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Research Papers:

Two common functional catalase gene polymorphisms (rs1001179 and rs794316) and cancer susceptibility: evidence from 14,942 cancer cases and 43,285 controls

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Oncotarget. 2016; 7:62954-62965. https://doi.org/10.18632/oncotarget.10617

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Kang Liu, Xinghan Liu, Meng Wang, Xijing Wang, Huafeng Kang, Shuai Lin, Pengtao Yang, Cong Dai, Peng Xu, Shanli Li and Zhijun Dai _

Abstract

Kang Liu1,*, Xinghan Liu1,*, Meng Wang1,*, Xijing Wang1, Huafeng Kang1, Shuai Lin1, Pengtao Yang1, Cong Dai1, Peng Xu1, Shanli Li1 and Zhijun Dai1

1 Department of Oncology, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

* These authors have contributed equally to this work

Correspondence to:

Zhijun Dai, email:

Keywords: catalase, polymorphism, cancer, susceptibility, meta-analysis

Received: May 12, 2016 Accepted: July 01, 2016 Published: July 15, 2016

Abstract

Recent studies have focused on the associations of catalase polymorphisms with various types of cancer, including cervical and prostate cancers. However, the results were inconsistent. To obtain a more reliable conclusion, we evaluated the relationship between the two common catalase gene polymorphisms (rs1001179 and rs794316) and cancer risk by a meta-analysis. Our meta-analysis included 37 published studies involving 14,942 cancer patients and 43,285 cancer-free controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the cancer risk. The results demonstrated that the rs1001179 polymorphism was associated with an increased cancer risk in the recessive and homozygote models (TT vs. CC: OR = 1.19, P = 0.01; TT vs. CT+CC: OR = 1.19, P <0.001). Furthermore, stratified analyses revealed a significant association between the rs1001179 polymorphism and prostate cancer in all models except the homozygote comparison. An association of the rs794316 polymorphism with cancer risk was detected in two genetic models (TT vs. AA: OR = 1.34, 95% CI = 1.03–1.74, P <0.001; TT vs. AT+AA: OR = 1.39, 95% CI = 1.09–1.77, P = 0.01). Additional well-designed studies with large samples should be performed to validate our results.


INTRODUCTION

Worldwide, cancer is currently the main cause of death and a public health problem that seriously threatens human health [1]. Biological and epidemiological studies have shown that carcinogenesis is a sophisticated, multivariate process resulting from interactions between genetic and environmental factors [2]. However, the exact mechanism of carcinogenesis has not been fully elucidated. Many aspects of malignant cancers, including carcinogenesis, aberrant growth, metastasis, and angiogenesis, have been attributed to reactive oxygen species (ROS) [3]. Such ROS-mediated damage to cellular macromolecules is thought to accumulate as a function of age, thus promoting carcinogenesis [4, 5].

Catalase (CAT) is an important endogenous antioxidant enzyme that decomposes hydrogen peroxide to oxygen and water, thus limiting the deleterious effects of ROS[6]; accordingly, the CAT gene may play an important role in substance metabolism. CAT is located on the nuclear chromosome 11p13, and polymorphisms in this gene have been reported to associate with the development of many types of cancer, such as invasive cervical cancer and prostate cancer [7].

The rs1001179 polymorphism (C-262T) is located in the promoter region of CAT, where it influences transcription factor binding and alters the basal transcription and consequent expression of the encoded enzyme [8]. The rs794316 polymorphism (A-15 T) has been identified in the promoter region near the CAT start site, and the endogenous variability of this promoter likely plays a role in the host response to oxidative stress [9]. A large number of previous studies in humans have suggested a possible correlation between genetic polymorphisms of CAT and susceptibility to cancers, such as prostate cancer [10-14], breast cancer [15], and hepatocellular carcinoma [16-19]. However, those studies published inconsistent results. Accordingly, we conducted a meta-analysis to combine data from all of the available case-control studies in order to validate the association of CAT polymorphisms with cancer risk.

