COX-2 rs5275 and rs689466 polymorphism and risk of lung cancer
Bibliographic record
Abstract
BACKGROUND: Cyclooxygenase-2 (COX-2) is an inducible enzyme that mediates the synthesis of prostaglandin, which plays an important role in the inflammation response. The overexpression of COX-2 in lung cancer has been found in several studies, suggesting that COX-2 contributes to carcinogenesis. There are many previous case-control studies focused on the association between COX-2 polymorphism and lung cancer risk, however, the conclusion remained controversial. OBJECTIVES: We performed this meta-analysis to evaluate the association between COX-2 rs5275 and rs689466 polymorphism and susceptibility to lung cancer. METHODS: A systematic literature research was conducted on PubMed, Embase, Cochrane Library, OVID, Web of Science, and Google Scholar up to November 30, 2017. The quality of studies was assessed by Newcastle-Ottawa scale. We combined odds ratios (ORs) and 95% confidence intervals (CIs) in 5 different genetic models for evaluation under a fixed-effect model or random-effect model. Subgroup analysis was performed according to source of control, ethnicity, pathological types, and smoking status. Sensitivity analysis and publication bias were also conducted. RESULTS: Eventually, 14 eligible studies were included in our meta-analysis. We found rs5275 gene polymorphism decreased the risk of lung cancer under heterozygote model (OR: 0.91, 95% CI: 0.84-0.98, P = .02). COX-2 rs689466 gene polymorphism was also related to a significantly reduced risk under allele (OR: 0.88, 95% CI: 0.82-0.95, P = .001), homozygote (OR: 0.81, 95% CI: 0.68-0.95, P = .01), heterozygote (OR: 0.81, 95% CI: 0.72-0.91, P < .001), and dominant model (OR: 0.81, 95% CI: 0.72-0.91, P < .001), except for recessive model. Subgroup analysis suggested a similar association in Asians, but not in Caucasians. Polymorphism of rs5275 was strongly associated with a reduced risk of lung adenocarcinoma according to stratified analysis by pathological types. Egger test identified no significant publication bias. CONCLUSIONS: Our meta-analysis demonstrated that COX-2 rs5275 and rs689466 polymorphism significantly decrease the risk of lung cancer in Asians but not in Caucasians, indicating COX-2 could serve as a potential diagnostic marker for lung cancer.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".