Fingerprint of novel circulating microRNAs identify patients with stroke-embolic stroke of undetermined source
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".