Association between the CYP1A1 T3801C polymorphism and risk of cancer: evidence from 268 case-control studies.

Gene 534 (2014) 324–344

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Association between the CYP1A1 T3801C polymorphism and risk of cancer: Evidence from 268 case–control studies Xiao-Feng He a,1, Wu Wei b,⁎,1, Zhi-Zhong Liu c,1, Xu-Liang Shen b,1, Xian-Bin Yang a, Su-Lan Wang b, Dao-Lin Xie d,1 a

Department of Research, Peace Hospital of Changzhi Medical College, Changzhi 046000, PR China Department of Hematology, Peace Hospital of Changzhi Medical College, Changzhi 046000, PR China Gastroenterology, The Second People's Hospital of Zhuhai, Zhuhai 519000, PR China d Department of Ultrasound Diagnosis, Peace Hospital of Changzhi Medical College, Changzhi 046000, PR China b c

a r t i c l e

i n f o

Article history: Accepted 7 October 2013 Available online 25 October 2013 Keywords: CYP1A1 Polymorphism Susceptibility Meta-analysis Cancer

a b s t r a c t T3801C is a common polymorphism in CYP1A1, showing differences in its biological functions. Case–control studies have been performed to elucidate the role of T3801C in cancer, although the results are conflicting and heterogeneous. Hence, we performed a meta-analysis to investigate the association between cancer susceptibility and T3801C (55,963 cases and 76,631 controls from 268 studies) polymorphism in different inheritance models. We used odds ratios with 95% confidence intervals to assess the strength of the association. Overall, significantly increased cancer risk was observed in any genetic model (dominant model: odds ratio [OR]= 1.14, 95% confidence interval [CI] = 1.09–1.19; recessive model: OR = 1.23, 95% CI = 1.12–1.34; CC vs. TT: OR = 1.31, 95% CI = 1.19–1.45; TC vs. TT: OR = 1.12, 95% CI = 1.07–1.18; additive model: OR = 1.14, 95% CI = 1.09–1.19) when all eligible studies were pooled into the meta-analysis. In further stratified and sensitivity analyses, the elevated risk remained for subgroups of cervical cancer, head and neck cancer, hepatocellular cancer, leukemia, lung cancer, prostate cancer and breast cancer. In addition, significantly decreased colorectal cancer risk was also observed. In summary, this meta-analysis suggests that the participation of CYP1A1 T3801C is a genetic susceptibility for some cancer types. Moreover, our work also points out the importance of new studies for T3801C association in some cancer types, such as gallbladder cancer, Asians of acute myeloid leukemia, and thyroid cancer, where at least some of the covariates responsible for heterogeneity could be controlled, to obtain a more conclusive understanding about the function of the CYP1A1 T3801C polymorphism in cancer development. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Polycyclic aromatic hydrocarbons (PAHs) are environmental compounds ubiquitously distributed in smoked, foods, tobacco smoke and the urban outdoor environment in large cities (Shimada, 2006). PAHs acquire carcinogenicity following activation by xenobiotic-metabolizing enzymes to highly reactive metabolites (Elovaara et al., 2007). Cytochrome P450 (CYP) enzymes are pivotal to the metabolic activation of

Abbreviations: PAHs, polycyclic aromatic hydrocarbons; CYP, cytochrome P450; CYP1A1, cytochrome P450 1A1; SNP, single nucleotide polymorphism; OR, odds ratios; HWE, Hardy–Weinberg equilibrium; NSCLC, non-small cells lung cancer; AC, adenocarcinoma; SCC, squamous cell carcinomas; SCLC, small cell lung cancer; AML, acute myeloid leukemia; ALL, acute lymphoblastic leukemia; CALL, acute lymphoblastic leukemia; SZ, sample size; HNC, head and neck cancer; SC, source of controls; PB, population-based studies; HB, hospital-based studies. ⁎ Corresponding author. Tel./fax: +86 3553034125. E-mail address: [email protected] (W. Wei). 1 These authors contributed equally to this work and should be considered as co-first authors. 0378-1119/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gene.2013.10.025

PAHs to epoxide intermediates. These intermediates are converted into the ultimate carcinogens, diol-epoxides, via epoxide hydrolase. CYP1A1 was believed to be the most important enzyme catalyzing activation of these pro-carcinogenic PAHs (Quiñones and Gil, 1995). Cytochrome P450 (CYP) enzyme catalyze phase I metabolism reaction. Cytochrome P450 1A1 (CYP1A1) is a member of the CYP family that participates in the metabolism of xenobiotics and endogenous compounds, particularly polycyclic aromatic hydrocarbons such as benzo[a]pyrene (Guengerich and Shimada, 1998). A commonly studied single nucleotide polymorphism (SNP) in the CYP1A1 gene has been indicated to associate with cancer risk, which was localized on chromosome 15q22 (Crofts et al., 1993). The commonly studied is the 3801TNC polymorphism (also referred to as 2A, m1, or rs4646903), which is characterized by the T to C mutation at nucleotide 3801 in the 30 flanking region of the CYP1A1 gene. The 3801TNC polymorphism can alter the level of gene expression or messenger RNA stability, resulting in a highly inducible activity of the enzyme (Shah et al., 2009). Hence, certain variant genotypes of the CYP1A1 gene which may cause enhanced enzymatic activity appear to play a role in susceptibility to adduct formation and presumably cancer risk (Rojas et al., 2000).

X.-F. He et al. / Gene 534 (2014) 324–344 Table 1 Scale for quality assessment criterion. Criterion

Score

Source of cases Selected from population or cancer registry Selected from hospital Selected from pathology archives, but without description Not described Source of controls Population-based Blood donors or volunteers Hospital-based (cancer-free patients) Not described Specimens used for determining genotypes White blood cells or normal tissues Tumor tissues or exfoliated cells of tissue Hardy–Weinberg equilibrium in controls Hardy–Weinberg equilibrium Hardy–Weinberg disequilibrium Total sample size N1000 N500 and b 1000 N200 and b500 b200

3 2 1 0 3 2 1 0 3 0 3 0 3 2 1 0

In the past decade, a number of molecular epidemiological studies have been done to evaluate the association between CYP1A1 T3801C polymorphism and different types of cancer risk in diverse populations.

325

However, the results were inconsistent or even contradictory. Partially because of the possible small effect of the polymorphism on cancer risk and the relatively small sample size in each of published studies. In addition, some recent meta-analyses analyzed such an association only for single cancer such as prostate cancer, leukemia, oral cancer, ovarian cancer, lung cancer, and so on (Ding et al., 2013; Ji et al., 2012; Sergentanis et al., 2012; Zhuo et al., 2012a, 2012b). Therefore, we performed a comprehensive meta-analysis by including the most recent and relevant articles to identify statistical evidence of the association between CYP1A1 T3801C polymorphism and risk of all cancers that have been investigated. Meta-analysis is a powerful tool for summarizing the different studies. It cannot only overcome the problem of small size and inadequate statistical power of genetic studies of complex traits, but also can provide more reliable results than a single case–control study. 2. Materials and methods 2.1. Identification and eligibility of relevant studies A comprehensive literature search was performed using the PubMed, ISI, and EMBASE database for relevant articles published (the last search update was Apr. 15, 2013) with the following key words “CYP1A1,” “polymorphism,” and “cancer” or “carcinoma.” The search was not limited to language. Additional studies were identified by hand searching references in original articles and review articles. Authors were contacted directly regarding crucial data not reported in

Potentially relevant papers identified and screened for retrieval (n = 2134) Duplicate articles

PUBMED: 798; EMBASE: 921; ISI: 415 Irrelevant articles were

were excluded

excluded (n = 385)

(n = 863) Studies have possible associations (n = 886)

Review articles, Case reports, and other polymorphisms were excluded (n = 610)

Publications about CYP1A1 T3801C polymorphism and risk of cancer (n = 276)

Excluded studies due to overlapping populations (n = 26)

Articles about CYP1A1 T3801C polymorphism and cancer risk (n = 250) 47 breast cancer studies, 8 cervical cancer studies, 18 colorectal cancer studies, 8 endometrial cancer, 9 esophageal cancer studies, 3 gallbladder cancer, 8 gastric cancer studies, 5 hepatocellular cancer studies, 32 head and neck cancerstudies, 18 leukemia studies, 71 lung cancer studies, 4 lymphoma studies, 7 ovarian cancer, 5 pancreatic cancer studies, 15 prostate cancer studies, and 10 studies with the “other cancers” Fig. 1. Study flow chart explaining the selection of the 250 publications included in the meta-analysis.

326

Table 2 Stratified analysis of CYP1A1 T3801C polymorphism on cancer risk.a Variables

268 (55,963/76,631) 47 (16,272/20,930) 8 (1245/1298) 18 (7171/8957) 8 (1450/2359) 9 (869/1996) 3 (236/299) 8 (739/2203) 5 (827/1965) 32 (5596/6897) 18 (3556/5222) 71 (11,035/14,678) 4 (1257/2821) 7 (752/1235) 5 (376/1028) 15 (1838/2107) 10 (2744/2636)

Dominant model

Recessive model

Homozygote

Heterozygote

Additive model

TC + CC versus TT

CC versus TC + TT

CC versus TT

TC versus TT

C versus T

OR (95% CI)

Ph/I2 (%)

OR (95% CI)

Ph/I2 (%)

OR (95% CI)

Ph/I2 (%)

OR (95% CI)

Ph/I2 (%)

1.14 (1.09–1.19)*

b0.001/65.7

1.23 (1.12–1.34)*

b0.001/57.0

1.31 (1.19–1.45)*

b0.001/61.5

1.12 (1.07–1.18)*

1.07 (0.97–1.19)* b

0.97 (0.89–1.04) 0.85 (0.60–1.21)* 0.99 (0.75–1.29)* b

0.92 (0.76–1.11) 1.21 (0.90–1.62)* 1.29 (1.11–1.49)* 1.29 (1.06–1.57)* 1.19 (1.10–1.30)* 0.88 (0.67–1.16)* 1.01 (0.81–1.26) 1.08 (0.82–1.42) 1.22 (0.96–1.55)* b

b0.001/72.9 b0.001/80.4 0.247/17.2 0.001/70.2 0.032/52.6 0.005/81.3 0.106/40.9 0.080/52.1 b0.001/68.2 b0.001/70.9 b0.001/48.5 0.081/55.5 0.314/15.1 0.666/0.0 0.001/62.3 b0.001/82.7

1.18 (0.97–1.45)* 2.21 (1.27–3.85)* 0.89 (0.68–1.17)* 1.04 (0.40–2.70)* 0.98 (0.75–1.29) 0.88 (0.28–2.78)* 0.97 (0.70–1.35) 1.03 (0.82–1.29) 1.81 (1.30–2.53)* b

1.25 (1.13–1.39) 0.93 (0.74–1.18) 0.94 (0.53–1.65) 0.58 (0.27–1.24) 0.85 (0.68–1.06) 1.17 (0.65–2.11)*

b0.001/66.4 0.084/44.2 0.091/34.7 0.066/51.6 0.947/0.0 0.034/70.5 0.210/27.4 0.873/0.0 b0.001/70.9 b0.001/81.1 0.105/20.2 0.616/0.0 0.166/40.9 0.542/0.0 0.681/0.0 0.011/59.5

1.23 (0.97–1.56)* 2.72 (1.34–5.55)* 0.86 (0.72–1.04) 0.96 (0.34–2.71)* 0.97 (0.72–1.30) b

0.97 (0.68–1.38) 1.19 (0.92–1.54) 1.99 (1.39–2.85)* 1.61 (0.98–2.63)* 1.35 (1.16–1.57)* 0.99 (0.77–1.28) 1.05 (0.59–1.89) 0.60 (0.27–1.32) 1.01 (0.79–1.28) 1.27 (0.64–2.50)*

b0.001/72.6 0.010/61.9 0.109/32.4 0.036/58.0 0.931/0.0 0.008/79.0 0.214/26.8 0.295/19.1 b0.001/71.0 b0.001/78.6 0.021/30.8 0.421/0.0 0.252/26.7 0.458/0.0 0.340/10.3 0.002/67.0

1.06 (0.95–1.18)* 1.29 (0.89–1.92)* 0.99 (0.91–1.07) 0.83 (0.59–1.16)* 1.02 (0.74–1.40)* 0.99 (0.49–1.98)* 0.92 (0.75–1.13) 1.25 (1.02–1.54) 1.29 (1.11–1.51)* 1.21 (1.02–1.44)* 1.14 (1.04–1.25)* 0.91 (0.65–1.27)* 1.04 (0.84–1.37) 1.16 (0.87–1.53) 1.33 (1.05–1.69)* 1.14 (0.86–1.52)*

OR (95% CI)

Ph/I2 (%)

b0.001/57.7

1.14 (1.09–1.19)*

b0.001/73.1

b0.001/65.7 b0.001/75.1 0.174/24.8 0.006/64.8 0.009/62.9 0.091/58.4 0.242/23.5 0.121/48.3 b0.001/57.2 0.002/56.6 0.001/41.5 0.056/65.4 0.213/29.7 0.633/0.0 0.004/57.1 b0.001/76.4

b

b0.001/81.3 b0.001/81.3 0.192/23.1 0.001/72.4 0.140/36.1 b0.001/87.9 0.038/52.9 0.214/33.0 b0.001/75.9 b0.001/84.0 0.001/42.9 0.066/63.1 0.370/7.8 0.797/0.0 0.001/62.3 b0.001/85.5

b

0.97 (0.91–1.03) 0.90 (0.64–1.25)* 0.99 (0.87–1.13) b

0.99 (0.79–1.25)* 1.10 (0.97–1.26) 1.38 (1.19–1.61)* b

1.15 (1.07–1.23)* 0.88 (0.67–1.15) 1.03 (0.84–1.26) 1.00 (0.79–1.27) 1.10 (0.91–1.31)* b

SZ sample size, TT indicates wild-type, TC indicates heterozygote, CC indicates variant homozygote, T the major allele, C the minor allele, Ph P value of heterogeneity test, HNC head and neck cancer, and the bold values indicate that the results are statistically significant. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models. b The results were excluded due to high heterogeneity.

