Nuclear Factor-κB Nuclear Localization Is Predictive of Biochemical Recurrence in Patients with Positive Margin Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Radical prostatectomy (RP) patients with positive surgical margins are at increased risk for recurrence, emphasizing the need for prognostic markers to stratify probable outcome for optimal patient management decisions. We tested the hypothesis that nuclear localization of nuclear factor (NF)-kappaB, a transcription factor involved in the regulation of cell growth, angiogenesis, invasion, and apoptosis, is associated with an increased risk of biochemical recurrence after RP. EXPERIMENTAL DESIGN: Analyses addressed data from 42 patients (age range, 52-72 years; mean age, 63.7 years) who exhibited positive surgical margins after RP. Immunohistochemical analysis of NF-kappaB (p65) was performed on the positive margin tissue. A nuclear staining cutoff of >5% was considered positive. The relation between nuclear NF-kappaB expression and biochemical recurrence (prostate-specific antigen >0.3 ng/mL and rising) after RP was tested in univariate and multivariate Cox regression models. RESULTS: Biochemical recurrence was recorded in 23 patients (54.8%; median follow-up, 3.2 years). Univariate Cox regression demonstrated a 4.9-fold (95% confidence interval, 1.5-16.7; P = 0.01) higher rate of recurrence in men with NF-kappaB > 5%. In the multivariate model, after controlling for primary (P = 0.004) and secondary (P = 0.7) Gleason patterns, lymph node (P = 0.06) and seminal vesicle invasion (P = 0.2), and preoperative prostate-specific antigen (P = 0.009), NF-kappaB > 5% was associated with a 6.2-fold higher risk of biochemical recurrence (95% confidence interval, 1.7-23.5; P = 0.007). CONCLUSIONS: In univariate and multivariate analysis, NF-kappaB nuclear expression was strongly predictive of biochemical recurrence in patients with positive surgical margins after RP. We propose that nuclear NF-kappaB may serve as a useful independent molecular marker for stratifying patients at risk for recurrence.
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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 | 0.000 | 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".