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Journal of Cancer Research and Clinical Oncology logoLink to Journal of Cancer Research and Clinical Oncology
. 2014 Dec 11;141(7):1205–1220. doi: 10.1007/s00432-014-1894-x

Systematic evaluation of cancer risk associated with DNMT3B polymorphisms

Fujiao Duan 1,✉, Shuli Cui 2, Chunhua Song 3,4, Liping Dai 3,4, Xia Zhao 1, Xiaoqin Zhang 1
PMCID: PMC11824120  PMID: 25515408

Abstract

Purpose

The aim of our study is to provide a precise quantification for the association between DNA methyltransferase 3B (DNMT3B) variations (rs2424913 C/T, rs1569686 G/T, rs6087990 T/C and rs2424908 T/C) and the risk of cancer.

Methods

We performed a systematic literature review and assessed the methodological quality of included case–control designed studies based on Newcastle–Ottawa Scale. Pooled odds ratios (ORs) and corresponding 95 % confidence intervals (95 % CIs) were calculated to assess the strengths of the associations.

Results

We identified 34 studies for pooled analyses. Overall, the results demonstrated that rs2424913 polymorphism was significantly associated with negative cancer risk in the African population (CT vs TT: OR 0.10, 95 % CI 0.02–0.63, P = 0.01; CT+CC vs TT: OR 0.14, 95 % CI 0.03–0.76, P = 0.02), and the rs1569686 polymorphism was significantly associated with a subtly decreased cancer risk (GT vs TT: OR 0.80, 95 % CI 0.72–0.90, P < 0.01; GT+GG vs TT: OR 0.84, 95 % CI 0.76–0.94, P < 0.01), particularly in the Asian population (GT vs TT: OR 0.79, 95 % CI 0.66–0.96, P < 0.01) and in colorectal cancer subgroup (G vs T: OR 0.69, 95 % CI 0.54–0.88, P < 0.01). In addition, the rs6087990 polymorphism was associated with decreased risk in Asian population (T vs C: OR 0.77, 95 % CI 0.62–0.96, P = 0.02). Similarly, the rs2424908 polymorphism was observed as a protective factor for cancer in the Asian population (CT+CC vs TT: OR 0.79, 95 % CI 0.66–0.95, P = 0.01).

Conclusions

DNMT3B polymorphisms might be associated with decreased cancer risk especially in the Asian population and for colorectal cancer. Further multicentric studies are still needed to confirm the results.

Keywords: DNMT3B, Polymorphisms, Cancer, Systematic evaluation

Introduction

Cancer is a major public health problem in the most parts of the world. Approximately 12.7 million cancer cases and 7.6 million cancer deaths are estimated to have occurred in 2008 worldwide (Jemal et al. 2011). In the United States, one in 4 deaths is due to cancer (Siegel et al. 2014). The primary prevention strategies aimed to reduce incidence and the early detection of subclinical cancer cases were discovered and increase the chance of curing early stage patients or prolonging their survival time (Adami et al. 2001). As a complex disease, there is a clear evidence that cancer is strongly influenced by genetic and environmental factors, of which gene polymorphism is a critical cause for the difference of individual genetic susceptibility to cancer (Chen and Hunter 2005; Foulkes 2008).

In humans, the most intensely studied epigenetic phenomenon is DNA methylation, an essential regulator of transcription and chromatin structure. It is a major epigenetic modification, and aberrant DNA cytosine methylation may play an important role in carcinogenesis (Paz et al. 2003). In mammals, this mainly refers to the covalent post-replicative addition of a methyl group to the 5′position of a cytosine in a CpG dinucleotide, which is conferred by DNA methyltransferases (DNMTs) (Bheemanaik et al. 2006). DNMTs are classified into three families: DNMT1, DNMT2 and DNMT3 (DNMT3A and DNMT3B and DNMT3L), and each plays a different functional role (Goll and Bestor 2005; Bourc’his et al. 2001.

DNA methyltransferase 3B (DNMT3B) gene is located on chromosome 20q11.2, including 23 exons and 22 introns. DNMT3B is required for the establishment and maintenance of genomic methylation patterns and proper murine development (Bachman et al. 2001). It encodes DNA methyltransferase 3B enzyme, which is primarily responsible for de novo methylation process and probably plays an oncogenic role during tumorigenesis (Okano et al. 1999). DNMT3B promoter polymorphisms have been reported to be associated with varying risk of malignant solid tumors and hematologic neoplasms, such as colorectal cancer, hepatocellular carcinoma, head and neck cancer, lung cancer, breast cancer and acute myeloid leukemia, albeit to varying degrees. However, the results from the individual studies were inconsistent.

So far, no multiple sites or large-scale systematic evaluation has investigated the overall cancer risk in relation to DNMT3B polymorphisms, except for one meta-analysis which only concerned two sites in 2012 (Zhu et al. 2012). Additionally, the corresponding meta-analysis dataset without excluding the genotype distribution did not follow Hardy–Weinberg equilibrium (HWE) studies. To further explore whether DNMT3B polymorphisms were associated with risks of the overall cancer and specific cancer subtypes, we conducted a meta-analysis on the association between the four most frequently studied DNMT3B polymorphisms (rs2424913 C/T, rs1569686 G/T, rs6087990 T/C and rs2424908 T/C) and cancer risk.

Materials and methods

Data sources and identification of eligible studies

We conducted a comprehensive literature search using the PubMed, Springer, Elsevier and Chinese BioMedical Literature Database (CBM). We searched the databases from January 1, 1989 through April 1, 2014 with the following MeSH terms: ‘rs2424913/C46359T/149C>T,’ ‘rs1569686/G39179T/579G>T,’ ‘rs6087990/283T>C,’ ‘rs2424908,’ ‘DNMT3B polymorphism’ and ‘Cancer.’ We also manually searched original studies on this topic in order to identify any studies that may have been missed by the computer-assisted search. Finally, the consultation with epidemiological specialist was performed to identify additional references or unpublished data. The present study was conducted in accordance with PRISMA guidelines (Moher et al. 2009).

