Gonadotropins Activate Proteolysis and Increase Invasion through Protein Kinase A and Phosphatidylinositol 3-Kinase Pathways in Human Epithelial Ovarian Cancer Cells
Bibliographic record
Abstract
Despite evidence that gonadotropins may facilitate peritoneal metastasis of ovarian cancer by increasing cell adhesion, the action and molecular mechanism of follicle-stimulating hormone (FSH) and luteinizing hormone (LH) in ovarian cancer invasion is not well characterized. In the present study, we investigated the effects of FSH and LH on the invasive activity and the expression of metastasis-related proteinases in human epithelial ovarian cancer by Western blot, zymography, reverse transcription-PCR (RT-PCR), ELISA, and Boyden chamber assay. Treatment with FSH or LH (10, 100, or 1,000 ng/mL) significantly increased the invasion of ovarian cancer cell lines, including BG-1, CaOV-3, and SKOV-3 cells but not OVCAR-3 cells. In addition, treatment of SKOV-3 cells with FSH or LH (100 or 1,000 ng/mL) enhanced the expression and activation of matrix metalloproteinases (MMP-2 and MMP-9) as shown by RT-PCR, gelatin zymography, and ELISA. Pretreatment with [(2R)-2-(hydroxamido-carbonylmethyl)-4-methylpentanoyl]-l-tryptophan methylamide (10 micromol/L), a total MMP inhibitor, and 3-(4-phenoxyphenylsulfonyl)-propylthiirane (20 micromol/L), a specific gelatinase inhibitor, neutralized the proinvasive effect of gonadotropins in SKOV-3 cells. In addition, the secretion of tissue inhibitor of metalloproteinases (TIMP-1 and TIMP-2) and plasminogen activator inhibitor-1 was significantly decreased by FSH and LH (100 or 1,000 ng/mL). We further showed that gonadotropins induced an increase in SKOV-3 invasiveness via the activation of protein kinase A (PKA) and phosphatidylinositol 3-kinase (PI3K) signaling pathways. Taken together, these results suggest that gonadotropins may contribute to ovarian cancer metastasis via activation of proteolysis and increase in invasion through the PKA and PI3K pathways.
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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".