Gonadotropin-Releasing Hormone-II Increases Membrane Type I Metalloproteinase Production via β-Catenin Signaling in Ovarian Cancer Cells
Bibliographic record
Abstract
GnRH-II is produced by ovarian cancer cells and enhances their invasiveness in vitro. In our studies of OVCAR-3 and CaOV-3 ovarian cancer cell lines, GnRH-II treatment induced phosphorylation of Akt and glycogen synthase kinase (GSK)3β, as well as β-catenin accumulation in the nucleus, and the latter was reduced by small interfering RNA (siRNA)-mediated depletion of the GnRH receptor. The phosphatidylinositol 3 kinase (PI3K)/Akt pathway is involved in β-catenin-dependent signaling, and pretreatment of these human ovarian cancer cells with a PI3K/Akt inhibitor, LY294002, attenuated GnRH-II-stimulated phosphorylation of GSK3β and inhibited GnRH-II-induced invasion. It also attenuated GnRH-II induced trans-activation of a β-catenin-dependent reporter gene, most likely because GSK3β phosphorylation promotes translocation of β-catenin to the nucleus. Membrane type I matrix metalloproteinase (MT1-MMP) contributes to tumor progression directly, or by processing the latent MMP-2 zymogen, and is a known target of β-catenin signaling. When OVCAR-3 and CaOV-3 cells were treated with GnRH-II, MT1-MMP levels increased approximately 3-fold, whereas siRNA-mediated depletion of GnRH receptor or pretreatment with LY294002 abrogated this. In addition, lithium chloride, which increases GSK3β phosphorylation and the nuclear translocation of β-catenin, increased MT1-MMP levels in these ovarian cancer cells. By contrast, depletion of β-catenin by siRNA treatment abolished GnRH-II-induced MT1-MMP synthesis and reduced their invasive potential. Furthermore, siRNA-mediated reduction of MT1-MMP levels reduced GnRH-II-induced invasion in ovarian cancer cells. We therefore conclude that GnRH-II stimulates the PI3K/Akt pathway, and the phosphorylation of GSK3β, thereby enhancing the β-catenin-dependent up-regulation of MT1-MMP production, which contributes to ovarian cancer metastasis.
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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".