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  • Primary research
  • Open Access

Analysis of the role of rs2031920 and rs3813867 polymorphisms within the cytochrome P450 2E1 gene in the risk of squamous cell carcinoma

Cancer Cell International201818:67

https://doi.org/10.1186/s12935-018-0561-8

  • Received: 14 March 2018
  • Accepted: 19 April 2018
  • Published:

Abstract

Background

To explore the genetic effect of rs2031920 and rs3813867 polymorphisms within the cytochrome P450 2E1 (CYP2E1) gene on the risk of squamous cell carcinoma (SCC), a meta-analysis was performed.

Methods

The eligible case–control studies were obtained by database searching and screening, and the specific statistical analysis was performed with STATA 12.0 software.

Results

After the process of database searching and screening, a total of 32 case–control studies with 7435 cases and 10,466 controls were ultimately included in our meta-analysis. With regard to the rs2031920 C/T polymorphism, in comparison to controls, a reduced risk in cases of esophageal squamous cell carcinoma (ESCC) was detected for the models of allele T vs. allele C [P = 0.025, odds ratio (OR) = 0.67], carrier T vs. carrier C (P = 0.014, OR = 0.70), TT vs. CC (P = 0.029, OR = 0.65), CT vs. CC (P = 0.040, OR = 0.56), CT + TT vs. CC (P = 0.035, OR = 0.58). Similarly, a decreased SCC risk was observed for the rs3813867 G/C polymorphism in the allele, carrier, homozygote, dominant, and recessive models of overall SCC meta-analysis and “ESCC” subgroup analysis (all P < 0.05, OR < 1) and in all genetic models of “Asian” and “population-based control (PB)” subgroup analysis (all P < 0.05, OR < 1). Additionally, for the rs2031920/rs3813867 haplotype, a decreased SCC risk was also detected in the overall SCC meta-analysis under the allele, carrier, homozygote and dominant model (all P < 0.05, OR < 1) and the subgroup analysis of “PB” under the allele, carrier, and dominant models (all P < 0.05, OR < 1).

Conclusions

Our meta-analysis supports the “T” allele carrier of the CYP2E1 rs2031920 C/T polymorphism and “C” allele carrier of the rs3813867 G/C polymorphism as protective factors for ESCC patients, especially in Asian populations.

Keywords

  • CYP2E1
  • SCC
  • Polymorphism
  • Risk

Background

The cytochrome P450 2E1 (CYP2E1) gene in Homo sapiens is located on chromosome 10 and is responsible for encoding a membrane-bound CYP2E1 protein, an important member of the human cytochrome P450 system [1]. The cytochrome P450 system works as a series of phase I enzymes participating in a group of biological events, such as drug metabolism, oxidative reactions, or the detoxification of endogenous and exogenous substances [2, 3]. Polymorphic variants, existing in the functional genes of the cytochrome P450 system, are associated with the pathogenesis of several clinical cancers [2, 3]. For example, rs2031920 C/T with an RsaI restriction enzyme site and rs3813867 C/T with a PstI restriction enzyme site are two common single nucleotide polymorphisms (SNP) within the 5′-flanking regions of the CYP2E1 gene [46]. Three genotypes of c1/c1, c1/c2, c2/c2 were generated; rs2031920 and rs3813867 were in close linkage disequilibrium [46]. Furthermore, CYP2E1 polymorphisms were reported to be linked to several cancers, such as nasopharyngeal carcinoma [7], urinary cancers [6] and head and neck carcinoma [5], particularly in Asian populations.

Squamous cell carcinoma (SCC) is the most common histological type of several clinical cancers, such as head and neck cancer, esophageal cancer, skin cancer, lung cancer, and cervical cancer [8, 9]. The exact pathogenesis of SCC remains unclear. Living habits (e.g., smoking, drinking), viral infection [e.g., human papillomavirus (HPV)], immune system, and polymorphic variants with many genes may be related to the risk of different SCC diseases [1012]. Previously, we conducted an updated meta-analysis to explore the impact of MDM2 (MDM2 Proto-Oncogene) polymorphisms on SCC susceptibility and found that the GG genotype of MDM2 rs2279744 polymorphism may be associated with an increased risk of esophageal SCC in Asian populations [8].

We observed a different conclusion regarding the role of rs2031920 and rs3813867 polymorphisms within the CYP2E1 gene in the risk of SCC. Thus, we are very interested in investigating the role of the rs2031920 and rs3813867 polymorphisms within the CYP2E1 gene in the susceptibility to SCC, considering the lack of publications of specific meta-analyses. We included a total of 32 case–control studies in our meta-analysis, which followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) [13].

