NF-κB p65 as a prognostic tool in prostate cancer: An immunohistochemical study from biopsy samples.
Bibliographic record
Abstract
e15202 Background: Our previous immunohistochemical studies of NF-κB p65 in prostate cancer (PCa) highlight its clinical potential as a prognostic marker in different cohorts of Canadian and European men. However these studies are essentially based on tissue microarrays (TMAs) built from samples obtained from radical prostatectomy specimens where other prognostic parameters, such as pathologic stage, Gleason score, and margin status are also available. However, limited prognostic parameters are available to clinicians at the time of diagnosis. The current study aims to assess whether the immunohistochemical staining of p65 could offer prognostic information at the time of diagnosis and help risk stratify patients. Methods: Prostatebiopsies were obtained from a cohort of 328 prostate cancer patients who were further treated by radical prostatectomy. NF-κB p65 was stained by immunochemistry on biopsy samples containing malignant tissue. The nuclear frequency was quantified as a percentage of positive p65 nuclear cells. Results: Our first statistical analyses revealed that the nuclear frequency of NF-κB p65 on biopsy samples was correlated with clinical paramaters as final Gleason score and BCR. Patients with Gleason score post-prostatectomy of 7 or more had a higher NF-κB p65 nuclear frequency in biopsy samples (p<0.05, Student test). NF-κB p65 nuclear frequency was also higher in patients developing a BCR and bone metastasis (p<0.05, Student test). Conclusions: These preliminary results show an association between NF-κB p65 nuclear distribution in biopsy samples and PCa aggressivity. We showed that NF-κB p65 immunohistochemical staining on biopsies was predictive of BCR. NFkB p65 may be prognostic marker at the time of diagnosis and could eventually be useful in clinical decision making.
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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 | 0.001 |
| 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 | 0.000 | 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".