X.-F. He et al. / Gene 534 (2014) 324–344

Overall Cancer type Breast cancer Cervical cancer Colorectal cancer Endometrial cancer Esophageal cancer Gallbladder cancer Gastric cancer Hepatocellular cancer HNC Leukemia Lung cancer Lymphoma Ovarian cancer Pancreatic cancer Prostate cancer Other cancer

No. comparisons (SZ case/control)

X.-F. He et al. / Gene 534 (2014) 324–344

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Table 3 Summary ORs (95% CI) categorized by ethnicity for the CYP1A1 T3801C polymorphism under different genetic models and cancer type.a Ethnicity

Cancer type

Caucasian Breast cancer

Asian


15 (7467/11,710)

Colorectal cancer Endometrial cancer

11 (4025/5066)

Esophageal cancer

3 (140/542)

HNC

8 (1535/1602)

Leukemia

7 (1542/1976)

5 (640/900)

Lung cancer

20 (4060/5302)

Ovarian cancer Breast cancer

3 (409/837)

Cervical cancer Colorectal cancer Esophageal cancer

12 (3852/4045)

3 (377/329) 4 (1095/2432) 4 (433/1058)

Gastric cancer

3 (273/640)

Hepatocellular cancer

4 (736/1611)

HNC

7 (932/1103)

Leukemia

3 (923/2066)

Lung cancer

29 (4124/5603)

Prostate cancer Breast cancer

6 (787/1012)

Lung cancer

7 (662/1001)

South Indian

Breast cancer

4 (863/782)

North Indian Mixed

HNC

3 (505/501)

Breast cancer

8 (2608/2840)

Colorectal cancer

3 (2051/2432)

African (USA)

HNC

Leukemia

Lung cancer

5 (1011/1057)

11 (1964/2717)

5 (772/786)

15 (2970/5032)

Dominant model

Recessive model

Homozygote

Heterozygote

Additive model

TC + CC versus TT

CC versus TC + TT

CC versus TT

TC versus TT

C versus T

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

0.98 (0.56–1.72) * 0.83 (0.56–1.23) 1.14 (0.20–6.45) * 1.34 (0.22–8.25)

1.05 (0.88–1.26) * 1.07 (0.95–1.21) 0.83 (0.47–1.47) *

1.05 (0.89–1.23) * 1.03 (0.92–1.15) 0.86 (0.48–1.57) * 1.25 (0.52–3.01) * 1.27 (0.97–1.66) * 1.04 (0.78–1.39) * 1.14 (1.02–1.26) 0.97 (0.56–1.68) 1.00 (0.83–1.21) * 1.17 (0.87–1.60) 0.89 (0.76–1.05) 0.92 (0.63–1.36) * 0.96 (0.70–1.31) 1.30 (0.95–1.78) * 1.49 (1.00–2.20) * 1.19 (1.00– 1.43) 1.19 (1.04–1.37) * 1.23 (1.01–1.49) 1.01 (0.76–1.35) * 1.05 (0.84–1.31) 3.40 (2.35–4.94)

b0.001/67.9 0.96 (0.56–1.65) * 0.312/13.9 0.82 (0.55–1.22) 0.002/76.0 1.22 (0.27–5.51) * 0.048/67.1 1.23 (0.20–7.46)

0.057/ 44.1 0.109/ 40.5 0.093/ 57.8 0.869/ 0.0

0.117/ 39.3 0.046/ 67.4 0.987/ 0.0

0.061/48.1 1.27 0.207/ (0.58–2.78) 29.0

1.32 0.206/ (0.60–2.90) 29.1

0.019/60.3 1.22 0.125/ (0.74–1.99) 42.0

1.15 0.254/ (0.69–1.92) 24.0

1.77 (1.19–2.61) 0.27 (0.03–2.23) 1.05 (0.80–1.38) * 1.30 (0.82–2.05) 0.90 (0.72–1.13) 0.98 (0.71–1.36)

0.671/ 0.0 –

0.241/29.7 1.08 (0.72–1.61) 0.094/53.1 1.03 (0.82–1.29)

0.430/ 0.0 0.873/ 0.0

b0.001/76.9 1.92 (1.19–3.10) * 0.500/0.0 0.87 (0.69–1.09) b0.001/54.9 1.26 (1.07–1.49) * 0.886/0.0 0.79 (0.61–1.04) 0.085/51.2 1.04 (0.54–2.03) * 0.633/0.0 1.09 (0.64–1.86) 0.473/0.0 2.12 (1.28–3.49) * b b0.001/89.2 1.33 (0.84–2.13) 0.096/42.4 1.03 0.98 (0.81–1.29) (0.81–1.18) * 0.92 0.732/0.0 1.39 (0.81–1.06) (0.43–4.44) * b b0.001/78.6 1.13 (0.88–1.45) * b b b0.001/80.1

0.006/ 66.6

0.96 (0.80–1.15)

0.041/ 47.3

0.480/0.0 0.133/50.4 b0.001/71.6

0.789/0.0 0.124/48.0 0.063/58.9

0.935/0.0

0.001/ 70.2 0.572/ 0.0 0.424/ 0.0 0.576/ 0.0

0.545/ 0.0 0.036/ 37.7 0.755/ 0. 0.057/ 56.4 0.279/ 21.2 0.002/ 79.4

0.001/64.5 1.08 b0.001/75.6 (0.88–1.31) * 0.371/7.7 1.03 0.310/14.9 (0.93–1.15) 0.002/75.8 0.005/72.8 0.92 (0.53–1.58) * b 0.085/66.3 b0.028/79.4 1.46 (0.62–3.45) * 1.41 0.181/32.3 1.36 0.136/38.4 (1.14–1.74) (1.12–1.64)

1.04 (0.76–1.41) * 1.80 0.684/ 1.12 (1.22–2.67) 0.0 (0.99–1.27) 0.26 – 0.97 (0.03–2.18) (0.71–1.32) b0.001/ 1.00 1.07 (0.83–1.21) (0.77–1.49) 76.4 * * 1.41 0.575/ 1.12 (0.86–2.32) 0.0 (0.81–1.55) 0.86 0.221/ 0.91 (0.67–1.09) 31.9 (0.76–1.07) 0.96 0.604/ 0.92 (0.68–1.37) 0.0 (0.58–1.45) * 1.06 0.497/ 0.93 (0.68–1.64) 0.0 (0.66–1.29) 1.19 0.295/ 1.25 (0.92–1.54) 19.1 (1.02–1.54)

0.012/63.3 1.04 0.016/61.4 (0.81–1.35) * 0.405/4.2 1.19 0.591/0.0 (1.06–1.32) 0.156/46.1 0.91 0.132/50.6 (0.69–1.21) b0.001/78.6 0.010/61.9 1.02 (0.87–1.21) * 0.838/0.0 1.15 0.680/0.0 (0.92–1.43) 0.186/37.7 0.92 0.130/46.8 (0.82–1.03) 0.030/66.4 0.97 0.260/25.3 (0.82–1.15)

2.13 (1.24–3.68) * 1.00 (0.78–1.29) 1.32 (1.06–1.63) * 0.93 (0.69–1.25) 1.08 (0.52–2.25) * 1.09 (0.64–1.88) 4.12 (2.82–6.03)

b0.001/78.6 0.001/74.5 1.48 (1.10–1.98) * 0.652/0.0 1.05 0.386/0.0 (0.93–1.18) 0.001/56.2 0.011/45.5 1.15 (1.03–1.29) * 0.920/0.0 1.05 0.731/0.0 (0.91–1.20) 0.028/63.2 0.177/36.7 1.03 (0.78–1.35) * 0.538/0.0 1.04 0.427/0.0 (0.85–1.27) 0.002/79.1 0.004/77.1 2.05 (1.37–3.06) * 0.552/0.0 2 b0.001/89.8

0.003/ 70.1 0.405/ 0.0 0.005/ 49.4

1.31 (0.88–1.96) * 1.27 (1.05–1.53) 1.13 (0.97–1.31)

0.775/ 0.0 0.032/ 62.1

1.35 (1.10–1.66) 1.04 (0.87–1.25)

0.284/ 20.4 0.208/ 34.0

1.03 (0.80–1.32) 1.84 (1.11–3.05) * b 0.055/ b0.001/ 1.42 (1.08– 65.6 87.4 1.88) 0.045/ 0.92 0.126/ 1.22 (0.80–1.05) 47.6 (0.73–2.04) 62.8 * 0.094/ 0.93 0.035/ 1.18 (0.80–1.07) 70.2 (0.44–3.12) 57.8 * b0.001/ b b0.001/ 1.17 72.5 83.7 (0.78–1.75) * b 0.001/ 0.002/ 1.31 78.3 76.1 (0.87–1.98) * b0.001/ 1.03 0.583/ 1.06 1.14 (0.85–1.25) 0.0 (0.85–1.34) (0.92–1.42) 72.6 * *

0.141/48.9 1.00 (0.80–1.25) 0.121/48.3 1.11 (0.97–1.26)

0.178/39.0 1.03 (0.83–1.27) * 0.239/30.1 0.94 (0.83–1.06)

0.539/0.0 0.214/33.0

0.046/62.6

0.358/2.7

0.009/70.4

b

b0.001/88.3

0.016/67.3

b

b0.001/88.7

0.001/66.6 1.00 (0.86–1.16) *

0.011/54.8

(continued on next page)

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X.-F. He et al. / Gene 534 (2014) 324–344

Table 3 (continued) Ethnicity

Cancer type

Prostate cancer


5 (581/598)

Dominant model

Recessive model

Homozygote

Heterozygote

Additive model

TC + CC versus TT

CC versus TC + TT

CC versus TT

TC versus TT

C versus T

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

0.96 (0.63–1.47)

b

b

b

0.212/31.4

b0.001/ 1.17 0.042/ 83.1 (0.51–2.71) 59.6 *

b0.001/79.7

b0.001/82.1

SZ sample size, TT indicates wild-type, TC indicates heterozygote, CC indicates variant homozygote, T the major allele, C the minor allele, Ph P value of heterogeneity test, HNC head and neck cancer, and the bold values indicate that the results are statistically significant. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models. b The results were excluded due to high heterogeneity.

original articles. In addition, studies were identified by a manual search of the reference lists of reviews and retrieved studies. We included all the case–control studies and cohort studies that investigated the association between CYP1A1 T3801C polymorphism and cancer risk with genotyping data. All eligible studies were retrieved, and their bibliographies were checked for other relevant publications. 2.2. Inclusion criteria The included studies needed to have met the following criteria: (1) only the case–control studies or cohort studies were considered, (2) evaluated the CYP1A1 T3801C polymorphism and the risk of cancer, and (3) the genotype distribution of the polymorphisms in cases and controls were described in detail and the results were expressed as odds ratio (OR) and corresponding 95% confidence interval (95% CI). Major reasons for exclusion of studies were as follows: (1) not for cancer research, (2) only case population, and (3) duplicate of previous publication (When the same sample was used in several publications, only the most complete information was included following careful examination). 2.3. Data extraction Information was carefully extracted from all eligible studies independently by two investigators according to the inclusion criteria listed above. The following data were collected from each study: first author's name, year of publication, country of origin, ethnicity, source of controls, sample size, and numbers of cases and controls in the CYP1A1 T3801C genotypes whenever possible. Ethnicity was categorized as “Caucasian,” “African,” and “Asian.” However, Africans from Africa and America certainly have a different exposition to PAHs, and in such case should be considered in separate groups. We considered the samples from Middle Eastern countries as “Middle Eastern” ethnicity. However, Indian was considered as “north Indian” and “south Indian” because the north Indians are different from the south Indians according to the race origin. When one study did not state which ethnic groups was included or if it was impossible to separate participants according to phenotype, the sample was termed as “mixed population.” Meanwhile, studies investigating more than one kind of cancer were counted as individual data set only in subgroup analyses by cancer type. We did not define any minimum number of patients to include in this meta-analysis. Articles that reported different ethnic groups and different countries or locations, we considered them different study samples for each category cited above. 2.4. Quality score assessment The quality of the studies was also independently assessed by the same two reviewers according to the predefined scale for quality assessment (Table 1). These scores were based on both traditional epidemiological considerations and cancer genetic issues. Any disagreement was resolved by a discussion between the two reviewers. Total scores

ranged from 0 (worst) to 15 (best). Reports scoring b10 were classified as “low quality”, and those ≥10 as “high quality”.

2.5. Statistical analysis Crude odds ratios (ORs) together with their corresponding 95% CIs were used to assess the strength of association between the CYP1A1 T3801C polymorphism and the risk of cancer. The pooled ORs were performed for codominant model (CC versus TT and TC versus TT); dominant model (TC + CC versus TT); recessive model (CC versus TC + TT); and additive model (C versus T), respectively. Betweenstudy heterogeneity was assessed by calculating Q-statistic (Heterogeneity was considered statistically significant if P b 0.10) (Davey and Egger, 1997) and quantified using the I2 value, a value that describes the percentage of variation across studies that are due to heterogeneity rather than chance, where I2 = 0% indicates no observed heterogeneity, with 25% regarded as low, 50% as moderate, and 75% as high (Higgins et al., 2003). If results were not heterogeneous, the pooled ORs were calculated by the fixed-effect model (we used the Q-statistic, which represents the magnitude of heterogeneity between-studies) (Mantel and Haenszel, 1959). Otherwise, a random-effect model was used (when the heterogeneity between-studies were significant) (DerSimonian and Laird, 1986). In addition to the comparison among all subjects, we also performed stratification analyses by cancer type (if one cancer type contained less than three individual studies, it was combined into the “other cancers” group), source of control, and ethnicity. Moreover, the extent to which the combined risk estimate might be affected by individual studies was assessed by consecutively omitting every study from the meta-analysis (leave-one-out sensitivity analysis). This approach would also capture the effect of the oldest or first positive study (first study effect). In addition, we also ranked studies according to sample size, and then repeated this meta-analysis. Sample size was classified according to a minimum of 200 participants and those with fewer than 200 participants. The cite criteria were previously described (Klug et al., 2009). Last, sensitivity analysis was also performed, excluding studies whose allele frequencies in controls exhibited significant deviation from the Hardy–Weinberg equilibrium (HWE), given that the deviation may denote bias. Deviation of HWE may reflect methodological problems such as genotyping errors, population stratification or selection bias. HWE was calculated by using the goodness-of-fit test, and deviation was considered when P b 0.05. Begg's funnel plots (Begg and Mazumdar, 1994) and Egger's linear regression test (Egger et al., 1997) were used to assess publication bias. If publication bias existed, the Duval and Tweedie nonparametric “trim and fill” method was used to adjust for it (Dual and Tweedie, 2000). We also performed publication bias by fail-safe number for P = 0.05 (Nfs0.05) (Rosenthal, 1979). A meta-regression analysis was carried out to identify the major sources of between-studies variation in the results, using the log of the ORs from each study as dependent variables, and cancer type, ethnicity, sample size, and source of controls as the possible sources of heterogeneity. All of the calculations were performed using STATA version 10.0 (STATA Corporation, College Station, TX).

