Assessment of Serum microRNA Biomarkers to Predict Reclassification of Prostate Cancer in Patients on Active Surveillance
Bibliographic record
Abstract
PURPOSE: Conventional clinical variables cannot accurately differentiate indolent from aggressive prostate cancer in patients on active surveillance. We investigated promising circulating miRNA biomarkers to predict the reclassification of active surveillance cases. MATERIALS AND METHODS: We collected serum samples from 2 independent active surveillance cohorts of 196 and 133 patients for the training and validation, respectively, of candidate miRNAs. All patients were treatment naïve and diagnosed with Gleason score 6 prostate cancer. Samples were collected prior to potential reclassification. We analyzed 9 circulating miRNAs previously shown to be associated with prostate cancer progression. Logistic regression and ROC analyses were performed to assess the predictive ability of miRNAs and clinical variables. RESULTS: A 3-miR (miRNA-223, miRNA-24 and miRNA-375) score was significant to predict patient reclassification (training OR 2.72, 95% CI 1.50-4.94 and validation OR 3.70, 95% CI 1.29-10.6). It was independent of clinical characteristics in multivariable models. The ROC AUC was maximized when combining the 3-miR score and prostate specific antigen, indicating additive predictive value. The 3-miR score plus the prostate specific antigen panel cutoff achieved 89% to 90% negative predictive value and 66% to 81% specificity. CONCLUSIONS: The 3-miR score combined with prostate specific antigen represents a noninvasive biomarker panel with high negative predictive value. It may be used to identify patients on active surveillance who have truly indolent 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 | 0.001 | 0.004 |
| 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 | 0.000 | 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".