Two investigators (DFJ and SLC) reviewed references and identified by the search strategy to generate a list of potentially relevant studies. The abstract of each potentially relevant article was reviewed by both of the two investigators, respectively. If the applicability of study could not be determined solely by the title or abstract, then the full text would be reviewed. For conflicting evaluations, the third investigator (LPD) was consulted to resolve the disagreements, and a final decision was made through consensus.

The inclusion criteria were as follows: (1) hypothesis tests on the association of DNMT3B polymorphisms with cancer risk; (2) case–control or cohort-designed study; (3) sufficiently presented data for estimating an odds ratio (OR) with 95 % confidence interval (CI); and (4) available genotype frequencies.

The studies, in which the genotypes of controls were not in accordance with the assumptions of HWE, were excluded from the analysis of this polymorphism.

Data extraction and quality assessment

The data were obtained according to a standard protocol. Data extraction was conducted independently by two reviewers (FJD and SLC). For controversial evaluations, the third reviewer (CHS) was consulted to resolve the dispute, and a final decision was made by discussion among the three reviewers. The following characteristics and numbers were collected from each study if available: first author’s name, year of publication, country of origin, ethnicity, demographics, cancer type, design of study, genotype distributions and HWE of controls, respectively. Different ethnicity descents were categorized as Asian, Caucasian and others. Study design was stratified into hospital-based and population-based studies. If original genotype frequency data were unavailable in relevant articles, a request for additional data was sent to the corresponding author. If duplicate publications using the same cohort of patients were found, data from the most recent publication were included.

Two investigators (FJD and LPD) assessed the methodological quality with a modified checklist based upon the Newcastle–Ottawa Scale (NOS) for quality of case–control studies (Wells et al. 2000), with discrepancies resolved by consensus. This instrument rates observational studies on a nine-point scale based on appropriateness of selection, comparability and exposure.

Data synthesis and statistical analysis

The analyses were conducted in Review Manager 5.0 (Version 5 for Windows, Cochrane Collaboration, Oxford, UK). The overall strength of an association between DNMT3B polymorphisms and cancer risk was assessed by crude ORs together with their corresponding 95 % CIs. The stratified assessments were further carried out for rs2424913, rs1569686 and rs6087990 (just only ethnicity) by ethnicity and cancer subtypes separately. For the limited studies available, stratified analysis was not carried out for rs2424908.

We calculated the departure from the HWE for the control group in each study using Pearson’s goodness-of-fit χ 2 test with 1 degree of freedom.

Heterogeneity was evaluated graphically by examination of forest plots and then statistically by the χ 2 test of heterogeneity and the inconsistency index (I 2). By heterogeneity test, heterogeneity was considered significant when P value (P heterogeneity) < 0.05 or I 2 > 50 % were consistent with possible substantial heterogeneity. If P heterogeneity ≥ 0.05, we used the fixed-effect model to calculate the combined OR (the Mantel–Haenszel method) (Mantel and Haenszel 2004), otherwise, random-effects model (DerSimonian and Laird method) was conducted (DerSimonian and Laird 1986). The significance of overall OR was determined by the Z test. If there was significant heterogeneity among included studies, the sources of heterogeneity would be explored using meta-regression in Stata 12.0 (StataCorp, College Station, TX, USA).

To assess the stability of the results, meta-influence analysis (sensitivity) was performed by omitting each study in turn to assess the quality and consistency of the results. Publication bias was evaluated graphically by funnel plot analysis, Begg’s test (rank correlation test) and Egger’s test (weighted linear regression test) (Peters et al. 2006). An asymmetric funnel plot would suggest that the possibility of small studies not being published due to unfavorable results. These analyses were performed using Stata 12.0.

Statistical tests conducted were considered significant whenever the corresponding null-hypothesis probability was less than 0.05 (P < 0.05).

Results

Literature search and study characteristics

The comprehensive literature search yielded 252 potentially relevant published articles between January 1, 1989 and April 1, 2014. 124 articles were potentially appropriate, and these articles further identified and screened, 39 articles (Succi et al. 2014; Lao et al. 2013; Mostowska et al. 2013; Hernández-Sotelo et al. 2013; Sun et al. 2012; Xu 2013; Zheng et al. 2013; Liu et al. 2008, 2012; Yang et al. 2012; Bao et al. 2011; Daraei et al. 2011; Zhang et al. 2011; Guo et al. 2010; Hu et al. 2010; Karpinski et al. 2010; Srivastava et al. 2010; Ezzikouri et al. 2009; Iacopetta et al. 2009; de Vogel et al. 2009; Chang et al. 2008; Fan et al. 2008a, b; Liu 2008a, b; Reeves et al. 2008; Yang 2006; Zhang 2008; Chang et al. 2007; Hong et al. 2007; Wu and Lin 2007; Jones et al. 2006; Aung et al. 2005; Lee et al. 2005; Li et al. 2005; Singal et al. 2005; Wang et al. 2005; Montgomery et al. 2004; Shen et al. 2002) underwent full-text assessment, five studies (Daraei et al. 2011; Liu 2008; Hong et al. 2007; Jones et al. 2006; Shen et al. 2002) were excluded due to not consistent with HWE, and one study (Aung et al. 2005) had no polymorphism of the target site (Fig. 1). Finally, 33 articles (34 studies) (Succi et al. 2014; Lao et al. 2013; Mostowska et al. 2013; Hernández-Sotelo et al. 2013; Sun et al. 2012; Xu 2013; Zheng et al. 2013; Liu et al. 2012; Yang et al. 2012; Bao et al. 2011; Zhang et al. 2011; Guo et al. 2010; Hu et al. 2010; Karpinski et al. 2010; Srivastava et al. 2010; Ezzikouri et al. 2009; Iacopetta et al. 2009; de Vogel et al. 2009; Chang et al. 2007, 2008; Fan et al. 2008a, b; Liu et al. 2008; Liu 2008; Reeves et al. 2008; Yang 2006; Zhang 2008; Wu and Lin 2007; Lee et al. 2005; Li et al. 2005; Singal et al. 2005; Wang et al. 2005; Montgomery et al. 2004) were conducted in quantitative synthesis, in which one article (Singal et al. 2005) included two populations.

