Zearalenone at environmental levels promoted ER- positive breast cancer cell lines through the hedgehog pathway
Bibliographic record
Abstract
Zearalenone (ZEA) has been recognized as a common exogenous nonsteroidal estrogen in environments which possessed a disruptive effect on organisms. Exposure to exogenous estrogen has been revealed to affect breast cancer development. However, whether ZEA exerts estrogenic effects to influence and cause breast cancer is poorly documented. We hypothesized that ZEA might act as an estrogen and causative factor in breast cancer, mainly due to its similarity to naturally occurring estrogens. The aim of this study was to further explore the potential risks of ZEA by comparing the effects of ZEA on the proliferation and invasion of estrogen receptor (ER) positive/negative breast cancer cell lines, and to explore its underlying molecular mechanisms. We found that low ZEA concentrations, similar with the real environment, promoted the proliferation, migration and invasion only in ER-positive breast cancer cells, while high concentrations of ZEA inhibited malignant biological behaviors in both ER positive and negative breast cancer cells. Consistently, ZEA demonstrated the same biphasic effect (promotion at low dose and inhibition at high dose) on cell stemness and epithelial mesenchymal transition processes, which were further confirmed to be controlled by the Hedgehog pathway. These results highlight the additional health risks posed by ZEA, which warrant greater attention in environmental management.
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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".