RESULTS

Characteristics of included studies

A flow chart of the study selection process is shown in Figure 1. Initially, 374 articles were identified. After reading the titles and abstracts of all the articles, 310 were excluded (164 articles were not related to cancer patients, 137 articles were not case-control studies and 9 articles were about other polymorphisms). After searching through the full texts of the remaining articles, an additional 15 were excluded, including 9 articles that contained no useful data and 6 articles that had re-reported data. Finally, a total of 37 studies from 29 published articles, involving 14,942 cases and 43,285 cancer-free controls, were included in this meta-analysis. The eligible studies presented data for several different cancer types, including prostate cancer, hepatocellular carcinoma, breast cancer, and colorectal cancer. Among these studies, 10 were based on Asian populations [9, 13, 15-17, 20-22], 20 on Caucasian populations [7, 10, 11, 14, 18, 23-33], 1 on an African population [14], and 6 on mixed-ethnicity populations [12, 19, 31, 34-36]. Furthermore, in 3 studies, the genotype distributions of the control groups departed from Hardy-Weinberg equilibrium (HWE) [7, 10, 20]. The characteristics of the eligible studies are presented in Table 1.

Table 1: Characteristics of the studies included in the meta-analysis

First author

Year

Country

Ethnicity

Genotyping medthod

Source of

control

Cancer type

Total sample size (case/control)

