Reperfusion stroke therapy is a modern treatment that involves thrombolysis and the mechanical removal of thrombus from the extracranial and/or cerebral arteries, thereby increasing penumbra reperfusion. After reperfusion therapy, 46% of patients are able to live independently 3 months after stroke onset. MicroRNAs (miRNAs) are essential regulators in the development of cerebral ischemia/reperfusion injury and the efficacy of the applied treatment. The first aim of this study was to examine the change in serum miRNA levels via next-generation sequencing (NGS) 10 days after the onset of acute stroke and reperfusion treatment. Next, the predictive values of the bioinformatics analysis of miRNA gene targets for the assessment of brain ischemic response to reperfusion treatment were explored. Human serum samples were collected from patients on days 1 and 10 after stroke onset and reperfusion treatment. The samples were subjected to NGS and then validated using qRT-PCR. Differentially expressed miRNAs (DEmiRNAs) were used for enrichment analysis. Hsa-miR-9-3p and hsa-miR-9-5p expression were downregulated on day 10 compared to reperfusion treatment on day 1 after stroke. The functional analysis of miRNA target genes revealed a strong association between the identified miRNA and stroke-related biological processes related to neuroregeneration signaling pathways. Hsa-miR-9-3p and hsa-miR-9-5p are potential candidates for the further exploration of reperfusion treatment efficacy in stroke patients.

Reperfusion therapy uses thrombolysis and clot removal to restore blood flow in the brain after stroke; however, three months after reperfusion therapy, roughly 46% of stroke patients become independent again. MiRNAs (micro RNA) regulate cerebral ischemia/reperfusion injury, and their transfer between cells via exosomes may differentially affect recipient cells. We examined serum exosomal miRNA levels, stroke treatments, and functional outcomes in stroke patients, and we explored the potential role of estimated differentially expressed miRNA (DEmiRNA) target genes in the brain’s reaction to reperfusion after ischemia. The patients in the study received aspirin or reperfusion therapy with either intravenous thrombolysis (rt-PA), mechanical thrombectomy (MT), or a combination of both (rt-PA/MT). Serum samples were collected from stroke patients on days 1 and 10 post-stroke. Serum exosomes’ miRNA was analyzed using qRT-PCR. We identified DEmiRNAs, estimated their targets, and performed enrichment analysis. Functional outcomes were assessed using the modified Rankin Scale (mRS) on days 10 and 90 post-stroke. Among studied treatments, only rt-PA/MT lowered DEmiRNA by day 10 vs. other groups. Specifically, patients with unfavorable mRS score exhibited decreased levels of miR-17, miR-20, miR-186 and miR-222 after combined stroke therapy. Functional analysis identified target genes and pathways associated with cytoskeleton remodeling, cell death, autophagy, inflammation, and dementia. In conclusion, unfavorable stroke outcomes following poor rt-PA/MT response could result from lower miRNA expression levels, thus activating cell death and neurodegenerative processes in brain.

MicroRNAs (miRNAs) are scalable biomarkers and therapeutic nodes. In acute ischemic stroke, in which reperfusion triggers rapid pathway shifts, we built a standardised enrichment workflow to recover robust, clinically relevant biology and to clarify tool performance, quantify the impact of target estimation, and provide practical, reproducible recommendations that non-bioinformatic end-users can adopt. We prioritized miR-19a-3p based on repeated signals in external datasets and links to endothelial/rt-PA biology. Predicted targets defined DIANA, miRDB, and intersection lists. KEGG and GO:BP enrichment was run in STRING, ShinyGO, and miRNET under standardised parameters; cross-tool correlation used Spearman rank, and GO:BP terms were collapsed into nine themes. Across KEGG, we observed shared patterns with tool-specific emphasis. ShinyGO and STRING were highly concordant (r≈0.85-0.95), consistently recovering signalling and adhesion/trafficking. miRNet often diverged for miRDB and the intersection (|r|≤0.06), preferentially amplifying cancer terms; concordance improved only on DIANA. Input choice shaped stability: miRDB and the intersection yielded lower FDRs and higher fold-enrichment, whereas DIANA showed flatter effects and greater dispersion. GO:BP mirrored this: ShinyGO/STRING aligned broadly, miRNET inflated nucleic-acid metabolism terms, yet “negative regulation of translation” was a cross-tool consensus peak. Thus, shared core biology is robust, while tool/input biases drive differences. Our recommendations are: favour the intersection to stabilize ranks/FDR; if a single predictor is required, prefer miRDB; treat DIANA-only findings as exploratory and confirm on miRDB or the intersection; if constrained to DIANA, rely on ShinyGO/STRING; and for any inter-tool comparison, standardise the background and FE/FDR, reporting both alongside the tool used.