Fig. 1.

Fig. 1

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Flow chart of the literature search and study selection

Characteristics of included studies are presented in Table 1. A total of 34 eligible studies met the prespecified inclusion criteria, 22 studies consisting of 5,642 cases, and 7,695 controls investigated the association between DNMT3B rs2424913 polymorphism and cancer risk; 16 studies including 4,338 cases and 4,578 controls studied the association between DNMT3B rs1569686 polymorphism and cancer risk; four articles including 1,103 cases and 1,071 controls investigated the association between DNMT3B rs6087990 polymorphism and cancer risk; for DNMT3B rs2424908 polymorphism, three studies consisting of 967 cases and 1,169 controls were included. All studies were case–control studies, including 8 studies on colorectal cancer, 4 gastric cancer, 4 lung cancer, 3 hepatocellular carcinoma (HCC), 3 head and neck cancer and 12 on other cancer types. There were 22 studies of Asian descendent, 11 of Caucasian descendent and one of African. A classic PCR–RFLP assay was used in 26 out of 34 studies. In addition, 22 studies were included based on the control sex or age matched for the case groups, 22 studies were population-based and 12 were hospital-based, and 20 studies were randomly repeated a portion of samples as quality control while genotyping.

Table 1.

Characteristics of included studies

References Ethnicity Cancer type Source of control Genotyping Matching Y/N Sample size case/control Qualityc control P HWE
rs2424913 rs1569686 rs6087990 rs2424908
Succi et al. (2014) Caucasian HNC Population RT-PCR N 237/488 N 0.123
Lao et al. (2013) Asian HCC Hospital PCR–RFLP Y

108/216a

114/240b

Y 0.836 0.343
Mostowska et al. (2013) Caucasian Ovarian cancer Population PCR–RFLP Y 159/180 Y 0.826 0.739
Hernández-Sotelo et al. (2013) Caucasian Cervical cancer Hospital PCR–RFLP N 70/60 Y 0.043 0.086
Sun et al. (2012) Asian Breast cancer Hospital MassARRAY Y 408/469 Y 0.281
Xu (2013) Asian Colorectal cancer Population MassARRAY Y 89/94 N 0.457 0.001
Zheng et al. (2013) Asian AML Population RT-PCR Y 317/406 Y NP 0.268 <0.001 0.122
Liu et al. (2012) Asian Lung cancer Population PCR–RFLP Y 174/135 N 0.216
Yang et al. (2012) Asian Gastric cancer Hospital MassARRAY N 242/294 N 0.562
Bao et al. (2011) Asian Colorectal cancer Population PCR–RFLP Y 544/533 Y 0.793 0.182
Daraei et al. (2011) Caucasian Colorectal cancer Hospital PCR–RFLP N 125/135 Y <0.001
Zhang et al. (2011) Asian Lung cancer Hospital PCR–RFLP Y 50/60 N 0.895
Guo (2010) Asian Colorectal cancer Population PCR–RFLP Y 316/157 Y NA
Hu et al. (2010) Asian Gastric cancer Population PCR–RFLP Y 259/262 Y 0.926 0.902
Karpinski et al. (2010) Caucasian Colorectal cancer Hospital PCR–RFLP N 186/140 Y 0.736 0.062
Srivastava et al. (2010) Asian Gallbladder cancer Population PCR–RFLP Y 212/219 Y 0.253
Ezzikouri et al. (2009) Caucasian HCC Population PCR–RFLP Y 96/222 Y 0.881
Iacopetta et al. (2009) Caucasian Colorectal cancer Population PCR–RFLP Y 828/949 Y 0.537
de Vogel et al. (2009) Caucasian Colorectal cancer Population PCR-SBR Y 703/1810 Y 0.580
de Vogel et al. (2008) Asian NPC Population MALDI-TOF N 259/250 N 0.193 0.567
Fan et al. (2008a) Asian Colorectal cancer Population PCR–RFLP Y 137/308 Y 0.090 0.286
Fan et al. (2008b) Asian Esophagus cancer Population PCR–RFLP Y 194/210 Y NP 0.399
Liu et al. (2008) Caucasian HNC Hospital PCR–RFLP Y 832/843 Y 0.153 0.787
Liu (2008) Asian Esophagus cancer Population PCR–RFLP Y 195/189 N 0.942
Liu-G (Liu 2008) Asian Gastric cancer Population PCR–RFLP Y 313/350 N <0.001
Reeves et al. (2008) Caucasian Colorectal cancer Hospital PCR–RFLP N 194/210 N 0.293
Yang (2006) Asian Lung cancer Population PCR–RFLP Y 52/55 N 0.835
Zhang (2008) Asian Gastric cancer Hospital PCR–RFLP Y 156/156 N 0.968 0.010
Chang et al. (2007) Asian HNC Population PCR–RFLP N 226/249 N 0.219 0.603
Hong et al. (2007) Asian Colorectal cancer Population PCR–RFLP Y 248/248 N 0.019
Wu and Lin (2007) Asian HCC Population PCR–RFLP Y 100/140 Y 0.966
Jones et al. (2006) Mix Colorectal cancer Hospital PCR-SSCP N 74/72 N 0.049
Aung et al. (2005) Asian Gastric cancer Hospital PCR–RFLP N 152/247 N NP
Lee et al. (2005) Asian Lung cancer Population PCR–RFLP Y 432/432 Y 0.522 0.592
Li et al. (2005) Asian Acute leukemia Population PCR–RFLP N 160/240 N 0.845
Singal et al. (2005) African Prostate cancer Hospital PCR–RFLP N 25/15 N 0.390
Singal et al. (2005) Caucasian Prostate cancer Hospital PCR–RFLP N 56/27 N 0.847
Wang et al. (2005) Asian Gastric cancer Population PCR–RFLP N 212/294 Y 0.654
Montgomery et al. (2004) Caucasian Breast cancer Hospital PCR–RFLP N 352/258 Y 0.130
Shen et al. (2002) Caucasian Lung cancer Population PCR–RFLP Y 319/340 Y 0.001
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HCC hepatocellular carcinoma, HNC head and neck cancer, AML acute myeloid leukemia, NPC Nasopharyngeal carcinomas, NP no polymorphisms, NA not applicable for the lack of C allele

