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BMC Medical Genetics

, 20:79 | Cite as

Associations between XRCC3 Thr241Met polymorphisms and breast cancer risk: systematic-review and meta-analysis of 55 case-control studies

  • Sepideh Dashti
  • Zahra Taherian-Esfahani
  • Abbasali Keshtkar
  • Soudeh Ghafouri-FardEmail author
Open Access
Research article
Part of the following topical collections:
  1. Clinical-Molecular Genetics and Cytogenetics

Abstract

Background

The X-ray repair cross-complementing group 3 (XRCC3) is an efficient component of homologous recombination and is required for the preservation of chromosomal integrity in mammalian cells. The association between Thr241Met single-nucleotide polymorphism (SNP) in this gene and susceptibility to breast cancer has been assessed in several studies. Yet, reports are controversial. The present meta-analysis has been designed to identify whether this SNP is associated with susceptibility to breast cancer.

Methods

We performed a systematic review and meta-analysis for retrieving the case-control studies on the associations between T241 M SNP and the risk of breast cancer. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to verify the association in dominant, recessive, and homozygote inheritance models.

Results

We included 55 studies containing 30,966 sporadic breast cancer cases, 1174 familial breast cancer cases and 32,890 controls in the meta-analysis. In crude analyses, no association was detected between the mentioned SNP and breast cancer risk in recessive, homozygote or dominant models. However, ethnic based analysis showed that in sporadic breast cancer, the SNP was associated with breast cancer risk in Arab populations in homozygous (OR (95% CI) = 3.649 (2.029–6.563), p = 0.0001) and recessive models (OR (95% CI) = 4.092 (1.806–9.271), p = 0.001). The association was significant in Asian population in dominant model (OR (95% CI) = 1.296, p = 0.029). However, the associations was significant in familial breast cancer in mixed ethnic-based subgroup in homozygote and recessive models (OR (95% CI) = 0.451 (0.309–0.659), p = 0.0001, OR (95% CI) = 0.462 (0.298–0.716), p = 0.001 respectively).

Conclusions

Taken together, our results in a large sample of both sporadic and familial cases of breast cancer showed insignificant role of Thr241Met in the pathogenesis of this type of malignancy. Such results were more conclusive in sporadic cases. In familial cases, future studies are needed to verify our results.

Keywords

Genes Neoplasm Single nucleotide polymorphism Breast Cancer 

Abbreviations

CIs

95% confidence intervals

DSB

DNA double strand break

HB

Hospital based

HR

Homologous recombination

HWE

Hardy-Weinberg Equilibrium

NHEJ

Non-homologous end-joining

NOS

Newcastle–Ottawa Scale

ORs

Crude odds ratios

PB

Population based

SNP

Single-nucleotide polymorphism

XRCC3

X-ray repair cross-complementing group 3

Background

Breast cancer ranks first among all women’s cancers regarding its incidence and rank second among them regarding its cancer-related mortality rate [1]. Several genetic and environmental factors have been associated with breast cancer risk. Among the most relevant factors is the ability to repair DNA double strand break (DSB). The homologous recombination (HR) and the non-homologous end-joining (NHEJ) pathways have been developed in eukaryotic cells for repair of such defects [2]. Numerous single nucleotide polymorphisms (SNPs) within genes coding the NHEJ pathway have been associated with breast cancer risk [3]. More importantly, the mostly recognized breast cancer susceptibility genes BRCA1 and BRCA2 participate in the process of HR. Deficiencies in HR have been detected both in BRCA1/2 germline mutation–associated and remarkable fraction BRCA1/2 wild-type breast cancer patients [4]. The X-ray repair cross-complementing group 3 (XRCC3) is an efficient component of HR and is required for the preservation of chromosomal integrity in mammalian cells [5]. Consequently, it has been regarded as a supposed candidate gene for breast cancer susceptibility. However, the data regarding its participation in breast cancer risk are inconsistent. Hang et al. conducted a meta-analysis of 48 case-control studies (including 14 studies in breast cancer) and reported that XRCC3 Thr241Met significantly increased risk of breast cancer. However, they suggested that a single larger study should be performed to assess tissue-specific cancer risk in different ethnicities [6]. Garcı’a-Closas et al. meta-analyzed the studies in Caucasian populations (10,979 cases and 10,423 controls) and reported a weak association between homozygous variants for XRCC3 Thr241Met and risk of breast cancer. They concluded that this variant is implausible to have a considerable role in breast cancer risk. However, they suggested studies with larger sample sizes to assess probable underlying gene–gene interactions or associations in ethnic-based subgroups [7]. Lee et al. in their meta-analysis of 12 studies demonstrated that Thr/Met and Met/Met weakly elevated the risk of breast cancer compared to Thr/Thr genotype [8]. Economopoulos et al. conducted a meta-analysis on 20 case–control studies in non-Chinese individuals and three case–control studies on Chinese individuals and reported association between T allele of this polymorphism (corresponding to Met) and breast cancer risk in recessive model. However, the association was only detected in non-Chinese population [9]. He et al. reported the mentioned association in recessive and additive models, but suggested conduction of a study with the larger sample size to assess gene-environment interaction [10]. In another study, He et al. have conducted a meta-analysis of 157 case-control studies including 34 studies in breast cancer (22,917 cases and 24,313 controls) and suggested the XRCC3 Thr241Met as a susceptibility locus for breast cancer, especially in Caucasians [11]. Mao et al. demonstrated a significantly higher risk of breast cancer in heterozygote model but not in other models. Such association was significant in Asians. Based on the reported weak association, they suggested conduction of a study with larger sample size [12]. Finally, using 23 case-control studies, Chai et al. reported association between the mentioned polymorphism and breast cancer risk, especially in Asian populations and in patients without family history of breast cancer [13].

