Open Access

Association of COMT Val158Met polymorphism and breast cancer risk: an updated meta-analysis

  • Xue Qin1,
  • Qiliu Peng1, 5,
  • Aiping Qin2,
  • Zhiping Chen3,
  • Liwen Lin1,
  • Yan Deng1,
  • Li Xie1,
  • Juanjuan Xu1,
  • Haiwei Li1,
  • Taijie Li1,
  • Shan Li1Email author and
  • Jinmin Zhao4Email author
Contributed equally
Diagnostic Pathology20127:136

DOI: 10.1186/1746-1596-7-136

Received: 25 July 2012

Accepted: 5 October 2012

Published: 8 October 2012

Abstract

Background

Catechol-O-methyltransferase (COMT) is one of the most important enzymes involved in estrogen metabolism and its functional genetic polymorphisms may be associated with breast cancer (BC) risk. Many epidemiological studies have been conducted to explore the association between the COMT Val158Met polymorphism and breast cancer risk. However, the results remain inconclusive. In order to derive a more precise estimation of this relationship, a large meta-analysis was performed in this study.

Methods

Systematic searches of the PubMed, Embase and Cochrane Library were performed. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to estimate the strength of the association.

Results

A total of 56 studies including 34,358 breast cancer cases and 45,429 controls were included. Overall, no significant associations between the COMT Val158Met polymorphism and breast cancer risk were found for LL versus HH, HL versus HH, LL versus HL, recessive model LL versus HL+HH, and dominant model LL+HL versus HH. In subgroup analysis by ethnicity, source of controls, and menopausal status, there was still no significant association detected in any of the genetic models.

Conclusion

Our meta-analysis results suggest that the COMT Val158Met polymorphism may not contribute to breast cancer susceptibility.

Virtual slides

The virtual slides(s) for this article can be found here:http://​www.​diagnosticpathol​ogy.​diagnomx.​eu/​vs48061235777084​17

Keywords

COMT Polymorphism Breast cancer Meta-analysis

Introduction

Breast cancer is one of the most frequently occurring cancer and cancer-related deaths are highly prevalent worldwide, which has become a major public health challenge[1]. The mechanism of developing breast cancer is still unclear. It has been widely accepted that exposure to circulating estrogen may be important in the development of breast cancer. Since estrogen biosynthesis and metabolism consist of many translation and transcription steps, the genes involved in these processes may contribute to the level of estrogen and thereby influence the susceptibility to breast cancer. Among the genes identified, BRCA1 and BRCA2 mutations have been reported to be associated with a dominantly inherited increased risk of the disease. However, they only account for about 5% of breast cancer occurrences[2]. This fact leaves the possibility that low-penetrance genetic factors are likely to explain most of disease cases.

Catechol-O-methyltransferase (COMT) is an important phase II enzyme involved in the conjugation and inactivation of catechol estrogens[3]. COMT is expressed at high levels in a variety of human tissues including liver, kidney, breast, and red blood cells[4]. The COMT gene is located on chromosome 22q11[5]. A G to A transition in the COMT gene results in valine to methionine amino acid change in codon 108/158 in the cytosolic/membrane-bound form of the protein. This amino acid change is believed to result in a 3–4-fold decrease in enzymatic activity[6, 7]. Since the variant form (Met) has been associated with decreased activity of the COMT compared with the wildtype (Val), these two forms are represented as COMT-L allele and COMT-H allele, respectively. It has been hypothesized that the individuals who inherit the low activity COMT-L gene may be at increased risk for breast cancer because of an increased accumulation of the catechol estrogen intermediates[811].

The role of COMT Val158Met polymorphism in the development of breast cancer has been investigated in the past decade, with conflicting results. Several studies have previously suggested an association between the COMT Val158Met polymorphism and an increased risk of breast caner[1214]. However, other studies have failed to confirm such an association[15, 16]. Moreover, two meta-analyses investigating the same hypothesis[17, 18], quite similar in methods and performed almost at the same time, yielded different conclusions. The exact relationship between genetic polymorphisms of COMT Val158Met and susceptibility to breast cancer has not been entirely established. To clarify the effect of COMT Val158Met on the risk of breast cancer, our study undertakes a meta-analysis of all published case–control observational studies.

