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Association between the BRCA2 rs144848 polymorphism and cancer susceptibility: a meta-analysis

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Oncotarget. 2017; 8:39818-39832. https://doi.org/10.18632/oncotarget.16242

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Qiuyan Li, Rongwei Guan, Yuandong Qiao, Chang Liu, Ning He, Xuelong Zhang, Xueyuan Jia, Haiming Sun, Jingcui Yu and Lidan Xu _

Abstract

Qiuyan Li1,*, Rongwei Guan1,*, Yuandong Qiao1, Chang Liu1, Ning He2, Xuelong Zhang1, Xueyuan Jia1, Haiming Sun1, Jingcui Yu3 and Lidan Xu1

1 Laboratory of Medical Genetics, Harbin Medical University, Harbin, People’s Republic of China

2 Department of Clinical Laboratory, Qiqihar Traditional Chinese Medicine Hospital, Qiqihar, People’s Republic of China

3 The Second Affiliated Hospital, Harbin Medical University, Harbin, People’s Republic of China

* These authors have contributed equally to this work

Correspondence to:

Lidan Xu, email:

Jingcui Yu, email:

Keywords: meta-analysis; BRCA2; cancer; polymorphism; susceptibility

Received: August 26, 2016 Accepted: February 06, 2017 Published: March 15, 2017

Abstract

The BRCA2 gene plays an important role in cancer carcinogenesis, and polymorphisms in this gene have been associated with cancer risk. The BRCA2 rs144848 polymorphism has been associated with several cancers, but results have been inconsistent. In the present study, a meta-analysis was performed to assess the association between the rs144848 polymorphism and cancer risk. Literature was searched from the databases of PubMed, Embase and Google Scholar before April 2016. The fixed or random effects model was used to calculate pooled odd ratios on the basis of heterogeneity. Meta-regression, sensitivity analysis, subgroup analysis and publication bias assessment were also performed using STATA 11.0 software according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009. A total of 40 relevant studies from 30 publications including 34,911 cases and 48,329 controls were included in the final meta-analysis. Among them, 22 studies focused on breast cancer, seven on ovarian cancer, five on non-Hodgkin lymphoma, and the remaining six studies examined various other cancers. The meta-analysis results showed that there were significant associations between the rs144848 polymorphism and cancer risk in all genetic models. Stratified by cancer type, the rs144848 polymorphism was associated with non-Hodgkin lymphoma. Stratified by study design, the allele model was associated with breast cancer risk in population-based studies. The meta-analysis suggests that the BRCA2 rs144848 polymorphism may play a role in cancer risk. Further well-designed studies are warranted to confirm these results.


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INTRODUCTION

Cancer is one of the most common diseases causing considerable morbidity and mortality worldwide. Environmental and genetic factors together contribute to the development of cancers [1-4]. It has been reported that DNA damage and repair is an important factor in carcinogenesis [5-7]. BRCA2 is a well-known cancer susceptibility gene involved in the repair of double-stranded DNA breaks which functions by regulating the intracellular shuttling and activity of RAD51, another critical protein in homologous recombination [8-10]. Studies have shown that cancer carcinogenesis is related to abnormalities in DNA repair mechanisms partially caused by a change in gene function which can result from genetic polymorphisms [11, 12].

Within the last few years, many studies have focused on the association between BRCA2 gene polymorphisms and cancer risk, including breast cancer, ovarian cancer, non-Hodgkin lymphoma, prostate cancer and others [13-18]. The rs144848 is the only common non-synonymous polymorphism in exon 10 of the BRCA2 gene [19]. The change from A to C in the rs144848 polymorphism results in an asparagine-to-histidine transition (N372H) which may affect BRCA2 structure at residues 290-453, a region which has been determined to interact with the histone acetyltransferase P/CAF prior to transcriptional activation of target genes [20]. Over the past decade, many association studies have been conducted to explore the role of the rs144848 N372H polymorphism in cancer risk [13, 15, 17, 18, 21-40], but it is still inconclusive whether this polymorphism in the BRCA2 gene is associated with susceptibility to cancer. Therefore, we performed a systematic review and meta-analysis of published studies focused on the association between the rs144848 polymorphism and cancer risk. Our in-depth analysis may drive a more precise estimation of risk which could in turn help identify additional genetic targets for future therapeutic interventions.

