The glucocorticoid receptor interferes with progesterone receptor-dependent genomic regulation in breast cancer cells
Bibliographic record
Abstract
The glucocorticoid and progesterone receptors (GR and PR) are closely related members of the steroid receptor family. Despite sharing similar structural and functional characteristics; the cognate hormones display very distinct physiological responses. In mammary epithelial cells, PR activation is associated with the incidence and progression of breast cancer, whereas the GR is related to growth suppression and differentiation. Despite their pharmacological relevance, only a few studies have compared GR and PR activities in the same system. Using a PR+/GR+ breast cancer cell line, here we report that either glucocorticoid-free or dexamethasone (DEX)-activated GR inhibits progestin-dependent gene expression associated to epithelial-mesenchymal-transition and cell proliferation. When both receptors are activated with their cognate hormones, PR and GR can form part of the same complex according to co-immunoprecipitation, quantitative microscopy and sequential ChIP experiments. Moreover, genome-wide studies in cells treated with either DEX or R5020, revealed the presence of several regions co-bound by both receptors. Surprisingly, GR also binds novel genomic sites in cells treated with R5020 alone. This progestin-induced GR binding was enriched in REL DNA motifs and located close to genes coding for chromatin remodelers. Understanding GR behavior in the context of progestin-dependent breast cancer could provide new targets for tumor therapy.
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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) | 0.002 | 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".