Gonadotropin‐releasing hormone and ovarian cancer: a functional and mechanistic overview
Bibliographic record
Abstract
The hypothalamic decapeptide gonadotropin-releasing hormone (GnRH) is well known for its role in the control of pituitary gonadotropin secretion, but the hormone and receptor are also expressed in extrapituitary tissues and tumor cells, including epithelial ovarian cancers. It is hypothesized that they may function as a local autocrine regulatory system in nonpituitary contexts. Numerous studies have demonstrated a direct antiproliferative effect on ovarian cancer cell lines of GnRH and its synthetic analogs. This effect appears to be attributable to multiple steps in the GnRH signaling cascade, such as cell cycle arrest at G(0)/G(1). In contrast to GnRH signaling in pituitary gonadotropes, the involvement of G(alpha q), protein kinase C and mitogen-activated protein kinases is less apparent in neoplastic cells. Instead, in ovarian cancer cells, GnRH receptors appear to couple to the pertussis toxin-sensitive protein G(alpha i), leading to the activation of protein phosphatase, which in turn interferes with growth factor-induced mitogenic signals. Apoptotic involvement is still controversial, although GnRH analogs have been shown to protect cancer cells from doxorubicin-induced apoptosis. Recently, data supporting a regulatory role of GnRH analogs in ovarian cancer cell migration/invasion have started to emerge. In this minireview, we summarize the current understanding of the antiproliferative actions of GnRH analogs, as well as the recent observations of GnRH effects on ovarian cancer cell apoptosis and motogenesis. The molecular mechanisms that mediate GnRH actions and the clinical applications of GnRH analogs in ovarian cancer patients are also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".