Document Detail

Association of COMT Val158Met polymorphism and breast cancer risk: an updated meta-analysis.
Jump to Full Text
MedLine Citation:
PMID:  23039364     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.diagnosticpathology.diagnomx.eu/vs4806123577708417.
Authors:
Xue Qin; Qiliu Peng; Aiping Qin; Zhiping Chen; Liwen Lin; Yan Deng; Li Xie; Juanjuan Xu; Haiwei Li; Taijie Li; Shan Li; Jinmin Zhao
Related Documents :
6120284 - Breast cancer and alcoholic-beverage consumption.
15173444 - Biomarkers for cancer diagnosis: implications for nutritional research.
23873024 - Prolactin cooperates with loss of p53 to promote claudin-low mammary carcinomas.
8347784 - Alcoholic beverage consumption and risk of breast cancer in spain.
2663144 - Elimination of malignant clonogenic breast cancer cells from human bone marrow.
17392714 - Drug insight: role of the androgen receptor in the development and progression of prost...
Publication Detail:
Type:  Journal Article; Meta-Analysis; Review     Date:  2012-10-08
Journal Detail:
Title:  Diagnostic pathology     Volume:  7     ISSN:  1746-1596     ISO Abbreviation:  Diagn Pathol     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-14     Completed Date:  2013-05-10     Revised Date:  2013-07-15    
Medline Journal Info:
Nlm Unique ID:  101251558     Medline TA:  Diagn Pathol     Country:  England    
Other Details:
Languages:  eng     Pagination:  136     Citation Subset:  IM    
Affiliation:
Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Breast Neoplasms / enzymology,  ethnology,  genetics*,  pathology
Case-Control Studies
Catechol O-Methyltransferase / genetics*
Chi-Square Distribution
Female
Genetic Predisposition to Disease
Humans
Odds Ratio
Phenotype
Polymorphism, Genetic*
Risk Assessment
Risk Factors
Chemical
Reg. No./Substance:
EC 2.1.1.6/Catechol O-Methyltransferase
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-ta): Diagn Pathol
Journal ID (iso-abbrev): Diagn Pathol
ISSN: 1746-1596
Publisher: BioMed Central
Article Information
Download PDF
Copyright ©2012 Qin et al.; licensee BioMed Central Ltd.
open-access:
Received Day: 25 Month: 7 Year: 2012
Accepted Day: 5 Month: 10 Year: 2012
collection publication date: Year: 2012
Electronic publication date: Day: 8 Month: 10 Year: 2012
Volume: 7First Page: 136 Last Page: 136
PubMed Id: 23039364
ID: 3543196
Publisher Id: 1746-1596-7-136
DOI: 10.1186/1746-1596-7-136

Association of COMT Val158Met polymorphism and breast cancer risk: an updated meta-analysis
Xue Qin1 Email: qinxue919@yahoo.cn
Qiliu Peng15 Email: Pengql45@163.com
Aiping Qin2 Email: aipingqin@gmail.com
Zhiping Chen3 Email: cczzpp919@163.com
Liwen Lin1 Email: llw17@sina.com
Yan Deng1 Email: 260126145@qq.com
Li Xie1 Email: drlixie@163.com
Juanjuan Xu1 Email: 949979938@qq.com
Haiwei Li1 Email: 121171493@qq.com
Taijie Li1 Email: 54963242@qq.com
Shan Li1 Email: lis8858@126.com
Jinmin Zhao4 Email: zhaojinmin@126.com
1Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
2Department of Obstetrics and Gynecology and Reproductive center, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
3Department of Occupational Health and Environmental Health, School of Public Health at Guangxi Medical University, Nanning, Guangxi, China
4Department of Orthopedic Trauma Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
5Department of Clinical Laboratory, Baise City People's Hospital, Baise, Guangxi, China

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 [8-11].

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 [12-14]. 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.thecochranelibrary.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, I2 = 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 I2 > 50%) or absence (P ≥ 0.10 and I2 ≤ 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 [22-25], four did not provide allele or genotyping data [26-29], 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 [9-16,31-73]. 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.