X.-F. He et al. / Gene 534 (2014) 324–344

329

Table 4 Summary ORs (95% CI) and value of value of the heterogeneity of CYP1A1 T3801C polymorphism for studies according to source of controls and cancer type.a Cancer type

SC


Dominant model

Recessive model

Homozygote

Heterozygote

TC + CC versus TT

CC versus TC + TT

CC versus TT

TC versus TT

2

OR (95% CI) Ph/I (%) Breast cancer

PB

17 (10,112/13,996)

0.95 (0.86–1.04) *

HB 30 (6160/6934)

b

Cervical cancer

HB

8 (1245/1298)

b

Colorectal cancer

PB

7 (4792/5718)

0.97 (0.88–1.06)

HB 11 (2379/3239)

0.96 (0.83–1.11) 0.83 (0.58–1.19) *

Endometrial cancer

PB

4 (1033/1915)

HB

4 (417/444)

b

Esophageal cancer

HB

8 (738/1542)

1.02 (0.74–1.39) *

Gallbladder cancer

HB

3 (236/299)

b

Gastric cancer

HB

6 (406/809)

1.12 (0.87–1.45)

HNC

HB 30 (5258/6560)

Hepatocellular HB cancer

4 (746/1556)

Leukemia

HB 17 (3141/3522)

Lung cancer

PB

10 (1882/2677)

HB 61 (9153/12,001)

Lymphoma

HB

3 (546/1121)

Ovarian cancer

PB

4 (468/917)

HB

3 (284/318)

PB

3 (301/941)

Pancreatic cancer Prostate cancer

HB 13 (1512/1797)

1.28 (1.09–1.49) * 1.20 (0.83–1.73) * 1.29 (1.03–1.61) * 1.19 (1.04–1.36) 1.19 (1.08–1.31) * 0.75 (0.58–0.97) * 0.96 (0.74–1.26) 1.11 (0.76–1.62) 1.13 (0.84–1.53) 1.24 (0.99–1.56) *

OR (95% CI)

0.024/44.8 0.96 (0.83–1.09)

2

2

Ph/I (%)

OR (95% CI) Ph/I (%)

0.469/ 0.0

0.95 0.218/ (0.82–1.09) 22.3

Additive model C versus T 2

OR (95% CI) Ph/I (%)

OR (95% CI) Ph/I2 (%)

0.95 0.020/50.2 0.96 (0.85–1.06) (0.89–1.05) * * b0.001/67.7 b b0.001/77.2 1.39 (0.99–1.95) b0.001/ b b0.001/ 1.16 75.4 79.7 (0.97–1.37) * b0.001/75.1 b b0.001/80.4 2.21 (1.27–3.85)* 0.084/ 2.72 (1.34– 0.010/ 1.29 44.2 5.55)* 61.9 (0.89–1.92) * 0.067/49.1 0.96 0.038/ 0.97 0.185/31.8 0.86 (0.57–1.30)* 0.018/ 0.80 (0.89–1.03) (0.84–1.13) 60.7 (0.54–1.19) 54.9 * * 0.303/14.8 0.95 (0.67–1.35) 0.533/ 0.99 0.457/ 1.00 0.420/1.7 0.99 0.0 (0.69–1.44) 0.0 (0.85–1.17) (0.87–1.13) 0.073/57.0 1.01 (0.21–4.76)* 0.031/ 0.98 0.028/ 0.87 0.149/43.8 0.85 71.2 (0.20–4.72) 71.9 (0.70–1.07) (0.57–1.25) * * b b0.001/81.1 0.75 (0.30–1.86) 0.257/ 0.61 0.176/ 0.003/78.8 b 26.5 (0.24–1.52) 42.5 0.009/64.8 1.00 0.026/56.0 0.93 (0.67–1.29) 0.938/ 0.97 0.875/ 1.07 (0.81–1.24) 0.0 (0.68–1.37) 0.0 (0.74–1.55) * * b 0.091/58.4 b 0.005/81.3 0.88 (0.28–2.78)* 0.034/ 0.008/ 0.99 70.5 79.0 (0.49–1.98) * 0.281/20.2 0.96 (0.64–1.44) 0.135/ 1.08 0.155/ 1.14 0.616/0.0 1.12 40.5 (0.69–1.67) 37.6 (0.87–1.49) (0.83–1.50) * b0.001/57.9 1.38 b0.001/ 1.31 b0.001/69.6 1.78 (1.25–2.54)* b0.001/ 1.97 (1.18–1.62) (1.12–1.54) 71.9 (1.35–2.88) 72.2 * * * 0.056/65.2 1.09 0.044/62.9 1.02 (0.80–1.29) 0.735/ 1.17 0.165/ 1.36 (0.95–1.26) 0.0 (0.88–1.54) 44.5 (0.89–2.10) * b0.001/72.6 b 0.001/58.8 b b0.001/ b b0.001/ 1.21 80.4 78.1 (0.99–1.46) * 0.023/55.0 1.16 0.126/35.2 1.17 (0.88–1.54) 0.136/ 1.38 0.250/ 1.19 (1.03–1.30) 36.7 (1.03–1.85) 22.5 (0.94–1.51) * 0.004/39.6 1.14 1.32 (1.12– 0.018/ 1.13 b0.001/50.8 1.26 (1.13–1.41) 0.145/ (1.05–1.24) 18.4 1.56)* 33.4 (1.01–1.25) * * 0.850/0.0 0.62 (0.25–1.51) 0.771/ 0.57 0.814/ 0.75 0.656/0.0 0.74 0.0 (0.23–1.39) 0.0 (0.55–1.01) (0.57–0.97) 0.224/31.4 0.85 (0.47–1.54) 0.300/16.9 2.86 (0.30–26.9) 0.475/0.0

0.39 (0.13–1.16)

0.019/50.6 0.84 (0.67–1.06)

0.124/ 52.0 – 0.419/ 0.0 0.965/ 0.0

0.96 (0.51–1.77) 3.16 (0.32–31.3) 0.44 (0.14–1.35) 0.99 (0.77–1.28)

0.196/ 38.7 – 0.261/ 25.6 0.805/ 0.0

0.99 (0.75–1.31) 1.38 (0.85–2.24) 1.23 (0.91–1.68) 1.37 (1.08–1.73) *

0.122/48.2 0.95 (0.76–1.20) 0.749/0.0 1.36 (0.90–2.08) 0.483/0.0 1.02 (0.78–1.33) 0.034/47.5 1.11 (0.94–1.30) *

0.039/45.1

b0.001/85.0

b0.001/81.3

0.192/30.9

0.222/25.0 0.024/68.1

0.002/80.4 0.094/44.5

b0.001/87.9

0.067/51.4

b0.001/76.9

0.115/53.7

b0.001/84.3

0.535/0.0

b0.001/48.8

0.742/0.0

0.334/11.8 0.962/0.0 0.650/0.0 0.051/43.9

SC source of controls, SZ sample size, TT indicates wild-type, TC indicates heterozygote, CC indicates variant homozygote, T the major allele, C the minor allele, Ph P value of heterogeneity test, HNC head and neck cancer, PB Population-based studies, HB Hospital-based studies, TT TC CC T the major allele, C the minor allele, the bold values indicate that the results are statistically significant. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models. b The results were excluded due to high heterogeneity.

3. Results 3.1. Eligible studies and meta-analysis databases Fig. 1 graphically illustrates the trial flow chart. A total of 2134 articles regarding CYP1A1 T3801C polymorphism with respect to cancer were identified. After screening the titles and abstracts, 863 articles were excluded because they were duplicated. In addition, 995 articles

were excluded because they were review articles, case reports, other polymorphisms of CYP1A1, or irrelevant to the current study. Last, of these published articles, 26 publications (Acevedo et al., 2003; Adonis et al., 2005b; Belogubova et al., 2006; Bolufer et al., 2007a; Chen et al., 2005; Cote et al., 2007; Dialyna et al., 2003; Duell et al., 2002a; Hirvonen et al., 1992b, 1993; Huang et al., 1999a; Justenhoven et al., 2008; Li et al., 2005; Liu et al., 2005; Matthias et al., 1998; Pisani et al., 2006; Sam et al., 2010a; Sam et al., 2010b; Sobti et al., 2003; Taioli

330

X.-F. He et al. / Gene 534 (2014) 324–344

Table 5 Summary ORs (95% CI) for the CYP1A1 T3801C polymorphism categorized by histological type or anatomical area in a specific tumor site.a Cancer type

Lung cancer

HNC

Histological type or anatomical area

NSCLC


Dominant model

Recessive model

Homozygote

Heterozygote

Additive model

TC + CC versus TT

CC versus TC + TT

CC versus TT

TC versus TT

C versus T

OR (95% CI) Ph/I2 (%)

OR (95% CI)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

Ph/I2 (%)

OR (95% CI)

Ph/I2 (%) 0.908/ 0.0

27 (4909/6850)

1.24 (1.09– 1.40)*

0.007/45.1 1.34 (1.09– 1.65)

0.783/ 0.0

1.51 (1.19– 1.91)

NSCLC/Caucasian

9 (2519/2815)

NSCLC/Asian

9 (1174/2173)

NSCLC/African

3 (332/638)

1.10 (0.97–1.26) 1.42 (1.21– 1.67) 1.16 (0.85–1.57) 1.07 (0.92–1.25) 1.41 (1.16– 1.72)*

0.131/35.9 1.84 (1.15– 2.92) 0.221/26.1 1.26 (0.94–1.70) 0.286/20.2 1.73 (0.84–3.54) 0.772/0.0 1.01 (0.71–1.42) 0.013/45.3 1.50 (1.14– 1.98)

0.890/ 0.0 0.340/ 11.6 0.449/ 0.0 0.973/ 0.0 0.126/ 31.3

1.87 (1.17– 2.97) 1.59 (1.08– 2.35) 1.72 (0.83–3.56) 1.04 (0.70–1.55) 1.84 (1.32– 2.56)

0.730/ 0.0 0.037/ 60.8 0.985/ 0.0 b0.001/ 74.6

2.45 (0.89–6.71) 1.74 (1.11– 2.72) 1.28 (0.72–2.26) 1.74 (1.11–2.74)*

0.343/ 10.0

2.37 (1.58–3.56)

b0.001/ 80.1

b

AC

19 (1165/4524)

SC

22 (1447/5060)

SC/Caucasian

7 (412/1376)

SC/Asian

7 (455/1941)

SCLC

11 (354/3095)

Oral cancer

15 (1779/2751)

Oral cancer/Asian

4 (420/551)

Oral/Mixed

7 (924/1543)

Larynx cancer

6 (335/899)

Pharynx cancer

4 (344/665)

Thyroid Cancer

3 (411/981)

c

Other sites

Leukemia ALL

11 (2555/2713)

14 (2081/2995)

ALL/Asian

4 (431/634)

ALL/Caucasian

6 (1038/1708)

ALL/Mixed

4 (612/653)

CALL

1.13 0.186/31.7 2.37 (0.86–1.47) (0.86–6.49) 1.76 (1.39– 0.440/0.0% 1.49 2.24) (0.81–2.74)* 1.07 0.437/0.3 1.26 (0.83–1.37) (0.73–2.17) b0.001/68.8 1.63 1.26 (1.06–2.50)* (0.99–1.61) * 0.006/76.1 2.32 1.43 (1.60–3.38) (0.82–2.49) * b0.001/76.9 b 1.31 (0.87–1.97) * 0.010/66.8 3.74 1.51 (2.14–6.53) (0.92–2.48) * 0.065/58.4 3.59 1.62 (2.02–6.40) (1.05–2.49) * b 0.005/80.8 2.30 (0.84–6.33) 0.005/60.1 2.10 1.35 (1.12–3.95)* (1.10–1.67) * 1.30 (1.04– 0.001/63.4 1.30 1.63)* (0.67–2.53)* 1.51 (0.98–2.32) * 1.14 (0.94–1.38) 9.27 (4.05– 21.22)*

0.182/ 38.3 0.862/ 0.0 – 0.065/ 47.5

1.20 (1.04–1.39) * 0.888/ 1.10 0.0 (0.95–1.27) 0.626/ 1.73 0.0 (1.35–2.23) 1.08 0.324/ (0.74–1.57) 0.0 0.882/ 1.02 0.0 (0.84–1.25) 0.337/ 1.37 10.8 (1.07–1.77) * 0.718/ 1.25 0.0 (0.91–1.71) 0.188/ 1.96 37.4 (1.45–2.65) 0.977/ 0.94 0.0 (0.69–1.29) b0.001/ 1.16 72.9 (0.91–1.46) * b 0.381/ 2.2

0.053/ 38.6

1.20 0.542/ (1.10–1.31) 0.0

0.191/ 29.7 0.494/ 0.0 0.130/ 56.4 0.766/ 0.0 0.053/ 42.3

1.17 (1.03–1.33) 1.34 (1.13–1.59) 1.21 (0.90–1.63) 0.99 (0.85–1.16) 1.35 (1.18–1.54) * 1.32 (0.99–1.73) 1.41 (1.16–1.72) 1.01 (0.79–1.28)

b0.001/ 1.17 83.7 (0.78–1.75) * 5.50 0.587/ 1.59 (3.03–10.00) 0.0 (0.92–2.76) * 4.98 0.961/ 1.92 (2.71–9.15) 0.0 (1.35–2.74)

0.009/ 70.4

b

0.046/ 62.5

1.88 0.299/ (1.49–2.38) 18.4

0.961/ 0.0

2.06 0.904/ (1.61–2.65) 0.0

1.16 – (0.60–2.24) 1.49 0.362/ (1.26–1.76) 8.8

1.42 – (0.88–2.30) 1.62 0.224/ (1.42–1.85) 25.6

2.42 (0.86–6.83) 2.35 (1.26–4.38)*

b0.001/ 1.42 79.9 (0.72–2.78)*

– 0.082/ 44.6

0.332/ 12.8 0.320/ 14.4 0.987/ 0.0 0.002/ 60.9

b0.001/ 1.51 (1.1080.1 2.06)*

b0.001/ 82.4

0.214/ 33.0

b

0.003/ 78.3

0.047/62.3 1.01 (0.54–1.87)*

0.075/ 56.5

1.26 (0.61–2.58)*

0.188/33.0 0.80 (0.29–2.21)* 0.079/55.7 2.96 (0.70–12.48) * 0.113/37.0 1.14 (0.57–2.28)*

0.051/ 54.7 0.003/ 78.3

0.126/ 41.9 0.096/ 52.8

1.16 (0.95–1.42) 1.12 (0.86–1.45)

0.109/ 44.5 0.303/ 17.6

1.11 0.141/ (0.94–1.32) 39.6 b b0.001/ 91.3

0.008/ 59.4

1.02 (0.59–1.76) 3.16 (0.72–13.79) * 1.21 (0.63–2.33)*

0.031/ 50.9

0.084/ 41.0

1.23 (1.01– 1.49)*

0.036/ 49.8

0.234/ 28.2 0.347/ 5.5 0.005/ 61.8

1.23 (1.02–1.47) 1.08 (0.83–1.42) 1.13 (0.87–1.46) * 0.85 (0.66–1.09) *

0.464/ 0.0 0.708/ 0.0 b0.001/ 71.8

b

b0.001/ 81.9

CALL/Caucasian

5 (884/1305)

0.484/0.0

CALL/Mixed

3 (402/425)

1.27 (1.03– 1.57) 1.04 (0.76–1.42) 1.16 (0.87–1.56) * 0.81 (0.62–1.06)

0.86 (0.30–2.45)* 0.584/0.0 1.72 (0.35–8.40)* 0.002/65.8 1.44 (0.82–2.54)*

0.053/ 57.1 0.091/ 58.3 0.006/ 64.4

1.10 (0.63–1.94) 1.64 (0.68–3.92) 1.68 (0.97–2.91)*

0.139/ 42.3 0.125/ 51.9 0.022/ 57.2

0.219/30.4 1.34 (0.56–3.23)

0.415/ 0.0

1.35 (0.56–3.26)

0.415/ 0.0

1.33 (1.10–1.62)

0.374/0.0

b0.001/ 87.4

b

0.002/ 84.0

1.33 0.713/ (1.08–1.62) 0.0

5 (547/1504)

AML/Asian

3 (645/2030)

b0.001/ 88.3

b

1.29 (1.03–1.61) * 1.30 (1.04–1.62) 0.99 (0.72–1.37) 1.13 (0.85–1.51) * 0.79 (0.60–1.04)

AML/Caucasian

0.042/ 63.5

0.086/ 36.2

1.27 (1.08– 1.49)

10 (1352/3772)

0.392/ 2.6 0.325/ 13.4 0.894/ 0.0 b0.001/ 80.8

b0.001/ 1.24 77.5 (1.04–1.48) * 0.051/ 1.54 61.4 (1.17–2.04)

10 (1506/2010)

AML

b

0.314/ 14.8 0.812/ 0.0 0.129/ 56.4 0.602/ 0.0 0.128/ 31.9

b

0.391/ 2.8

0.100/ 48.6

NSCLC non-small cell lung cancer, AC adenocarcinoma, SC squamous cell carcinoma, SCLC small cell lung cancer, AML acute myeloid leukemia, ALL acute lymphoblastic leukemia, CALL childhood acute lymphoblastic leukemia. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models. b The results were excluded due to high heterogeneity. c Includes a diversity of head and neck cancer not separated by anatomical area in the studies analyzed. The bold values indicate that the results are statistically significant.