Materials and methods

Search strategy

Electronic databases PubMed (http://​www.​ncbi.​nlm.​nih.​gov/​pubmed/​), Embase (http://​www.​embase.​com/​) and Cochrane Library (http://​www.​thecochranelibra​ry.​com/​view/​0/​index.​html) were used to search for all genetic association studies evaluating the COMT Val158Met polymorphism and breast cancer risk up to February 2012, the search strategy was based on combinations of “Breast cancer”, “Catechol-O-methyltransferase”, “COMT”, “polymorphism”, and “mutation”. No language or country restrictions were applied. All eligible studies were retrieved, and their bibliographies were checked for other relevant publications. Review articles and bibliographies of other relevant studies identified were searched by hand to find additional eligible studies. When multiple publications reported on the same or overlapping data, we chose the most recent or largest population. When a study reported the results on different subpopulations, we treated it as separate studies in the meta-analysis.

Selection criteria

Studies included in our meta-analysis had to meet the following inclusion criteria: (1) evaluate the association between COMT Val108/158Met polymorphism and breast cancer risk; (2) case–control design; (3) sufficient data for estimating an odds ratio (OR) with 95% confidence interval (CI); and (4) studies with full text articles. Studies were excluded if one of the following existed: (1) no control population; and (2) duplicate of previous publication.

Data extraction

Information was carefully extracted from all eligible publications by two investigators (Xue Qin and Qiliu Peng) independently according to the inclusion criteria listed above. For conflicting evaluation, an agreement was reached following discussion during a consensus meeting with a third reviewer (Aiping Qin). For each study, the following information were collected: First author’s name, year of publication, country, ethnicity of the studied population, total numbers of cases and controls, breast cancer diagnosis criteria, matching criteria, genotyping method, menopausal status, sources of the control population, quality control of genotyping and P value for control population in Hardy–Weinberg equilibrium (HWE). We did not define any minimum number of patients to include in our meta-analysis.

Statistical analysis

Crude odds ratios (ORs) together with their corresponding 95% CIs were used to assess the strength of association between the COMT Val158Met polymorphism and breast cancer risk. The pooled ORs were performed for co-dominant model (LL vs. HH, HL vs. HH, and LL vs. HL), dominant model (LL+ HL vs. HH), and recessive model (LL vs. HL+HH), respectively. Departure from the Hardy–Weinberg equilibrium for the control group in each study was assessed using a web-based program (http://​ihg2.​helmholtz-muenchen.​de/​cgibin/​hw/​hwa1.​pl). In subgroup analysis, we evaluated the effect of COMT Val108/158Met polymorphism on the susceptibility of BC in different population stratified by ethnicity (Caucasian, Asian, and Mixed/other), menopausal status (Pre-, and Post-) and sources of the control population (HB, PB, and FB).

For each genetic comparison, a chi-square-based Q-statistic test was used to evaluate the between-study heterogeneity of the studies. If P < 0.10, the between-study heterogeneity was considered to be significant, we chose the random-effects model to calculate the OR. Otherwise, when P ≥ 0.10, the between study heterogeneity was not significant, then the fixed effects model was used. We also measured the effect of heterogeneity using a quantitative measure, I 2 = 100% × (Qdf)/Q[19]. The I statistic measures the degree of inconsistency in the studies by calculating what percentage of the total variation across studies is due to heterogeneity rather than by chance[20]. Finally, the overall or pooled estimate of risk (OR) was calculated by a random effects model (DerSimonian–Laird) or a fixed effects model (Mantel–Haenszel) according to the presence (P < 0.10 or I 2 > 50%) or absence (P ≥ 0.10 and I 2 ≤ 50%) of heterogeneity, respectively.

Cumulative meta-analysis was conducted to identify the influence of the first published study on the subsequent publications, and the evolution of the combined estimates over time according to the ascending date of publication. To identify potentially influential studies, sensitivity analysis was also performed by excluding the studies without definite diagnostic criteria, the studies without quality control when genotyping and the studies whose genotype frequencies in control populations exhibited significant deviation from the Hardy–Weinberg equilibrium (HWE), given that the deviation may denote bias. The funnel plots and Egger regression asymmetry test were used to assess publication bias. Egger’s test can detect funnel plot asymmetry by determining whether the intercept deviates significantly from zero in a regression of the standardized effect estimates against their precision. A T test was performed to determine the significance of the asymmetry. An asymmetric plot suggested possible publication bias (P ≥ 0.05 suggests no bias). All analyses were performed using Stata software, version 10.0 (Stata Corp., College Station, TX, USA).