Methods

Database searching and screening

Five electronic databases, including PubMed, Web of Science, Cochrane, Scopus and Chinese National Knowledge Infrastructure (CNKI), were searched prior to January 2018. We used a group of keywords: Carcinoma, Squamous Cell; Carcinomas, Squamous Cell; Squamous Cell Carcinomas; Squamous Cell Carcinoma; Carcinoma, Squamous; Carcinomas, Squamous; Squamous Carcinoma; Squamous Carcinomas; Carcinoma, Epidermoid; Carcinomas, Epidermoid; Epidermoid Carcinoma; Epidermoid Carcinomas; Carcinoma, Planocellular; Carcinomas, Planocellular; Planocellular Carcinoma; Planocellular Carcinomas; esophageal squamous cell carcinoma head and neck squamous cell carcinoma; lung squamous cell carcinoma; skin squamous cell carcinoma; oral squamous cell carcinoma; cervix squamous cell carcinoma; vagina squamous cell carcinoma; SCC; ESCC; HNSCC; LSCC; SSCC; OSCC; Cytochrome P-450 CYP2E1; Cytochrome P 450 CYP2E1; Cytochrome P-450-J; Cytochrome P 450 J; 4-Nitrophenol-2-Hydroxylase; 4 Nitrophenol 2 Hydroxylase; Dimethylnitrosamine N-Demethylase; Dimethylnitrosamine N Demethylase; Cytochrome P450 2E1; N-Nitrosodimethylamine Demethylase; N Nitrosodimethylamine Demethylase; CYP2E1; Cytochrome P-450 IIE1; Cytochrome P 450 IIE1; CYP IIE1; CYPIIE1; Cytochrome P-450 (ALC).

The retrieved studies were then reviewed and screened with the following exclusion criteria: (1) data based on animal experiments; (2) case reports, cohort studies or meeting abstracts; (3) without SNP data; (4) meta-analyses or reviews; (5) no SCC or CYP2E1 data; (6) duplicate studies; (7) no pathological typing data; (8) no genotype data. The data of genotype frequencies in cases and controls must have been provided in the selected studies.

Characteristics and quality assessment

Based on the eligible articles, the authors extracted and summarized the usable information, including the first author’s name, year, country, race, SNP, genotype frequency, SCC type, control source, genotyping assay, and HWE (Hardy–Weinberg equilibrium), in a table. The Newcastle–Ottawa Scale (NOS) system was also used to assess the methodological quality of individual studies. Only the studies with NOS score > 5 were ultimately included in our meta-analysis.

Heterogeneity and association test

STATA software (Stata Corporation, College Station, TX, USA) was used for our heterogeneity and association tests. In the case of heterogeneity, the P value of Cochran’s Q statistic < 0.05 or I2 value > 50% were considered to represent high heterogeneity among studies, which led to the use of a random effects model (DerSimonian and Laird method). Otherwise, the fixed effects model (Mantel–Haenszel statistics) was used. In the association test, odds ratio (OR), 95% confidence interval (CI) and P value were computed to assess the association strength in the allele, carrier, homozygote, heterozygote, dominant, and recessive models. In addition, based on the factors of race, SCC type, control source and HWE, a series of subgroup analyses were performed as well.

Publication bias and sensitivity analysis

Begg’s test and Egger’s test were used to assess the potential publication bias. A P value larger than 0.05 indicated the absence of potential publication bias. In addition, sensitivity analysis was used to evaluate the data stability and possible sources of heterogeneity.

Results

Process for identifying eligible studies

After our initial database retrieval, a total of 393 records [PubMed (n = 89), Web of Science (n = 161), Cochrane (n = 1), Scopus (n = 116) and CNKI (n = 26)] were obtained, as presented in Fig. 1. Then, 113 duplicate records were excluded. Based on the exclusion criteria, 223 records were removed. Moreover, the lack of confirmed pathological typing data or genotype frequency distribution resulted in the exclusion of another 25 articles. Finally, our meta-analysis involved a total of 32 articles [1445] containing 7435 cases and 10,466 controls. The characteristics of each study are presented in Table 1. No study had poor quality; the NOS score of all studies was greater than five (Table 1).
Fig. 1
Fig. 1

The process for identifying eligible studies

Table 1

Characteristics of each study included in the meta-analysis

First author

Year

Country

Race

NOS

SNP

Case

Assay

Control

AA/AB/BB

Type

AA/AB/BB

Source

HWE

Balaji

2011

India

Mixed

8

rs3813867

151/6/0

HNSCC

TaqMan allelic discrimination

125/7/0

PB

Y

     

rs2031920

151/6/0

HNSCC

TaqMan allelic discrimination

125/7/0

PB

Y

     

