Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Lack of association between polymorphisms in the CYP1A2 gene and risk of cancer: evidence from meta-analyses

  • Vladimir Vukovic1Email author,
  • Carolina Ianuale1,
  • Emanuele Leoncini1,
  • Roberta Pastorino1,
  • Maria Rosaria Gualano1,
  • Rosarita Amore1 and
  • Stefania Boccia1
BMC Cancer201616:83

https://doi.org/10.1186/s12885-016-2096-5

Received: 8 April 2015

Accepted: 28 January 2016

Published: 10 February 2016

Abstract

Background

Polymorphisms in the CYP1A2 genes have the potential to affect the individual capacity to convert pre-carcinogens into carcinogens. With these comprehensive meta-analyses, we aimed to provide a quantitative assessment of the association between the published genetic association studies on CYP1A2 single nucleotide polymorphisms (SNPs) and the risk of cancer.

Methods

We searched MEDLINE, ISI Web of Science and SCOPUS bibliographic online databases and databases of genome-wide association studies (GWAS). After data extraction, we calculated Odds Ratios (ORs) and 95 % confidence intervals (CIs) for the association between the retrieved CYP1A2 SNPs and cancer. Random effect model was used to calculate the pooled ORs. Begg and Egger tests, one-way sensitivity analysis were performed, when appropriate. We conducted stratified analyses by study design, sample size, ethnicity and tumour site.

Results

Seventy case-control studies and one GWA study detailing on six different SNPs were included. Among the 71 included studies, 42 were population-based case-control studies, 28 hospital-based case-control studies and one genome-wide association study, including total of 47,413 cancer cases and 58,546 controls. The meta-analysis of 62 studies on rs762551, reported an OR of 1.03 (95 % CI, 0.96–1.12) for overall cancer (P for heterogeneity < 0.01; I 2  = 50.4 %). When stratifying for tumour site, an OR of 0.84 (95 % CI, 0.70–1.01; P for heterogeneity = 0.23, I 2  = 28.5 %) was reported for bladder cancer for those homozygous mutant of rs762551. An OR of 0.79 (95 % CI, 0.65–0.95; P for heterogeneity = 0.09, I 2  = 58.1 %) was obtained for the bladder cancer from the hospital-based studies and on Caucasians.

Conclusions

This large meta-analysis suggests no significant effect of the investigated CYP1A2 SNPs on cancer overall risk under various genetic models. However, when stratifying according to the tumour site, our results showed a borderline not significant OR of 0.84 (95 % CI, 0.70–1.01) for bladder cancer for those homozygous mutant of rs762551. Due to the limitations of our meta-analyses, the results should be interpreted with attention and need to be further confirmed by high-quality studies, for all the potential CYP1A2 SNPs.

Keywords

CYP1A2PolymorphismCancerMeta-analysisSusceptibility

Background

Cancer is a complex disease that develops as a result of the interactions between environmental factors and genetic inheritance. In 2012 there were 14.1 million new cancer cases and 8.2 million cancer deaths worldwide [1]. Endogenous or exogenous xenobiotics are activated or inactivated through two metabolic steps by phase I and phase II enzymes [2]. The majority of chemical carcinogens require activation to electrophilic reactive forms to produce DNA adducts and this is mainly catalyzed by phase I enzymes. Although there are some exceptions, phase II enzymes, in contrast, detoxify such intermediates through conjugative reactions. The consequent formation of reactive metabolites and their binding to DNA to give stable adducts are considered to be critical in the carcinogenic process. It might therefore be expected that individuals with increased activation or low detoxifying potential have a higher susceptibility for cancer [3].

Cytochrome P450 1A2 (CYP1A2) enzyme is a member of the cytochrome P450 oxidase system and is involved in the phase I metabolism of xenobiotics. In humans, the CYP1A2 enzyme is encoded by the CYP1A2 gene [4]. In vivo, CYP1A2 activity exhibits a remarkable degree of interindividual variations, as the gene expression is highly inducible by a number of dietary and environmental chemicals, including tobacco smoking, heterocyclic amines (HAs), coffee and cruciferous vegetables. Another possible contributor to interindividual variability in CYP1A2 activity is the occurrence of polymorphisms in the CYP1A2 gene [5], which have the potential for determining individual’s different susceptibility to carcinogenesis [6]. CYP1A2 is expressed mainly in the liver, but also, expression of the CYP1A2 enzyme in pancreas and lung has been detected. The CYP1A2 gene consists of 7 exons and is located at chromosome 15q22-qter. More than 40 single nucleotide polymorphisms (SNPs) of the CYP1A2 gene have been discovered so far [7, 8].

High in vivo CYP1A2 activity has been suggested to be a susceptibility factor for cancers of the bladder, colon and rectum, where exposure to compounds such as aromatic amines and HAs has been implicated in the etiology of the disease [5, 6]. Additionally, it has been reported that among the CYP1A2 polymorphisms, CYP1A2*1C (rs2069514) and CYP1A2*1 F (rs762551) are associated with reduced enzyme activity in smokers [5].

In recent years, efforts have been put into investigating the association of CYP1A2 polymorphisms and the risk of several cancers, among them, colorectal [923], lung [7, 2432], breast [3346], bladder [4, 4752], and other in different population groups, with inconsistent results. Therefore, with these meta-analyses we aimed to provide a quantitative assessment of the association between all CYP1A2 polymorphisms and risk of cancer at various sites.

Methods

Selection criteria

Identification of the studies was carried out through a search of MEDLINE, ISI Web of Science and SCOPUS databases up to February 15th, 2015, by two independent researchers (R.A. and V.V.). The following terms were used: [(Cytochrome P450 1A2) OR (CYP1A2)] AND (Cancer) AND (Humans [MeSH]), without any restriction on language. All eligible studies were retrieved, and their bibliographies were hand-searched to find additional eligible studies. We only included published studies with full-text articles available.

Also, detail search of several publically available databases of genome-wide association studies (GWAS) - GWAS Central, Genetic Associations and Mechanisms in Oncology (GAME-ON), the Human Genome Epidemiology (HuGE) Navigator, National Human Genome Research Institute (NHGRI GWAS Catalog), The database of Genotypes and Phenotypes (dbGaP), The GWASdb, VarySysDB Disease Edition (VaDE), The genome wide association database (GWAS DB), was carried out up to February 15th, 2015 for the association between CYP1A2 and various cancers using the combinations of following terms: (Cytochrome P450 1A2) OR (CYP1A2) OR (Chromosome 15q24.1) AND (Cancer). Additional consultation of principal investigators (PI) of the retrieved GWAS was undertaken in order to obtain the primary data and include them in the analyses.

Studies were considered eligible if they were assessing the frequency of any CYP1A2 gene polymorphism in relation to the number of cancer cases and controls, according to the three variant genotypes (wild-type homozygous (wtwt), heterozygous (wtmt) and homozygous mutant (mtmt)). Case-only and case series studies with no control population were excluded, as well as studies based only on phenotypic tests, reviews, meta-analysis and studies focused entirely on individuals younger than 16 years old. When the same sample was used in several publications, we only considered the most recent or complete study to be used in our meta-analyses. Meanwhile, for studies that investigated more types of cancer, we counted them as individual data only in a subgroup analysis by the tumour type, while when they reported different ethnicity or location within the same study, we considered them as a separate studies.

Data extraction

Two investigators (C.I. and V.V.) independently extracted the data from each article using a structured sheet and entered them into the database. The following items were considered: rs number, first author, year and location of the study, tumour site, ethnicity, study design, number of cases and controls, number of heterozygous and homozygous individuals for the CYP1A2 polymorphisms in the compared groups. We used widely accepted National Center for Biotechnology Information (NCBI) CYP classification [53] to determine which specific genotype should be considered as wtwt, wtmt and mtmt. We also ranked studies according to their sample size, where studies with minimum of 200 cases were classified as small and above 200 cases as large.

Statistical analysis

The estimated Odds Ratios (ORs) and 95 % confidence interval (CI) for the association between each CYP1A2 SNP and cancer were defined as follows:
  • wtmt vs wtwt (OR1)

  • mtmt vs wtwt (OR2).

According to the following algorithm on the criteria to identify the best genetic model [54] for each SNP:
  • Recessive model (mtmt versus wt carriers): if OR2 ≠ 1 and OR1 = 1

  • Dominant model (mt carriers versus wtwt): if OR2 = OR1 ≠ 1,

we used the dominant model of inheritance for rs2069514, rs2069526 and rs35694136 and recessive model for rs762551, rs2470890 and rs2472304 in the meta-analysis. Random effect model was used to calculate the pooled ORs, taking into account the possibility of between studies heterogeneity [55], that was evaluated by the χ2-based Q statistics and the I 2 statistics [56], where I 2  = 0 % indicates no observed heterogeneity, within 25 % regarded as low, 50 % as moderate, and 75 % as high [57]. A visual inspection of Begg’s funnel plot and Begg’s and Egger’s asymmetry tests [58] were used to investigate publication bias, where appropriate [59]. To determinate the deviation from the Hardy-Weinberg Equilibrium (HWE) we used a publicly available program (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl ). Additionally, the Galbraith’s test [60] was performed to evaluate the weight each study had on the overall estimate and its contribution on Q-statistics. We also performed a one-way sensitivity analysis to explore the effect that each study had on the overall effect estimate, by computing the meta-analysis estimates repeatedly after every study has been omitted.

Studies whose allele frequency in the control population deviated significantly from the Hardy-Weinberg Equilibrium (HWE) at the p-value ≤ 0.01 were excluded from the meta-analyses, given that this deviation may represent bias. We conducted stratified analysis by study design, ethnicity, sample size and tumour site to investigate the potential sources of heterogeneity across the studies. Statistical analyses were performed using the STATA software package v. 13 (Stata Corporation, College 162 Station, TX, USA), and all statistical tests were two-sided.

Results

Characteristics of the studies

We identified a total of 2541 studies through MEDLINE, ISI Web of Science and SCOPUS online databases. One thousand and sixteen studies were left after duplicates removal, and after carefully reading the titles, only 175 studies were assessed for eligibility. After reviewing the abstracts, 120 full text articles were obtained for further eligibility. By not fulfilling the inclusion criteria, 61 full text articles were excluded, leaving 59 studies for quantitative synthesis. Additional hand-search of the reference lists of 59 included studies was done and 11 new eligible studies were found, resulting in 70 included studies.

