Dataset Information

Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology.

ABSTRACT:

Background

Determining stroke etiology is crucial for secondary prevention, but intensive workups fail to classify ~30% of strokes that are cryptogenic.

Objective

To examine the hypothesis that the transcriptomic profiles of clots retrieved during mechanical thrombectomy are unique to strokes of different subtypes.

Methods

We isolated RNA from the clots of 73 patients undergoing mechanical thrombectomy. Samples of sufficient quality were subjected to 100-cycle, paired-end RNAseq, and transcriptomes with less than 10 million unique reads were excluded from analysis. Significant differentially expressed genes (DEGs) between subtypes (defined by the Trial of Org 10 172 in Acute Stroke Treatment) were identified by expression analysis in edgeR. Gene ontology enrichment analysis was used to study the biologic differences between stroke etiologies.

Results

In all, 38 clot transcriptomes were analyzed; 6 from large artery atherosclerosis (LAA), 21 from cardioembolism (CE), 5 from strokes of other determined origin, and 6 from cryptogenic strokes. Among all comparisons, there were 816 unique DEGs, 174 of which were shared by at least two comparisons, and 20 of which were shared by all three. Gene ontology analysis showed that CE clots reflected high levels of inflammation, LAA clots had greater oxidoreduction and T-cell processes, and clots of other determined origin were enriched for aberrant platelet and hemoglobin-related processes. Principal component analysis indicated separation between these subtypes and showed cryptogenic samples clustered among several different groups.

Conclusions

Expression profiles of stroke clots were identified between stroke etiologies and reflected different biologic responses. Cryptogenic thrombi may be related to multiple etiologies.

SUBMITTER: Tutino VM

PROVIDER: S-EPMC9789205 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology.

Tutino Vincent M VM Fricano Sarah S Chien Aichi A Patel Tatsat R TR Monteiro Andre A Rai Hamid H HH Dmytriw Adam A AA Chaves Lee D LD Waqas Muhammad M Levy Elad I EI Poppenberg Kerry E KE Siddiqui Adnan H AH

Journal of neurointerventional surgery 20220624 e1

<h4>Background</h4>Determining stroke etiology is crucial for secondary prevention, but intensive workups fail to classify ~30% of strokes that are cryptogenic.<h4>Objective</h4>To examine the hypothesis that the transcriptomic profiles of clots retrieved during mechanical thrombectomy are unique to strokes of different subtypes.<h4>Methods</h4>We isolated RNA from the clots of 73 patients undergoing mechanical thrombectomy. Samples of sufficient quality were subjected to 100-cycle, paired-end R ...[more]

PMID: 35750484

Similar Datasets

Project description:Background: Brain clots retrieved following endovascular thrombectomy in acute ischemic stroke patients may offer unique opportunities to characterise stroke aetiology and aid stroke decision-making in select groups of patients. However, the evidence around the putative association of clot morphology with stroke aetiology is limited and remains inconclusive. This study aims to perform a systematic review and meta-analysis to delineate the association of brain clot composition with stroke aetiology and post-reperfusion outcomes in patients receiving endovascular thrombectomy. Methods: The authors conducted a systematic literature review and meta-analysis by extracting data from several research databases (MEDLINE/PubMed, Cochrane, and Google Scholar) published since 2010. We used appropriate key search terms to identify clinical studies concerning stroke thrombus composition, aetiology, and clinical outcomes, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: The authors identified 30 articles reporting on the relationship between stroke thrombus composition or morphology and aetiology, imaging, or clinical outcomes, of which 21 were included in the meta-analysis. The study found that strokes of cardioembolic origin (SMD = 0.388; 95% CI, 0.032-0.745) and cryptogenic origin (SMD = 0.468; 95% CI, 0.172-0.765) had significantly higher fibrin content than strokes of non-cardioembolic origin. Large artery atherosclerosis strokes had significantly lower fibrin content than cardioembolic (SMD = 0.552; 95% CI, 0.099-1.004) or cryptogenic (SMD = 0.455; 95% CI, 0.137-0.774) strokes. Greater red blood cell content was also significantly associated with a thrombolysis in cerebral infarction score of 2b-3 (SMD = 0.450; 95% CI, 0.177-0.722), and a positive hyperdense middle cerebral artery sign (SMD = 0.827; 95% CI, 0.472-1.183). No significant associations were found between red blood cell, platelet, or white blood cell content and aetiology, or between clot composition and bridging thrombolysis. Conclusions: This meta-analysis found that fibrin composition is significantly higher in strokes of cardioembolic and cryptogenic origin, and that red blood cell content is positively associated with the hyperdense middle cerebral artery sign and better reperfusion outcomes. Important advances to stroke clinical workup can be derived from these findings, in which many aspects of stroke workflow remain to be optimised. As data are still limited in terms of the association of various thrombus components with stroke aetiology as well as a standardised method of analysis, further studies are required to validate these findings to guide their use in clinical decision-making.

Dataset Information

Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology.

Background

Objective

Methods

Results

Conclusions

Publications

Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology.

Similar Datasets

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