological processes, mainly through the post-transcriptional regulation of gene expression (17). Their stability and detectability in various tissues, including blood, have attracted significant attention in the last decade, leading to their exploration as potential diagnostic and prognostic biomarkers, particularly in oncology (18). Circulating miRNAs have also emerged as valuable tools in stroke medicine. Numerous studies have identified miRNAs as diagnostic markers for ischemic stroke, with hsa-let-7e-5p, hsa-miR-124-3p, hsa-miR-17-5p, and hsa-miR-185-5p showing consistent differential expression (19). Furthermore, the combination of miR-124-3p, miR-125b-5p, and miR-192-5p expression has been shown to predict the extent of neurological deterioration in ischemic stroke patients treated with rtPA (20). In another study, miR-21-5p, miR-206, and miR-3123 were implicated in predicting the risk of hemorrhagic transformation in patients with cardioembolic stroke (21). Additionally, the assessment of RNA markers, including miRNA-23a, miRNA-193a, miRNA-128, miRNA-99a, miRNA-let-7a, miRNA-494, miRNA-424, and the long non-coding (lnc)RNA H19, has been shown to improve the prediction of symptomatic intracranial hemorrhage (sICH) after rtPA (22).

The findings of the above studies suggest that quantitative miRNA and proteomic data may increase the current power of the tools for predicting thrombolysis-associated sICH in patients with acute ischemic stroke [as we showed in our previous study (16)]. However, the main objective of the presented studies is to demonstrate a methodology for and the feasibility of such an approach. This pilot study only aimed to identify potential miRNAs indicative of an increased risk of HT occurrence.

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