NOXA and PUMA Expression Add to Clinical Markers in Predicting Biochemical Recurrence of Prostate Cancer Patients in a Survival Tree Model
Bibliographic record
Abstract
PURPOSE: To assess the expression of proapoptotic NOXA and PUMA in prostate tissues and delineate their association with prostate cancer (PCa) recurrence. EXPERIMENTAL DESIGN: Normal, prostatic intraepithelial neoplasia (PIN), hormone-sensitive (HS) PCa, and hormone-refractory (HR) PCa tissues were used to build tissue microarrays encompassing a total of 135 patients. Two observers assessed the intensity of NOXA and PUMA immunohistochemical staining using a composite color scale. One hundred and eighty recursive partitioning and regression tree (RPART) models were generated to predict biochemical recurrence (BCR) within HS cancer patients using NOXA, PUMA, and clinical parameters. Models were then ranked according to the integrated Brier score (IBS). RESULTS: Increasing NOXA expression was associated with PCa progression, reaching the highest levels in HR PCa. Increased NOXA expression was observed in 68% of HS cancer patients and was predictive of BCR (LR = 8.64; P = 0.003). In contrast, PUMA expression was highest in HS cancer, and although 70% of HS cancer patients exhibited increased PUMA expression, PUMA alone could not predict the onset of BCR. Interestingly, the top-ranking RPART model generated [IBS = 0.107; 95% confidence interval (95% CI), 0.065-0.128] included surgical margin status and NOXA and PUMA expression, although recurrent prognostic classification schemes obtained in the top 10 models favored a survival tree model containing margin status, NOXA expression, and preoperative prostate-specific antigen (PSA) (IBS = 0.114; 95% CI, 0.069-0.142). CONCLUSION: We conclude that NOXA and PUMA expression may be linked to PCa progression and propose further validation of a survival tree model including surgical margin status, NOXA expression, and preoperative PSA for predicting BCR.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".