Antagonists of Growth Hormone-Releasing Hormone and Somatostatin Analog RC-160 Inhibit the Growth of the OV-1063 Human Epithelial Ovarian Cancer Cell Line Xenografted into Nude Mice1
Bibliographic record
Abstract
The effects of antagonists of GHRH and the somatostatin analog RC-160 on the growth of OV-1063 human epithelial ovarian cancer cells xenografted into nude mice were investigated. Treatment with 20 microg/day of the GHRH antagonist JV-1-36 or MZ-5-156 and 60 microg/day of the somatostatin analog RC-160 for 25 days decreased tumor volume by 70.9% (P < 0.01), 58.3% (P < 0.05), and 60.6% (P < 0.01), respectively, vs. the control value. The levels of GH in serum were decreased in all of the treated groups, but only RC-160 significantly reduced serum insulin-like growth factor I (IGF-I). The levels of messenger ribonucleic acid (mRNA) for IGF-I and -II and for their receptors in OV-1063 tumors were investigated by multiplex RT-PCR. No expression of mRNA for IGF-I was detected, but treatment with JV-1-136 caused a 51.8% decrease (P < 0.05) in the level of mRNA for IGF-II in tumors. Exposure of OV-1063 cells cultured in vitro to GHRH, IGF-I, or IGF-II significantly (P < 0.05) stimulated cell growth, but 10(-5) mol/L JV-1-36 nearly completely inhibited (P < 0.001) OV-1063 cell proliferation. OV-1063 tumors expressed mRNA for GHRH receptors and showed the presence of binding sites for GHRH. Our results indicate that antagonistic analogs of GHRH and the somatostatin analog RC-160 inhibit the growth of epithelial ovarian cancers. The effects of RC-160 seem to be exerted more on the pituitary GH-hepatic IGF-I axis, whereas GHRH antagonists appear to reduce IGF-II production and interfere with the autocrine regulatory pathway. The antitumorigenic action of GHRH antagonists appears to be mediated by GHRH receptors found in OV-1063 tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".