Oncostatin M suppresses oestrogen receptor-α expression and is associated with poor outcome in human breast cancer
Bibliographic record
Abstract
The most important clinical biomarker for breast cancer management is oestrogen receptor alpha (ERα). Tumours that express ER are candidates for endocrine therapy and are biologically less aggressive, while ER-negative tumours are largely treated with conventional chemotherapy and have a poor prognosis. Despite its significance, the mechanisms regulating ER expression are poorly understood. We hypothesised that the inflammatory cytokine oncostatin M (OSM) can downregulate ER expression in breast cancer. Recombinant OSM potently suppressed ER protein and mRNA expression in vitro in a dose- and time-dependent manner in two human ER+ breast cancer cell lines, MCF7 and T47D. This was dependent on the expression of OSM receptor beta (OSMRβ) and could be blocked by inhibition of the MEKK1/2 mitogen-activated protein kinases. ER loss was also necessary for maximal OSM-induced signal transduction and migratory activity. In vivo, high expression of OSM and OSMR mRNA (determined by RT-PCR) was associated with reduced ER (P<0.01) and progesterone receptor (P<0.05) protein levels in a cohort of 70 invasive breast cancers. High OSM and OSMR mRNA expression was also associated with low expression of ESR1 (ER, P<0.0001) and ER-regulated genes in a previously published breast cancer gene expression dataset (n=321 cases). In the latter cohort, high OSMR expression was associated with shorter recurrence-free and overall survival in univariate (P<0.0001) and multivariate (P=0.022) analyses. OSM signalling may be a novel factor causing suppression of ER and disease progression in breast cancer.
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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".