Discovery of circulating microRNAs associated with human prostate cancer using a mouse model of disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Circulating microRNAs (miRNAs) are emerging as useful non-invasive markers of disease. The objective of this study was to use a mouse model of prostate cancer as a tool to discover serum miRNAs that could be assessed in a clinical setting. Global miRNA profiling identified 46 miRNAs at significantly altered levels (p ≤ 0.05) in the serum of TRansgenic Adenocarcinoma of Mouse Prostate (TRAMP) mice with advanced prostate cancer compared to healthy controls. A subset of these miRNAs with known human homologues were validated in an independent cohort of mice and then measured in serum from men with metastatic castration-resistant prostate cancer (mCRPC; n = 25) or healthy men (n = 25). Four miRNAs altered in mice, mmu-miR-141, mmu-miR-298, mmu-miR-346 and mmu-miR-375, were also found to be at differential levels in the serum of men with mCRPC. Three of these (hsa-miR-141, hsa-miR-298 and hsa-miR-375) were upregulated in prostate tumors compared with normal prostate tissue, suggesting that they are released into the blood as disease progresses. Moreover, the intra-tumoral expression of hsa-miR-141 and hsa-miR-375 were predictors of biochemical relapse after surgery. This study is the first to demonstrate that specific serum miRNAs are common between human prostate cancer and a mouse model of the disease, highlighting the potential of such models for the discovery of novel biomarkers.
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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 it