Identification of a Novel MicroRNA Panel Associated with Metastasis Following Radical Prostatectomy for Prostate Cancer
Bibliographic record
Abstract
BACKGROUND/AIM: This is a case control study designed to identify one or more novel microRNA sequences associated with metastasis following radical prostatectomy for clinically localized prostate cancer. MATERIALS AND METHODS: Samples were obtained from patients with clinical evidence of metastatic disease following surgery (cases) and patients who showed no evidence of metastasis or biochemical recurrence at least 5 years following surgery (controls) as identified from a single-center, institutional database. Cases and controls were matched for tumor grade and duration of follow-up. RESULTS: Whole miRNome analysis identified 2,792 expressed miRNAs in 19 patient pairs. The 497 miRNA sequences with reads per million over 10, were used for analysis, bootstrapping with backward selection identified a panel of 5-miRNA (miR-17-3p, miR-27a-3p, miR-200a-3p, miR-375, and miR-376b-3p) with a risk score strongly associated with metastasis (AUC=89.5%, 95%CI=79.5-99.5%). Methodologically, most studies use the magnitude of differential expression with or without clinical judgement for selection of predictors for inclusion in panels. In order to strengthen the predictive model, a selection strategy was employed, bootstrapping with automated backwards selection, which relied on the strength of association for inclusion. CONCLUSION: A genome-wide analysis of microRNA expression identified a panel of 5 miRNAs strongly associated with prostate cancer metastasis following radical prostatectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".