Pigment Epithelium-Derived Factor Is Estrogen Sensitive and Inhibits the Growth of Human Ovarian Cancer and Ovarian Surface Epithelial Cells
Bibliographic record
Abstract
Epithelial ovarian carcinoma is the most lethal gynecological cancer. However, little is known about the molecular mechanisms underlying the disease development and progression. In this study, we found that the expression of pigment epithelium-derived factor (PEDF) was greatly reduced in ovarian tumors and in ovarian cancer cell lines when compared with their normal precursor, ovarian surface epithelium (OSE). In addition, we showed that exogenous PEDF inhibited the growth of cultured human OSE as well as ovarian cancer cell lines, whereas targeted inhibition of endogenous PEDF using small interfering RNA or neutralizing PEDF antibody promoted the growth of these cells, confirming that the growth-inhibitory effect was PEDF specific. We also report for the first time that estrogen is an important upstream regulator of PEDF in human OSE. Treatment of the cultured cells with 17 beta-estradiol (E2) inhibited the expression of PEDF protein and mRNA in a dose- and time-dependent manner, which could be reversed by the specific estrogen receptor antagonist, ICI 182,780, indicating that the regulation was estrogen receptor-mediated. We further showed that this down-regulation of PEDF gene transcription was a direct, primary effect of E2. E2 promoted OSE and ovarian cancer cell growth, whereas simultaneous treatment with E2 and PEDF abrogated the estrogenic growth stimulation of these cells. This study is the first to demonstrate a role of PEDF in OSE biology and ovarian cancer and suggests that the loss of PEDF may e of relevance in carcinogenesis.
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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".