Estrogen Regulates Snail and Slug in the Down-Regulation of E-Cadherin and Induces Metastatic Potential of Ovarian Cancer Cells through Estrogen Receptor α
Bibliographic record
Abstract
Tumorigenesis is a multistep process involving dysregulated cell growth and metastasis. Considerable evidence implicates a mitogenic action of estrogen in early ovarian carcinogenesis. In contrast, its influence in the metastatic cascade of ovarian tumor cells remains obscure. In the present study, we showed that 17beta-estradiol (E2) increased the metastatic potential of human epithelial ovarian cancer cell lines. E2 treatment led to clear morphological changes characteristic of epithelial-mesenchymal transition (EMT) and an enhanced cell migratory propensity. These morphological and functional alterations were associated with changes in the abundance of EMT-related genes. Upon E2 stimulation, expression and promoter activity of the epithelial marker E-cadherin were strikingly suppressed, whereas EMT-associated transcription factors, Snail and Slug, were significantly up-regulated. This up-regulation was attributed to the increase in gene transcription activated by E2. Depletion of endogenous Snail or Slug using small interfering RNA (siRNA) attenuated E2-mediated decrease in E-cadherin. In addition, E2-induced cell migration was also neutralized by the siRNAs, suggesting that both transcription factors are indispensable for the prometastatic actions of E2. More importantly, by using selective estrogen receptor (ER) agonists, forced expression, and siRNA approaches, we identified that E2 triggered the metastatic behaviors exclusively through an ERalpha-dependent pathway. We also showed that ERbeta had an opposing action on ERalpha because the presence of ERbeta completely inhibited the EMT and down-regulation of E-cadherin induced by ERalpha. Collectively, this study provides a compelling argument that estrogen can potentiate tumor progression by EMT induction and highlights the crucial role of ERalpha in ovarian tumorigenesis.
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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".