Association between IL-27 Gene Polymorphisms and Cancer Susceptibility in Asian Population: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Interleukin 27 (IL-27) has potent antitumor activity. Several epidemiological studies have designated that genetic variants of the IL-27 gene may contribute to various cancer susceptibility, but the data were inconclusive. Objective: The current meta-analysis aimed to address the association between IL-27 rs153109, rs17855750, and rs181206 polymorphisms and the risk of cancer. DATA SOURCES: Our team has selected eligible studies up to May 1, 2020, from several electronic databases, including Web of Science, PubMed, Scopus, and Google Scholar databases. RESULTS: Our meta-analysis revealed that the carriers rs153109 A>G polymorphism in the IL-27 gene have higher risks of diseases in the heterozygous (OR=1.26, 95%CI=1.06-1.49, P=0.007, AG vs AA), homozygous (OR=1.18, 95%CI=1.01-1.37, p=0.33, GG vs AA), dominant (OR=1.25, 95%CI=1.07-1.47, P=0.006, AG+GG vs AA), and allele (OR=1.15, 95%CI=1.04-1.27, P=0.008, G vs A) genetic models. Stratified analysis by cancer type indicated that this variant was significantly associated with gastrointestinal cancer, colorectal cancer and breast cancer. The findings did not support an association between rs17855750 T>G, rs181206 T>C polymorphisms of IL-27 and cancer risk. CONCLUSION: the current study findings suggest that IL-27 rs153109 polymorphism significantly increased the risk of cancer susceptibility. Well-designed replication in a larger independent genetic association study with larger sample sizes in diverse ethnicities is required to verify the 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.033 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".