Prognostic and clinicopathological value of Beclin-1 expression in hepatocellular carcinoma: a meta-analysis
Bibliographic record
Abstract
Abstract Background The abnormal expression of Beclin-1 has recently been investigated in a variety of tumors. However, previous studies have obtained contradicting results regarding the clinical and prognostic value of Beclin-1 in hepatocellular carcinoma (HCC). We performed a meta-analysis to clarify the prognostic value of Beclin-1 and its correlations with clinical pathological parameters in HCC. Methods Relevant studies were systematically retrieved from PubMed, EMBASE, China National Knowledge Infrastructure (CNKI), Wan Fang and Chinese VIP databases. We used the Newcastle-Ottawa scale (NOS) to estimate the quality of the involved studies. Results Ten eligible studies with 1086 HCC patients were included in this study. Our results showed that decreased Beclin-1 expression in HCC related to histological grade [poor-undifferentiated vs. well-moderate: odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.65–3.32, P < 0.00001]. The pooled hazard ratio (HR) (HR = 1.43, 95% CI = 1.17–1.75, P = 0.0004) indicated that decreased Beclin-1 expression correlated with poor overall survival (OS). Conclusions This meta-analysis indicated that decreased Beclin-1 expression might relate to poor differentiation and unfavorable outcome in HCC.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".