rs2031920/rs3813867

151/6/0

HNSCC

TaqMan allelic discrimination

125/7/0

PB

Y

Bhat

2014

India

Asian

6

rs2031920

366/148/12

ESCC

PCR–RFLP

207/308/11

HB

N

Bouchardy

2000

France

Caucasian

7

rs2031920

109/11/1

HNSCC

PCR–RFLP

164/8/0

HB

Y

Brocic

2011

Serbia

Caucasian

9

rs2031920

105/13/5

HNSCC

PCR–RFLP

160/16/1

PB

Y

Cao

2014

China

Asian

7

rs3813867

143/44/2

LSCC

PCR–RFLP

340/168/18

HB

Y

Cury

2012

Brazil

Africa

7

rs3813867

160/141

HNSCC

PCR–RFLP

242/361

PB

Y

Ferreira

2006

Portugal

Caucasian

7

rs2031920

113/91

CSCC

PCR–RFLP

224/111

PB

Y

Gajecka

2005

Poland

Caucasian

6

rs2031920

279/9/0

HNSCC

PCR–RFLP

305/18/0

PB

Y

Gattas

2006

Brazil

Africa

6

rs3813867

90/13/0

HNSCC

PCR–RFLP

96/6/0

HB

Y

     

rs3813867

31/7/0a

HNSCC

PCR–RFLP

96/6/0

HB

Y

Gonzalez

1998

Spain

Caucasian

6

rs3813867

68/6/1

HNSCC

PCR–RFLP

179/21/0

PB

Y

Guo

2012

China

Asian

8

rs2031920

195/1252

HNSCC

PCR–RFLP

254/662

PB

NR

Guo

2008

China

Asian

8

rs2031920/rs3813867

57/16/7

ESCC

PCR–RFLP

225/180/75

PB

N

Huang

2000

China

Asian

8

rs2031920

10/13/1

LSCC

PCR–RFLP

152/101/7

PB

N

Le

1998

USA

Mixed

8

rs2031920

56/17/1

LSCC

PCR–RFLP

338/102/14

PB

Y

Lee

2006

Korea

Asian

6

rs2031920

30/37/6

LSCC

PCR–RFLP

90/89/12

HB

Y

Li, D

2005

South Africa

Mixed

8

rs2031920

184/5/0

ESCC

SSCP

191/7/0

PB

Y

     

rs3813867

184/5/0

ESCC

SSCP

187/11/0

PB

Y

Li

2008

China

Asian

7

rs2031920/rs3813867

39/311

LSCC

PCR–RFLP

83/691

PB

Y

Li, G

2005

USA

Caucasian

6

rs3813867

684/37/3

HNSCC

PCR–RFLP

1137/86/3

HB

Y

Li

2011

China

Asian

6

rs3813867

159/67/0

ESCC

PCR–RFLP

173/62/11

HB

Y

Li

2000

China

Asian

7

rs2031920

40/11/2

LSCC

PCR–RFLP

75/57/5

PB

Y

Liu

2007

China

Asian

8

rs2031920

34/33/10

ESCC

PCR–RFLP

45/29/5

PB

Y

Matthias

2003

Germany

Caucasian

6

rs2031920/rs3813867

307/18/1b

HNSCC

PCR–RFLP

165/10/0

HB

Y

     

rs2031920/rs3813867

35/3/0c

HNSCC

PCR–RFLP

165/10/0

HB

Y

Morita

1997

Japan

Asian

8

rs2031920/rs3813867

34/18/1

ESCC

PCR–RFLP

85/42/5

PB

Y

Neuhaus

2004

Germany

Caucasian

6

rs2031920

304/8/0

HNSCC

PCR

282/13/2

PB

N

Nishino

2008

Japan

Asian

6

rs2031920

74/44/6

CSCC

PCR–RFLP

68/42/7

PB

Y

Oyama

2002

Japan

Asian

6

rs2031920

40/8/5

LSCC

PCR–RFLP

391/196/25

PB

Y

Pandey

2012

India

Caucasian

7

rs2031920

47/31

HNSCC

PCR–RFLP

35/151

PB

NR

Ruwali

2010

India

Asian

6

rs2031920

327/231

HNSCC

NR

343/71

PB

NR

Soya

2008

India

Asian

7

rs2031920/rs3813867

394/141

HNSCC

PCR–RFLP

212/81

HB

Y

Tai

2010

China

Asian

9

rs2031920

184/81/13

HNSCC

PCR–RFLP

182/84/12

PB

Y

Tan

2010

China

Asian

9

rs2031920

107/31/12

ESCC

PCR–RFLP

66/77/7

PB

N

Wang

2012

China

Asian

8

rs3813867

156/74/10d

ESCC

Gel-based DNA microarray

131/94/20

PB

Y

     

rs3813867

149/85/8e

ESCC

Gel-based DNA microarray

109/94/18

PB

Y

     

rs2031920

154/76/10d

ESCC

Gel-based DNA microarray

131/94/20

PB

Y

     

rs2031920

141/93/8e

ESCC

Gel-based DNA microarray

108/95/18

PB

Y

SNP single nucleotide polymorphisms, NOS Newcastle–Ottawa Scale, HNSCC head and neck squamous cell carcinoma, ESCC esophageal squamous cell carcinoma; LSCC lung squamous cell carcinoma, CSCC cervical squamous cell carcinoma, A major allele, B minor allele, PCR polymerase chain reaction, RFLP restriction fragment-length polymorphism, SSCP single-strand conformation polymorphism, NR not reported, PB population-based control, HB hospital-based control, HWE hardy–weinberg equilibrium, Y P value of HWE > 0.05, N P value of HWE > 0.05