Therefore, according to inconclusive results of the previous meta-analyses and lack of systematic review in this regard, we conducted a systematic review and meta-analysis to assess the association between the Thr241Met SNP (rs861539) within XRCC3 and breast cancer risk in diverse inheritance models.

Methods

Registration

We conducted the present systematic review protocol according to the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) [14]. We also registered the study protocol on the international prospective register of systematic review (PROSPERO) network. The registration number was CRD42018104217.

Information source and searching strategy

We searched PubMed, Scopus, EMBASE, Web of Science and ProQuest databases, the key journals (Breast Cancer Research and Treatment, Cancer Research), conferences/ congress research papers (as Grey literature) and the reference list of the included primary studies until March 2018 T(1990/01/01:2018/03/31) using the following syntaxes: “x-ray repair cross-complementing group 3” or “XRCC3”and“polymorphisms” or “single nucleotide polymorphism” and “breast tumor” or “breast cancer” and “rs861539” or “c.722C > T” or “p.Thr241Met” or “T241 M” (see Additional file 1). The complete search syntaxes were developed based on MeSH database and Emtree. The syntaxes for each database are shown in supplementary file. We did not implement any language restriction.

Eligibility criteria and selection process

We included: i) all observational studies such as cross-sectional, case-control and cohort studies ii) studies that assessed associations between Thr241Met within XRCC3 and breast cancer risk. iii) Studies with available genotype frequencies in both case and control groups. We excluded books, reviews, editorial, letters and articles which did not intend to assess the association between XRCC3 Thr241Met SNP and breast cancer risk and those without control group data. Our participants are post- or pre-menopause women with breast cancer which is pathologically confirmed. Studies with male breast cancer cases were excluded. Our exposure is rs861539 (T241 M) that was evaluated with various genotyping methods such as PCR-RFLP, Taq-Man, Sequencing and etc. We performed search in the different mentioned sources and exported the search outputs into the End-Note software. The duplicated primary studies were deleted (only one version of the duplicated documents was kept). The screening phase (selecting included/ probable included versus excluded primary studies using the title or/ and the abstract) were performed. The selection or verification process (selecting included versus excluded primary studies) were performed based on the eligibility criteria. All steps for preparing this systematic review such as searching, screening based on titles of papers and abstracts, selection according to examination of full text of articles, risk of bias assessment and data extraction were done independently by two authors (SD and ZTE). Any disagreement regarding the inclusion/exclusion criteria and data extraction were resolved by consensus of the reviewers.

Quality assessment and data extraction

Methodological quality assessment (risk of bias assessment) was based on the Newcastle–Ottawa Scale (NOS). Checklist of each study was filled with two reviewers independently. Any disagreements (between two reviewers) were resolved by the discussion or consensus otherwise opinion of third expert reviewer. For assessing total quality status in primary study we used sum score of quality items. According to this score, we classified the papers in three groups (Good, Fair, Poor) [6]. Data was extracted by two reviewers as described above. Dataincluded general information of studies, study eligibility, method, risk of bias assessment and results including odds ratio. If there were some unclear information, we contacted with corresponding authors of studies. Our data extraction form includes the following items: First author, Publication year, Source of study participants, Name of Country, Ethnicity, Genotyping method and Reference number. Association between the mentioned polymorphism and breast cancer was evaluated by calculating crude OR based on 2-by-2 table. Furthermore, this association was assessed after controlling potentially confounder variables. For this reason, we extracted adjusted OR values which were calculated by logistic regression in primary studies. Since multi-variable logistic regression models in primary studies were not similar, all adjusted OR values were extracted from primary studies in order to combine similar adjusted OR values in data synthesis step.

Data synthesis (meta-analysis)

All of data analyses were performed in two distinct groups of familial breast cancer and sporadic breast cancer. Data were analyzed using STATA 13 software. Association between the mentioned SNP and breast cancer risk were analyzed by pooling odds ratio (ORs) with 95% confidence interval (CIs) in three models including dominant (TM + MM vs.TT), recessive (MM vs. TM + TT), and homozygote (MM vs.TT) models using STATA metan module. Z test was applied to assess the significance of the ORs, The heterogeneity between included publications was evaluated using I2 parameter as described previously [14] where the higher values indicate higher level of heterogeneity. Furthermore, we checked heterogeneity by the chi-square-based Q-test (Heterogeneity was considered statistically significant if p < 0.05) (Egger et al., 1997). We combined genotype frequencies to calculate univariable (crude) OR. In addition, combination of adjusted OR values was based on the similarity of adjusted OR values restricted in two models including age-adjusted (association between rs861539 and breast cancer after controlling age of patients) and age and other factors. The random-effects model was used to combine parameters acquired from discrete studies due to methodological variation. Sensitivity analyses were performed using leave-one-out sensitivity analysis to indicate the effect of the quality score on the results. Subgroup analyses were done for evaluating potential sources of heterogeneity based on ethnicity, case selection methods case group (hospital vs. population), methodological quality status (Good, Fair, Poor) and-case enrollment strategies (incident vs. prevalent).