0.025/54.5 0.032/78.2 0.021/62.2 0.248/21.2 0.381/0.0 0.943/0.0 0.669/0.0 0.086/42.2 1.25 (0.95–1.65)* 0.84 (0.34–2.07)* 1.04 (0.68–1.60)* 1.06 (0.92–1.22) 1.06 (0.79–1.42) 2.09 (1.75–2.50) 0.97 (0.87–1.08) 1.07 (0.92–1.23) Premenopausal Postmenopausal

Smokers

The bold values indicate that the results are statistically significant, HNC head and neck cancer, SZ sample size. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models.

1.21 (0.82–1.77)* 1.61 (0.41–6.26)* 0.68 (0.35–1.32)* 1.15 (0.96–1.38) 1.00 (0.65–1.53) 1.94 (1.51–2.49) 0.93 (0.80–1.09) 0.99 (0.89–1.09) 0.014/60.3 0.087/65.8 0.457/0.0 0.393/5.2 0.496/0.0 0.673/0.0 0.675/0.0 0.044/49.7 1.49 (0.74–3.02)* 0.51 (0.09–2.75)* 2.63 (1.50–4.62) 1.01 (0.72–1.43) 1.15 (0.62–2.13) 4.40 (2.84–6.81) 0.98 (0.76–1.25) 1.22 (0.80–1.87)* 1.20 (0.72–2.01)* 0.36 (0.15–0.88) 2.74 (1.62–4.63) 1.01 (0.76–1.34) 1.15 (0.65–2.06) 3.04 (2.06–4.48) 1.01 (0.80–1.27) 1.21 (0.82–1.78)* Lung cancer Colorectal cancer HNC Lung cancer Colorectal cancer HNC Breast cancer Breast cancer Non-smokers

22 (1350/2522) 3 (146/254) 7 (388/681) 24 (2785/2960) 4 (548/608) 9 (912/643) 10 (1612/1738) 13 (5308/7959)

1.31 (0.99–1.75)* 1.15 (0.52–2.57)* 0.84 (0.52–1.37)* 1.35 (1.10–1.66)* 0.90 (0.70–1.15) 2.37 (1.90–2.97) 0.98 (0.85–1.13) 1.08 (0.92–1.26)*

b0.001/65.1 0.073/61.8 0.015/62.0 b0.001/59.8 0.528/0.0 0.612/0.0 0.136/33.9 0.051/42.8

0.021/53.9 0.334/0.0 0.284/19.8 0.116/34.2 0.659/0.0 0.341/11.5 0.668/0.0 0.070/44.8

OR (95% CI) Ph/I2 (%) OR (95% CI) Ph/I2 (%) OR (95% CI) Ph/I2 (%) OR (95% CI)

CC versus TC + TT

331

et al., 1995a, 1995b; Tsuchiya et al., 2002; Wang et al., 2004; Wang et al., 2006; Yamaguti et al., 2010; Yu et al., 1999a) were excluded because their populations overlapped with another 23 included studies (Adonis et al., 2005a; Belogubova et al., 2004; Bolufer et al., 2007b; Cáceres et al., 2005; Chen et al., 2008; Cote et al., 2009; Duell et al., 2002b; Fan et al., 2006; Gu et al., 2007; Hirvonen et al., 1992a; Huang et al., 1999b; Li et al., 2004a; MARIE-GENICA, 2010; Matthias et al., 2003; Pisani et al., 2006; Sam et al., 2008; Sobti et al., 2004; Taioli et al., 1998, 1999; Tsuchiya et al., 2010; Yamaguti et al., 2009; Yu et al., 1999b). As summarized in Supplemental Table 1, 250 publications with 268 case–control studies were selected among the meta-analysis, including 55,963 cases and 76,631 controls. Among these studies, there were 47 breast cancer studies, 8 cervical cancer studies, 18 colorectal cancer studies, 8 endometrial cancer, 9 esophageal cancer studies, 3 gallbladder cancer, 8 gastric cancer studies, 5 hepatocellular cancer studies, 32 head and neck cancer studies, 18 leukemia studies, 71 lung cancer studies, 4 lymphoma studies, 7 ovarian cancer, 5 pancreatic cancer studies, 15 prostate cancer studies, and 10 studies with the “other cancers”. All of the cases were pathologically confirmed.

0.042/50.2 0.042/75.9 0.005/69.8 0.251/20.8 0.437/0.0 0.275/20.3 0.837/0.0 0.508/0.0

OR (95% CI)

TC versus TT

Homozygote Recessive model Dominant model

TC + CC versus TT

No. comparisons (SZ case/control) Cancer type Smoking status

Table 6 Summary ORs (95% CI) for the CYP1A1 T3801C polymorphism categorized by smoking status or menopausal status in a specific tumor site.a

CC versus TT

Ph/I2 (%)

Additive model

C versus T

Heterozygote

Ph/I2 (%)

X.-F. He et al. / Gene 534 (2014) 324–344

3.2. Quantitative synthesis The evaluations of the association of CYP1A1 T3801C polymorphism with cancer risk are shown in Table 2. Overall, significantly increased cancer risk was observed in any genetic model (dominant model: OR = 1.14, 95% CI = 1.09–1.19, P value of heterogeneity test [Ph] b 0.001, I2 = 65.7%; recessive model: OR = 1.23, 95% CI = 1.12–1.34, Ph b 0.001, I2 = 57.0%; CC vs. TT: OR = 1.31, 95% CI = 1.19–1.45, Ph b 0.001, I2 = 61.5%; TC vs. TT: OR = 1.12, 95% CI = 1.07–1.18, Ph b 0.001, I2 = 57.7%; additive model: OR = 1.14, 95% CI = 1.09–1.19, Ph b0.001, I2 =73.1%). However, there was significant heterogeneity between studies. Hence, we then performed subgroup analysis by cancer type. We found that individuals with the minor variant genotypes had a higher risk of cervical cancer (recessive model: OR = 2.21, 95% CI = 1.27–3.85, Ph = 0.084, I2 = 44.2%; CC vs. TT: OR = 2.72, 95% CI = 1.34–5.55, Ph = 0.010, I2 = 61.9%), hepatocellular cancer (TC vs. TT: OR = 1.25, 95% CI = 1.02–1.54, Ph = 0.121, I2 = 48.3%), head and neck cancer (dominant model: OR = 1.29, 95% CI = 1.11–1.49, Ph b 0.001, I2 = 68.2%; recessive model: OR = 1.81, 95% CI = 1.30–2.53, Ph b 0.001, I2 = 70.9%; CC vs. TT: OR = 1.99, 95% CI = 1.39–2.85, Ph b 0.001, I2 = 71.0%; TC vs. TT: OR = 1.29, 95% CI = 1.11–1.51, Ph b 0.001, I2 = 57.2%; additive model: OR = 1.38, 95% CI = 1.19–1.61, Ph b 0.001, I2 = 75.9%), leukemia (dominant model: OR = 1.29, 95% CI = 1.06–1.57, Ph b 0.001, I2 = 70.9%; TC vs. TT: OR = 1.21, 95% CI = 1.02–1.44, Ph = 0.002, I2 = 56.6%), lung cancer (dominant model: OR = 1.19, 95% CI = 1.10–1.30, Ph b 0.001, I2 = 48.5%; recessive model: OR = 1.25, 95% CI = 1.13–1.39, Ph = 0.105, I2 = 20.2%; CC vs. TT: OR = 1.35, 95% CI = 1.16–1.57, Ph = 0.021, I2 = 30.8%; TC vs. TT: OR = 1.14, 95% CI = 1.04–1.25, Ph = 0.001, I2 = 41.5%; additive model: OR=1.15, 95% CI=1.07–1.23, Ph =0.001, I2 =42.9%), and prostate cancer (TC vs. TT: OR = 1.33, 95% CI = 1.05–1.69, Ph = 0.004, I2 = 57.1%). 3.3. Ethnicity and cancer risk attributed to the CYP1A1 T3801C polymorphism We further examined the association of the CYP1A1 T3801C polymorphism and cancer risk according to cancer type and ethnicity (Table 3) because there was significant heterogeneity between studies. For samples of Caucasians, we found that individuals with the minor variant genotypes had a higher risk of head and neck cancer (TC vs. TT: OR = 1.41, 95% CI = 1.14–1.74, Ph = 0.181, I2 = 32.3%; additive model: OR = 1.36, 95% CI = 1.12–1.64, Ph = 0.136, I2 = 38.4%) and lung cancer (dominant model: OR = 1.14, 95% CI = 1.02–1.26, Ph = 0.480, I2 = 0.0%; recessive model: OR = 1.77, 95% CI = 1.19–2.61, Ph = 0.671, I2 = 0.0%; CC vs. TT: OR = 1.80, 95% CI = 1.22–2.67, Ph = 0.684, I2 = 0.0%; additive model: OR = 1.19, 95% CI = 1.06–1.32, Ph =

332

Table 7 Summary ORs (95% CI) and value of the heterogeneity of CYP1A1 T3801C polymorphism under different genetic models according to literature quality on cancer risk.a Variables

Literature quality

No. comparisons (SZ case/ control)

186 (48,192/66,185)

b10

82 (7771/10,446)

≥10

35 (14,941/19,479)

b10

12 (1331/1451)

Cervical cancer

≥10

6 (1130/1146)

Colorectal cancer

≥10 b10 ≥10

14 (6975/8651) 4 (196/306) 4 (1118/1880)

b10

4 (332/479)

Esophageal cancer

≥10

7 (762/1916)

Gastric cancer

≥10 b10 ≥10

4 (564/1718) 4 (175/485) 4 (773/1879)

≥10

24 (4470/5301)

b10

8 (1126/1596)

Leukemia

≥10

14 (2936/4494)

Lung cancer

b10 ≥10

4 (620/728) 46 (8887/11,781)

b10

25 (2148/2897)

Lymphoma Ovarian cancer

≥10 ≥10

3 (1105/2312) 4 (488/872)

Pancreatic cancer Prostate cancer

b10 ≥10 ≥10

3 (264/363) 3 (301/941) 6 (544/685)

b10

9 (1294/1422)

Breast cancer

Endometrial cancer

Hepatocellular cancer HNC

Recessive model

Homozygote

Heterozygote

TC + CC versus TT

CC versus TC + TT

CC versus TT

TC versus TT

OR (95% CI)

Ph/I2 (%)

1.11 (1.06–1.17) * 1.22 (1.08–1.38) * 1.04 (0.94–1.16) * 1.27 (0.90–1.78) *

b0.001/65.2 1.20 (1.09–1.32) * b0.001/65.8 1.32 (1.08–1.63) * b0.001/73.9 1.19 (0.97–1.47) * b0.001/70.0 1.27 (0.66–2.42) * b0.001/84.8 2.26 (1.20–4.27)

OR (95% CI)

Ph/I2 (%)

OR (95% CI)

0.059/59.7 2.73 (0.42–17.94) 0.065/49.5 1.00 (0.76–1.32)

b0.001/53.1 1.27 (1.14–1.41) * b0.001/63.8 1.46 (1.16–1.83) * b0.001/65.7 1.21 (0.95–1.54) * 0.001/68.6 1.59 (0.67–3.76) * 0.033/58.7 2.86 (1.27–6.43) * 0.228/21.9 0.81 (0.67–0.98) 0.105/55.7 2.05 (0.98–4.28) 0.026/67.5 0.73 (0.20–2.61) * 0.893/0.0 2.70 (0.40–18.21) 0.946/0.0 1.01 (0.74–1.36)

0.270/23.6 0.84 (0.56–1.24) 0.481/0.0 1.39 (0.76–2.53) 0.428/0.0 1.01 (0.80–1.27)

0.294/19.2 0.79 (0.51–1.21) 0.213/33.3 1.57 (0.84–2.96) 0.870/0.0 1.12 (0.86–1.47)

1.25 (1.06–1.47) b0.001/68.0 1.89 (1.27–2.82) * * 1.45 (1.02–2.06) 0.001/72.7 b * 1.20 (1.02–1.43) 0.013/51.5 1.29 (0.86–1.94) * * b b0.001/86.9 b 1.25 (1.14–1.36) 0.001 43.3 1.28 (1.09–1.51) * * 1.07 (0.88–1.29) 0.001/52.7 1.21 (0.99–1.48) * 1.05 (0.89–1.23) 0.220/33.9 0.96 (0.75–1.22) 0.80 (0.60–1.07) 0.736/0.0 0.98 (0.10–10.1) * 1.41 (1.00–1.98) 0.988/0.0 0.89 (0.45–1.73) 1.13 (0.84–1.53) 0.475/0.0 0.39 (0.13–1.16) 1.52 (0.96–2.43) 0.007/68.3 0.81 (0.59–1.11) * 1.09 (0.83–1.44) 0.013/58.8 0.89 (0.65–1.22) *

b0.001/62.6 1.99 (1.25–3.16) * b0.001/81.6 1.99 (1.09–3.64) * 0.001/63.3 1.39 (0.93–2.07) * b0.001/89.1 b 0.048/31.0 1.42 (1.18–1.71) * 0.478/0.0 1.19 (0.96–1.49)

b

0.96 (0.88–1.03) 1.33 (0.85–2.06) 0.73 (0.47–1.13) * 1.11 (0.55–2.25) * 1.07 (0.82–1.40) * 0.80 (0.64–1.01) 1.34 (0.92–1.94) 1.11 (0.91–1.34)