1 The genotype frequencies of “AA/AB + BB”

2 The genotype frequencies of “AA + AB/BB”

a Data of oral squamous cell carcinoma

b Single HNSCC

c Multiple HNSCC

d Data from “Chaoshan” region

e Data from “Taihang” region

The rs2031920 polymorphism

A meta-analysis of rs2031920 and SCC risk was conducted on the allele model (allele T vs. allele C), carrier model (carrier T vs. carrier C), homozygote model (TT vs. CC), heterozygote model (CT vs. CC), dominant model (CT + TT vs. CC), and recessive model (TT vs. CC + CT). As shown in Table 2, 18 case–control studies were enrolled for the allele, carrier, heterozygote models, 15 case–control studies were enrolled for the homozygote model, 21 case–control studies were enrolled for the dominant model, and 16 case–control studies were enrolled for the recessive model. Pooling results suggested that there was no statistically significant difference for the overall SCC risk between the case and control groups under any model (Table 2, P value of association test > 0.05).
Table 2

Meta-analysis of CYP2E1 rs2031920 C/T polymorphism and SCC risk

Comparisons

Group

Number (study)

OR

95% CI

P (association)

Allele model (allele T vs. allele C)

All

18

0.84

0.67–1.06

0.144

 

Asian

11

0.80

0.61–1.05

0.106

 

Caucasian

4

1.04

0.46–2.37

0.929

 

HNSCC

6

0.99

0.62–1.59

0.971

 

ESCC

6

0.67

0.48–0.95

0.025

 

LSCC

5

0.94

0.67–1.32

0.722

 

PB

15

0.83

0.68–1.02

0.076

 

HB

3

1.00

0.38–2.58

0.994

 

Y

14

0.92

0.75–1.13

0.449

 

N

4

0.60

0.37–0.99

0.048

Carrier model (carrier T vs. carrier C)

All

18

0.83

0.69–1.01

0.064

 

Asian

11

0.80

0.60–1.00

0.053

 

Caucasian

4

0.99

0.49–1.99

0.982

 

HNSCC

6

0.96

0.65–1.43

0.849

 

ESCC

6

0.70

0.53–0.93

0.014

 

LSCC

5

0.92

0.68–1.25

0.602

 

PB

15

0.83

0.71–0.98

0.027

 

HB

3

0.98

0.44–2.16

0.955

 

Y

14

0.91

0.78–1.06

0.236

 

N

4

0.62

0.42–0.92

0.018

Homozygote model (TT vs. CC)

All

15

0.87

0.65–1.15

0.324

 

Asian

11

0.83

0.62–1.12

0.324

 

Caucasian

3

2.18

0.66–7.19

0.202

 

HNSCC

4

1.35

0.69–2.62

0.379

 

ESCC

5

0.65

0.44–0.96

0.029

 

LSCC

5

1.27

0.69–2.33

0.440

 

PB

12

0.85

0.62–1.17

0.316

 

HB

3

0.94

0.49–1.79

0.847

 

Y

11

0.90

0.65–1.24

0.522

 

N

4

0.76

0.42–1.38

0.371

Heterozygote model (CT vs. CC)

All

18

0.74

0.54–1.02

0.067

 

Asian

11

0.68

0.45–1.02

0.064

 

Caucasian

4

0.93

0.51–1.71

0.825

 

HNSCC

6

0.92

0.66–1.28

0.617

 

ESCC

6

0.56

0.32–0.97

0.040

 

LSCC

5

0.82

0.45–1.47

0.503

 

PB

15

0.73

0.56–0.96

0.024

 

HB

3

0.85

0.23–3.17

0.804

 

Y

14

0.85

0.69–1.05

0.139

 

N

4

0.48

0.23–1.01

0.054

Dominant model (CT + TT vs. CC)

All

21

0.81

0.60–1.11

0.189

 

Asian

12

0.80

0.54–1.19

0.263

 

Caucasian

6

0.85

0.42–1.71

0.644

 

HNSCC

8

0.95

0.56–1.62

0.844

 

ESCC

6

0.58

0.35–0.96

0.035

 

LSCC

5

0.87

0.53–1.44

0.591

 

PB

18

0.81

0.61–1.07

0.138

 

HB

3

0.89

0.24–3.33

0.864

 

