EGF-Induced EMT and Invasiveness in Serous Borderline Ovarian Tumor Cells: A Possible Step in the Transition to Low-Grade Serous Carcinoma Cells?
Bibliographic record
Abstract
In high-grade ovarian cancer cultures, it has been shown that epidermal growth factor (EGF) induces cell invasion by activating an epithelial-mesenchymal transition (EMT). However, the effect of EGF on serous borderline ovarian tumors (SBOT) and low-grade serous carcinomas (LGC) cell invasion remains unknown. Here, we show that EGF receptor (EGFR) was expressed, that EGF treatment increased cell migration and invasion in two cultured SBOT cell lines, SBOT3.1 and SV40 large T antigen-infected SBOT cells (SBOT4-LT), and in two cultured LGC cell lines, MPSC1 and SV40 LT/ST-immortalized LGC cells (ILGC). However, EGF induced down-regulation of E-cadherin and concurrent up-regulation of N-cadherin in SBOT cells but not in LGC cells. In SBOT cells, the expression of the transcriptional repressors of E-cadherin, Snail, Slug and ZEB1 were increased by EGF treatment. Treatment with EGF led to the activation of the downstream ERK1/2 and PI3K/Akt. The MEK1 inhibitor PD98059 diminished the EGF-induced cadherin switch and the up-regulation of Snail, Slug and ZEB1 and the EGF-mediated increase in SBOT cell migration and invasion. The PI3K inhibitor LY294002 had similar effects, but it could not block the EGF-induced up-regulation of N-cadherin and ZEB1. This study demonstrates that EGF induces SBOT cell migration and invasion by activating EMT, which involves the activation of the ERK1/2 and PI3K/Akt pathways and, subsequently, Snail, Slug and ZEB1 expression. Moreover, our results suggest that there are EMT-independent mechanisms that mediate the EGF-induced LGC cell migration and invasion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".