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Research Papers:

SMAD7 polymorphisms and colorectal cancer risk: a meta-analysis of case-control studies

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Oncotarget. 2016; 7:75561-75570. https://doi.org/10.18632/oncotarget.12285

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Yongsheng Huang _, Wenting Wu, Meng Nie, Chuang Li and Lin Wang

Abstract

Yongsheng Huang1, Wenting Wu2, Meng Nie1, Chuang Li1, Lin Wang1

1Institute of Basic Medical Sciences and School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China

2Department of Epidemiology, Richard M. Fairbanks School of Public Health, Melvin & Bren Simon Cancer Center, Indiana University, Indianapolis, IN 46202, USA

Correspondence to:

Yongsheng Huang, email: yongsheng@ibms.pumc.edu.cn

Lin Wang, email: lin.wang@ibms.pumc.edu.cn

Keywords: SMAD7, colorectal cancer, polymorphism, meta-analysis

Received: April 15, 2016    Accepted: September 14, 2016    Published: September 27, 2016

ABSTRACT

Mothers against decapentaplegic homolog 7 (SMAD7) inhibits the transforming growth factor-β (TGF-β) signaling pathway, which regulates carcinogenesis and cancer progression. A number of studies have reported that SMAD7 polymorphisms (rs4464148, rs4939827, and rs12953717) are associated with colorectal cancer (CRC) risk, but the results from these studies remain conflicting. To determine a more precise estimation of the relationship between SMAD7 and CRC, we undertook a large-scale meta-analysis of 63 studies, which included a total of 187,181 subjects (86,585 cases and 100,596 controls). The results of our meta-analysis revealed that the C allele of rs4464148 [CC vs. TT+TC, odds ratio (OR) =1.23, 95% confidence interval (CI): 1.14–1.33, P < 0.01], the T allele of rs4939827 [TT vs. CC+TC, odds ratio OR=1.15, 95%CI:1.07–1.22, P < 0.01] and the T allele of rs12953717 [TT vs. CC+TC, OR =1.22, 95%CI:1.16–1.29, P < 0.01] were all associated with the increased CRC risk. Subgroup analysis according to ethnicity showed rs4464148 and rs12953717 were associated with the risk of CRC in both Caucasians and Asians, whereas rs4939827 was a risk polymorphism for CRC specifically in Caucasians. In summary, this large-scale meta-analysis indicated that SMAD7 polymorphisms (rs4464148, rs4939827, and rs12953717) correlate with CRC.


INTRODUCTION

Cancer is caused by the dysfunction of intricate signaling pathways, leading to abnormal growth, metastasis, and many other events [1]. The transforming growth factor β (TGF-β) signaling pathway is one of major tumor-regulatory pathways, exerting critical tumor-suppressive functions in the early stages of tumorigenesis [2, 3]. When TGF-β signaling is activated, downstream SMAD2 and SMAD3 proteins are phosphorylated, forming a complex with SMAD4 and then translocating to the nucleus to turn on and off the transcription of a wide range of target genes [4, 5]. SMAD7 inhibits TGF-β signaling by preventing the formation of the SMAD2/SMAD4 complex [6]. It also interacts with activated TGF-β type I receptor and blocks the phosphorylation and activation of SMAD2 [6].

SMAD7 has also been reported to affect tumorigenesis via several other mechanisms. First, in FET-1 colon cancer cells, SMAD7 induces the expression of IκB, thereby repressing NF-κB activity [7]. Secondly, SMAD7 up-regulates MYC expression and WNT signaling via interactions with β-catenin in breast cancer [8] and hepatocellular carcinoma [9]. In addition, SMAD7 inhibits ERK1/2, JNK1/2, and p38 MAPKs under some circumstances related with tumorigenesis, such as erythroid differentiation [10] and chondrocyte differentiation [11].