aCorresponding to the rs2424913, bcorresponding to the rs1569686, cQuality control was conducted when sample of cases and controls was genotyped

Assessment of methodological quality

The quality assessment for included studies is described in Table 2. We assessed the methodological quality of included studies based on NOS for quality of case–control studies. A star system of the NOS (range 0–9 stars) has been developed for the evaluation (Table 2). Out of a maximum 9 stars, 23 studies had quality scores of 4–6, nine studies had a star of 7, and two had quality stars of 8 or 9. Most studies had appropriate case–control selection, including representativeness of the selection, comparability and exposure.

Table 2.

Quality assessment of studies

Studies Selection (score) Comparability (score) Exposure (score) Total score
Adequate definition of patient case Representativeness of patients cases Selection of controls Definition of control Control for important factor or additional factor Ascertainment of exposure (blinding) Same method of ascertainment for participants Non-response rate
Succi et al. (2014) 1 1 1 1 2 1 1 0 8
Lao et al. (2013) 1 1 0 1 1 0 1 0 5
Mostowska et al. (2013) 1 1 1 1 1 0 1 0 6
Hernández-Sotelo et al. (2013) 1 1 0 1 0 0 1 0 4
Sun et al. (2012) 1 1 0 1 1 0 1 0 5
Xu (2013) 1 1 1 1 2 0 1 0 7
Zheng et al. (2013) 1 1 1 1 1 0 1 0 6
Liu et al. (2012) 1 1 1 1 1 0 1 0 6
Yang et al. (2012) 1 1 0 1 0 0 1 0 4
Bao et al. (2011) 1 1 1 1 2 0 1 0 7
Zhang et al. (2011) 1 1 0 1 1 0 1 0 5
Guo et al. (2010) 1 1 1 1 2 0 1 0 7
Hu et al. (2010) 1 1 1 1 1 0 1 0 6
Karpinski et al. (2010) 1 1 0 1 0 0 1 0 7
Srivastava et al. (2010) 1 1 1 1 1 0 1 0 6
Ezzikouri et al. (2009) 1 1 1 1 2 0 1 0 7
Iacopetta et al. (2009) 1 1 1 1 1 0 1 1 7
de Vogel et al. (2009) 1 1 1 1 2 1 1 0 8
Chang et al. (2008) 1 1 1 1 1 0 1 0 6
Fan et al. (2008a) 1 1 1 1 1 0 1 0 6
Fan et al. (2008b) 1 1 1 1 1 0 1 0 6
Liu et al. (2008) 1 1 0 1 1 0 1 1 6
Liu (2008) 1 1 1 1 1 0 1 0 6
Reeves et al. (2008) 1 1 0 1 0 0 1 0 4
Yang (2006) 1 1 1 1 2 0 1 0 7
Zhang (2008) 1 1 0 1 1 0 1 0 5
Chang et al. (2007) 1 1 1 1 1 0 1 0 6
Wu and Lin (2007) 1 1 1 1 2 0 1 0 7
Lee et al. (2005) 1 1 1 1 1 0 1 1 7
Li et al. (2005) 1 0 1 1 0 0 1 0 4
Singal et al. (2005) 1 1 0 1 0 0 1 0 4

Wang et al.

(2005)

 1 1 1 1 0 0 1 0 5
Shen et al. (2002) 1 1 0 1 0 0 1 0 4
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Quantitative synthesis

For all of 34 datasets, the frequencies of risk C allele in rs2424913, G allele in rs1569686, T allele in rs6087990 and C allele in rs2424908 varied greatly among different control populations (Figs. 2a, b, 3a, b), and there was a statistically significant difference between the Caucasian populations and Asian populations for the rs2424913 and rs1569686, respectively (data not shown). Caucasian populations presented the highest frequencies of risk alleles of the three SNPs (rs2424913, rs1569686, rs6087990), whereas the lowest frequencies appeared in Asian populations. For the rs2424908, we observed a wide variation of C allele frequencies in Asian populations.

Fig. 2.

Fig. 2

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a frequencies of C allele in rs2424193 among controls stratified by ethnicity. b Frequencies of G allele in rs1569686 among controls stratified by ethnicity

Fig. 3.

Fig. 3

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a frequencies of T allele in rs6087990 among controls stratified by ethnicity. b Frequencies of C allele in rs2424908 among controls stratified by ethnicity

For the rs2424913 polymorphism, no significant risk association was observed in the overall pooled analysis (Table 3; Fig. 4). Subgroup analysis by ethnicity revealed a decreased risk in the African population (CT vs TT: OR 0.10, 95 % CI 0.02–0.63, P = 0.01; CT+CC vs TT: OR 0.14, 95 % CI 0.03–0.76, P = 0.02) but not Caucasian population and Asian population (Table 4).