Y

15

0.90

0.72–1.12

0.345

 

N

4

0.50

0.25–0.99

0.046

Recessive model (TT vs. CC + CT)

All

16

1.21

0.80–1.83

0.362

 

Asian

12

1.20

0.78–1.84

0.402

 

Caucasian

3

2.11

0.23–19.71

0.512

 

HNSCC

5

1.88

0.91–3.90

0.089

 

ESCC

5

0.91

0.47–1.74

0.770

 

LSCC

5

1.47

0.81–2.69

0.206

 

PB

13

1.18

0.72–1.94

0.514

 

HB

3

1.24

0.66–2.34

0.497

 

Y

11

1.05

0.65–1.71

0.829

 

N

4

1.26

0.70–2.28

0.438

OR odds ratio, CI confidence interval, HNSCC head and neck squamous cell carcinoma, ESCC esophageal squamous cell carcinoma, LSCC lung squamous cell carcinoma, PB population-based control, HB hospital-based control, Y P value of hardy–weinberg equilibrium > 0.05, N P value of hardy–weinberg equilibrium > 0.05

Moreover, we conducted a statistical analysis of the subgroup of race (Asian/Caucasian), SCC type (HNSCC/ESCC/LSCC), control source (PB/HB), and HWE (Y/N). As shown in Table 2, in comparison with controls, a reduced ESCC risk was observed in the models of allele T vs. allele C (P = 0.025, OR = 0.67), carrier T vs. carrier C (P = 0.014, OR = 0.70), TT vs. CC (P = 0.029, OR = 0.65), CT vs. CC (P = 0.040, OR = 0.56), CT + TT vs. CC (P = 0.035, OR = 0.58), but not TT vs. CC + CT (P = 0.770). Figure 2a shows forest plot data in subgroup analysis by SCC type under the allele model. The “T” allele carrier of the rs2031920 polymorphism within the CYP2E1 gene seems to be linked to ESCC risk.
Fig. 2
Fig. 2

Meta-analysis data of rs2031920 under the allele model. a Subgroup analysis according to the SCC type; b Egger’s test; c sensitivity analysis

The rs3813867 polymorphism

We also conducted the overall and subgroup meta-analysis of rs3813867 and SCC risk under the allele (10 case–control studies), carrier (10 case–control studies), homozygote (6 case–control studies), heterozygote (10 case–control studies), dominant (11 case–control studies), and recessive (6 case–control studies) models. The positive results regarding the association between CYP2E1 rs3813867 and SCC risk were detected in the overall SCC meta-analysis and subgroup analysis of “ESCC” and “Y” (P value of Hardy–Weinberg equilibrium > 0.05) under all genetic models (Table 3, all P < 0.05, OR < 1), only apart from the heterozygote model (P = 0.150). A decreased SCC risk was also detected in the subgroup analysis of “Asian” and “PB” under all genetic models (Table 3, all P < 0.05, OR < 1). Figure 3a shows the forest plot data of subgroup analysis by SCC type under the allele model. The “C” allele carrier of CYP2E1 rs3813867 polymorphism may be associated with the risk of SCC, especially the ESCC cases in Asian populations.
Table 3

Meta-analysis of CYP2E1 rs3813867 G/C polymorphism and SCC risk

Comparisons

Group

Number (study)

OR

95% CI

P (association)

Allele model (allele C vs. allele G)

All

10

0.72

0.63–0.83

< 0.001

 

Asian

4

0.67

0.57–0.78

< 0.001

 

HNSCC

5

0.97

0.73–1.30

0.863

 

ESCC

4

0.68

0.57–0.82

< 0.001

 

PB

5

0.65

0.53–0.79

< 0.001

 

HB

5

0.80

0.66–0.97

0.021

 

Y

10

0.72

0.63–0.83

< 0.001

Carrier model (carrier C vs. carrier G)

All

10

0.79

0.68–0.92

0.002

 

Asian

4

0.75

0.63–0.90

0.001

 

HNSCC

5

0.94

0.70–1.27

0.698

 

ESCC

4

0.77

0.63–0.93

0.008

 

PB

5

0.72

0.58–0.89

0.003

 

HB

5

0.85

0.70–1.05

0.133

 

Y

10

0.79

0.68–0.92

0.002

Homozygote model (CC vs. GG)

All

6

0.38

0.24–0.61

< 0.001

 

Asian

4

0.30

0.18–0.50

< 0.001

 

HNSCC

2

2.34

0.58–9.34

0.230

 

ESCC

3

0.30

0.17–0.53

< 0.001

 

PB

3

0.43

0.24–0.75

0.003

 

HB

3

0.30

0.12–0.74

0.009

 

Y

6

0.38

0.24–0.61

< 0.001

Heterozygote model (GC vs. GG)

All

10

0.82

0.63–107

0.150

 

