Over‐expression of IκB‐kinase‐ε (IKKε/IKKi) induces secretion of inflammatory cytokines in prostate cancer cell lines
Bibliographic record
Abstract
BACKGROUND: Elevated inflammatory cytokine levels in serum have been associated with advanced stage metastasis-related morbidity in prostate cancer. Several studies have shown that IL-6 and IL-8 can accelerate the growth of human prostate cancer cell lines. Previous studies, in murine embryonic fibroblasts, have shown that Ikappa-B kinase-epsilon (IKKepsilon/IKKi)-deficiency results in the reduction of lipopolysaccharide-mediated expression of IL-6. RESULTS: In this study, we report that over-expression of IKKepsilon in hormone-sensitive 22Rv1 and LNCaP prostate cancer cells induces the secretion of several inflammatory cytokines including IL-6 and IL-8. Both of these cytokines are secreted by hormone-refractory PC-3 prostate cancer cells and IKKepsilon knock-down in these cells correlates with a strong decrease in IL-6 secretion. Furthermore, we demonstrate that IKKepsilon over-expression does not induce the activation of the IKKepsilon classical targets NF-kappaB and IRF-3, two transcription factors involved in the regulation of several cytokines. Finally, we observe that high IKKepsilon expression results in its nuclear translocation, a phenomena that is TBK1-independent. CONCLUSIONS: This study identifies IKKepsilon as a potential prostate cancer gene that may favor chronic inflammation and create a tumor-supporting microenvironment that promotes prostate cancer progression, particularly by the induction of IL-6 secretion that may act as a positive growth factor in prostate cancer.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".