0.222/21.3 0.85 (0.71–1.02) 0.567/0.0 1.07 (0.92–3.41) 0.003/78.6 0.82 (0.25–2.66)

0.511/0.0 1.04 (0.79–1.35) 0.060/71.6 0.92 (0.10–8.63) * 0.269/18.2 1.08 (0.53–2.20) 0.419/0.0 0.44 (0.14–1.35) 0.676/0.0 0.96 (0.68–1.37) 0.466/0.0

1.04 (0.75–1.45)

Ph/I2 (%)

OR (95% CI)

b0.001/59.6 1.10 (1.05–1.16) * b0.001/65.1 1.18 (1.05–1.33) * b0.001/71.8 1.04 (0.93–1.17) * b0.001/76.7 1.14 (0.85–1.54) * 0.003/72.1 b 0.224/22.3 0.98 (0.91–1.07) 0.380/0.0 1.15 (0.72–1.84) 0.014/71.9 0.73 (0.49–1.08) * 0.979/0.0 1.07 (0.53–2.15) * 0.992/0.0% 1.10 (0.81–1.50) * 0.304/17.3 0.82 (0.65–1.04) 0.324/13.6 1.28 (0.86–1.89) 0.637/0.0 1.18 (0.95–1.46) b0.001/69.9 1.29 (1.09–1.52) * b0.001/75.7 1.38 (0.96–1.98) * 0.004/57.0 1.18 (0.99–1.39) * b0.001/87.9 b 0.032/34.2 1.20 (1.11–1.30) 0.156/24.5 1.03 (0.83–1.28) * 0.447/0.0 1.09 (0.91–1.30) 0.073/68.8 0.81 (0.57–1.13) 0.324/0.0 1.48 (1.04–2.12) 0.261/25.6 1.23 (0.91–1.68) 0.281/20.2 1.65 (1.06–2.57) * 0.335/12.2 1.18 (0.90–1.55)

The bold values indicate that the results are statistically significant. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models. b The results were excluded due to high heterogeneity.

Additive model C versus T Ph/I2 (%)

OR (95% CI)

Ph/I2 (%)

b0.001/56.9 1.11 (1.06–1.17) b0.001/71.6 * b0.001/58.7 1.19 (1.07–1.34) b0.001/74.9 * b0.001/81.7 b0.001/69.2 b 0.036/51.4

b

b0.001/82.4

b0.001/80.7

b

b0.001/85.5

0.222/22.6 0.95 (0.89–1.02) 0.151/43.3 1.35 (0.97–1.88) 0.012/72.7 b

24.3 0.823/0.0 b0.001/81.2

0.064/58.6 1.16 (0.62–2.15) * 0.022/59.3 1.02 (0.89–1.16)

0.071/57.4 0.293/17.9

0.201/35.2 0.84 (0.70–1.01) 0.791/0.0 1.27 (0.96–1.68) 0.590/0.0 1.07 (0.93–1.22)

0.323/13.8 0.143/44.7 0.613/0.0

0.021/45.8 1.39 (1.16–1.67) b0.001/74.4 * 0.001/71.8 b b0.001/79.4 0.035/44.9 1.18 (1.00–1.39) b0.001/66.5 * 0.002/79.3 b b0.001/92.1 0.116/23.7 1.17 (1.09–1.26) 0.025/35.8 * 0.001/55.1 1.10 (0.94–1.27) 0.003/51.0 * 0.146/52.6 1.02 (0.91–1.16) 0.185/43.1 0.520/0.0 0.85 (0.63–1.14) 0.341/7.1 0.864/0.0 1.23 (0.93–1.61) 0.483/0.0 1.02 (0.78–1.33) 0.027/60.4 1.21 (0.87–1.67) * 0.039/52.7 1.04 (0.83–1.31) *

0.804/0.0 0.650/0.0 0.007/68.9 0.011/61.7

X.-F. He et al. / Gene 534 (2014) 324–344

≥10

Overall

Dominant model

X.-F. He et al. / Gene 534 (2014) 324–344

0.591, I2 = 0.0%). For samples of Asians, we found that individuals with the minor variant genotypes had a higher risk of hepatocellular cancer (TC vs. TT: OR = 1.25, 95% CI = 1.02–1.54, Ph = 0.121, I2 = 48.3%), head and neck cancer (dominant model: OR = 1.49, 95% CI = 1.00–2.20, Ph b 0.001, I2 = 76.9%; recessive model: OR = 1.92, 95% CI = 1.19–3.10, Ph = 0.006, I2 = 66.6%; CC vs. TT: OR = 2.13, 95% CI = 1.24–3.68, Ph = 0.003, I2 = 70.1%; additive model: OR = 1.48, 95% CI = 1.10–1.98, Ph = 0.001, I2 = 78.6%), leukemia (dominant model: OR = 1.19, 95% CI = 1.00–1.43, Ph = 0.500, I2 = 0.0%; TC vs. TT: OR = 1.27, 95% CI = 1.05–1.53, Ph = 0.652, I2 = 0.0%), lung cancer (dominant model: OR = 1.19, 95% CI = 1.04–1.37, Ph b 0.001, I2 = 54.9%; recessive model: OR = 1.26, 95% CI = 1.07–1.49, Ph = 0.036, I2 = 37.7%; CC vs. TT: OR = 1.32, 95% CI = 1.06–1.63, Ph = 0.005, I2 = 49.4%; additive model: OR=1.15, 95% CI= 1.03–1.29, Ph =0.001, I2 =56.2%), and prostate cancer (dominant model: OR = 1.23, 95% CI = 1.01–1.49, Ph = 0.886, I2 = 0.0%; TC vs. TT: OR = 1.35, 95% CI = 1.10–1.66, Ph = 0.920, I2 = 0.0%). For samples of south Indians, significant association was observed among breast cancer (dominant model: OR = 3.40, 95% CI = 2.35–4.94, Ph = 0.473, I2 = 0.0%; recessive model: OR = 2.12, 95% CI = 1.28–3.49, Ph = 0.002, I2 = 79.4%; CC vs. TT: OR = 4.12, 95% CI = 2.82–6.03, Ph = 0.208, I2 = 34.0%; TC vs. TT: OR = 1.84, 95% CI = 1.11–3.05, Ph = 0.004, I2 = 77.1%; additive model: OR = 2.05, 95% CI=1.37–3.06, Ph =0.002, I2 =79.1%). For samples of north Indians, significant association was observed among head and neck cancer (TC vs. TT: OR = 1.42, 95% CI = 1.08–1.88, Ph = 0.552, I2 = 0.0%). 3.4. Source of controls and cancer risk attributed to the CYP1A1 T3801C polymorphism We also examined the association of the CYP1A1 T3801C polymorphism and cancer risk according to cancer type and source of controls (Table 4). For the population-based studies, significant association was found among lung cancer (dominant model: OR = 1.19, 95% CI = 1.04–1.36, Ph = 0.126, I2 = 35.2%; CC vs. TT: OR = 1.38, 95% CI = 1.03–1.85, Ph = 0.250, I2 = 22.4%; additive model: OR = 1.16, 95% CI = 1.03–1.30, Ph = 0.535, I2 = 0.0%). For the hospital-based studies, significant association was observed among cervical cancer (recessive model: OR = 2.21, 95% CI = 1.27–3.85, Ph = 0.084, I2 = 44.2%; CC vs. TT: OR = 2.72, 95% CI = 1.34–5.55, Ph = 0.010, I2 = 61.9%), leukemia

333

(dominant model: OR = 1.29, 95% CI = 1.03–1.61, Ph b 0.001, I2 = 72.6%), head and neck cancer (dominant model: OR = 1.28, 95% CI = 1.09–1.49, Ph b 0.001, I2 = 69.6%; recessive model: OR = 1.78, 95% CI = 1.25–2.54, Ph b 0.001, I2 = 71.9%; CC vs. TT: OR = 1.97, 95% CI = 1.35–2.88, Ph b 0.001, I2 = 72.2%; TC vs. TT: OR = 1.31, 95% CI = 1.12–1.54, Ph b 0.001, I2 = 57.9%; additive model: OR = 1.38, 95% CI = 1.18–1.62, Ph b 0.001, I2 = 76.9%), lymphoma (dominant model: OR = 0.75, 95% CI = 0.58–0.97, Ph = 0.850, I2 = 0.0%; additive model: OR = 0.74, 95% CI = 0.57–0.97, Ph = 0.742, I2 = 0.0%), prostate cancer (TC vs. TT: OR = 1.37, 95% CI = 1.08–1.73, Ph = 0.034; I2 = 47.5%), and lung cancer (dominant model: OR = 1.19, 95% CI = 1.08–1.31, Ph b 0.001, I2 = 50.8%; recessive model: OR = 1.26, 95% CI = 1.13–1.41, Ph = 0.145, I2 = 18.4%; CC vs. TT: OR = 1.32, 95% CI = 1.12–1.56, Ph = 0.018, I2 = 33.4%; TC vs. TT: OR = 1.13, 95% CI = 1.01–1.25, Ph = 0.004, I2 = 39.6%; additive model: OR = 1.14, 95% CI = 1.05–1.24, Ph b 0.001, I2 = 48.8%). 3.5. Anatomical site, histological type, and association of the CYP1A1 T3801C polymorphism with cancer risk We next completed a subgroup analysis by tumor site and histological type or anatomical location (Table 5). Regarding lung cancer, the CYP1A1 T3801C polymorphism was associated with non-small cells lung cancer (NSCLC). The pooled analysis for NSCLC was based on a large sample size with a good number of studies and we did not find important heterogeneity between studies. Conversely, there was no association between the CYP1A1 T3801C polymorphism and small cell lung cancer. For NSCLC, ethnicity also seemed to be crucial covariate, indicating that the CYP1A1 T3801C polymorphism has a different penetrance by ethnicity. Significant association was observed among Caucasians and Asians, but not USA Africans for NSCLC. When examining NSCLC sub-types, significant association was only observed among squamous cell carcinomas (SCC). In the stratified analysis by ethnicity, significant association was observed among Asians, but not Caucasians for SCC. For head and neck cancer, the results suggested a differential contribution of the CYP1A1 C allele, with a significant association among oral cancer, larynx cancer, pharynx cancer, and other site, but not thyroid cancer. In the stratified analysis by ethnicity, significant association was observed among Asians for oral cancer. High heterogeneity between

Table 8 Summary ORs (95% CI) and value of the heterogeneity of CYP1A1 T3801C polymorphism under different genetic models according to genotype material on cancer risk.a Variables Literature quality


Dominant model TC + CC versus TT OR (95% CI)

Overall

Breast cancer

Lung cancer

Blood

240 (51,129/68,969)

Normal tissue

22 (3791/6190)

Tumor tissue Blood

6 (1043/1472)

Normal tissue Blood

Normal tissue

38 (14,437/18,203)

7 (1570/2438) 64 (9732/12,619)

5 (785/1320)

Recessive model

Ph/I2 (%)

Homozygote

Heterozygote

Additive model

CC versus TC + TT

CC versus TT

TC versus TT

C versus T

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

1.16 (1.10–1.22)* b0.001/66.2 1.23 (1.12–1.35) * 1.00 (0.85–1.19)* b0.001/61.9 1.24 (0.89–1.73) * 0.91 (0.74–1.11) 0.184/33.6 1.06 (0.63–1.77) 1.11 (0.99–1.25) b0.001/74.6 1.19 (0.96–1.47) * 0.96 (0.72–1.28) 0.002/70.6 – 1.20 (1.10–1.31)* b0.001/48.4 1.28 (1.10–1.48) * 1.21 (0.80–1.84)* 0.016/71.0 1.19 (0.93–1.51)

b0.001/ 57.9

1.15 (1.09–1.20) * 1.08 (0.89–1.30) * 0.94 (0.79–1.13)

b0.001/ 73.8

0.166/ 38.2 b0.001/ 64.5

b

0.109/ 47.2 b0.001/ 81.6

–

–

–

–

–

–

1.37 (1.15–1.64) * 1.25 (0.90–1.72)

0.007/ 37.2

1.14 (1.03–1.25) * 1.21 (0.76–1.94) *

0.004/ 38.4

1.15 (1.07–1.25) * 1.15 (0.99–1.33)

b0.001/ 46.6

– 0.054/ 26.5 0.495/ 0.0

b0.001/ 57.7

0.589/ 0.0 b0.001/ 72.6

0.635/ 0.0 b0.001/ 67.2

0.008/ 55.1

b0.001/ 62.6

1.13 (1.07–1.19) * 1.11 (0.88–1.40) * 0.90 (0.72–1.12) 1.05 (0.95–1.17)

1.32 (1.19–1.47) * 1.27 (0.87–1.86) * 1.00 (0.60–1.69) 1.22 (0.961.56)

0.018/ 52.1

0.682/ 0.0

0.001/ 62.2

0.007/ 75.0

The bold values indicate that the results are statistically significant. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models. b The results were excluded due to high heterogeneity.

b0.001/ 68.4

0.217/ 32.5

334

X.-F. He et al. / Gene 534 (2014) 324–344

Table 9 Summary ORs (95% CI) and value of the heterogeneity of CYP1A1 T3801C polymorphism under different genetic models according to studies with HWE on cancer risk.a Variables


N0.05 Overall cancer

251 (52,893/72,590)

Cancer type Breast cancer

45 (15,728/20,482)

Colorectal cancer Gastric cancer

17 (6438/8236) 7 (666/1940)

HNC

27 (4723/5538)

Lung cancer

68 (10,834/14,297)

Ovarian cancer

6 (630/1091)

Prostate cancer

13 (1532/1772)

Cancer type and ethnicity Breast cancer/Caucasian

14 (7303/11,548)

Breast cancer/Asian

11 (3472/3759)

Colorectal cancer/Caucasian

10 (3292/4345)

HNC/Asian

6 (860/940)

HNC/Mixed

9 (1470/1866)

Lung cancer/Asian

28 (4033/5465)

Lung cancer/Mixed

9 (1603/1968)

Prostate cancer/Asian

4 (481/677)

Cancer type and source of controls Breast cancer/HB

28 (5616/6486)

Colorectal cancer/PB

6 (4059/4997)

Gastric cancer/HB

5 (333/546)

