Expression level of <i>beta protein 1</i> mRNA in Chinese breast cancer patients: A potential molecular marker for poor prognosis
Bibliographic record
Abstract
Recent studies revealed high ectopic beta protein 1 (BP1) expression in breast cancer. Remarkably, up to 100% (18/18) of estrogen receptor (ER)-negative tumors and 89% (25/28) of tumors from African American women were BP1-positive. However, the role of BP1 in breast cancer development and its clinical significance still has not been well defined. In the present study, we analyzed the quantitative level of BP1 mRNA in breast carcinomas using real-time polymerase chain reaction and aimed to elucidate its association with tumor characteristics and patient prognosis. Our data showed that BP1 mRNA was expressed at significantly higher levels in tumors with lymph node metastasis, with a high histological grade, and in those that were of ER-negative status. Furthermore, overexpression of BP1 was significantly associated with poor outcome of patients harboring tumors with a high histological grade and negative ER. Using both in vitro and in vivo systems, we also showed that the transcript level of BP1 was positively correlated to the growth rate of breast tumor cells. Taken together, our results support the notion that BP1 might contribute to breast neoplastic transformation or tumor progression and suggest for the first time that BP1 mRNA level has potential as a prognostic predictor for 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 | 0.001 |
| 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".