IκB‐Kinase‐ε (IKKε/IKKi/IκBKε) expression and localization in prostate cancer tissues
Bibliographic record
Abstract
BACKGROUND: Advanced prostate cancer (PCa) remains a one of the leading causes of cancer related death and is often due to the progression from a hormone sensitive (HS) to a castrate resistant (CR) state for which therapeutic alternatives remain palliative. Molecular events involved in the progression to CR-PCa remain largely unknown. A previous study reported significantly higher levels of Iκ-B kinase-epsilon (IKKε) expression in CR compared to androgen-responsive cell lines. In the present study, we evaluate IKKε expression in human prostate tissue. METHODS: In order to evaluate the modulation of IKKε expression in PCa tissue IKKε immunostaining was performed on paraffin-embedded prostate tissue microarrays containing cores from normal tissues (n = 47), non-malignant tissues adjacent to the tumor (n = 53), prostatic intraepithelial neoplasia (PIN) (n = 28), HS (n = 62), and CR tumors (n = 31). RESULTS: We found a low cytoplasmic expression of IKKε in non-malignant tissue. HS tumors showed a significant increase in cytoplasmic IKKε expression compared to non-malignant tissues. CR tissues presented the highest cytoplasmic IKKε expression levels. We also report, for the first time, the presence of a nuclear localization of IKKε in prostate epithelial cells, in particular we observed an increase of IKKε nuclear localization in HS malignant tissues. Finally, we found a strong link between an increase of IKKε cytoplasmic expression in PCa and metastatic progression. CONCLUSION: This study strongly suggests the role of IKKε as a PCa oncogene that may be involved in the emergence of a CR state.
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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 | 0.000 | 0.000 |
| 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".