Prognostic value of c-MET protein expression in gastric cancer patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Background The heterogeneous nature of c-MET overexpression in gastric cancer (GC) leads to a lack of consensus on its prognostic significance.Objective To evaluate the predictive value of c-MET protein expression in gastric cancer patients.Methods A systematic review of studies from PubMed, Web of Science, Embase, and Cochrane Library up to April 2025. Heterogeneity and robustness were assessed using the Cochrane Q test, I2 statistic, and sensitivity analysis. Publication bias was evaluated with Egger’s and Begg’s tests. The Newcastle-Ottawa Scale (NOS) assessed methodological quality.Results From 2,322 articles, 22 studies were included. High c-MET expression was significantly associated with reduced overall survival (OS) (Hazard Ratio [HR] = 1.22; 95% Confidence Interval [CI]: 1.13, 1.31; I2 = 6.8%; P = 0.371) and disease-free survival (DFS) (pooled HR = 1.39; 95% CI: 1.11, 1.68; I2 = 42.4%; P = 0.139). Definitions of c-MET positivity varied across studies regarding thresholds, staining intensity, and detection methods. Subgroup analysis of OS revealed conflicting conclusions based on study design, cutoff values, and c-MET assays.Conclusion High c-MET expression may independently predict poor GC prognosis. Future efforts should focus on standardized detection methods and high-quality prospective studies to validate its prognostic value.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".