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An Updated and Comprehensive Meta-Analysis of Association Between Seven Hot Loci Polymorphisms from Eight GWAS and Glioma Risk

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Abstract

Eight genome-wide association studies (GWASs) found that seven loci (rs2736100, rs4295627, rs4977756, rs498872, rs11979158, rs2252586, rs6010620) polymorphisms could elevate the risk of glioma, one of the most common types of primary brain cancer in adults. However, the replication studies about these seven loci obtained inconsistent results. In order to derive a more accurate estimation about the relationship between the selected single-nucleotide polymorphism (SNP) and susceptibility to glioma, we conducted a meta-analysis containing all eligible published case control studies to evaluate the association. An overall literature search was conducted using the database of PubMed, Science Direct, China national knowledge infrastructure (CNKI), and Embase. Seventeen articles with 25 studies were included in the meta-analysis. Glioma risk (odds ratio, OR; 95 % confidential interval, 95 %CI) was estimated with the random-effect model or the fixed-effects model. STATA 12.0 was applied to analyze all statistical data. Results showed that seven hot loci were all associated with increased risk of glioma (rs2736100, OR = 1.28, 95 %CI = 1.23–1.32; rs4295627, OR = 1.34, 95 %CI = 1.21–1.47; rs4977756, OR = 1.24, 95 %CI = 1.20–1.28; rs498872, OR = 1.24, 95 %CI = 1.15–1.33; rs6010620, OR = 1.29, 95 %CI = 1.24–1.35; rs11979158: OR = 1.18, 95 %CI = 1.10–1.25; rs2252586: OR = 1.18, 95 %CI = 1.10–1.25). Additionally, subgroup analysis by stages of glioma found that variation of rs11979158 had stronger relationship with high-grade (OR = 1.32, 95 %CI = 1.19–1.45) than low-grade glioma (OR = 1.12, 95 % CI = 1.03–1.21). Similarly, stratified analysis of rs2252586 by stages revealed the similar trend, with OR of 1.26 (95 %CI = 1.17–1.35) in high-grade glioma and OR of 1.15 (95 %CI = 1.08–1.22) in low-grade glioma. In summary, the present study showed that mutations of the seven loci could elevate the risk of glioma significantly. However, more other factors that could be related with glioma should be considered in further studies.

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Authors and Affiliations

  1. Department of Clinical Laboratory Medicine, Shanghai Fifth People’s Hospital, Fudan University, Minhang District, No. 801 Heqing Road, Shanghai, 200240, China

    Qiang Wu

  2. Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA

    Yanyan Peng & Xiaotao Zhao

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  1. Qiang Wu
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Correspondence to Qiang Wu.

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Supplement Figure 1

Forest plot for the meta-analysis of the association between rs11979158 mutation and glioma in allelic model. (EPS 671 kb)

High resolution image (GIF 37 kb)

Supplement Figure 2

Forest plot for the meta-analysis of the association between rs2252586 mutation and glioma in allelic model. (EPS 666 kb)

High resolution image (GIF 37 kb)

Supplement Figure 3

Forest plot for the meta-analysis of the association between rs6010620 mutation and glioma in allelic model. (EPS 758 kb)

High resolution image (GIF 53 kb)

Supplement Figure 4

Forest plot for the meta-analysis of the association between rs4977756 mutation and glioma in allelic model. (EPS 770 kb)

High resolution image (GIF 53 kb)

Supplement Figure 5

Forest plot for the meta-analysis of the association between rs498872 mutation and glioma in allelic model. (EPS 766 kb)

High resolution image (GIF 54 kb)

Supplement Figure 6

Forest plot for the meta-analysis of the association between rs2736100 mutation and glioma in allelic model. (EPS 732 kb)

High resolution image (GIF 50 kb)

Supplement Figure 7

Forest plot for the meta-analysis of the association between rs4295627 mutation and glioma in allelic model. (EPS 770 kb)

High resolution image (GIF 55 kb)

Supplement Figure 8

Cumulative meta-analysis of the association between rs11979158 mutation and glioma in allelic model. (EPS 611 kb)

High resolution image (GIF 23 kb)

Supplement Figure 9

Cumulative meta-analysis of the association between rs2252586 mutation and glioma in allelic model. (EPS 485 kb)

High resolution image (GIF 23 kb)

Supplement Figure 10

Cumulative meta-analysis of the association between rs6010620 mutation and glioma in allelic model. (EPS 522 kb)

High resolution image (GIF 34 kb)

Supplement Figure 11

Cumulative meta-analysis of the association between rs4977756 mutation and glioma in allelic model. (EPS 587 kb)

High resolution image (GIF 30 kb)

Supplement Figure 12

Cumulative meta-analysis of the association between rs498872 mutation and glioma in allelic model. (EPS 574 kb)

High resolution image (GIF 34 kb)

Supplement Figure 13

Cumulative meta-analysis of the association between rs2736100 mutation and glioma in allelic model. (EPS 560 kb)

High resolution image (GIF 32 kb)

Supplement Figure 14

Cumulative meta-analysis of the association between rs4295627 mutation and glioma in allelic model. (EPS 595 kb)

High resolution image (GIF 33 kb)

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Wu, Q., Peng, Y. & Zhao, X. An Updated and Comprehensive Meta-Analysis of Association Between Seven Hot Loci Polymorphisms from Eight GWAS and Glioma Risk. Mol Neurobiol 53, 4397–4405 (2016). https://doi.org/10.1007/s12035-015-9346-4

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