Publication bias

Funnel plots, Begg’s and Egger’s test were used to measure publication bias (p-value< 0.1) [6, 11].

Results

Literature search

Figure 1 shows the data collection flow diagram for the present study. At the first step of database search, 4795 items were obtained. The initial screening and removal of duplicate items led to identification of 287 publications. Further screening resulted in removal of 187 items. Finally, full texts of the remained items were assessed for eligibility and 55 publications containing 30,966 sporadic breast cancer cases, 1174 familial breast cancer cases and 32,890 controls were included in the syntheses [8, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]. Tables 1 and 2 show the features of selected studies which assessed the association between the mentioned SNP and breast cancer in familial and sporadic cases respectively.
Fig. 1

PRISMA flow diagram showing the selection of the 55 eligible case control studies

Table 1

General characteristics of studies reporting associations in familial breast cancer (HB: hospital based, PB: population based, N/M: Not mentioned, HWE: Hardy-Weinberg Equilibrium, NOS: The Newcastle-Ottawa Scale, Quality of studies based on NOS star scoring system: 1–2 stars: poor, 3–5 stars: fair and 6–10 stars: good)

First Author

Year

Society

Country

Ethnicity

Genotyping Method

Case-enrollment strategy

Frequency in Cases

Frequency in Controls

HWE

NOS score

TT

TM

MM

Total

TT

TM

MM

Total

Costa

2007

HB

Portugal

Caucasian

PCR-RFLP

Prevalent

40

29

12

81

225

140

66

431

0

5

Dufloth

2005

HB

Brazil

Mixed

PCR-RFLP

Prevalent

27

18

7

52

68

35

15

118

0.005

3

Figueiredo

2004

PB

Canada

Caucasian

MALDI-TOF MS

Incident

29

38

16

83

13

20

4

37

0.341

9

Forsti

2004

PB

Finland

Caucasian

PCR-RFLP

Prevalent

72

85

15

172

89

88

25

202

0.654

4

Smith b

2003

HB

USA

Caucasian

PCR-RFLP

Incident

10

14

3

27

42

55

24

121

> 0.05

7

Vral

2011

HB

Italy

Caucasian

PCR-RFLP or SnapShot technique

N/M

60

87

23

170

54

84

30

168

0.964

2

Gonzalez-Hormazabal

2012

PB

Chile

Mixed

Taq-Man

Prevalent

187

103

32

322

335

209

23

567

0.177

7

Jara

2010

PB

Chile

Mixed

Conformation-sensitive gel electrophoresis (CSGE)

Prevalent

149

91

27

267

296

182

22

500

0.52

8

Table 2

General characteristics of studies reporting associations in sporadic breast cancer (HB: hospital based, PB: population based, HWE: Hardy-Weinberg Equilibrium, NOS: The Newcastle-Ottawa Scale)

