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  • Research article
  • Open Access
  • Open Peer Review

Association between cyclooxygenase-2 (COX-2) 8473 T > C polymorphism and cancer risk: a meta-analysis and trial sequential analysis

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  • 1Email author
BMC Cancer201818:847

https://doi.org/10.1186/s12885-018-4753-3

  • Received: 31 March 2018
  • Accepted: 14 August 2018
  • Published:
Open Peer Review reports

Abstract

Background

Numerous studies have investigated the relationship between COX-2 8473 T > C polymorphism and cancer susceptibility, however, the results remain controversial. Therefore, we carried out the present meta-analysis to obtain a more accurate assessment of this potential association.

Methods

In this meta-analysis, 79 case-control studies were included with a total of 38,634 cases and 55,206 controls. We searched all relevant articles published in PubMed, EMBASE, OVID, Web of Science, CNKI and Wanfang Data, till September 29, 2017. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the strength of the association. We performed subgroup analysis according to ethnicity, source of controls, genotyping method and cancer type. Moreover, Trial sequential analysis (TSA) was implemented to decrease the risk of type I error and estimate whether the current evidence of the results was sufficient and conclusive.

Results

Overall, our results indicated that 8473 T > C polymorphism was not associated with cancer susceptibility. However, stratified analysis showed that the polymorphism was associated with a statistically significant decreased risk for nasopharyngeal cancer and bladder cancer, but an increased risk for esophageal cancer and skin cancer. Interestingly, TSA demonstrated that the evidence of the result was sufficient in this study.

Conclusion

No significant association between COX-2 8473 T > C polymorphism and cancer risk was detected.

Keywords

  • COX-2 gene
  • 8473 T > C polymorphism
  • Cancer
  • Risk
  • Meta-analysis

Background

Currently, cancer is still considered as a global public health problem and the leading cause of human death [1], with an estimate of 14.1 million new cancer cases and 8.2 million cancer deaths in 2012 worldwide [2]. A large number of epidemiological and biological researches have demonstrated that cancer, as a multifactorial disease, is caused by a series of potential risk factors, including genetic and environmental factors [3]. However, the accurate mechanisms of carcinogenesis remained unclear. In recent years, many studies have pointed that the expression of tumor suppressor genes and oncogenes is closely associated with inflammation, which can also promote the transformation of cancer [46].

Cyclooxygenase-2 (COX-2), also called prostaglandin endoperoxide synthetase (PTGS-2), is an inducible isoform of COX enzyme that converts arachidonic acid to prostaglandins, and prostaglandins are generally regarded as the effective mediators of inflammation [7]. By producing prostaglandins, COX-2 is considered to participate in several biological processes, such as carcinogenesis, cell proliferation, angiogenesis and mediating immune suppression. More and more evidence has pointed that increased expression of COX-2 is closely associated with malignant progression [810]. In addition, it is also shown that carcinogenesis could be prevented by using selective COX-2 inhibitors [11]. The human COX-2 gene, with a length of 8.3 kb and consisting of 10 exons, is located on chromosome lq25.2-q25.3. Different polymorphism sites in the COX-2 gene have been clarified. One of these functional polymorphisms, the 8473 T > C polymorphism in the 3′-untranslated region (3’UTR) of COX-2 gene is the most widely investigated polymorphism.

Previous functional researches have indicated that 8473 T > C polymorphism is related to the alteration of the mRNA level of COX-2 gene via playing an important role in message stability and translational efficiency [12]. There are numerous case-control studies that have investigated the role of 8473 T > C polymorphism in cancer risk. However, the results of these studies remain inconclusive. Therefore, to draw a more precise conclusion, we conduct the present meta-analysis to evaluate the association of 8473 T > C polymorphism in COX-2 gene with cancer susceptibility.

Methods

Identification and eligibility of relevant studies

Literature in electronic databases, including PubMed, EMBASE, OVID and Web of Science, were systematically searched using the following terms: “cyclooxygenase-2 or COX-2 or PTGS2” and “polymorphism or variant or genotype” and “cancer or carcinoma or neoplasm”. To expand our investigation, we also searched China National Knowledge Infrastructure (CNKI) and Wanfang Data using the corresponding Chinese terms. Furthermore, references cited in each included study were also searched manually to identify potential additional relevant studies. When the information provided in the article was unclear, we contacted the author for detailed raw data. If data were overlapping, we adopted the most recent and comprehensive research for this meta-analysis. The last search date was September 29, 2017.

Inclusion and exclusion criteria

The inclusion criteria were as follows: studies investigating the association of COX-2 8473 T > C polymorphism with cancer risk; studies with essential information on genotype or allele frequencies to estimate ORs and 95% CIs; studies with human subjects; and case-controlled studies. Exclusion criteria included: reviews or meta-analyses; animal or cytology experiments; duplicate publications; studies not involving cancer; no controls, not according with Hardy-Weinberg equilibrium (PHWE < 0.05) in the control group, and studies published neither in English nor Chinese.

Data extraction

From all eligible publications, the following data, including the first author, year of publication, population ethnicity, country, source of controls, cancer type, detection genotype methods of COX-2 8473 T > C polymorphism, and number of cases and controls, were carefully extracted by two authors (Qiuping Li and Chao Ma) independently. Inconsistencies were resolved after discussion, and a consensus was reached for all extracted data.

Quality assessment

The quality of the included studies was evaluated using the Newcastle–Ottawa scale (NOS) [13] with eight items (Additional file 1: Table S1). We awarded a study a maximum of nine star scale based on selection (four stars maximum), comparability (two stars maximum) and exposure (three stars maximum). Studies with NOS scores of 1–3, 4–6 and 7–9 were considered as low-quality, medium-quality and high-quality studies, respectively. Medium-quality and high-quality studies were included in the present meta-analysis.