Eleven GWASs on the association between CYP1A2 SNPs and cancer risk were identified after detail search of GWAS online databases. Studies did not report full data on investigated SNPs, so we contacted principal investigators (PIs) to retrieve the information and include into our analyses. After 3 repeated solicitations, only one PI provided us with the full data on CYP1A2 SNPs of breast cancer cases and controls, and by this making total of 71 studies included in our meta-analyses [4, 752, 6184]. Figure 1 shows the process of literature search and study selection.
Figure 1
Fig. 1

Flowchart depicting literature search and study selection. *GWAS data bases searched: GWAS Central, Genetic Associations and Mechanisms in Oncology (GAME-ON), the Human Genome Epidemiology (HuGE) Navigator, National Human Genome Research Institute (NHGRI GWAS Catalog), The database of Genotypes and Phenotypes (dbGaP), The GWASdb, VarySysDB Disease Edition (VaDE), The genome wide association database (GWAS DB)

Among the 71 included studies, 42 were population-based case-control studies, 28 hospital-based case-control studies and one genome-wide association study, including total of 47,413 cancer cases and 58,546 controls (Table 1). The total investigated SNPs were six, of which 62 studies on the rs762551 [4, 721, 23, 24, 2646, 4850, 52, 6165, 67, 68, 7275, 7779, 8184]. Thirty five studies out of 62 were conducted on Caucasians (56.5 %), 17 on mixed populations (27.4 %) and 10 on Asians (16.1 %), including 33,181 cancer cases and 40,195 controls. Among them, 15 were on breast cancer, 14 studies on colorectal, and 9 on lung cancer.
Table 1

Description of 45 studies included in meta-analysis of association between different CYP1A2 SNPs and cancer

Rs number

First author

Year

Tumour site

Country

Ethnicity

Sample size (No. cases/controls)

Crude OR° (95 % CI) recessive model

Crude OR (95 % CI) dominant model

rs762551

Goodman MT. [73]

2001

Ovaries

USA

Mixed

116/138*a

0.52 (0.19–1.43)

 

Sachse C. [18]

2002

Colorectum

UK

Caucasian

490/593*ª

1.15 (0.70–1.88)

 

Goodman MT. [74]

2003

Ovaries

USA

Mixed

164/194*ª

0.73 (0.34–1.55)

 

Hopper J. [36]

2003

Breast

Australia

Caucasian

204/287*c

0.55 (0.27–1.13)

 

Doherty JA. [68]

2005

Endometrium

USA

Mixed

371/420*ª

1.27 (0.75–2.15)

 

Landi S. [16]

2005

Colorectum

Spain

Caucasian

361/321*b

1.74 (1.05–2.88)

 

Le Marchand L. [39]

2005

Breast

USA

Mixed

1339/1369*a

0.73 (0.55–0.96)

 

Prawan A. [81]

2005

Liver

Thailand

Asian

216/233*a

0.52 (0.24–1.13)

 

Mochizuki J. [79]

2005

Liver

Japan

Asian

31/123*a

1.35 (0.26–7.01)

 

Agudo A. [61]

2006

Stomach

European countries1

Caucasian

242/943*a

0.88 (0.50–1.55)

 

Bae SY. [9]

2006

Colorectum

S. Korea

Asian

111/93*b

1.14 (0.51–2.54)

 

De Roos AJ. [67]

2006

Lymphoma

USA

Mixed

745/640*a

0.91 (0.63–1.31)

 

Li D. [8]

2006

Pancreas

USA

Mixed

307/333*b

1.10 (0.65–1.84)

 

Long JR. [41]

2006

Breast

China

Asian

1082/1139*a

0.89 (0.71–1.13)

 

Rebbeck TR [82]

2006

Endometrium

USA

Mixed

475/1233*a

1.03 (0.73–1.46)

 

Kiss I. [13]

2007

Colorectum

Hungary

Caucasian

500/500*b

1.07 (0.74–1.54)

 

Kury S. [15]

2007

Colorectum

France

Caucasian

1013/1118*a

1.03 (0.75–1.41)

 

Osawa Y. [29]

2007

Lung

Japan

Asian

103/111*a

1.17 (0.57–2.42)

 

Takata Y. [46]

2007

Breast

USA (Hawaii)

Mixed

325/250*a

0.76 (0.39–1.49)

 

Yoshida K. [23]

2007

Colorectum

Japan

Asian

64/111*a

0.57 (0.21–1.53)

 

Gemignani F. [26]

2007

Lung

European countries2

Caucasian

297/310*b

0.86 (0.50–1.49)

 

Kotsopoulos J. [38]

2007

Breast

Canada

Caucasian

170/241*b

2.12 (0.99–4.57)

 

Gulyaeva LF. [35]

2008

Endometrium

Russia

Caucasian

166/180*a

2.20 (0.40–12.16)

 

Gulyaeva LF. [35]

2008

Ovaries

Russia

Caucasian

96/180*a

9.21 (1.95–43.53)

 

Gulyaeva LF. [35]

2008

Breast

Russia

Caucasian

93/180*a

27.58 (6.32–120.35)

 

Hirata H. [75]

2008

Endometrium

USA

Caucasian

150/165*a

0.96 (0.62–1.51)

 

Saebo M. [19]

2008

Colorectum

Norway

Caucasian

198/222*a

1.05 (0.49–2.23)

 

Suzuki H. [84]

2008

Pancreas

USA

Caucasian

649/585*a

0.93 (0.56–1.54)

 

Figueroa JD [48]

2008

Bladder

Spain

Caucasian

1101/1021*b

0.80 (0.62–1.04)

 

Zienolddiny S. [32]

2008

Lung

Norway

Caucasian

335/393*a

1.43 (0.88–2.32)

 

Cotterchio M. [11]

2008

Colorectum

Canada

Caucasian

835/1247*a

0.91 (0.67–1.23)

 

Aldrich MC. [7]

2009

Lung

USA

Mixed

113/299*a

3.36 (1.58–7.13)

 

Altayli E. [4]

2009

Bladder

Turkey

Caucasian

135/128*b

1.51 (0.88–2.60)

 

B’chir F. [24]

2009

Lung

Tunisia

Caucasian

101/98*b

0.90 (0.47–1.70)

 

Kobayashi M. [78]

2009

Stomach

Japan

Asian

141/286*b

0.62 (0.33–1.18)

 

Kobayashi M. [14]

2009

Colorectum

Japan

Asian

104/225*b

0.64 (0.31–1.32)

 

Shimada N (a) [45]

2009

Breast

Japan and Brazil

Asian

483/484*b

1.02 (0.71–1.47)

 

Shimada N (b) [45]

2009

Breast

Brazil

Mixed

389/389*b

0.50 (0.31–0.80)

 

Sangrajrang S. [44]

2009

Breast

Thailand

Asian

552/483*b

2.72 (1.52–4.86)

 

Villanueva C. [52]

2009

Bladder

Spain

Caucasian

1034/911*b

0.82 (0.62–1.07)

 

Canova C. [64]

2009

UADT

European countries3

Caucasian

1480/1437*b

0.88 (0.69–1.13)

 

Cleary SP [10]

2010

Colorectum

Canada

Caucasian

1165/1290*a

0.93 (0.71–1.22)

 

Pavanello S. [50]

2010

Bladder

Italy

Caucasian

155/161*b

0.57 (0.25–1.30)

 

Singh A. [31]

2010

Lung

India

Caucasian

200/200*a

0.61 (0.37–1.00)

 

The MARIE-GENICA Consortium [43]

2010

Breast

Germany

Caucasian

3147/5485*a

1.04 (0.88–1.22)

 

Canova C. [65]

2010

UADT

Italy

Caucasian

376/386*b

1.21 (0.77–1.89)

 

Ashton KA [62]

2010

Endometrium

Australia

Caucasian

191/291*a

1.03 (0.71–1.49)

 

Guey LT [49]

2010

Bladder

Spain

Caucasian

1005/1021*b

0.77 (0.58–1.00)

 

Rudolph A. [17]

2011

Colorectum

Germany

Caucasian

678/680*a

1.38 (0.93–2.05)

 

Sainz J. [20]

2011

Colorectum

Germany

Caucasian

1764/1786*a

0.95 (0.75–1.19)

 

Jang JH [77]

2012

Pancreas

Canada

Mixed

447/880*a

1.08 (0.73–1.59)

 

Khvostova EP [37]

2012

Breast

Russia

Caucasian

323/526*b

1.82 (1.14–2.90)

 

Pavanello S. [30]

2012

Lung

Denmark

Caucasian

421/776*a

1.63 (1.08–2.48)

 

Wang J. [21]

2012

Colorectum

USA

Mixed

305/357*a

0.97 (0.55–1.70)

 

Anderson LN [33]

2012

Breast

Canada

Mixed

886/932*a

1.50 (1.09–2.07)

 

Ayari I. [34]

2013

Breast

Tunisia

Caucasian

117/42*b

1.62 (0.51–5.11)

 

Barbieri RB [63]

2013

Thyroid gland

Brasil

Mixed

123/339*a

2.12 (1.16–3.87)

 

Dik VK [12]

2013

Colorectum

The Netherlands

Caucasian

970/1590*a

1.10 (0.85–1.43)

 

Gervasini G. [27]

2013

Lung

Spain

Caucasian

95/196*b

1.25 (0.60–2.61)

 

Lee HJ. [40]

2013

Breast

USA

Mixed

579/981*a

1.22 (0.85–1.75)

 

Lowcock E. [42]

2013

Breast

Canada

Mixed

1693/1761*a

1.24 (0.97–1.57)

 

Ghoshal U. [72]

2014

Stomach

India

Caucasian

88/170*a

1.13 (0.57–2.22)

 

Mikhalenko AP. [28]

2014

Lung

Belarus

Caucasian

92/328*a

1.14 (0.44–2.93)

 

Shahabi A. [83]

2014

Prostate

USA

Mixed

1480/777*a

0.97 (0.72–1.30)

rs2069514

Sachse C. [18]

2002

Colorectum

UK

Caucasian

60/73*a

12.71 (1.56–103.44)

