Consideration of GREB1 as a potential therapeutic target for hormone-responsive or endocrine-resistant cancers
Bibliographic record
Abstract
INTRODUCTION: Steroid hormones increase the incidence and promote the progression of many types of cancer. Exogenous estrogens increase the risk of developing breast, ovarian and endometrial cancer and many breast cancers initially respond to estrogen deprivation. Although steroid hormone signaling has been extensively studied, the mechanisms of hormone-stimulated cancer growth have not yet been fully elucidated, limiting opportunities for novel approaches to therapeutic intervention. AREAS COVERED: This review examines growing evidence for the important role played by the steroid hormone-induced gene called GREB1, or growth regulation by estrogen in breast cancer 1. GREB1 is a critical mediator of both the estrogen-stimulated proliferation of breast cancer cells and the androgen-stimulated proliferation of prostate cancer cells. EXPERT OPINION: Although its exact function in the cascade of hormone action remains unclear, the ability of GREB1 to modulate tumor progression in models of breast, ovarian and prostate cancer renders this gene an excellent candidate for further consideration as a potential therapeutic target. Research examining the mechanism of GREB1 action will help to elucidate its role in proliferation and its potential contribution to endocrine resistance and will determine whether GREB1 interference may have therapeutic efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".