Results

Study characteristics

According to our search criteria, 61 studies relevant to the role of COMT Val158Met polymorphism on BC risk were identified. Ten of these articles were excluded: one of these articles was a review[21], four were overlapped subjects[2225], four did not provide allele or genotyping data[2629], and one was a study concerned with COMT 1222 G>A polymorphism[30]. Manual search of references cited in the published studies did not reveal any additional articles. As a result, a total of 51 relevant studies met the inclusion criteria for the meta-analysis[916, 3173]. Among them, five of the eligible studies contained data on two different ethnic groups, and we treated them independently[31, 51, 56, 60, 69]. Therefore, a total of 56 separate comparisons consisting of 34,358 BC patients and 45,429 controls were included in our meta-analysis. The characteristics of the 56 case–control comparisons selected for determining the relationship between COMT Val108/158Met polymorphism and risk of BC are summarized in Table 1. These 56 comparisons were consisted of 33 Caucasian samples, 18 Asian populations and 5 mixed/other populations. Thirty of the studies were population-based case–control studies and 20 were hospital-based studies, four of these studies[44, 54, 60, 69] presented COMT Val158Met polymorphism genotype distributions according to family history (familial-based breast cancer). There were 22 comparisons concerned with COMT Val158Met polymorphism and premenopausal BC patients and 27 comparisons concerned with COMT Val158Met polymorphism and postmenopausal BC patients (see Table 1). Seventy-one percent (40/56) studies in the present meta-analysis used the golden criteria of “histologically confirmed” or “pathologically conformed” as BC diagnosis. Eighty-two (46/56) percent of the control populations matched to BC patients with age and 52% (29/56) studies used the classic PCR-RFLP assay to genotype the COMT Val158Met polymorphism, about 52% (29/56) of the case–control studies included mentioned the quality control when genotyping. The genotype frequencies of control group in 3 studies were not consistent with HWE[33, 41, 70]. We could not calculate the P value of HWE in two studies[66, 73] because they only provided data with dominant model. To remove possible HWE stratification, for each analysis involving any of these 5 studies, sensitivity analysis would be carried out by excluding the studies the genotype frequencies for control group of which deviate from HWE and the studies whose P value of HWE in the control group could not be calculated.
Table 1

General characteristics of individual studies in the meta-analysis of COMT Val158Met polymorphism and breast cancer

Study, year

Country

Ethnicity

No. of cases/controls

BC diagnosis

Matching criteria

Genotyping method

Menopausal status

Control sources

Quality control

HWE6(p value)

