Association of TLR4 gene 2026A/G (rs1927914), 896A/G (rs4986790), and 1196C/T (rs4986791) polymorphisms and cancer susceptibility: Meta-analysis and trial sequential analysis
Bibliographic record
Abstract
BACKGROUND: This study was performed to assess the association of TLR4 gene 2026A/G (rs1927914), 896A/G (rs4986790), and 1196C/T (rs4986791) polymorphisms and cancer susceptibility based on published case-control studies. METHODS: Web of Science, PubMed, Embase, CBM, WanFang Data, CNKI, and VIP database were used for article retrieving. Then, these articles were screened according to the study inclusion and exclusion criteria. The data was extracted, and the study quality was evaluated according to the principle of Newcastle-Ottawa Scale. Meta-analysis was performed by RevMan 5.4 and Stata MP-17 software. Trial sequential analysis was performed by TSA 0.9.5.10 Beta software. RESULTS: Eighty-seven case-control studies including 25,969 cases and 32,119 controls were included in the meta-analysis. The diseases involved in case groups include prostate cancer, lung cancer, gastric cancer, hepatocellular carcinoma, colorectal cancer, etc. A versus G model of rs1927914, A versus G model of rs4986790 and C versus T model of rs4986791 showed that odds ratio (OR) = 1.08, OR = 0.85, and OR = 0.74 respectively. All the 3 comparisons were statistically significant. Sensitivity analysis showed that the results were stable. Publication bias analysis and trial sequential analysis showed that no significant publication bias was found in the results of the meta-analysis, and the probability of false positives was small. CONCLUSION: People with A allele of rs1927914, G allele of rs4986790, or T allele of rs4986791 have higher risks of cancer. The results of meta-analysis are stable and have less probability of false positives.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".