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; I2=55.0 and P = 0.000 for heterogeneity), HL versus HH (OR = 1.005, 95% CI 0.959–1.052; I2=27.1 and P = 0.038 for heterogeneity), LL versus HL (OR = 0.983, 95% CI 0.926–1.045; I2=44.4 and P = 0.000 for heterogeneity), recessive model LL versus HL+HH (OR = 0.988, 95% CI 0.929–1.050; I2=51.3 and P = 0.000 for heterogeneity) and dominant model LL+HL versus HH (OR = 1.001, 95% CI 0.954–1.051; I2=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.

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.


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.


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.


Competing interest

The authors declared that they have no conflict of interest in relation to this study.


Authors’ contributions

All authors have read and approved the final files for this manuscript.


Acknowledgements

This work was not supported by any kind of fund.


References
Parkin DM,Bray F,Ferlay J,Pisani P,Global cancer statistics, 2002CA Cancer J ClinYear: 20055527410810.3322/canjclin.55.2.7415761078
Lux MP,Fasching PA,Beckmann MW,Hereditary breast and ovarian cancer: review and future perspectivesJ Mol Med (Berl)Year: 2006841162810.1007/s00109-005-0696-716283147
Guldberg HC,Marsden CA,Catechol-O-methyl transferase: pharmacological aspects and physiological rolePharmacol RevYear: 19752721352061103160
Mannisto PT,Ulmanen I,Lundstrom K,Taskinen J,Tenhunen J,Tilgmann C,Kaakkola S,Characteristics of catechol O-methyl-transferase (COMT) and properties of selective COMT inhibitorsProg Drug ResYear: 1992392913501475365
Grossman MH,Emanuel BS,Budarf ML,Chromosomal mapping of the human catechol-O-methyltransferase gene to 22q11.1----q11.2GenomicsYear: 199212482282510.1016/0888-7543(92)90316-K1572656
Weinshilboum RM,Raymond FA,Inheritance of low erythrocyte catechol-o-methyltransferase activity in manAm J Hum GenetYear: 1977292125135848488
Dawling S,Roodi N,Mernaugh RL,Wang X,Parl FF,Catechol-O-methyltransferase (COMT)-mediated metabolism of catechol estrogens: comparison of wild-type and variant COMT isoformsCancer ResYear: 200161186716672211559542
Goodman JE,Jensen LT,He P,Yager JD,Characterization of human soluble high and low activity catechol-O-methyltransferase catalyzed catechol estrogen methylationPharmacogeneticsYear: 200212751752810.1097/00008571-200210000-0000312360102
Lavigne JA,Helzlsouer KJ,Huang HY,Strickland PT,Bell DA,Selmin O,Watson MA,Hoffman S,Comstock GW,Yager JD,An association between the allele coding for a low activity variant of catechol-O-methyltransferase and the risk for breast cancerCancer ResYear: 19975724549354979407957
Thompson PA,Shields PG,Freudenheim JL,Stone A,Vena JE,Marshall JR,Graham S,Laughlin R,Nemoto T,Kadlubar FF,Ambrosone CB,Genetic polymorphisms in catechol-O-methyltransferase, menopausal status, and breast cancer riskCancer ResYear: 19985810210721109605753
Mitrunen K,Jourenkova N,Kataja V,Eskelinen M,Kosma VM,Benhamou S,Kang D,Vainio H,Uusitupa M,Hirvonen A,Polymorphic catechol-O-methyltransferase gene and breast cancer riskCancer Epidemiol Biomarkers PrevYear: 200110663564011401913
Huang CS,Chern HD,Chang KJ,Cheng CW,Hsu SM,Shen CY,Breast cancer risk associated with genotype polymorphism of the estrogen-metabolizing genes CYP17, CYP1A1, and COMT: a multigenic study on cancer susceptibilityCancer ResYear: 199959194870487510519398