In 2007, Broderick and co-workers [12] conducted a genome-wide association study and identified three polymorphic variants in intron 3 of SMAD7 (rs4464148, rs4939827, and rs12953717). Furthermore, they found these SMAD7 polymorphisms were associated with CRC adenomas and carcinomas [12]. In a number of other studies these SMAD7 polymorphisms have been associated with the risk of developing multiple cancers, including CRC [1214], renal [15], and liver cancer [16]. However, other case-control studies have reported that these polymorphisms are not associated with cancer risk, in CRC [1719], breast cancer [20], and lymphocytic leukemia [21]. These inconsistencies may be partially due to the relatively small sample sizes in each of these studies. Therefore, we performed a large-scale meta-analysis of all eligible published studies to derive a more precise quantitative assessment of the association between SMAD7 polymorphisms and CRC risk.

RESULTS

Study selection and characteristics

Figure 1 is a flowchart explaining the study selection process. A total of 62 articles were initially retrieved from PubMed, Web of Science, EBSCO, and Embase electronic databases (last updated in June, 2016). Based on the search criteria, we excluded 33 ineligible records after carefully reviewing the full text and data, leaving 29 articles published between 2007 and 2016 for our quantitative meta-analysis.

Flowchart of the literature selection process.

Figure 1: Flowchart of the literature selection process.

The characteristics of SMAD7 polymorphisms (rs4464148, rs4939827, and rs12953717) in selected studies are shown in Table 1. There were 64 eligible studies from 29 articles analyzing the relationship of SMAD7 polymorphisms and CRC risk. Among these studies, one was conducted on rs12953717, with a relatively small sample size (308 subjects) [22], which seems to have affected the results dramatically. Therefore, this study was excluded from analysis. Finally, 63 studies (published from 2007-2016) including 187,181 subjects (86,585 cases and 100,596 controls) were used to estimate the risk of developing CRC with SMAD7 polymorphisms. Each subpopulation in the literature was treated as a separate study in our meta-analysis. Populations were divided into ethnic categories. The Newcastle-Ottawa Scale (NOS) was used for quality assessment [23] and all of the studies achieved moderately high quality scores above 6 (Table 1). Among the included studies, 12 were conducted on rs4464148 (18,303 cases and 16,964 controls), 37 on rs4939827 (48,751 cases and 61,529 controls), and 14 on rs12953717 (19,531 cases and 22,103 controls).

Table 1: Main characteristics of all case-control studies included in the meta-analysis

SNP

Author

Year

Ethnicity

Cancer type

Case

Control

HWE (Control P value)

Study design

Genotyping method

Quality assessment

rs4464148

TT

TC

CC

TT

TC

CC

Broderick et al. [12]
-A group

2007

Caucasian

Colon

389

425

116

486

394

80

0.991

GWAS

Illumina

8

-B group

2007

Caucasian

Colon

2017

1952

472

1886

1617

346

0.982

Replication

Allele-PCR

8

-C group

2007

Caucasian

Colon

922

845

193

827

696

146

0.980

Replication

Allele-PCR

8

-D group

2007

Caucasian

Colon

422

408

99

171

137

27

0.952

Replication

Allele-PCR

8

Thompson et al. [28]

2009

Caucasian

Colon

269

231

61

342

324

53

0.045

Replication

TaqMan

8

Curtin et al. [43]

2009

Caucasian

Colon

503

472

95

535

423

89

0.678

Replication

SNPlex

8

Pittman et al. [44]

2009

Caucasian

Colon

1161

1107

264

1095

1277

235

0.996

Replication

Allele-PCR

8

Ho et al. [35]

2011

Asian

Colon

739

146

7

770

116

4

0.869

Replication

Sequenom

7

Zhang et al. [38]

2014

Asian

Colon

1

52

675

14

305

2957

0.999

Replication

TaqMan

8

Kurlapska et al. [17]

2014

Caucasian

Colon

1214

1228

400

84

96

33

0.523

Replication

Sequenom

7

Damavand et al. [29]

2015

Caucasian

Colon

138

78

37

113

101

20

0.700

Replication

Taqman

7

Serrano-Fernandez et al. [45]

