Hydrocortisone down‐regulates the tumor suppressor gene <i>BRCA1</i> in mammary cells: A possible molecular link between stress and breast cancer
Bibliographic record
Abstract
Psychological stress has been correlated with breast cancer development in numerous epidemiological studies. However, physiological and molecular models which may account for this association are not readily available. We have found that the stress hormone hydrocortisone (cortisol) down-regulates the expression of the breast cancer susceptibility gene BRCA1 in the nonmalignant mouse mammary cell line EPH4. This effect is concentration-dependent, is reliant on the continuous presence of hydrocortisone, and is not affected by the addition of lactogenic hormones, or growth conditions. Hydrocortisone was also found to negate a known positive effect of estrogen on BRCA1 expression and, therefore, may interfere with estrogen-related signaling in mammary epithelial cells. The repressive effect of hydrocortisone is diminished or lost in the mouse mammary lines HC-11 and SP1, respectively, suggesting regulation of the BRCA1 may differ between lines. We have uncovered two promoter regulatory sites, which are involved in BRCA1 regulation by hydrocortisone, namely the RIBS and UP regulatory elements. Binding of the transcription factor GABP to both sites is lost upon hydrocortisone addition, though the levels of these factors are not altered by hydrocortisone treatment. Because BRCA1 activity is important for a number of intracellular pathways involved in prevention of tumorigenesis, its observed down-regulation may represent a novel molecular mechanism for cortisol's involvement in breast cancer development.
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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".