Fingerprint of novel circulating microRNAs identify patients with stroke-embolic stroke of undetermined source
Notice bibliographique
Résumé
Abstract Background Stroke is the second-most common cause of death worldwide. Circulating levels of selected microRNAs (miRNAs) were found to be modulated both in animal experimental models and in patients with stroke, opening up new avenues for the identification of more effective and specific biomarkers to identify and risk-stratify stroke patients. Aim of the present study is to identify all circulating miRNAs that are modulated in patients with stroke, to select specific miRNAs to be used as disease biomarkers to improve prognosis. Methods 48 patients with stroke- ESUS were involved in the study. We have divided the patient groups based on patients who had a second stroke or TIA and did not have (safety vs safety control). Total RNA was extracted from plasma samples quality of extracted material was assessed using a fluorometric electrophoretic assay. MiRNA profiling was performed using the Affymetrix platform using. Statistical analysis was performed in TAC software. Additional analyses were performed in and R using Signal information obtained from the TAC output. We performed the following tests using log2 transformed data and all comparison groups (A-F). We performed additional FDR correction, logistic regression, Mann-whitney test t-test depending if variances were equal or differing. We calculated Area under the curve using ROCp R package. Scores were ranging from 0–1. Co-expression analysis to identify genes authentically expressed was performed using Spearman correlation (cutoff=0.9, Rpval=0.05). In order to identify the targets of DE miRNAs we used our wizbionet R package and previously developed pipelines [1,2]. We performed target screening using multimiR package, selecting top 20% predictions from all available databases. Results MiR-4786, miR-1205, miR-548ar-3p and miR-518e-3p were found the most differentially expressed miRNAs between the groups. So far, miR-4786 was studied only in patients with acute leukemia [3]. Several studies showed the importance of miR-1205 in cell carcinoma and ovarian cancer progression [4]. Moreover, so far only one study showed the regulation of miR-548ar-3p in breast cancer [5]. Finally only one study showed the alteration of miR-518e-3p in Parkinsons disease patients [6]. Besides, our enrichment analysis showed Interleukin-2 signaling pathway, Lipid and lipoprotein metabolism, BDNF signaling pathway, MAPK signaling pathway, Intellectual Disability, Alzheimer's Disease are significantly related to ESUS- patients. Conclusions Any of those miRNAs were never studied in stroke before, our results identified several novel circulating prognostic biomarkers miRNAs that are down- of up-regulated in ESUS-stroke patients (who had only one vs multiple stroke). Among those several miRNAs were identified that are known to play a role in the pathophysiology of neurovascular diseases, paving the way to a new class of smart pathophysiology-based biomarkers in stroke. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Polish National Science Center OPUS Figure 1Figure 2
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».