Abstract 13144: MicroRNA Sequencing Highlights Regulatory Networks Upstream of Osteopontin and B-type Natriuretic Peptide in Acute Coronary Sydrome
Bibliographic record
Abstract
Introduction: The genomic regulatory networks underlying the pathogenesis of acute coronary syndrome (ACS) are incompletely understood. As intermediate traits, circulating protein biomarkers report on underlying disease severity and are powerfully prognostic in ACS. We hypothesized that integration of dense microRNA (miRNA) profiling with measurement of biomarkers would highlight potential regulatory pathways. Methods: We studied 186 patients enrolled in the biomarker substudy of the TRILOGY clinical trial of ACS. MiRNA sequencing was performed on RNA extracted from whole blood, and seven known prognostic protein biomarkers were measured from plasma (N-terminal pro B-type natriuretic peptide [NT-proBNP], C-reactive protein, osteopontin [OPN], myeloperoxidase, growth differentiation factor 15, monocyte chemoattractant protein 1, and neopterin). MiRNAs were tested for association with these biomarkers using generalized linear models. Target genes putatively regulated by the associated miRNAs were examined using pathway analysis. Results: Fourteen miRNAs, including cardiac-related miRs 20b-5p and 320a,b and d, were associated with OPN levels (min. p=1.1x10 -4 ), and five miRNAs, including cardiac-related miRs 25-3p and 423-3p, were associated with NT-proBNP levels (min. p=3.4x10 -4 ); no other biomarkers showed significant associations. Sixty-three KEGG pathways were enriched in the target genes of either NT-proBNP- or OPN-associated miRNAs, with five pathways found in the top ten for both biomarkers: prion diseases, fatty acid biosynthesis, lysine degradation, protein processing in endoplasmic reticulum, and viral carcinogenesis. Conclusions: By integrating large-scale microRNA profiling with circulating biomarkers as intermediate traits, we identified associations of known cardiac-related and novel miRs with two prognostic biomarkers (OPN and NT-proBNP), and identified potential genomic regulatory networks underlying these biomarkers. We further identified novel non-cardiac genomic pathways associated with these biomarkers. These results may inform future studies delineating genomic pathways underlying ACS outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".