Table 3.

Main results of pooled ORs in the meta-analysis

Comparisons Cases n/N Controls n/N Heterogeneity test Summary OR (95 % CI) Hypothesis test Studies
Q P I 2 (%) Z P
rs2424193
 C versus T 4,066/11,100 573,315,192 30.40 0.08 31 1.01 (0.95,1.07) 0.30 0.76 22
 CT versus TT 1,838/4,527 2,609/6,033 38.56 0.01 46 1.01 (0.83,1.22) 0.08 0.94 22
 CC versus TT 1,161/3,756 1,562/4,986 14.85 0.32 12 1.00 (0.89,1.14) 0.07 0.94 14
 CT+CC versus TT 2,852/5,642 4,170/7,695 35.10 0.03 40 0.97 (0.82,1.15) 0.30 0.77 22
 CC versus CT+TT 1,114/5,642 1,562/7,695 13.99 0.37 7 1.06 (0.96,1.16) 1.16 0.25 14
rs1569686
 G versus A 1,811/7,956 2,017/8,894 70.44 <0.01 80 0.91 (0.74,1.13) 0.85 0.40 15
 AG versus AA 979/3,512 1,217/4,018 20.60 0.11 32 0.80 (0.72,0.90) 3.79 <0.01 15
 GG versus AA 378/2,790 392/2,875 9.92 0.62 0 0.89 (0.72,1.10) 1.07 0.28 13
 AG+GG versus AA 1,382/4,338 1,552/4,578 38.24 <0.01 61 0.84 (0.76,0.94) 3.16 <0.01 16
 GG versus AG+AA 410/4,018 400/4,241 26.49 0.01 51 1.15 (0.76,1.74) 0.67 0.50 14
rs6087990
 T versus C 389/2,226 359/2,142 6.20 0.10 52 0.90 (0.75,1.08) 1.11 0.27 4
 CT versus CC 243/1,030 241/1,012 10.38 0.02 71 0.98 (0.63,1.53) 0.10 0.92 4
 TT versus CC 73/860 59/830 3.12 0.37 4 1.36 (0.79,2.32) 1.12 0.26 4
 CT+TT versus CC 316/1,103 300/1,071 10.00 0.02 70 0.96 (0.62,1.47) 0.21 0.84 4
 TT versus CT+CC 73/1,103 59/1,071 0.66 0.88 0 0.95 (0.63,1.43) 0.26 0.80 4
rs2424908
 C versus T 787/1,832 1,021/2,838 5.80 0.06 65 0.97 (0.86,1.10) 0.45 0.65 3
 CT versus TT 449/797 597/957 4.49 0.11 55 0.78 (0.64,0.94) 2.57 0.01 3
 CC versus TT 169/517 212/572 4.16 0.12 52 0.82 (0.64,1.06) 1.52 0.13 3
 CT+CC versus TT 618/967 809/1,169 3.82 0.15 48 0.79 (0.66,0.95) 2.55 0.01 3
 CC versus CT+TT 169/967 212/1,169 4.93 0.08 59 0.95 (0.76,1.19) 0.43 0.67 3
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Fig. 4.

Fig. 4

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Forest plot of cancer risk associated with DNMT3B rs2424913 for the allele comparison (C vs T). The squares and horizontal lines correspond to the study-specific OR and 95 % CI. The area of the squares reflects the study-specific weight. The diamond represents the pooled OR and 95 % CI

Table 4.

