SBEM protein expression correlates with estrogen receptor and tumor grade in breast cancer.
Bibliographic record
Abstract
3282 We recently identified small breast epithelial mucin (SBEM) as a novel 90 amino-acid protein, the expression of which appears to be highly breast-specific. Structurally, this protein contains tandem repeats of the octapeptide neutral core sequence TTAAXTTA, that is characteristic of the large family of glycoproteins known as mucins. Since the epithelial surface mucin, MUC1, a member of this family, is abnormally glycosylated and its expression is up-regulated in breast cancer, we were interested in examining protein expression of SBEM in breast cancer cell lines, normal breast tissue and breast tumors. Western blot analysis of eight different breast cancer cell lines indicated variable SBEM protein expression. The MDA-231 and BT-20 cell lines exhibited high expression of SBEM protein, while the T5 and MCF-7 cell lines expressed SBEM at lower levels. In normal mammary tissue, SBEM was present, but there was a low level of expression. 88 breast tumors were analyzed by Western blot, and were classified as being SBEM-positive (n = 64) or SBEM-negative (n = 24). When compared to ER levels, SBEM positive tumors demonstrated lower ER levels (22.5fmol/mg) than SBEM negative tumors (63.0fmol/mg). There was also a positive correlation between SBEM level and tumor grade (P = 0.02). No correlation between SBEM and PR levels was observed (P = 0.71). All other normal tissues studied were negative for SBEM protein. These data suggest that a higher expression of the SBEM protein could help monitor breast cancer progression.
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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 | 0.001 | 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".