Expression of small breast epithelial mucin (SBEM) protein in tissue microarrays (TMAs) of primary invasive breast cancers
Bibliographic record
Abstract
AIMS: Small breast epithelial mucin (SBEM) is a recently described gene product that shows promise as a new breast biomarker. The aim was to investigate for the first time SBEM protein expression in a large cohort (n = 300) of invasive breast cancers, its relationship to established clinical variables and its association with clinical outcome. METHODS AND RESULTS: Immunohistochemical analysis was performed on tissue microarrays consisting of 149 oestrogen receptor (ER) alpha- and 151 ERalpha+ breast cancers. Overall, 18% of tumours were SBEM+ (n = 53/300). However, SBEM protein was more frequently observed in ER- (22%) than in ER+ cancers (13%; P = 0.049). A significant association with psoriasin/S100A7 expression (P < or = 0.0001) was observed in the entire cohort. SBEM was also positively associated with HER-2 (P = 0.046) in ER- cancers, and increased levels of SBEM were strongly associated with higher tumour grade (P = 0.0015). Furthermore, SBEM expression showed a trend towards an association with reduced overall survival and relapse-free survival in the ER+ cohort (P = 0.063 and P = 0.072, respectively). CONCLUSIONS: Our results suggest that SBEM may identify a unique subset of breast cancers with poor prognosis and may have future implications for therapeutic management of this disease.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".