Oncotarget

Research Papers:

Association between hOGG1 polymorphism rs1052133 and gastric cancer

Dingding Zhang, Xiaoxin Guo, Jinliang Hu, Guangqun Zeng, Maomin Huang, Dandan Qi and Bo Gong _

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Oncotarget. 2017; 8:34321-34329. https://doi.org/10.18632/oncotarget.16124

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Abstract

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

Correspondence to:

Bo Gong, email: gongbo2007@hotmail.com

Keywords: gastric cancer, meta-analysis, hOGG1 rs1052133, association

Received: January 10, 2017     Accepted: March 04, 2017     Published: March 11, 2017

ABSTRACT

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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