GREB1 is an estrogen receptor-regulated tumour promoter that is frequently expressed in ovarian cancer
Bibliographic record
Abstract
Estrogenic hormone replacement therapy increases the risk of developing ovarian cancer, and estrogen promotes tumour initiation and growth in mouse models of this disease. GREB1 (Growth regulation by estrogen in breast cancer 1) is an ESR1 (estrogen receptor 1)-upregulated protein which may mediate estrogen action. GREB1 knockdown prevents hormone-driven proliferation of several breast and prostate cancer cell lines and prolongs survival of mice engrafted with ovarian cancer cells, but its mechanism of action remains unclear. In this study, we explored GREB1 function in ovarian cancer. GREB1 overexpression in ovarian cancer cell lines increased cell proliferation and migration and promoted a mesenchymal morphology associated with increased Col1a2, which encodes a collagen I subunit. GREB1 knockdown inhibited proliferation and promoted an epithelial morphology associated with decreased Col1a2. In human tissues, GREB1 was expressed in all ESR1-expressing tissues throughout the normal female reproductive tract, in addition to several tissues that did not show ESR1 expression. In a TMA of ovarian cancer cases, GREB1 was expressed in 75-85% of serous, endometrioid, mucinous, and clear cell carcinomas. Serous, endometrioid, and mucinous ovarian cancers were almost always positive for either ESR1 or GREB1, suggesting a possible reliance on signalling through ESR1 and/or GREB1. Targeting GREB1 may inhibit tumour-promoting pathways both downstream and independent of ESR1 and is therefore a possible treatment strategy worthy of further investigation.
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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 | 0.001 |
| 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) | 0.003 | 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".