Positive Regulation of Estrogen Receptor Alpha in Breast Tumorigenesis
Bibliographic record
Abstract
Estrogen receptor alpha (ERα, NR3A1) contributes through its expression in different tissues to a spectrum of physiological processes, including reproductive system development and physiology, bone mass maintenance, as well as cardiovascular and central nervous system functions. It is also one of the main drivers of tumorigenesis in breast and uterine cancer and can be targeted by several types of hormonal therapies. ERα is expressed in a subset of luminal cells corresponding to less than 10% of normal mammary epithelial cells and in over 70% of breast tumors (ER+ tumors), but the basis for its selective expression in normal or cancer tissues remains incompletely understood. The mapping of alternative promoters and regulatory elements has delineated the complex genomic structure of the ESR1 gene and shed light on the mechanistic basis for the tissue-specific regulation of ESR1 expression. However, much remains to be uncovered to better understand how ESR1 expression is regulated in breast cancer. This review recapitulates the current body of knowledge on the structure of the ESR1 gene and the complex mechanisms controlling its expression in breast tumors. In particular, we discuss the impact of genetic alterations, chromatin modifications, and enhanced expression of other luminal transcription regulators on ESR1 expression in tumor cells.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".