Lavigne 1997

America

Caucasian

113/114

NR

Age, race

PCR-RFLP

Pre-, Post-

HB

NR

0.862

Thompson 1998

America

Caucasian

281/289

Histologically confirmed

Age, region

PCR-RFLP

Pre-, Post-

PB

NR

0.522

Millikana 1998

America

Caucasian

389/379

Histologically confirmed

Age, race

PCR-RFLP

Pre-, Post-

PB

Yes

0.916

Millikanb 1998

America

Mixed/other

265/263

Histologically confirmed

Age, race

PCR-RFLP

Pre-, Post-

PB

Yes

0.838

Huang 1999

China

Asian

118/125

Pathologically conformed

NR

PCR-RFLP

Pre-, Post-

HB

NR

0.612

Goodman 2001

America

Caucasian

112/113

Histologically confirmed

Age, race

PCR-RFLP

Mixed

PB

Yes

0.788

Mitrunen 2001

Finland

Caucasian

481/480

Histologically confirmed

NR

PCR-RFLP

Pre-, Post-

PB

NR

0.921

Yim 2001

Korea

Asian

163/163

Histopathologically confirmed

Age

PCR-RFLP

Pre-, Post-

HB

Yes

0.004

Jungestrom 2001

Sweden

Caucasian

126/117

NR

Age

PCR-RFLP

Pre-

HB

NR

0.209

Hamajima 2001

Japan

Asian

150/165

Histologically confirmed

NR

PCR-RFLP

Pre-, Post-

HB

NR

0.079

Kocabas 2002

Turkey

Caucasian

84/103

Histologically confirmed

Age

PCR-RFLP

Pre-, Post-

HB

NR

0.227

Comings 2003

America

Caucasian

67/145

NR

Region

PCR-RFLP

Post-

PB

NR

0.335

Wedren 2003

Sweden

Caucasian

1490/1340

NR

Age

DASH

Post-

PB

Yes

0.772

Wu 2003

America

Asian

589/562

NR

Age, race

TaqMan

Mixed

PB

NR

0.646

Tan 2003

China

Asian

250/250

Histopathologically confirmed

Age

PCR-RFLP

Pre-, Post-

HB

NR

0.174

Sazci 2004

Turkey

Caucasian

130/224

Histopathologically confirmed

Age

PCR-RFLP

Pre-

PB

NR

0.000

Dunning 2004

UK

Caucasian

2850/1908

NR

Age, region

TaqMan

Post-

PB

Yes

0.232

Hefler 2004

Austria

Caucasian

391/1698

Histologically confirmed

Age, region

Sequencing

Mixed

HB

Yes

0.577

Ahsan 2004

America

Caucasian

313/262

Histopathologically confirmed

Age

LP

Mixed

FB

Yes

0.108

Modugno 2005

America

Caucasian

250/3950

Histopathologically confirmed

NR

TaqMan

Post-

PB

NR

0.391

Lin 2005

China

Asian

99/366

Pathologically conformed

Age, region

PCR-RFLP

Mixed

PB

Yes

0.972

Lin 2005

China

Asian

87/341

Pathologically conformed

Age, region

PCR-RFLP

Mixed

PB

Yes

0.393

Marchand 2005

America

Mixed/other

1339/1370

NR

Age

PCR-RFLP

Post-

PB

NR

0.109

Wen 2005

China

Asian

1120/1191

Pathologically conformed

Age

PCR-RFLP

Pre-, post-

PB

Yes

0.698

Cheng 2005

China

Asian

496/740

Pathologically conformed

Age

NR

Mixed

HB

Yes

0.006

Gaudeta 2006

America

Caucasian

1048/1092

Pathologically conformed

Age

MALDI-TOF

Pre-, post-

PB

Yes

0.853

Gaudetb 2006

Poland

Caucasian

1983/2279

Histopathologically confirmed

Age

TaqMan

Mixed

PB

Yes

0.525

Gallicchio 2006

America

Caucasian

81/1251

Pathologically conformed

NR

TaqMan

Mixed

PB

NR

0.440

Chang 2006

China

Asian

189/321

Histologically confirmed

Age

PCR-RFLP

Mixed

HB

NR

0.068

Onay 2006

Canada

Caucasian

398/372

Pathologically conformed

Age

TaqMan

Pre-

FB

Yes

0.283

Pharoah 2007

UK

Caucasian

2176/2012

NR

NR

TaqMan

Mixed

PB

NR

0.287

Ralpha 2007

America

Caucasian

1626/3286

NR

Age

TaqMan

Pre-, post-

HB

Yes

0.758

Ralphb 2007

America

Caucasian

500/1005

NR

Age

TaqMan

Pre-, post-

HB

Yes

0.549

Akisik 2007

Turkey

Caucasian

114/108

NR

Age

PCR-RFLP

Mixed

NR

NR

0.966

Hu 2007

China

Asian

112/110

Pathologically conformed

Age

Sequencing

Pre-, post-

HB

NR

0.252

Takata 2007

America

Mixed/other

325/250

Mammographically examed

Age

PCR-RFLP

Pre-, post-

PB

NR

0.104

Onaya 2008

Canada

Caucasian

1217/714

Pathologically conformed

Age

TaqMan

Mixed

FB

Yes

0.832

Onayb 2008

Finland

Caucasian

708/549

Pathologically conformed

Age

TaqMan

Mixed

FB

Yes

0.676

Justenhoven 2008

Germany

Caucasian

606/622

NR

Age

MALDI-TOF MS

Mixed

PB

Yes

0.654

He 2009

America

Caucasian

1212/1683

Pathologically conformed

Age

TaqMan

Mixed

HB

Yes

0.850

Reding 2009

America

Caucasian

891/878

NR

Age

TaqMan

post-

PB

Yes

0.606

GENICA 2009

Germany

Caucasian

3144/5481

Histologically conformed

Age, region

MALDI-TOF MS

post-

PB

Yes

0.094

Yadav 2009

India

Asian

154/166

NR

Region

PCR-RFLP

Pre-, post-

HB

NR

0.570

Shrubsole 2009

China

Asian

1093/1169

Pathologically conformed

Age

PCR-RFLP

Pre-, post-

PB

Yes

Sangrajrang 2009

Thailand

Asian

565/486

Histologically conformed

