# A Meta-Analysis of the Association betweenDNMT1 Polymorphisms and Cancer Risk

Authors: Hao Li; Jing-wei Liu; Li-ping Sun; Yuan Yuan
Journal: BioMed Research International
 (2017)

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## Abstract

Previous studies have examined the associations of DNA methyltransferase 1 (DNMT1) polymorphisms, including single nucleotide polymorphisms rs16999593 (T/C), rs2228611 (G/A), and rs2228612 (A/G), with cancer risk. However, the results are inconclusive. The aim of this meta-analysis is to elucidate the associations betweenDNMT1 polymorphisms and cancer susceptibility. The PubMed, Embase, Web of Science, and Chinese National Knowledge Infrastructure databases were searched systematically to identify potentially eligible reports. Odd ratios and 95% confidence intervals were used to evaluate the strength of association between threeDNMT1 polymorphisms and cancer risk. A total of 16 studies were finally included in the meta-analysis, namely, nine studies of 3378 cases and 4244 controls for rs16999593, 11 studies of 3643 cases and 3866 controls for rs2228611, and three studies of 1343 cases and 1309 controls for rs2228612. TheDNMT1 rs2228612 (A/G) polymorphism was significantly related to cancer risk in the recessive model. The meta-analysis also suggested thatDNMT1 rs16999593 (T/C) may be associated with gastric cancer, while rs2228611 (G/A) may be associated with breast cancer. In future research, large-scale and well-designed studies are required to verify these findings.


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## Body

## 1. Introduction

DNA methylation is one of the most commonly occurring epigenetic events in the mammalian genome. DNA methyltransferases (DNMTs) are critical to establishing and maintaining DNA methylation patterns by converting cytosine residues to 5-methylcytosine (5mC) in cytosine–guanine (CpG) dinucleotides [1, 2]. The DNMT family comprises three active forms: DNMT1, DNMT3A, and DNMT3B. DNMT1 is the major enzyme responsible for methylation maintenance [3, 4], while DNMT3A and DNMT3B are thought to function in de novo methylation rather than in methylation maintenance [3, 4].DNMT1 is located on human chromosome 19p13.2 and encodes a protein comprising 1632 amino acids, which may be implicated in occurrence progression and prognosis of the cancer. DNMT1 consists of three major structural elements: an N-terminal regulatory domain that is necessary for the localization of DNMT1; a C-terminal catalytic domain that is involved in the binding of substrates; and a central linker that contains repeated glycine-lysine dipeptides [5]. Genetic variation caused by single nucleotide polymorphisms (SNPs) is the most common form of altered gene structure. The most commonly studiedDNMT1 SNPs are rs16999593 (T/C), rs2228611 (G/A), and rs2228612 (A/G), which are present in coding regions and, therefore, may influenceDNMT1expression.In recent years, various studies have indicated thatDNMT1 polymorphisms may play pivotal roles in carcinogenesis. The SNPs rs16999593 (T/C) and rs2228612 (G/A) were initially found to be associated with risk of breast cancer [6, 7], while SNP rs2228611 (A/G) was linked to gastric cancer [8]. Subsequently, a number of studies have concentrated on the relationships betweenDNMT1 polymorphisms and risks of different cancers [6, 7, 9–21]. However, the results from these studies are inconsistent.Until now, no meta-analysis has been carried out to investigate the relation of the threeDNMT1 polymorphisms (rs16999593 (T/C), rs2228611 (G/A), and rs2228612 (A/G)) with risk of cancer. Individual studies have lacked the ability to obtain overall reliable conclusions because of limited sample sizes and/or variations in ethnicities. To obtain further insights into the roles ofDNMT1 polymorphisms in carcinogenesis, we mainly performed a meta-analysis on the associations between these three SNPs and cancer risk.


## 2. Materials and Methods

### 2.1. Search Strategy

We systematically searched the PubMed, Embase, Web of Science, and Chinese National Knowledge Infrastructure databases using different combinations of the search terms “DNMT1 or DNA methyltransferase 1,” “polymorphism or mutation or variant,” and “cancer or neoplasm or tumor.” The search was last updated on 06 June 2016. When overlapping data were found, only the largest and latest study was selected. We contacted the authors and requested their specific raw data when the data provided in the published article were not sufficient.


### 2.2. Inclusion and Exclusion Criteria

Studies were included when they met the following criteria: case-control study; the subject was the association ofDNMT1 polymorphisms (rs16999593 (T/C), rs2228611 (G/A), and rs2228612 (A/G)) with risk of cancer; and essential information on genotype or allele frequencies was available to assess the odds ratios (ORs) and 95% confidence intervals (CIs). Exclusion criteria included review articles; systematic reviews and meta-analyses; animal studies; sample size less than 100; and scarce or insufficient information on genotype or allele frequencies for the rs16999593 (T/C), rs2228611 (G/A), or rs2228612 (A/G) polymorphisms ofDNMT1 despite us contacting the authors.


### 2.3. Data Extraction

Two of the authors (HL and JL) independently selected the articles and extracted the original data using a standardized and consistent method. The following information was collected from each study: first author, year of publication, ethnicity of the subjects, cancer type, numbers of cases and controls, and genotyping methods. Conflicts were resolved after discussion and consensus was finally reached on all the extracted information.


### 2.4. Statistical Analysis

All statistical analyses were conducted using STATA software (version 12.0; Stata Corp LP, College Station, TX). ORs and their corresponding 95% CIs were employed to assess the strength of relationships between theDNMT1 polymorphisms and cancer risk. P values < 0.05 were considered as statistically significant. Heterogeneity was calculated using the Q statistic (P value < 0.10 indicates significant heterogeneity among studies) and I-squared value. The Mantel-Haenszel fixed-effects model was used to calculate the pooled ORs when the heterogeneity of studies was not significant. Otherwise, the DerSimonian and Laird random-effects model was used. We conducted the sensitivity analysis to explore heterogeneity when significant heterogeneity existed. Subgroup analysis was applied to explore the effects of cancer type and genotyping method. In addition, Begg’s test and Egger’s test were performed to evaluate publication bias; P values < 0.05 for Begg’s and Egger’s tests indicate significant publication bias.