 

Tsukino H. [51]

2004

Bladder

Japan

Asian

306/306*a

0.95 (0.69–1.31)

 

Landi S. [16]

2005

Colorectum

Spain

Caucasian

328/295*b

0.90 (0.38–2.10)

 

Chiou HL [25]

2005

Lung

China

Asian

162/208*b

1.04 (0.69–1.57)

 

Agudo A. [61]

2006

Stomach

European countries1

Caucasian

243/945*a

1.66 (0.72–3.84)

 

Chen X. [66]

2006

Liver

China

Asian

430/546*a

0.97 (0.75–1.24)

 

Bae SY. [9]

2006

Colorectum

S. Korea

Asian

111/93*b

0.68 (0.39–1.18)

 

Yoshida K. [23]

2007

Colorectum

Japan

Asian

66/113*a

0.82 (0.44–1.52)

 

Osawa Y. [29]

2007

Lung

Japan

Asian

106/113*a

0.80 (0.46–1.36)

 

Gemignani F. [26]

2007

Lung

European countries2

Caucasian

278/294*b

0.52 (0.16–1.75)

 

Zienolddiny S. [32]

2008

Lung

Norway

Caucasian

243/214*a

0.65 (0.22–1.91)

 

Imaizumi T. [76]

2009

Liver

Japan

Asian

209/256*a

0.88 (0.61–1.27)

 

B’chir F. [24]

2009

Lung

Tunisia

Caucasian

101/98*b

5.88 (2.96–11.70)

 

Yeh CC [22]

2009

Colorectum

Taiwan

Asian

718/731*b

1.08 (0.88–1.32)

 

Gemignani F. [71]

2009

Pleura

Italy

Caucasian

92/643*b

0.33 (0.04–2.45)

 

Singh A. [31]

2010

Lung

India

Caucasian

200/200*a

0.84 (0.47–1.50)

 

Pavanello S. [30]

2012

Lung

Denmark

Caucasian

423/777*a

0.85 (0.32–2.24)

 

Ayari I. [34]

2013

Breast

Tunisia

Caucasian

109/41*b

0.35 (0.14–0.90)

 

Gervasini G. [27]

2013

Lung

Spain

Caucasian

95/196*b

2.67 (0.70–10.17)

 

Cui X. [47]

2013

Bladder

Japan

Asian

282/257*b

0.89 (0.63–1.26)

rs2069526

Sachse C. [18]

2002

Colorectum

UK

Caucasian

490/593*a

0.86 (0.60–1.22)

 

Landi S. [16]

2005

Colorectum

Spain

Caucasian

321/288*b

1.27 (0.55–2.90)

 

Gemignani F. [26]

2007

Lung

European countries2

Caucasian

247/251*b

0.34 (0.14–0.81)

 

Zienolddiny S. [32]

2008

Lung

Norway

Caucasian

194/239*a

1.66 (0.37–7.49)

 

Gemignani F. [71]

2009

Pleura

Italy

Caucasian

78/579*b

1.10 (0.42–2.90)

 

Singh A. [31]

2010

Lung

India

Caucasian

200/200*a

1.07 (0.65–1.75)

 

Gervasini G. [27]

2013

Lung

Spain

Caucasian

95/196*b

1.36 (0.57–3.27)

rs2470890

Hopper J. [36]

2003

Breast

Australia

Caucasian

204/287*c

0.82 (0.47–1.43)

 

Landi S. [16]

2005

Colorectum

Spain

Caucasian

353/320*b

1.24 (0.84–1.82)

 

Chen X. [66]

2006

Liver

China

Asian

428/545*a

0.53 (0.27–1.06)

 

Kury S. [15]

2007

Colorectum

France

Caucasian

1013/1118*a

1.07 (0.90–1.27)

 

Gemignani F. [26]

2007

Lung

European countries2

Caucasian

283/298*b

0.83 (0.51–1.35)

 

Aldrich MC. [7]

2009

Lung

USA

Mixed

113/299*a

1.12 (0.59–2.13)

 

Gemignani F. [71]

2009

Pleura

Italy

Caucasian

85/669*b

1.02 (0.56–1.88)

 

Canova C. [64]

2009

UADT

European countries3

Caucasian

1455/1403*b

1.03 (0.84–1.26)

 

Canova C. [65]

2010

UADT

Italy

Caucasian

374/387*b

1.51 (1.02–2.23)

 

Anderson LN [33]

2012

Breast

Canada

Mixed

884/927*a

1.49 (1.18–1.89)

 

Eom SY. [69]

2013

Stomach

S. Korea

Asian

473/472*b

1.15 (0.55–2.37)

rs2472304

Hopper J. [36]

2003

Breast

Australia

Caucasian

204/286*c

0.81 (0.46–1.43)

 

Sangrajrang S. [44]

2009

Breast

Thailand

Asian

552/478*b

1.16 (0.59–2.29)

 

Aldrich MC. [7]

2009

Lung

USA

Mixed

112/297*a

1.12 (0.59–2.14)

 

Ferlin A. [70]

2010

Testicles

Italy

Caucasian

234/218*a

0.68 (0.46–1.01)

rs35694136

Li D. [8]

2006

Pancreas

USA

Mixed

307/329*b

0.87 (0.63–1.18)

 

Olivieri EH [80]

2009

Head and Neck

Brasil

Mixed

81/134*b

8.98 (4.49–17.93)

 

Pavanello S. [50]

2010

Bladder

Italy

Caucasian

167/141*b

0.73 (0.46–1.14)

 

Singh A. [31]

2010

Lung

India

Caucasian

200/200*a

1.65 (1.11–2.45)

 

Pavanello S. [30]

2012

Lung

Denmark

Caucasian

415/760*a

0.98 (0.65–1.49)

 

Ayari I. [34]

2013

Breast

Tunisia

Caucasian

108/38*b

0.88 (0.40–1.93)

Statistically significant results are presented in bold. °OR (95 % CI) Odds Ratio and 95 % Confidence Interval 1Ten European countries: Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom. 2Six European countries: Romania, Hungary, Poland, Russia, Slovakia, Czech Republic. 3Ten European countries: Czech Republic, Germany, Greece, Italy, Ireland, Norway, United Kingdom, Spain, Croatia, France. *Hardy-Weinberg Equilibrium (HWE), P value ˃0.01. aPopulation-based study bHospital-based study cGenome-wide Association Study. (a), (b) One study with two different population

Twenty studies investigated the rs2069514 [9, 16, 18, 2227, 2932, 34, 47, 51, 61, 66, 71, 76], of which 11 were conducted on Caucasians (55 %) and 9 on Asians (45 %). Eight studies investigated the effect on lung cancer (40 %), 5 studies on colorectal cancer (25 %), 2 on liver cancer (10 %), 2 on bladder (10 %) and by 1 study on stomach (5 %), breast (5 %) and pleura (5 %), totaling for 4562 cancer cases and 6399 controls (Table 1).

The remaining four SNPs were investigated by a reduced number of studies and details are presented in Table 1. Genotype frequencies in all control groups did not deviate from values predicted by HWE (Table 1). As some studies on different cancer types shared the same control group [35], these studies were aggregated when performing the meta-analyses, except when stratified by tumour site.

Quantitative synthesis

As the crude analysis for rs762551 provided an OR1 of 1.03 (95 % CI 0.98–1.07) and an OR2 of 1.06 (95 % CI 0.97–1.16), for rs2470890 OR1 1.03 (95 % CI 0.93–1.14) and OR2 of 1.14 (95 % CI 0.97–1.34) and for rs2472304 OR1 of 0.98 (95 % CI 0.79–1.22) and OR2 of 0.89 (95 % CI 0.66–1.22) according to the criteria proposed in the methods section, we applied the recessive model of inheritance for the meta-analyses. On the other hand, for rs2069514, rs2069526 and rs35694136 original papers did not report enough data to calculate OR1 and OR2, so we were able only to apply the dominant model for the data analyses.

The Figs. 2 and 3 depict the forest plots of the ORs of the six CYP1A2 SNPs and cancer. By pooling 62 studies on rs762551, the meta-analysis reported an OR of 1.03 (95 % CI 0.96–1.12) for overall cancer (P for heterogeneity < 0.01; I 2  = 50.4 %). Egger test and the Begg’s correlation method did not provide statistical evidence of publication bias (P = 0.19 and P = 0.39, respectively) (Fig. 4). To explore the potential sources of heterogeneity, we performed the Galbraith’s test which identified the study of Shimada N. (b) [45] and Sangrajrang S. [44], as the main contributors to heterogeneity (graph not shown). In the one-way sensitivity analysis, these two outlying studies were omitted from meta-analysis and the overall OR slightly changed to 1.03 (95 % CI 0.96–1.11), with a reduced heterogeneity (P for heterogeneity <0.01; I 2  = 43.0 %).
Figure 2
Fig. 2

Forest plot of the CYP1A2 rs762551 and cancer meta-analysis under recessive models of inheritance. The diamonds and horizontal lines correspond to the study-specific odds ratio (OR) and 95 % confidence interval (CI)

Figure 3
Fig. 3

Forest plot of the remaining five CYP1A2 SNPs and cancer meta-analyses under different models of inheritance. The diamonds and horizontal lines correspond to the study-specific odds ratio (OR) and 95 % confidence interval (CI)

Figure 4
Fig. 4

Funnel plot for publication bias for studies with CYP1A2 rs762551. Each point represents an individual study for the indicated association

