Potential Cross-Talk between Alternative and Classical NF-κB Pathways in Prostate Cancer Tissues as Measured by a Multi-Staining Immunofluorescence Co-Localization Assay
Bibliographic record
Abstract
BACKGROUND: While the classical NF-κB/p65 pathway is known to be involved in prostate cancer progression and is associated with poor patient outcome, the role of the NF-κB /RelB alternative protein is not well defined. Here we analyzed the activation of both NF-κB pathways in prostate cancer tissues and correlate this activation with clinical features of the disease. METHODS: A multiple immunofluorescence technique was employed to concomitantly and quantitatively visualize the nuclear localization of p65 and RelB in 200 paraffin embedded samples. Epithelia were defined using appropriate fluorochrome markers and the resulting immunofluorescent signals were quantified with an automated scoring system. RESULTS: The nuclear frequency of p65 was found to be significantly increased in tumor tissues as compared with normal adjacent tissue, whereas the frequency for RelB was decreased (p < 0.001, Wilcoxon test). As previously reported, p65 nuclear frequency was associated with a risk of biochemical recurrence. Although, RelB nuclear frequency alone did not predict recurrence, the presence of activated RelB reduced the risk of recurrence associated with the activation of p65. CONCLUSION: For the first time p65/RelB co-distribution was assessed in prostate cancer tissues and suggested a negative crosstalk between the two NF-κB pathways in prostate cancer progression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".