## 2.1. Search Strategy

We systematically searched the PubMed, Embase, Web of Science, and Chinese National Knowledge Infrastructure databases using different combinations of the search terms “DNMT1 or DNA methyltransferase 1,” “polymorphism or mutation or variant,” and “cancer or neoplasm or tumor.” The search was last updated on 06 June 2016. When overlapping data were found, only the largest and latest study was selected. We contacted the authors and requested their specific raw data when the data provided in the published article were not sufficient.


## 2.2. Inclusion and Exclusion Criteria

Studies were included when they met the following criteria: case-control study; the subject was the association ofDNMT1 polymorphisms (rs16999593 (T/C), rs2228611 (G/A), and rs2228612 (A/G)) with risk of cancer; and essential information on genotype or allele frequencies was available to assess the odds ratios (ORs) and 95% confidence intervals (CIs). Exclusion criteria included review articles; systematic reviews and meta-analyses; animal studies; sample size less than 100; and scarce or insufficient information on genotype or allele frequencies for the rs16999593 (T/C), rs2228611 (G/A), or rs2228612 (A/G) polymorphisms ofDNMT1 despite us contacting the authors.


## 2.3. Data Extraction

Two of the authors (HL and JL) independently selected the articles and extracted the original data using a standardized and consistent method. The following information was collected from each study: first author, year of publication, ethnicity of the subjects, cancer type, numbers of cases and controls, and genotyping methods. Conflicts were resolved after discussion and consensus was finally reached on all the extracted information.


## 2.4. Statistical Analysis

All statistical analyses were conducted using STATA software (version 12.0; Stata Corp LP, College Station, TX). ORs and their corresponding 95% CIs were employed to assess the strength of relationships between theDNMT1 polymorphisms and cancer risk. P values < 0.05 were considered as statistically significant. Heterogeneity was calculated using the Q statistic (P value < 0.10 indicates significant heterogeneity among studies) and I-squared value. The Mantel-Haenszel fixed-effects model was used to calculate the pooled ORs when the heterogeneity of studies was not significant. Otherwise, the DerSimonian and Laird random-effects model was used. We conducted the sensitivity analysis to explore heterogeneity when significant heterogeneity existed. Subgroup analysis was applied to explore the effects of cancer type and genotyping method. In addition, Begg’s test and Egger’s test were performed to evaluate publication bias; P values < 0.05 for Begg’s and Egger’s tests indicate significant publication bias.


## 3. Results

### 3.1. Characteristics of the Studies

This meta-analysis was organized according to the PRISMA (Supplementary File 1 in Supplementary Material available online athttps://doi.org/10.1155/2017/3971259). A detailed flow chart of the study selection process is shown in Figure 1. A total of 215 potentially relevant articles were found by searching the four databases and after removing duplicates. Altogether 187 publications were excluded mainly due to no relevance, animal not human experiments, reviews, or meeting abstract. The 28 remaining articles were evaluated further for eligibility. Finally, 16 articles were included in the present meta-analysis [6–21].Figure 1
The flowchart of literature inclusion and exclusion.The baseline characteristics of the 16 included studies are summarized in Table1. Among them, nine articles containing 3378 cases and 4244 controls surveyed the association between rs16999593 (T/C) and cancer risk [6, 7, 9–15]; 11 studies on the relation between rs2228611 (G/A) and cancer risk included 3643 cases and 3866 controls [6–8, 10, 11, 13, 14, 18–21]; and three publications containing 1343 cases and 1309 controls explored the correlation of rs2228612 (A/G) with cancer risk [7, 16, 17]. The populations surveyed in the nine rs16999593 (T/C) studies were all Chinese [6, 7, 9–15]. In the rs2228611 (G/A) studies, nine of the populations were Chinese [6, 7, 10, 11, 13, 14, 19–21], one was Iranian [8], and one was Polish [18]. In the rs2228612 (A/G) studies the populations were either Caucasian [16] or Chinese [7, 17]. The genotyping methods used to detect theDNMT1 polymorphisms included sequencing, MassARRAY, PCR-RFLP, MALDI-TOF, TaqMan, and SNPlex [6–21]. We used subgroup analyses to explore the effects of different cancer types and genotyping methods on the associations of increased risk of cancer with theDNMT1 rs16999593 (T/C) and rs2228611 (G/A) polymorphisms. We did not perform subgroup analysis for rs2228612 (A/G) because of the limited number of articles that was available.Table 1
Characteristics of the included studies in this meta-analysis.
Author
Year
Ethnicity
Cancer type
Genotyping method
Case
Control
Total
MM
WM
WW
Total
MM
WM
WW
For DNMT1 rs16999593 T/C polymorphism
Wang [12]
2014
Chinese
Cervical cancer
Sequencing
100
48
44
8
100
70
25
5
Gao [13]
2015
Chinese
Gastric cancer
Sequencing
310
180
112
18
420
281
117
22
Li [14]
2015
Chinese
Esophageal cancer
MassARRAY
258
138
80
40
260
127
109
24
He et al. [11]
2014
Chinese
Prostate cancer
MassARRAY
155
94
53
8
155
73
67
15
Sun [7]
2012
Chinese
Breast cancer
MassARRAY
1327
425
224
29
1440
504
202
28
Xiang et al. [6]
2010
Chinese
Breast cancer
PCR-RFLP
305
239
64
2
314
220
89
5
Jiang et al. [9]
2012
Chinese
Gastric cancer
Sequencing
447
283
144
20
961
659
273
29
Yang et al. [10]
2012
Chinese
Gastric cancer
MALDI-TOF
242
141
89
12
294
196
83
15
Tao et al. [15]
2015
Chinese
Breast cancer
Sequencing
234
68
164
2
300
180
105
15
For DNMT1 rs2228611 G/A polymorphism
Gao [13]
2015
Chinese
Gastric cancer
Sequencing
310
167
128
15
420
232
163
25
Li [14]
2015
Chinese
Esophageal cancer
MassARRAY
258
131
85
42
260
119
113
28
He et al. [11]
2014
Chinese
Prostate cancer
MassARRAY
155
82
61
12
155
79
64
12
Xiang et al. [6]
2010
Chinese
Breast cancer
PCR–RFLP
305
125
149
31
314
154
121
39
Yang et al. [21]
2016
Chinese
Renal cell carcinoma
PCR–RFLP
293
152
117
24
293
139
133
21
Yang et al. [10]
2012
Chinese
Gastric cancer
MALDI-TOF
242
132
97
13
285
160
99
26
Sun [7]
2012
Chinese
Breast cancer
MassARRAY
678
341
279
58
733
369
303
61
Mostowska et al. [18]
2013
Polish
Ovarian cancer
PCR–RFLP
159
28
74
57
210
44
94
72
Xi et al. [19]
2014
Chinese
Breast cancer
MALDI-TOF
810
385
362
63
848
432
343
73
Lin et al. [20]
2015
Chinese
Breast cancer
MALDI-TOF
233
107
109
17
236
120
94
22
Khatami et al. [8]
2009
Iranian
Gastric cancer
PCR–RFLP
200
34
50
16
112
32
62
18
For DNMT1 rs2228612 A/G polymorphism
Sun [7]
2012
Chinese
Breast cancer
MassARRAY
675
254
273
148
731
308
290
133
Chang et al. [17]
2014
Chinese
Esophageal cancer
SNPlex
137
52
56
29
357
100
200
57
Chang et al. [17]
2014
Chinese
Stomach cancer
SNPlex
143
43
72
28
357
100
200
57
Chang et al. [17]
2014
Chinese
Liver cancer
SNPlex
158
48
74
36
357
100
200
57
Kullmann et al. [16]
2013
Caucasian
Breast cancer
TaqMan
221
193
28
0
221
180
35
6
Abbreviations: W, wild-type allele; M, mutant-type allele.


