Meta‐analysis: tumour invasion‐related genetic polymorphisms and gastric cancer susceptibility
Bibliographic record
Abstract
BACKGROUND: Host genetic susceptibility has been suggested as one of the most important possible explanations for interindividual difference in gastric cancer (GC) risk. AIM: To evaluate the impact of tumour invasion-related gene polymorphisms, which may be involved in a variety of processes during GC development, such as cell adhesion and angiogenesis, on the risk of GC. METHODS: We reviewed published studies on tumour invasion-related gene polymorphisms and GC susceptibility until 31 March 2008, and then quantitatively summarized associations of the most widely-studied polymorphism, CDH1 -160C>A, with GC using meta-analysis. RESULTS: Twenty-seven eligible studies were included in this review. Fourteen polymorphisms significantly related to GC in at least one study were identified. For several polymorphisms, heterogeneous results were observed and associations in opposite directions were seen among Asian and Caucasian populations. In meta-analysis, CDH1 -160C>A showed an inverse association with GC among Asians (OR, 0.76; 95% CI, 0.55-1.05) and a positive association among Caucasians (OR, 1.40; 95% CI, 0.95-2.04). CONCLUSIONS: This review suggests that genetic polymorphisms in tumour invasion could be candidate biomarkers of GC risk. However, differences between populations and stages of cancer need to be taken into account and may explain some of the inconsistencies found in previous studies.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".