Statistical analysis

We analyzed the association of COX-2 8473 T > C polymorphism with cancer risk using Stata software (Version 11.0; StataCorp, College Station, TX). Cumulative ORs and the corresponding 95% CIs were employed to measure the strength of associations. All p values were two-sided, and p < 0.05 was considered as statistically significant. Heterogeneity was assessed using a Q statistic (considered significant heterogeneity among the studies if P value< 0.10) and an I-squared (I2) value [14]. When heterogeneity of studies was significant, the DerSimonian and Laird random-effects model [15] was performed to calculate the pooled ORs. Otherwise, the Mantel–Haenszel fixed-effects model was used [16]. We performed the sensitivity analysis to explore heterogeneity when significant heterogeneity was detected. Subgroup analysis was used to explore the effect of ethnicity, study design, cancer type and genotype method. Moreover, publication bias was evaluated quantitatively using Begg’s [17] and Egger’s [18] tests. Significant publication bias was indicated if P value< 0.05.

Trial sequential analysis

Type I errors may be caused by meta-analysis due to random error because of insufficient sample size in this meta-analysis. And the conclusions of the meta-analysis tended to be changed by later studies with a larger sample size [19]. When TSA was performed in a meta-analysis, both inadequate information size and false positive conclusions were revealed, and the above limitations were also overcome [19, 20]. Therefore, we used TSA software version 0.9 beta in this meta-analysis on the basis of two-sided tests, with an overall type I error risk of 5%, a statistical test power of 80%, and relative risk reduction of 10%. Trails were ignored in interim due to too low information to use (< 1.0%) by the TSA software. When the cumulative Z-curve in results crosses the TSA boundary or enters the insignificance area, a sufficient level of evidence has been reached, and no further studies are necessary. However, when the Z curve does not exceed any of the boundaries and the required sample size has not been reached, evidence to reach a conclusion is insufficient [21].

Results

Characteristics of the included studies

A detailed flow chart of included studies is shown in Fig. 1. A systematic search through five electronic databases yielded 652 citations after duplicate removal. After reviewing the titles, abstracts and full texts, articles that were not related with this analysis, meeting, animal or cytology experiments and reviews were removed, leading to the exclusion of 561 publications. The remaining 91 articles were further evaluated for eligibility. Finally, 65 full-text articles (79 studies) that met the inclusion criteria were included in the present meta-analysis.
Fig. 1
Fig. 1

Flow chart of literature search and study selection

The primary characteristics of the 79 included studies in this meta-analysis are summarized in Table 1. In our included studies, 38,634 cases and 55,206 controls surveyed the association between COX-2 8473 T > C polymorphism and cancer risk. Among these publications, there were 12 colorectal cancer [2231], 1 ampulla of vater (AV) cancer [32], 4 bladder cancer [3336], 13 breast cancer [3746], 2 cervical cancer [47, 48], 1 endometrial cancer [49], 4 esophageal cancer [5053], 1 extrahepatic bile duct (EHBD) cancer [32], 2 gallbladder cancer [32, 54], 4 gastric cancer [5558], 1 glioma [59], 2 hepatocellular cancer (HCC) [60, 61], 1 head and neck (HN) cancer [62], 2 laryngeal cancer [50, 63], 11 lung cancer [6474], 3 nasopharyngeal cancer [50, 75, 76], 3 oral cancer [50, 63, 77], 2 ovarian cancer [78], 1 pancreatic cancer [79], 6 prostate cancer [8083] and 3 skin cancer [8486]. Ethnic subgroups were divided into Asian, Caucasian, Australian and African. If it was difficult to distinguish the ethnicity of participants according to content included in the study, ethnicity of the study was termed “Mixed”. Study designs were categorized as PB and HB. The COX2 8473 T > C polymorphism was primarily detected by genotyping methods including TaqMan, PCR-RFLP and PCR-PIRA, in addition to the methods of SNPlex, SNP-IT, PCR-KASP, Invader, Illumina GoldenGate, Pyrosequencing and MassARRAY. We used subgroup analysis to search the effects of ethnicity, study design, genotype method and cancer type for the relationship of COX2 8473 T > C polymorphism with cancer risk.
Table 1

Characteristics of studies included in the meta-analysis

First author

Year

Ethnicity

Country

Control source

Cancer type

Genotype method

cases

controls

HWE

MAF

TT

TC

CC

TT

TC

CC

Cox, D.G.

2004

Caucasian

Spain

HB

colorectal

Invader

140

121

29

126

120

25

0.639

0.314

Campa, D.

2004

Caucasian

France

PB

lung

TaqMan

31

107

112

65

99

50

0.304

0.465

Hu, Z.

2005

Asian

China

HB

lung

PCR-PIRA

234

83

5

209

107

7

0.113

0.187

Sorensen, M.

2005

Caucasian

Denmark

PB

lung

TaqMan

127

111

18

115

126

27

0.377

0.336

Campa, D.

2005

Caucasian

France

PB

lung

TaqMan

855

886

224

805

904

228

0.285

0.351

Sakoda, L.C.

2006

Asian

China

PB

AV

TaqMan

30

11

4

541

216

21

0.920

0.166

Gallicchio, L.

2006

Mixed

USA

PB

breast

TaqMan

9

5

0

158

164

34

0.360

0.326

Gallicchio, L.

2006

Mixed

USA

PB

breast

TaqMan

29

26

11

396

416

95

0.353

0.334

Siezen, C.L.

2006

Caucasian

Netherlands

PB

colorectal

Pyrosequencing

97

83

20

190

163

35

0.996

0.300

Siezen, C.L.

2006

Caucasian

Netherlands

PB

colorectal

Pyrosequencing

216

171

55

339

281

73

0.198

0.308

Sakoda, L.C.