First Author

Year

Society

Country

Ethnicity

Genotyping Method

Case-enrollment strategy

Frequency in Cases

Frequency in Controls

HWE

NOS

Score

TT

TM

MM

Total

TT

TM

MM

Total

Al Zoubi

2015

HB

Jordan

Arab

Sequencing

Prevalent

16

26

4

46

8

18

5

31

0.33

5

Al Zoubi

2017

HB

Italy

Caucasian

Sequencing

Prevalent

8

13

2

23

4

9

2

15

0.72

5

Ali

2016

HB

Saudi Arabian

Arab

PCR-RFLP

Incident

43

73

27

143

32

32

78

35

>  0.05

6

Brooks

2008

PB

USA

Mixed

PCR-RFLP

Incident

254

259

98

611

249

286

76

611

0.661

9

Costa

2007

HB

Portugal

Caucasian

PCR-RFLP

Prevalent

68

77

31

176

121

61

29

211

0

5

Devi

2017

HB

India

Asian

PCR-RFLP

Prevalent

350

100

14

464

426

99

9

534

0.25

10

Ding

2015

HB

China

Asian

PCR-LDR

Prevalent

510

91

5

606

557

74

2

633

0.25

7

Dufloth

2005

HB

Brazil

Mixed

PCR-RFLP

Prevalent

15

16

2

33

68

35

15

118

0.005

3

Figueiredo

2004

PB

Canada

Caucasian

MALDI-TOF MS

incident

110

148

61

319

133

180

52

365

0.39

9

Forsti

2004

PB

Finland

Caucasian

PCR-RFLP

Prevalent

111

80

32

223

161

110

27

298

0.654

4

Garcia-Closas

2006

PB

Poland

Caucasian

NA

Incident

785

907

282

1974

980

1039

266

2285

0.709

7

Garcia-Closas

2006

PB

USA

Caucasian

NA

Incident

1102

1419

457

2978

973

1213

368

2554

0.748

7

Gohari-Lasaki

2015

HB

Iran

Mixed

PCR-RFLP

Prevalent

70

13

17

100

69

22

9

100

NA

2

Han

2004

PB

USA

Mixed

Taq-Man

Incident

388

429

135

952

468

607

170

1245

0.225

8

Jacobsen

2003

PB

Denmark

Caucasian

Taq-Man / PCR-RFLP

Incident

163

203

59

425

160

198

65

423

0.772

4

Kipen

2017

HB

Belarus

Caucasian

PCR-RFLP

Incident

86

68

15

169

84

94

7

185

>  0.05

5

Krupa

2009

HB

Poland

Caucasian

PCR-RFLP

Prevalent

29

68

38

135

29

107

39

175

0.003

4

Kuschel

2002

PB

UK

Caucasian

Taq-Man

Incident

790

1026

327

2143

728

827

229

1784

0.8

4

Lavanya

2015

HB

India

Asian

PCR-RFLP

N/M

42

7

1

50

40

8

2

50

> 0.05

6

Lee

2007

HB

South Korea

Asian

Single base extension assay

Prevalent

437

51

1

489

349

29

0

378

0.74

6

Loizidou

2008

PB

Cyprus

Mixed

PCR-RFLP

Incident

312

560

220

1092

351

600

226

1177

0.285

8

Millikan

2005

PB

USA

Caucasian

Taq-Man

Incident

505

578

171

1254

435

555

142

1132

0.086

9

Millikan

2005

PB

USA

African-American

Taq-Man

Incident

482

222

41

745

421

211

44

676

0.015

9

Ozgoz

2017

HB

Turkey

Mixed

Multiplex-PCR & MALDI-TOF

Prevalent

42

46

14

102

37

40

23

100

0.234

7

Qureshi

2014

HB

Pakistan

Mixed

PCR-RFLP

Prevalent

74

67

15

156

101

44

5

105

>  0.05

6

Rafii

2003

HB

UK

Caucasian

Taq-Man

Prevalent

201

248

72

521

341

416

129

886

0.87

8

Ramadan

2014

HB

Egypt

Mixed

PCR-RFLP

Incident

28

57

15

100

30

37

8

75

0.491

7

Romanowicz

2017

HB

Poland

Caucasian

HRM

Prevalent

48

72

80

200

52

72

76

200

0.862

6

Romanowicz-Makowska

2012

HB

Poland

Caucasian

PCR-RFLP

Prevalent

210

370

180

760

178

366

216

760

0.343

5

Romanowicz-Makowska

2011

HB

Poland

Caucasian

PCR-RFLP

Prevalent

220

378

192

790

188

384

226

798

0.939

5

Sangrajrang

2007

HB

Thai

Asian

Melting curve analysis

Incident

437

69

1

507

384

38

2

424

0.322

6

Santos

2010

HB

Brazil

Mixed

PCR-RFLP

Incident

28

31

6

65

49

29

7

85

0.37

6

Shadrina

2016

PB

Russia

Caucasian

Taq-Man

Prevalent

285

284

95

664

294

278

72

644

0.59

6

Silva

2010

HB

Portugal

Caucasian

PCR-RFLP

N/M

109

138

42

289

178

276

94

548

0.46

6

Smith

2008

HB

USA

Caucasian

Mass ARRAY system

Incident

124

137

54

315

158

184

59

401

0.649

5

Smith

2008

HB

USA

African-American

Mass ARRAY system

Incident

32

19

1

52

48

20

5

73

0.169

7

Smith a

2003

HB

USA

Caucasian

PCR-RFLP

Incident

96

105

51

252

104

129

35

268

0.611

7

Smith b

2003

PB

USA

Caucasian

PCR-RFLP

Incident

30

40

17

87

39

55

15

109

0.68

7

Smolarz

2015

HB

Poland

Caucasian

PCR-RFLP

Prevalent

19

35

16

70

15

35

20

70

0.718

6

Sobczuk

2009

HB

Poland

Caucasian

PCR-RFLP

Prevalent

29

71

50

150

24

50

32

106

0.567

5

Sterpone

2010

HB

Italy

Caucasian