### 3.2. Quantitative Data Synthesis

The results for the association ofDNMT1 rs16999593 (T/C) with cancer risk are summarized in Table 2. Overall, neither the heterozygous nor dominant genetic models found significant associations between rs16999593 (T/C) and cancer risk (TC versus TT: OR = 1.29, 95% CI = 0.90–1.84, P=0.163; TC + CC versus TT: OR = 1.28, 95% CI = 0.93–1.77, P=0.135). The allele analysis also found no significant association (C allele versus T allele: OR = 1.18, 95% CI = 0.96–1.45, P=0.127). For the subgroup analysis according to cancer type (Figure 2), rs16999593 (T/C) was consistently associated with increased risk of gastric cancer (TC versus TT: OR = 1.36, 95% CI = 1.14–1.61, P=0.001; TC + CC versus TT: OR = 1.36, 95% CI = 1.15–1.60, P<0.001; C allele versus T allele: OR = 1.28, 95% CI = 1.11–1.47, P=0.001), but no significant association was found with breast cancer. For the different genotyping methods, rs16999593 (T/C) demonstrated increased risk of cancer in the sequencing subgroup but not in the MassARRAY subgroup.Table 2
Meta-analysis results of the association betweenDNMT1 rs16999593 (T/C) polymorphism and cancer risk.
Genetic model
Group/subgroup
N
Heterogeneity test
Statistical model
Test for overall effect
I
2 (%)
P
h
e
t
OR (95% CI)
P
CC versus TT
Overall
9
37.5
0.119
F
1.17 (0.92–1.49)
0.213
Gastric cancer
3
0.0
0.743
F
1.36 (0.93–1.99)
0.117
Breast cancer
3
48.1
0.146
F
0.93 (0.58–1.48)
0.748
Sequencing
4
30.1
0.232
F
1.33 (0.90–1.95)
0.149
MassARRAY
3
66.1
0.052
R
1.01 (0.53–1.93)
0.968
TC versus TT
Overall
9
89.70
<0.001
R
1.29 (0.90–1.84)
0.163
Gastric cancer
3
00.00
0.540
F
1.36 (1.14–1.61)
0.001
Breast cancer
3
95.90
<0.001
R
1.53 (0.62–3.80)
0.360
Sequencing
4
90.30
<0.001
R
2.06 (1.16–3.65)
0.013
MassARRAY
3
85.40
0.001
R
0.84 (0.49–1.43)
0.517
(TC + CC) versus TT
Overall
9
88.60
<0.001
R
1.28 (0.93–1.72)
0.135
Gastric cancer
3
00.00
0.720
F
1.36 (1.15–1.60)
<0.001
Breast cancer
3
95.50
<0.001
R
1.45 (0.62–3.40)
0.388
Sequencing
4
88.40
<0.001
R
1.99 (1.20–3.28)
0.007
MassARRAY
3
83.60
0.002
R
0.88 (0.55–1.41)
0.603
CC versus (TC + TT)
Overall
9
48.7
0.049
R
1.03 (0.72–1.49)
0.861
Gastric cancer
3
0.00
0.635
F
1.22 (0.84–1.78)
0.303
Breast cancer
3
70.5
0.034
R
0.49 (0.13–1.76)
0.274
Sequencing
4
62.9
0.044
R
1.00 (0.49–2.04)
0.998
MassARRAY
3
65.8
0.054
R
1.10 (0.59–2.05)
0.767
C allele versus T allele
Overall
9
81.40
<0.001
R
1.18 (0.95–1.45)
0.127
Gastric cancer
3
00.00
0.936
F
1.28 (1.11–1.47)
0.001
Breast cancer
3
91.50
<0.001
R
1.18 (0.71–1.97)
0.529
Sequencing
4
66.30
0.030
R
1.53 (1.26–1.95)
<0.001
MassARRAY
3
81.10
0.005
R
0.96 (0.67–1.36)
0.805
Abbreviations: R, random effect model; F, fixed effect model.Figure 2
Forest plot for the association betweenDNMT1 rs16999593 (T/C) polymorphism and cancer risk in the cancer type subgroup. (a) TC versus TT; (b) TC + CC versus TT.
(a)
(b)The results for the association ofDNMT1 rs2228611 (G/A) with cancer risk are summarized in Table 3. Overall, the GA genotype was not significantly associated with risk of cancer compared with the GG genotype (OR = 1.05, 95% CI = 0.92–1.21, P=0.075), and the GA + AA genotype was not related to cancer risk compared with the GG genotype (OR = 1.05, 95% CI = 0.96–1.15, P=0.284). Similarly, no significant association was observed in the allele analysis (A allele versus G allele: OR = 1.02, 95% CI = 0.95–1.10, P=0.532). In the subgroup analysis according to cancer type (Figure 3), the rs2228611 (G/A) was associated with higher risk of breast cancer (GA versus GG: OR = 1.17, 95% CI = 1.03–1.33, P=0.016; GA + AA versus GG: OR = 1.13, 95% CI = 1.00–1.28, P=0.043). A similar result was found in the MALDI-TOF subgroup (GA versus GG: OR = 1.20, 95% CI = 1.03–1.41, P=0.022) in the analysis according to different genotyping methods. For the gastric cancer and PCR-RFLP subgroups, no significant associations were found in any of the compared genetic models.Table 3
