Mifepristone increases mRNA translation rate, triggers the unfolded protein response, increases autophagic flux, and kills ovarian cancer cells in combination with proteasome or lysosome inhibitors
Bibliographic record
Abstract
The synthetic steroid mifepristone blocks the growth of ovarian cancer cells, yet the mechanism driving such effect is not entirely understood. Unbiased genomic and proteomic screenings using ovarian cancer cell lines of different genetic backgrounds and sensitivities to platinum led to the identification of two key genes upregulated by mifepristone and involved in the unfolded protein response (UPR): the master chaperone of the endoplasmic reticulum (ER), glucose regulated protein (GRP) of 78 kDa, and the CCAAT/enhancer binding protein homologous transcription factor (CHOP). GRP78 and CHOP were upregulated by mifepristone in ovarian cancer cells regardless of p53 status and platinum sensitivity. Further studies revealed that the three UPR-associated pathways, PERK, IRE1α, and ATF6, were activated by mifepristone. Also, the synthetic steroid acutely increased mRNA translation rate, which, if prevented, abrogated the splicing of XBP1 mRNA, a non-translatable readout of IRE1α activation. Moreover, mifepristone increased LC3-II levels due to increased autophagic flux. When the autophagic-lysosomal pathway was inhibited with chloroquine, mifepristone was lethal to the cells. Lastly, doses of proteasome inhibitors that are inadequate to block the activity of the proteasomes, caused cell death when combined with mifepristone; this phenotype was accompanied by accumulation of poly-ubiquitinated proteins denoting proteasome inhibition. The stimulation by mifepristone of ER stress and autophagic flux offers a therapeutic opportunity for utilizing this compound to sensitize ovarian cancer cells to proteasome or lysosome inhibitors.
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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".