Use of microRNA signature to distinguish early from late biochemical failure in prostate cancer.
Bibliographic record
Abstract
194 Background: With the introduction of PSA testing, the problem of over-treatment emerged in prostate cancer. Only a small subset of prostate cancer patients will require more intensive adjuvant therapy. There is currently no biomarker that can predict disease aggressiveness at the time of surgery. Methods: We analyzed miRNA expression in 41 patients (the discovery set) which were dichotomized into; 'high risk'- experienced biochemical failure within 24 months after radical prostatectomy (n=26) and 'low risk' who did not have biochemical failure for at least 35 months (n=15). The validation set consisted of 72 cases. Total RNA was isolated from FFPE cores. cDNA was prepared for each patients and expression miRNA expression was screened by qRT-PCR –based panel. miRNAs were ranked by non-parametric tests. Linear regression models were built to predict biochemical failure. We used TargetScan for miRNA target prediction. Targets were validated by transient transfection of synthetic miRNA precursors followed by qRT-PCR quantification of the targets. Proliferation was assessed by measuring cell viability. Results: We compared the expression of 754 mature human miRNAs in patients with ‘high’ or ‘low’ risk for biochemical failure. We identified 24 miRNAs that were differentially expressed between the risk groups. We developed three logistic regression models, based on the expression of 2-3 miRNAs (PPV=100% and NPV ranges 86.4-100%). We confirmed the differential expression on the study set and on a larger, independent set of PCa pateints. We also validated one model on an independent set of patients. Further, we show that transfection of miR-152 and miR-331-3p, featured in the logistic regression models, altered proliferation of PCa3 and DU145 cells. Target prediction indicated Erbb3 and Erbb2 as potential direct targets and their mRNA expression significantly reduced when miR-152 and miR-331-3p were overexpressed. Conclusions: Altered miR-331-3p and miR-152 expression represent a potential tool for assessing the risk of early biochemical failure. These miRNAs may act through the Erbb family to induce an alternative way of AR activation.
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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) | 0.000 | 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".