Gonadotropins and Ovarian Cancer
Bibliographic record
Abstract
Ovarian epithelial cancer (OEC) accounts for 90% of all ovarian cancers and is the leading cause of death from gynecological cancers in North America and Europe. Despite its clinical significance, the factors that regulate the development and progression of ovarian cancer are among the least understood of all major human malignancies. The two gonadotropins, FSH and LH, are key regulators of ovarian cell functions, and the potential role of gonadotropins in the pathogenesis of ovarian cancer is suggested. Ovarian carcinomas have been found to express specific receptors for gonadotropins. The presence of gonadotropins in ovarian tumor fluid suggests the importance of these factors in the transformation and progression of ovarian cancers as well as being prognostic indicators. Functionally, there is evidence showing a direct action of gonadotropins on ovarian tumor cell growth. This review summarizes the key findings and recent advances in our understanding of these peptide hormones in ovarian cancer development and progression and their role in potential future cancer therapy. We will first discuss the supporting evidence and controversies in the "gonadotropin theory" and the use of animal models for exploring the involvement of gonadotropins in the etiology of ovarian cancer. The role of gonadotropins in regulating the proliferation, survival, and metastasis of OEC is next summarized. Relevant data from ovarian surface epithelium, which is widely believed to be the precursor of OEC, are also described. Finally, we will discuss the clinical applications of gonadotropins in ovarian cancer and the recent progress in drug development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".