Meta-analysis results of the association betweenDNMT1 rs2228611 (G/A) polymorphism and cancer risk.
Genetic model
Group/subgroup
Studies
Heterogeneity test
Statistical model
Test for overall effect
I
2 (%)
P
h
e
t
OR (95% CI)
P
AA versus GG
Overall
11
0.00
0.925
F
0.99 (0.84–1.19)
0.898
Gastric cancer
3
0.00
0.774
F
0.74 (0.49–1.13)
0.165
Breast cancer
4
0.00
0.979
F
0.98 (0.78–1.22)
0.848
PCR–RFLP
4
0.00
0.880
F
1.04 (0.76–1.42)
0.803
MALDI-TOF
3
0.00
0.511
F
0.87 (0.65–1.17)
0.360
GA versus GG
Overall
11
41.00
0.075
R
1.05 (0.92–1.21)
0.445
Gastric cancer
3
0.00
0.461
F
1.07 (0.86–1.33)
0.522
Breast cancer
4
35.60
0.198
F
1.17 (1.03–1.33)
0.016
PCR–RFLP
4
64.00
0.040
R
1.05 (0.73–1.52)
0.782
MALDI-TOF
3
0.00
0.910
F
1.20 (1.03–1.41)
0.022
(GA + AA) versus GG
Overall
11
1.80
0.425
F
1.05 (0.96–1.15)
0.284
Gastric cancer
3
0.00
0.618
F
1.02 (0.83–1.26)
0.860
Breast cancer
4
1.50
0.385
F
1.13 (1.00–1.28)
0.043
PCR–RFLP
4
51.70
0.102
F
1.06 (0.87–1.29)
0.542
MALDI-TOF
3
0.00
0.872
F
1.14 (0.98–1.33)
0.087
AA versus (GA + GG)
Overall
11
0.00
0.596
F
0.97 (0.83–1.13)
0.671
Gastric cancer
3
0.00
0.535
F
0.76 (0.51–1.12)
0.169
Breast cancer
4
0.00
0.813
F
0.90 (0.73–1.12)
0.351
PCR–RFLP
4
0.00
0.779
F
0.99 (0.76–1.29)
0.933
MALDI-TOF
3
0.00
0.501
F
0.80 (0.60–1.06)
0.126
A allele versus G allele
Overall
11
0.00
0.978
F
1.02 (0.95–1.10)
0.532
Gastric cancer
3
0.00
0.863
F
0.96 (0.82–1.14)
0.660
Breast cancer
4
0.00
0.861
F
1.06 (0.96–1.16)
0.249
PCR–RFLP
4
0.00
0.530
F
1.03 (0.90–1.18)
0.696
MALDI-TOF
3
0.00
0.745
F
1.04 (0.93–1.17)
0.494
R: random effect model; F: fixed effect model.Figure 3
Forest plot for the association betweenDNMT1 rs2228611 (G/A) polymorphism and risk of cancer in the subgroup of cancer type. (a) GA versus GG; (b) GA + AA versus GG.
(a)
(b)The results for the association ofDNMT1 rs2228612 (A/G) with cancer risk are summarized in Table 4 and Figure 4. Significant association with cancer risk was observed in the recessive model (GG versus AG + AA: OR = 1.29, 95% CI = 1.06–1.56, P=0.009), but no significant association was revealed in the allele analysis (G allele versus A allele: OR = 1.00, 95% CI = 0.83–1.20, P=0.980).Table 4
Meta-analysis results of the association betweenDNMT1 rs2228612 (A/G) polymorphism and cancer risk.
Genetic model
Heterogeneity test
Statistical model
Test for overall effect
I
2 (%)
P
h
e
t
OR (95% CI)
P
GG versus AA
19.20
0.292
F
1.20 (0.97–1.49)
0.088
AG versus AA
60.30
0.039
R
0.81 (0.61–1.08)
0.156
(GG + AG) versus AA
62.7
0.030
R
0.87 (0.66–1.14)
0.310
GG versus (AA + AG)
11.50
0.340
F
1.29 (1.06–1.56)
0.009
G allele versus A allele
58.70
0.046
R
1.00 (0.83–1.20)
0.980
R: random effect model; F: fixed effect model.Figure 4
Forest plot for the association betweenDNMT1 rs2228612 (A/G) polymorphism and cancer risk. (a) GG versus AA; (b) GG versus AA + AG.
(a)
(b)


### 3.3. Heterogeneity Test and Sensitivity Analysis

In most of the comparisons of DNMT1 rs16999593 and rs2228612 polymorphisms and one comparison of DNMT1 rs2228611, significant heterogeneity was observed. We next performed a leave-one-out sensitivity analysis. The results show that no individual study significantly affected the pooled OR (figure not shown), suggesting that the results of the meta-analysis were robust.