Wang Q,Li H,Tao P,Wang YP,Yuan P,Yang CX,Li JY,Yang F,Lee H,Huang Y,Soy isoflavones, CYP1A1, CYP1B1, and COMT polymorphisms, and breast cancer: a case–control study in southwestern ChinaDNA and cell biologyYear: 201130858559510.1089/dna.2010.119521438753
Naushad SM,Pavani A,Rupasree Y,Sripurna D,Gottumukkala SR,Digumarti RR,Kutala VK,Modulatory effect of plasma folate and polymorphisms in one-carbon metabolism on catecholamine methyltransferase (COMT) H108L associated oxidative DNA damage and breast cancer riskIndian journal of biochemistry & biophysicsYear: 2011484283289
Cribb AE,Joy Knight M,Guernsey J,Dryer D,Hender K,Shawwa A,Tesch M,Saleh TM,CYP17, catechol-o-methyltransferase, and glutathione transferase M1 genetic polymorphisms, lifestyle factors, and breast cancer risk in women on Prince Edward IslandThe breast journalYear: 2011171243110.1111/j.1524-4741.2010.01025.x21129090
Cerne JZ,Pohar-Perme M,Novakovic S,Frkovic-Grazio S,Stegel V,Gersak K,Combined effect of CYP1B1, COMT, GSTP1, and MnSOD genotypes and risk of postmenopausal breast cancerJournal of gynecologic oncologyYear: 201122211011910.3802/jgo.2011.22.2.11021860737
Mao C,Wang XW,Qiu LX,Liao RY,Ding H,Chen Q,Lack of association between catechol-O-methyltransferase Val108/158Met polymorphism and breast cancer risk: a meta-analysis of 25,627 cases and 34,222 controlsBreast Cancer Res TreatYear: 2010121371972510.1007/s10549-009-0650-420464630
Ding H,Fu Y,Chen W,Wang Z,COMT Val158Met polymorphism and breast cancer risk: evidence from 26 case–control studiesBreast Cancer Res TreatYear: 2010123126527010.1007/s10549-010-0759-520130981
Higgins JP,Thompson SG,Quantifying heterogeneity in a meta-analysisStat MedYear: 200221111539155810.1002/sim.118612111919
Higgins JP,Thompson SG,Deeks JJ,Altman DG,Measuring inconsistency in meta-analysesBMJYear: 2003327741455756010.1136/bmj.327.7414.55712958120
Kang D,Genetic polymorphisms and cancer susceptibility of breast cancer in Korean womenJ Biochem Mol BiolYear: 2003361283410.5483/BMBRep.2003.36.1.02812542972
Naushad SM,Reddy CA,Rupasree Y,Pavani A,Digumarti RR,Gottumukkala SR,Kuppusamy P,Kutala VK,Cross-talk between one-carbon metabolism and xenobiotic metabolism: implications on oxidative DNA damage and susceptibility to breast cancerCell Biochem BiophysYear: 201161371572310.1007/s12013-011-9245-x21792634
Song CG,Hu Z,Yuan WT,Di GH,Shen ZZ,Huang W,Shao ZM,Prevalence of Val158Met polymorphism in COMT gene on non-BRCA1/2 hereditary breast cancerZhonghua Wai Ke Za ZhiYear: 200644191310131317217814
Wang Q,Wang YP,Li JY,Yuan P,Yang F,Li H,Polymorphic catechol-O-methyltransferase gene, soy isoflavone intake and breast cancer in postmenopausal women: a case–control studyChin J CancerYear: 201029768368810.5732/cjc.009.1070020591221
Park SK,Yim DS,Yoon KS,Choi IM,Choi JY,Yoo KY,Noh DY,Choe KJ,Ahn SH,Hirvonen A,Kang D,Combined effect of GSTM1, GSTT1, and COMT genotypes in individual breast cancer riskBreast Cancer Res TreatYear: 2004881556210.1007/s10549-004-0745-x15538046
Ji Y,Olson J,Zhang J,Hildebrandt M,Wang L,Ingle J,Fredericksen Z,Sellers T,Miller W,Dixon JM,Brauch H,Eichelbaum M,Justenhoven C,Hamann U,Ko Y,Bruning T,Chang-Claude J,Wang-Gohrke S,Schaid D,Weinshilboum R,Breast cancer risk reduction and membrane-bound catechol O-methyltransferase genetic polymorphismsCancer ResYear: 200868145997600510.1158/0008-5472.CAN-08-004318632656