Asian

4

0.75

0.56–0.99

0.045

 

HNSCC

5

1.15

0.62–2.16

0.657

 

ESCC

4

0.76

0.54–1.07

0.116

 

PB

5

0.66

0.51–0.84

0.001

 

HB

5

1.09

0.67–1.76

0.730

 

Y

10

0.82

0.63–107

0.150

Dominant model (GC + CC vs. GG)

All

11

0.76

0.60–0.97

0.024

 

Asian

4

0.68

0.54–0.86

0.002

 

HNSCC

6

1.01

0.62–1.65

0.961

 

ESCC

4

0.70

0.53–0.92

0.011

 

PB

6

0.62

0.50–0.77

< 0.001

 

HB

5

1.03

0.65–1.62

0.916

 

Y

11

0.76

0.60–0.97

0.024

Recessive model (CC vs. GG + GC)

All

6

0.43

0.27–0.68

< 0.001

 

Asian

4

0.34

0.20–0.57

< 0.001

 

HNSCC

2

2.39

0.60–9.53

0.218

 

ESCC

3

0.35

0.20–0.60

< 0.001

 

PB

3

0.49

0.28–0.86

0.013

 

HB

3

0.31

0.13–0.77

0.011

 

Y

6

0.43

0.27–0.68

< 0.001

OR odds ratio, CI confidence interval, HNSCC head and neck squamous cell carcinoma, ESCC esophageal squamous cell carcinoma, PB population-based control, HB hospital-based control, Y P value of hardy–weinberg equilibrium > 0.05

Fig. 3
Fig. 3

Meta-analysis data of rs3813867 under the allele model. a Subgroup analysis according to the SCC type; b Egger’s test; c sensitivity analysis

The rs2031920/rs3813867 haplotype

The results of overall and subgroup meta-analysis of the rs2031920/rs3813867 haplotype and SCC risk under the allele (five case–control studies), carrier (five studies), homozygote (three studies), heterozygote (five studies), dominant (seven studies), and recessive (three studies) models are shown in Table 4. We observed a decreased SCC risk in the overall SCC meta-analysis under the allele, carrier, homozygote, and dominant models (Table 4, all P < 0.05, OR < 1), and the subgroup analysis of “PB” under the allele, carrier, and dominant models (all P < 0.05, OR < 1). These results suggested a potential link between the c1/c2 or c2/c2 of rs2031920/rs3813867 haplotype and SCC risk, which still requires more case–control studies.
Table 4

Meta-analysis of CYP2E1 rs2031920/rs3813867 haplotype and SCC risk

Comparisons

Group

Number (study)

OR

95% CI

P (association)

Allele c2 vs. allele c1

All

5

0.65

0.49–0.86

0.003

 

HNSCC

3

1.01

0.57–1.78

0.977

 

PB

3

0.57

0.42–0.79

0.001

 

Y

4

0.98

0.65–1.46

0.913

Carrier c2 vs. carrier c1

All

5

0.73

0.53–1.00

0.047

 

HNSCC

3

0.98

0.55–1.75

0.945

 

PB

3

0.65

0.45–0.93

0.019

 

Y

4

0.98

0.64–1.50

0.938

c2c2 vs. c1c1

All

3

0.41

0.20–0.86

0.018

c1c2 vs. c1c1

All

5

0.75

0.43–1.30

0.309

 

HNSCC

3

0.96

0.53–1.71

0.877

 

PB

3

0.63

0.29–1.35

0.231

 

Y

4

1.00

0.64–1.56

0.990

c1c2 + c2c2 vs. c1c1

All

7

0.72

0.55–0.94

0.016

 

HNSCC

4

0.97

0.59–1.57

0.892

 

PB

4

0.64

0.47–0.87

0.005

 

Y

6

0.97

0.70–1.35

0.871

c2c2 vs. c1c1 + c1c2

All

3

0.55

0.26–1.13

0.103

OR odds ratio, CI confidence interval, HNSCC head and neck squamous cell carcinoma, PB population-based control, Y P value of hardy–weinberg equilibrium > 0.05

Heterogeneity evaluation

When assessing the heterogeneity level, the fixed model was used for the TT vs. CC model of rs2031920 due to the lack of high heterogeneity (Table 5, I2 = 38.3%, P value of heterogeneity = 0.066), however, the random model was utilized for others. The fixed model was used for the allele, carrier, homozygote and recessive models of rs3813867 (Table 5, all I2 < 50.0%, P value of heterogeneity > 0.05); and the allele, carrier, homozygote, dominant, and recessive models of the rs2031920/rs3813867 haplotype (Table 5, all I2 < 50.0%, P value of heterogeneity > 0.05).
Table 5

Heterogeneity test and publication analysis

SNP

Comparisons

I2 (%)

P (heterogeneity)

