Reversibility of pre-malignant estrogen-induced epigenetic changes
Bibliographic record
Abstract
The development of early detection and prevention strategies of breast cancer relies on defining molecular and cellular events that characterize progressive alterations underlying preneoplastic changes in the mammary epithelium. Studies have shown that estrogen exerts its carcinogenic effects through both genetic and epigenetic pathways to promote imbalances in proliferation and apoptosis, genomic instability and cancer. The purpose of this study was to identify the earliest epigenetic changes that could be detected in response to estrogen treatment. More importantly, having detected these early pre-malignant epigenetic changes, a follow-up study was designed to address the potential to reverse these estrogen-induced alterations. Using a well-established ACI rat model, morphological and epigenetic changes were identified in the mammary gland tissue as early as 2 days after exposure to constitutively elevated estrogen levels produced by continuous release estrogen mini-pellets. Progressive hyperproliferative changes were paralleled by epigenetic disturbances, including the upregulation of DNA methyltransferases and hyperacetylation of histone residues. These changes could be detected early, and they continued to persist if estrogen was maintained within a high physiological range. Epigenetic features of short-term estrogen exposure were strikingly similar to hallmarks of cancer promotion and progression. Yet, importantly, these changes exhibited a degree of reversibility if a source of elevated levels of estrogen was removed. Knowing that operational reversibility during the promotion stage of carcinogenesis provides a window for intervention, the potential to reverse the effects of elevated levels of estrogen prior to tumor development may prove to be a promising avenue to explore.
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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".