Epidermal Growth Factor-Induced GnRH-II Synthesis Contributes to Ovarian Cancer Cell Invasion
Bibliographic record
Abstract
GnRH-II modulates ovarian cancer cells invasion and is expressed in normal ovary and ovarian epithelial cancer cells; however, the upstream regulator(s) of GnRH-II expression in these cells remains unclear. We now demonstrate that epidermal growth factor (EGF) increases GnRH-II mRNA levels in several human ovarian carcinoma cell lines and up-regulates GnRH-II promoter activity in OVCAR-3 cells in a dose-dependent manner, whereas an EGF receptor inhibitor (AG148) abolishes EGF-induced increases in GnRH-II promoter activity and GnRH-II mRNA levels. EGF increases the phosphorylation of cAMP-responsive element-binding protein (p-CREB) and its association with the coregulator, CCAAT/enhancer binding protein beta, whereas blocking the EGF-induced ERK1/2 phosphorylation with MAPK inhibitors (PD98059/U0126) markedly reduced these effects. Moreover, depletion of CREB using small interfering RNA attenuated EGF-induced GnRH-II promoter activity. Chromatin immunoprecipitation assays demonstrated that EGF induces p-CREB binding to a cAMP responsive-element within the GnRH-II promoter, likely in association with CCAAT/enhancer binding protein beta, and mutagenesis of this cAMP responsive-element prevented EGF-induced GnRH-II promoter activity in OVCAR-3 cells. Importantly, GnRH-II acts additively with EGF to promote invasion of OVCAR-3 and CaOV-3 cells, but not SKOV-3 cells that express low levels of GnRH receptor (GnRHR). Treatment with GnRHR small interfering RNA also partially inhibited the EGF-induced invasion of OVCAR-3 and CaOV-3 cells. Furthermore, EGF treatment transiently increases GnRHR levels in OVCAR-3 and CaOV-3, which likely accentuates the effects of increase GnRH-II production on cell invasion. These results provide evidence that EGF is an upstream regulator of the autocrine actions of GnRH-II on the invasive properties of ovarian cancer cells.
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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".