HOTAIR and its surrogate DNA methylation signature indicate carboplatin resistance in ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Understanding carboplatin resistance in ovarian cancer is critical for the improvement of patients' lives. Multipotent mesenchymal stem cells or an aggravated epithelial to mesenchymal transition phenotype of a cancer are integrally involved in pathways conferring chemo-resistance. Long non-coding RNA HOTAIR (HOX transcript antisense intergenic RNA) is involved in mesenchymal stem cell fate and cancer biology. METHODS: We analyzed HOTAIR expression and associated surrogate DNA methylation (DNAme) in 134 primary ovarian cancer cases (63 received carboplatin, 55 received cisplatin and 16 no chemotherapy). We validated our findings by HOTAIR expression and DNAme analysis in a multicentre setting of five additional sets, encompassing 946 ovarian cancers. Chemo-sensitivity has been assessed in cell culture experiments. RESULTS: HOTAIR expression was significantly associated with poor survival in carboplatin-treated patients with adjusted hazard ratios for death of 3.64 (95 % confidence interval [CI] 1.78-7.42; P < 0.001) in the discovery and 1.63 (95 % CI 1.04-2.56; P = 0.032) in the validation set. This effect was not seen in patients who did not receive carboplatin (0.97 [95 % CI 0.52-1.80; P = 0.932]). HOTAIR expression or its surrogate DNAme signature predicted poor outcome in all additional sets of carboplatin-treated ovarian cancer patients while HOTAIR expressors responded preferentially to cisplatin (multivariate interaction P = 0.008). CONCLUSIONS: Non-coding RNA HOTAIR or its more stable DNAme surrogate may indicate the presence of a subset of cells which confer resistance to carboplatin and can serve as (1) a marker to personalise treatment and (2) a novel target to overcome carboplatin resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".