Results of the stratified meta-analyses are reported in the Table 2. When stratifying the results of meta-analysis for rs762551 by ethnicity, we found no significant effect of CYP1A2 on cancer risk for Caucasians (OR = 1.03; 95 % CI 0.94–1.13), Asians (OR = 0.95; 95 % CI 0.72–1.27) nor among a mixed population (OR = 1.05; 95 % CI 0.89–1.25). When stratifying according to the tumour site, results showed an OR of 0.84 (95 % CI 0.70–1.01; P for heterogeneity = 0.23, I 2  = 28.5 %) for bladder cancer for those homozygous mutant types of rs762551 (Table 2). We further examined the association between the CYP1A2 polymorphism and cancer risk according to ethnicity, source of controls and sample size and then stratified by cancer type. We found a significant OR of 0.79 (95 % CI 0.65–0.95; P for heterogeneity = 0.09, I 2  = 58.1 %) for bladder cancer among the hospital-based population and among Caucasians. There was no significant association among Caucasians for breast cancer (OR = 1.71; 95 % CI 0.94–3.10; P for heterogeneity < 0.01, I 2  = 83.4 %), lung cancer (OR = 1.07; 95 % CI 0.79–1.44; P for heterogeneity = 0.07, I 2  = 48.1 %,) or colorectal cancer (OR = 1.05, 95 % CI 0.94–1.16; P for heterogeneity = 0.49, I 2  = 0.0 %). Among Asians, when stratifying for cancer type, we obtained an OR of 0.76 (95 % CI 0.47–1.22; P for heterogeneity = 0.48, I 2  = 0.0 %) for colorectal cancer and OR = 1.27 (95 % CI 0.75–2.16; P for heterogeneity <0.01, I 2  = 83.6 %) for breast cancer.
Table 2

Subgroup meta-analyses of CYP1A2 SNPs and cancer risk according to study design, ethnicity and tumour site

 

Number cases/controls

Recessive model

 

Exposed

Not exposed

OR°

95 % CI°

I 2 (%)

P value for heterogeneity

rs762551

3373/4006

29,808/36,562

1.03

0.96–1.12

50.4

<0.01

Study design

      

 Hospital based

1048/1110

8289/8482

1.03

0.88–1.20

60.3

<0.01

 Population based

2314/2869

21,326/27,820

1.05

0.96–1.15

41.8

<0.01

Study sample size

      

 Large

2883/3387

27,381/32,680

1.02

0.94–1.11

55.9

<0.01

 Small

490/619

2427/3882

1.09

0.90–1.32

36.0

0.05

Ethnicity

      

 Asian

348/414

2539/2874

0.95

0.72–1.27

54.6

0.02

 Caucasian

2132/2600

18,305/23,388

1.03

0.94–1.13

42.4

<0.01

 Mixed

893/992

8964/10,300

1.05

0.89–1.25

62.8

<0.01

Tumour site

      

 Bladder

392/436

3038/2806

0.84

0.70–1.01

28.5

0.23

 Breast

1097/1280

10,285/13,269

1.17

0.94–1.45

79.2

<0.01

 Colorectum

803/934

7755/9199

1.03

0.93–1.14

0.0

0.56

 Endometrium

258/391

1095/1898

1.06

0.87–1.30

0.0

0.85

 Liver

12/26

235/330

0.63

0.30–1.32

5.0

0.31

 Lung

221/265

1536/2446

1.20

0.87–1.64

58.9

0.01

 Ovaries

27/34

349/478

1.31

0.33–5.19

80.3

0.01

 Pancreas

107/142

1296/1656

1.04

0.80–1.36

0.0

0.87

 Stomach

46/141

425/1258

0.85

0.59–1.21

0.0

0.45

 UADT

186/192

1670/1631

0.97

0.73–1.29

29.9

0.23

 

Number cases/controls

Dominant model

 

Exposed

Not exposed

OR

95 % CI

I 2 (%)

P value for heterogeneity

rs2069514

1229/1373

3333/5026

0.99

0.81–1.21

60.0

<0.01

Study design

      

 Hospital based

758/783

1727/2329

1.01

0.73–1.40

73.7

<0.01

 Population based

471/590

1606/2697

0.94

0.78–1.14

10.6

0.35

Study sample size

      

 Large

969/1085

2691/3736

0.97

0.86–1.09

0.0

0.89

 Small

260/288

642/1290

1.18

0.65–2.11

81.1

<0.01

Ethnicity

      

 Asian

1093/1235

1297/1388

0.96

0.86–1.07

0.0

0.86

 Caucasian

136/138

2036/3638

1.16

0.63–2.14

75.7

<0.01

Tumour site

      

 Bladder

236/237

352/326

0.92

0.73–1.17

0.0

0.81

 Colorectum

447/458

836/847

0.96

0.65–1.43

53.2

0.07

 Liver

315/409

324/393

0.94

0.76–1.15

0.0

0.68

 Lung

211/219

1397/1881

1.16

0.68–1.99

76.3

<0.01

 

Number cases/controls

Dominant model

 

Exposed

Not exposed

OR

95 % CI

I 2 (%)

P value for heterogeneity

rs2069526

139/202

1486/2144

0.94

0.70–1.26

20.7

0.27

Study design

      

 Hospital based

35/78

706/1236

0.89

0.47–1.72

53.9

0.09

 Population based

104/124

780/908

0.94

0.71–1.25

0.0

0.59

Study sample size

      

 Large

121/151

1137/1181

0.85

0.56–1.28

49.4

0.12

 Small

18/51

349/963

1.29

0.71–2.35

0.0

0.89

Ethnicity

      

 Caucasian

139/202

1486/2144

0.94

0.70–1.26

20.7

0.27

Tumour site

      

 Colorectum

74/93

737/788

0.91

0.66–1.26

0.0

0.40

 Lung

60/75

676/811

0.90

0.47–1.71

55.4

0.08

 

Number cases/controls

Recessive model

 

Exposed

Not exposed

OR

95 % CI

I 2 (%)

P value for heterogeneity

rs2470890

1106/1187

4559/5538

1.11

0.96–1.28

39.0

0.09

Study design

      

 Hospital based

429/480

2594/3069

1.10

0.95–1.27

0.0

0.46

 Population based

655/670

1783/2219

1.09

0.80–1.50

70.5

0.02

Study sample size

      

 Large

1077/1043

4390/4714

1.11

0.94–1.30

50.9

0.04

 Small

29/144

169/824

1.07

0.69–1.66

0.0

0.85

Ethnicity

      

 Asian

28/42

873/975

0.77

0.37–1.64

55.4

0.13

 Caucasian

863/957

2904/3525

1.07

0.96–1.20

0.0

0.47

 Mixed

215/188

782/1038

1.44

1.16–1.80

0.0

0.41

Tumour site

      

 Breast

222/189

866/1025

1.17

0.65–2.08

73.4

0.05

 Colorectum

500/509

866/929

1.10

0.94–1.28

0.0

0.51

 Lung

48/77

348/520

0.92

0.63–1.37

0.0

0.47

 UADT

294/262

1535/1528

1.20

0.83–1.74

65.7

0.09

 

Number cases/controls

Recessive model

 

Exposed

Not exposed

OR

95 % CI

I 2 (%)

P value for heterogeneity

rs2472304

127/172

975/1107

0.84

0.64–1.09

0.0

0.43

Study design

      

 Population based

85/120

261/395

0.82

0.51–1.30

40.4

0.20

Study sample size

      

 Large

112/136

878/846

0.79

0.59–1.05

0.0

0.41

Ethnicity

      

 Caucasian

92/121

346/383

0.72

0.52–0.99

0.0

0.61

Tumour site

      

 Breast

42/52

714/712

0.94

0.61–1.45

0.0

0.43

 

Number cases/controls

Dominant model

 

Exposed

Not exposed

OR

95 % CI

I 2 (%)

P value for heterogeneity

rs35694136

439/419

839/1183

1.37

0.78–2.42

89.0

<0.01

Study design

      

 Hospital based

290/263

373/379

1.46

0.56–3.77

92.7

<0.01

 Population based

149/156

466/804

1.28

0.77–2.13

68.4

0.08

Study sample size

      

 Large

290/319

632/970

1.11

0.75–1.64

69.8

0.04

 Small

149/100

207/213

1.78

0.37–8.60

94.6

<0.01

Ethnicity

      

 Caucasian

255/241

635/898

1.04

0.70–1.53

62.0

0.05

 Mixed

184/178

204/285

2.73

0.28–27.09

97.3

<0.01

Tumour site

      

 Lung

149/156

466/804

1.28

0.77–2.13

68.4

0.08

Statistically significant ORs are presented in bold. °OR (95 % CI) Odds Ratio and 95 % Confidence Interval

When pooling the 20 studies on rs2069514, the meta-analysis provided an OR of 0.99 (95 % CI 0.81–1.21) for overall cancer (P for heterogeneity <0.01; I 2  = 60 %) (Fig. 2). Egger test and the Begg’s correlation method provided no statistical evidence of publication bias (P = 0.86 and P = 0.56, respectively). We performed the Galbraith’s test to explore the source of heterogeneity and accordingly singled out the study of B’chir F. et al. [24] as the main contributor to heterogeneity (graph not shown). In the one-way sensitivity analysis, the study of B’chir F. et al. [24] was omitted from the overall meta-analysis and the heterogeneity dropped down to 14 % (P = 0.28), with the OR of 0.93 (95 % CI 0.82–1.06).

We evaluated the effect of the rs2069514 polymorphism according to the tumour site and obtained an OR of 0.96 (95 % CI 0.65–1.43; P for heterogeneity = 0.07, I 2  = 53.2 %) for colorectal cancer, an OR of 1.29 (95 % CI 0.60–2.79; P for heterogeneity = 0.00; I 2  = 82.1 %) for lung cancer (Table 2). Analyses on different ethnicity and study design did not provide any significant results (Caucasians OR = 1.16; 95 % CI 0.63–2.14; I 2  = 75.7 %, P < 0.01, for Asians OR = 0.96; 95 % CI 0.86–1.07, I 2  = 0.0 %; P = 0.86 and Hospital-based study design OR = 1.01; 95 % CI 0.73–1.40; I 2  = 73.7 %, P < 0.01, for Population-based design OR = 0.94; 95 % CI 0.78–1.14; I 2  = 10.6 %, P = 0.35). We did not observe any significant association between rs2069514 polymorphism and cancer risk when subgrouping data according to ethnicity, source of controls and sample size and then stratified by cancer type. Among Caucasians, we obtained an OR of 1.28 (95 % CI 0.55–2.98; I 2  = 80.9 %, P < 0.01) for lung cancer, while among Asians OR = 0.94 (95 % CI 0.68–1.31; I 2  = 0.0 %, P = 0.44) for lung and OR = 0.94 (95 % CI 0.71–1.24; I 2  = 28.8 %, P = 0.25) for colorectal cancer.