Stratified analyses of the DNMT3B rs2424193 polymorphism on cancer risk

Comparisons Heterogeneity test Summary OR (95 % CI) Hypothesis test Studies
Q P I 2 (%) Z P
Ethnic
Asian
 C versus T 20.08 0.03 50 1.25 (0.77, 2.04) 0.90 0.37 11
 CT versus TT 16.87 0.08 41 1.17 (0.79, 1.73) 0.79 0.43 11
 CC versus TT 0.05 0.98 0 3.77 (0.59, 24.11) 1.40 0.16 3
 CT+CC versus TT 18.30 0.05 45 1.26 (0.86, 1.86) 1.19 0.23 11
 CC versus CT+TT 0.03 0.89 0 3.68 (0.58, 23.53) 1.38 0.17 3
Caucasian
 C versus T 8.28 0.51 0 1.00 (0.94, 1.07) 0.12 0.91 10
 CT versus TT 15.19 0.09 41 1.00 (0.85, 1.18) 0.01 0.99 10
 CC versus TT 10.67 0.30 16 1.00 (0.89, 1.14) 0.06 0.95 10
 CT+CC versus TT 10.64 0.30 15 0.96 (0.85, 1.08) 0.66 0.51 10
 CC versus CT+TT 12.15 0.21 26 1.05 (0.96, 1.16) 1.09 0.28 10
African
 C versus T – – – 0.43 (0.17, 1.08) 1.79 0.07 1
 CT versus TT – – – 0.10 (0.02, 0.63) 2.46 0.01 1
 CC versus TT – – – 0.23 (0.03, 1.63) 1.47 0.14 1
 CT+CC versus TT – – – 0.14 (0.03, 0.76) 2.27 0.02 1
 CC versus CT+TT – – – 0.87 (0.20, 3.77) 0.19 0.85 1
Cancer subtypes
Colorectal cancer
 C versus T 6.39 0.38 6 1.03 (0.95, 1.12) 0.68 0.50 6
 CT versus TT 2.84 0.72 0 1.06 (0.91, 1.24) 0.78 0.43 6
 CC versus TT 0.68 0.88 0 1.09 (0.92, 1.30) 1.04 0.30 6
 CT+CC versus TT 3.02 0.70 0 1.07 (0.92, 1.23) 0.86 0.39 6
 CC versus CT+TT 0.42 0.94 0 1.03 (0.91, 1.17) 0.49 0.62 6
HCC
 C versus T
 CT versus TT 2.65 0.27 25 1.18 (0.64, 2.18) 0.54 0.59 3
 CC versus TT – – – 1.16 (0.55, 2.43) 0.39 0.70 1
 CT+CC versus TT 3.40 0.18 41 1.21 (0.67, 2.18) 0.65 0.52 3
 CC versus CT+TT – – – 0.98 (0.59, 1.63) 0.08 0.94 1
Gastric cancer
 C versus T 1.04 0.59 0 1.18 (0.56, 2.48) 0.43 0.67 3
 CT versus TT 0.10 0.95 0 0.66 (0.30, 1.45) 1.03 0.31 3
 CC versus TT – – – 3.02 (0.12, 74.69) 0.68 0.50 1
 CT+CC versus TT 0.78 0.68 0 0.73 (0.34, 1.55) 0.83 0.41 3
 CC versus CT+TT – – – 3.02 (0.12, 74.69) 0.68 0.50 1
Other cancers
 C versus T 22.78 <0.01 60 1.06 (0.83, 1.27) 0.25 0.80 10
 CT versus TT 28.92 <0.01 69 1.04 (0.69, 1.55) 0.18 0.86 10
 CC versus TT 11.53 0.17 31 0.89 (0.74,1.08) 1.17 0.24 9
 CT+CC versus TT 22.95 <0.01 61 0.97 (0.71, 1.34) 0.16 0.87 10
 CC versus CT+TT 13.11 0.11 39 1.11 (0.85, 1.43) 0.76 0.45 9
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For the rs1569686 polymorphism, significant differences were observed for the comparison of heterozygote comparison and dominant model (GA vs AA: OR 0.80, 95 % CI 0.72–0.90, P < 0.01; GG+GA vs AA: OR 0.84, 95 % CI 0.76–0.94, P < 0.01) (Table 3; Fig. 5). Subgroup analysis by the ethnicity revealed a significant association in the comparison of GT vs TT (OR 0.79, 95 % CI 0.66–0.96, P < 0.01) in the Asian population (Table 5). When grouped by the cancer types, significant associations were found in colorectal cancer (G vs T: OR 0.69, 95 % CI 0.54–0.88, P < 0.01; GT vs TT: OR 0.66, 95 % CI 0.50–0.87, P < 0.01; GT+GG vs TT: OR 0.64, 95 % CI 0.51–0.80, P < 0.01). In addition to the decreased risk of colorectal cancer, a decreased risk was also observed in other cancer groups (GT vs TT: OR 0.84, 95 % CI 0.74–0.95, P = 0.01).

Fig. 5.

Fig. 5

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Forest plot of cancer risk associated with DNMT3B rs1569686 for the heterozygous genetic model (GT vs TT)

Table 5.

Stratified analyses of the DNMT3B rs1569686 polymorphism on cancer risk

Comparisons Heterogeneity test Summary OR (95 % CI) Hypothesis test Studies
Q P I 2 (%) Z P
Ethnic
Asian
 G versus T 69.77 <0.01 84 0.90 (0.67, 1.21) 0.68 0.49 12
 GT versus TT 20.46 0.04 46 0.79 (0.66, 0.96) 3.43 <0.01 12
 GG versus TT 9.18 0.42 2 0.97 (0.63, 1.49) 0.15 0.88 11
 GT+GG versus TT 37.84 <0.01 68 0.82 (0.65, 1.02) 1.77 0.08 13
 GG versus GT+TT 21.42 0.02 53 1.14 (0.55, 2.35) 0.35 0.72 11
Caucasian
 G versus T 0.37 0.83 0 0.94 (0.84, 1.07) 0.91 0.36 3
 GT versus TT 0.06 0.97 0 0.83 (0.66, 1.04) 1.63 0.10 3
 GG versus TT 0.34 0.84 0 0.86 (0.67, 1.11) 1.16 0.25 3
 GT+GG versus TT 0.65 0.72 0 0.87 (0.70, 1.09) 1.23 0.22 3
 GG versus GT+TT 0.40 0.82 0 0.99 (0.82, 1.19) 0.10 0.92 3
Cancer subtypes
Colorectal cancer
 G versus T 3.80 0.15 47 0.69 (0.54, 0.88) 2.98 <0.01 3
 GT versus TT 2.17 0.34 8 0.66 (0.50, 0.87) 2.94 <0.01 3
 GG versus TT 2.63 0.27 24 0.59 (0.20, 1.72) 0.97 0.33 3
 GT+GG versus TT 6.08 0.11 51 0.64 (0.51, 0.80) 3.82 <0.01 4
 GG versus GT+TT 2.78 0.25 28 0.63 (0.22, 1.84) 0.84 0.40 3
Other cancers
 G versus T 58.38 <0.01 81 0.96 (0.76, 1.21) 0.35 0.73 12
 GT versus TT 16.09 0.14 32 0.84 (0.74, 0.95) 2.82 0.01 12
 GG versus TT 26.64 <0.01 62 1.13 (0.67, 1.88) 0.45 0.65 11
 GT+GG versus TT 20.54 0.01 56 0.89 (0.73, 1.08) 1.15 0.25 12
 GG versus GT+TT 23.49 0.01 57 1.21 (0.78, 1.21) 0.84 0.40 11
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For the rs6087990 polymorphism, no significant risk association was observed in the overall pooled analysis (Table 3). However, Subgroup analysis by ethnicity revealed a decreased in the Asian population (T vs C: OR 0.77, 95 % CI 0.62–0.96, P = 0.02; CT vs CC: OR 0.77, 95 % CI 0.61–0.98, P = 0.03; CT+TT vs CC: OR 0.76, 95 % CI 0.60–0.97, P = 0.03). In contrast, a subtly increased risk was observed in the Caucasian population (CT vs CC: OR 2.30, 95 % CI 1.23–4.31, P = 0.01; CT+TT vs CC: OR 2.10, 95 % CI 1.17–3.78, P = 0.01) (Table 6).