### 3.4. Publication Bias

Begg’s test and Egger’s test were used to quantitatively evaluate the publication bias of the selected studies; the details are listed in Table5. For the associations ofDNMT1 rs16999593 (T/C), rs2228611 (G/A), and rs2228612 (A/G) with cancer risk, rs2228612 (A/G) showed publication bias (GG versus AA; G allele versus A allele) and rs16999593 (T/C) showed publication bias in the comparison with the recessive model.Table 5
Results of publication bias test.
Polymorphism
Compared genotype
Begg’s test
Egger’s test
z-value
P value
t-value
P value
DNMT1 rs16999593 T/C
CC versus TT
0.94
0.348
−2.00
0.085
TC versus TT
0.10
0.917
0.03
0.976
(TC + CC) versus TT
0.10
0.917
0.02
0.982
CC versus (TC + TT)
1.98
0.048
−2.72
0.030
C allele versus T allele
0.10
0.917
−0.45
0.669
DNMT1 rs2228611 G/A
AA versus GG
1.40
0.161
−0.99
0.348
GA versus GG
0.78
0.436
−0.65
0.530
(GA + AA) versus GG
0.78
0.436
−0.69
0.505
AA versus (GA + GG)
0.47
0.640
−0.61
0.556
A allele versus G allele
0.93
0.350
1.17
0.273
DNMT1 rs2228612 (A/G)
GG versus AA
1.71
0.086
−3.97
0.029
AG versus AA
0.24
0.806
−2.86
0.065
(GG + GA) versus AA
0.73
0.462
−3.81
0.032
GG versus (AA + AG)
0.73
0.462
−1.37
0.263
G allele versus A allele
2.20
0.027
−4.26
0.024


## 3.1. Characteristics of the Studies

This meta-analysis was organized according to the PRISMA (Supplementary File 1 in Supplementary Material available online athttps://doi.org/10.1155/2017/3971259). A detailed flow chart of the study selection process is shown in Figure 1. A total of 215 potentially relevant articles were found by searching the four databases and after removing duplicates. Altogether 187 publications were excluded mainly due to no relevance, animal not human experiments, reviews, or meeting abstract. The 28 remaining articles were evaluated further for eligibility. Finally, 16 articles were included in the present meta-analysis [6–21].Figure 1
The flowchart of literature inclusion and exclusion.The baseline characteristics of the 16 included studies are summarized in Table1. Among them, nine articles containing 3378 cases and 4244 controls surveyed the association between rs16999593 (T/C) and cancer risk [6, 7, 9–15]; 11 studies on the relation between rs2228611 (G/A) and cancer risk included 3643 cases and 3866 controls [6–8, 10, 11, 13, 14, 18–21]; and three publications containing 1343 cases and 1309 controls explored the correlation of rs2228612 (A/G) with cancer risk [7, 16, 17]. The populations surveyed in the nine rs16999593 (T/C) studies were all Chinese [6, 7, 9–15]. In the rs2228611 (G/A) studies, nine of the populations were Chinese [6, 7, 10, 11, 13, 14, 19–21], one was Iranian [8], and one was Polish [18]. In the rs2228612 (A/G) studies the populations were either Caucasian [16] or Chinese [7, 17]. The genotyping methods used to detect theDNMT1 polymorphisms included sequencing, MassARRAY, PCR-RFLP, MALDI-TOF, TaqMan, and SNPlex [6–21]. We used subgroup analyses to explore the effects of different cancer types and genotyping methods on the associations of increased risk of cancer with theDNMT1 rs16999593 (T/C) and rs2228611 (G/A) polymorphisms. We did not perform subgroup analysis for rs2228612 (A/G) because of the limited number of articles that was available.Table 1
Characteristics of the included studies in this meta-analysis.
Author
Year
Ethnicity
Cancer type
Genotyping method
Case
Control
Total
MM
WM
WW
Total
MM
WM
WW
For DNMT1 rs16999593 T/C polymorphism
Wang [12]
2014
Chinese
Cervical cancer
Sequencing
100
48
44
8
100
70
25
5
Gao [13]
2015
Chinese
Gastric cancer
Sequencing
310
180
112
18
420
281
117
22
Li [14]
2015
Chinese
Esophageal cancer
MassARRAY
258
138
80
40
260
127
109
24
He et al. [11]
2014
Chinese
Prostate cancer
MassARRAY
155
94
53
8
155
73
67
15
Sun [7]
2012
Chinese
Breast cancer
MassARRAY
1327
425
224
29
1440
504
202
28
Xiang et al. [6]
2010
Chinese
Breast cancer
PCR-RFLP
305
239
64
2
314
220
89
5
Jiang et al. [9]
2012
Chinese
Gastric cancer
Sequencing
447
283
144
20
961
659
273
29
Yang et al. [10]
2012
Chinese
Gastric cancer
MALDI-TOF
242
141
89
12
294
196
83
15
Tao et al. [15]
2015
Chinese
Breast cancer
Sequencing
234
68
164
2
300
180
105
15
For DNMT1 rs2228611 G/A polymorphism
Gao [13]
2015
Chinese
Gastric cancer
Sequencing
310
167
128
15
420
232
163
25
Li [14]
2015
Chinese
Esophageal cancer
MassARRAY
258
131
85
42
260
119
113
28
He et al. [11]
2014
Chinese
Prostate cancer
MassARRAY
155
82
61
12
155
79
64
12
Xiang et al. [6]
2010
Chinese
Breast cancer
PCR–RFLP
305
125
149
31
314
154
121
39
Yang et al. [21]
2016
Chinese
Renal cell carcinoma
PCR–RFLP
293
152
117
24
293
139
133
21
Yang et al. [10]
2012
Chinese
Gastric cancer
MALDI-TOF
242
132
97
13
285
160
99
26
Sun [7]
2012
Chinese
Breast cancer
MassARRAY
678
341
279
58
733
369
303
61
Mostowska et al. [18]
2013
Polish
Ovarian cancer
PCR–RFLP
159
28
74
57
210
44
94
72
Xi et al. [19]
2014
Chinese
Breast cancer
MALDI-TOF
810
385
362
63
848
432
343
73
Lin et al. [20]
2015
Chinese
Breast cancer
MALDI-TOF
233
107
109
17
236
120
94
22
Khatami et al. [8]
2009
Iranian
Gastric cancer
PCR–RFLP
200
34
50
16
112
32
62
18
For DNMT1 rs2228612 A/G polymorphism
Sun [7]
2012
Chinese
Breast cancer
MassARRAY
675
254
273
148
731
308
290
133
Chang et al. [17]
2014
Chinese
Esophageal cancer
SNPlex
137
52
56
29
357
100
200
57
Chang et al. [17]
2014
Chinese
Stomach cancer
SNPlex
143
43
72
28
357
100
200
57
Chang et al. [17]
2014
Chinese
Liver cancer
SNPlex
158
48
74
36
357
100
200
57
Kullmann et al. [16]
2013
Caucasian
Breast cancer
TaqMan
221
193
28
0
221
180
35
6
Abbreviations: W, wild-type allele; M, mutant-type allele.