We performed meta-analysis of 11 studies on rs2470890 which provided an OR of 1.11 (95 % CI 0.96-1.28) for the overall cancer risk (P for heterogeneity 0.09; I 2  = 39 %) (Fig. 2). Egger test and the Begg’s correlation method provided no statistical evidence of publication bias (P = 0.42 and P = 0.59, respectively). The Galbraith’s test singled out the study of Anderson LN et al. [33] as the main contributor to heterogeneity (graph not shown). In one-way sensitivity analysis, this study was omitted from the overall meta-analysis and the heterogeneity dropped down to 6 % (P = 0.39), with still not significant OR of 1.06 (95 % CI, 0.94–1.19). The effect of rs2470890 polymorphism according to the tumour site was also evaluated and was obtained non-significant result of OR of 1.10 (95 % CI, 0.94–1.28) P for heterogeneity = 0.51, I 2  = 0.0 % for colorectal cancer and an OR of 1.20 (95 % CI, 0.83–1.74), P for heterogeneity = 0.09; I 2  = 65.7 % for cancer of upper aero-digestive tract (UADT) (Table 2). Subgroups analyses by different ethnicity showed a significant association between rs2470890 polymorphism and cancer for Mixed population OR = 1.44; 95 % CI 1.16–1.80; I 2  = 0.0 %, P = 0.41, while not among Caucasians (OR = 1.07; 95 % CI 0.96–1.20; I 2  = 0.0 %, P = 0.41) nor Asians (OR = 0.77; 95 % CI 0.37–1.64; I 2  = 55.4 %, P = 0.13).

Results of the remaining three SNPs of CYP1A2 are presented in the Fig. 3 and the Table 2. Absence of significant association with overall risk of cancer was reported. Only for rs2472304 we rendered an OR of 0.72 (95 % CI 0.52–0.99) I 2  = 0.0 %, P = 0.61 for Caucasians, when doing a subgroup analyses on ethnicity. No evidence of significant heterogeneity was detected (data not shown).

When the meta-analyses were performed excluding small sample size studies for all examined SNPs, there were still no significant results obtained for the association between CYP1A2 SNPs and cancer risk (Table 2).

Discussion

The current meta-analysis included 71 studies with more than 47,000 cancer cases and 58,000 controls, detailing on all the CYP1A2 gene polymorphisms and risk of cancer, shows no significant effect of investigated CYP1A2 SNPs on cancer overall risk under various genetic models. Meta-analysis is a common tool for summarizing different studies to resolve the problem of small size statistical power and discrepancy in genetic association studies [85] and also it provides more reliable results than a single case-control study. To the best of our knowledge, this is the largest and most comprehensive meta-analysis on CYP1A2 SNPs and cancer performed so far. Several previous meta-analyses have been reported on the association between CYP1A2 gene polymorphisms and risk of cancer [8695]. Deng et al. [87] reported no association between CYP1A2 rs762551 polymorphism and lung cancer risk by including 1675 cases and 2393 controls. In the paper of Xue et al. [94], combined mutational homozygous and wild type homozygous genotype compared with mutational heterozygous genotype, had protective effect against gastric cancer by including 383 cases and 1229 controls. Wen-Xia Sun et al. [91] reported a significant protective effect of homozygous mutant of rs762551 CYP1A2 SNP on bladder cancer in Caucasian population. Based on 19 studies, Wang et al. [93] found a borderline significantly increased risk of overall cancer among homozygous mutant of CYP1A2 rs762551, mainly in Caucasians. The meta-analysis of 46 case-control studies by Tian et al. [92] suggested that the wild-type allele of CYP1A2 rs762551 polymorphism might be associated with breast and ovarian cancer risk, especially among Caucasians. These inconclusive results could be explained by differences in study design, sample size, ethnicity, and cancer subtypes included.

The CYP1A2 gene is a member of the CYP1 family and is involved in metabolism of carcinogens and estrogens. In particular, it plays an essential role in the metabolic activation of pro-carcinogens, such as polycyclic aromatic hydrocarbons (PAHs) and heterocyclic aromatic amines (HAA) [93]. Therefore, increased levels of this enzyme could explain the association with increased risk for cancer [16]. The wild genotype of CYP1A2*1 F represents a highly inducible genotype, and this high CYP1A2 activity may increase the hydroxylated forms as proximate carcinogens, from HCAs and aryl-amines [29].

In our meta-analyses, we showed that none of the investigated CYP1A2 polymorphisms were significantly associated with overall risk of cancer at various sites. These results confirm the findings of a recent meta-analysis from Li Zhenzhen et al. [95] where was reported no significant associations with cancer risk in any genetic model (allele contrast, codominant, dominant, or recessive model) in terms of rs2069514 and rs3569413. For rs762551, they found that carriers of C-allele have an increased overall risk of developing cancer in allele genetic model (C-allele vs. A-allele) while not in other models. Their further subgroup analyses demonstrated that rs762551 polymorphism was associated with an increased risk of cancer in Caucasians under dominant model, while we investigated rs762551 under recessive model and did not obtain significant association. Moreover, their meta-analysis included only 37 case-control studies of rs762551 involving 16,825 cancer cases and 21,513 controls. Our meta-analysis may be the most comprehensive meta-analysis of the relationship between the CYP1A2 rs762551 polymorphisms and the risk of cancer, to date.

When stratifying according to tumour site, our results showed a borderline not significant OR of 0.84 (95 % CI, 0.70–1.01) for bladder cancer for those homozygous mutant of rs762551 with total of 3430 cases and 3242 controls included (Table 2), thus confronting the previous evidence from Wen-Xia Sun et al. [91] that reported an OR = 0.79 (95 % CI 0.66–0.94) from 2415 cases and 2208 controls, and suggesting that on even bigger number of subjects investigated, this significance might disappear. Pavanello et al. [96] stressed that polymorphisms of rs762551 might be the crucial modulating factor along the continuum from the exposure to relevant environmental and occupational factors, in increased CYP1A2 activity of smokers measured by the urinary caffeine metabolic ratio.

We also found a significant decreased risk for bladder cancer for mutant carriers of rs762551 among the hospital-based population. Hospital-based studies have certain biases since those controls may have some benign diseases which can progress and also may not be representative of the general population. Using a population-based control would reduce the chance of bias in these studies.

In one recent meta-analysis by Zhi-Bin Bu et al. [86] on the association between CYP1A2 rs762551, rs2069514, rs2069526, and rs2470890 polymorphisms and lung cancer risk, there was no evidence of significant association between lung cancer risk and CYP1A2 rs2069514, s2470890, and rs2069526 polymorphisms. They found increased lung cancer risk for rs762551 polymorphism in Caucasians from 3 studies, while in our analysis there was no such connection on a bigger sample of studies [24, 2628, 3032].

Lastly, when stratifying our results for breast and colorectal cancer, we did not report any significant association between rs762551 and these cancers, thus confirming previous meta-analyses of Li-Xin Qiu et al. [90] on breast and Xiao-Feng He et al. [88] on colorectal cancer risk. Other meta-analysis by Jianbing Hu et al. [89] also suggested that CYP1A2 rs762551 polymorphism was not a risk factor for colorectal cancer susceptibility, since no association was detected after all studies were pooled together nor in a subgroup analysis by ethnicity or source of controls, in all genetic models. The influence of the different CYP1A2 SNPs might be camouflaged by the presence of some yet unidentified causal genes involved in many other types of cancer.

When stratifying the results according to ethnicity, the protective effect of rs2472304 in our study was restricted only to Caucasians, while for rs2470890, we noticed an increased risk among a mixed population. A possible explanation for these results could be that the same polymorphisms may play different roles in cancer susceptibility in different ethnic populations as well as different tumour positions, due to a difference in genetic backgrounds, the environment they live in, lifestyle and migrations, which all may have a critical role in cancer pathogenesis [97]. Also, some low penetrance genetic effects of single polymorphism could be determined by their interaction with other polymorphisms and/or a specific environmental exposure.

No other relevant results were reported for the remaining SNPs, however there were available only few studies regarding these associations, involving relatively small number of participants.

In interpreting the results, some limitations of our study should be considered. Firstly, only published studies were included, so there was space for publication bias, which in fact was confirmed by formal statistical tests. Secondly, the study size for most of the CYP1A2 polymorphisms was limited to perform any meaningful subgroup analyses. Thirdly, it would have been valuable to stratify the results according to environmental effect modifiers, though this was not possible, as the original data sets were not available. Indeed, due to lack of access to original data used in included studies, our meta-analyses are based on the unadjusted data, so the effects might be confounded or modified by relevant covariates. Fourthly, beside breast cancer, there are no genome-wide association studies of the effects of CYP1A2 polymorphisms on cancer risk. We were able to include only one breast cancer GWAS into our analyses, therefore our results might be affected by additional publication bias.

Despite these limitations, our meta-analyses also have some advantages. First, the statistical power of the analyses was noticeably increased as a huge number of cases and controls were pooled from different studies and has more statistical powerful than any single case-control study. Secondly, in our analyses, we included more studies than any previously published meta-analysis on the association between CYP1A2 polymorphism and cancer risks and investigated 6 different CYP1A2 SNPs.

Conclusions

In conclusion, our meta-analysis suggests that investigated CYP1A2 polymorphisms are not associated with cancer susceptibility under various genetic models. In order to reach a more definitive conclusion, there is a necessity for further gene-gene and gene-environment interaction studies to be conducted on different populations and larger sample size, for diverse CYP1A2 SNPs.