Crooke PS,Ritchie MD,Hachey DL,Dawling S,Roodi N,Parl FF,Estrogens, enzyme variants, and breast cancer: a risk modelCancer Epidemiol Biomarkers PrevYear: 20061591620162910.1158/1055-9965.EPI-06-019816985022
Silva SN,Cabral MN,Bezerra de Castro G,Pires M,Azevedo AP,Manita I,Pina JE,Rueff J,Gaspar J,Breast cancer risk and polymorphisms in genes involved in metabolism of estrogens (CYP17, HSD17beta1, COMT and MnSOD): possible protective role of MnSOD gene polymorphism Val/Ala and Ala/Ala in women that never breast fedOncol RepYear: 200616478178816969494
Corder EH,Hefler LA,Multilocus genotypes spanning estrogen metabolism associated with breast cancer and fibroadenomaRejuvenation ResYear: 200691566010.1089/rej.2006.9.5616608396
Jakubowska A,Gronwald J,Menkiszak J,Gorski B,Huzarski T,Byrski T,Toloczko-Grabarek A,Gilbert M,Edler L,Zapatka M,Eils R,Lubinski J,Scott RJ,Hamann U,BRCA1-associated breast and ovarian cancer risks in Poland: no association with commonly studied polymorphismsBreast Cancer Res TreatYear: 2010119120121110.1007/s10549-009-0390-519360465
Millikan RC,Pittman GS,Tse CK,Duell E,Newman B,Savitz D,Moorman PG,Boissy RJ,Bell DA,Catechol-O-methyltransferase and breast cancer riskCarcinogenesisYear: 199819111943194710.1093/carcin/19.11.19439855007
Goodman JE,Lavigne JA,Wu K,Helzlsouer KJ,Strickland PT,Selhub J,Yager JD,COMT genotype, micronutrients in the folate metabolic pathway and breast cancer riskCarcinogenesisYear: 200122101661166510.1093/carcin/22.10.166111577006
Yim DS,Parkb SK,Yoo KY,Yoon KS,Chung HH,Kang HL,Ahn SH,Noh DY,Choe KJ,Jang IJ,Shin SG,Strickland PT,Hirvonen A,Kang D,Relationship between the Val158Met polymorphism of catechol O-methyl transferase and breast cancerPharmacogeneticsYear: 200111427928610.1097/00008571-200106000-0000111434504
Bergman-Jungestrom M,Wingren S,Catechol-O-Methyltransferase (COMT) gene polymorphism and breast cancer risk in young womenBritish journal of cancerYear: 200185685986210.1054/bjoc.2001.200911556837
Hamajima N,Matsuo K,Tajima K,Mizutani M,Iwata H,Iwase T,Miura S,Oya H,Obata Y,Limited association between a catechol-O-methyltransferase (COMT) polymorphism and breast cancer risk in JapanInternational journal of clinical oncology / Japan Society of Clinical OncologyYear: 200161131810.1007/PL00012073
Kocabas NA,Sardas S,Cholerton S,Daly AK,Karakaya AE,Cytochrome P450 CYP1B1 and catechol O-methyltransferase (COMT) genetic polymorphisms and breast cancer susceptibility in a Turkish populationArchives of toxicologyYear: 2002761164364910.1007/s00204-002-0387-x12415427
Comings DE,Gade-Andavolu R,Cone LA,Muhleman D,MacMurray JP,A multigene test for the risk of sporadic breast carcinomaCancerYear: 20039792160217010.1002/cncr.1134012712467
Wedren S,Rudqvist TR,Granath F,Weiderpass E,Ingelman-Sundberg M,Persson I,Magnusson C,Catechol-O-methyltransferase gene polymorphism and post-menopausal breast cancer riskCarcinogenesisYear: 200324468168710.1093/carcin/bgg02212727796
Wu AH,Tseng CC,Van Den Berg D,Yu MC,Tea intake, COMT genotype, and breast cancer in Asian-American womenCancer ResYear: 200363217526752914612555
