Estrogen receptor alpha pathway is involved in leptin-induced ovarian cancer cell growth
Bibliographic record
Abstract
Previously, we demonstrated that leptin, a pleiotropic hormone produced by adipocytes, stimulates the growth of BG-1 ovarian cancer cells via the extracellular signal-regulated kinase signaling pathway. In this study, we further investigated the involvement of estrogen receptor (ER) pathway in the mechanism of leptin-induced ovarian cancer cell growth. Treatment with leptin (100 ng/ml) resulted in a significant increase in the cell growth of ERα-transfected OVCAR-3 and A2780 cells, whereas no significant difference was observed in ERβ-transfected cells. Downregulation of ERα using small interfering RNA completely reversed leptin-induced growth of BG-1 cells. Treatment with leptin resulted in ER transcriptional activation, i.e. nuclear localization of ER and increased expression of pS2, an estrogen-dependent gene. Luciferase reporter assay revealed that treatment of BG-1 cells with leptin (100 ng/ml) stimulated the expression of the reporter gene in the absence of estradiol (E2). To examine an involvement of Janus kinase 2/signal transducers and activators of transcription 3 (STAT-3) and phosphatidyl-inositol 3-kinase (PI3K)/Akt in leptin-induced pathway, we demonstrated that leptin increased phosphorylation of STAT-3 and Akt in BG-1 cells in a time- and dose-dependent manner. On the other hand, leptin-induced cell growth and ER transactivation were effectively blocked by specific STAT-3 inhibitor AG490 and, to a lesser extent, by PI3K inhibition. Further study with coimmunoprecipitation assay revealed that stimulation with leptin induced STAT-3 binding to ERα. Taken together, these results indicate that the stimulation of ovarian cancer cell growth by leptin involves, at least in part, ER transcriptional activation via the STAT-3 signaling 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) | 0.002 | 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".