2006

Asian

China

PB

EHBD

TaqMan

70

51

5

541

216

21

0.920

0.166

Sakoda, L.C.

2006

Asian

China

PB

gallbladder

TaqMan

165

61

10

541

216

21

0.920

0.166

Park, J.M.

2006

Asian

Korea

HB

lung

PCR-PIRA

352

205

25

330

220

32

0.552

0.244

Shahedi, K.

2006

Caucasian

Sweden

PB

prostate

MassARRAY

571

618

158

306

363

88

0.208

0.356

Cox, D.G.

2007

Mixed

USA

PB

breast

TaqMan

541

567

141

699

808

213

0.383

0.359

Cox, D.G.

2007

Mixed

USA

PB

breast

TaqMan

140

131

30

270

259

81

0.134

0.345

Cox, D.G.

2007

Mixed

USA

PB

breast

TaqMan

281

296

67

278

294

79

0.925

0.347

Gao, J.

2007

Asian

China

HB

breast

PCR-RFLP

404

179

18

429

194

20

0.733

0.182

Vogel, U.

2007

Caucasian

Denmark

PB

breast

PCR-RFLP

167

150

44

155

165

41

0.770

0.342

Lee, T.S.

2007

Asian

Korea

HB

cervical

SNP-IT

115

52

8

101

50

2

0.124

0.176

Campa, D.

2007

Caucasian

France

PB

esophageal

TaqMan

64

84

11

389

377

87

0.756

0.323

Jiang, G.J.

2007

Asian

China

HB

gastric

PCR-PIRA

159

86

9

199

96

9

0.525

0.188

Hou, L.F.

2007

Caucasian

Poland

PB

gastric

TaqMan

137

132

35

165

202

49

0.279

0.361

Campa, D.

2007

Caucasian

France

PB

laryngeal

TaqMan

139

120

22

313

321

77

0.694

0.334

Campa, D.

2007

Caucasian

France

PB

nasopharyngeal

TaqMan

41

47

11

313

321

77

0.694

0.334

Campa, D.

2007

Caucasian

France

PB

oral

TaqMan

72

70

11

313

321

77

0.694

0.334

Cheng, I.

2007

African

USA

HB

prostate

TaqMan

12

39

38

11

49

29

0.162

0.601

Cheng, I.

2007

Caucasian

USA

HB

prostate

TaqMan

183

199

34

196

177

44

0.668

0.318

Lira, M.G.

2007

Caucasian

Italy

HB

skin

PCR-RFLP

44

47

12

64

51

15

0.330

0.312

Vogel, U.

2007

Caucasian

Denmark

PB

skin

TaqMan

123

140

41

145

148

22

0.054

0.305

Yang, H

2008

Mixed

USA

HB

bladder

SNPlex

279

268

76

236

312

85

0.255

0.381

Song, D.K.

2008

Asian

China

HB

bladder

PCR-PIRA

132

39

4

113

61

5

0.337

0.198

Ferguson, H.R.

2008

Caucasian

UK

HB

esophageal

TaqMan

73

106

30

111

113

24

0.537

0.325

Vogel, U.

2008

Caucasian

Denmark

PB

lung

PCR-RFLP

182

183

38

310

341

93

0.959

0.354

Danforth, K.N.

2008

Caucasian

USA

PB

prostate

TaqMan

488

515

143

641

605

137

0.741

0.318

Danforth, K.N.

2008

Caucasian

USA

PB

prostate

TaqMan

517

507

113

501

517

117

0.332

0.331

Abraham, J.E.

2009

Caucasian

UK

PB

breast

TaqMan

927

985

260

996

1010

259

0.903

0.337

Andersen, V

2009

Caucasian

Denmark

PB

colorectal

TaqMan

147

178

34

315

355

95

0.745

0.356

Gong, Z.H

2009

Mixed

USA

PB

colorectal

PCR-RFLP

64

70

28

69

109

33

0.351

0.415

Thompson, C.L.

2009

Caucasian

USA

PB

colorectal

TaqMan

176

189

56

216

199

65

0.081

0.343

Upadhyay, R.

2009

Asian

India

HB

esophageal

PCR-RFLP

63

89

22

81

102

33

0.924

0.389

Srivastava, K.

2009

Asian

India

HB

gallbladder

PCR-RFLP

51

91

25

67

88

29

0.991

0.397

Piranda, D.N.

2010

Mixed

Brazil

PB

breast

TaqMan

125

149

20

120

99

25

0.496

0.305

Dossus, L.

2010

Mixed

Germany

PB

breast

IGG

2697

2664

772

3512

3501

933

0.180

0.338

Pandey, S.

2010

Asian

India

HB

cervical

PCR-RFLP

104

90

6

102

82

16

0.932

0.285

Pereira, C.

2010

Caucasian

Portugal

HB

colorectal

TaqMan

54

51

10

118

114

24

0.638

0.316

Lurie, G.

2010

Mixed

USA

PB

ovarian

TaqMan

169

120

13

338

207

47

0.058

0.254

Lurie, G.

2010

Caucasian

USA

PB

ovarian

TaqMan

333

304

86

490

469

136

0.151

0.338

Gangwar, R.

2011

Asian

India

PB

bladder

PCR-RFLP

82

106

24

97

119

34

0.794

0.374

Brasky, T.M.

2011

Caucasian

USA

PB

breast

TaqMan

432

447

108

732

782

226

0.450

0.355

Akkiz, H.

2011

Caucasian

Turkey

HB

HCC

PCR-RFLP

65

56

8

58

62

9

0.161

0.310

Lim, W.Y.

2011

Asian

Singapore

HB

lung

TaqMan

182

100

15

462

228

28

0.984

0.198

Ozhan, G.

2011

Caucasian

Turkey

HB

pancreatic

PCR-RFLP

74

60

19

71

59

20

0.176

0.330

Mandal, R.K.

