Lysophosphatidic Acid Induction of Vascular Endothelial Growth Factor Expression in Human Ovarian Cancer Cells
Bibliographic record
Abstract
BACKGROUND: Lysophosphatidic acid (LPA) stimulates ovarian tumor growth at concentrations present in ascitic fluid. Vascular endothelial growth factor (VEGF) stimulates angiogenesis and plays a pivotal role in the formation of ovarian cancer-associated ascites. We examined whether LPA promotes ovarian tumor growth by increasing angiogenesis via VEGF. METHODS: VEGF expression was examined in a simian virus 40 T-antigen-immortalized ovarian surface epithelial cell line (IOSE-29) and in ovarian cancer cell lines (OVCAR-3, SKOV-3, and CAOV-3) treated with LPA. VEGF promoter activity was measured in OVCAR-3 cells after transfection or cotransfection with c-Fos and c-Jun, components of AP1 transcription factor, potential binding sites for which are present in the VEGF promoter. The expression of the LPA receptors Edg2 and Edg4 was also assessed. All statistical tests were two-sided. RESULTS: LPA treatment increased steady-state VEGF messenger RNA (mRNA) levels in OVCAR-3 cells in a time- and dose-dependent fashion and stimulated VEGF promoter activity without prolonging mRNA half-life in these cells, but LPA had little effect on IOSE-29 cells. Forced overexpression of c-Jun and c-Fos in OVCAR-3 cells stimulated VEGF promoter activity fourfold. LPA also elevated VEGF protein levels by 1.5-fold in SKOV-3 cells (P =.0148), 1.9-fold in CAOV-3 cells (P<.001), and threefold in OVCAR-3 cells (P<.0001). Both Edg2 and Edg4 were detected in ovarian cancer cells; however, only Edg2 was present in normal ovarian surface epithelial cells and IOSE-29 cells. CONCLUSIONS: LPA stimulates ovarian tumor growth, at least in part, via induction of VEGF expression through transcriptional activation. However, this LPA response is not evident in normal ovarian surface epithelial cells. Our data suggest that Edg4, but not Edg2, plays a role in LPA stimulation of ovarian tumor growth.
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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".