2015

Caucasian

Colon

507

517

141

561

490

114

0.643

Replication

Taqman

8

rs4939827

CC

TC

TT

CC

TC

TT

Broderick et al. [12]
-A group

2007

Caucasian

Colon

153

449

328

229

480

251

0.987

GWAS

Illumina

8

-B group

2007

Caucasian

Colon

852

2178

1392

845

1915

1084

0.989

Replication

Allele-PCR

8

-C group

2007

Caucasian

Colon

387

982

623

410

840

430

0.995

Replication

Allele-PCR

8

-D group

2007

Caucasian

Colon

194

477

292

76

171

96

0.923

Replication

Allele-PCR

8

Tenesa et al. [14]
-Scotland(GWAS)

2008

Caucasian

Colon

538

1521

926

706

1508

845

0.506

GWAS

Illumina

8

-Japan

2008

Asian

Colon

233

1582

2576

131

1028

2019

0.992

Replication

TaqMan

8

-Canada

2008

Caucasian

Colon

225

593

355

284

576

322

0.402

Replication

TaqMan

8

-England

2008

Caucasian

Colon

418

1120

694

546

1126

578

0.959

Replication

TaqMan

8

-Spain

2008

Caucasian

Colon

62

156

131

57

143

95

0,808

Replication

TaqMan

8

-Germany

2008

Caucasian

Colon

420

1071

659

541

1057

530

0.765

Replication

TaqMan-

8

-Germany

2008

Caucasian

Colon

289

617

412

378

704

358

0.403

Replication

TaqMan

8

-Scotland

2008

Caucasian

Colon

156

420

254

189

446

288

0.497

Replication

TaqMan

8

-Israel

2008

Caucasian

Colon

267

638

447

312

627

397

0.035

Replication

TaqMan

8

Curtin et al. [43]

2009

Caucasian

Colon

221

520

324

229

538

274

0.251

Replication

SNPlex

8

Thompson et al. [28]

2009

Caucasian

Colon

125

275

154

146

378

185

0.064

Replication

TaqMan

8

Pittman et al. [44]

2009

Caucasian

Colon

785

1250

497

725

1300

582

0.987

Replication

Allele-PCR

8

Slattery et al. [46]

2010

Caucasian

Colon

360

773

457

492

992

503

0.947

Replication

TaqMan

8

Xiong et al. [33]

2010

Asian

Colon

1370

677

77

1442

570

74

0.061

Replication

T-ARMS-PCR

8

von Hoslt et al. [47]

2010

Caucasian

Colon

395

886

501

387

884

408

0.930

Replication

deCode test

8

Kupfer et al. [48]

2010

African

Colon

379

340

76

455

429

101

0.994

Replication

Sequenom

7

Caucasian

Colon

88

199

112

85

183

99

0.981

Replication

Sequenom

7

Mates et al. [49]

2010

Caucasian

Colon

28

37

27

15

57

23

0.061

Replication

Centaurus

6

Mates et al. [50]

2011

Caucasian

Colon

42

69

42

32

106

43

0.225

Replication

Centaurus

7

Cui et al. [34]

2011

Asian

Colon

1628

1007

155

2247

1190

147

0.501

Replication

Illumina

8

Li et al. [22]

2011

Asian

Colon

73

53

12

81

73

14

0.665

Replication

Sequenom

7

Ho et al. [35]

2011

Asian

Colon

343

420

129

376

405

109

0.997

Replication

Sequenom

7

Song et al. [36]

2012

Asian

Colon

399

232

10

732

272

33

0.214

Replication

TaqMan

6

Lubbe et al. [51]

2012

Caucasian

Colon

444

969

624

1394

3021

1636

0.993

Replication

Allele-PCR

7

Garcia-Albeniz et al. [52]

2012

Caucasian

Colon

90

233

118

538

1120

600

0.731

Replication

TaqMan

8

Phipps et al. [53]

2012

Caucasian

Colon

657

1526

884

574

1597

1112

0.988

Replication

TaqMan

7

Kirac et al. [54]