RESULTS

Study characteristics

A flow diagram for the search strategy is shown in Figure 1. Based on the search strategy, 2,174 articles were identified in the initial search. After reading titles and abstracts, 1,788 articles were excluded and 386 articles were reviewed for full text. According to the study inclusion/exclusion criteria, 40 relevant studies from 30 publications including 34,911 cases and 48,329 controls were used for the final meta-analysis [13-15, 17, 18, 21, 23-40, 46-52]. Nine studies were medium quality and 31 studies were high quality. The main characteristics of these included studies are shown in Table 1.

Table 1: Characteristics of included studies that contributed to associations between rs144848 and cancer risk.

Study [ref] per SNP

Year

Race/ethnicity

Sourcea

Cases

Controls

Allele frequencies

NOS assessment

Cancer type

Total

NN

NH

HH

Total

NN

NH

HH

Casesb

Controlsb

Healey et al. [12]

2000

Caucasian

PB

234

116

99

19

266

138

115

13

0.71

0.73

7

Breast

Healey et al. [12]

2000

Caucasian

PB

1667

858

664

145

1201

631

493

77

0.71

0.73

7

Breast

Healey et al. [12]

2000

Caucasian

PB

450

236

180

34

228

124

94

10

0.72

0.75

7

Breast

Healey et al. [12]

2000

Caucasian

PB

659

325

285

49

866

433

373

60

0.71

0.72

7

Breast

Healey et al. [12]

2000

Caucasian

PB

449

270

154

25

453

277

152

24

0.77

0.78

7

Breast

Spurdle et al. [45]

2002

Caucasian

PB

1397

720

548

129

775

417

308

50

0.71

0.74

7

Breast

Ishitobi et al. [22]

2003

Asian

HB

149

97

47

5

144

85

56

3

0.81

0.78

7

Breast

Menzel et al. [24]

2004

Caucasian

PB

211

104

91

16

912

482

361

69

0.71

0.73

7

Breast

Menzel et al. [24]

2004

Caucasian

PB

94

53

35

6

152

84

57

11

0.75

0.74

7

Breast

Cox et al. [44]

2005

Caucasian

Nested

1285

695

501

89

1660

884

647

129

0.74

0.73

7

Breast

Millikan et al. [25]

2005

African

PB

762

564

183

15

675

510

153

12

0.86

0.87

7

Breast

Millikan et al. [25]

2005

Caucasian

PB

1265

662

521

82

1135

579

467

89

0.73

0.72

7

Breast

Garcia-Closas et al. [21]

2006

Caucasian

PB

3161

1617

1278

266

2701

1412

1057

232

0.71

0.72

7

Breast

Garcia-Closas et al. [21]

2006

Caucasian

PB

1968

1007

826

135

2276

1239

897

140

0.72

0.74

7

Breast

Johnson et al. [47]

2007

Caucasian

NA

473

233

201

39

2461

1278

993

190

0.71

0.72

6

Breast

Palli et al. [48]

2007

Caucasian

PB

91

48

31

12

261

127

107

27

0.70

0.69

6

Breast

Baynes et al. [46]

2007

Caucasian

PB

4537

2306

1892

339

4339

2182

1824

333

0.72

0.71

7

Breast

Seymour et al. [49]

2008

Caucasian

HB

252

127

111

14

100

50

44

6

0.72

0.72

6

Breast

Dombernowsky et al. [19]

2009

Caucasian

PB

1200

604

503

93

4119

2129

1677

313

0.71

0.72

6

Breast

Juwle et al. [23]

2012

Asian

NA

100

68

28

4

50

39

8

3

0.82

0.86

6

Breast

Hasan et al. [11]

2013

African

HB

100

38

33

29

100

33

32

35

0.55

0.49

6

Breast

Jumaah et al. [50]

2014

African

NA

36

26

10

0

10

10

0

0

0.86

1.00

6

Breast

Auranen et al. [26]

2003

Caucasian

PB

680

355

272

53

1546

819

629

98

0.72

0.73

7

Ovarian

Auranen et al. [26]

2003

Caucasian

PB

441

222

176

43

1097

578

445

74

0.70

0.73

7

Ovarian

Wenham et al. [28]

2003

Caucasian

PB

312

169

128

15

398

227

146

25

0.75

0.75

7

Ovarian

Beesley et al. [32]