Abbreviations

95 % CI: 

95 % confidence interval

CYP1A2: 

cytochrome P450 1A2

dbGaP: 

The database of Genotypes and Phenotypes

GAME-ON: 

Genetic Associations and Mechanisms in Oncology

GWAS: 

genome-wide association studies

GWAS DB: 

The genome wide association database

HAA: 

heterocyclic aromatic amines

HAs: 

heterocyclic amines

HuGE: 

the Human Genome Epidemiology Navigator

HWE: 

Hardy-Weinberg Equilibrium

mtmt: 

homozygous mutant genotype

NCBI: 

National Center for Biotechnology Information

NHGRI: 

National Human Genome Research Institute Catalog

ORs: 

Odds Ratios

PAHs: 

polycyclic aromatic hydrocarbons

PIs: 

principal investigators

SNPs: 

single nucleotide polymorphisms

VaDE: 

VarySysDB Disease Edition

wtmt: 

wild-type mutant-type heterozygous genotype

wtwt: 

wild-type homozygous genotype

Declarations

Acknowledgements

The Authors would like to thank Professor John Hopper and his working team from the Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, for providing us with the primary GWAS data from their study of association between CYP1A2 SNPs and breast cancer risk. Also, would like to thank the ERAWEB mobility programme, under the European Commission, for financially supporting the work of VV., the Fondazione Veronesi for supporting the work of Emanuele Leoncini, and the Associazione Italiana per la Ricerca sul Cancro (AIRC) for supporting the work of Roberta Pastorino.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Public Health- Section of Hygiene, Università Cattolica del Sacro Cuore, Rome, Italy

