Clinicopathological and Prognostic Roles of the Expression Levels of the Programmed Cell Death-1 Gene in Patients with Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Multiple studies have explored the prognostic role and clinical significance of the expression of the programmed cell death-1 ( PD-1 ) gene in hepatocellular carcinoma (HCC). However, the results have been inconsistent. This study evaluated PD-1 expression and its clinical significance in patients with HCC, as well as the correlation between HCC pathological features and prognoses. Methods: All related research in PubMed, Embase, and Web of Science prior to October 31, 2019, was retrieved. The Newcastle-Ottawa Scale was used to evaluate the quality of the literature. Stata 14.0 statistical software was used to analyze the data, and the correlations between PD-1 expression and the clinicopathological characteristics of patients were analyzed using the odds ratio (OR) and its 95% confidence interval (CI). The hazard ratio (HR) and its 95% CI were used to analyze the correlation between PD-1 high expression and patient prognosis. Begg's test was used to evaluate publication bias. Results: A total of 581 patients were analyzed in the six studies included in the meta-analysis. Pooled analysis revealed that high levels of PD-1 expression did not correlate with overall survival (HR = 0.79; 95% CI: [0.41-1.54]; p = 0.493). PD-1 positivity was associated with better disease-free survival (HR = 0.52; 95% CI: [0.38-0.72]; p < 0.0001). Furthermore, elevated PD-1 expression corrected for age (OR = 0.62, 95% CI: [0.41-0.96]; p = 0.030) and alpha-fetoprotein levels (OR = 2.27, 95% CI: [1.46-3.55]; p < 0.0001), were not correlated with patient sex, tumor size, tumor multiplicity, hepatitis B virus history, tumor node metastasis stage or Barcelona Clinic Liver Cancer stage. Conclusions: This meta-analysis revealed that PD-1 expression may be a useful prognostic marker in HCC patients. Prospective clinical studies are needed to support these findings.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".