Expression of oestrogen receptor-β in oestrogen receptor-α negative human breast tumours
Bibliographic record
Abstract
To analyse the phenotype of breast tumours that express oestrogen receptor-beta (ERbeta) alone tissue microarrays were used to investigate if ERbeta isoforms are associated with specific prognostic markers and gene expression phenotypes in ERalpha-negative tumours. ERalpha-negative tumours were positive for ERbeta1 in 58% of cases (n=122/210), total ERbeta in 60% (n=115/192) and ERbeta2/cx in 57% of cases (n=114/199). Oestrogen receptor-beta1 and total ERbeta were significantly correlated with Ki67 (r=0.28, P<0.0001, n=209; r=0.29, P<0.0001, n=191) and with CK5/6, a marker of the basal phenotype (r=0.20, P=0.0106, n=170; r=0.18, P=0.0223, n=158). ERbeta2/cx was strongly associated with p-c-Jun and NF-kappaBp65 (r=0.53, P<0.0001, n=93; r=0.35, P<0.0001, n=176). This study shows that a range of ERbeta isoform expression occurs in ERalpha-negative breast tumours. While expression of ERbeta1, total and ERbeta2/cx are correlated, individual forms show associations with certain phenotypes that suggest different roles in subsets of ERalpha-negative cancers. Based on our in vivo observations, ERbeta may have the potential to become a therapeutic target in the specific subcohort of ERalpha-negative breast cancers.
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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 | 0.000 |
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".