2011

Asian

India

HB

prostate

PCR-RFLP

71

86

38

105

113

32

0.853

0.354

Gomez, L.M.

2011

Caucasian

Italy

PB

skin

PCR-RFLP

56

65

17

56

50

18

0.221

0.347

Li, H.Z.

2012

Asian

China

PB

gastric

TaqMan

1048

534

67

1276

568

56

0.450

0.179

Guo, S.J

2012

Asian

China

HB

lung

PCR-RFLP

486

185

15

389

181

32

0.075

0.203

Fawzy, M.S.

2013

Caucasian

Egypt

HB

breast

PCR-RFLP

53

71

36

69

67

14

0.694

0.317

Andersen, V

2013

Caucasian

Denmark

PB

colorectal

PCR-KASP

430

404

97

720

815

203

0.228

0.351

Makar, K.W.

2013

Mixed

USA

PB

colorectal

IGG

851

920

232

1067

1149

333

0.392

0.356

Makar, K.W.

2013

Mixed

USA

PB

colorectal

IGG

552

582

157

887

940

258

0.713

0.349

Ruan, Y.F.

2013

Asian

China

HB

colorectal

PCR-PIRA

98

27

5

80

37

3

0.597

0.179

Song, H.L.

2013

Asian

China

HB

endometrial

PCR-RFLP

68

27

5

69

26

5

0.233

0.180

Lu, Y.J.

2013

Asian

China

HB

esophageal

PCR-RFLP

76

36

7

179

54

5

0.698

0.134

Chang, J.S.

2013

Asian

China

HB

HN

TaqMan

209

89

15

199

86

10

0.850

0.180

Qian, Q.

2014

Asian

China

HB

bladder

TaqMan

4

26

24

1

32

64

0.164

0.825

Gao, J.

2014

Asian

China

HB

breast

TaqMan

299

132

34

515

244

40

0.117

0.203

Vogel, L.K.

2014

Caucasian

Denmark

PB

colorectal

TaqMan

69

87

33

169

191

39

0.156

0.337

Shao, S.S.

2014

Asian

China

HB

HCC

PCR-RFLP

160

92

18

357

164

19

0.975

0.187

Niu, Y.

2014

Asian

China

PB

laryngeal

TaqMan

59

27

4

691

316

25

0.112

0.177

Bhat, I.A.

2014

Asian

India

HB

lung

PCR-RFLP

133

53

4

128

66

6

0.470

0.195

Lan, X.H.

2014

Asian

China

HB

oral

PCR-RFLP

35

14

2

65

32

10

0.053

0.243

Niu, Y.

2014

Asian

China

PB

oral

TaqMan

118

45

5

691

316

25

0.112

0.177

Gao, F.

2015

Asian

China

HB

gastric

TaqMan

171

100

13

193

77

4

0.232

0.155

Lin, R.P.

2015

Asian

China

HB

glioma

TaqMan

129

66

5

109

77

14

0.936

0.263

Cao, Q.

2015

Asian

China

HB

lung

PCR-RFLP

16

19

7

22

25

3

0.233

0.310

Mamoghli, T.

2015

Caucasian

Tunisia

HB

nasopharyngeal

PCR-RFLP

100

80

9

110

99

28

0.433

0.327

Wang, J.L.

2015

Asian

China

HB

nasopharyngeal

PCR-RFLP

139

129

28

110

149

41

0.398

0.385

Moraes, J.L.

2017

Mixed

Brazil

HB

lung

TaqMan

44

43

17

69

106

25

0.107

0.390

Abbreviations: HWE Hardy-Weinberg equilibrium, MAF minor allele frequecy, HB hospital based, PB population based, AV ampulla of vater, EHBD extrahepatic bile duct, HCC hepatocellular carcinoma, HN head and neck, PCR-RFLP polymorphism chain reaction restriction fragment length polymorphism, PCR-PIRA polymorphism chain reaction based primer-introduced restriction analysis, PCR-KASP polymorphism chain reaction based kompetitive allele specific, IGG Illumina GoldenGate

Meta-analysis

Overall analysis

The main results of our meta-analysis are listed in Table 2. The association between COX2 8473 T > C polymorphism and cancer risk was evaluated in five comparison models: homozygote comparison, heterozygote comparison, dominant model, recessive model and allele analysis. When the homozygote and heterozygote comparisons were carried out, no significant association was found (CC vs.TT: OR = 1.01, 95% CI = 0.93–1.11, p = 0.799; TC vs. TT: OR = 0.99, 95% CI = 0.95–1.03, p = 0.462). Furthermore, neither dominant nor recessive model discovered significant associations of 8473 T > C polymorphism with cancer risk ((CC + TC) vs. TT: OR = 0.99, 95% CI = 0.95–1.04, p = 0.644; CC vs. (TC + TT): OR = 1.01, 95%CI = 0.94–1.09, p = 0.779). The allele analysis also didn’t find significant association (C allele vs. T allele: OR = 1.00, 95% CI = 0.96–1.04, p = 0.921). Overall, the results of this meta-analysis showed no significant association between COX-2 8473 T > C polymorphism and cancer risk.
Table 2

Results of overall and stratifed meta-analysis

Genetic model

Group/subgroup

Studies

Heterogeneity test

Statistical model

Test for overall effect

I2 (%)

Phet

OR (95% CI)

P

CC vs. TT

Overall

79

57.4

0

R

1.01(0.93–1.11)

0.799

PB

42

58.6

0

R

1.01(0.92–1.11)

0.870

HB

37

57.3

0

R

1.01(0.83–1.23)

0.915

Asian

32

55.8

0

R

1.10(0.88–1.37)

0.403

Caucasian

33

65.9

0

R

1.03(0.90–1.18)

0.652

Taqman

41

63.9

0

R

1.08(0.94–1.23)

