DN200434, an orally available inverse agonist of estrogen-related receptor γ, induces ferroptosis in sorafenib-resistant hepatocellular carcinoma
Bibliographic record
Abstract
Sorafenib, originally identified as an inhibitor of multiple oncogenic kinases, induces ferroptosis in hepatocellular carcinoma (HCC) cells. Several pathways that mitigate sorafenib-induced ferroptosis confer drug resistance; thus strategies that enhance ferroptosis increase sorafenib efficacy. Orphan nuclear receptor estrogen-related receptor γ (ERRγ) is upregulated in human HCC tissues and plays a role in cancer cell proliferation. The aim of this study was to determine whether inhibition of ERRγ with DN200434, an orally available inverse agonist, can overcome resistance to sorafenib through induction of ferroptosis. Sorafenib-resistant HCC cells were less sensitive to sorafenibinduced ferroptosis and showed significantly higher ERRγ levels than sorafenib-sensitive HCC cells. DN200434 induced lipid peroxidation and ferroptosis in sorafenib-resistant HCC cells. Mechanistically, DN200434 increased mitochondrial ROS generation by reducing glutathione/glutathione disulfide levels, which subsequently reduced mTOR activity and GPX4 levels. DN200434 induced amplification of the antitumor effects of sorafenib was confirmed in a tumor xenograft model. The present results indicate that DN200434 may be a novel therapeutic strategy to re-sensitize HCC cells to sorafenib. [BMB Reports 2022; 55(11): 547-552].
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) | 0.000 | 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".