Chelerythrine induces apoptosis and ferroptosis through Nrf2 in ovarian cancercells
Bibliographic record
Abstract
Ovarian cancer is a prevalent malignancy in the female reproductive system, representing a significantly fatal and incurable tumor. Chelerythrine (CHE), a natural benzopyridine alkaloid, has demonstrated a broad spectrum of anticancer activities. Nevertheless, the ovarian cancer inhibitory impact of CHE remains unclear. In this study, we investigated the cytotoxic mechanism and potential targets of CHE on in vitro cultures of A2780 and SKOV3 cells derived from ovarian cancer. Additionally, in vivo experiments were conducted to confirm the suppressive impact of CHE on tumor growth in nude mice. The findings revealed that CHE impeded the growth of A2780 and SKOV3 cells in a concentration-time-dependent manner and significantly suppressed the development of tumors in nude mice. CHE elevated the level of oxidative stress in tumor cells, prompted cell cycle halt in the S phase, and increased their mitochondrial membrane potential. Western blotting results demonstrated that CHE could modulate the expression of proteins associated with apoptotic and ferroptosis processes in A2780 and SKOV3 cells. Nrf2 was verified to be an upstream key target mediating the inhibitory impact of CHE on ovarian cancer cells. In summary, CHE exerts its anti-cancer effects on ovarian cancer by modulating Nrf2, inhibiting cellular proliferation, and promoting apoptosis and ferroptosis.
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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".