Table 6.

Stratified analyses of the DNMT3B rs6087990 polymorphism on cancer risk

Comparisons Heterogeneity test Summary OR (95 % CI) Hypothesis test Studies
Q P I 2 (%) Z P
Ethnic
Asian
 T versus C 0.14 0.93 0 0.77 (0.62, 0.96) 2.31 0.02 3
 CT versus CC 0.18 0.92 0 0.77 (0.61, 0.98) 2.12 0.03 3
 TT versus CC 0.21 0.90 0 0.65 (0.24, 1.77) 0.85 0.40 3
 CT+TT versus CC 0.23 0.98 0 0.76 (0.60, 0.97) 2.23 0.03 3
 TT versus CT+CC 0.21 0.90 0 0.69 (0.5, 1.87) 0.74 0.46 3
Caucasian
 T versus C – – – 1.25 (0.91,1.71) 1.39 0.16 1
 CT versus CC – – – 2.30 (1.23,4.31) 2.61 0.01 1
 TT versus CC – – – 1.86 (0.97, 3.57) 1.88 0.06 1
 CT+TT versus CC – – – 2.10 (1.17, 3.78) 2.47 0.01 1
 TT versus CT+CC – – – 1.01 (0.64, 1.60) 0.06 0.95 1
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For the rs2424908 polymorphism, a significant association in the comparison of CT vs TT (OR 0.78, 95 % CI 0.64–0.94, P = 0.01) and CT+CC vs TT (OR 0.79, 95 % CI 0.66–0.95, P = 0.01) (Table 3). Due to the limited availability of eligible studies, we had not stratified these studies for this site (included study populations were all Asians).

Test of heterogeneity and sensitivity analysis

When evaluating the association between the DNMT3B polymorphism and the susceptibility to cancer, we found that there was significant heterogeneity for the part of the four sites’ comparison, but we just only evaluated the source of heterogeneity for rs2424913 and rs1569686 due to lack of relevant published data. Thus, we assessed the source of heterogeneity for the two sites. Meta-regression in Stata 12.0 was used to assess by publication language and year, ethnic, cancer type, matched controls (yes or not), source of control (population or hospital), assay, sample size (300 as the boundary) and quality control (with or not). It was detected that the systemic results were not affected by these characteristics (Table 7).

Table 7.

Results of heterogeneity test for rs2424913 and rs1569686

Comparisons Language Publication year Ethnic Cancer type Match Source of control Assay Sample size Quality control
rs2424913
 C versus T 0.064 0.339 0.256 0.812 0.327 0.178 0.785 0.605 0.781
 CT versus TT 0.057 0.286 0.438 0.964 0.220 0.066 0.667 0.765 0.529
 CC versus TT 0.748 0.578 0.608 0.784 0.433 0.366 0.772 0.687 0.765
 CT+CC versus TT 0.387 0.718 0.574 0.956 0.853 0.768 0.949 0.552 0.888
rs1569686
 G versus T 0.790 0.497 0.275 0.960 0.593 0.581 0.419 0.978 0.988
 GT versus TT 0.456 0.679 0.559 0.415 0.596 0.797 0.554 0.907 0.442
 GG versus TT 0.311 0.652 0.487 0.777 0.827 0.998 0.156 0.890 0.478
 GT+GG versus TT 0.579 0.441 0.479 0.351 0.656 0.989 0.359 0.883 0.532
 GG versus GT+TT 0.327 0.062 0.881 0.082 0.914 0.077 0.073 0.350 0.990
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Sensitivity analyses were performed to assess the influence of each individual dataset to the pooled ORs by the systematic omission of the individual study. The corresponding pooled ORs were not materially altered, except the Karpinski et al. (2010) for the rs6087990 (data not shown), the heterogeneity was decreased when this study was removed.

Evaluation of publication bias

Begg’s funnel plot and Egger’s linear regression test were performed to assess the publication biases of rs2424913 and rs1569686 included studies. The shape of the funnel plots did not reveal any evidence of obvious asymmetry in all comparison models (Figs. 6, 7). Then, Egger’s test was used to provide statistical evidence of funnel plot symmetry. The results still did not show any obvious evidence of publication bias (Table 8), indicating that our results were statistically robust.

Fig. 6.

Fig. 6

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Funnel plot of DNMT3B rs2424913 polymorphism and cancer risk of dominant model (CT+CC vs TT). The horizontal line in the funnel plot indicates the fixed-effects summary estimate, whereas the sloping lines indicate the expected 95 % CI for a given SE

Fig. 7.

Fig. 7

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Funnel plot of DNMT3B rs1569686 polymorphism and cancer risk of dominant model (GT+GG vs TT)

Table 8.

Publication bias of DNMT3B rs2424913 and rs1569686 for Egger’s test

Comparisons t P 95 % CI
rs2424913
 C versus T 0.32 0.751 −0.674 to 0.920
 CT versus TT 0.06 0.951 −0.954 to 1.018
 CC versus TT −0.88 0.400 −1.923 to 0.834
 CT+CC versus TT 0.15 0.879 −0.913 to 1.059
 CC versus CT+TT −1.83 0.097 −1.274 to 0.316
rs1569686
 G versus T 0.15 0.880 −2.796 to 3.225
 GT versus TT −0.05 0.957 −2.857 to 2.716
 GG versus TT 0.23 0.825 −2.271 to 2.758
 GT+GG versus TT −0.58 0.575 −4.036 to 2.338
 GG versus GT+TT 0.29 0.780 −1.804 to 2.310
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Discussion

Patterning of DNA methylation plays a critical role in epigenetic gene regulation during normal development (Weber et al. 2007), it is the most common epigenetic modification of DNA in mammalian genomes, presenting a heritable epigenetic feature that is associated with transcriptional silencing, X-chromosome inactivation, genetic imprinting, genomic stability and gene expression (Jones and Baylin 2002). However, aberrant DNA methylation patterns involving hypermethylation or hypomethylation have been associated with the development and progression of various cancers (Bjornsson et al. 2004).

