<i>MLH1</i> Promoter Methylation and Prediction/Prognosis of Gastric Cancer: A Systematic Review and Meta and Bioinformatic Analysis
Bibliographic record
Abstract
The promoter methylation of MLH1 gene and gastric cancer (GC)has been investigated previously. To get a more credible conclusion, we performed a systematic review and meta and bioinformatic analysis to clarify the role of MLH1 methylation in the prediction and prognosis of GC. Methods: Eligible studies were targeted after searching the PubMed, Web of Science, Embase, BIOSIS, CNKI and Wanfang Data to collect the information of MLH1 methylation and GC. The link strength between the two was estimated by odds ratio with its 95% confidence interval. The Newcastle-Ottawa scale was used for quantity assessment. Subgroup and sensitivity analysis were conducted to explore sources of heterogeneity. The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were employed for bioinformatics analysis on the correlation between MLH1 methylation and GC risk, clinicopathological behavior as well as prognosis. Results: 2365 GC and 1563 controls were included in the meta-analysis. The pooled OR of MLH1 methylation in GC was 4.895 (95% CI: 3.149-7.611, P<0.001), which considerably associated with increased GC risk. No significant difference was found in relation to Lauren classification, tumor invasion, lymph node/distant metastasis and tumor stage in GC. Analysis based on GEO and TCGA showed that high MLH1 methylation enhanced GC risk but might not related with GC clinicopathological features and prognosis. Conclusion: MLH1 methylation is an alive biomarker for the prediction of GC and it might not affect GC behavior. Further study could be conducted to verify the impact of MLH1 methylation on GC prognosis.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".