Changes in subcellular localisation of MI-ER1α, a novel oestrogen receptor-α interacting protein, is associated with breast cancer progression
Bibliographic record
Abstract
The oestrogen receptor-alpha (ER alpha) plays a key role in breast development and tumorigenesis and inhibiting its activity remains a prime strategy in the treatment of ER alpha-positive breast cancers. Thus, elucidation of the molecular mechanisms responsible for regulating ER alpha activity may facilitate the design of new, more effective breast cancer therapies. The MI-ER1 alpha is a novel transcriptional repressor that contains an LXXLL motif for interaction with nuclear hormone receptors. We investigated the ability of MI-ER1 alpha to bind to ER alpha in HEK293 and MCF-7 breast carcinoma cells, using co-immunoprecipitation assays. In both cell lines, MI-ER1 alpha interacted with ER alpha in the presence and absence of oestrogen, but the interaction was stronger in the absence of ligand. Functional analysis revealed that overexpression of MI-ER1 alpha in T47D breast carcinoma cells results in inhibition of oestrogen-stimulated anchorage-independent growth, suggesting that MI-ER1 alpha may play a role in regulating breast carcinoma cell proliferation in vivo. To explore this further, we performed an immunohistochemical analysis of normal breast tissue and breast carcinoma; a total of 110 cases were examined in whole tissue sections and 771 cases were analysed in tissue microarrays. No consistent difference in the MI-ER1 alpha expression level between normal breast tissue and breast carcinoma was discernible. However, there was a dramatic shift in the subcellular localisation: nuclear MI-ER1 alpha was detectable in 75% of normal breast samples and in 77% of hyperplasia, but in breast carcinoma, only 51% of DCIS, 25% of ILC and 4% of IDC contained nuclear staining. This shift from nuclear to cytoplasmic localisation of MI-ER1 alpha during breast cancer progression suggests that loss of nuclear MI-ER1 alpha might contribute to the development of invasive breast carcinoma.
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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 | 0.000 |
| 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".