NR

TaqMan

Mixed

HB

Yes

0.610

Mónica 2010

Mexico

Caucasian

91/94

Pathologically conformed

Age, education

PCR-RFLP

Pre-, post-

HB

NR

0.669

Syamalaa 2010

India

Asian

219/367

Histologically conformed

Age

PCR-RFLP

Mixed

PB

NR

0.183

Syamalab 2010

India

Asian

140/367

Histologically conformed

Age

PCR-RFLP

Mixed

FB

NR

0.183

Peterson 2010

America

Caucasian

1584/1416

Pathologically conformed

Age

TaqMan

Mixed

PB

Yes

0.026

Delort 2010

France

Caucasian

910/1000

Pathologically conformed

Age

TaqMan

Mixed

PB

Yes

0.230

Wang 2011

China

Asian

400/400

Histopathologically conformed

Age

Sequencing

Pre-, post-

PB

Yes

0.389

Naushad 2011

India

Asian

212/233

Histopathologically conformed

NR

PCR-RFLP

Mixed

HB

NR

0.201

Cribb 2011

Canada

Caucasian

207/621

Histopathologically conformed

Age

PCR-RFLP

Mixed

HB

NR

0.208

Cerne 2011

Slovenia

Caucasian

530/270

NR

Age

TaqMan

post-

HB

Yes

0.903

Lajin 2011

Syria

Mixed/other

135/107

Pathologically conformed

Age

PCR-RFLP

Pre-, post-

PB

NR

0.887

Santos 2011

Brazil

Mixed/other

62/62

Pathologically conformed

Age

PCR-RFLP

Pre-, post-

PB

NR

PB Population-based FB family-based, HB hospital-based, HWE Hardy–Weinberg equilibrium, NR not reported, Pre- premenopausal, Post- postmenopausal, PCR-RFLP PCR-based restriction fragment length polymorphism, MALDI-TOF MS matrix assisted laser desorption/ionization time-of-flight mass spectrometry, LP Luorescence polarization.

a, b They were two different case–control studies in one publication.

Quantitative synthesis of data

The pooled ORs along with their 95% CIs and the results of the heterogeneity test are presented in detail in Table 2. Overall, no significant associations between COMT Val158Met polymorphism and breast cancer susceptibility were observed in all genetic models when all the eligible studies were pooled into the meta-analysis. No significant associations were found for LL versus HH (OR = 0.999, 95% CI 0.0.925–1.078; I 2 = 55.0 and P = 0.000 for heterogeneity), HL versus HH (OR = 1.005, 95% CI 0.959–1.052; I 2 = 27.1 and P = 0.038 for heterogeneity), LL versus HL (OR = 0.983, 95% CI 0.926–1.045; I 2 = 44.4 and P = 0.000 for heterogeneity), recessive model LL versus HL+HH (OR = 0.988, 95% CI 0.929–1.050; I 2 = 51.3 and P = 0.000 for heterogeneity) and dominant model LL+HL versus HH (OR = 1.001, 95% CI 0.954–1.051; I 2 = 41.0 and P = 0.001 for heterogeneity). Next, the effect of COMT Val158Met polymorphism on breast cancer risk was evaluated according to ethnicity, menopausal status (Figure 1; Figure 2) and sources of controls. Similarly, no significant association was found in any of the genetic models. We further conducted a meta-analysis after the five studies[33, 41, 66, 70, 73] whose genotype frequencies significantly deviated from HWE or whose P values of HWE in the control population unable to be calculated were excluded. The results were not materially changed in any genetic models. Sensitivity analysis by excluding the studies without definite diagnostic criteria and the studies without quality control when genotyping did not alter the pattern of the results. Cumulative meta-analysis was performed for dominant model LL +LH versus HH in the overall populations. In the overall populations, the random effects odds ratio was always insignificantly larger or smaller than 1. It changed little from around 0.998 after the year 2007 (Figure 3), indicating the stability of the association.
Table 2

Meta-analysis of the COMT Val158Met polymorphism on BC susceptibility

Comparison

Population

Sample size

No. of studies

Test of association

Mode

Test of heterogeneity

  

Case

Control

 

OR

95% CI

P value

 

χ 2

P value

I 2

LL vs. HH

Overall

17,223

23,069

54

0.999

0.925-1.078

0.976

R

117.76

0.000

55.0

 

Caucasian

12,942

18,066

32

0.960

0.897-1.028

0.240

R

49.28

0.020

37.1

 

Asian

3,009

3,790

17

1.243

0.942-1.641

0.125

R

54.34

0.000

70.6

 

Pre-

2,095

2,523

21

1.049

0.825-1.334

0.697

R

48.22

0.000

58.5

 

Post-

7,215

10,138

26

0.982

0.875-1.102

0.757

R

45.40

0.008

44.9

 

PB

17,223

23,069

28

0.999

0.925-1.078

0.381

R

48.00

0.008

43.7

 

HB

3,800

6,169

20

1.151

0.946-1.402

0.160

R

58.86

0.000

67.7

 

FB

1,351

1,140

5

0.848

0.712-1.010

0.064

F

4.43

0.351

9.7

HL vs. HH

Overall

22,589

33,568

54

1.005

0.959-1.052

0.845

R

72.70

0.038

27.1

 

