Epidermal Growth Factor Induces Human Oviductal Epithelial Cell Invasion by Down-Regulating E-Cadherin Expression
Bibliographic record
Abstract
CONTEXT: The loss of E-cadherin enhances cell invasiveness. There is increasing evidence that high-grade serous ovarian cancer may arise from oviductal epithelial cells rather than the ovarian surface epithelium. Despite the controversy over the cellular origins of this disease, the roles of epidermal growth factor (EGF) in human oviductal epithelial cells are largely unknown. OBJECTIVE: We examined whether EGF could induce oviductal epithelial cell invasion by its down-regulation of E-cadherin. METHODS: Matrigel-coated transwells were used for the invasion assay. Small interfering RNA was used to knock down the expression of EGF receptor (EGFR). Specific mRNA and protein levels were examined by quantitative RT-PCR and Western blot, respectively. RESULTS: The expression of Pax8 confirmed the secretory type of the cultured human oviductal epithelial cell line OE-E6/E7. EGFR was expressed in OE-E6/E7 cells, and treatment with EGF down-regulated E-cadherin expression. The effect of EGF on the down-regulation of E-cadherin was abolished by small interfering RNA-mediated depletion of EGFR. EGF treatment led to the activation of ERK1/2, p38, and Akt. Snail and Slug are transcriptional repressors of E-cadherin. Interestingly, our results show that EGF induced Slug but not Snail expression. Moreover, the inhibition of EGF-induced ERK1/2, p38, and Akt activation by pharmacological inhibitors attenuated EGF-induced Slug expression and the down-regulation of E-cadherin, as well as subsequent cell invasion. CONCLUSIONS: EGF induces human oviductal epithelial cell invasion through the activation of ERK1/2, p38, and Akt, the up-regulation of Slug, and the down-regulation of E-cadherin.
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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".