The associations between common SNPs of EFEMP1 gene and glioma risk in Chinese population

Jun Hu,¹ Dong Dong,² Dandan Lu<sup>3

1</sup>Research Centre of Biomedical Technology Co., Ltd., Jiangsu Vocational College of Medicine, Yancheng, Jiangsu, ²Department of Pharmacy, Women and Infants Hospital of Zhengzhou, Zhengzhou, Henan, ³Department of Dermatology, The Third Hospital of Jinan, Jinan, Shandong, China

Background: Although the associations between common single nucleotide polymorphisms (SNPs) of EFEMP1 gene and glioma risk have been investigated in Chinese population-based case–control studies, investigation results for several SNPs are inconsistent. In addition, the single-center study has a poor statistical power due to finite sample size. Therefore, a meta-analysis was conducted to comprehensively determine the associations.
Methods: All eligible case–control studies were obtained by searching PubMed, EMBASE, Web of Science, and Chinese National Knowledge Infrastructure. Pooled odds ratio (OR) with 95% confidence interval (CI) was used to assess the strength of the associations in fixed- or random-effects model.
Results: EFEMP1 rs1346787 polymorphism was significantly associated with glioma risk in Chinese population under all genetic models (GG vs AA, OR =2.22, 95% CI =1.46–3.36; AG vs AA, OR =1.54, 95% CI =1.27–1.87; (GG+AG) vs AA, OR =1.60, 95% CI =1.34–1.93; GG vs (AG+AA), OR =1.86, 95% CI =1.24–2.78; G vs A, OR =1.54, 95% CI =1.32–1.79). However, the significant association of EFEMP1 rs1346786 with glioma risk in Chinese population was observed only under heterozygous model of AG vs AA (OR =1.34, 95% CI =1.10–1.62), dominant model of (GG+AG) vs AA (OR =1.36, 95% CI =1.13–1.63), and allelic model of G vs A (OR =1.28, 95% CI =1.10–1.50).
Conclusion: Our study demonstrated that EFEMP1 polymorphisms, especially rs1346787 and rs1346786, might predict glioma risk in Chinese population. However, high-quality case–control studies with larger sample sizes are warranted to confirm the above-mentioned findings.

Keywords: polymorphism, glioma, risk, meta-analysis

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