2007

Caucasian

PB

492

249

203

40

948

502

383

63

0.71

0.73

8

Ovarian

Beesley et al. [32]

2007

Caucasian

PB

930

460

401

69

825

461

296

68

0.71

0.74

8

Ovarian

Ramus et al. [36]

2008

Mixed

Nested

4174

2196

1655

323

7402

3859

2979

564

0.72

0.72

7

Ovarian

Quaye et al. [37]

2009

Caucasian

PB

1459

779

569

111

2294

1200

925

169

0.73

0.72

7

Ovarian

Shen et al. [30]

2006

Mixed

PB

476

250

191

35

555

301

220

34

0.73

0.74

7

NHL c

Scott et al. [33]

2007

Caucasian

PB

757

387

307

63

676

375

253

48

0.71

0.74

7

NHL

Shen et al. [34]

2007

Caucasian

PB

556

271

236

49

498

246

203

49

0.70

0.70

7

NHL

Hill et al. [16]

2006

Mixed

PB

1116

577

441

98

926

505

361

60

0.71

0.74

7

NHL

Salagovic et al. [39]

2012

Caucasian

HB

107

62

34

11

127

82

40

5

0.74

0.80

7

NHL

Hu et al. [27]

2003

Asian

PB

120

69

39

12

231

126

95

10

0.74

0.75

6

Esophageal

Wu et al. [31]

2006

Caucasian

PB

604

306

246

52

595

332

223

40

0.71

0.75

8

Bladder

Debniak et al. [35]

2008

Caucasian

Nested

627

288

280

59

3819

1994

1580

245

0.68

0.73

6

Melanoma

Agalliu et al. [15]

2010

Caucasian

PB

1269

655

498

116

1243

654

500

89

0.71

0.73

8

Prostate

Agalliu et al. [15]

2010

African

PB

142

104

36

2

79

59

18

2

0.86

0.86

8

Prostate

Kotnis et al. [38]

2012

Asian

HB

109

35

56

18

186

81

70

35

0.58

0.62

7

Multiple

a Source in control, PB population-based study, HB hospital-based study

b Major allele frequency

c non-Hodgkin lymphoma

Study flow diagram.

Figure 1: Study flow diagram.

Association between BRCA2 rs144848 polymorphism and cancer risk

As shown in Table 2, there was no heterogeneity in any genetic model. The meta-analysis results showed that there were significant associations between the rs144848 polymorphism and cancer risk in all genetic models (H allele vs. N allele, OR = 1.044, 95% CI = 1.021-1.068, p < 0.001; NH vs. NN, OR = 1.037, 95% CI = 1.006-1.069, p = 0.018; HH vs. NN, OR = 1.104, 95% CI = 1.044-1.168, p = 0.001; dominant model, OR = 1.047, 95% CI = 1.018-1.078, p = 0.002; recessive model, OR = 1.086, 95% CI = 1.028-1.146, p = 0.003; Figure 2-6).

Forest plot for pooled ORs for the associations between allele model (H allele vs.

Figure 2: Forest plot for pooled ORs for the associations between allele model (H allele vs. N allele) of rs144844 and cancer risk in the overall population. Each square is proportional to the study-specific weight.

Forest plot for pooled ORs for the associations between additive model (NH vs.

Figure 3: Forest plot for pooled ORs for the associations between additive model (NH vs. NN) of rs144844 and cancer risk in the overall population. Each square is proportional to the study-specific weight.

Forest plot for pooled ORs for the associations between additive model (HH vs.

Figure 4: Forest plot for pooled ORs for the associations between additive model (HH vs. NN) of rs144844 and cancer risk in the overall population. Each square is proportional to the study-specific weight.

Forest plot for pooled ORs for the associations between dominant model (NH+HH vs.

Figure 5: Forest plot for pooled ORs for the associations between dominant model (NH+HH vs. NN) of rs144844 and cancer risk in the overall population. Each square is proportional to the study-specific weight.

Forest plot for pooled ORs for the associations between recessive model (HH vs.

Figure 6: Forest plot for pooled ORs for the associations between recessive model (HH vs. NH+NN) of rs144844 and cancer risk in the overall population. Each square is proportional to the study-specific weight.