HWE

SNP

Sousa

2016

Brazil

Mixed

Taqman

hospital

HCC

106/139

0.44

rs1001179

Castaldo

2015

Portugal

Caucasian

PCR

population

CC

119/106

0.00

rs1001179

Geybels

2015

Netherland

Caucasian

PCR

population

PC

1529/25184

0.00

rs1001179

Liu

2015

China

Asian

PCR-RFLP

hospital

HCC

266/248

0.68

rs1001179

Saadat

2015

Iran

Caucasian

PCR

population

BC

407/395

0.40

rs1001179

Su-1

2015

China

Asian

PCR-RFLP

hospital

HCC

301/186

0.49

rs1001179

Su-2

2015

China

Asian

PCR-RFLP

hospital

HCC

99/294

0.83

rs1001179

Banescu

2014

Romania

Caucasian

PCR-RFLP

population

CML

168/321

0.47

rs1001179

Aynali

2013

Turkey

Caucasian

PCR-RFLP

hospital

Laryngeal cancer

25/23

0.13

rs1001179

Tefik

2013

Turkey

Caucasian

PCR-RFLP

population

PC

155/195

0.07

rs1001179

Ding

2012

China

Asian

PCR

population

PC

1417/1008

0.86

rs1001179

Farawela

2012

Egypt

Caucasian

PCR-RFLP

population

NHL

100/100

0.49

rs1001179

Karunasinghe

2012

New Zealand

Mixed

Taqman

population

PC

258/567

0.42

rs1001179

Tsai

2012

Taiwan

Asian

PCR

hospital

BC

260/224

0.44

rs1001179

Chang

2012

China

Asian

PCR-RFLP

population

CRC

880/848

0.00

rs794316

Nahon

2011

France

Caucasian

Taqman

hospital

HCC

84/55

0.62

rs1001179

Ezzikouri

2010

France

Mixed

PCR-RFLP

population

HCC

96/222

0.59

rs1001179

He-1

2010

USA

Caucasian

Taqman

population

BCC

270/796

0.89

rs1001179

He-2

2010

USA

Caucasian

Taqman

population

Melanoma

211/796

0.89

rs1001179

He-3

2010

USA

Caucasian

Taqman

population

SCC

266/796

0.89

rs1001179

Tang

2010

USA

Mixed

Taqman

population

Pancreatic cancer

551/602

0.97

rs1001179

Wu

2010

Taiwan

Asian

PCR-RFLP

hospital

OCC

122/122

0.18

rs794316

Funke

2009

Germany

Caucasian

PCR

population

CRC

632/605

0.11

rs1001179

Li

2009

USA

Caucasian

Taqman

population

BC

497/493

1.00

rs1001179

Quick-1

2008

USA

Caucasian

HM L/I MS

population

BC

569/974

0.70

rs1001179

Quick-2

2008

USA

Mixed

HM L/I MS

population

BC

47/108

0.22

rs1001179

Rajaraman-1

2008

USA

Caucasian

Taqman

hospital

Glioma

330/438

0.57

rs1001179

Rajaraman-2

2008

USA

Caucasian

Taqman

hospital

Meningioma

120/438

0.57

rs1001179

Rajaraman-3

2008

USA

Caucasian

Taqman

hospital

Acoustic neuroma

63/438

0.57

rs1001179

Choi-1

2007

USA

Caucasian

HM L/I MS

population

PC

463/1233

0.26

rs1001179

Choi-2

2007

USA

African

HM L/I MS

population

PC

27/120

0.60

rs1001179

Cebrian

2006

UK

Caucasian

Taqman

population

BC

2171/2262

0.96

rs1001179

Ho

2006

China

Asian

PCR-RFLP

hospital

LC

230/240

0.44

rs1001179

Lightfoot

2006

USA/UK

Mixed

Taqman

population

NHL

909/1437

0.96

rs1001179

Ahn

2005

USA

Caucasian

HM L/I MS

population

BC

1008/1056

0.93

rs1001179

Lee-1

2002

South Korea

Asian

PCR-RFLP

population

GC

80/108

0.47

rs794316

Lee-2

2002

South Korea

Asian

PCR-RFLP

population

HCC

106/108

0.47

rs794316

PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; HM L/I MS: high-throughput, matrixassisted, laser desorption/ionization time-of-flight mass spectrometry; HCC: hepatocellular carcinoma; CC: cervical cancer; BC: breast cancer; CML: chronic myeloid leukemia; NHL: non-Hodgkin lymphoma; BCC: basal cell carcinoma; SCC: squamous cell carcinoma; PC: Prostate cancer; CRC: colorectal cancer; OCC: Oral cavity cancer; GC: gastric cancer; LC: lung cancer; SNP: single-nucleotide polymorphisms; HWE: Hardy-Weinberg equilibrium.

Flow diagram of included studies for the meta-analysis.

Figure 1: Flow diagram of included studies for the meta-analysis. CNKI = China National Knowledge Infrastructure.

Meta-analysis of CAT polymorphisms and cancer risk

As shown in Table 2, the minor allele frequencies varied widely among cancer patients across the eligible studies, ranging from 0.04 to 0.50 for rs1001179 polymorphism and 0.31 to 0.43 for rs794316 polymorphism. The average minor allele frequencies for these polymorphisms were 0.19 and 0.40, respectively.

Table 2: Genotype Distribution and Allele Frequency of CAT polymorphisms in Cases and Controls

First author

Genotype (N)

Allele frequency (N)

