NF‐κB and estrogen receptor α interactions: Differential function in estrogen receptor‐negative and ‐positive hormone‐independent breast cancer cells
Bibliographic record
Abstract
Estrogen receptor (ER)-positive breast cancer cells have low levels of constitutive NF-kappaB activity while ER negative (-) cells and hormone-independent cells have relatively high constitutive levels of NF-kappaB activity. In this study, we have examined the aspects of mutual repression between the ERalpha and NF-kappaB proteins in ER+ and ER- hormone-independent cells. Ectopic expression of the ERalpha reduced cell numbers in ER+ and ER- breast cancer cell lines while NF-kappaB-binding activity and the expression of several NF-kappaB-regulated proteins were reduced in ER- cells. ER overexpression in ER+/E2-independent LCC1 cells only weakly inhibited the predominant p50 NF-kappaB. GST-ERalpha fusion protein pull downs and in vivo co-immunoprecipitations of NF-kappaB:ERalpha complexes showed that the ERalpha interacts with p50 and p65 in vitro and in vivo. Inhibition of NF-kappaB increased the expression of diverse E2-regulated proteins. p50 differentially associated directly with the ER:ERE complex in LCC1 and MCF-7 cells by supershift analysis while p65 antibody reduced ERalpha:ERE complexes in the absence of a supershift. ChIP analysis demonstrated that NF-kappaB proteins are present on an endogenous ERE. Together these results demonstrate that the ER and NF-kappaB undergo mutual repression, which may explain, in part, why expression of the ERalpha in ER- cells does not confer growth signaling. Secondly, the acquisition of E2-independence in ER+ cells is associated with predominantly p50:p50 NF-kappaB, which may reflect alterations in the ER in these cells. Since the p50 homodimer is less sensitive to the presence of the ER, this may allow for the activation of both pathways in the same cell.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".