HNC/HB

25 (4385/5201)

Lung cancer/HB

58 (8952/11,620)

Ovarian cancer/PB

3 (346/773)

Prostate cancer/HB

11 (1206/1462)

Dominant model

Recessive model

Homozygote

Heterozygote

Additive model

TC + CC versus TT

CC versus TC + TT

CC versus TT

TC versus TT

C versus T

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

OR (95% CI) Ph/I2 (%)

1.13 b0.001/ 1.25 b0.001/ 1.33 b0.001/ 1.11 (1.05– (1.08–1.19) 64.6 (1.15–1.37) 52.0 (1.20–1.47) 58.7 1.16)

1.08 (0.97–1.20) * 0.94 (0.87–1.02) 0.87 (0.71–1.07) 1.32 (1.11–1.56) * 1.19 (1.10–1.29) * 0.93 (0.73–1.19) 1.25 (0.94–1.65)

b0.001/ 1.23 64.2 (1.01–1.51) * 0.441/ 0.92 1.1 (0.70–1.22) 0.121/ 0.94 40.5 (0.66–1.35) b0.001/ 1.91 70.4 (1.36–2.69) * b0.001/ 1.28 42.4 (1.15–1.42)

b0.001/ 1.29 64.2 (1.01–1.64) * 0.096/ 0.88 34.9 (0.73–1.06) 0.144/ 0.92 37.3 (0.63–1.36) 0.001/ 2.13 55.7 (1.42–3.18) * 0.145/ 1.37 17.6 (1.17–1.59) * 0.416/ 1.32 0.152/ 1.25 0.0 (0.53–3.26) 46.9 (0.50–3.14) b0.001/ 0.94 0.735/ 1.11 66.4 (0.72–1.22) 0.0 (0.84–1.48)

b0.001/ 1.00 69.2 (0.55–1.84) * b0.001/ 1.14 73.1 (0.88–1.47) * 0.459/ 0.91 0.0 (0.59–1.41) b b0.001/ 1.70 80.3 (1.04–2.78) * b0.001/ b 1.11 (0.81–1.51) 76.1 * 0.001/ 1.29 1.22 (1.09–1.53) (1.07–1.40) 51.1 * * 0.001/ 1.01 1.13 (0.74–1.38) (0.86–1.50) 69.1 * 1.27 0.992/ 0.90 (0.99–1.62) 0.0 (0.63–1.29) 1.03 (0.87–1.22) * 1.03 (0.84–1.27) * 0.98 (0.87–1.11)

b

b0.001/ 1.53 77.8 (1.08–2.16) * 0.93 0.502/ 0.92 (0.85–1.03) 0.0 (0.59–1.45) * 1.08 0.202/ 1.07 (0.81–1.44) 32.9 (0.44–2.56) b0.001/ 1.87 72.1 (1.30–2.70) * b0.001/ 1.36 44.4 (1.16–1.59) * 0.538/ 0.98 0.0 (0.10–10.1) * 0.012/ 0.94 1.28 (0.71–1.24) (0.97–1.69) 55.8 *

1.31 (1.09–1.57) * 1.18 (1.08–1.29) * 0.81 (0.58–1.13)

b0.001/ b0.001/ 1.14 56.0 (1.09–1.19) 71.6 *

b0.001/ 1.06 b0.001/ 71.7 (0.95–1.18) 67.1

b

b0.001/ 81.4

0.105/ 33.8 0.156/ 35.7 b0.001/ 65.5

0.95 (0.89–1.02) 0.97 (0.74–1.26) 1.45 (1.23–1.72) * 1.15 (1.07–1.23) * 0.99 (0.78–1.26) 1.14 (0.92–1.41) *

0.250/ 18.2 0.036/ 20.4 b0.001/ 72.5

1.06 (0.87–1.31) * 1.07 (0.91–1.27) * 1.00 (0.90–1.13)

b0.001/ 77.4

0.96 (0.88–1.04) 0.87 (0.70–1.09) 1.36 (1.15–1.62) * 1.13 (1.03–1.22) * 0.96 (0.73–1.26) 1.34 (1.01–1.78) *

0.415/ 3.3 0.274/ 20.4 0.002/ 54.8

0.001/ 65.7

0.995/ 0.0

1.03 (0.85–1.24) * 0.002/ 1.01 71.7 (0.81–1.26) * 0.105/ 1.02 42.9 (0.89–1.16) 0.002/ 1.43 73.1 (0.92–2.20) * b b0.001/ 1.12 90.3 (0.58–2.15) * 0.014/ 1.16 (1.00– 1.37 1.34) (1.11–1.69) 45.0 * 0.98 0.950/ 0.97 (0.71–1.35) 0.0 (0.82–1.16)

0.742/ 0.0

1.06 0.856/ (0.72–1.57) 0.0

b0.001/ 72.4

b

0.036/ 49.8 0.014/ 60.2 0.104/ 43.0 0.021/ 62.4 0.001/ 86.1 0.041/ 37.3

0.016/ 64.0 0.081/ 51.8 0.001/ 57.8 0.057/ 27.1 0.060/ 71.6 0.987/ 0.0

1.02 (0.54–1.91) * 1.19 (0.85–1.65) * 0.91 (0.59–1.42) 2.00 (1.08–3.72) *

0.055/ 25.8 0.162/ 45.1 0.392/ 5.4

0.025/ 52.7

1.33 (1.03– 1.72)

b0.001/ 1.16 78.0 (0.96–1.40) * 0.030/ 0.94 0.85 (0.85–1.04) (0.55–1.31) 59.7 * 0.093/ 1.10 1.19 (0.81–1.49) (0.48–2.91) 49.8 * b0.001/ 1.39 (1.16– 2.10 1.66) (1.36–3.26) 67.3 * 0.051/ 1.14 (1.05– 1.34 1.23) (1.13–1.58) 28,1 * 0.073/ 0.81 0.92 (0.57–1.13) (0.10–8.63) 68.8 * 1.11 0.868/ 1.39 (1.04– (0.82–1.51) 0.0 1.85)*

0.040/ 27.3 0.361/ 8.0 0.002/ 62.3

0.006/ 67.2 0.616/ 0.0 0.001/ 76.2

b

0.007/ 35.9 0.271/ 22.5 0.001/ 65.3

b0.001/ 75.9 0.313/ 15.0 b0.001/ 82.0

0.016/ 75.9

b

b0.001/ 90.5

0.031/ 40.0

0.003/ 52.6

0.370/ 0.0

1.18 (1.06–1.31) * 0.99 (0.86–1.13)

0.953/ 0.0

1.11 0.898/ (0.93–1.32) 0.0

b0.001/ 69.8

b

0.302/ 17.2

0.94 0.305/ (0.87–1.02) 16.8

0.511/ 0.0

1.13 0.043/ (0.77–1.65) 59.5

0.002/ 54.7 0.138/ 19.5

1.45 (1.21–1.74) * 1.14 (1.05– 1.23)

0.520/ 0.0

0.85 0.341/ (0.63–1.14) 7.1

0.019/ 54.7

0.055/ 1.16 (0.96–1.40) 45.8 *

0.557/ 0.0

b0.001/ 84.5

b0.001/ 73.9 0.003/ 42.3

X.-F. He et al. / Gene 534 (2014) 324–344

335

Table 9 (continued) Variables


Dominant model

Recessive model

Homozygote

Heterozygote

TC + CC versus TT

CC versus TC + TT

CC versus TT

TC versus TT

2

2

OR (95% CI) Ph/I (%) Histological type or anatomical area in a specific tumor site Oral cancer

11 (1111/1637)

b0.001/ 1.97 1.34 (1.29–2.99) (0.98–1.83) 71.6 * * b 0.003/ 1.95 (1.2683.3 3.04)

0.035/ 51.8

0.001/ 86.0

Oral cancer/Asian

3 (348/388)

Oral/Mixed

5 (430/692)

b

Other sites

9 (2350/2468)

0.003/ 1.35 (1.06–1.71) 64.0 *

Smoking status in a specific tumor site Lung cancer/non-smokers

Lung cancer/smokers

HNC/non-smokers

21 (1329/2469)

21 (2234/2401)

5 (326/521)

b0.05 Overall cancer

17 (3070/4041)

Breast cancer

2 (544/448)

Colorectal cancer

1 (733/721)

Gastric cancer

1 (73/263)

HNC

5 (873/1359)

Lung cancer

3 (201/381)

Ovarian cancer

1 (122/144)

Prostate cancer

2 (306/335)

OR (95% CI) Ph/I (%)

1.29 (0.96–1.73) * 1.44 (1.15–1.81) * 1.14 (0.85–1.54)

0.001/ 80.2

b

1.97 0.207/ (1.30–2.98) 29.0

b0.001/ 1.36 66.5 (0.84–2.20) * b0.001/ 1.28 60.4 (0.89–1.85) 0.106/ 47.5

0.634/ 0.0

0.060/ 46.5 0.193/ 28.3

3.34 0.316/ (1.84–6.06) 15.2

b

b0.001/ b 76.0 0.096/ 0.56 0.99 (0.39–0.81) (0.58–1.68) 63.9 * – 1.27 0.49 (0.18–1.31) (0.97–1.65) 1.28 – 1.12 (0.76–2.16) (0.49–2.60) b 1.09 0.172/ (0.91–1.30) 37.4 b b0.001/ 0.91 89.1 (0.61–1.36) 1.41 – 0.76 (0.87–2.28) (0.37–1.57) 1.16 0.232/ 0.67 (0.85–1.59) 30.0 (0.44–1.01)

2

OR (95% CI) Ph/I (%)

2.10 (1.22–3.61) * 2.16 (1.35–3.46)

0.002/ 67.3 0.296/ 17.9

OR (95% CI) Ph/I (%)

OR (95% CI) Ph/I2 (%)

0.003/ 1.20 (0.88–1.63) 65.3 * b 0.001/ 84.7

b

b0.001/ 1.12 90.3 (0.58–2.15) * 2.24 0.168/ 1.60 (1.33– (1.47–3.42) 34.1 1.92)

0.013/ 62.9 0.756/ 0.0

3.43 0.701/ (1.81–6.52) 0.0

b0.001/ b 80.7 0.610/ 0.55 0.0 (0.37–0.83)

1.12 (0.77–1.62) * 1.14 (0.91–1.43)

1.33 (1.07– 1.65)* 1.06 (0.79–1.43)

0.51 – (0.19–1.38) – 1.24 – (0.52–2.95) b b0.001/ b0.001/ 89.3 84.0 0.198/ 1.25 0.019/ 38.3 (0.48–3.28) 74.7 – 0.94 – (0.44–2.01) 0.648/ 0.78 0.405/ 0.0 (0.50–1.23) 0.0

1.35 (1.02–1.77) 1.29 (0.74–2.27) 0.99 (0.82–1.20) b

1.62 (0.96–2.73) 1.38 (0.98–1.93)

b0.001/ 79.7

0.016/ 75.9

0.018/ 1.49 (0.96–2.31) 75.2 * b b0.001/ 92.5

0.607/ 0.0

1.61 0.152/ (1.38–1.88) 36.2

0.074/ 45.8

1.30 (0.97–1.75) * 1.14 (0.95–1.36)

0.141/ 37.7

0.025/ 0.71 (0.32–1.55) 67.8 *

b0.001/ 78.6 0.525/ 0.0

–

C versus T 2

b

1.68 (0.80–3.56) * 1.39 (0.85–2.25)

Additive model

0.019/ 58.4 0.679/ 0.0

0.194/ 1.41 (1.08–1.83) 36.4 *

b0.001/ b 68.9 0.202/ 0.91 38.6 (0.55–1.50) * – 1.16 (0.91–1.49) – 1.20 (0.80–1.80) b 0.249/ 25.8 b0.001/ b 89.1 – 1.13 (0.79–1.63) 0.298/ 0.96 7.5 (0.76–1.20)

b0.001/ 84.4 0.060/ 71.8 – – b0.001/ 81.0 0.001/ 86.5 – 0.261/ 20.7

HNC head and neck cancer, the bold values indicate that the results are statistically significant, HB hospital-based study, PB population-based study, SZ sample size. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models. b The results were excluded due to high heterogeneity.

studies was observed for thyroid cancer. Hence, new studies should be performed to investigate the CYP1A1 T3801C polymorphism and thyroid cancer risk. For leukemia, the CYP1A1 T3801C polymorphism was associated with acute lymphoblastic leukemia (ALL). Conversely, there was no association between the CYP1A1 T3801C polymorphism and acute myeloid leukemia. Significant association was observed among Asians, but not Caucasians for ALL. When examining ALL sub-types, significant association was only observed among children acute lymphoblastic leukemia (CALL). In the stratified analysis by ethnicity, significant association was observed among Caucasians for CALL. 3.6. Interaction of CYP1A1 T3801C and other risk factors for cancer susceptibility We next tested if the CYP1A1 T3801C polymorphism could interact with other known risk factors (Table 6). For lung cancer, a positive interaction between smokers and the CYP1A1 C allele was indicated among dominant model. For head and neck cancer, significant association was observed among smokers and non-smokers. For breast cancer, no association was found among menopausal status.