## 3.2. Quantitative Data Synthesis

The results for the association ofDNMT1 rs16999593 (T/C) with cancer risk are summarized in Table 2. Overall, neither the heterozygous nor dominant genetic models found significant associations between rs16999593 (T/C) and cancer risk (TC versus TT: OR = 1.29, 95% CI = 0.90–1.84, P=0.163; TC + CC versus TT: OR = 1.28, 95% CI = 0.93–1.77, P=0.135). The allele analysis also found no significant association (C allele versus T allele: OR = 1.18, 95% CI = 0.96–1.45, P=0.127). For the subgroup analysis according to cancer type (Figure 2), rs16999593 (T/C) was consistently associated with increased risk of gastric cancer (TC versus TT: OR = 1.36, 95% CI = 1.14–1.61, P=0.001; TC + CC versus TT: OR = 1.36, 95% CI = 1.15–1.60, P<0.001; C allele versus T allele: OR = 1.28, 95% CI = 1.11–1.47, P=0.001), but no significant association was found with breast cancer. For the different genotyping methods, rs16999593 (T/C) demonstrated increased risk of cancer in the sequencing subgroup but not in the MassARRAY subgroup.Table 2
Meta-analysis results of the association betweenDNMT1 rs16999593 (T/C) polymorphism and cancer risk.
Genetic model
Group/subgroup
N
Heterogeneity test
Statistical model
Test for overall effect
I
2 (%)
P
h
e
t
OR (95% CI)
P
CC versus TT
Overall
9
37.5
0.119
F
1.17 (0.92–1.49)
0.213
Gastric cancer
3
0.0
0.743
F
1.36 (0.93–1.99)
0.117
Breast cancer
3
48.1
0.146
F
0.93 (0.58–1.48)
0.748
Sequencing
4
30.1
0.232
F
1.33 (0.90–1.95)
0.149
MassARRAY
3
66.1
0.052
R
1.01 (0.53–1.93)
0.968
TC versus TT
Overall
9
89.70
<0.001
R
1.29 (0.90–1.84)
0.163
Gastric cancer
3
00.00
0.540
F
1.36 (1.14–1.61)
0.001
Breast cancer
3
95.90
<0.001
R
1.53 (0.62–3.80)
0.360
Sequencing
4
90.30
<0.001
R
2.06 (1.16–3.65)
0.013
MassARRAY
3
85.40
0.001
R
0.84 (0.49–1.43)
0.517
(TC + CC) versus TT
Overall
9
88.60
<0.001
R
1.28 (0.93–1.72)
0.135
Gastric cancer
3
00.00
0.720
F
1.36 (1.15–1.60)
<0.001
Breast cancer
3
95.50
<0.001
R
1.45 (0.62–3.40)
0.388
Sequencing
4
88.40
<0.001
R
1.99 (1.20–3.28)
0.007
MassARRAY
3
83.60
0.002
R
0.88 (0.55–1.41)
0.603
CC versus (TC + TT)
Overall
9
48.7
0.049
R
1.03 (0.72–1.49)
0.861
Gastric cancer
3
0.00
0.635
F
1.22 (0.84–1.78)
0.303
Breast cancer
3
70.5
0.034
R
0.49 (0.13–1.76)
0.274
Sequencing
4
62.9
0.044
R
1.00 (0.49–2.04)
0.998
MassARRAY
3
65.8
0.054
R
1.10 (0.59–2.05)
0.767
C allele versus T allele
Overall
9
81.40
<0.001
R
1.18 (0.95–1.45)
0.127
Gastric cancer
3
00.00
0.936
F
1.28 (1.11–1.47)
0.001
Breast cancer
3
91.50
<0.001
R
1.18 (0.71–1.97)
0.529
Sequencing
4
66.30
0.030
R
1.53 (1.26–1.95)
<0.001
MassARRAY
3
81.10
0.005
R
0.96 (0.67–1.36)
0.805
Abbreviations: R, random effect model; F, fixed effect model.Figure 2
Forest plot for the association betweenDNMT1 rs16999593 (T/C) polymorphism and cancer risk in the cancer type subgroup. (a) TC versus TT; (b) TC + CC versus TT.
(a)
(b)The results for the association ofDNMT1 rs2228611 (G/A) with cancer risk are summarized in Table 3. Overall, the GA genotype was not significantly associated with risk of cancer compared with the GG genotype (OR = 1.05, 95% CI = 0.92–1.21, P=0.075), and the GA + AA genotype was not related to cancer risk compared with the GG genotype (OR = 1.05, 95% CI = 0.96–1.15, P=0.284). Similarly, no significant association was observed in the allele analysis (A allele versus G allele: OR = 1.02, 95% CI = 0.95–1.10, P=0.532). In the subgroup analysis according to cancer type (Figure 3), the rs2228611 (G/A) was associated with higher risk of breast cancer (GA versus GG: OR = 1.17, 95% CI = 1.03–1.33, P=0.016; GA + AA versus GG: OR = 1.13, 95% CI = 1.00–1.28, P=0.043). A similar result was found in the MALDI-TOF subgroup (GA versus GG: OR = 1.20, 95% CI = 1.03–1.41, P=0.022) in the analysis according to different genotyping methods. For the gastric cancer and PCR-RFLP subgroups, no significant associations were found in any of the compared genetic models.Table 3
Meta-analysis results of the association betweenDNMT1 rs2228611 (G/A) polymorphism and cancer risk.
Genetic model
Group/subgroup
Studies
Heterogeneity test
Statistical model
Test for overall effect
I
2 (%)
P
h
e
t
OR (95% CI)
P
AA versus GG
Overall
11
0.00
0.925
F
0.99 (0.84–1.19)
0.898
Gastric cancer
3
0.00
0.774
F
0.74 (0.49–1.13)
0.165
Breast cancer
4
0.00
0.979
F
0.98 (0.78–1.22)
0.848
PCR–RFLP
4
0.00
0.880
F
1.04 (0.76–1.42)
0.803
MALDI-TOF
3
0.00
0.511
F
0.87 (0.65–1.17)
0.360
GA versus GG
Overall
11
41.00
0.075
R
1.05 (0.92–1.21)
0.445
Gastric cancer
3
0.00
0.461
F
1.07 (0.86–1.33)
0.522
Breast cancer
4
35.60
0.198
F
1.17 (1.03–1.33)
0.016
PCR–RFLP
4
64.00
0.040
R
1.05 (0.73–1.52)
0.782
MALDI-TOF
3
0.00
0.910
F
1.20 (1.03–1.41)
0.022
(GA + AA) versus GG
Overall
11
1.80
0.425
F
1.05 (0.96–1.15)
0.284
Gastric cancer
3
0.00
0.618
F
1.02 (0.83–1.26)
0.860
Breast cancer
4
1.50
0.385
F
1.13 (1.00–1.28)
0.043
PCR–RFLP
4
51.70
0.102
F
1.06 (0.87–1.29)
0.542
MALDI-TOF
3
0.00
0.872
F
1.14 (0.98–1.33)
0.087
AA versus (GA + GG)
Overall
11
0.00
0.596
F
0.97 (0.83–1.13)
0.671
Gastric cancer
3
0.00
0.535
F
0.76 (0.51–1.12)
0.169
Breast cancer
4
0.00
0.813
F
0.90 (0.73–1.12)
0.351
PCR–RFLP
4
0.00
0.779
F
0.99 (0.76–1.29)
0.933
MALDI-TOF
3
0.00
0.501
F
0.80 (0.60–1.06)
0.126
A allele versus G allele
Overall
11
0.00
0.978
F
1.02 (0.95–1.10)
0.532
Gastric cancer
3
0.00
0.863
F
0.96 (0.82–1.14)
0.660
Breast cancer
4
0.00
0.861
F
1.06 (0.96–1.16)
0.249
PCR–RFLP
4
0.00
0.530
F
1.03 (0.90–1.18)
0.696
MALDI-TOF
3
0.00
0.745
F
1.04 (0.93–1.17)
0.494
R: random effect model; F: fixed effect model.Figure 3
Forest plot for the association betweenDNMT1 rs2228611 (G/A) polymorphism and risk of cancer in the subgroup of cancer type. (a) GA versus GG; (b) GA + AA versus GG.
(a)
(b)The results for the association ofDNMT1 rs2228612 (A/G) with cancer risk are summarized in Table 4 and Figure 4. Significant association with cancer risk was observed in the recessive model (GG versus AG + AA: OR = 1.29, 95% CI = 1.06–1.56, P=0.009), but no significant association was revealed in the allele analysis (G allele versus A allele: OR = 1.00, 95% CI = 0.83–1.20, P=0.980).Table 4
Meta-analysis results of the association betweenDNMT1 rs2228612 (A/G) polymorphism and cancer risk.
Genetic model
Heterogeneity test
Statistical model
Test for overall effect
I
2 (%)
P
h
e
t
OR (95% CI)
P
GG versus AA
19.20
0.292
F
1.20 (0.97–1.49)
0.088
AG versus AA
60.30
0.039
R
0.81 (0.61–1.08)
0.156
(GG + AG) versus AA
62.7
0.030
R
0.87 (0.66–1.14)
0.310
GG versus (AA + AG)
11.50
0.340
F
1.29 (1.06–1.56)
0.009
G allele versus A allele
58.70
0.046
R
1.00 (0.83–1.20)
0.980
R: random effect model; F: fixed effect model.Figure 4
Forest plot for the association betweenDNMT1 rs2228612 (A/G) polymorphism and cancer risk. (a) GG versus AA; (b) GG versus AA + AG.
(a)
(b)


