Non-classical estrogen signaling in ovarian cancer improves chemo-sensitivity and patients outcome
Bibliographic record
Abstract
Deficiency in homologous recombination repair (HRR) is frequently associated with hormone-responsive cancers, especially the epithelial ovarian cancer (EOC) which shows defects of HRR in up to half of cases.However, whether there are molecular connections between estrogen signaling and HRR deficiency in EOC remains unknown.Methods: We analyzed the estrogen receptor α (ERα) binding profile in EOC cell lines and investigated its association with genome instability, HRR deficiency and sensitivity to chemotherapy using extensive public datasets and in vitro/in vivo experiments. Results:We found an inverse correlation between estrogen signaling and HRR activity in EOC, and the genome-wide collaboration between ERα and the co-repressor CtBP.Though the non-classical AP-1-mediated ERα signaling, their targets were highly enriched by HRR genes.We found that depleting ERα in EOC cells up-regulates HRR activity and HRR gene expression.Consequently, estrogen signaling enhances the sensitivity of ovarian cancer cells to chemotherapy agents in vitro and in vivo.Large-scale analyses further indicate that estrogen replacement and ESR1 expression are associated with chemo-sensitivity and the favorable survival of EOC patients.Conclusion: These findings characterize a novel role of ERα in mediating the molecular connection between hormone and HRR in EOC and encourage hormone replacement therapy for EOC patients.
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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 | 0.001 | 0.001 |
| 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".