Identification and Validation of a Five MicroRNA Signature Predictive of Prostate Cancer Recurrence and Metastasis: A Cohort Study
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".