3.7. Literature quality and cancer risk attributed to the CYP1A1 T3801C polymorphism We also examined the association of the CYP1A1 T3801C polymorphism and cancer risk according to cancer type and literature quality (Table 7). For the literature quality ≥10, significant association was observed among overall cancer (dominant model: OR = 1.11, 95% CI = 1.06–1.17, Ph b 0.001, I2 = 65.2%; recessive model: OR = 1.20, 95% CI = 1.09–1.32, Ph b 0.001, I2 = 53.1%; CC vs. TT: OR = 1.27, 95% CI = 1.14–1.41, Ph b 0.001, I2 = 59.6%; TC vs. TT: OR = 1.10, 95% CI = 1.05–1.16, Ph b 0.001, I2 = 56.9%; additive model: OR = 1.11, 95% CI = 1.06–1.17, Ph b 0.001, I2 = 71.6%), cervical cancer (recessive model: OR = 2.26, 95% CI = 1.20–4.27, Ph = 0.033, I2 = 58.7%; CC vs. TT: OR = 2.86, 95% CI = 1.27–6.43, Ph = 0.003, I2 = 72.1%), colorectal cancer (CC vs. TT: OR = 0.81, 95% CI = 0.67–0.98, Ph = 0.224, I2 = 22.3%), head and neck cancer (dominant model: OR = 1.25, 95% CI = 1.06–1.47, Ph b 0.001, I2 = 68.0%; recessive model: OR = 1.89, 95% CI = 1.27–2.82, Ph b 0.001, I2 = 62.6%; CC vs. TT: OR = 1.99, 95% CI = 1.25–3.16, Ph b 0.001, I2 = 69.9%; TC vs. TT: OR = 1.29, 95% CI = 1.09–1.52, Ph = 0.021, I2 = 45.8%; additive model: OR = 1.39, 95% CI = 1.16–1.67, Ph b 0.001, I2 = 74.4%), leukemia (dominant model: OR = 1.20, 95%

336

X.-F. He et al. / Gene 534 (2014) 324–344

Table 10 Summary ORs (95% CI) and value of the heterogeneity of CYP1A1 T3801C polymorphism under different genetic models according to sample size on cancer risk.a SZ

Variables

N200 Overall cancer

Breast cancer

Cervical cancer


Homozygote

Heterozygote

Additive model

CC versus TC + TT

CC versus TT

TC versus TT

C versus T

Ph/I2 (%) OR (95% CI)

Ph/I2 (%) OR (95% CI)

Ph/I2 (%) OR (95% CI)

Ph/I2 (%) OR (95% CI)

Ph/I2 (%)

208 (52,430/71,949)

1.12 (1.07– 1.18)*

b0.001/ 67.5

1.23 (1.11– 1.35)*

b0.001/ 63.3

1.29 (1.15–1.44)*

b0.001/ 66.4

b0.001/ 58.0

1.13 (1.07–1.18) *

b0.001/ 76.0

39 (15,797/20,304)

1.05 (0.95–1.16) *

b0.001/ 73.4

1.14 (0.92–1.41)*

b0.001/ 68.3

1.16 (0.91–1.47)*

b0.001/ 72.8

b0.001/ 66.6

b

b0.001/ 81.9

b

b0.001/ 84.8 0.222/ 21.3 0.009/ 67.4

2.26 (1.20– 4.27)* 0.85 (0.71–1.02) 0.81 (0.25–2.66)*

0.033/ 58.7 0.228/ 21.9 0.026/ 67.5

2.86 (1.27–6.43)*

0.003/ 72.1 0.224/ 22.3 0.014/ 71.9%

b0.001/ 80.7 0.222/ 22.6 0.031/ 59.2

b

b0.001/ 85.5 24.3

0.065/ 49.5

1.00 (0.76–1.32)

0.946/ 0.0

1.01 (0.74–1.36)

0.992/ 0.0%

0.162/ 38.9 0.428/ 0.0 b0.001/ 65.9

0.88 (0.62–1.26) 1.01 (0.80–1.27) 1.86 (1.26–2.72)*

0.412/ 0.0 0.870/ 0.0 b0.001/ 75.1

0.85 (0.58–1.26)

0.372/ 6.0 0.637/ 0.0 b0.001/ 74.7

b0.001/ 71.0

b

b0.001/ 82.2

1.67 (0.99–2.81)*

b0.001/ 79.8

b0.001/ 44.2

1.29 (1.11–1.50)*

0.051/ 29.0

1.40 (1.18–1.68)*

0.016/ 36.3

0.241/ 25.8 0.015/ 56.1

0.85 (0.47–1.54) 0.82 (0.63–1.06)

0.124/ 52.0 0.406/ 3.5

0.95 (0.51–1.77)

0.196/ 38.7 0.258/ 20.8

b0.001/ 56.6

1.21 (1.04–1.41)

0.476/ 0.0

1.41 (1.15–1.75)*

0.088/ 22.2

0.005/ 67.6

1.67 (0.99–2.81)

0.175/ 37.0

2.68 (1.35–5.33)

0.127/ 47.5

0.547/ 0.0 0.567/ 0.0 0.055/ 72.9

2.35 (0.44–12.37) 1.07 (0.92–3.41) 2.73 (0.42–17.94)

0.523/ 0.0 0.105/ 55.7 0.893/ 0.0

2.24 (0.42–12.00) 0.514/ 0.0 2.05 (0.98–4.28) 0.380/ 0.0 2.70 (0.40–18.18) 0.970/ 0.0

0.469/ 0.0 0.302/ 16.5 –

0.59 (0.14–2.50) 1.76 (0.73–4.25) 1.35 (0.58–3.16) 1.60 (0.94–2.71)

–

0.36 (0.08–1.69)

–

0.144/ 48.3 –

2.09 (0.81–5.37)

0.249/ 28.1 –

7 (1130/1146) 14 (6975/8651)

Esophageal cancer

7 (762/1916)

Gastric cancer

5 (637/1981)


4 (773/1879) 28 (5295/6536)

Leukemia

17 (3457/5123)

Lung cancer

51 (9676/12,943)

6 (1337/2268)

Ovarian cancer

6 (706/1204)

Prostate cancer

10 (1517/1642)

60 (3533/4682)

Breast cancer

8 (475/626)

Cervical cancer

2 (115/152)


4 (196/306)

Esophageal cancer Gastric cancer

2 (107/80)

2 (113/91)

3 (102/222)


4 (301/361)

Leukemia

1 (99/99)

Lung cancer

Recessive model

TC + CC versus TT OR (95% CI)


b200 Overall cancer

Dominant model

1 (54/86)

20 (1359/1735)

Ovarian cancer

2 (79/155)

Prostate cancer

5 (321/465)

0.96 (0.88–1.03) 0.75 (0.53–1.05) * 1.07 (0.82–1.40) * 0.86 (0.70–1.06) 1.11 (0.91–1.34) 1.22 (1.05–1.41) * 1.29 (1.06–1.59) * 1.24 (1.13–1.35) * 1.00 (0.79–1.25) 1.08 (0.85–1.38) * 1.23 (1.05–1.44) * 1.34 (0.79–2.27) * 0.92 (0.51–1.66) 1.33 (0.85–2.06) 1.74 (0.46–6.56) * 0.49 (0.25–0.95) 1.40 (0.82–2.38) 3.02 (1.34–6.79) 2.20 (1.35–3.58) * 1.25 (0.70–2.23) 1.05 (0.83–1.32) * 1.08 (0.60–1.96) 1.76 (1.00–3.11) *

0.084/ 54.9 –

0.81 (0.67–0.98) 0.73 (0.20–2.61)*

1.12 (0.86–1.47) 1.93 (1.29–2.90)*

0.95 (0.72–1.26)

2.80 (0.99–7.88)

0.575/ 0.0

2.35 (1.35–4.08)

0.340/ 10.5

–

0.54 (0.13–2.26)

–

0.003/ 53.4

0.48 (0.12–1.99) 1.18 (0.93–1.51)

0.541/ 0.0

1.19 (0.91–1.55)

0.299/ 13.8

0.560/ 0.0 0.018/ 66.5

2.76 (0.87–8.78) 0.94 (0.63–1.41)

0.968/ 0.0 0.794/ 0.0

2.65 (0.82–8.52)

0.846/ 0.0 0.407/ 0.0

1.18 (0.74–1.87)

1.11 (1.06–1.16) * 1.06 (0.95–1.17) * b

0.98 (0.91–1.07) 0.75 (0.55–1.02) * 1.10 (0.81–1.50) * 0.88 (0.70–1.09) 1.18 (0.95–1.46) 1.21 (1.04–1.40) * 1.20 (1.01–1.44) * 1.17 (1.07–1.28) * 1.10 (0.85–1.43) 1.18 (0.93–1.51) * 1.20 (1.01–1.43) * 1.08 (0.56–2.09) * 0.87 (0.48–1.59) 1.15 (0.72–1.84) b

0.41 (0.17–0.99) 1.26 (0.72–2.20) 3.11 (1.33–7.27) 2.20 (1.56–3.09) 1.40 (0.76–2.58) 1.02 (0.77–1.35) * 0.92 (0.49–1.73) 1.86 (1.07–3.24) *

0.022/ 59.3

0.95 (0.89–1.02) 0.78 (0.56–1.09) * 1.02 (0.89–1.16)

0.004/ 70.9 0.293/ 17.9

0.148/ 41.0 0.590/ 0.0 0.007/ 48.7

0.89 (0.75–1.04) 1.07 (0.93–1.22) 1.33 (1.13–1.57) *

0.209/ 31.9 0.613/ 0.0 b0.001/ 78.0

0.001/ 58.9

b

b0.001/ 84.9

0.065/ 27.4

1.17 (1.09–1.26) * 0.99 (0.79–1.24) 1.01 (0.83–1.24) * 1.18 (1.05–1.34) * 1.19 (0.65–2.15) * 1.01 (0.62–1.66) 1.35 (0.97–1.88) 1.75 (0.98–3.10) * 0.56 (0.30–1.02) 1.61 (0.79–3.30) 1.73 (1.06–2.81) 1.77 (1.38–2.26)

0.007/ 39.9

0.103/ 51.6 0.046/ 49.3 b0.001/ 56.0 0.019/ 66.1 0.746/ 0.0 0.151/ 43.3 0.044/ 75.4 – 0.595/ 0.0 – 0.102/ 51.6 – 0.001/ 58.6 0.439/ 0.0 0.035/ 61.3

1.06 (0.65–1.74) 1.07 (0.89–1.28) * 1.24 (0.78–1.98) 1.35 (0.91–2.00) *

HNC head and neck cancer, SZ sample size, and the bold values indicate that the results are statistically significant. a All summary ORs were calculated using fixed-effects models. In the case of significant heterogeneity (indicated by *), ORs were calculated using random-effects models. b The results were excluded due to high heterogeneity.

0.195/ 36.2 0.010/ 60.3 b0.001/ 54.9 0.002/ 75.8 0.356/ 0.0 0.823/ 0.0 0.076/ 68.3 – 0.073/ 61.8 – 0.199/ 35.6 – 0.012/ 48.9 0.762/ 0.0 0.017/ 66.8

(–0.22; 0.13) (0.02; 0.07) (–0.25; –0.03) (–0.01; 0.01) (–0.16; 0.09) –0.044 0.045 –0.140 0.001 –0.036 0.159 0.027 0.042 0.169 0.446

0.615 0.002 0.015 0.843 0.570

95% CI Coef. P 95% CI

(–0.05; 0.32) (0.004; 0.06) (–0.24; –0.004) (–0.003; 0.02) (–0.20; 0.09) 0.136 0.035 –0.122 0.007 –0.057 (–0.60; 0.07) (0.04; 0.16) (–0.49; –0.01) (–0.02; 0.02) (–0.07; 1.39)

Coef. P

0.120 0.001 0.039 0.958 0.076

95% CI

–0.266 0.102 –0.250 0.0006 0.659

Coef. P 95% CI

(–0.06; –0.006) (0.034; 0.14) (–0.44; –0.02) (–0.02; 0.02) (–0.23; 0.24)

Coef.

–0.300 0.851 –0.229 –0.003 0.004 (–0.14; 0.25) (0.01; 0.08) (–0.23; 0.01) (–0.004; 0.02) (–0.22; 0.06) 0.06 0.046 –0.112 0.007 –0.081

95% CI

0.574 0.004 0.066 0.199 0.280

Coef.

HWE Ethnicity Source of controls Cancer type Sample size

P

Recessive model Dominant model Study characteristics

Table 11 Meta-regression analysis of the main characteristics of the 268 studies.

0.046 0.001 0.033 0.765 0.970

Homozygote

Heterozygote

Additive model

P

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337

CI = 1.02–1.43, Ph = 0.013, I2 = 51.5%; additive model: OR = 1.18, 95% CI = 1.00–1.39, Ph b 0.001, I2 = 66.5%), lung cancer (dominant model: OR = 1.25, 95% CI = 1.14–1.36, Ph = 0.001, I2 = 43.3%; recessive model: OR = 1.28, 95% CI = 1.09–1.51, Ph = 0.048, I2 = 31.0%; CC vs. TT: OR = 1.42, 95% CI = 1.18–1.71, Ph = 0.032, I2 = 34.2%; TC vs. TT: OR = 1.20, 95% CI = 1.11–1.30, Ph = 0.116, I2 = 23.7%; additive model: OR=1.17, 95% CI=1.09–1.26, Ph =0.025, I2 =35.8%), and prostate cancer (TC vs. TT: OR = 1.65, 95% CI = 1.06–2.57, Ph = 0.027, I2 = 60.4%). 3.8. Genotype material and cancer risk attributed to the CYP1A1 T3801C polymorphism Finally, we also examined the association of the CYP1A1 T3801C polymorphism and cancer risk according to cancer type and genotype material (Table 8). For the blood samples, significant association was observed among overall cancer (dominant model: OR = 1.16, 95% CI = 1.10–1.22, Ph b 0.001, I2 = 66.2%; recessive model: OR = 1.23, 95% CI = 1.12–1.35, Ph b 0.001, I2 = 57.9%; CC vs. TT: OR = 1.32, 95% CI = 1.19–1.47, Ph b 0.001, I2 = 62.6%; TC vs. TT: OR = 1.13, 95% CI = 1.07–1.19, Ph b 0.001, I2 = 57.7%; additive model: OR = 1.15, 95% CI = 1.09–1.20, Ph b 0.001, I2 = 73.8%) and lung cancer (dominant model: OR = 1.20, 95% CI = 1.10–1.31, Ph b 0.001, I2 = 48.4%; recessive model: OR = 1.28, 95% CI = 1.10–1.48, Ph = 0.054, I2 = 26.5%; CC vs. TT: OR = 1.37, 95% CI = 1.15–1.64, Ph = 0.007, I2 = 37.2%; TC vs. TT: OR = 1.14, 95% CI = 1.03–1.25, Ph = 0.004, I2 = 38.4%; additive model: OR = 1.15, 95% CI = 1.07–1.25, Ph b 0.001, I2 = 46.6%). 3.9. Test of heterogeneity and sensitivity There was significant heterogeneity among these studies for dominant model comparison (Ph b 0.001), recessive model comparison (Ph b 0.001), additive model comparison (Ph b 0.001), CC versus TT (Ph b 0.001), and TC versus TT (Ph b 0.001). Then, we assessed the source of heterogeneity by ethnicity, cancer type, source of controls, HWE, and sample size. Table 11 lists the results of meta-regression. The results indicated that HWE (recessive model: P = 0.046), ethnicity (dominant model: P = 0.004; recessive model: P = 0.001; CC vs. TT: P = 0.001; CT vs. TT: P=0.001; additive model: P=0.002), and source of controls (recessive model: P = 0.033; CC vs. TT: P = 0.039; CT vs. TT: P = 0.042; additive model: P = 0.015) but not cancer type (d dominant model: P = 0.199; recessive model: P = 0.765; CC vs. TT: P = 0.958; CT vs. TT: P = 0.169; additive model: P = 0.843) and sample size (dominant model: P = 0.280; recessive model: P = 0.970; CC vs. TT: P = 0.076; CT vs. TT: P = 0.446; additive model: P = 0.570) contributed to substantial heterogeneity among the meta-analysis. Examining genotype frequencies in the controls, significant deviation from HWE was detected in the 17 studies (Adonis et al., 2005a; Anantharaman et al., 2007; Bailey et al., 1998; Cha et al., 2007; Chatterjee et al., 2009; Chen et al., 2012; Cordero et al., 2010; Goodman et al., 2001; Kimura et al., 2008; Lee et al., 2006; Li et al., 2008b; Quiñones et al., 2001; Sabitha et al., 2010; Sainz et al., 2011; Shin et al., 2007; Suzuki et al., 2003; Wang et al., 2004). For cancer type, when these studies were excluded, the results of CYP1A1 T3801C were changed among breast cancer (recessive model: OR = 1.23, 95% CI = 1.01–1.51, Ph b 0.001, I2 = 64.2%; additive model: OR = 1.29, 95% CI = 1.01–1.64, Ph b 0.001, I2 = 71.7%) and hospital-based studies of breast cancer (recessive model: OR = 1.53, 95% CI = 1.08–2.16, Ph b 0.001, I2 = 72.4%), as shown in Table 9. In addition, when the meta-analysis was performed excluding studies with small sample sizes, the results of CYP1A1 T3801C were changed among colorectal cancer (CC vs. TT: OR = 0.81, 95% CI = 0.67–0.98, Ph = 0.224, I2 = 22.3%), hepatocellular cancer (TC vs. TT: OR = 1.12, 95% CI = 0.86–1.47, Ph = 0.637, I2 = 0.0%), and prostate cancer (TC vs. TT: OR = 1.18, 95% CI = 0.93–1.51, Ph = 0.046, I2 = 49.3%), as shown in Table 10. Last, a single study involved in the meta-analysis was deleted each time to reflect the influence of individual data set to the pooled

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A

B Filled funnel plot with pseudo 95% confidence limits

Filled funnel plot with pseudo 95% confidence limits

2

2

1

1

theta, filled

theta, filled

0

0

-1

-1

-2

-2 0

.5

1

0

s.e. of: theta, filled

C

.5

1

s.e. of: theta, filled

Filled funnel plot with pseudo 95% confidence limits 4

2

theta, filled 0

-2

-4 0

.5

1

1.5

2

s.e. of: theta, filled Fig. 2. The Duval and Tweedie nonparametric “trim and fill” method's funnel plot of the meta-analysis of cancer risk and CYP1A1 T3801C polymorphism (A: dominant model; B: additive model; C: TC vs. TT).