References

  1. Boffetta P, Boccia S, La Vecchia C. A Quick Guide to Cancer Epidemiology. Springer International Publishing; 2014. p. 11–4.Google Scholar
  2. Heller F. Genetics/genomics and drug effects. Acta Clin Belg. 2013;68(2):77–80.PubMedView ArticleGoogle Scholar
  3. Raunio H, Husgafvel-Pursiainen K, Anttila S, Hietanen E, Hirvonen A, Pelkonen O. Diagnosis of polymorphisms in carcinogen-activating and inactivating enzymes and cancer susceptibility--a review. Gene. 1995;159(1):113–21.PubMedView ArticleGoogle Scholar
  4. Altayli E, Gunes S, Yilmaz AF, Goktas S, Bek Y. CYP1A2, CYP2D6, GSTM1, GSTP1, and GSTT1 gene polymorphisms in patients with bladder cancer in a Turkish population. Int Urol Nephrol. 2009;41(2):259–66.PubMedView ArticleGoogle Scholar
  5. Sachse C, Bhambra U, Smith G, Lightfoot TJ, Barrett JH, Scollay J, et al. Polymorphisms in the cytochrome P450 CYP1A2 gene (CYP1A2) in colorectal cancer patients and controls: allele frequencies, linkage disequilibrium and influence on caffeine metabolism. Br J Clin Pharmacol. 2003;55(1):68–76.PubMedPubMed CentralView ArticleGoogle Scholar
  6. Nakajima M, Yokoi T, Mizutani M, Kinoshita M, Funayama M, Kamataki T. Genetic polymorphism in the 5′-flanking region of human CYP1A2 gene: effect on the CYP1A2 inducibility in humans. J Biochem. 1999;125(4):803–8.PubMedView ArticleGoogle Scholar
  7. Aldrich MC, Selvin S, Hansen HM, Barcellos LF, Wrensch MR, Sison JD, et al. CYP1A1/2 haplotypes and lung cancer and assessment of confounding by population stratification. Cancer Res. 2009;69(6):2340–8.PubMedPubMed CentralView ArticleGoogle Scholar
  8. Li D, Jiao L, Li Y, Doll MA, Hein DW, Bondy ML, et al. Polymorphisms of cytochrome P4501A2 and N-acetyltransferase genes, smoking, and risk of pancreatic cancer. Carcinogenesis. 2006;27(1):103–11.PubMedView ArticleGoogle Scholar
  9. Bae SY, Choi SK, Kim KR, Park CS, Lee SK, Roh HK, et al. Effects of genetic polymorphisms of MDR1, FMO3 and CYP1A2 on susceptibility to colorectal cancer in Koreans. Cancer Sci. 2006;97(8):774–9.PubMedView ArticleGoogle Scholar
  10. Cleary SP, Cotterchio M, Shi E, Gallinger S, Harper P. Cigarette smoking, genetic variants in carcinogen-metabolizing enzymes, and colorectal cancer risk. Am J Epidemiol. 2010;172(9):1000–14.PubMedPubMed CentralView ArticleGoogle Scholar
  11. Cotterchio M, Boucher BA, Manno M, Gallinger S, Okey AB, Harper PA. Red meat intake, doneness, polymorphisms in genes that encode carcinogen-metabolizing enzymes, and colorectal cancer risk. Cancer Epidemiol Biomarkers Prev. 2008;17(11):3098–107.PubMedPubMed CentralView ArticleGoogle Scholar
  12. Dik VK, van Oijen MG, Uiterwaal C, van Gils C, van Duijnhoven FJ, Cauchi S, et al. Coffee consumption, genetic polymorphisms in CYP1A2 and NAT2, and colorectal cancer risk. Gastroenterology. 2013;144(5):S589–90.Google Scholar
  13. Kiss I, Orsos Z, Gombos K, Bogner B, Csejtei A, Tibold A, et al. Association between allelic polymorphisms of metabolizing enzymes (CYP 1A1, CYP 1A2, CYP 2E1, mEH) and occurrence of colorectal cancer in Hungary. Anticancer Res. 2007;27(4C):2931–7.PubMedGoogle Scholar
  14. Kobayashi M, Otani T, Iwasaki M, Natsukawa S, Shaura K, Koizumi Y, et al. Association between dietary heterocyclic amine levels, genetic polymorphisms of NAT2, CYP1A1, and CYP1A2 and risk of colorectal cancer: a hospital-based case-control study in Japan. Scand J Gastroenterol. 2009;44(8):952–9.PubMedView ArticleGoogle Scholar
  15. Kury S, Buecher B, Robiou-du-Pont S, Scoul C, Sebille V, Colman H, et al. Combinations of cytochrome P450 gene polymorphisms enhancing the risk for sporadic colorectal cancer related to red meat consumption. Cancer Epidemiol Biomarkers Prev. 2007;16(7):1460–7.PubMedView ArticleGoogle Scholar
  16. Landi S, Gemignani F, Moreno V, Gioia-Patricola L, Chabrier A, Guino E, et al. A comprehensive analysis of phase I and phase II metabolism gene polymorphisms and risk of colorectal cancer. Pharmacogenet Genomics. 2005;15(8):535–46.PubMedView ArticleGoogle Scholar
  17. Rudolph A, Sainz J, Hein R, Hoffmeister M, Frank B, Forsti A, et al. Modification of menopausal hormone therapy-associated colorectal cancer risk by polymorphisms in sex steroid signaling, metabolism and transport related genes. Endocr Relat Cancer. 2011;18(3):371–84.PubMedView ArticleGoogle Scholar
  18. Sachse C, Smith G, Wilkie MJ, Barrett JH, Waxman R, Sullivan F, et al. A pharmacogenetic study to investigate the role of dietary carcinogens in the etiology of colorectal cancer. Carcinogenesis. 2002;23(11):1839–49.PubMedView ArticleGoogle Scholar
  19. Saebo M, Skjelbred CF, Brekke Li K, Bowitz Lothe IM, Hagen PC, Johnsen E, et al. CYP1A2 164 A-->C polymorphism, cigarette smoking, consumption of well-done red meat and risk of developing colorectal adenomas and carcinomas. Anticancer Res. 2008;28(4C):2289–95.PubMedGoogle Scholar
  20. Sainz J, Rudolph A, Hein R, Hoffmeister M, Buch S, von Schonfels W, et al. Association of genetic polymorphisms in ESR2, HSD17B1, ABCB1, and SHBG genes with colorectal cancer risk. Endocr Relat Cancer. 2011;18(2):265–76.PubMedView ArticleGoogle Scholar
  21. Wang J, Joshi AD, Corral R, Siegmund KD, Marchand LL, Martinez ME, et al. Carcinogen metabolism genes, red meat and poultry intake, and colorectal cancer risk. Int J Cancer. 2012;130(8):1898–907.PubMedView ArticleGoogle Scholar
  22. Yeh CC, Sung FC, Tang R, Chang-Chieh CR, Hsieh LL. Polymorphisms of cytochrome P450 1A2 and N-acetyltransferase genes, meat consumption, and risk of colorectal cancer. Dis Colon Rectum. 2009;52(1):104–11.PubMedView ArticleGoogle Scholar
  23. Yoshida K, Osawa K, Kasahara M, Miyaishi A, Nakanishi K, Hayamizu S, et al. Association of CYP1A1, CYP1A2, GSTM1 and NAT2 gene polymorphisms with colorectal cancer and smoking. Asian Pac J Cancer Prev. 2007;8(3):438–44.PubMedGoogle Scholar
  24. B’Chir F, Pavanello S, Knani J, Boughattas S, Arnaud MJ, Saguem S. CYP1A2 genetic polymorphisms and adenocarcinoma lung cancer risk in the Tunisian population. Life Sci. 2009;84(21–22):779–84.PubMedView ArticleGoogle Scholar
  25. Chiou HL, Wu MF, Chien WP, Cheng YW, Wong RH, Chen CY, et al. NAT2 fast acetylator genotype is associated with an increased risk of lung cancer among never-smoking women in Taiwan. Cancer Lett. 2005;223(1):93–101.PubMedView ArticleGoogle Scholar
  26. Gemignani F, Landi S, Szeszenia-Dabrowska N, Zaridze D, Lissowska J, Rudnai P, et al. Development of lung cancer before the age of 50: the role of xenobiotic metabolizing genes. Carcinogenesis. 2007;28(6):1287–93.PubMedView ArticleGoogle Scholar
  27. Gervasini G, Ghotbi R, Aklillu E, San Jose C, Cabanillas A, Kishikawa J, et al. Haplotypes in the 5′-untranslated region of the CYP1A2 gene are inversely associated with lung cancer risk but do not correlate with caffeine metabolism. Environ Mol Mutagen. 2013;54(2):124–32.PubMedView ArticleGoogle Scholar
  28. Mikhalenko AP, Krupnova EV, Chakova NN, Chebotareva NV, Demidchik YE. Assessment of the relationship between combinations of polymorphic variants of xenobiotic-metabolizing enzyme genes and predisposition to lung cancer. Cytol Genet. 2014;48(2):111–6.View ArticleGoogle Scholar
  29. Osawa Y, Osawa KK, Miyaishi A, Higuchi M, Tsutou A, Matsumura S, et al. NAT2 and CYP1A2 polymorphisms and lung cancer risk in relation to smoking status. Asian Pac J Cancer Prev. 2007;8(1):103–8.PubMedGoogle Scholar
  30. Pavanello S, Fedeli U, Mastrangelo G, Rota F, Overvad K, Raaschou-Nielsen O, et al. Role of CYP1A2 polymorphisms on lung cancer risk in a prospective study. Cancer Genet. 2012;205(6):278–84.PubMedView ArticleGoogle Scholar
  31. Singh AP, Pant MC, Ruwali M, Shah PP, Prasad R, Mathur N, et al. Polymorphism in cytochrome P450 1A2 and their interaction with risk factors in determining risk of squamous cell lung carcinoma in men. Cancer Biomark. 2010;8(6):351–9.PubMedView ArticleGoogle Scholar
  32. Zienolddiny S, Campa D, Lind H, Ryberg D, Skaug V, Stangeland LB, et al. A comprehensive analysis of phase I and phase II metabolism gene polymorphisms and risk of non-small cell lung cancer in smokers. Carcinogenesis. 2008;29(6):1164–9.PubMedView ArticleGoogle Scholar
  33. Anderson LN, Cotterchio M, Mirea L, Ozcelik H, Kreiger N. Passive cigarette smoke exposure during various periods of life, genetic variants, and breast cancer risk among never smokers. Am J Epidemiol. 2012;175(4):289–301.PubMedView ArticleGoogle Scholar
  34. Ayari I, Fedeli U, Saguem S, Hidar S, Khlifi S, Pavanello S. Role of CYP1A2 polymorphisms in breast cancer risk in women. Mol Med Rep. 2013;7(1):280–6.PubMedView ArticleGoogle Scholar
  35. Gulyaeva LF, Mikhailova ON, PustyInyak VO, Kim IV, Gerasimov AV, Krasilnikov SE, et al. Comparative analysis of SNP in estrogen-metabolizing enzymes for ovarian, endometrial, and breast cancers in Novosibirsk, Russia. Adv Exp Med Biol. 2008;617:359–66.PubMedView ArticleGoogle Scholar
  36. Hopper JL, Dite GS, Jenkins MA, Southey MC, Hocking JS, Giles GG, et al. Familial risks, early-onset breast cancer, and BRCA1 and BRCA2 germline mutations. J Natl Cancer Inst. 2003;95(6):448–57.PubMedView ArticleGoogle Scholar
  37. Khvostova EP, Pustylnyak VO, Gulyaeva LF. Genetic polymorphism of estrogen metabolizing enzymes in Siberian women with breast cancer. Genet Test Mol Biomarkers. 2012;16(3):167–73.PubMedView ArticleGoogle Scholar
  38. Kotsopoulos J, Ghadirian P, El-Sohemy A, Lynch HT, Snyder C, Daly M, et al. The CYP1A2 genotype modifies the association between coffee consumption and breast cancer risk among BRCA1 mutation carriers. Cancer Epidemiol Biomarkers Prev. 2007;16(5):912–6.PubMedView ArticleGoogle Scholar
  39. Le Marchand L, Donlon T, Kolonel LN, Henderson BE, Wilkens LR. Estrogen metabolism-related genes and breast cancer risk: the multiethnic cohort study. Cancer Epidemiol Biomarkers Prev. 2005;14(8):1998–2003.PubMedView ArticleGoogle Scholar
  40. Lee HJ, Wu K, Cox DG, Hunter D, Hankinson SE, Willett WC, et al. Polymorphisms in xenobiotic metabolizing genes, intakes of heterocyclic amines and red meat, and postmenopausal breast cancer. Nutr Cancer. 2013;65(8):1122–31.PubMedView ArticleGoogle Scholar
  41. Long JR, Egan KM, Dunning L, Shu XO, Cai Q, Cai H, et al. Population-based case-control study of AhR (aryl hydrocarbon receptor) and CYP1A2 polymorphisms and breast cancer risk. Pharmacogenet Genomics. 2006;16(4):237–43.PubMedView ArticleGoogle Scholar
  42. Lowcock EC, Cotterchio M, Anderson LN, Boucher BA, El-Sohemy A. High coffee intake, but not caffeine, is associated with reduced estrogen receptor negative and postmenopausal breast cancer risk with no effect modification by CYP1A2 genotype. Nutr Cancer. 2013;65(3):398–409.PubMedView ArticleGoogle Scholar
  43. Marie-Genica C. Genetic polymorphisms in phase I and phase II enzymes and breast cancer risk associated with menopausal hormone therapy in postmenopausal women. Breast Cancer Res Treat. 2010;119(2):463–74.View ArticleGoogle Scholar
  44. Sangrajrang S, Sato Y, Sakamoto H, Ohnami S, Laird NM, Khuhaprema T, et al. Genetic polymorphisms of estrogen metabolizing enzyme and breast cancer risk in Thai women. Int J Cancer. 2009;125(4):837–43.PubMedView ArticleGoogle Scholar
  45. Shimada N, Iwasaki M, Kasuga Y, Yokoyama S, Onuma H, Nishimura H, et al. Genetic polymorphisms in estrogen metabolism and breast cancer risk in case-control studies in Japanese, Japanese Brazilians and non-Japanese Brazilians. J Hum Genet. 2009;54(4):209–15.PubMedView ArticleGoogle Scholar
  46. Takata Y, Maskarinec G, Le Marchand L. Breast density and polymorphisms in genes coding for CYP1A2 and COMT: the Multiethnic Cohort. BMC Cancer. 2007;7:30.PubMedPubMed CentralView ArticleGoogle Scholar
  47. Cui X, Lu X, Hiura M, Omori H, Miyazaki W, Katoh T. Association of genotypes of carcinogen-metabolizing enzymes and smoking status with bladder cancer in a Japanese population. Environ Health Prev Med. 2013;18(2):136–42.PubMedView ArticleGoogle Scholar
