Abstract P745: Whole Blood MicroRNA and Their Target Messenger RNA Reveal Distinct Transcriptional Changes in Ischemic Stroke Patients With and Without Comorbid Cancer
Bibliographic record
Abstract
Introduction: One-tenth of patients with stroke have cancer. We previously identified mRNA profiles differentiating patients with stroke and cancer, stroke only, and cancer only. In this study, we investigated mRNA and microRNA (miRNA) transcriptomes to identify potential miRNA regulators that underlie the observed mRNA changes. Methods: We prospectively enrolled 4 groups of subjects at 3 centers from 2009-2020. This analysis included 41 subjects with ischemic stroke plus cancer, 42 subjects with ischemic stroke only, 28 subjects with cancer only, and 30 vascular risk factor controls. Stroke-only and cancer-only subjects were matched to stroke-plus-cancer subjects by age, sex, and cancer type. We performed miRNA and mRNA sequencing on blood drawn 72-120 hours after stroke. ANCOVA estimated differential expression of miRNA and mRNA between groups (FDR p<0.05, |fold-change|>1.2). Analyses were adjusted for time from stroke onset, sex, age, vascular risk factors and batch. Results: We identified differential expression in 36 miRNA and 264 corresponding mRNA targets between the stroke-plus-cancer and stroke-only groups after accounting for cancer-only expression (Fig 1). Immune and coagulation pathways, including complement, platelet glycoproteins, TGF-β, and mTOR signaling, were overrepresented in stroke-plus-cancer vs stroke-only subjects. T cell, B cell and platelet precursor-specific genes were also overrepresented in stroke-plus-cancer subjects. When compared to other groups, stroke-plus-cancer subjects had 230 unique mRNA encoding for transcriptional regulators, including those involving splicing, epigenetics, and mediator complex genes bridging transcription factors and RNA transcriptional machinery. Conclusion: Patients with stroke and cancer had distinct signatures of miRNA and target mRNA compared to stroke patients without cancer, supporting the hypothesis that cancer-related stroke is a unique subgroup of ischemic stroke.
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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 | 0.000 |
| 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) | 0.003 | 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".