Lapatinib Inhibits Amphiregulin-induced BeWo Choriocarcinoma Cell Proliferation by Reducing ERK1/2 and AKT Signaling Pathways
Bibliographic record
Abstract
BACKGROUND: Human choriocarcinoma is the most aggressive type of gestational trophoblastic neoplasia. The expression of epidermal growth factor receptor (EGFR) in choriocarcinomas is significantly higher than those of trophoblastic cells in healthy placentas. Lapatinib is a potent EGFR and human epidermal growth factor receptor 2 (HER2) inhibitor that inhibits cell proliferation and induces apoptosis in various human cancer cells. Amphiregulin (AREG) is the most abundant EGFR ligand in amniotic fluid during human pregnancy. AIM: To explore the role of AREG in human choriocarcinoma cell proliferation. MATERIALS AND METHODS: The effect of lapatinib and AREG on cell proliferation was examined by the MTT assay. Western blots were used to investigate EGFR and HER2 expression, and the activation of caspase-3, extracellular signal-regulated kinases 1/2 (ERK1/2) and phosphatidylinositol 3-kinase /protein kinase B (PI3K/AKT) signaling pathways. RESULTS: Treatment with lapatinib reduced BeWo cell proliferation by inducing apoptosis. Moreover, AREG treatment stimulated BeWo cell proliferation by activating ERK1/2 and PI3K/AKT signaling pathways, which was blocked by lapatinib. CONCLUSION: Targeting EGFR/HER2 might be a useful therapeutic strategy for human choriocarcinoma.
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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) | 0.002 | 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".