Abstract WP40: Common and Distinct Circulating MicroRNAs Across Four Neurovascular Disorders
Bibliographic record
Abstract
Introduction: Familial cerebral cavernous malformations (FCCM), Sturge-Weber Syndrome (SWS), and hereditary hemorrhagic telangiectasia with brain arteriovenous malformations (HHT) are neurovascular disorders driven by genetic mutations while cerebral microbleeds (CMBs) are primarily associated with the aging process. All are associated with different vascular dysmorphisms and/or propensity to bleed. Hypothesis: We hypothesized that common and distinct circulating microribonucleic acids (miRNAs), reflecting shared and different pathobiology, can serve as potential biomarkers and therapeutic targets. Methods: Plasma miRNAs from patients with FCCM (n=10), SWS (n=10), and HHT (n=10) compared to age and sex-propensity-matched healthy young (HY) subjects (n=10) were extracted and sequenced. Similarly, CMB patients (n=10) were age and sex propensity-matched with healthy old (HO) subjects (n=10). Differentially expressed (DE) miRNAs of each disorder were identified (p<0.05, FDR corrected, absolute fold change [|FC|]>1.5]). Ingenuity Pathway Analysis (IPA) was conducted to determine gene targets and pathways of DE miRNAs. DE genes based on the transcriptome of each disorder were identified and utilized to filter gene targets of circulating miRNAs. Results: Eleven DE miRNAs were identified in FCCM, 40 in SWS, 41 in HHT, and 26 in CMB (p<0.05, FDR-corrected, [|FC|]>1.5]). Further analyses showed that 18 miRNAs were commonly dysregulated in two of the studied neurovascular disorders. IPA identified 17 genes targeted by at least two DE miRNAs in each of the four cerebrovascular disorders. Functional enrichment of those shared gene targets showed that PTEN, CDKN1A, NCL2L1, and CCND2 were involved in the PI3K-Akt Signaling Pathway. Moreover, the ROBO SLIT Signaling Pathway was identified as an involved pathway across all four disorders. Conclusion: The common dysregulated miRNAs across the disorders underscore their potential as biomarkers and therapeutic targets, reflecting their mechanistic involvement in shared pathophysiological pathways. Furthermore, the commonly targeted genes and implicated pathways suggest shared functionality of the miRNAs. These findings pave the way for further exploration of these miRNAs, aiming at the clinical application for disease monitoring and therapeutic intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".