ORs, and the corresponding pooled ORs were not essentially altered (data not shown).

3.10. Publication bias We performed Begg's funnel plot and Egger's test to assess the publication bias of literatures. Begg's funnel plots and Egger's test suggested that there might be publication bias in dominant model (P = 0.010), additive model (P = 0.015), and TC vs. CC (P = 0.015). This might be a limitation for the meta-analysis because studies with null findings, especially those with small sample size, are less likely to be published. Adjusting for possible publication bias using the Duval and Tweedie nonparametric “trim and fill” method for overall studies, the results did not change between CYP1A1 T3801C polymorphism with the risk of cancer. Fig. 2 lists the Duval and Tweedie nonparametric “trim and fill” methods funnel plot. Then, we used fail-safe number, which is defined as the number of negative results that could reverse the significant finding, for the evaluation of the reliability of meta-analysis. The Nfs0.05 for data in dominant model, additive model, and TC vs. CC were 291, 260, and 244, respectively, suggesting that the publication biases may not have a remarkable influence on the results of the meta-analyses.

4. Discussion In our meta-analysis of more than 260 case-control studies, the T3801C polymorphism of CYP1A1 was associated with different patterns of cancer susceptibility according to cancer type, ethnicity, source of controls, and histological type of cancer. The heterogeneity between studies was high, and was at least partly by the covariates listed above. Cytochrome P450s are enzymes that catalyze phase-I metabolism reactions, such as C-, N- and S-oxidation and dealkylation (Androutsopoulos et al., 2009). Cytochrome P450 1A1 (CYP1A1) is a member of the CYP1 family and participates in the metabolism of a vast number of xenobiotics, as well as endogenous substrates (McManus et al., 1990). The metabolism of xenobiotics may well lead to their activation, and in the case of CYP1A1, the activation of benzo[a]pyrene represents a well-studied reaction (Androutsopoulos et al., 2009; McManus et al., 1990). Importantly, among endogenous substrates, the involvement of CYP1A1 in the metabolism of estrogen is worth reporting (Spink et al., 1992). CYP 1A1 plays a key role in phase I metabolism of polycyclic aromatic hydrocarbons and in estrogen metabolism, and the dysfunction of CYP 1A1 can cause the damages to DNA, lipids, and proteins, which further result in carcinogenesis (Androutsopoulos et al., 2009; Nebert and Dalton, 2006). In addition, many polymorphisms

X.-F. He et al. / Gene 534 (2014) 324–344

have been identified in the CYP family, which have impacts on the activity of CYP functions (Zhou et al., 2009). Since CYPs play a role in the bioactivation of many procarcinogens, polymorphisms of these enzymes may contribute to the variable susceptibility to carcinogenesis.CYP1A1T3801C is a polymorphism in the 30-noncoding region of CYP1A1, containing a single T to C base substitution which gives rise to an MspI restriction site (Androutsopoulos et al., 2009; Zhou et al., 2009). The CYP1A1 variant C was found to be more readily inducible than the variant T, and it is conceivable that the CYP1A1 T3801C polymorphism may have a relationship with the cervical cancer susceptibility. Many studies have reported the association of CYP1A1 T3801C polymorphism with risk of cancer, however, the results remained controversial, although some original studies thought that T3801C polymorphism was associated with risk of cancer, others had different opinions. In order to resolve this conflict, the meta-analysis including 55,614 cases and 76,509 controls was performed to derive a more precise estimation of the association between CYP1A1 T3801C polymorphism and risk of different types of cancer. Overall, our results indicate that T3801C polymorphism is associated with increased cancer risk when all eligible studies were pooled into the meta-analysis. The previous meta-analysis indicated that Asians and Indians were more susceptible to cancer related to the CYP1A1 T3801C polymorphism, and the effect size was also higher than in Caucasians. Curiously, ethnicity also modulated the genetic model for the CYP1A1 polymorphism susceptibility to cancer (Table 3), mainly in the histological types of lung cancer, head and neck cancer, and leukemia (Table 5). It should be considered that the apparent inconsistency of these results may underlie differences in ethnicity, lifestyle and disease prevalence as well as possible limitations due to the relatively small sample size. The current knowledge of carcinogenesis indicates a multi-factorial and multistep process that involves various genetic alterations and several biological pathways. Thus, it is unlikely that risk factors of cancer work in isolation from each other. And the same polymorphisms may play different roles in cancer susceptibility, because cancer is a complicated multi-genetic disease, and different genetic backgrounds may contribute to the discrepancy. And even more importantly, the low penetrance genetic effects of single polymorphism may largely depend on interaction with other polymorphisms and/or a particular environmental exposure. These results suggest that association between the CYP1A1 T3801C and cancer is modified by gene–gene and gene–environment interactions that differ between and within ethnic groups. We also observed a wide variation of the Gln allele frequencies of control resources in Asians (0.27), Indians (0.35), Caucasians (0.35) and Africans (0.17), and this different allele frequency might account for the association between the T3801C polymorphism and cancer susceptibility among different ethnicities. Based on biochemical properties described for CYP1A1 polymorphism, we would expect that the C allele would be associated with higher susceptibility for all types of cancer. However, our results showed that such association was observed just for cervical cancer, head and neck cancer, leukemia, lung cancer, prostate cancer and breast cancer, suggesting that other factors may be modulating the CYP1A1 polymorphism functionality. Several previous studies assessed the effect of CYP1A1 T3801C polymorphism on these cancer risks, which finding is consistent with our results. However, the exact mechanism for association between different tumor sites and CYP1A1 T3801C polymorphism was not clear, carcinogenetic mechanism may differ by different tumor sites and the CYP1A1 genetic variants may exert varying effects in different cancers. Possible sources of heterogeneity, we found that controls source demonstrated the evidence of significant variation by meta-regression. The reason may be that the hospital-based studies have some biases because such controls may contain certain benign diseases which are prone to develop malignancy and may not be very representative of the general population. Thus, the use of a proper and representative cancer-free control subjects is very important in reducing biases in such genotype association studies. And this indicates that

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it may be not appropriate to use an overall estimation of the relationship between CYP1A1 T3801C polymorphism and cancer risk. The current meta-analysis has some strength compared with individual studies and previous meta-analyses. First, differently from previous meta-analyses (Ding et al., 2013; Ji et al., 2012; Sergentanis et al., 2012; Zhuo et al., 2012a, 2012b), we explored the impact of CYP1A1 T3801C on a great diversity of cancer sites, allowing for a general view of its influence on cancer susceptibility. Second, we explored the interaction of CYP1A1 T3801C polymorphism and other risk factors, such as smoking or menopausal status. Third, we performed subgroup analyses according to literature quality and genotyping material. Fourth, our meta-analysis explores and analyzes the sources of heterogeneity between studies about CYP1A1 T3801C in cancer. There was some evidence of publication bias for some cancers, which may also have contributed to the high heterogeneity observed. However, such limitations highlight the need for further studies in specific tumor sites and different ethnicities, using population-based sources of cases and controls and adequate sample size. A potential limitation of our results is the small number of studies for some tumor sites and subgroups, which hinders the ability of drawing more definite conclusions for some results. For these cases, the interpretation of the results should be taken carefully. In summary, this meta-analysis suggests the participation of T3801C in the susceptibility for some cancers, but some results were limited by the small number of studies and participants in some subgroups. Moreover, ethnicity, histological type of cancer, and smokers seem to contribute to varying expressions of the T3801C on cancer risk. Finally, our work also points out the importance of new studies for T3801C association in some cancer types and ethnicities, such as gallbladder cancer, Indians with oral cancer, and thyroid cancer, where at least some of the covariates responsible for heterogeneity could be controlled, to obtain a more conclusive understanding about the function of the CYP1A1 T3801C polymorphism in cancer development. Conflict of Interest We declare that we have no conflict of interest. References Acevedo, C., et al., 2003. Positive correlation between single or combined genotypes of CYP1A1 and GSTM1 in relation to prostate cancer in Chilean people. Prostate 57, 111–117. Adonis, M., Martínez, V., Marín, P., Berrios, D., Gil, L., 2005a. Smoking habit and genetic factors associated with lung cancer in a population highly exposed to arsenic. Toxicol. Lett. 159, 32–37. Adonis, M., Martínez, V., Marín, P., Gil, L., 2005b. CYP1A1 and GSTM1 genetic polymorphisms in lung cancer populations exposed to arsenic in drinking water. Xenobiotica 35, 519–530. Agudo, A., et al., 2006. 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. 15, 2427–2434. Al-Dayel, F., et al., 2008. Polymorphisms of drug-metabolizing enzymes CYP1A1, GSTT and GSTP contribute to the development of diffuse large B-cell lymphoma risk in the Saudi Arabian population. Leuk. Lymphoma 49, 122–129. Alexandrie, A.K., Sundberg, M.I., Seidegård, J., Tornling, G., Rannug, A., 1994. Genetic susceptibility to lung cancer with special emphasis on CYP1A1 and GSTM1: a study on host factors in relation to age at onset, gender and histological cancer types. Carcinogenesis 15, 1785–1790. Anantharaman, D., Chaubal, P.M., Kannan, S., Bhisey, R.A., Mahimkar, M.B., 2007. Susceptibility to oral cancer by genetic polymorphisms at CYP1A1, GSTM1 and GSTT1 loci among Indians: tobacco exposure as a risk modulator. Carcinogenesis 28, 1455–1462. Androutsopoulos, V.P., Tsatsakis, A.M., Spandidos, D.A., 2009. Cytochrome P450 CYP1A1: wider roles in cancer progression and prevention. BMC Cancer 9, 187. Ashley-Martin, J., VanLeeuwen, J., Cribb, A., Andreou, P., Guernsey, J.R., 2012. Breast cancer risk, fungicide exposure and CYP1A1*2A gene–environment interactions in a province-wide case–control study in Prince Edward Island, Canada. Int. J. Environ. Res. Public Health 9, 1846–1858. Ashton, K.A., et al., 2010. Polymorphisms in genes of the steroid hormone biosynthesis and metabolism pathways and endometrial cancer risk. Cancer Epidemiol. 34, 328–337. Aydin-Sayitoglu, M., Hatirnaz, O., Erensoy, N., Ozbek, U., 2006. Role of CYP2D6, CYP1A1, CYP2E1, GSTT1, and GSTM1 genes in the susceptibility to acute leukemias. Am. J. Hematol. 81, 162–170.

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Association between the CYP1A1 A2455G polymorphism and risk of cancer: evidence from 272 case-control studies.

Association between the COMT Val158Met polymorphism and risk of cancer: evidence from 99 case-control studies.

Association between the XRCC1 Arg399Gln polymorphism and risk of cancer: evidence from 297 case-control studies.

Association between the OGG1 Ser326Cys Polymorphism and Cancer Risk: Evidence from 152 Case-Control Studies.

Association between the PARP1 Val762Ala polymorphism and cancer risk: evidence from 43 studies.

Association between GSTP1 Ile105Val polymorphism and urinary system cancer risk: evidence from 51 studies.

Association between CYP1A1 Ile462Val Polymorphism and Oral Squamous Cell Carcinoma Susceptibility: Evidence from 13 Investigations.

The association between RFC1 G80A polymorphism and cancer susceptibility: Evidence from 33 studies.

The association of the CYP1A1 Ile462Val polymorphism with head and neck cancer risk: evidence based on a cumulative meta-analysis.

Association between TERT rs2736100 polymorphism and lung cancer susceptibility: evidence from 22 case-control studies.

Association between CD95L polymorphism and cervical cancer risk: evidence from a meta-analysis.

Association between BHMT gene rs3733890 polymorphism and cancer risk: evidence from a meta-analysis.

Association between BRCA1 P871L polymorphism and cancer risk: evidence from a meta-analysis.

CYP1A1 MspI polymorphism and the risk of oral squamous cell carcinoma: Evidence from a meta-analysis.

Association between dietary nitrate and nitrite intake and sitespecific cancer risk: evidence from observational studies.

CYP1A1 Ile462Val polymorphism and colorectal cancer risk in Polish patients.

Lack of association between the PIN1 promoter -667T>C (rs2233679) polymorphism and cancer risk: Evidence from meta-analysis.

The association between RAD23B Ala249Val polymorphism and cancer susceptibility: evidence from a meta-analysis.

The association between alcohol and breast cancer risk: evidence from the combined analysis of six dietary case-control studies.

Association between NQO1 C609T polymorphism and prostate cancer risk.

Association between IRS-1 Gly972Arg polymorphism and colorectal cancer risk.

Association between 3801T>C polymorphism of CYP1A1 and idiopathic male infertility risk: a systematic review and meta-analysis.

Genetic association of NQO1 609C>T polymorphism with risk of gastrointestinal cancer: evidence from case-control studies.

Genetic association between hOGG1 C8069G polymorphism and colorectal cancer risk.