Meta-regression analysis

The following covariates were considered for meta-regression: ethnicity, study design and cancer type. The results showed that cancer type contributed to effect in the meta-analysis (H allele vs. N allele, p = 0.011; HH vs. NN, p = 0.006; dominant model, p = 0.039; recessive model, p = 0.011).

Table 2: Summary of OR and 95%CI for association between rs144848 polymorphism and susceptibility to cancer.

Variable per SNP

I2 (%)

p for heterogeneity

OR (95% CI)

p value

p for publication bias

Effects model

Sensitive analysis

exclude

OR (95% CI )

p value

p for publication bias

H allele vs N allele

7.0

0.345

1.044 (1.021-1.068)

<0.001 a

0.045

fixed

[36]

1.053 (1.028-1.080)

<0.001 a

0.143

NH vs NN

0.0

0.491

1.037 (1.006-1.069)

0.018 a

0.147

fixed

[36]

1.048 (1.014-1.082)

0.005 a

0.352

HH vs NN

16.8

0.183

1.104 (1.044-1.168)

0.001 a

0.066

fixed

[46]

1.125 (1.060-1.194)

<0.001 a

0.148

Dominant model

0.0

0.470

1.047 (1.018-1.078)

0.002 a

0.069

fixed

[36]

1.059 (1.026-1.092)

<0.001 a

0.069

Recessive model

16.8

0.184

1.086 (1.028-1.146)

0.003 a

0.114

fixed

[46]

1.102 (1.040-1.168)

0.001 a

0.214

a Statistically significant

Subgroup analysis by cancer type stratification

Based on cancer type, four groups were included in the meta-analysis: breast cancer group, ovarian cancer group, non-Hodgkin lymphoma group and other cancers group. The results showed that the rs144848 polymorphism was not associated with breast cancer or ovarian cancer in any model. However, the rs144848 polymorphism was associated with non-Hodgkin lymphoma in four models (H allele vs. N allele, OR = 1.110, 95% CI = 1.023-1.205, p = 0.012; HH vs. NN, OR = 1.263, 95% CI = 1.035-1.542, p = 0.022; dominant model, OR = 1.118, 95% CI = 1.008-1.240, p = 0.035; recessive model, OR = 1.216, 95% CI = 1.002-1.476, p = 0.048) and with other cancers in all genetic models (Table 3).

Table 3: Summary of OR and 95% CI for association of rs144848 polymorphism with cancer risk by cancer type stratification.

Subgroup

p for heterogeneity

I2 (%)

OR (95% CI)

p value

Effects model

N allele vs H allele

Breast cancer

0.679

0.0

1.028 (0.997-1.060)

0.075

fixed

Ovarian cancer

0.359

9.1

1.024 (0.981-1.068)

0.280

fixed

NHL

0.518

0.0

1.110 (1.023-1.205)

0.012 a

fixed

Others

0.658

0.0

1.158 (1.074-1.249)

<0.001 a

fixed

NH vs NN

Breast cancer

0.890

0.0

1.029 (0.988-1.072)

0.166

fixed

Ovarian cancer

0.080

46.8

1.015 (0.959-1.074)

0.604

fixed

NHL

0.954

0.0

1.090 (0.977-1.215)

0.122

fixed

Others

0.090

47.5

1.117 (1.009-1.236)

0.033 a

fixed

HH vs NN

Breast cancer

0.491

0.0

1.056 (0.978-1.139)

0.162

fixed

Ovarian cancer

0.446

0.0

1.063 (0.957-1.180)

0.253

fixed

NHL

0.294

19.0

1.263 (1.035-1.542)

0.022 a

fixed

Others

0.653

0.0

1.439 (1.199-1.726)

<0.001 a

fixed

Dominant model

Breast cancer

0.852

0.0

1.033 (0.994-1.074)

0.097

fixed

Ovarian cancer

0.156

35.7

1.022 (0.969-1.079)

0.420

fixed

NHL

0.855

0.0

1.118 (1.008-1.240)

0.035 a

fixed

Others

0.237

26.3

1.162 (1.055-1.280)

0.002 a

fixed

Recessive model

Breast cancer

0.477

0.0

1.044 (0.969-1.124)

0.259

fixed

Ovarian cancer

0.351

10.3

1.057 (0.954-1.170)

0.290

fixed

NHL

0.277

21.6

1.216 (1.002-1.476)

0.048 a

fixed

Others

0.377

6.2

1.346 (1.130-1.603)