Tan W,Qi J,Xing DY,Miao XP,Pan KF,Zhang L,Lin DX,[Relation between single nucleotide polymorphism in estrogen-metabolizing genes COMT, CYP17 and breast cancer risk among Chinese women]Zhonghua zhong liu za zhi [Chinese journal of oncology]Year: 2003255453456
Sazci A,Ergul E,Utkan NZ,Canturk NZ,Kaya G,Catechol-O-methyltransferase Val 108/158 Met polymorphism in premenopausal breast cancer patientsToxicologyYear: 20042042–319720215388245
Dunning AM,Dowsett M,Healey CS,Tee L,Luben RN,Folkerd E,Novik KL,Kelemen L,Ogata S,Pharoah PD,Easton DF,Day NE,Ponder BA,Polymorphisms associated with circulating sex hormone levels in postmenopausal womenJournal of the National Cancer InstituteYear: 2004961293694510.1093/jnci/djh16715199113
Hefler LA,Tempfer CB,Grimm C,Lebrecht A,Ulbrich E,Heinze G,Leodolter S,Schneeberger C,Mueller MW,Muendlein A,Koelbl H,Estrogen-metabolizing gene polymorphisms in the assessment of breast carcinoma risk and fibroadenoma risk in Caucasian womenCancerYear: 2004101226426910.1002/cncr.2036115241822
Ahsan H,Chen Y,Whittemore AS,Kibriya MG,Gurvich I,Senie RT,Santella RM,A family-based genetic association study of variants in estrogen-metabolism genes COMT and CYP1B1 and breast cancer riskBreast Cancer Res TreatYear: 200485212113110.1023/B:BREA.0000025401.60794.6815111770
Modugno F,Zmuda JM,Potter D,Cai C,Ziv E,Cummings SR,Stone KL,Morin PA,Greene D,Cauley JA,Estrogen metabolizing polymorphisms and breast cancer risk among older white womenBreast Cancer Res TreatYear: 200593326127010.1007/s10549-005-5347-816142442
Lin SC,Chou YC,Wu MH,Wu CC,Lin WY,Yu CP,Yu JC,You SL,Chen CJ,Sun CA,Genetic variants of myeloperoxidase and catechol-O-methyltransferase and breast cancer riskEuropean journal of cancer prevention: the official journal of the European Cancer Prevention Organisation (ECP)Year: 200514325726110.1097/00008469-200506000-0001015901995
Lin WY,Chou YC,Wu MH,Jeng YL,Huang HB,You SL,Chu TY,Chen CJ,Sun CA,Polymorphic catechol-O-methyltransferase gene, duration of estrogen exposure, and breast cancer risk: a nested case–control study in TaiwanCancer detection and preventionYear: 200529542743210.1016/j.cdp.2005.07.00316191465
Le Marchand L,Donlon T,Kolonel LN,Henderson BE,Wilkens LR,Estrogen metabolism-related genes and breast cancer risk: the multiethnic cohort studyCancer Epidemiol Biomarkers PrevYear: 20051481998200310.1158/1055-9965.EPI-05-007616103451
Wen W,Cai Q,Shu XO,Cheng JR,Parl F,Pierce L,Gao YT,Zheng W,Cytochrome P450 1B1 and catechol-O-methyltransferase genetic polymorphisms and breast cancer risk in Chinese women: results from the shanghai breast cancer study and a meta-analysisCancer Epidemiol Biomarkers PrevYear: 200514232933510.1158/1055-9965.EPI-04-039215734954
Cheng TC,Chen ST,Huang CS,Fu YP,Yu JC,Cheng CW,Wu PE,Shen CY,Breast cancer risk associated with genotype polymorphism of the catechol estrogen-metabolizing genes: a multigenic study on cancer susceptibilityInternational journal of cancerYear: 2005113334535310.1002/ijc.20630
Gaudet MM,Bensen JT,Schroeder J,Olshan AF,Terry MB,Eng SM,Teitelbaum SL,Britton JA,Lehman TA,Neugut AI,Ambrosone CB,Santella RM,Gammon MD,Catechol-O-methyltransferase haplotypes and breast cancer among women on Long Island, New YorkBreast Cancer Res TreatYear: 200699223524010.1007/s10549-006-9205-016596327