Caucasian

19,059

25,912

32

0.999

0.958-1.042

0.968

F

30.14

0.510

0.0

 

Asian

4,525

5,781

17

1.052

0.923-1.200

0.448

R

36.85

0.002

56.6

 

Pre-

3,204

3,877

21

0.962

0.871-1.062

0.440

F

27.59

0.119

27.5

 

Post-

10,480

14,476

26

1.009

0.954-1.067

0.762

F

33.83

0.112

26.1

 

PB

17,648

22,679

28

0.987

0.945-1.030

0.547

F

3.60

0.463

0.0

 

HB

5,751

9,128

20

1.075

0.966-1.195

0.187

R

33.89

0.019

43.9

 

FB

2,102

1,674

5

0.950

0.824-1.094

0.476

F

30.98

0.272

12.9

LL vs. HL

Overall

23,594

31,759

54

0.983

0.926-1.045

0.586

R

95.26

0.000

44.4

 

Caucasian

19,579

27,208

32

0.954

0.911-1.001

0.055

F

36.02

0.245

13.9

 

Asian

2,538

3,135

17

1.170

0.895-1.528

0.251

R

49.83

0.000

67.9

 

Pre-

2,507

3,200

21

1.060

0.851-1.320

0.606

R

49.32

0.000

59.4

 

Post-

10,243

14,548

26

0.969

0.915-1.025

0.271

F

32.47

0.271

23.0

 

PB

16,437

21,768

28

0.969

0.909-1.032

0.324

R

39.76

0.054

32.1

 

HB

4,973

8,203

20

1.060

0.902-1.245

0.478

R

48.71

0.000

61.0

 

FB

2,099

1,714

5

0.882

0.769-1.012

0.073

F

4.37

0.358

8.6

LL vs. HL+HH

Overall

34,358

45,429

56

0.988

0.929-1.050

0.702

R

108.88

0.000

51.3

 

Caucasian

25,790

35,593

32

0.956

0.909-1.006

0.081

R

43.54

0.067

28.8

 

Asian

5,770

7,552

17

1.204

0.927-1.564

0.164

R

52.91

0.000

69.8

 

Pre-

3,903

4.800

21

1.053

0.855-1.297

0.627

R

49.44

0.000

59.5

 

Post-

13,969

19,581

26

0.980

0.901-1.065

0.629

R

37.85

0.048

33.9

 

PB

24,205

31,307

30

0.966

0.906-1.030

0.290

R

45.36

0.015

40.5

 

HB

7,262

11,750

20

1.098

0.934-1.289

0.257

R

54.38

0.000

65.1

 

FB

2,776

2,264

5

0.877

0.760-1.013

0.074

F

4.62

0.328

13.5

LL+HL vs. HH

Overall

34,358

45,429

56

1.001

0.954-1.051

0.953

R

93.20

0.001

41.0

 

Caucasian

25,790

35,593

32

0.982

0.944-1.022

0.369

F

37.71

0.189

17.8

 

Asian

5,770

7,552

17

1.072

0.952-1.208

0.253

R

42.65

0.001

60.1

 

Pre-

3,933

4.839

22

1.016

0.890-1.160

0.815

R

33.81

0.038

37.9

 

Post-

14,001

19,604

27

1.001

0.924-1.084

0.987

R

40.17

0.038

35.3

 

PB

24,205

31,307

30

0.975

0.924-1.028

0.343

R

42.89

0.047

32.4

 

HB

7,262

11,750

20

1.091

0.978-1.216

0.118

R

39.26

0.004

51.6

 

FB

2,776

2,264

5

0.914

0.799-1.044

0.186

F

3.81

0.432

0.0

OR odds ratio, CI confidence intervals, R random effects model, F fixed effects model, PB Population-based study, HB Hospital-based study, FB Familial-based study, Pre- Premenopausal, Post- Postmenopausal.

https://static-content.springer.com/image/art%3A10.1186%2F1746-1596-7-136/MediaObjects/13000_2012_Article_662_Fig1_HTML.jpg
Figure 1

OR and 95% CI of individual studies and pooled data for the association between the COMT Val158Met polymorphism and BC in premenopausal populations using a random-effect model (dominant model LL+HL vs. HH).

https://static-content.springer.com/image/art%3A10.1186%2F1746-1596-7-136/MediaObjects/13000_2012_Article_662_Fig2_HTML.jpg
Figure 2

OR and 95% CI of individual studies and pooled data for the association between the COMT Val158Met polymorphism and BC in postmenopausal populations using a random-effect model (dominant model LL+HL vs. HH).

https://static-content.springer.com/image/art%3A10.1186%2F1746-1596-7-136/MediaObjects/13000_2012_Article_662_Fig3_HTML.jpg
Figure 3

Cumulative meta-analysis of the association between COMT Val158Met polymorphism and breast cancer susceptibility risk of the overall populations using a random effects model (dominant model LL+HL versus HH) . Each study was used as an information step. The vertical dotted line is the summary odds ratio. Bars, 95% confidence interval (CI).