0.272

PCR-RFLP

23

60.4

0

R

0.94(0.74–1.20)

0.615

PCR-PIRA

5

0

0.802

F

0.83(0.56–1.23)

0.345

bladder cancer

4

13.1

0.327

F

0.74(0.55–0.99)

0.040

breast cancer

13

53.5

0.012

R

1.01(0.87–1.17)

0.939

cervical cancer

2

82.6

0.016

R

1.04(0.11–9.53)

0.971

colorectal cancer

12

17.7

0.270

F

0.95(0.86–1.06)

0.340

esophageal cancer

4

61.1

0.052

R

1.30(0.72–2.33)

0.390

gallbladder cancer

2

0

0.532

F

1.28(0.78–2.12)

0.326

gastric cancer

4

52.4

0.098

R

1.34(0.85–2.13)

0.210

HCC

2

59.9

0.114

F

1.54(0.88–2.70)

0.128

laryngeal cancer

2

67.3

0.080

R

0.98(0.35–2.75)

0.973

lung cancer

11

80.5

0

R

0.97(0.65–1.45)

0.883

nasopharyngeal cancer

3

56.1

0.103

F

0.59(0.40–0.86)

0.007

oral cancer

3

0

0.404

F

0.68(0.40–1.16)

0.158

ovarian cancer

2

51.6

0.151

F

0.84(0.64–1.10)

0.205

prostate cancer

6

42.8

0.120

F

1.10(0.95–1.28)

0.192

skin cancer

3

42.6

0.175

F

1.51(1.02–2.25)

0.041

TC vs. TT

Overall

79

33.1

0.003

R

0.99(0.95–1.03)

0.462

PB

42

28.4

0.047

R

1.00(0.96–1.04)

0.908

HB

37

37.7

0.012

R

0.96(0.88–1.04)

0.303

Asian

32

43.4

0.005

R

0.98(0.90–1.07)

0.675

Caucasian

33

23.1

0.119

F

0.99(0.95–1.04)

0.679

Taqman

41

36.2

0.012

R

1.03(0.97–1.09)

0.313

PCR-RFLP

23

11.6

0.303

F

0.97(0.90–1.05)

0.494

PCR-PIRA

5

50.4

0.089

R

0.78(0.61–0.99)

0.037

bladder cancer

4

49.4

0.115

F

0.75(0.62–0.90)

0.002

breast cancer

13

0

0.540

F

0.99(0.94–1.04)

0.676

cervical cancer

2

0

0.604

F

1.00(0.74–1.37)

0.980

colorectal cancer

12

3.8

0.408

F

0.97(0.90–1.03)

0.305

esophageal cancer

4

0

0.772

F

1.35(1.10–1.66)

0.004

gallbladder cancer

2

41.6

0.191

F

1.05(0.80–1.38)

0.706

gastric cancer

4

57.2

0.071

R

1.10(0.89–1.36)

0.389

HCC

2

52.2

0.148

F

1.11(0.85–1.44)

0.467

laryngeal cancer

2

0

0.542

F

0.88(0.69–1.13)

0.322

lung cancer

11

51.3

0.025

R

0.90(0.79–1.03)

0.140

nasopharyngeal cancer

3

33.3

0.223

F

0.84(0.67–1.06)

0.135

oral cancer

3

0

0.867

F

0.88(0.69–1.12)

0.307

ovarian cancer

2

14.7

0.279

F

1.02(0.86–1.20)

0.855

prostate cancer

6

3.1

0.397

F

1.02(0.93–1.12)

0.662

skin cancer

3

0

0.806

F

1.20(0.93–1.54)

0.154

(CC + TC) vs. TT

Overall

79

50.0

0

R

0.99(0.95–1.04)

0.644

PB

42

46.4

0.001

R

1.00(0.95–1.05)

0.992

HB

37

53.9

0

R

0.97(0.88–1.06)

0.490

Asian

32

57.0

0

R

0.99(0.90–1.10)

0.892

Caucasian

33

51.5

0

R

1.01(0.95–1.08)

0.775

Taqman

41

53.3

0

R

1.04(0.97–1.11)

0.249

PCR-RFLP

23

43.4

0.015

R

0.98(0.88–1.10)

0.758

PCR-PIRA

5

48.8

0.099

R

0.79(0.63–0.98)

0.035

bladder cancer

4

52.9

0.095

R

0.73(0.53–1.00)

0.052

breast cancer

13

19.0

0.251

F

1.00(0.95–1.04)

0.877

cervical cancer

2

0

0.862

F

0.98(0.73–1.32)

0.909

colorectal cancer

12

4.3

0.403

F

0.96(0.90–1.03)

0.237

esophageal cancer

4

0

0.414

F

1.33(1.10–1.63)

0.004

gallbladder cancer

2

2.2

0.312

F

1.08(0.84–1.40)

0.557

gastric cancer

4

65.6

0.033

R

1.13(0.90–1.42)

0.300

HCC

2

67.1

0.081

R

1.08(0.66–1.77)

0.764

laryngeal cancer

2

7.3

0.299

F

0.87(0.68–1.10)

0.238

lung cancer

11

72.7

0

R

0.92(0.78–1.10)

0.363

nasopharyngeal cancer

3

47.0

0.152

F

0.79(0.64–0.98)

0.030

oral cancer

3

0

0.856

F

0.85(0.67–1.08)

0.180

ovarian cancer

2

0

0.565

F

0.98(0.84–1.14)

0.784

prostate cancer

6

21.0

0.275

F

1.04(0.95–1.13)

0.408

skin cancer

3

0

0.979

F

1.25(0.99–1.59)

0.063

CC vs. (TC + TT)

Overall

79

52.6

0

R

1.01(0.94–1.09)

0.779

PB

42

53.2

0

R

1.01(0.93–1.09)