MAF

Case

Control

Case

Control

total

AA

AB

BB

total

AA

AB

BB

A

B

A

B

rs1001179

Sousa 2016

106

68

35

3

139

103

32

4

171

41

238

40

0.19

Castaldo 2015

119

58

25

36

106

65

27

14

141

97

157

55

0.41

Geybels 2015

1529

887

539

103

25184

15794

8108

1282

2313

745

39696

10672

0.24

Liu 2015

266

239

27

0

248

223

24

1

505

27

470

26

0.05

Saadat 2015

407

261

129

17

395

240

132

23

651

163

612

178

0.20

Su-1 2015

301

273

27

1

186

168

18

0

573

29

354

18

0.05

Su-2 2015

99

92

7

0

294

264

29

1

191

7

557

31

0.04

Banescu 2014

168

105

49

14

321

168

132

21

259

77

468

174

0.23

Aynali 2013

25

13

10

2

23

12

11

0

36

14

35

11

0.28

Tefik 2013

155

58

64

33

195

107

68

20

180

130

282

108

0.42

Ding 2012

1417

1316

99

2

1008

940

67

1

2731

103

1947

69

0.04

Farawela 2012

100

26

49

25

100

28

53

19

101

99

109

91

0.50

Karunasinghe 2012

258

144

99

15

567

350

195

22

387

129

895

239

0.25

Tsai 2012

260

225

35

0

224

202

22

0

485

35

426

22

0.07

Nahon 2011

84

62

21

1

55

32

19

4

145

23

83

27

0.14

Ezzikouri 2010

96

76

14

6

222

173

45

4

166

26

391

53

0.14

He-1 2010

270

161

97

12

796

512

252

32

419

121

1276

316

0.22

He-2 2010

211

129

75

7

796

512

252

32

333

89

1276

316

0.21

He-3 2010

266

160

96

10

796

512

252

32

416

116

1276

316

0.22

Tang 2010

551

349

174

28

602

366

207

29

872

230

939

265

0.21

Funke 2009

632

374

235

23

605

348

231

26

983

281

927

283

0.22

Li 2009

497

295

176

26

493

303

167

23

766

228

773

213

0.23

Quick-1 2008

569

345

197

27

974

598

333

43

887

251

1529

419

0.22

Quick-2 2008

47

34

13

0

108

97

10

1

81

13

204

12

0.14

Rajaraman-1 2008

330

195

124

11

438

251

164

23

514

146

666

210

0.22

Rajaraman-2 2008

120

73

39

8

438

251

164

23

185

55

666

210

0.23

Rajaraman-3 2008

63

43

17

3

438

251

164

23

103

23

666

210

0.18

Choi-1 2007

463

281

157

25

1233

732

445

56

719

207

1909

557

0.22

Choi-2 2007

27

24

3

0

120

109

11

0

51

3

229

11

0.06

Cebrian 2006

2171

1351

707

113

2262

1362

787

113

3409

933

3511

1013

0.21

Ho 2006

230

209

19

2

240

217

23

0

437

23

457

23

0.05

Lightfoot 2006

909

554

298

57

1437

867

498

72

1406

412

2232

642

0.23

Ahn 2005

1008

614

349

45

1056

679

335

42

1577

439

1693

419

0.22

rs794316

Chang 2012

880

280

448

152

848

272

472

104

1008

752

1016

680

0.43

Wu 2010

122

57

55

10

122

62

54

6

169

75

178

66

0.31

Lee-1 2002

80

35

38

7

108

51

44

13

108

52

146

70

0.33

Lee-2 2002

106

51

42

13

108

51

44

13

144

68

146

70

0.32

A: the major allele; B: the minor allele; MAF: minor allele frequencies.

The main results of this meta-analysis are listed in Table 3. Thirty-three studies involving 13,754 cases and 42,099 controls were included for rs1001179. As shown in Table 3 and Figure 2, we observed an increased cancer risk associated with the rs1001179 polymorphism under the homozygote and recessive models (TT vs. CC: odds ratio [OR] = 1.19, 95% confidence interval [CI] = 1.04-1.37, P = 0.01; TT vs. CT+CC: OR = 1.19, 95% CI = 1.06- 1.34, P < 0.001.) In the cancer-specific analysis, the results showed significant correlations between the rs1001179 polymorphism and prostate cancer risk in different comparison models (T vs. C: OR = 1.21, 95% CI = 1.04-1.41, P = 0.02; TT vs. CC: OR = 1.57, 95% CI = 1.17-2.10, P = 0.00; TT+CT vs. CC: OR = 1.20, 95% CI = 1.01-1.42, P = 0.04; TT vs. CT+CC: OR = 1.40, 95% CI = 1.18-1.67, P < 0.001). However, no meaningful correlations were observed in analyses stratified by ethnicity or the source of controls.

Table 3: Meta-analysis of the association between CAT polymorphisms and cancer risk

