Identification and Validation of a Five MicroRNA Signature Predictive of Prostate Cancer Recurrence and Metastasis: A Cohort Study
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Bibliographic record
Abstract
BACKGROUND: MicroRNA (miRNA) have been shown to be important in regulating gene expression in prostate cancer. We used next generation miRNA sequencing to conduct a whole miRNome analysis to identify miRNAs associated with prostate cancer metastasis. METHODS: We conducted discovery and validation analyses of miRNAs among a total of 546 men who underwent surgery for prostate cancer using the development of metastasis as an endpoint. Genome wide analysis was conducted among the discovery group (n=31) to identify new miRNAs associated with prostate cancer metastasis. Selected miRNAs were then analyzed using qPCR on prostatectomy specimens from an independent cohort (n=515) to determine whether their expression could predict the development of metastasis after surgery. To examine the biology underlying these associations, we created prostate cancer cell lines which overexpressed miR-301a for in vitro and in vivo functional assays. RESULTS: We identified 33 miRNAs associated with prostate cancer metastasis and selected a panel comprising miRs-301a, 652, 454, 223 and 139 which strongly predicted metastasis (AUC=95.3%, 95%C.I.:84%-99%). Among the validation cohort, the 15-year metastasis-free survival was 77.5% (95% C.I.:63.9%-86.4%) for patients with a high miRNA panel score and 98.8% (95% C.I.:94.9%-99.7%, p<0.0001 for difference) for those with a low score. After adjusting for grade, stage, and PSA, the hazard ratio for metastasis was 4.3 (95% C.I.: 1.7-11.1, p=0.002) for patients with a high miRNA panel score, compared to those with a low score. Prostate cancer cell lines overexpressing miR-301a had in significantly higher tumor growth and metastasis in a xenograft mouse model. CONCLUSIONS: A panel of miRNAs is associated with prostate cancer metastasis. These could be used as potential new prognostic factors in the surgical management of prostate cancer.
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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