Inducible IκB Kinase/IκB Kinase ε Expression Is Induced by CK2 and Promotes Aberrant Nuclear Factor-κB Activation in Breast Cancer Cells
Bibliographic record
Abstract
Aberrant activation of nuclear factor-kappaB (NF-kappaB) transcription factors has been implicated in the pathogenesis of breast cancer. We previously showed elevated activity of IkappaB kinase alpha (IKKalpha), IKKbeta, and protein kinase CK2 in primary human breast cancer specimens and cultured cells. A novel inducible IKK protein termed IKK-i/IKKepsilon has been characterized as a potential NF-kappaB activator. Here, we provide evidence that implicates IKK-i/IKKepsilon in the pathogenesis of breast cancer. We show IKK-i/IKKepsilon expression in primary human breast cancer specimens and carcinogen-induced mouse mammary tumors. Multiple breast cancer cell lines showed higher levels of IKK-i/IKKepsilon and kinase activity compared with untransformed MCF-10F breast epithelial cells. Interestingly, IKK-i/IKKepsilon expression correlated with CK2alpha expression in mammary glands and breast tumors derived from MMTV-CK2alpha transgenic mice. Ectopic CK2 expression in untransformed cells led to increased IKK-i/IKKepsilon mRNA and protein levels. Inhibition of CK2alpha via the pharmacologic inhibitor apigenin or upon transfection of a CK2 kinase-inactive subunit reduced IKK-i/IKKepsilon levels. Expression of a kinase-inactive IKK-i/IKKepsilon mutant in breast cancer cells reduced NF-kappaB activity as judged by transfection assays of reporters driven either by NF-kappaB elements or the promoters of two NF-kappaB target genes, cyclin D1 and relB. Importantly, the kinase-inactive IKK-i/IKKepsilon mutant reduced the endogenous levels of these genes as well as the ability of breast cancer cells to grow in soft agar or form invasive colonies in Matrigel. Thus, CK2 induces functional IKK-i/IKKepsilon, which is an important mediator of the activation of NF-kappaB that plays a critical role in the pathogenesis of breast 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) | 0.001 | 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".