Role of Beclin1 expression in patients with hepatocellular carcinoma: a meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Beclin1 has been reported as a vital marker for a number of malignant tumors. However, the role of Beclin1 in hepatocellular carcinoma (HCC) remains inconclusive. Thus, we conducted a meta-analysis to assess the correlation between Beclin1 and its clinicopathological and prognostic values in HCC. METHODS: PubMed, Cochrane Library, Web of Science, EMBASE, Chinese CNKI, and Chinese WanFang databases were searched for published articles on Beclin1 expression in hepatocellular tissues. Standard-compliant articles were screened using the Newcastle-Ottawa Scale for strict quality control of the literature. The correlation of Beclin1 expression with the clinicopathological features and survival outcomes was analyzed. Pooled odds ratios and hazard ratios with 95% confidence intervals were calculated using STATA14.2. RESULTS: Eleven articles with 1,279 patients were included in this meta-analysis. Positive Beclin1 expression was found to be correlated with alpha fetoprotein, liver cirrhosis, and vascular invasion, but not with gender, age, HBsAg, size of tumor, number of tumors, differentiation, and TNM stage. Positive Beclin1 expression was also associated with favorable 5-year overall survival and disease-free survival rates. CONCLUSION: Our meta-analysis indicated that positive Beclin1 expression was negatively related to alpha fetoprotein, liver cirrhosis, and vascular invasion in HCC. Beclin1 could be used as a prognostic biomarker for 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".