2013

Caucasian

Colon

63

143

96

172

291

131

0.705

Replication

Illumina

8

Yang et al. [37]

2014

Asian

Colon

342

298

65

891

752

159

0.985

Replication

Allele-PCR

7

Kurlapska et al. [17]

2014

Caucasian

Colon

54

93

65

716

1394

730

0.330

Replication

Sequenom

7

Zhang et al. [38]

2014

Asian

Colon

400

277

51

1894

1170

212

0.858

Replication

TaqMan

7

Hong et al. [19]

2015

Asian

Colon

126

63

9

182

127

19

0.608

Replication

Illumina

7

Baert-Desurmont et al. [55]

2016

Caucasian

Colon

89

157

104

191

493

343

0.555

Replication

SNaPshot

8

Abd EI-Fattah et al. [18]

2016

Caucasian

Colon

20

35

22

11

15

10

0.319

Replication

TaqMan

7

rs12953717

CC

TC

TT

CC

TC

TT

Broderick et al.

-A group [12]

2007

Caucasian

Colon

159

309

151

326

467

167

0.991

GWAS

Illumina

8

-B group

2007

Caucasian

Colon

1247

2204

973

1248

1898

722

0.994

Replication

Allele-PCR

8

-C group

2007

Caucasian

Colon

582

991

422

558

834

312

0.990

Replication

Allele-PCR

8

-D group

2007

Caucasian

Colon

277

468

198

106

168

67

0.976

Replication

Allele-PCR

8

Middeldorp et al. [13]

2009

Caucasian

Colon

301

493

201

482

643

215

0.982

Replication

TaqMan

7

Curtin et al. [43]

2009

Caucasian

Colon

314

530

226

332

521

188

0.509

Replication

SNPlex

8

Thompson et al. [28]

2009

Mixed

Colon

196

248

116

220

370

129

0.218

Replication

TaqMan

8

Pittman et al. [56]

2009

Caucasian

Colon

716

1261

555

859

1275

473

0.998

Replication

Allele-PCR

8

Kupfer et al. [48]

2010

African

Colon

401

327

67

525

388

72

0.979

Replication

Sequenom

7

2010

Caucasian

Colon

197

121

81

119

180

68

0.996

Replication

Sequenom

7

Slattery et al. [46]

2010

Caucasian

Colon

503

754

332

676

928

327

0.779

Replication

Illumina

8

Ho et al. [35]

2011

Asian

Colon

276

343

97

304

345

65

0.557

Replication

Sequenom

7

Scollen et al. [56]

2011

Mixed

Colon

710

1031

425

730

1083

437

0.326

Replication

TaqMan

8

Zhang et al. [38]

2014

Asian

Colon

418

263

47

1947

1135

194

0.096

Replication

TaqMan

8

SNP: single nucleotide polymorphisms: HWE: Hardy-Weinberg equilibrium.

Quantitative data synthesis

SMAD7 rs4464148 polymorphism

For each study, we investigated the association between the SMAD7 rs4464148 polymorphism and CRC risk, assuming different inheritance models. When all eligible studies were pooled into the meta-analysis, significant associations were found for the recessive genetic model (Table 2): CC vs. TC+TT (OR = 1.23; 95% CI: 1.14–1.33; PZ < 0.01; PH = 0.43], while only a slight association was found for the dominant genetic model: CC +TC vs. TT (OR = 1.10; 95% CI: 0.99–1.22; PZ = 0.51; PH = 0.00). Subgroup analysis according to ethnicity showed that rs4464148 was significantly associated with CRC risk in both Caucasian and Asian populations (Table 2).