With regard to DNMT3B polymorphisms, previous studies focused on the transcriptional start site within the promoter region, especially the SNPs rs2424913 and rs1569686. In the present study, we conducted a meta-analysis on the association between the four most frequently studied DNMT3B polymorphisms and cancer risk, including rs2424913, rs1569686, rs6087990 and rs2424908, with the first 2 SNP sites found in promoter regions and the latter 2 in an exon region.

To the best of our knowledge, our meta-analysis is the first to critically examine the available literature and characterize the potential impact of DNMT3B on cancer risk, and it was evaluated by the pooled results from 34 published studies. The results demonstrated that the rs2424913 (CT genotype and dominant model) was associated with a significantly reduced risk of developing cancer in the African population and that the rs1569686 (AG genotype and dominant model) was associated with a decreased risk of developing cancer, in particular in the Asian population (AG genotype) and for colorectal cancer (G allele, AG genotype and dominant model) and other cancer subtypes (AG genotype). The similar results have been obtained for both rs2424193 and rs1569686 by Zhu et al. (2012). Moreover, the results showed rs2424913 polymorphism is not associated with colorectal cancer risk, which is consistent with the other two published studies (Fang et al. 2012; Meng et al. 2013).

Similarity to the above, rs6087990 (T allele, CT genotype and dominant) was associated with a decreased risk of developing cancer in the Asian populations, in contrast, a subtly increased risk was observed in a Caucasian population (CT genotype and dominant model). However, for the rs2424908 (CT genotype and dominant model) was associated with a significantly reduced risk of developing cancer, most importantly, the populations of included studies for the rs2424908 were all Asian population. To date, there is no any quantitative evaluation of the rs6087990 and rs2424908.

The DNMT3B rs2424913 polymorphism is at an alternative promoter region, locating −149 bp from the DNMT3B promoter transcription start site, it was reported that a C–T transition confers a 30 % increase in promoter activity for the DNMT3B in vitro assays (Shen et al. 2002), and the DNMT3B polymorphism may be associated with cancer prognosis (Wang et al. 2004). Due to the activity of the DNMT3B promoter up-regulate DNMT3B expression that contributes an aberrant de novo methylation of CpG islands in some tumor suppressor genes (Robertson et al. 1999), the functional alteration of the DNMT3B should be associated with cancer risk. In the present study, we found that the rs2424913 SNP demonstrated no polymorphisms in 11 included studies, with 100 % of this population presenting the TT genotype in either the case or control group of Asian populations, but the significant distribution difference between Asian populations and Caucasian populations. Furthermore, genetic polymorphisms often vary across ethnic groups (Bamshad et al. 2004), and the observed diversity in the rs2424913 SNP distribution in different ethnic populations may explain the different methylation status in Asian and Caucasian populations. This status was similar to the genotype distribution of rs1569686 between Asian and Caucasian populations.

The DNMT3B rs1569686 polymorphism is located in the CpG poor promoter region, which is located −579 bp from the exon 1B transcription start site. Although in vitro promoter assays have revealed that this polymorphism does not affect the transcriptional activity of the DNMT promoter (Lee et al. 2005), there is still no certain evidence for assuring a negative association between rs1569686 polymorphism and cancer risk. The present study does reveal that DNMT3B plays a protective role for developing cancer, especially in the Asian populations and colorectal carcinogenesis. It is suggested that there may be the ethnic and histological difference in risk, due to certain genotypes conferring a greater susceptibility to a particular histological type of cancer (Lee et al. 2005; Li et al. 2004).

The DNMT3B rs6087990 is located −283 bp from the exon 1A transcription start site, and the rs2424908 is located in the intron region. Our present study revealed that rs6087990 and rs2424908 were associated with a significantly reduced risk of developing cancer in the Asian populations, but a subtly increased risk was observed in a Caucasian population. So far, these associations were first revealed by the pooled results. However, more larger and well-designed multicentric studies should be carried out to validate our findings due to lack of relevant published data. In the future, this finding will be updated as evidence emerges.

Although meta-analysis is robust, our study still has some limitations. Firstly, because only published studies were retrieved in the meta-analysis, publication bias might be possible, even though the statistical test did not show it. Secondly, for each selected case–control study, our results were based on unadjusted estimates, whereas a more precise analysis could be performed if individual data were available such as diet habit, smoking, drinking status and environmental factors. Thirdly, our analysis did not consider any potential gene–gene interaction, gene–environment interaction and/or the possibility of linkage disequilibrium between polymorphisms. Fourthly, although all eligible studies were summarized, the relatively small sample size of studies may lead to reduced statistical power when stratified according to the ethnicity and cancer subtype. Finally, the datasets without excluding the studies with inefficient stars based on NOS.

In summary, our data suggest that DNMT3B polymorphisms might be associated with decreased cancer risk especially in the Asian population and for colorectal cancer. However, there are notable limitations in the current literature, and further large-scale and well-designed studies concerning different ethnicities are still needed to confirm the results.

Acknowledgments

This research was supported by People’s Republic of China National Natural Science Foundation of China (No. 81202278), and Program for Science & Technology Innovation Talents in Universities of Henan Province (No. 2010HASTIT027), and Excellent Youth Foundation of He’nan Scientific Committee (No. 124100510007).

Conflict of interest

The authors declare no potential conflicts of interest.

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