Ischemic Stroke with Comorbid Cancer Has Specific miRNA‐mRNA Networks in Blood That Vary by Ischemic Stroke Mechanism
Bibliographic record
Abstract
OBJECTIVE: Approximately half of ischemic strokes (IS) in cancer patients are cryptogenic, with many presumed cardioembolic. We evaluated whether there were specific miRNA and mRNA transcriptome architectures in peripheral blood of IS patients with and without comorbid cancer, and between cardioembolic versus noncardioembolic IS etiologies in comorbid cancer. METHODS: We studied patients with cancer and IS (CS; n = 42), stroke only (SO; n = 41), and cancer only (n = 28), and vascular risk factor-matched controls (n = 30). mRNA-Seq and miRNA-Seq data, analyzed with linear regression models, identified differentially expressed genes in CS versus SO and in cardioembolic versus noncardioembolic CS, and miRNA-mRNA regulatory pairs. Network-level analyses identified stroke etiology-specific responses in CS. RESULTS: A total of 2,085 mRNAs and 31 miRNAs were differentially expressed between CS and SO. In CS, 122 and 35 miRNA-mRNA regulatory pairs, and 5 and 3 coexpressed gene modules, were associated with cardioembolic and noncardioembolic CS, respectively. Complement, growth factor, and immune/inflammatory pathways showed differences between IS etiologies in CS. A 15-gene biomarker panel assembled from a derivation cohort (n = 50) correctly classified 81% of CS and 71% of SO participants in a validation cohort (n = 33). Another 15-gene panel correctly identified etiologies for 13 of 13 CS-cardioembolic and 11 of 11 CS-noncardioembolic participants upon cross-validation; 11 of 16 CS-cryptogenic participants were predicted cardioembolic. INTERPRETATION: We discovered unique mRNA and miRNA transcriptome architecture in CS and SO, and in CS with different IS etiologies. Cardioembolic and noncardioembolic etiologies in CS showed unique coexpression networks and potential master regulators. These may help distinguish CS from SO and identify IS etiology in cryptogenic CS patients. ANN NEUROL 2024;96:565-581.
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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) | 0.000 | 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) | 0.001 | 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".