Table 2: Meta-analysis of the association between SMAD7 polymorphisms and colorectal cancer risk

SNP

Comparison

Subgroup

Heterogeneity test

Model

PZ

PE

OR (95% CI)

I2 (%)

PH

rs4464148

CC vs. TT+TC

Overall

1.3

0.43

F

<0.01

0.13

1.23(1.14–1.33)

Caucasian

12.3

0.33

F

<0.01

1.22(1.13–1.32)

Asian

0

0.71

F

0.03

1.39(1.04–1.87)

CC+TC vs. TT

Overall

73.8

0.00

R

0.07

0.51

1.10(0.99–1.22)

Caucasian

76.8

0.00

R

0.16

1.08(0.97–1.21)

Asian

0

0.41

F

0.02

1.36(1.05–1.75)

C vs. T

Overall

66.2

0.00

R

<0.01

0.36

1.12(1.04–1.19)

Caucasian

67.7

0.00

R

0.01

1.10(1.02–1.18)

Asian

66

0.09

F

<0.01

1.35(1.12–1.63)

rs4939827

TT vs. CC+TC

Overall

73.3

0.00

R

<0.01

0.89

1.15(1.07–1.22)

Caucasian

61.2

0.00

R

<0.01

1.19(1.12–1.26)

Asian

75.8

0.00

R

0.73

1.04(0.84–1.28)

TT+TC vs. CC

Overall

71.8

0.00

R

<0.01

0.14

1.13(1.07–1.20)

Caucasian

71.6

0.00

R

<0.01

1.16(1.08–1.24)

Asian

74.0

0.00

R

0.31

1.07(0.94–1.23)

T vs. C

Overall

79.6

0.00

R

<0.01

0.45

1.11(1.06–1.16)

Caucasian

74.7

0.00

R

<0.01

1.13(1.08–1.18)

Asian

56.9

0.00

R

0.33

1.07(0.94–1.21)

rs12952717

TT vs. CC+TC

Overall

13.2

0.31

F

<0.01

0.54

1.22(1.16–1.29)

Caucasian

0

0.87

F

<0.01

1.25(1.18–1.32)

Asian

54.9

0.14

F

0.02

1.31(1.04–1.65)

TT+TC vs. CC

Overall

51.3

0.02

R

<0.01

0.66

1.15(1.08–1.23)

Caucasian

45.3

0.06

F

<0.01

1.19(1.13–1.25)

Asian

0.0

0.54

F

0.082

1.12(0.99–1.28)

T vs. C

Overall

51.5

0.02

R

<0.01

0.85

1.13(1.09–1.19)

Caucasian

29.8

0.17

F

<0.01

1.16(1.12–1.20)

Asian

19.6

0.27

F

0.02

1.13(1.02–1.25)

PH : P value of heterogeneity test; PZ : P value of Z test; PE : P value of Egger’s test. R: random-effects model. F: fixed-effects model

SMAD7 rs4939827 polymorphism

Similarly, we investigated the association between the SMAD7 rs4939827 polymorphism and CRC risk. Significant associations were found for both the recessive (Figure 2): TT vs. TC+CC (OR = 1.15; 95% CI: 1.07–1.22; PZ < 0.01; PH = 0.00) and the dominant genetic models: TT+ TC vs. CC (OR = 1.13; 95% CI: 1.07–1.20; PZ < 0.01; PH = 0.00; Table 2). Subgroup analysis according to ethnicity showed that rs4939837 was significantly associated with CRC risk in the Caucasian population (27 studies: 36,062 cases and 43,518 controls): TT vs. TC+CC (OR = 1.19; 95% CI: 1.12–1.26; PZ < 0.01; PH = 0.00 for heterogeneity), whereas it had no association with CRC risk among Asians (9 studies: 12,607 cases and 16,349 controls): TT vs. TC+CC (OR = 1.04; 95% CI: 0.84–1.28; PZ = 0.73; PH = 0.00; Table 2).

Forest plot of cancer risk associated with the SMAD7 polymorphisms in colorectal cancer studies with recessive genetic models.

Figure 2: Forest plot of cancer risk associated with the SMAD7 polymorphisms in colorectal cancer studies with recessive genetic models. The squares and horizontal lines correspond to the study-specific odds ratio (OR) and 95% confidence interval (95% CI). The area of the squares reflects the weight (inverse of the variance). A. SMAD7 rs4464148; B. SMAD7 rs4939827; C. SMAD7 rs12953717.