F/R

P (Begg’s test)

P (Egger’s test)

rs2031920 (C/T)

Allele T vs. allele C

77.2

< 0.001

R

0.649

0.054

 

Carrier T vs. carrier C

58.9

0.001

R

0.449

0.077

 

TT vs. CC

38.3

0.066

F

0.276

0.242

 

CT vs. CC

82.1

< 0.001

R

0.544

0.544

 

CT + TT vs. CC

83.1

< 0.001

R

0.608

0.037

 

TT vs. CC + CT

57.4

0.002

R

0.685

0.207

rs3813867 (G/C)

Allele C vs. allele G

46.1

0.054

F

0.074

0.072

 

Carrier C vs. carrier G

28.4

0.183

F

0.107

0.150

 

CC vs. GG

45.4

0.103

F

0.707

0.651

 

GC vs. GG

52.4

0.026

R

0.107

0.230

 

GC + CC vs. GG

47.3

0.041

R

0.062

0.150

 

CC vs. GG + GC

43.6

0.115

F

1.000

0.732

rs2031920 + rs3813867 (c1/c2)

Allele c2 vs. allele c1

49.8

0.093

F

1.000

0.184

 

Carrier c2 vs. carrier c1

15.5

0.316

F

0.806

0.245

 

c2c2 vs. c1c1

0.0

0.671

F

0.296

0.269

 

c1c2 vs. c1c1

53.1

0.074

R

0.806

0.327

 

c1c2 + c2c2 vs. c1c1

46.3

0.083

F

0.764

0.227

 

c2c2 vs. c1c1 + c1c2

0.0

0.792

F

0.296

0.501

SNP single nucleotide polymorphisms, F fixed, R random

Publication bias and sensitivity analysis

Begg’s and Egger’s tests did not provide confirmed evidence of obvious publication bias in the above comparisons (Table 5, all P value of Begg’s test and Egger’s test> 0.05) apart from the CT + TT vs. CC model of rs2031920 (P value of Egger’s test = 0.037). Figures 2b and 3b show the Egger’s publication bias plot of rs2031920 and rs3813867 under the allele model, respectively. Additionally, a relatively stable conclusion was obtained by sensitivity analysis results (Fig. 2c for allele model of rs2031920; Fig. 3c for allele model of rs3813867; data for others not shown).

Discussion

CYP2E1 rs2031920 was related to the risk of ESCC in a high-incidence region (Kashmir, India) [15]. Nevertheless, negative results were also reported in one study from South Africa [29] and in a Huai’an population from China [34]. Meta-analysis can address this conflicting issue. We did not observe published meta-analyses specific for the genetic relationship between CYP2E1 rs2031920, rs3813867 SNP and ESCC risk. In this study, we provide evidence that the “T” allele carrier of the rs2031920 polymorphism and the “C” allele carrier of the CYP2E1 rs3813867 polymorphism may be associated with a decreased risk of ESCC, especially in Asian populations because most of the included case–control studies were from China or India.

Tang et al. [46] selected 21 case–control studies for a meta-analysis in 2010 and investigated the potential effect of CYP2E1 rs2031920 and rs3813867 in the risk of head and neck cancer; they found that the homozygote genotype of CYP2E1 rs2031920/rs3813867 may be linked to the risk of head and neck cancer, especially in Asian populations. Zhuo et al. [5] performed another meta-analysis containing 43 case–control studies in 2016 and reported a positive association between CYP2E1 rs2031920/rs3813867 and head and neck cancer risk under the homozygote model. However, the subgroup analysis based of HNSCC was not performed in the two meta-analyses. In our meta-analysis, we failed to observe the statistical relationship between CYP2E1 rs2031920 SNP, rs3813867 SNP, rs2031920/rs3813867 haplotype and HNSCC risk.

Cao et al. [18] selected 17 case–control studies with 2639 cases and 3450 controls for a meta-analysis of the association between CYP2E1 rs3813867 and the risk of lung cancer in the Chinese population in 2014, and showed a potential link between the “C” allele carriers of CYP2E1 rs3813867 and a decreased risk of lung cancer. In our meta-analysis, very limited data were included after our strict selection; thus, no statistical evidence regarding the role of CYP2E1 rs3813867 in LSCC risk was provided. However, we enrolled five case–control studies [2628, 33, 39] in our subgroup analysis of “LSCC” for CYP2E1 rs2031920 and found a negative genetic relationship, which was partly in line with the previous data from LSCC subgroup analysis [47].

