Prognostic Value and Clinicopathological Differences of Bmi1 in Gastric Cancer: A Meta-analysis
Bibliographic record
Abstract
B cell-specific Moloney murine leukemia virus integration site 1 (Bmi1) was identified as a biomarker of cancer stem cells, and over-expression of Bmi1 might enhance tumor aggressive clinical behavior in gastric cancer (GC). Our aim of this meta-analysis is to investigate the prognostic role and clinicopathological differences of Bmi1 in GC patients. A total of 6 studies up to September 2014 were included in our study. Our results showed that there were no relationships between Bmi1 expression and the gender (pooled OR=0.87, 95%CI=0.66-1.14, P=0.319, fixed effect), age (pooled OR=1.22, 95%CI=0.95-1.59, P=0.126, fixed effect) and differentiation (pooled OR=1.15, 95%CI=0.71-1.86, P=0.582, random effect) in GC patients. But high Bmi1 expression was significantly correlated with the clinical stage (pooled OR=3.04, 95%CI=1.31-7.07, P=0.010, random effect), tumor size (pooled OR=2.01, 95%CI=1.14-3.55, P=0.016, random effect), T classification (pooled OR=2.79, 95%CI=1.94-4.03, P<0.001, fixed effect), lymph node metastasis (pooled OR=2.24, 95%CI=1.47-3.39, P<0.001, random effect) and distant metastasis (pooled OR=5.05, 95%CI=1.29-19.70, P=0.020, random effect), and led to a poor overall survival (OS) in GC patients (RR=3.38, 95%CI=2.43-4.69, P<0.001, fixed effect). These findings suggested that Bmi1 might serve as a novel and effective prognostic biomarker in GC, and could be a promising emerging molecular target in GC therapy.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.038 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".