0.001 a

fixed

a Statistically significant

Association between BRCA2 rs144848 polymorphism and breast cancer risk

There were 22 breast cancer studies with different ethnicities and study designs. To assess the role of genetic background and the source of the control population in breast cancer risk, we carried out a subgroup analysis. In the analysis of genetic background, the overall population was divided into three subgroups, Caucasian, Asian, and African. The results showed that no statistically significant association was observed in any population (Table 4). In the analysis of study design, the overall population was divided into two subgroups, population-based studies and hospital-based studies. The results showed that the allele model was associated with the risk of breast cancer based on population-based studies (H allele vs. N allele, OR = 1.034, 95% CI = 1.000-1.068, p = 0.047; Table 5).

Table 4: Summary of OR and 95% CI for association of rs144848 polymorphism with breast cancer risk by ethnicity stratification.

Subgroup

p for heterogeneity

I2 (%)

OR (95% CI)

p value

Effects model

N allele vs H allele

Caucasian

0.690

0.0

1.029 (0.997-1.061)

0.075

fixed

Asian

0.262

20.5

0.974 (0.692-1.372)

0.882

fixed

African

0.185

40.8

1.024 (0.850-1.235)

0.801

fixed

NH vs NN

Caucasian

0.970

0.0

1.028 (0.986-1.072)

0.189

random

Asian

0.050

74.0

1.133 (0.427-3.006)

0.801

random

African

0.337

8.1

1.069 (0.798-1.430)

0.656

random

HH vs NN

Caucasian

0.332

10.4

1.060 (0.981-1.146)

0.138

fixed

Asian

0.551

0.0

1.086 (0.377-3.124)

0.879

fixed

African

0.388

0.0

0.877 (0.529-1.455)

0.612

fixed

Dominant model

Caucasian

0.925

0.0

1.033 (0.993-1.075)

0.106

fixed

Asian

0.101

62.8

0.955 (0.640-1.424)

0.820

fixed

African

0.244

29.2

1.065 (0.855-1.325)

0.575

fixed

Recessive model

Caucasian

0.333

10.3

1.048 (0.972-1.130)

0.220

fixed

Asian

0.395

0.0

1.078 (0.378-3.072)

0.888

fixed

African

0.443

0.0

0.876 (0.548-1.399)

0.579

fixed

Table 5: Summary of OR and 95% CI for association of rs144848 polymorphism with breast cancer risk by the study design stratification.

Subgroup

p for heterogeneity

I2 (%)

OR (95% CI)

p value

Effects model

H allele vs N allele

PB

0.691

0.0

1.034 (1.000-1.068)

0.047 a

fixed

HB

0.759

0.0

0.883 (0.707-1.103)

0.273

fixed

Others

0.264

24.5

1.011 (0.923-1.108)

0.810

fixed

NH vs NN

PB

0.953

0.0

1.030 (0.986-1.076)

0.182

fixed

HB

0.684

0.0

0.864 (0.638-1.171)

0.346

fixed

Others

0.174

39.6

1.050 (0.930-1.186)

0.428

fixed

HH vs NN

PB

0.315

12.4

1.076 (0.991-1.168)

0.082

fixed

HB

0.677

0.0

0.844 (0.501-1.422)

0.525

fixed

Others

0.559

0.0

0.957 (0.763-1.200)

0.702

fixed

Dominant model

PB

0.916

0.0

1.037 (0.995-1.081)

0.085

fixed

HB

0.750

0.0

0.856 (0.642-1.141)

0.290

fixed

Others

0.195

36.2

1.035 (0.922-1.162)

0.558

fixed

Recessive model

PB

0.297

14.0

1.063 (0.982-1.151)

0.132

fixed

HB

0.625

0.0

0.867 (0.538-1.398)

0.558

fixed

Others

0.627

0.0

0.943 (0.757-1.175)

0.600

fixed

a Statistically significant

Sensitivity analysis

To determine the degree to which an individual study affected the overall OR estimates, one-way sensitivity analysis was performed by excluding one study at a time and sequentially recalculating the overall effect. The results showed no influence on pooled ORs and 95% CIs as individual studies were excluded.