PCR-RFLP

Prevalent

18

21

4

43

15

15

4

34

0.853

6

Su

2015

HB

Taiwan

Asian

PCR-RFLP

Prevalent

1052

141

39

1232

1131

87

14

1232

0.89

7

Thyagarajan

2006

PB

USA

Caucasian

PCR-RFLP

N/M

160

192

67

419

126

157

40

323

0.405

8

Vral

2011

HB

Italy

Caucasian

PCR-RFLP or SnapShot

N/M

13

22

9

44

54

84

30

168

0.964

2

Webb

2005

PB

Australia

Caucasian

Taq-Man

Prevalent

500

612

184

1296

248

321

91

660

0.425

8

Webb

2005

PB

Australia

Mixed

Taq-Man

Prevalent

91

44

14

149

59

54

15

128

0.625

8

Zhang

2005

HB

China

Asian

PCR-RFLP

Incident

33

80

107

220

29

115

166

310

0.17

3

BCAC HBBCS

2006

HB

Germany

Caucasian

Taq-Man & ARMS

N/M

95

119

42

1156

77

88

29

194

0.64

5

BCAC Madrid

2006

HB

Spain

Caucasian

Taq-Man & Illumina

N/M

255

274

92

621

281

287

105

673

0.028

6

BCAC SEARCH

2006

PB

UK

Caucasian

Taq-Man

N/M

1177

1462

465

3104

1607

1898

549

4054

0.76

9

BCAC Seoul

2006

HB

Korea

Asian

Taq-Man & SNPstream

N/M

502

53

1

556

355

31

0

386

0.411

8

BCAC Sheffield

2006

HB

UK

Caucasian

Taq-Man

N/M

458

555

168

1181

437

534

195

1166

0.144

7

BCAC USRTS

2006

PB

USA

Caucasian

Taq-Man

N/M

281

336

98

715

402

480

155

1037

0.55

7

Meta-analysis results

Initially, we conducted the analysis in the familial and sporadic studies after using the random-effects model. Random model was used for analysis of associations in three inheritance models based on its more conservative nature. Final results for familial and sporadic studies are shown in Tables 3 and 4.
Table 3

Meta-analysis of studies reporting sporadic cases in different subgroups

Potential

 

Odd Ratio

(CI 95%)

No of Studies

Heterogeneity χ2

P value

I2

Interaction p value

A Homozygote model: MM vs. TT

Ethnicity

Caucasian

0.922 (0.838–1.016)

31

63.02

0.000

52.4%

0.0001

Asian

0.725 (0.345–1.522)

8

18.89

0.009

62.9%

African-American

1.278 (0.826–1.977)

2

0.77

0.381

0.0%

Arab

3.649 (2.029–6.563)

2

0.26

0.609

0.0%

Mixed

0.889 (0.694–1.140)

10

16.49

0.009

45.4%

Study-based

Hospital-based

0.979 (0.825–1.162)

36

81.66

0.000

57.1%

0.655

Population-based

0.869 (0.796–0.950)

17

26.22

0.051

39.0%

Methodological quality

Good

0.974 (0.786–1.208)

15

36.70

0.001

61.9%

0.891

Fair

0.930 (0.830–1.041)

36

84.07

0.000

58.4%

Poor

0.644 (0.338–1.229)

2

0.37

0.544

0.0%

Case enrollment strategies

Incident

0.938 (0.819–1.075)

20

54.88

0.000

59.9%

0.455

Prevalent

0.887 (0.720–1.093)

23

45.70

0.001

58.4%

Not mentioned

0.975 (0.798–1.191)

10

21.53

0.011

58.2%

All studies

 

0.937 (0.849–1.034)

53

124.20

0.000

58.1%

B Dominant model: TM + MM vs. TT

Ethnicity

Caucasian

1.022 (0.969–1.079)

31

43.65

0.051

31.3%

0.0001

Asian

1.296 (1.027–1.636)

8

18.22

0.011

61.6%

African-American

0.921 (0.749–1.134)

2

0.53

0.465

0.0%

 

Arab

0.671 (0.419–1.074)

2

0.00

0.950

0.0%

Mixed

1.084 (0.863–1.361)

10

33.91

0.000

73.5%

Study-based

Hospital-based

1.089 (0.975–1.215)

36

89.81

0.000

61.0%

0.655

Population-based

1.017 (0.955–1.084)

17

31.38

0.012

49.0%

Methodological quality

Good

1.028 (0.950–1.112)

15

36.88

0.001

62.0%

0.891

Fair

1.050 (1.010–1.091)

36

84.16

0.000

58.4%

Poor

1.022 (0.643–1.624)

2

0.12

0.725

0.0%

Case enrollment strategies

Incident

1.011 (0.934–1.095)

20

37.53

0.007

49.4%

0.455

Prevalent

1.111 (0.958–1.289)

23

74.40

0.000

70.4%

Not mentioned

1.042 (0.975–1.113)

10

7.89

0.545

0.0%

All studies

 

1.045 (0.982–1.112)

53

121.39

0.000

57.2%

C Recessive model: MM vs. TM + TT

Ethnicity

Caucasian

0.921 (0.849–1.000)

31

56.42

0.002

46.8%

0.000

Asian

0.688 (0.374–1.266)

8

15.51

0.030

54.9%

African-American

1.265 (0.778–2.055)

2

1.02

0.312

2.2%

Arab

3.649 (2.029–6.563)

2

1.55

0.213

35.4%

Mixed

0.895 (0.728–1.101)

10

13.93

0.125

35.4%

Study-based

Hospital-based

0.989 (0.844–1.159)

36

90.43

0.000

61.3%

0.00

Population-based

0.868 (0.806–0.934)

17

21.79

0.150

26.6%

Methodological quality

Good

0.961 (0.822–1.125)

15

27.19

0.018

48.5%

0.153

Fair

0.942 (0.841–1.055)

36

99.37

0.000

64.8%

Poor

0.645 (0.355–1.173)

2

0.84

0.359

0.0%

Case enrollment strategies

Incident

0.950 (0.823–1.097)