Comparisons

OR

95%CI

P value

Heterogeneity

Effects model

I2

P value

B vs A

rs1001179

1.06

0.99-1.13

0.11

54%

0.00

R

HWE

1.04

0.97-1.11

0.28

39%

0.02

R

Caucasian

1.05

0.96-1.14

0.27

66%

0.00

R

Asian

1.05

0.86-1.29

0.64

0%

0.80

F

Mixed

1.10

0.92-1.32

0.29

54%

0.07

R

PC

1.21

1.04-1.41

0.02

61%

0.02

R

HCC

0.85

0.62-1.17

0.32

25%

0.25

F

BC

1.04

0.93-1.17

0.50

52%

0.05

R

rs794316

1.10

0.98-1.24

0.11

0%

0.88

F

HWE

1.06

0.84- 1.35

0.61

0%

0.76

F

BB vs AA

rs1001179

1.20

1.08-1.34

0.00

20%

0.16

F

HWE

1.12

1.00-1.27

0.05

0%

0.70

F

Caucasian

1.16

0.97-1.38

0.10

41%

0.03

R

Asian

1.37

0.37-5.14

0.64

0%

0.80

F

Mixed

1.29

0.98-1.68

0.07

0%

0.47

F

PC

1.57

1.17- 2.10

0.00

33%

0.20

F

HCC

0.88

0.20- 3.82

0.87

45%

0.12

F

BC

1.03

0.85- 1.25

0.75

0%

0.82

F

rs794316

1.34

1.03-1.74

0.00

0%

0.58

F

HWE

1.09

0.62-1.91

0.76

0%

0.52

F

AB vs AA

rs1001179

1.02

0.94- 1.09

0.68

39%

0.01

R

HWE

1.01

0.93- 1.09

0.82

35%

0.03

R

Caucasian

1.01

0.93- 1.11

0.76

47%

0.01

R

Asian

1.03

0.84- 1.28

0.77

0%

0.77

F

Mixed

1.05

0.80- 1.38

0.72

67%

0.02

R

PC

1.14

0.99- 1.31

0.06

33%

0.19

F

HCC

0.81

0.60- 1.09

0.17

0%

0.73

F

BC

1.07

0.91- 1.25

0.43

60%

0.02

R

rs794316

0.97

0.81- 1.16

0.74

0%

0.76

F

HWE

1.10

0.79- 1.52

0.59

0%

0.81

F

BB+AB vs AA

rs1001179

1.04

0.96- 1.12

0.33

48%

0.00

R

HWE

1.02

0.95- 1.11

0.54

39%

0.02

R

Caucasian

1.03

0.94- 1.14

0.50

59%

0.00

R

Asian

1.04

0.84- 1.29

0.70

0 %

0.79

F

Mixed

1.09

0.86- 1.38

0.49

62%

0.03

R

PC

1.20

1.01- 1.42

0.04

55%

0.05

R

HCC

0.83

0.62- 1.11

0.21

0%

0.56

F

BC

1.06

0.91- 1.23

0.44

59%

0.02

R

rs794316

1.04

0.87-1.23

0.68

0%

0.92

F

HWE

1.10

0.80- 1.49

0.57

0%

0.85

F

BB vs AB+AA

rs1001179

1.19

1.06- 1.34

0.00

10%

0.31

F

HWE

1.12

1.00- 1.27

0.05

0%

0.70

F

Caucasian

1.16

0.99- 1.35

0.06

29%

0.11

F

Asian

1.38

0.37- 5.18

0.63

0 %

0.80

F

Mixed

1.30

0.99- 1.70

0.05

0%

0.50

F

PC

1.40

1.18- 1.67

0.00

0%

0.48

F

HCC

0.95

0.23- 3.99

0.94

43%

0.14

F

BC

1.04

0.86- 1.25

0.70

0%

0.89

F

rs794316

1.39

1.09-1.77

0.01

0%

0.41

F

HWE

1.05

0.61- 1.79

0.87

0%

0.46

F

A: the major allele; B: the minor allele; F: fixed effects mode; R: random effects model; HCC: hepatocellular carcinoma; BC: breast cancer; PC: Prostate cancer; HWE: meta-analysis excluding the studies departing from HWE.

The association of the rs794316 polymorphism with cancer risk was investigated in 4 studies involving 1,188 cases and 1,186 controls. This polymorphism was associated with an increased cancer risk in the overall population under the two models (TT vs. AA: OR = 1.34, 95% CI = 1.03-1.74, P < 0.001; TT vs. AT+AA: OR = 1.39, 95% CI = 1.09-1.77, P = 0.01; Figure 3).

Table 4: Meta-regression analyses of potential source of heterogeneity

Heterogeneity

factors

Coefficient

SE

Z

P

95% CI

LL

UL

Sample size

0.047

0.042

1.12

0.273

-0.039

0.134

Publication year

0.026

0.014

1.77

0.088

-0.004

0.056

Ethnicity

0.146

0.159

0.92

0.368

-0.182

0.473

Genotype method

-0.023

0.054

-0.42

0.676

-0.135

0.089

Source of control

0.259

0.244

1.06

0.300

-0.244

0.761

SE: standard error; 95% CI: 95% confidence interval; LL: lower limit; UL: upper limit.

Forest plot of cancer risk related to rs1001179 polymorphism under TT versus CC genetic model.

Figure 2: Forest plot of cancer risk related to rs1001179 polymorphism under TT versus CC genetic model. T = the minor allele in rs1001179 polymorphism, C = the major allele in rs1001179 polymorphism, CI = confidence interval, OR = odds ratio.