The close linkage disequilibrium between rs2031920 and rs3813867 for the CYP2E1 gene was reported [46]. For example, the same genotype frequency distribution was observed in case and control groups of south Indians [14]. However, we observed different genotype frequency distributions between case and control in some other reports [29, 45]. For example, in the Taihang regions of China, the genotype frequency of rs2031920 differs from that of rs3813867 in both the case and control groups [45]. In addition, most case–control studies only measured the single SNP. Thus, we performed a meta-analysis of rs2031920 and rs3813867, respectively; then, we analyzed the role of the rs2031920/rs3813867 haplotype based on the available data. We also conducted an overall and subgroup meta-analysis with four factors (race, SCC type, control source and HWE) under the allele, carrier, heterozygote and dominant models.

To enroll as many eligible case–control studies as possible, a search of five independent online databases (PubMed, Web of Science, Cochrane, Scopus and CNKI) was performed using the overall SCC terms and specific terms, such as ESCC, HNSCC, LSCC and SSCC. Based on our strict criteria, we removed the articles that contained the unconfirmed pathological typing information or failed to provide a genotype frequency distribution in both case and control studies. We observed the absence of large publication bias and the stability of data through Begg’s/Egger’s tests and sensitivity analyses.

Despite this, the shortcomings of the small sample size may still have affected our statistical power. Only one case–control study [38] was included in the “cervical SCC” subgroup analysis of rs2031920 under the allele, carrier, homozygote, heterozygote, and recessive models. Only one case–control study [18] was enrolled in the “lung SCC” subgroup analysis of rs3813867 under all genetic models. Only two studies [25, 36] were enrolled in the “ESCC” subgroup analysis of the rs2031920/rs3813867 haplotype.

In this study, we focused on the genetic role of two polymorphisms within the CYP2E1 gene in our meta-analysis, and we still cannot rule out the potential genetic effect of other CYP2E1 polymorphisms (e.g., rs6413432 T/A) and the variant combination between CYP2E1 and other related genes (e.g., MDM2).

For rs3813867, we did not observe obvious heterogeneity in the allele, carrier, homozygote and recessive models, only apart from the heterozygote model. Reduced heterogeneity levels were also observed in the ESCC subgroup analysis compared to the overall analysis. For example, in the allele model, a relatively high heterogeneity level in overall meta-analysis (P value of heterogeneity = 0.054, I2 = 46.1%) changed to a relatively lower heterogeneity level in the ESCC subgroup (P value of heterogeneity = 0.517, I2 = 0.0%). A slight reduction was also observed for the heterozygote model (P value of heterogeneity from 0.026 to 0.101, I2 value from 52.4 to 51.9%), even though significant between-study heterogeneity existed in the ESCC subgroup. We thus performed another meta-analysis, which only enrolled the available case–control studies of ESCC, and similar results were obtained (data not shown).

In addition, we observed remarkable heterogeneity for the allele, carrier, heterozygote, dominant and recessive modes of rs2031920. Even though a stable result was detected in the sensitivity analysis, and no decreased heterogeneity level was observed in the subgroup of ESCC compared with overall meta-analysis. This suggested that mixed factors contributed to the source of heterogeneity of specific ESCC subgroups. We tried to analyze the clinical characterizations, such as gender, age or concomitant pathologies, within the enrolled case–control studies. However, in the ESCC, only six eligible case–control studies were included in the ESCC subgroup, and the adjustment data was very limited for categorization. A larger sample size is required to conduct a more in-depth analysis.

Conclusions

In conclusion, our meta-analysis data demonstrated that the CYP2E1 rs2031920 and rs3813867 polymorphisms may be associated with the risk of ESCC. However, this conclusion should be confirmed with more extractable case–control studies.

Abbreviations

CYP2E1: 

cytochrome P450 2E1

SCC: 

squamous cell carcinoma

ESCC: 

esophageal squamous cell carcinoma

SNP: 

single nucleotide polymorphisms

HPV: 

human papillomavirus

MDM2: 

MDM2 Proto-Oncogene

PRISMA: 

preferred reporting items for systematic reviews and meta-analyses

CNKI: 

Chinese National Knowledge Infrastructure

HWE: 

Hardy–Weinberg equilibrium

NOS: 

Newcastle–Ottawa Scale

OR: 

odds ratio

CI: 

confidence interval

PB: 

population-based control

HB: 

hospital-based control

HNSCC: 

head and neck squamous cell carcinoma

LSCC: 

lung squamous cell carcinoma

CSCC: 

cervical squamous cell carcinoma

RFLP: 

restriction fragment-length polymorphism

SSCP: 

single-strand conformation polymorphism

Declarations

Authors’ contributions

HZ and HY designed the study. HZ, HL and HY extracted, analyzed, and interpreted the data. HZ and HY drafted the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank American Journal Experts (http://bit.ly/AJE-HS) for professional help with English usage in this manuscript.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

All data generated or analyzed during the present study are included in this published article.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

Not applicable.

Publisher’s Note

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Authors’ Affiliations

(1)
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin Huanhu Hospital, No 6, Ji Zhao Road, Jinnan District, Tianjin, 300060, People’s Republic of China

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