## 3.3. Heterogeneity Test and Sensitivity Analysis

In most of the comparisons of DNMT1 rs16999593 and rs2228612 polymorphisms and one comparison of DNMT1 rs2228611, significant heterogeneity was observed. We next performed a leave-one-out sensitivity analysis. The results show that no individual study significantly affected the pooled OR (figure not shown), suggesting that the results of the meta-analysis were robust.


## 3.4. Publication Bias

Begg’s test and Egger’s test were used to quantitatively evaluate the publication bias of the selected studies; the details are listed in Table5. For the associations ofDNMT1 rs16999593 (T/C), rs2228611 (G/A), and rs2228612 (A/G) with cancer risk, rs2228612 (A/G) showed publication bias (GG versus AA; G allele versus A allele) and rs16999593 (T/C) showed publication bias in the comparison with the recessive model.Table 5
Results of publication bias test.
Polymorphism
Compared genotype
Begg’s test
Egger’s test
z-value
P value
t-value
P value
DNMT1 rs16999593 T/C
CC versus TT
0.94
0.348
−2.00
0.085
TC versus TT
0.10
0.917
0.03
0.976
(TC + CC) versus TT
0.10
0.917
0.02
0.982
CC versus (TC + TT)
1.98
0.048
−2.72
0.030
C allele versus T allele
0.10
0.917
−0.45
0.669
DNMT1 rs2228611 G/A
AA versus GG
1.40
0.161
−0.99
0.348
GA versus GG
0.78
0.436
−0.65
0.530
(GA + AA) versus GG
0.78
0.436
−0.69
0.505
AA versus (GA + GG)
0.47
0.640
−0.61
0.556
A allele versus G allele
0.93
0.350
1.17
0.273
DNMT1 rs2228612 (A/G)
GG versus AA
1.71
0.086
−3.97
0.029
AG versus AA
0.24
0.806
−2.86
0.065
(GG + GA) versus AA
0.73
0.462
−3.81
0.032
GG versus (AA + AG)
0.73
0.462
−1.37
0.263
G allele versus A allele
2.20
0.027
−4.26
0.024