Gallicchio L,Berndt SI,McSorley MA,Newschaffer CJ,Thuita LW,Argani P,Hoffman SC,Helzlsouer KJ,Polymorphisms in estrogen-metabolizing and estrogen receptor genes and the risk of developing breast cancer among a cohort of women with benign breast diseaseBMC cancerYear: 2006617310.1186/1471-2407-6-17316808847
Chang TW,Wang SM,Guo YL,Tsai PC,Huang CJ,Huang W,Glutathione S-transferase polymorphisms associated with risk of breast cancer in southern TaiwanBreast (Edinburgh, Scotland)Year: 200615675476110.1016/j.breast.2006.03.008
Onay VU,Briollais L,Knight JA,Shi E,Wang Y,Wells S,Li H,Rajendram I,Andrulis IL,Ozcelik H,SNP-SNP interactions in breast cancer susceptibilityBMC cancerYear: 2006611410.1186/1471-2407-6-11416672066
Pharoah PD,Tyrer J,Dunning AM,Easton DF,Ponder BA,Association between common variation in 120 candidate genes and breast cancer riskPLoS geneticsYear: 200733e4210.1371/journal.pgen.003004217367212
Ralph DA,Zhao LP,Aston CE,Manjeshwar S,Pugh TW,DeFreese DC,Gramling BA,Shimasaki CD,Jupe ER,Age-specific association of steroid hormone pathway gene polymorphisms with breast cancer riskCancerYear: 2007109101940194810.1002/cncr.2263417436274
Akisik E,Dalay N,Functional polymorphism of thymidylate synthase, but not of the COMT and IL-1B genes, is associated with breast cancerJournal of clinical laboratory analysisYear: 20072129710210.1002/jcla.2013917385677
Hu Z,Song CG,Lu JS,Luo JM,Shen ZZ,Huang W,Shao ZM,A multigenic study on breast cancer risk associated with genetic polymorphisms of ER Alpha, COMT and CYP19 gene in BRCA1/BRCA2 negative Shanghai women with early onset breast cancer or affected relativesJournal of cancer research and clinical oncologyYear: 20071331296997810.1007/s00432-007-0244-717562079
Takata Y,Maskarinec G,Le Marchand L,Breast density and polymorphisms in genes coding for CYP1A2 and COMT: the Multiethnic CohortBMC cancerYear: 200773010.1186/1471-2407-7-3017295924
Onay UV,Aaltonen K,Briollais L,Knight JA,Pabalan N,Kilpivaara O,Andrulis IL,Blomqvist C,Nevanlinna H,Ozcelik H,Combined effect of CCND1 and COMT polymorphisms and increased breast cancer riskBMC cancerYear: 20088610.1186/1471-2407-8-618194538
Justenhoven C,Hamann U,Schubert F,Zapatka M,Pierl CB,Rabstein S,Selinski S,Mueller T,Ickstadt K,Gilbert M,Ko YD,Baisch C,Pesch B,Harth V,Bolt HM,Vollmert C,Illig T,Eils R,Dippon J,Brauch H,Breast cancer: a candidate gene approach across the estrogen metabolic pathwayBreast Cancer Res TreatYear: 2008108113714910.1007/s10549-007-9586-817588204
He C,Tamimi RM,Hankinson SE,Hunter DJ,Han J,A prospective study of genetic polymorphism in MPO, antioxidant status, and breast cancer riskBreast Cancer Res TreatYear: 2009113358559410.1007/s10549-008-9962-z18340529
Reding KW,Weiss NS,Chen C,Li CI,Carlson CS,Wilkerson HW,Farin FM,Thummel KE,Daling JR,Malone KE,Genetic polymorphisms in the catechol estrogen metabolism pathway and breast cancer riskCancer Epidemiol Biomarkers PrevYear: 20091851461146710.1158/1055-9965.EPI-08-091719383894
MARIE-GENICA Consortium on Genetic Susceptibility for Menopausal Hormone Therapy Related Breast Cancer RiskGenetic polymorphisms in phase I and phase II enzymes and breast cancer risk associated with menopausal hormone therapy in postmenopausal womenBreast Cancer Res TreatYear: 2010119246347419424794