0.831

HB

37

53.3

0

R

1.01(0.85–1.21)

0.876

Asian

32

52.9

0

R

1.07(0.86–1.32)

0.500

Caucasian

33

58.5

0

R

1.02(0.91–1.14)

0.715

Taqman

41

60.9

0

R

1.05(0.94–1.18)

0.400

PCR-RFLP

23

55.3

0.001

R

0.94(0.76–1.17)

0.572

PCR-PIRA

5

0

0.845

F

0.88(0.59–1.30)

0.510

bladder cancer

4

25.9

0.256

F

0.78(0.61–1.01)

0.061

breast cancer

13

53.4

0.012

R

1.01(0.88–1.16)

0.884

cervical cancer

2

83.8

0.013

R

1.04(0.11–10.14)

0.972

colorectal cancer

12

19.1

0.256

F

0.97(0.88–1.06)

0.471

esophageal cancer

4

60.8

0.054

R

1.12(0.64–1.95)

0.695

gallbladder cancer

2

13.5

0.282

F

1.13(0.71–1.80)

0.615

gastric cancer

4

27.8

0.245

F

1.30(1.00–1.68)

0.052

HCC

2

42.5

0.187

F

1.51(0.87–2.61)

0.141

laryngeal cancer

2

62.6

0.102

F

0.80(0.51–1.26)

0.338

lung cancer

11

75.4

0

R

0.99(0.70–1.38)

0.932

nasopharyngeal cancer

3

46.9

0.152

F

0.65(0.46–0.94)

0.020

oral cancer

3

0

0.388

F

0.71(0.42–1.18)

0.182

ovarian cancer

2

65.5

0.088

R

0.75(0.42–1.34)

0.336

prostate cancer

6

44.6

0.108

F

1.11(0.97–1.27)

0.137

skin cancer

3

57.6

0.095

R

1.01(0.94–1.09)

0.454

C allele vs. T allele

Overall

79

62.0

0

R

1.00(0.96–1.04)

0.921

PB

42

59.9

0

R

1.01(0.96–1.05)

0.810

HB

37

64.8

0

R

0.98(0.90–1.07)

0.656

Asian

32

66.4

0

R

1.00(0.91–1.09)

0.956

Caucasian

33

66.9

0

R

1.02(0.96–1.08)

0.573

Taqman

41

66.5

0

R

1.04(0.98–1.10)

0.239

PCR-RFLP

23

61.4

0

R

0.99(0.89–1.09)

0.794

PCR-PIRA

5

39.9

0.155

F

0.84(0.74–0.96)

0.010

bladder cancer

4

57.4

0.070

R

0.76(0.60–0.96)

0.020

breast cancer

13

47.8

0.028

R

1.00(0.94–1.06)

0.938

cervical cancer

2

9.5

0.293

F

0.95(0.75–1.22)

0.699

colorectal cancer

12

12.8

0.319

F

0.97(0.93–1.02)

0.222

esophageal cancer

4

56.6

0.075

R

1.21(0.96–1.52)

0.100

gallbladder cancer

2

0

0.759

F

1.07(0.88–1.31)

0.496

gastric cancer

4

67.7

0.026

R

1.14(0.94–1.38)

0.195

HCC

2

73.4

0.052

R

1.10(0.71–1.71)

0.658

laryngeal cancer

2

47.3

0.168

F

0.88(0.73–1.06)

0.183

lung cancer

11

83.0

0

R

0.96(0.82–1.14)

0.661

nasopharyngeal cancer

3

54.1

0.113

F

0.80(0.68–0.94)

0.007

oral cancer

3

0

0.669

F

0.85(0.70–1.03)

0.106

ovarian cancer

2

0

0.850

F

0.95(0.85–1.07)

0.428

prostate cancer

6

44.2

0.111

F

1.05(0.98–1.12)

0.188

skin cancer

3

0

0.589

F

1.21(1.02–1.45)

0.031

Abbreviations: OR odds ratios, CI confidence intervals, R random effects model, F fixed effects model, HB hospital based, PB population based, PCR-RFLP polymorphism chain reaction restriction fragment length polymorphism, PCR-PIRA polymorphism chain reaction based primer-introduced restriction analysis, HCC hepatocellular carcinoma

The results are in bold italic if P <0.05

Subgroup analysis

In order to estimate the effects of specific study characteristics on the relationship between COX-2 8473 T > C polymorphism and cancer risk, we carried out subgroup analysis in control source, ethnicity, genotyping method and type of cancer under a variety of genetic models. For control source subgroup, whether the source of controls was population-based (PB) or hospital-based (HB), no association between 8473 T > C polymorphism and cancer risk was found. When stratified according to ethnicity, we observed no significant associations in Asians or Caucasians. Stratified by genotyping method, no relationship was detected in TaqMan and PCR-RFLP. However, by comparison, we discovered statistically significant decreased cancer risk in PCR-PIRA (TC vs. TT: OR = 0.78, 95% CI: 0.61–0.99, p = 0.037; (CC + TC) vs. TT: OR = 0.79, 95% CI: 0.63–0.78, P = 0.035; C allele vs. T allele: OR = 0.84, 95% CI: 0.74–0.96, P = 0.010). According to cancer type, 8473 T > C polymorphism was associated with a statistically significant decreased risk for nasopharyngeal cancer except for heterozygote comparison (CC vs. TT: OR = 0.59, 95% CI: 0.40–0.86, P = 0.007; (CC + TC) vs. TT: OR = 0.79, 95% CI: 0.64–0.98, P = 0.030; CC vs. (TC + TT): OR = 0.65, 95%CI: 0.46–0.94, P = 0.020; C allele vs. T allele: OR = 0.80, 95% CI: 0.68–0.94, P = 0.007). In the group with bladder cancer, we also found a decreased risk in the homozygote comparison, heterozygote comparison and allele analysis (CC vs. TT: OR = 0.74, 95% CI = 0.55–0.99, P = 0.040; TC vs. TT: OR = 0.75, 95% CI = 0.62–0.90, P = 0.002; C allele vs. T allele: OR = 0.76, 95% CI = 0.60–0.96, P = 0.020), but not in the dominant model and recessive model. However, for the esophageal cancer group, the COX-2 8473 T > C polymorphism was significantly associated with an increased risk in the heterozygote comparison and dominant model (TC vs. TT: OR = 1.35, 95% CI = 1.10–1.66, P = 0.004; (CC + TC) vs. TT: OR = 1.33, 95% CI = 1.10–1.63, P = 0.004), but not in the homozygote comparison, recessive model and allele analysis. For the group of skin cancer, we also observed the association of a significantly increased risk in the homozygote comparison and allele analysis (CC vs. TT: OR = 1.51, 95% CI = 1.02–2.25, P = 0.041; C allele vs. T allele: OR = 1.21, 95% CI = 1.02–1.45, P = 0.031, respectively), but not in heterozygote comparison, dominant model and recessive model. On the contrary, the result of breast cancer indicated no relationship with this polymorphism. Similarly, we also observed no significant association of 8473 T > C polymorphism with other cancers, including cervical cancer, colorectal cancer, gallbladder cancer, gastric cancer, HCC, lung cancer, oral cancer, ovarian cancer and prostate cancer. The detailed results were shown in Table 2.

