Synergistic Effects of Epidermal Growth Factor and Hepatocyte Growth Factor on Human Ovarian Cancer Cell Invasion and Migration: Role of Extracellular Signal-Regulated Kinase 1/2 and p38 Mitogen-Activated Protein Kinase
Bibliographic record
Abstract
Ovarian cancer is the primary cause of death from gynecological malignancies with a poor prognosis characterized by widespread peritoneal dissemination. However, mechanisms of invasion and metastasis in ovarian cancer remain poorly understood. Epidermal growth factor (EGF) and hepatocyte growth factor (HGF) are often both overexpressed and contribute to the growth of ovarian cancer by activating autocrine pathways. In the present study, we investigated the mechanisms of invasive activity of EGF, HGF, and their synergistic effects in human ovarian cancer cells. Here our data suggest that EGF and HGF may use unique and overlapping signaling cascades leading to the invasive phenotype. We revealed that HGF-mediated cell migration and invasion required the coordinate activation of the phosphatidylinositol 3-kinase/Akt and extracellular signal-regulated kinase 1/2. Although EGF-dependent invasive phenotype appeared to have similar requirements for phosphatidylinositol 3-kinase, this growth factor used the alternative p38 MAPK pathway for cell invasion. A significant role of p38 MAPK was further supported by the observation that expression of dominant negative p38 MAPK likewise inhibited EGF-dependent invasiveness and cell motility. We also showed that EGF cooperated with HGF to promote a highly invasive phenotype via the increased secretion of matrix metalloproteinase (MMP)-9. The coincident induction of MMP-9 was functionally significant because inclusion of MMP-9 inhibitor or an anti-MMP-9 neutralizing antibody abolished EGF- and HGF-induced cellular invasion. These findings provide insights into the mechanism of the malignant progression of ovarian cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".