- 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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