Test of heterogeneity and sensitivity analysis

Significant heterogeneity was obvious in all the comparisons of COX-2 8473 T > C polymorphism (Table 2). Studies were excluded one by one to evaluate their influence on the test of heterogeneity and the credibility of our results. The results revealed that the corresponding pooled ORs and 95% CIs were not changed (Additional file 2: Figure S1, Additional file 3: Figure S2, Additional file 4: Figure S3 and Additional file 5: Figure S4), implying that the results of the present meta-analysis were credible and robust.

Publication bias

The Begg’s and Egger’s tests were performed to quantitatively assess the publication bias of this meta-analysis. P < 0.05 observed in the allelic genetic models was considered representive of statistically significant publication bias. The P details for bias were presented in Table 3. There was no significant publication bias in the overall analysis under each model. Moreover, the funnel plots quantitatively evaluating the publication bias did not reveal any evidence of obvious asymmetry in any model (Fig. 2).
Table 3

Results of publication bias test

Compared genotype

Begg’s test

Egger’s test

z value

P value

t value

P value

CC vs. TT

1.10

0.273

0.34

0.734

TC vs. TT

−0.16

0.876

−0.14

0.890

(CC + TC) vs. TT

0.64

0.523

0.06

0.951

CC vs. (TC + TT)

0.93

0.354

0.24

0.807

C allele vs. T allele

0.79

0.429

0.14

0.891

P value < 0.05 was considered as significant publication bias

Fig. 2
Fig. 2

a. Funnel plots for the publication bias test in the overall analysis under homozygote comparison. b. Funnel plots for the publication bias test in the overall analysis under heterozygote comparison. c. Funnel plots for the publication bias test in the overall analysis under dominant model. d. Funnel plots for the publication bias test in the overall analysis under recessive model. e. Funnel plots for the publication bias test in the overall analysis under allele analysis

Trial sequential analysis (TSA) results

As shown in Fig. 3, in order to prove the conclusions, the sample size required in the overall analysis was 50,558 cases for homozygote comparison, and 68,302 cases for heterozygote comparison. The results showed that the cumulative Z-cure didn’t exceed the TSA boundary, but the total number of cases and controls exceeded the required sample size, indicating that adequate evidence of our conclusions were established and no further relevant trials were needed.
Fig. 3
Fig. 3

a. TSA for overall analysis under homozygote comparison. b. TSA for overall analysis under heterozygote comparison. The required information size was calculated based on a two side α = 5%, β = 20% (power 80%), and an anticipated relative risk reduction of 10%

Discussion

Inflammation has been considered as an acting element for the pathogenesis of cancer. Prostaglandins are important molecules in the inflammatory response, and they are produced from arachidonic aid through the catalytic activity of COX-2. COX-2 cannot be detected under normal conditions, but rapidly induced in response to various inflammatory stimulus [7]. The expression level of COX-2 gene is regulated by a series of regulatory elements located in COX-2 promoter region, including nuclear factor-κb(NF- κB)/nuclear factor interleukin-6 (NF-IL6)/CCAAT/enhancer-binding protein (C/EBP) binding sites, cyclic AMP-response element (CRE) and activation protein 1 (AP-1) [87]. Further studies indicated that 3’UTR of COX-2 gene of murine also contains several regulatory elements affecting the stability of mRNA and the efficiency of translation [12], which played vital roles in stabilization, degradation, and translation of the transcripts [88, 89]. According to the above studies, many researchers hypothesized that polymorphism sites in 3’UTR of COX-2 gene, with 8473 T > C polymorphism included, might increase the expression of COX-2 and affect the susceptibility of cancer. Therefore, the correlation between 8473 T > C polymorphism in 3’UTR of COX-2 gene and cancer susceptibility has been of great interest in polymorphism research. In this meta-analysis, not only did we try to make sure whether 8473 T > C polymorphism has any relationship with the susceptibility of overall cancer, but we also performed TSA to efficiently decrease the risk of type I error and evaluate whether our results were stable.