20

63.03

0.000

69.9%

0.377

Prevalent

0.900 (0.761–1.064)

23

45.19

0.003

51.3%

Not mentioned

0.974 (0.812–1.168)

10

21

0.013

57.1%

All studies

 

0.939 (0.857–1.029)

55

131.15

0.000

60.3%

Table 4

Meta-analysis of studies reporting familial cases in different subgroups

Potential

 

Odd Ratio

(CI 95%)

No of Studies

Heterogeneity χ2

P value

I2

Interaction p value

A Homozygote model: MM vs. TT

 Ethnicity

Caucasian

1.204 (0.835–1.735)

5

2.56

0.634

0.0%

0.000

Mixed

0.451 (0.309–0.659)

3

1.8

0.406

0.0%

 Study-based

Hospital-based

1.184 (0.784–1.788)

4

1.52

0.677

0.0%

0.690

Population-based

0.581 (0.318–1.060)

4

8.24

0.041

63.6%

 Methodological quality

Good

1.080 (0.691–1.688)

3

0.67

0.716

0.0%

0.002

Fair

0.504 (0.304–0.834)

4

4.51

0.211

33.5%

Poor

1.449 (0.752–2.793)

1

0.00

.

.%

 Case enrollment strategies

Incident

1.000 (0.300–3.327)

2

1.64

0.201

38.9%

0.068

Prevalent

0.683 (0.412–1.134)

5

10.69

0.030

62.6%

Not mentioned

1.449 (0.752–2.793)

1

0

.

.%

 All studies

 

0.809 (0.521–1.258)

8

17.7

0.013

60.4%

B Dominant model: TM + MM vs. TT

 Ethnicity

Caucasian

1.012 (0.800–1.280)

5

0.82

0.936

0.0%

0.576

Mixed

1.104 (0.909–1.341)

3

0.39

0.824

0.0%

 Study-based

Hospital-based

1.016 (0.770–1.341)

4

1.11

0.775

0.0%

0.690

Population-based

1.087 (0.910–1.299)

4

0.25

0.969

0.0%

 Methodological quality

Good

1.132 (0.855–1.499)

3

0.13

0.937

0.0%

0.614

Fair

1.075 (0.887–1.304)

4

0.41

. 0.937

0.0%

Poor

0.868 (0.553–1.364)

1

0.00

.

.%

 Case enrollment strategies

Incident

0.958 (0.530–1.733)

2

0.39

0.201

38.9%

0.579

Prevalent

1.104 (0.936–1.302)

5

0.03

0.856

0.0%

Not mentioned

0.868 (0.553–1.364)

1

0

.

.%

 All studies

 

1.066 (0.917–1.238)

8

1.52

0.982

0.0%

C Recessive model: MM vs. TM + TT

 Ethnicity

Caucasian

1.233 (0.877–1.732)

5

3.41

0.491

0.0%

0.576

Mixed

0.462 (0.298–0.716)

3

2.65

0.266

24.5%

 Study-based

Hospital-based

1.224 (0.834–1.796)

4

1.25

0.742

0.0%

0.690

Population-based

0.409 (0.228–0.734)

4

10.89

0.012

72.4%

 Methodological quality

Good

1.172 (0.765–1.793)

3

0.79

0.675

0.0%

0.614

Fair

0.515 (0.297–0.894)

4

5.63

0.131

46.7%

Poor

1.389 (0.770–2.508)

1

0.00

. 2.508

 Case enrollment strategies

Incident

0.977 (0.258–3.707)

5

14.05

0.007

71.5%

0.579

Prevalent

0.718 (0.410–1.257)

2

2.36

0.124

57.7%

Not mentioned

1.389 (0.770–2.508)

1

0.00

 All studies

 

0.831 (0.524–1.319)

8

21.53

0.003

67.5%

Bold entry is significant

The forest plots for each model are depicted in Figs. 2 and 3.
Fig. 2

Forest plots of XRCC3 Thr241Met polymorphism and sporadic breast cancer for all eligible studies. a Homozygote model: MM vs. TT. b Dominant model: TM + MM vs. TT. c Recessive model: MM vs. TM + TT

Fig. 3

Forest plots of XRCC3 Thr241Met polymorphism and familial breast cancer for all eligible studies. a Homozygote model: MM vs. TT. b Dominant model: TM + MM vs. TT. c Recessive model: MM vs. TM + TT

No significant associations were detected between the mentioned SNP and breast cancer risk in any inheritance model either in familial or in sporadic breast cancer cases.

