NF‐κB nuclear localization and its prognostic significance in prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To detect the subcellular localization of NF-kappa B (p65) in human prostate cancer tissues of different histological grades, and to test whether NF-kappa B localization alone, or combined with the histological grade, can be used to predict patient outcome. PATIENTS AND METHODS: Prostate cancer tissues were obtained from radical prostatectomy specimens; the histological grade was determined using the Gleason grading system. Clinical outcomes were defined as good (5-year disease-free survival with undetectable levels of prostate specific antigen) or poor (progression to bone metastases). The subcellular localization of NF-kappa B was visualized by immunohistochemistry using an anti-p65 antibody. RESULTS: The NF-kappa B subcellular localization was initially assessed in 45 specimens; in these samples a nuclear localization of NF-kappa B was specific to cancer tissues, but did not correlate with the Gleason score (P = 0.089). NF-kappa B was then assessed as a prognostic marker to complement Gleason score in predicting cancer progression. Tumour tissues from 30 men with a known clinical outcome were included; 10 of 17 patients who had a poor outcome were positive for NF-kappa B nuclear staining, whereas only two of 13 with a good outcome were positive (P = 0.026). When NF-kappa B subcellular localization and Gleason score were combined, two risk categories of progression were defined. Eleven of 13 specimens from those with a good outcome were in the low-risk category (Gleason 2-4 or Gleason 5-7 with negative nuclear NF-kappa B) and 12 of 17 in the poor outcome group were in the high-risk category (Gleason 8-10 or Gleason 5-7 with positive nuclear NF-kappa B; P = 0.004). CONCLUSION: NF-kappa B is detectable in the nucleus in prostate cancer tissues and positivity can be used to help predict patient outcome. Multivariate analyses using other clinical and molecular variables are underway, and will validate the usefulness of NF-kappa B as a prognostic factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".