Targeting Gonadotropin-releasing Hormone Receptor Inhibits the Early Step of Ovarian Cancer Metastasis by Modulating Tumor-mesothelial Adhesion
Bibliographic record
Abstract
Ovarian cancer has a clear predilection to metastasize to the peritoneum, which represents one of the most important prognostic factors of poor clinical outcome. Gonadotropin-releasing hormone (GnRH) receptor is significantly overexpressed during the malignant progression of human ovarian cancer. Here, using lentiviral-based small interfering RNA (siRNA) technology to downregulate GnRH receptor in metastatic ovarian cancer cells, we show that GnRH receptor is an important mediator of ovarian cancer peritoneal metastasis. GnRH receptor downregulation dramatically attenuated their adhesion to the peritoneal mesothelium. By inhibiting the expression of GnRH receptor, we showed decreased expression of α2β1 and α5β1 integrin and adhesion to specific extracellular matrix (ECM) proteins. This was also associated with a reduction of P-cadherin. Furthermore, adhesion of ovarian cancer cells to different ECMs and the mesothelium were abrogated in response to β1 integrin and P-cadherin reduction, confirming that the effects were β1 integrin- and P-cadherin-specific. Using a mouse model of human ovarian cancer metastasis, we found that the inhibition of GnRH receptor, β1 integrin, and P-cadherin significantly attenuated tumor growth, ascites formation, and the number of metastatic implants. These results define a new role for GnRH receptor in early metastasis and offer the possibility of novel therapeutic targets.
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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 | 0.001 |
| 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".