Overexpression of Follicle-Stimulating Hormone Receptor Activates Oncogenic Pathways in Preneoplastic Ovarian Surface Epithelial Cells
Bibliographic record
Abstract
It has been previously demonstrated that human ovarian cancer cells express FSH receptor (FSHR). However, whether FSHR plays a role in ovarian cancer development is still ambiguous. To investigate the role of FSHR in tumor progression, we overexpressed the receptor in SV40 Tag immortalized ovarian surface epithelium (OSE) cell lines (IOSE-80PC, a postcrisis line, and IOSE-398), which are preneoplastic and nontumorigenic. We compared the expression levels of several selected oncogenes in nontransfected (80PC), vector-transfected (80PCV), FSHR-transfected IOSE (80PCF) cells, and established ovarian cancer cell lines (OVCAR-3 and SKOV-3). Significantly increased protein levels of epithelial growth factor receptor, HER-2/neu, and c-Myc, but not K-Ras, were observed in FSHR-overexpressing 80PCF cells when compared with 80PCV cells. Constitutive phosphorylation of ERK1/2 was augmented in 80PCF cells, whereas phosphorylation of the other MAPK including p38 and Jun N-terminal kinase was unchanged. Considerable constitutive phosphorylation of ERK1/2 was also observed in OVCAR-3 and SKOV-3 cell lines when compared with 80PCV. More importantly, 80PCF cells grew more rapidly than 80PC and 80PCV cells. In conclusion, we have demonstrated that FSHR was highly expressed in OVCAR-3 and 80PCF cells transfected with FSHR overexpression vector. The 80PCF cell line showed increased levels of epithelial growth factor receptor, HER-2/neu, and c-myc and constitutive activation of ERK1/2. The rate of proliferation of the 80PCF cells was increased, compared with control cell lines. These results suggest that the overexpression of FSHR may be associated with enhanced levels of potential oncogenic pathways and increased proliferation in preneoplastic ovarian surface epithelial cells.
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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".