Next, we assessed association between this SNP and risk of familial or sporadic breast cancer in ethnic-based subgroups (Figs. 4 and 5). In sporadic breast cancer, the SNP was associated with breast cancer risk in Arab populations in homozygous (OR (95% CI) = 3.649 (2.029–6.563), p = 0.0001) and recessive models (OR (95% CI) = 4.092 (1.806–9.271), p = 0.001). However, the association was significant in Asian population in dominant model (OR (95% CI) = 1.296 (1.027–1.636), p = 0.029). Based on the calculated Interaction p-value in ethnic-based subgroup analyses (p = 0.0001), we conclude that such subgroup analysis strategy was appropriate and the calculated ORs are significant. However, the associations was significant in familial breast cancer in mixed ethnic-based subgroup in homozygote and recessive models (OR (95% CI) = 0.451 (0.309–0.659), p = 0.0001, OR (95% CI) = 0.462 (0.298–0.716), p = 0.001 respectively).
Fig. 4

Forest plots of XRCC3 Thr241Met polymorphism and risk of sporadic breast cancer in ethnic-based subgroups. a Homozygote model: MM vs. TT. b Dominant model: TM + MM vs. TT. c Recessive model: MM vs. TM + TT

Fig. 5

Forest plots of XRCC3 Thr241Met polymorphism and risk of familial breast cancer in ethnic-based subgroups. a Homozygote model: MM vs. TT. b Dominant model: TM + MM vs. TT. c Recessive model: MM vs. TM + TT

Subsequently, we appraised the associations based on the study-base for selecting case/control (society) subgroup (hospital-based vs. population-based). In sporadic cases, the associations were significant in population-based studies in homozygote and recessive models (OR (95% CI) = 0.869 (0.796–0.950), p = 0.002 and OR (95% CI) = 0.868 (0.806–0.934), p = 0.0001 respectively). The Interaction p-value was calculated as 0.655 which shows inappropriateness of such subgroup analysis strategy. No significant associations were found in society-based analysis in familial cases (Additional file 2: Figure S1 and Additional file 3: Figure S2).

We also assessed the associations in methodological quality subgroups (Based on NOS scores) and found no significant association in sporadic (Interaction p-value = 0.891) but in familial cases we found the association in studies with fair quality in homozygote and recessive models (OR (95% CI) = 0.504 (0.304–0.834), p = 0.008, OR (95% CI) = 0.515 (0.297–0.894), p = 0.018 respectively) (Additional file 4: Figure S3 and Additional file 5: Figure S4).

Finally, we evaluated associations based on the case enrollment strategy (Incident vs. Prevalent). No significant associations were detected either in sporadic or familial cases (Interaction p-value = 0.22) (Additional file 6: Figure S5 and Additional file 7: Figure S6).

Publication bias

We conducted both Begg’s funnel plot and Egger’s test for appraisal of the publication bias in sporadic and familial studies separately. The calculated parameters are shown in Tables 3 and 4. Moreover, the outlines of the funnel plots were rather symmetric implying absence of any significant publication bias (Figs. 6 and 7).
Fig. 6

Funnel plots for whole publications in sporadic cases. a Dominant model: TM + MM vs.TT. b Recessive model: MM vs. TM + TT. c Homozygote model: MM vs.TT

Fig. 7

Funnel plots for whole publications in familial cases. a Dominant model: TM + MM vs.TT. b Recessive model: MM vs. TM + TT. c Homozygote model: MM vs.TT

Adjusted OR

As we did not detected any association between the mentioned SNP and breast cancer risk in crude analysis, we subsequently assessed associations considering the effects of confounder variables using adjusted ORs. We retrieved adjusted ORs and confounder variables from the publications. Subsequently, we categorized confounder variables to two groups: 1. Age 2. Other variables including body mass index, smoking, hazardous life style and contraceptive use. Analyses were performed in sporadic subgroup based on the three inheritance models (Fig. 8). There was no significant association between this SNP and risk of sporadic breast cancer in any inheritance model considering adjusted ORs.
Fig. 8

Forest plots for adjusted OR (adjusted for Age and Other variables including body mass index, smoking, hazardous life style and contraceptive use.) in sporadic cases. a Dominant model: TM + MM vs.TT. b Recessive model: MM vs. TM + TT. c Homozygote model: MM vs.TT

Sensitivity analysis and cumulative meta-analysis

To assess the strength of the association results, we conducted a leave-one-out sensitivity analysis by repeatedly removing one study at a time and re-measuring the summary OR. The summary ORs did not change, showing that our results were not originated from any certain study (Table 1).

Discussion

In the present meta-analysis, we assessed the associations between Thr241Met SNP and familial/ sporadic breast cancer based on the results of 55 studies containing 30,966 sporadic breast cancer cases, 1174 familial breast cancer cases and 32,890 controls. Crude analyses revealed no associations. In spite of assessing potential confounder variables and adjusting odds ratio of the primary studies, we did not find any association.

In sporadic cases, the narrow confidence intervals indicate the high power of the meta-analysis, so the results are conclusive. However, in familial cases, the wide confidence intervals imply that further studies are needed to reach conclusive results. Based on such findings, we predict that inclusion of further studies would not change the results of the meta-analysis. Sensitivity analyses by repeatedly removing one study at a time showed that the results of crude analysis were consistent result, therefore signifying the robustness of the study according to sensitivity analysis results, no relation between quality of studies with results and non-considerable publication bias.

Another strong point of our study was that we considered adjusted ORs to control the effects of confounding variables. Such approach further verified our results.

