Discovery of circulating microRNAs associated with human prostate cancer using a mouse model of disease
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".