Publication bias

Publication bias was observed in only one model (H allele vs. N allele, p = 0.045; Table 2). However, there was no significant publication bias in any genetic model (p > 0.05) after sensitivity analysis. Trim and fill results showed that the adjusted risk estimate remained significant (H allele vs. N allele, OR = 1.028, 95% CI = 1.006-1.050, p = 0.014), which confirmed that the results of this meta-analysis were statistically robust.

DISCUSSION

The mechanisms underlying carcinogenesis are still not fully clear, but it has been suggested that genetic and environmental factors play the most important role in the development of cancer. The BRCA2 protein can regulate homologous recombination by interacting with the RAD51 recombinase, and many studies have suggested that the rs144848 polymorphism in the BRCA2 gene is a susceptibility locus for cancers [8]. However, until now, there has been no consistent result regarding the association between the rs144848 N372H polymorphism and cancer risk. To explain these contradictory results, a meta-analysis including 34,911 cases and 48,329 controls was conducted and five genetic models were utilized to assess the association between the BRCA2 rs144848 polymorphism and the risk of cancer.

In our meta-analysis, the results showed that there was no heterogeneity in any genetic model in overall population, while associations were observed between the rs144848 polymorphism and cancer risk in all genetic models. Meta-regression analysis suggested that ethnicity and study design had no influence on overall effect, but cancer type did contribute to effect (H allele vs. N allele, p = 0.011; HH vs. NN, p = 0.006; dominant model, p = 0.039; recessive model, p = 0.011). Based on cancer type, four groups were included in the meta-analysis: breast cancer group, ovarian cancer group, non-Hodgkin lymphoma group and other cancers group. The results showed that the rs144848 polymorphism was not associated with breast cancer or ovarian cancer in any model. However, the rs144848 polymorphism was associated with non-Hodgkin lymphoma in four models, and associated with other cancers in all genetic models.

The results showed a statistically significant association in all genetic models for overall population. Due to the relatively large number of research studies on breast cancer, we also did a subgroup analysis in the breast cancer group. To assess the role of genetic background in breast cancer, we stratified the population by ethnicity and found no association in Caucasian, Asian, and African subgroups. Considering that the number of publications in Asian and African populations was small, we believe our results may not be reliable due to insufficient statistical power, so additional studies should be conducted to confirm our results. However, after subgroup analysis by study design stratification, we found that the BRCA2 rs144848 N372H polymorphism was associated with increasing the risk of breast cancer in population-based studies (H allele vs. N allele, OR = 1.034, 95% CI = 1.000-1.068, p = 0.047). One-way sensitivity analysis suggested no influence of individual studies on pooled ORs and 95% CIs.

In 2006, a study from the breast cancer association consortium summarized the common breast cancer-associated polymorphisms but failed to show a significant association between the BRCA2 rs144848 polymorphism and breast cancer [53]. In 2010, Qiu et al. found in a meta-analysis that the BRCA2 rs144848 H allele may be a low-penetrant risk factor for developing breast cancer [54]. In 2014, Xue et al. conducted a meta-analysis to assess the association between the BRCA2 rs144848 polymorphism and cancer susceptibility [55]. In contrast to Qiu et al., they did not find an association between the BRCA2 rs144848 polymorphism and breast cancer, but did observe an association with ovarian cancer. Different results from Xue et al. were then obtained in 2015 by Wang et al., who found that the rs144848 polymorphism was not associated with ovarian cancer. Compared with this latter study, we updated and added several new studies which were strictly filtered by a quality assessment. In addition, we used five genetic models to assess the role of the BRCA2 rs144848 polymorphism in our meta-analysis. Another important difference from Wang et al. was that their results were based on the risk estimates obtained without the original genotype data, whereas all studies included in our meta-analysis provided genotype data, so that our results were more precise by calculating effect directly without potential deviations and biases.

The strength of this meta-analysis is that the most current literature was included. To guarantee the quality of the meta-analysis, the Newcastle-Ottawa scale was conducted to assess the quality of included studies, and a strict procedure for data extraction was performed by two investigators according to inclusion and exclusion criteria. Furthermore, no low-quality literature was included in this meta-analysis which might possibly have influenced our results. One-way sensitivity analysis and meta-regression were also performed to increase the robustness of our conclusions. Subgroup analysis by ethnicity and the source of the control population were used to explain the effect of genetic background and study design.

There are some limitations in this meta-analysis. First, the literature search strategy was limited by language, and only published papers in English were included. Second, because we excluded literature without original data, some studies were excluded. Third, other potential interactions including environment × gene, gene × gene and some potential covariates were not considered due to insufficient information.