Through calculation of Interaction p values we determined subgroup analysis based on ethnicity as being the most strategy in this regard. Ethnic based analysis showed that in sporadic breast cancer, the SNP was associated with breast cancer risk in Arab and Mixed populations in homozygous and recessive models. The association was significant in Asian population in dominant model. However, no associations were detected in familial breast cancer in any ethnic-based subgroup and any inheritance model. The detected associations between this SNP and risk of sporadic breast cancer in certain populations had wide confidence intervals which necessitate extra studies. The same situation has been seen in familial breast cancer cases in ethnic-based subgroup analyses.

Chai et al. have performed a meta-analysis of 23 case-controls studies on association between Thr241Met SNP and breast cancer. Their meta-analysis of the pooled data of 13,513 cases and 14,100 controls association between the mentioned SNP and breast cancer risk in recessive and homozygote models in total populations as well as within Asian populations [14]. Our study had the advantage of including higher numbers of cases and controls and assessment of adjusted ORs and sensitivity analysis. The results of our ethnic-based analysis were consistent with their results regarding the observed association in Asian population but not regarding the associated model. Although they found association between this SNP and risk of sporadic breast cancer, we disapprove such association based on the obtained conclusive results.

In brief, we have implemented the high quality systematic review and meta-analysis including comprehensiveness (inclusion of 5 databases), inclusion of grey literature (theses) and duplicate implementation of all steps of systematic review and meta-analysis (independent implementation of search, screening, selection, quality assessment and data extraction by two authors). In addition, priori principle (establishment and registration of protocol) was applied.

Our study had some limitations. Based on the unavailability of sufficient data from the primary studies, we could not assess the association between the mentioned SNP and breast cancer risk in pre−/post-menopause subgroups. In addition, the adjusted OR values of the primary studies were based on different parameters which might influence the validity of this kind of statistical analysis. Finally, there were some limitations in the primary studies and we did not find any genotyping data according to breast cancer subtypes except for 3 studies in triple negative breast cancer. Due to the low number of primary studies, the result of meta-analysis based on breast cancer subtypes was not reliable. So, we did not performed this type of analysis.

Conclusion

Taken together, our results in a large sample of both sporadic and familial cases of breast cancer showed insignificant role of Thr241Met in the pathogenesis of this type of malignancy. Such results were more conclusive in sporadic cases. In familial cases, future studies are needed to verify our results.

Notes

Acknowledgements

The authors declare that there is no conflict of interest.

Funding

Not applicable.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Authors’ contributions

SD and ZTE assessed the studies and performed the meta-analysis. AK and SGF supervised the study. AK contributed in data acquisition and analysis. SGF wrote the manuscript. All authors approved the manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12881_2019_809_MOESM1_ESM.docx (14 kb)
Additional file 1: The search syntaxes for each database. (DOCX 14 kb)
12881_2019_809_MOESM2_ESM.zip (22 kb)
Additional file 2: Figure S1. Forest plots of XRCC3 Thr241Met polymorphism and risk of sporadic breast cancer in Study-based subgroups. (D) Homozygote model: MM vs. TT. (E) Dominant model: TM + MM vs. TT. (F) Recessive model: MM vs. TM + TT. (ZIP 21 kb)
12881_2019_809_MOESM3_ESM.zip (9 kb)
Additional file 3: Figure S2. Forest plots of XRCC3 Thr241Met polymorphism and risk of familial breast cancer in society -based subgroups. (D) Homozygote model: MM vs. TT. (E) Dominant model: TM + MM vs. TT. (F) Recessive model: MM vs. TM + TT. (ZIP 8 kb)
12881_2019_809_MOESM4_ESM.zip (22 kb)
Additional file 4: Figure S3. Forest plots of XRCC3 T241 M Polymorphism and Sporadic Breast Cancer according to NOS subgroup analysis. (A) Homozygote model: MM vs. TT. (B) Dominant model: TM + MM vs. TT. (C) Recessive model: MM vs. TM + TT. (ZIP 22 kb)
12881_2019_809_MOESM5_ESM.zip (9 kb)
Additional file 5: Figure S4. Forest plots of XRCC3 T241 M Polymorphism and Familial Breast Cancer according to NOS subgroup analysis. (A) Homozygote model: MM vs. TT. (B) Dominant model: TM + MM vs. TT. (C) Recessive model: MM vs. TM + TT. (ZIP 9 kb)
12881_2019_809_MOESM6_ESM.zip (22 kb)
Additional file 6: Figure S5. Forest plots of XRCC3 T241 M Polymorphism and Sporadic Breast Cancer according to case enrollment subgroup analysis. (A) Homozygote model: MM vs. TT. (B) Dominant model: TM + MM vs. TT. (C) Recessive model: MM vs. TM + TT. (ZIP 22 kb)
12881_2019_809_MOESM7_ESM.zip (9 kb)
Additional file 7: Figure S6. Forest plots of XRCC3 T241 M Polymorphism and Familial Breast Cancer according to case enrollment subgroup analysis. (A) Homozygote model: MM vs. TT. (B) Dominant model: TM + MM vs. TT. (C) Recessive model: MM vs. TM + TT. (ZIP 9 kb)

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Authors and Affiliations

  1. 1.Department of Medical GeneticsShahid Beheshti University of Medical SciencesTehranIran
  2. 2.Department of Health Sciences Education DevelopmentSchool of Public Health, Tehran University of Medical SciencesTehranIran

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