SMAD7 rs12953717 polymorphism

In this meta-analysis, a strong association between the rs12953717 polymorphism and CRC risk was found for both the recessive: TT vs. CC+TC (OR = 1.22; 95% CI: 1.16–1.29; PZ < 0.01; PH = 0.31) and the dominant genetic models: TT+TC vs. CC (OR = 1.15; 95% CI: 1.08–1.23; PZ < 0.01; PH = 0.02; Table 2). Further subgroup analysis based on ethnicity showed that rs12953717 was significantly associated with the risk of CRC in both Caucasians and Asians (Table 2).

Sensitivity analyses and publication bias

Our results suggested that the influence of individual data sets to the pooled ORs were not significant. Sensitivity analysis showed that no single study qualitatively altered the pooled ORs, providing evidence of the stability of the meta-analysis (Supplementary Figure S1). Funnel plots and Egger’s test were performed to assess publication bias. The results suggested that there was no publication bias for the comparison of rs4464148 allele C vs. allele T (t =0.96, PE = 0.36), rs4939827 allele T vs. allele C (t =-0.76, PE = 0.45), or rs12953717 allele T vs. allele C (t =-0.19, PE = 0.85). The shape of Begg’s funnel plot did not reveal any obvious asymmetry (Supplementary Figure S2).

DISCUSSION

TGF-β signaling is essential for maintaining homeostasis, cell differentiation, and tumor suppression [3, 24, 25]. Increased production of TGF-β occurs in various tumor types, such as CRC [26]. As one of the key effectors of TGF-β signaling, perturbation of SMAD7 expression has been documented to influence CRC progression [7][27]. Though the functional role of the SMAD7 polymorphisms (rs4464148, rs4939827, and rs12953717) has not yet been interpreted, a number of published epidemiological studies have reported that these polymorphisms are correlated with the risk of developing multiple cancers [12, 28, 29]. However, other studies have reported that these polymorphisms are not associated with cancer development [1720].

These conflicting studies based their conclusions on small numbers of samples and different detection methods. Therefore ameta-analysis from large-scale samples of all available studies is required to have a more accurate assessment as to whether the SMAD7 polymorphisms are related to risk of developing CRC. Our group has already used meta-analysis to systematically investigate the association between cancer risk and several SNPs involved in TGF-β signaling [3032]. In this meta-analysis, we found SMAD7 polymorphisms (rs4464148, rs4939827, and rs12953717) in the combined population were all significantly associated with CRC risk. Subgroup analysis according to ethnicity showed that rs4464148 and rs12953717 were significantly associated with the risk of CRC among both Caucasian and Asian population, whereas rs4939827 seems to be a risk polymorphism for CRC only within a Caucasian population. There could be several possibilities to explain such a differential association. First, the difference in association may result from differences in socioeconomic environment, regional dietary habits, and race. Second, the number of rs4939827 in Asian studies is still not as large as desired. In addition, the results from nine studies incorporated in this meta-analysis conflict with each other [14, 19, 22, 3338]. Therefore, more Asian studies are still needed to clearly evaluate the interactions of SMAD7 rs4939827 and CRC in this ethnic group.

One recent study [39] also assessed the associations between these three SNPs and CRC risk by meta-analysis; however, there were significant limitations. First, the number of studies included in their analysis was smaller than ours. Only 4 publications for rs4464148, which also lack relevant studies for Asian population, and 13 publications for rs4939827 were included in their meta-analysis, while 9 publications for rs4464148 and 25 publications for rs4939827 were included in our work. Second, they only analyzed the relationship between SMAD7 polymorphisms and CRC risk under an allelic model, while we also analyzed under dominant and recessive models. Therefore, our updated meta-analysis at a much larger scale clearly provides a more credible and reliable assessment for the association between SMAD7 polymorphisms and the risk to develop CRC.