  48. Figueroa JD, Malats N, Garcia-Closas M, Real FX, Silverman D, Kogevinas M, et al. Bladder cancer risk and genetic variation in AKR1C3 and other metabolizing genes. Carcinogenesis. 2008;29(10):1955–62.PubMedPubMed CentralView ArticleGoogle Scholar
  49. Guey LT, Garcia-Closas M, Murta-Nascimento C, Lloreta J, Palencia L, Kogevinas M, et al. Genetic susceptibility to distinct bladder cancer subphenotypes. Eur Urol. 2010;57(2):283–92.PubMedView ArticleGoogle Scholar
  50. Pavanello S, Mastrangelo G, Placidi D, Campagna M, Pulliero A, Carta A, et al. CYP1A2 polymorphisms, occupational and environmental exposures and risk of bladder cancer. Eur J Epidemiol. 2010;25(7):491–500.PubMedView ArticleGoogle Scholar
  51. Tsukino H, Kuroda Y, Nakao H, Imai H, Inatomi H, Osada Y, et al. Cytochrome P450 (CYP) 1A2, sulfotransferase (SULT) 1A1, and N-acetyltransferase (NAT) 2 polymorphisms and susceptibility to urothelial cancer. J Cancer Res Clin Oncol. 2004;130(2):99–106.PubMedView ArticleGoogle Scholar
  52. Villanueva CM, Silverman DT, Murta-Nascimento C, Malats N, Garcia-Closas M, Castro F, et al. Coffee consumption, genetic susceptibility and bladder cancer risk. Cancer Causes Control. 2009;20(1):121–7.PubMedView ArticleGoogle Scholar
  53. National Center for Biotechnology Information. dbSNP Home Page. [cited 2015 02/15]. Available from: http://www.ncbi.nlm.nih.gov/SNP/index.html. Accessed 15 Feb 2015.
  54. Attia J, Thakkinstian A, D’Este C. Meta-analyses of molecular association studies: methodologic lessons for genetic epidemiology. J Clin Epidemiol. 2003;56(4):297–303.PubMedView ArticleGoogle Scholar
  55. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.PubMedView ArticleGoogle Scholar
  56. Deeks J, Altman D, Bradburn M. Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis. In: Egger M, Davey Smith G, Altman D, editors. Systematic reviews in health care: meta-analysis in context. 2nd ed. London: BMJ Books; 2001. p. 285–312.View ArticleGoogle Scholar
  57. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.PubMedPubMed CentralView ArticleGoogle Scholar
  58. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.PubMedPubMed CentralView ArticleGoogle Scholar
  59. Ioannidis JP, Trikalinos TA. The appropriateness of asymmetry tests for publication bias in meta-analyses: a large survey. CMAJ. 2007;176(8):1091–6.PubMedPubMed CentralView ArticleGoogle Scholar
  60. Galbraith RF. A note on graphical presentation of estimated odds ratios from several clinical trials. Stat Med. 1988;7(8):889–94.PubMedView ArticleGoogle Scholar
  61. Agudo A, Sala N, Pera G, Capella G, Berenguer A, Garcia N, et al. Polymorphisms in metabolic genes related to tobacco smoke and the risk of gastric cancer in the European prospective investigation into cancer and nutrition. Cancer Epidemiol Biomarkers Prev. 2006;15(12):2427–34.PubMedView ArticleGoogle Scholar
  62. Ashton KA, Proietto A, Otton G, Symonds I, McEvoy M, Attia J, et al. Polymorphisms in genes of the steroid hormone biosynthesis and metabolism pathways and endometrial cancer risk. Cancer Epidemiol. 2010;34(3):328–37.PubMedView ArticleGoogle Scholar
  63. Barbieri RB, Bufalo NE, Cunha LL, Assumpcao LV, Maciel RM, Cerutti JM, et al. Genes of detoxification are important modulators of hereditary medullary thyroid carcinoma risk. Clin Endocrinol. 2013;79(2):288–93.View ArticleGoogle Scholar
  64. Canova C, Hashibe M, Simonato L, Nelis M, Metspalu A, Lagiou P, et al. Genetic associations of 115 polymorphisms with cancers of the upper aerodigestive tract across 10 European countries: the ARCAGE project. Cancer Res. 2009;69(7):2956–65.PubMedView ArticleGoogle Scholar
  65. Canova C, Richiardi L, Merletti F, Pentenero M, Gervasio C, Tanturri G, et al. Alcohol, tobacco and genetic susceptibility in relation to cancers of the upper aerodigestive tract in northern Italy. Tumori. 2010;96(1):1–10.PubMedGoogle Scholar
  66. Chen X, Wang H, Xie W, Liang R, Wei Z, Zhi L, et al. Association of CYP1A2 genetic polymorphisms with hepatocellular carcinoma susceptibility: a case-control study in a high-risk region of China. Pharmacogenet Genomics. 2006;16(3):219–27.PubMedGoogle Scholar
  67. De Roos AJ, Gold LS, Wang S, Hartge P, Cerhan JR, Cozen W, et al. Metabolic gene variants and risk of non-Hodgkin’s lymphoma. Cancer Epidemiol Biomarkers Prev. 2006;15(9):1647–53.PubMedView ArticleGoogle Scholar
  68. Doherty JA, Weiss NS, Freeman RJ, Dightman DA, Thornton PJ, Houck JR, et al. Genetic factors in catechol estrogen metabolism in relation to the risk of endometrial cancer. Cancer Epidemiol Biomarkers Prev. 2005;14(2):357–66.PubMedView ArticleGoogle Scholar
  69. Eom SY, Yim DH, Zhang Y, Yun JK, Moon SI, Yun HY, et al. Dietary aflatoxin B1 intake, genetic polymorphisms of CYP1A2, CYP2E1, EPHX1, GSTM1, and GSTT1, and gastric cancer risk in Korean. Cancer Causes Control. 2013;24(11):1963–72.PubMedView ArticleGoogle Scholar
  70. Ferlin A, Ganz F, Pengo M, Selice R, Frigo AC, Foresta C. Association of testicular germ cell tumor with polymorphisms in estrogen receptor and steroid metabolism genes. Endocr Relat Cancer. 2010;17(1):17–25.PubMedView ArticleGoogle Scholar
  71. Gemignani F, Neri M, Bottari F, Barale R, Canessa PA, Canzian F, et al. Risk of malignant pleural mesothelioma and polymorphisms in genes involved in the genome stability and xenobiotics metabolism. Mutat Res. 2009;671(1–2):76–83.PubMedView ArticleGoogle Scholar
  72. Ghoshal U, Tripathi S, Kumar S, Mittal B, Chourasia D, Kumari N, et al. Genetic polymorphism of cytochrome P450 (CYP) 1A1, CYP1A2, and CYP2E1 genes modulate susceptibility to gastric cancer in patients with Helicobacter pylori infection. Gastric Cancer. 2014;17(2):226–34.PubMedView ArticleGoogle Scholar
  73. Goodman MT, McDuffie K, Kolonel LN, Terada K, Donlon TA, Wilkens LR, et al. Case-control study of ovarian cancer and polymorphisms in genes involved in catecholestrogen formation and metabolism. Cancer Epidemiol Biomarkers Prev. 2001;10(3):209–16.PubMedGoogle Scholar
  74. Goodman MT, Tung KH, McDuffie K, Wilkens LR, Donlon TA. Association of caffeine intake and CYP1A2 genotype with ovarian cancer. Nutr Cancer. 2003;46(1):23–9.PubMedView ArticleGoogle Scholar
  75. Hirata H, Hinoda Y, Okayama N, Suehiro Y, Kawamoto K, Kikuno N, et al. CYP1A1, SULT1A1, and SULT1E1 polymorphisms are risk factors for endometrial cancer susceptibility. Cancer. 2008;112(9):1964–73.PubMedView ArticleGoogle Scholar
  76. Imaizumi T, Higaki Y, Hara M, Sakamoto T, Horita M, Mizuta T, et al. Interaction between cytochrome P450 1A2 genetic polymorphism and cigarette smoking on the risk of hepatocellular carcinoma in a Japanese population. Carcinogenesis. 2009;30(10):1729–34.PubMedView ArticleGoogle Scholar
  77. Jang JH, Cotterchio M, Borgida A, Gallinger S, Cleary SP. Genetic variants in carcinogen-metabolizing enzymes, cigarette smoking and pancreatic cancer risk. Carcinogenesis. 2012;33(4):818–27.PubMedPubMed CentralView ArticleGoogle Scholar
  78. Kobayashi M, Otani T, Iwasaki M, Natsukawa S, Shaura K, Koizumi Y, et al. Association between dietary heterocyclic amine levels, genetic polymorphisms of NAT2, CYP1A1, and CYP1A2 and risk of stomach cancer: a hospital-based case-control study in Japan. Gastric Cancer. 2009;12(4):198–205.PubMedView ArticleGoogle Scholar
  79. Mochizuki J, Murakami S, Sanjo A, Takagi I, Akizuki S, Ohnishi A. Genetic polymorphisms of cytochrome P450 in patients with hepatitis C virus-associated hepatocellular carcinoma. J Gastroenterol Hepatol. 2005;20(8):1191–7.PubMedView ArticleGoogle Scholar
  80. Olivieri EH, da Silva SD, Mendonca FF, Urata YN, Vidal DO, Faria Mde A, et al. CYP1A2*1C, CYP2E1*5B, and GSTM1 polymorphisms are predictors of risk and poor outcome in head and neck squamous cell carcinoma patients. Oral Oncol. 2009;45(9):e73–9.PubMedView ArticleGoogle Scholar
  81. Prawan A, Kukongviriyapan V, Tassaneeyakul W, Pairojkul C, Bhudhisawasdi V. Association between genetic polymorphisms of CYP1A2, arylamine N-acetyltransferase 1 and 2 and susceptibility to cholangiocarcinoma. Eur J Cancer Prev. 2005;14(3):245–50.PubMedView ArticleGoogle Scholar
  82. Rebbeck TR, Troxel AB, Wang Y, Walker AH, Panossian S, Gallagher S, et al. Estrogen sulfation genes, hormone replacement therapy, and endometrial cancer risk. J Natl Cancer Inst. 2006;98(18):1311–20.PubMedView ArticleGoogle Scholar
  83. Shahabi A, Corral R, Catsburg C, Joshi AD, Kim A, Lewinger JP, et al. Tobacco smoking, polymorphisms in carcinogen metabolism enzyme genes, and risk of localized and advanced prostate cancer: results from the California Collaborative Prostate Cancer Study. Cancer Med. 2014;3(6):1644–55.PubMedPubMed CentralView ArticleGoogle Scholar
  84. Suzuki H, Morris JS, Li Y, Doll MA, Hein DW, Liu J, et al. Interaction of the cytochrome P4501A2, SULT1A1 and NAT gene polymorphisms with smoking and dietary mutagen intake in modification of the risk of pancreatic cancer. Carcinogenesis. 2008;29(6):1184–91.PubMedPubMed CentralView ArticleGoogle Scholar
  85. Munafo MR, Flint J. Meta-analysis of genetic association studies. Trends Genet. 2004;20(9):439–44.PubMedView ArticleGoogle Scholar
  86. Bu ZB, Ye M, Cheng Y, Wu WZ. Four polymorphisms in the cytochrome P450 1A2 (CYP1A2) gene and lung cancer risk: a meta-analysis. Asian Pac J Cancer Prev. 2014;15(14):5673–9.PubMedView ArticleGoogle Scholar
  87. Deng SQ, Zeng XT, Wang Y, Ke Q, Xu QL. Meta-analysis of the CYP1A2 -163C>A polymorphism and lung cancer risk. Asian Pac J Cancer Prev. 2013;14(5):3155–8.PubMedView ArticleGoogle Scholar
  88. He XF, Wei J, Liu ZZ, Xie JJ, Wang W, Du YP, et al. Association between CYP1A2 and CYP1B1 polymorphisms and colorectal cancer risk: a meta-analysis. PLoS One. 2014;9(8), e100487.PubMedPubMed CentralView ArticleGoogle Scholar
  89. Hu J, Liu C, Yin Q, Ying M, Li J, Li L, et al. Association between the CYP1A2-164 A/C polymorphism and colorectal cancer susceptibility: a meta-analysis. Mol Genet Genomics. 2014;289(3):271–7.PubMedView ArticleGoogle Scholar
  90. Qiu LX, Yao L, Mao C, Yu KD, Zhan P, Chen B, et al. Lack of association of CYP1A2-164 A/C polymorphism with breast cancer susceptibility: a meta-analysis involving 17,600 subjects. Breast Cancer Res Treat. 2010;122(2):521–5.PubMedView ArticleGoogle Scholar
  91. Sun WX, Chen YH, Liu ZZ, Xie JJ, Wang W, Du YP, et al. Association between the CYP1A2 polymorphisms and risk of cancer: a meta-analysis. Mol Genet Genomics. 2015;290(2):709–25.PubMedView ArticleGoogle Scholar
  92. Tian Z, Li YL, Zhao L, Zhang CL. Role of CYP1A2 1F polymorphism in cancer risk: evidence from a meta-analysis of 46 case-control studies. Gene. 2013;524(2):168–74.PubMedView ArticleGoogle Scholar
  93. Wang H, Zhang Z, Han S, Lu Y, Feng F, Yuan J. CYP1A2 rs762551 polymorphism contributes to cancer susceptibility: a meta-analysis from 19 case-control studies. BMC Cancer. 2012;12:528.PubMedPubMed CentralView ArticleGoogle Scholar
  94. Xue H, Lu Y, Xue Z, Lin B, Chen J, Tang F, et al. The effect of CYP1A1 and CYP1A2 polymorphisms on gastric cancer risk among different ethnicities: a systematic review and meta-analysis. Tumour Biol. 2014;35(5):4741–56.PubMedView ArticleGoogle Scholar
  95. Zhenzhen L, Xianghua L, Ning S, Zhan G, Chuanchuan R, Jie L. Current evidence on the relationship between three polymorphisms in the CYP1A2 gene and the risk of cancer. Eur J Cancer Prev. 2013;22(6):607–19.PubMedView ArticleGoogle Scholar
  96. Pavanello S, Pulliero A, Lupi S, Gregorio P, Clonfero E. Influence of the genetic polymorphism in the 5′-noncoding region of the CYP1A2 gene on CYP1A2 phenotype and urinary mutagenicity in smokers. Mutat Res. 2005;587(1–2):59–66.PubMedView ArticleGoogle Scholar
  97. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. A comprehensive review of genetic association studies. Genet Med. 2002;4(2):45–61.PubMedView ArticleGoogle Scholar

Copyright

© Vukovic et al. 2016

Advertisement