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Record W1999458378 · doi:10.1038/sj.bjc.6604518

Changes in subcellular localisation of MI-ER1α, a novel oestrogen receptor-α interacting protein, is associated with breast cancer progression

2008· article· en· W1999458378 on OpenAlexafffund
Patti McCarthy, FV Mercer, M W J Savicky, B A Carter, Gary D. Paterno, Laura L. Gillespie

Bibliographic record

VenueBritish Journal of Cancer · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsTissue microarrayBreast cancerCancer researchEstrogen receptor alphaBreast carcinomaCarcinogenesisEstrogen receptorImmunohistochemistryBiologyAlpha (finance)CancerPathologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.009
GPT teacher head0.252
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2008
Admission routes2
Has abstractyes

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