Inhibin Resistance Is Associated with Aggressive Tumorigenicity of Ovarian Cancer Cells
Bibliographic record
Abstract
Malignant ovarian epithelial tumors have been shown to have decreased inhibin production relative to activin production compared with normal ovarian surface epithelial (OSE) cells and nonmalignant ovarian tumors. Activin stimulates proliferation of many ovarian cancer cell lines. Inhibin antagonizes the action of activin, and inhibin-deficient mice develop gonadal tumors, suggesting that inhibin may be a tumor suppressor. However, its effects on OSE and ovarian cancer cells are unknown. We hypothesize that activin and inhibin are important regulators of biological activity in ovarian cancers. We found that inhibin A decreased murine OSE proliferation, whereas activin A had no effect. Activin A increased the proliferation of four of eight ovarian cancer cell lines (SKOV3, OCC1, OVCAR3, and A2780-s). Inhibin A decreased the proliferation of SKOV3, A2780-s, and OVCAR3 but had no effect on OCC1, ES-2, HEY, A2780-cp, and OVCA429 cells. When injected into nude mice, the inhibin-resistant cancer cell lines resulted in shorter survival time compared with the inhibin-responsive cells. Further investigations on SKOV3 and OCC1 cells showed that activin A increased invasion through Matrigel. Inhibin A decreased both basal and activin-induced proliferation and invasion of SKOV3 but had no effect on OCC1 cells. Reverse transcription-PCR analyses showed that the SKOV3 and OCC1 cells produced activin, but only SKOV3 produced inhibin. Analysis of the activin/inhibin signaling pathways indicated that Smad anchor for receptor activation was elevated in SKOV3 and OCC1 cells and that an up-regulation of the activin receptor expression may explain the inhibin resistance of OCC1 cells. Our results suggest that activin responsiveness may be gained during transformation of OSE cells and that inhibin resistance may contribute to the aggressive behavior of ovarian cancer cells.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".