Mechanism of gonadotropin-releasing hormone (GnRH)-I and -II-induced cell growth inhibition in ovarian cancer cells: role of the GnRH-I receptor and protein kinase C pathway
Bibliographic record
Abstract
In our previous studies, we demonstrated that ERK1/2 (extracellular signal-regulated protein kinase) and p38 MAPK (mitogen-activated protein kinase) are required for gonadotropin-releasing hormone (GnRH)-II-induced anti-proliferation of ovarian cancer cells. In the present study, we examined the role of the GnRH-I receptor, as well as the activation of protein kinase C (PKC), in the anti-proliferative effect induced by GnRH-I or II in ovarian cancer cells. Our results demonstrated that Antide, a GnRH-I antagonist, reversed the activation of ERK1/2 induced by GnRH-I or II and abolished the anti-proliferative effect of GnRH-I and II in ovarian cancer cells. Transfection of short-interfering RNA to abrogate the gene expression of the GnRH-I receptor reversed GnRH-I and II-induced anti-proliferation. These results indicate that GnRH-I or II induce anti-proliferation through the GnRH-I receptor in ovarian cancer cells. In addition, the activation of ERK1/2 by GnRH-I or II was mimicked by phorbol-12-myristate 13-acetate, a PKC activator. Pretreatment with GF109203X, an inhibitor of PKC, blocked GnRH-induced ERK1/2 activation and anti-proliferation. These results suggest that the activation of PKC is responsible for GnRH-induced ERK1/2 activation and anti-proliferation in ovarian cancer cells. Taken together, these results indicate that binding of GnRH-I and II to the GnRH-I receptor activates ERK1/2 through a PKC-dependent pathway and is essential for GnRH-induced anti-proliferation 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".