Forest plot of cancer risk related to rs794316 polymorphism under TT versus AA genetic model.

Figure 3: Forest plot of cancer risk related to rs794316 polymorphism under TT versus AA genetic model. T = the minor allele in rs794316 polymorphism, A = the major allele in rs794316 polymorphism, CI = confidence interval, OR = odds ratio.

Heterogeneity analysis and publication bias

In this meta-analysis, Q-statistic test was used to detect between-study heterogeneity that arose from methodological or clinical dissimilarity across studies. When the P value of the heterogeneity test was more than 0.1 (P ≥0.1), a fixed-effects model was performed. Otherwise, the random-effects model was used. To explore the other factors which may influence our results, we performed a meta-regression analysis. As shown in the Table 4, sample size was not the factor which could be involved in cancer susceptibility (P = 0.134). Furthermore, the results revealed that the publication year, ethnicity, genotype method and the source of controls were all not the factors that could impact on our results (P = 0.088, 0.368, 0.676 and 0.300, respectively). We also performed a funnel plot and Egger’s test to assess publication bias. As shown in Figure 4, the funnel plots failed to reveal any obvious asymmetries of the 2 polymorphisms in the overall population, and the results of Egger’s test revealed no publication bias (P > 0.05). Therefore, the results revealed that publication bias was not significant in this meta-analysis.

Begg&#x2019;s funnel plot for publication bias test of CAT polymorphisms: rs1001179 (A), rs794316 (B), under the homozygous model.

Figure 4: Begg’s funnel plot for publication bias test of CAT polymorphisms: rs1001179 (A), rs794316 (B), under the homozygous model.

Sensitivity analysis

A single study was deleted one at a time from the meta-analysis to reflect the influence of each individual dataset on the pooled ORs. The analysis results demonstrated that no single study greatly influenced the overall cancer risk estimations with respect to the CAT polymorphisms (Figure 5), which indicates that our results are statistically robust.

Sensitivity analysis of the association between CAT rs1001179 polymorphism and cancer risk under the homozygous model.

Figure 5: Sensitivity analysis of the association between CAT rs1001179 polymorphism and cancer risk under the homozygous model.

DISCUSSION

Previous case-control studies have investigated the association between the rs1001179 polymorphism and cancer risk. No significant associations were observed between rs1001179 polymorphism and hepatocellular carcinoma or breast cancer risk in studies by Liu et al. [17] and Saadat et al. [23], respectively. However, Geybels et al. [10] and Castaldo et al.[7] reported significant associations between rs1001179 polymorphism and increased prostate and cervical cancer risks, respectively, and Nahon et al. [18] and Su et al. [16] demonstrated that rs1001179 polymorphism was a protective factor with respect to hepatocellular carcinoma susceptibility.

We combined all the case-control studies concerning rs1001179 polymorphism and cancer risk to perform this meta-analysis, and found that individuals harboring the rs1001179 TT and rs794316 TT genotypes had a higher cancer risk than did those with other genotypes. This is likely attributable to the relationship between rs1001179 polymorphism and lower CAT activity, which further hinders the response to oxidative stress and might lead to tumorigenesis [37, 38]. The stratified analysis results indicated that the CAT rs1001179 polymorphism was only associated with prostate cancer, but not other cancers. These results were in accordance with others’ findings. Geybels et al. observed that the CAT rs1001179 polymorphism was associated with the risk of stage III/IV prostate cancer, which might be explained by the effect of CAT expression on oxidative stress and the link between increased oxidative stress and prostate cancer.

A previous meta-analysis including 9,777 cancer patients and 12,223 controls showed significant association between rs1001179 polymorphism and cancer risk in the recessive model [39]. Compared with that meta-analysis, our meta-analysis included 11 new independent studies of hepatocellular carcinoma [16, 17, 22, 34], chronic myeloid leukemia [24], laryngeal cancer [25], colorectal cancer [20], and oral cavity cancer [9]. Different from the previous result, we observed an association between the rs1001179 polymorphism and an increased cancer risk in the homozygote model. And it is worth mentioning that we found an association of the rs794316 polymorphism with cancer risk in recessive model and homozygote model, which wasn’t detected by anyone before.

