Association between hOGG1 polymorphism rs1052133 and gastric cancer
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Dingding Zhang1,*, Xiaoxin Guo1,*, Jinliang Hu2,3,*, Guangqun Zeng4, Maomin Huang1,5, Dandan Qi1, Bo Gong1
1Sichuan Provincial Key Laboratory for Disease Gene Study, Hospital of University of Electronic Science and Technology of China and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610072, China
2Institute of Health Policy and Hospital Management, Sichuan Academy of Medical Science and Sichuan Provincial People’s Hospital, Chengdu, Sichuan, 610072, China
3School of Public Health, Sichuan University, Chengdu, Sichuan, 610041, China
4Department of Clinical Laboratory, People’s Hospital of Pengzhou, Pengzhou, Sichuan, 611930, China
5Department of Immunology, Zunyi Medical College, Zunyi, Guizhou, 563000, China
*These authors contributed equally to this study
Bo Gong, email: email@example.com
Keywords: gastric cancer, meta-analysis, hOGG1 rs1052133, association
Received: January 10, 2017 Accepted: March 04, 2017 Published: March 11, 2017
Purpose: To conduct a comprehensive evaluation of the association of the human8-oxoguanine glycosylase 1 (hOGG1) gene polymorphism rs1052133 with gastric cancer (GC) through a systematic review and meta-analysis of genetic association study.
Results: A total of 15 articles from published papers were included in our analysis. The meta-analyses for hOGG1 rs1052133, composed of 4024GC patients and 6022controls, showed low heterogeneity for the included populations in all the genetic models, except for the Caucasian population under allelic genetic model, the Asian population under addictive model and Caucasian population under dominant model. The analyses of all the genetic models in overall pooled populations did not identify any significant association between GC and hOGG1 rs1052133 (Allelic model: C vs. G , p = 0.746; Addictive model: CC vs. GG, p = 0.888; Recessive model: CC +GC vs. GG, p = 0.628; Dominant model: CC vs. GG+GC, p = 0.147), even though stratified analyses were conducted in different ethnicities under each genetic model.
Materials and Methods: All case-control association studies on hOGG1 and GC reported up to December 15, 2016 in PubMed, Embase, Web of Science, and the Chinese Biomedical Database were retrieved. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated for single-nucleotide polymorphism (SNP) using fixed- and random- effects models according to between-study heterogeneity. Publication bias analyses were conducted using Begg test.
Conclusions: This meta-analysis showed there was no association between hOGG1 rs1052133 and GC. Given the limited sample size, further investigations including more ethnic groups are required to validate the association.
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