## 4. Discussion

DNMT1, the major methyltransferase in mammals, lies in the replication fork and methylates newly synthesized DNA strands directly in S phase of DNA replication sites [22], which is essential for epigenetic inheritance. Regional aberrant DNA hypermethylation has been identified as a possible mechanism of inactivation of tumor suppressor genes [23]. Many studies have indicated that the overexpression ofDNMT1 could silence vital tumor suppressor genes such asAPC,P16, andRUNX3 through DNA methylation [24, 25]. Therefore, DNMT1 might be implicated in the occurrence, development, and prognosis of multiple types of cancer.Polymorphisms have been identified as a powerful tool for predicting hereditary susceptibility of some complex diseases including cancer. However, previous individual studies about the association betweenDNMT1 polymorphisms and cancer risk were not only limited but also inconclusive. To our knowledge, this is the first comprehensive meta-analysis investigating the possible correlations of SNPs rs16999593 (T/C), rs2228611 (G/A), and rs2228612 (A/G) inDNMT1 with risk of overall cancer and specific cancer types, which is anticipated to shed light on the role ofDNMT1 polymorphisms in carcinogenesis.SNP rs2228612 (A/G) causes an isoleucine to phenylalanine substitution at amino acid 327 in the DNMT1 protein, which may alter the function of DNMT1 and influence its effect in the carcinogenesis. In this meta-analysis, we found thatDNMT1 rs2228612 (A/G) was associated with risk of overall cancer in the recessive model. However, only three articles with small-scale studies were available for analysis; therefore, the results should to be interpreted with caution.We did not find significant association between SNP rs16999593 (T/C) and overall cancer risk in any genetic comparison. Different types of cancer have distinct initiation and progression mechanisms, in which polymorphisms in key genes play critical roles. This meta-analysis elucidated that theDNMT1 rs16999593 (T/C) polymorphism was associated with different cancer types. In the subgroup analysis according to cancer type, the TC genotype of rs16999593 (T/C) was associated with risk of gastric cancer, but not breast cancer. Extensive evidence has suggested that DNA methylation is involved in the initiation and progression of gastric cancer and increased expression ofDNMT1 had been confirmed to be related to gastric cancer [25]. The AKT-NFκB and STAT3 signaling pathways can upregulateDNMT1 expression, which could cause aberrant DNA methylation of tumor suppressor genes and lead to gastric cancer [26, 27]. Therefore, SNP rs16999593 (T/C), which causes a histidine to arginine substitution at 97 positions of the amino acid sequence, might affect the function of DNMT1, thus increasing susceptibility to gastric cancer. The subgroup of breast cancer involved two types of breast cancer: sporadic triple-negative breast cancer (TNBC) [15] and infiltrating ductal beast carcinoma (IDBC) [6]. The TC genotype of rs16999593 (T/C) was related to increased TNBC risk but decreased IDBC risk. Therefore,DNMT1 might have diverse functions in different types of breast cancer. Further studies of the effects ofDNMT1 polymorphisms on specific breast cancer types are still needed. In addition, only in the subgroup of sequencing,DNMT1 rs16999593 (T/C) were constantly associated with increased cancer risk in all compared genetic models, indicating that different genotype detecting methods might influence the results.According to the functional prediction tool F-SNP (http://compbio.cs.queensu.ca/F-SNP/), SNP rs2228611 (G/A) may change the regulation ofDNMT1 splicing by leading to a synonymous mutation at amino acid 463. Multiple transcript variants of DNMT1 gene as a result of alternative splicing have been found. Therefore, we speculated that the rs2228611 (G/A) might influence the process of carcinogenesis by regulating the pattern of alternative splicing ofDNMT1. Here, we did not find any significant associations betweenDNMT1 rs2228611 (G/A) with altered risk of cancer in any genetic model. However, in the subgroup analysis, individuals with the GA genotype of rs2228611 (G/A) were associated with higher risk of breast cancer in both heterozygous and dominant models. In addition, individuals with the GA genotype of rs2228611 (G/A) had decreased risk in one study on esophageal cancer [14], which was opposite to the results for breast cancer. This reverse outcome may be because various types of cancer have different mechanisms of carcinogenesis. Future studies on different types of cancer may help to better understand these findings.Some limitations of our meta-analysis should be noted. Firstly, the number of studies was not sufficiently large, especially for subgroup analysis ofDNMT1 rs2228612 (A/G). Secondly, the languages of the publications were limited to English and Chinese. Thirdly, although this meta-analysis was based on the whole population, most studies were from Chinese populations, except for one Iranian case and one Polish case for rs2228611 (G/A) and another one Caucasian case for rs2228612 (A/G), which showed the same results with the Chinese population. So this study had a certain universality, especially for Chinese population. Finally, publication bias was found in two comparisons of rs2228612 (A/G) and one comparison of rs16999593 (T/C).


## 5. Conclusion

TheDNMT1 rs2228612 (A/G) GG genotype may be associated with increased risk of cancer compared with the AA + AG genotype. SNP rs16999593 (T/C) is significantly associated with gastric cancer risk while SNP rs2228611 (G/A) is significantly related to breast cancer risk. Further large-scale and well-designed investigations including different cancers are required to verify the findings of this study.



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*Source: 3971259-2017-04-03.xml*