Because the control group genotype distributions departed from HWE in 3 studies, we performed a subgroup analysis that excluded those studies. Regarding the rs1001179 polymorphism, the result was remained consistent with the overall analysis; in other words, an association between an increased cancer risk and rs1001179 polymorphism was observed in recessive model and homozygote model. Nevertheless, we observed no significant association between the rs794316 polymorphism and cancer risk with any of the genetic models, although this might be a consequence of the small number of studies.

Several limitations of this meta-analysis should be acknowledged. First, only Asian population was involved in the analysis of rs794316, and most studies of rs1001179 are for Caucasian and Asian population. Accordingly, it would be better to include more studies with various ethnic groups to identify their definite roles in different populations. Second, some detailed information (e.g., sex, age, lifestyle, and environmental factors) was not considered. Third, the overall outcomes were based on individual unadjusted ORs, whereas a more precise evaluation should be adjusted using other potentially suspect factors. Fourth, the genotyping methods used in the eligible studies differed widely, which might have influenced the results. Moreover, although we have summarized all data on rs794316 polymorphism and cancer risk, the number of relative studies still needs further expansion.

In summary, this meta-analysis has shown associations of the CAT rs1001179 and rs794316 polymorphisms with an increased cancer risk. Additional larger-scale multicenter studies with larger sample sizes are needed to further validate the possible roles of these polymorphisms in cancers.

MATERIALS AND METHODS

Search strategy

The PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) databases were searched for publications from 2002 to January 2016 using the terms “cancer” or “tumor”, “CAT” or “Catalase”, “polymorphism” or “SNP”, “rs1001179” or “C-262T”, and “rs794316” or “A-15 T”. We also used the “Related Articles” option in PubMed to identify additional studies of the same topic. The reference lists of the retrieved articles were also screened. All included studies were selected using the following criteria: (a) studies must have featured a case-control design and focused on CAT polymorphism and cancer risk; (b) published data must have been sufficient to allow OR estimation with a 95% CI; and (c) for multiple publications reporting the same data or overlapping data, the largest or most recent publication was selected.

Data extraction

Initially, 2 investigators (Liu K and Liu XH) independently checked all potentially relevant studies, and disagreements were resolved through discussions with a third researcher. We extracted the following items from each article: first author, year of publication, country of origin, ethnicity, cancer types, control source, genotyping method, total numbers of cases and controls, and numbers of different genotypes among cases and controls. All data were extracted from published articles. All cancers were confirmed by histology or pathology. The non-cancer controls had no evidence of any malignant disease at the time of the study.

Statistical analysis

We used ORs and 95% CIs to evaluate the cancer risks associated with CAT polymorphisms. Heterogeneity between studies was evaluated using the I2 test, with a higher I2 value indicating a higher level of heterogeneity (I2 = 75-100%: extreme heterogeneity; I2 = 50-75%: great heterogeneity; I2 = 25-50%: moderate heterogeneity; I2 < 25%: no heterogeneity). During the heterogeneity evaluation, the fixed-effects model would be used if the P value was ≥0.10; otherwise, the random-effects model was used. Subgroup analyses were performed according to cancer type, control source, and ethnicity. A sensitivity analysis was performed to assess the stability of the final results by sequentially omitting each individual study at a time. Egger’s test and Begg’s test were adopted to assess publication bias. The meta-analysis assessed the following genetic models: dominant model (AB+BB vs. AA), recessive model (BB vs. AA + AB), homozygote comparison (BB vs. AA), heterozygote comparison (AB vs. AA), and allele comparison (B vs. A). All analyses were performed using the Stata software, version 12.0 (Stata Corp., College Station, TX, USA). A P value < 0.5 was considered statistically significant, and all P values were 2-sided.

CONFLICTS OF INTEREST

The authors have no conflicts of interest to declare.

GRANT SUPPORT

This study was supported by the National Natural Science Foundation, China (No. 81471670; 81274136); the China Postdoctoral Science Foundation (No. 2015T81037); the Fundamental Research Funds for the Central Universities, China (No. 2014qngz-04); and the specialized Research Fund of the Second Affiliated Hospital of Xi’an Jiaotong University, China [RC (GG) 201203].

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