Publication bias

Begg’s funnel plots and Egger’s tests were performed to assess publication bias. The shapes of the funnel plots revealed no obvious asymmetry (Figure 4). The Egger’s test was then used to statistically assess funnel plot symmetry. The results suggested no evidence of publication bias (t = 0.94 and P = 0.352 for dominant model). The results indicated that the results of these meta-analyses are relatively stable and that publication bias is unlikely to affect the results of the meta-analyses.
https://static-content.springer.com/image/art%3A10.1186%2F1746-1596-7-136/MediaObjects/13000_2012_Article_662_Fig4_HTML.jpg
Figure 4

Funnel plots for publication bias in the studies of the meta-analysis on the association between COMT Val158Met polymorphism and breast cancer risk of the overall populations (dominant LL+HL versus HH).

Discussion

Estrogens, estrone, and estradiol are catabolized to catechol estrogens. Estrogen metabolites, such as 4-hydroxyestrone and 4-hydroxyestrone, shown to be involved in breast carcinogenesis[74]. Catechol-O-methyltransferase (COMT) catalyzes the O-methylation of these carcinogenic estrogens to methoxyes tradiols and methoxyestrones. In the COMT gene, a G to A transition results in an amino acid change (Val/Met) at codon 108 of soluble COMT and codon 158 of membrane-bound COMT. This amino acid change is believed to result in a 3–4-fold decrease in enzymatic activity[6, 7]. It has been hypothesized that individuals who inherit the low activity COMT gene may be at increased risk for breast cancer because of an increased accumulation of the catechol estrogen intermediates. The potential association between the COMT Val108/158Met polymorphism and the risk of subsequent BC has evoked a huge interest from clinicians, scientists, and the public. During the past few years a large number of studies with case–control design have been carried out to investigate this topic but consistent results have not been reported. We therefore conducted a meta-analysis of the evidence obtained from all published studies in order to elucidate and provide a quantitative reassessment of the association. To our knowledge, this is the most comprehensive meta-analysis to date to evaluate the association between COMT Val108/158Met polymorphism and breast cancer risk.

We did not observe a positive relationship between COMT Val108/158Met polymorphism and breast cancer risk either overall or among subgroups of women defined by ethnicity, menopausal status or sources of the control population. In previous studies, overall the findings were inconsistent. Lavigne et al. observed a large increase in the risk of breast cancer among postmenopausal obese women carrying the COMT-LL genotype, and an inverse association among premenopausal women with the relative risk (RR) for COMT-LL stronger among postmenopausal women with high BMI[9]. Thompson et al. reported positive associations for the COMT-HL and COMT-LL genotypes among premenopausal women and found that modification of RRs by BMI was highest among premenopausal women with a high BMI[10]. A comprehensive study of the entire estrogen-metabolizing pathway (CYP17, CYP1A1, COMT) also reported that breast cancer is only associated with the low activity COMT genotype in women with a high BMI and that the COMT-LL genotype was strongly associated with breast cancer risk, with an adjusted OR of as high as 4.02[12]. In contrast to the other studies but in line with the findings of the current study, Lajin et al. did not observe any association between one or two copies of the COMT-L allele and breast cancer risk, and did not find strong modification of RR estimates by menopausal status[72]. In an effort to shed some light on the impact of COMT Val108/158Met polymorphism on breast cancer risk, two previous meta-analyses[17, 18] were conducted almost at the same time to explore the relationship between COMT Val108/158Met polymorphism and breast cancer. Ding et al.[18] examined the effect of COMT Val158Met polymorphism on breast cancer risk by combining results in meta-analysis. They concluded that COMT Val158Met polymorphism was significantly associated with increased breast cancer risk in European population. However, Mao et al.[17] did not find any relationship between COMT Val158Met polymorphism and breast cancer risk in any genetic models including among Caucasian, Asian, premenopausal, and postmenopausal women in their meta-analysis, which was consistent with the findings of our study. The discrepancy in previously reported findings was most probably because that the previous studies with relatively small sample size may have insufficient statistical power to detect the exact effect or may have generated a fluctuated risk estimate. However, in our study, large number of cases and controls were pooled from all published studies, which greatly increased statistical power of the analysis and provided enough evidence for us to draw a safe and reliable conclusion.

