PUMA and NOXA Expression in Tumor-Associated Benign Prostatic Epithelial Cells Are Predictive of Prostate Cancer Biochemical Recurrence
Bibliographic record
Abstract
Background: Given that treatment decisions in prostate cancer (PC) are often based on risk, there remains a need to find clinically relevant prognostic biomarkers to stratify PC patients. We evaluated PUMA and NOXA expression in benign and tumor regions of the prostate using immunofluorescence techniques and determined their prognostic significance in PC. Methods: PUMA and NOXA expression levels were quantified on six tissue microarrays (TMAs) generated from radical prostatectomy samples (n = 285). TMAs were constructed using two cores of benign tissue and two cores of tumor tissue from each patient. Association between biomarker expression and biochemical recurrence (BCR) at 3 years was established using log-rank (LR) and multivariate Cox regression analyses. Results: Kaplan–Meier analysis showed a significant association between BCR and extreme levels (low or high) of PUMA expression in benign epithelial cells (LR = 8.831, p = 0.003). Further analysis revealed a significant association between high NOXA expression in benign epithelial cells and BCR (LR = 14.854, p < 0.001). The combination of extreme PUMA and high NOXA expression identified patients with the highest risk of BCR (LR = 16.778, p < 0.001) in Kaplan–Meier and in a multivariate Cox regression analyses (HR: 2.935 (1.645–5.236), p < 0.001). Conclusions: The combination of PUMA and NOXA protein expression in benign epithelial cells was predictive of recurrence following radical prostatectomy and was independent of PSA at diagnosis, Gleason score and pathologic stage.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".