Yadav S,Singhal NK,Singh V,Rastogi N,Srivastava PK,Singh MP,Association of single nucleotide polymorphisms in CYP1B1 and COMT genes with breast cancer susceptibility in Indian womenDisease markersYear: 200927520321020037207
Shrubsole MJ,Lu W,Chen Z,Shu XO,Zheng Y,Dai Q,Cai Q,Gu K,Ruan ZX,Gao YT,Zheng W,Drinking green tea modestly reduces breast cancer riskThe Journal of nutritionYear: 2009139231031619074205
Sangrajrang S,Sato Y,Sakamoto H,Ohnami S,Laird NM,Khuhaprema T,Brennan P,Boffetta P,Yoshida T,Genetic polymorphisms of estrogen metabolizing enzyme and breast cancer risk in Thai womenInternational journal of cancerYear: 2009125483784310.1002/ijc.24434
Moreno-Galvan M,Herrera-Gonzalez NE,Robles-Perez V,Velasco-Rodriguez JC,Tapia-Conyer R,Sarti E,Impact of CYP1A1 and COMT genotypes on breast cancer risk in Mexican women: a pilot studyThe International journal of biological markersYear: 201025315716320878621
Syamala VS,Syamala V,Sheeja VR,Kuttan R,Balakrishnan R,Ankathil R,Possible risk modification by polymorphisms of estrogen metabolizing genes in familial breast cancer susceptibility in an Indian populationCancer investigationYear: 201028330431110.3109/0735790090274449419863350
Peterson NB,Trentham-Dietz A,Garcia-Closas M,Newcomb PA,Titus-Ernstoff L,Huang Y,Chanock SJ,Haines JL,Egan KM,Association of COMT haplotypes and breast cancer risk in caucasian womenAnticancer researchYear: 201030121722020150638
Delort L,Satih S,Kwiatkowski F,Bignon YJ,Bernard-Gallon DJ,Evaluation of breast cancer risk in a multigenic model including low penetrance genes involved in xenobiotic and estrogen metabolismsNutrition and cancerYear: 201062224325110.1080/0163558090330530020099199
Lajin B,Hamzeh AR,Ghabreau L,Mohamed A,Al Moustafa AE,Alachkar A,Catechol-O-methyltransferase Val 108/158 Met polymorphism and breast cancer risk: a case control study in SyriaBreast cancer (Tokyo, Japan)Year: 2011 Epub ahead of print.
dos Santos RA,Teixeira AC,Mayorano MB,Carrara HH,de Andrade J,Takahashi CS,Variability in estrogen-metabolizing genes and their association with genomic instability in untreated breast cancer patients and healthy womenJournal of biomedicine & biotechnologyYear: 2011201157178421716904
Zhu BT,Conney AH,Functional role of estrogen metabolism in target cells: review and perspectivesCarcinogenesisYear: 199819112710.1093/carcin/19.1.19472688
Lau J,Antman EM,Jimenez-Silva J,Kupelnick B,Mosteller F,Chalmers TC,Cumulative meta-analysis of therapeutic trials for myocardial infarctionThe New England journal of medicineYear: 1992327424825410.1056/NEJM1992072332704061614465

Figures

[Figure ID: F1]
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).



[Figure ID: F2]
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).



[Figure ID: F3]
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).



[Figure ID: F4]
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).



Tables
[TableWrap ID: T1] 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.


[TableWrap ID: T2] 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   χ2 P value I2
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.



Article Categories:
  • Research

Keywords: COMT, Polymorphism, Breast cancer, Meta-analysis.

Previous Document:  Lupus autoimmunity altered by cellular methylation metabolism.
Next Document:  Overexpression of CD44 accompanies acquired tamoxifen resistance in MCF7 cells and augments their se...