In the present meta-analysis, we comprehensively researched the association of the 8473 T > C polymorphism in the 3’UTR region of COX-2 with cancer risk in all population through 79 studies. The results showed that no significant association between 8473 T > C polymorphism we studied and overall cancer risk was detected under all five genetic comparisons. However, we discovered significant heterogeneity among studies, therefore, further sensitivity analyses were conducted. Though the studies were eliminated one by one, heterogeneity remained significant. Moreover, several subgroup analyses, performed according to control source, ethnicity, genotyping method and type of cancer in all compared genetic models, could not explain the source of heterogeneity. In control source subgroup, no statistical significance association was found neither in PB nor HB. For ethnicity subgroup, whether in Asians or Caucasians, the polymorphism had no influence on cancer risk. The results might indicate that different individuals in the studies have the same risk to cancer. Moreover, only in the subgroup of PCR-PIRA, 8473 T > C polymorphism was linked to decrease risk to overall cancer in heterozygote comparison, recessive model and allele analysis, suggesting that different genotype detecting methods used in studies might influence the results. In the stratification analysis by type of cancer, the results indicated that the 8473 T > C polymorphism was associated with a statistically significant decreased risk for nasopharyngeal cancer in other four models except for heterozygote comparison, and bladder cancer in the homozygote comparison, heterozygote comparison and allele analysis. However, we observed an increased risk for esophageal cancer in heterozygote comparison and dominant model, and for skin cancer in homozygote comparison and allele analysis. The factors that contributed to this contradiction might include the following three aspects. Firstly, inconsistent results might be attributed to the different pathogenesis of the cancer. Secondly, 8473 T > C polymorphism might play different roles in different cancers. Most importantly, the influence of COX-2 gene 8473 T > C polymorphism on cancer risk might be affected by complex interactions between gene and environment. For example, smoking, the most important risk factor of lung cancer, could induce COX-2 expression [90].

Currently, some meta-analysis have investigated the relationship of 8473 T > C polymorphism with susceptibility to some types of cancer. Interestingly, part of the previous studies found some strong associations inconsistent with the result of our meta-analysis. Such as the report by Liu et al. [91] indicated that COX-2 gene 8473 T > C polymorphism was a factor for suffering from lung cancer, and Zhu et al. [92] suggested that 8473 T > C polymorphism might cause a decreased risk of lung cancer. Like Pan et al. [93], the current study supports the view that no significant association between 8473 T > C polymorphism and lung cancer risk. The reasons for this result may be as follows, firstly, the quality of original studies directly influences the reliability of the meta-analysis. In our meta-analysis the quality assessment of all the studies related with cancer was performed by using NOS, and low-quality studies were excluded. Secondly, the studies with the most recent or larger sample size were included, we therefore carried out a more systematic review of all eligible studies on the COX-2 8473 T > C polymorphisms and risk of lung cancer. Thirdly, the result of this polymorphism on cancer susceptibility might be influenced by some environmental factors or other polymorphisms, such as smoking. Meanwhile, some significant correlations we found were not shown in previous meta-analysis. For example, 8473 T > C polymorphism was associated with a decreased risk in nasopharyngeal cancer. When later studies were included in the meta-analysis, the contradiction didn’t appear, suggesting that the conclusions of previous meta-analysis with less number of studies might be reliable. More studies are required to achieve a more reliable result.

Obviously, we clarified the association in this meta-analysis, including more studies with the larger information size. Besides, it is the first TSA that comprehensively elaborated the influence of COX-2 8473 T > C polymorphism in response to cancer risk. However, several limitations should be taken into consideration in this meta-analysis. To begin with, only publications written in English or Chinese were included in our analysis. Therefore, selection bias might be inevitable. Secondly, there was significant heterogeneity in this meta-analysis between the polymorphism and cancer under all five genetic models. Moreover, the source of heterogeneity could not be explained by using subgroup and sensitivity analysis. Finally, as a complicated disease, the pathogenesis of cancer is strongly associated with environmental factors and the interactions with multifarious genetic factors rather than the effect of any single gene. Therefore, gene-to-environment interactions play a vital role in evaluating genetic polymorphisms. More original studies are required to estimate potential interactions between gene and gene, as well as gene and environment.

Conclusions

The results of this meta-analysis manifested that the association between COX-2 8473 T > C polymorphism and overall cancer was not detected under all five genetic comparisons. In the stratification analysis of cancer type, 8473 T > C polymorphism might be associated with a statistically significant decreased risk for nasopharyngeal cancer and bladder cancer, but an increased risk for esophageal cancer and skin cancer. And most importantly, in order to verify the conclusions of this analysis, further studies are needed to assess the potential gene-gene and gene-environment interactions.

Abbreviations

AV: 

Ampulla of vater

CI: 

Confidence intervals

EHBD: 

Extrahepatic bile duct

F: 

Fixed effects model

HB: 

Hospital based

HCC: 

Hepatocellular carcinoma

HN: 

Head and neck

HWE: 

Hardy-Weinberg equilibrium

IGG: 

Illumina GoldenGate

MAF: 

Minor allele frequecy

OR: 

Odds ratios

PB: 

Population based

PCR-KASP: 

Polymorphism chain reaction based kompetitive allele specific

PCR-PIRA: 

Polymorphism chain reaction based primer-introduced restriction analysis

PCR-RFLP: 

Polymorphism chain reaction restriction fragment length polymorphism

R: 

Random effects model

Declarations

Acknowledgements

We would like to thank the reviewers whose comments and suggestions greatly improved this manuscript.

Availability of data and materials

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

Authors’ contributions

QPL and TJM were responsible for conception and design of the study. QPL and CM did the studies selection, data extraction, statistical analyses and the writing of paper. SHC, GYZ, LS and FC participated in studies selection and data extraction and provided statistical expertise. QPL, WGZ and LZ contributed to the literature search, studies selection and figures. ZHZ and TJM reviewed and edited the manuscript extensively. All authors were involved in interpretation of results, read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Department of Medical Oncology, Luohe Central Hospital, Luohe First People’s Hospital, No. 56 People’s East Road, Luohe City, 462000, Henan Province, China

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