Heterogeneity is a potential problem that may affect the interpretation of the results. The present meta-analysis showed that there was large heterogeneity between studies (table 2). Common reasons for heterogeneity may include differences in the studied populations (e.g., ethnicity, menopausal status), or in methods (e.g., genotyping), or in sample selection (e.g., source of control populations), or it may be due to interaction with other risk factors (e.g., BRCA variants). Finding of the source of heterogeneity is one of the most important goals of a meta-analysis. Therefore, we stratified the studies according to ethnicity, source of control subjects of the studies, and menopausal status. Subsequent subgroup analysis stratified by ethnicity, source of control subjects, and menopausal status identified large heterogeneity as well, indicating that menopausal status, ethnicity or source of control subjects contributed little to the existence of overall heterogeneity. Unfortunately, our study had insufficient information for subgroup analysis to detect whether the variants in BRCA gene might be great sources of heterogeneity. We found that in three studies[33, 41, 70] the genotypic frequencies showed significant deviation from the expected frequencies based on Hardy–Weinberg equilibrium and two studies[66, 73] provide insufficient data for calculating P value of HWE in the control populations. Excluding these five studies did not alter the heterogeneity between studies. However, when heterogeneity between the studies exists, the results could be interpreted in the context of cumulative meta-analysis, which provides a measure of how much the genetic effect changes as more data accumulate over time[75]. In our study, the results of cumulative meta-analysis for dominant model LL+HL versus HH showed stability in pooled odds ratio after the year 2007 in the overall populations, which provide evidence for drawing safe conclusion about the insignificant association between COMT Val158Met polymorphism and breast cancer risk.

Some limitations of this meta-analysis should be acknowledged. First, some studies found significant associations between COMT Val108/158Met polymorphism and breast cancer risk in several subgroups of populations, such as associations among postmenopausal women with a low body mass index (BMI)[10, 11], a high BMI[9] or women at young ages[11]. It is difficult for a meta-anlysis to derive such specific associations because the results from previous studies were not presented in a uniform standard. Second, our results were based on unadjusted estimates and a more precise analysis should be carried out if individual data were available, this would allow for adjustment by other covariates including age, BMI, ethnicity, lifestyle, and environmental factors. Third, all of the studies were performed in Asian and Caucasian populations. Further studies are needed in other ethnic populations because of possible ethnic differences of the COMT polymorphisms. In spite of these, our present meta-analysis also had some advantages. First, substantial number of cases and controls were pooled from all publications concerned with COMT Val158Met polymorphism and BC risk, which greatly increased statistical power of the analysis and provided enough evidence for us to draw a safe conclusion. Second, the quality of case–control studies included in this meta-analysis was satisfactory according to our selection criteria. Third, no publication bias was detected in this meta-analysis, which indicated that the pooled results of our study should be reliable.

In conclusion, this meta-analysis suggests that the COMT Val158Met polymorphism may not be associated with breast cancer risk. However, it is necessary to conduct large sample studies using standardized unbiased genotyping methods, homogeneous breast cancer patients, and well-matched controls. Moreover, gene-gene and gene-environment interactions should also be considered in the analysis. Such studies taking these factors into account may eventually lead to a better, more comprehensive understanding of the association between COMT Val158Met polymorphism and BC risk.

Notes

Abbreviations

BC: 

Breast cancer

HWE: 

Hardy–Weinberg equilibrium

OR: 

Odds ratio

CI: 

Confidence interval

COMT: 

Catechol-O-methyltransferase

BMI: 

Body mass index

PB: 

Population-based

FB: 

Family-based

HB: 

Hospital-based

Pre: 

Premenopausal

Post: 

Postmenopausal

PCR-RFLP PCR: 

based restriction fragment length polymorphism

MALDI-TOF MS: 

matrix assisted laser desorption/ionization time-of-flight mass spectrometry

LP: 

Luorescence polarization.

Declarations

Acknowledgements

This work was not supported by any kind of fund.

Authors’ Affiliations

(1)
Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University
(2)
Department of Obstetrics and Gynecology and Reproductive center, First Affiliated Hospital of Guangxi Medical University
(3)
Department of Occupational Health and Environmental Health, School of Public Health at Guangxi Medical University
(4)
Department of Orthopedic Trauma Surgery, First Affiliated Hospital of Guangxi Medical University
(5)
Department of Clinical Laboratory, Baise City People's Hospital

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