In conclusion, our meta-analysis determined that the BRCA2 rs144848 polymorphism was associated with non-Hodgkin lymphoma, and indicated that the rs144848 H allele of the BRCA2 gene may be a low-penetrate risk factor enhancing carcinogenesis in breast cancer. Further well-designed studies are warranted to clarify the mechanism and increase comprehensive understanding of the role of the BRCA2 rs144848 polymorphism in cancer.

MATERIALS AND METHODS

Publication research

Studies were retrieved by searching PubMed, Embase and Google Scholar following the guidelines in Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 [41]. The last search was updated on April 2016 with the terms “cancer”, “tumor”, “BRCA”, “polymorphism”, “genetic”, “variant”, “rs144848” and “N372H”. References in potential articles were also included in order to find more relevant studies.

Inclusion criteria

All articles were reviewed by two investigators independently. Studies were included in the meta-analysis if they met the following criteria: (1) Studies were case-control or cohort studies; (2) articles were original studies of human participants; (3) genotype distributions were available; (4) studies were published in English; and (5) articles were association studies between rs144848 polymorphism and cancer risk. If studies were drawn from the same population, only the study with the largest sample size or with a sufficient quantity of useful data was included. If an article reported the results from different studies, each study was treated as a separate comparison in our meta-analysis.

Quality score assessment

The Newcastle-Ottawa scale was used to assess the quality of studies [42]. Three items including selection, comparability and exposure were used to calculate the score of studies with a maximum score of nine. Any disagreements were adjusted by a third reviewer. A total score of three or lower, four to six and seven or greater was considered to indicate low, medium and high quality studies, respectively.

Data extraction

Data were extracted from included studies using a standardized form. For each study, the following information was extracted: (1) name of first author, (2) year of publication, (3) ethnicity of population, (4) source of control population and (5) sample size and genotype distribution. Ethnicity was categorized as Caucasian, Asian or African, and the study design was categorized as population-based study, hospital-based study or nested study.

Statistical analysis

The odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were calculated to assess the association between the rs144848 polymorphism and cancer risk. Five models were used in this meta-analysis: (1) H allele vs. N allele, (2) NH vs. NN, (3) HH vs. NN, (4) dominant model, (NH+HH vs. NN), and (5) recessive model, (HH vs. NH+NN). Statistical analysis was performed using STATA 11.0 (Stata Corporation, College Station, TX, USA). The chi-square test was conducted to evaluate if the studies deviated from Hardy-Weinberg equilibrium, and the threshold for disequilibrium was p < 0.05. Cochran’s Q test and I2 statistic test were performed to assess heterogeneity across individual studies (p < 0.10 and I2 > 50% suggested heterogeneity). The fixed-effects model (the Mantel-Haenszel method) was used to estimate the pooled OR if I2 < 50%; otherwise, the random-effects model (the DerSimonian and Laird method) was used [43]. A value of p < 0.05 was accepted as the significance threshold for each genetic model.

Subgroup analysis was conducted based on ethnicity (Caucasian, Asian and African) and study design (population-based and hospital-based). If heterogeneity was present, meta-regression was conducted to explore the source of heterogeneity. One-way sensitivity analysis was used to assess the influence of the individual study set to the pooled ORs by sequential exclusion.

A funnel plot was performed to estimate the potential publication bias using Begg’s test, in which the standard error of log (OR) was plotted against its log (OR) [44]. Egger’s liner regression test was also used to evaluate publication bias with quantitative analysis as a supplement to the funnel plot [45]. The trim and fill method was used to adjust pooled ORs and 95% CIs if bias was detected.

ACKNOWLEDGMENTS

This work was supported by grants from the National Natural Science Foundation of China [81102278], the China Postdoctoral Science Foundation [20100481019], the Postdoctoral Science Special Foundation of Heilongjiang Province, China [LBH-TZ1208], the Postdoctoral Science Research Foundation of Heilongjiang Province, China [LBH-Q13128], and Wu lien-teh Youth Science Foundation of Harbin Medical University [WLD-QN1405].

CONFLICTS OF INTEREST

We declared that there is no duality of interest associated with this manuscript.

Submission declaration

Submission of the article implies that the work described has not been published previously.

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