IκB‐Kinase‐ε (IKKε/IKKi/IκBKε) expression and localization in prostate cancer tissues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it