Nonetheless, we also wish to acknowledge the limitations in our study. First, we stratified the studies by ethnic subtypes as Caucasian and Asian. However, we could not assess the association in the African population due to the insufficient number of African studies. Second, further subtle adjusted analysis could be carried out if more detailed individual information was available. Third, we only assessed the association of SMAD7 polymorphisms with CRC risk, because there were not sufficient studies conducted on other cancers.

To date, a large number of studies have focused on the relationship between SMAD7 polymorphisms and cancer. However, controversies remain as whether those polymorphisms indeed associate with increased cancer risks. Our large-scale meta-analysis demonstrated that the C allele of rs4464148, the T allele of rs4939827, and the T allele of rs12953717 were all significantly associated with the increased CRC risk, which may provide a basis for genetic testing in the development of CRC. Consistent with our findings, Noci et al. [40] recently showed that SMAD7 rs4939827 is also associated with cancer survival rate after therapy. Therefore, the identification of SMAD7 polymorphisms may also benefit developing targeted and personalized therapy against CRC. However, more comparative studies are needed to evaluate interactions of SMAD7 polymorphisms and cancer risk in other specific cancer subtypes and ethnic subtypes

MATERIALS AND METHODS

Literature Search strategy

We searched for relevant case-control studies using the following words and terms: ‘‘SMAD7’’, “Mothers against decapentaplegic homolog 7”, “rs4464148”, “rs4939827”, “rs12953717”, ‘‘polymorphism’’ or ‘‘variation’’, “susceptibility”, and “tumor” or ‘‘cancer’’ or ‘‘carcinoma’’ or ‘‘neoplasia’’ or “colorectal caner” or “CRC” in PubMed, the Web of Science, EBSCO, and Embase databases. There were no limitations on the language and year for the literature search. The last search was updated on June 30, 2016. References of the retrieved publications were also screened.

Inclusion criteria

Two authors independently screened titles and abstracts to identify relevant studies. Full-text articles of these studies were then carefully read to select eligible studies. Studies had to meet the following inclusion criteria: (a) were a case-control study, nested case-control or a cohort study; (b) evaluated the association between SMAD7 polymorphisms (“rs4464148”, “rs4939827”, and “rs12953717”) and CRC risk; (c) had available genotype frequencies both in cases and controls; (d) the genotype distribution in control groups was in Hardy-Weinberg equilibrium (HWE). (e) In cases of multiple studies with overlapping, redundant data published, only the most recent or complete study was included.

Qualitative assessment

Two authors independently conducted the quality assessment. The Newcastle–Ottawa Scale (NOS) was used to evaluate the study quality, which scored studies by the selection of the groups, the comparability of cases and controls, and the ascertainment of the exposure. We considered a study awarded 0-3, 4-6, or 7-9 as a low-, moderate-, or high-quality study, respectively [23].

Data extraction

Two authors independently selected the relevant articles and extracted the following data: first author’s name, publication date, ethnicity, cancer type, genotyping methods, number of cases and controls, and number of genotypes in case-control groups. In addition, P values according to the HWE in controls were extracted from the included studies.

Statistical analysis

Our meta-analysis was performed using Stata software (version 12.0; StataCorp LP, College Station, TX, USA). We first calculated the strength of the association between SMAD7 polymorphisms and CRC by odds ratio (OR) corresponding to 95% confidence interval (CI) for different genetic models. Then we stratified the studies by ethnic subtypes and examined the association between SMAD7 polymorphisms and the CRC risk (Table 2). A chi-square-based Q-statistic test [41] was performed to evaluate the between-study heterogeneity of the studies. PH < 0.05 was considered significant for heterogeneity. We also calculated the quantity I2 that represents the percentage of total variation across studies. As a guide, values of I2 less than 25% were considered “low”, values about 50% were considered “moderate”, and values greater than 75% were considered “high”[42]. The fixed effects model was used when there was no heterogeneity of the results of studies; otherwise, the random-effects model was chosen. A pooled OR obtained by meta-analysis was used to give a more reasonable evaluation of the association. The significance of the pooled OR was determined by Z test (PZ ≤0.05 suggests a significant OR). Funnel plots were used to access publication bias by the method of Begg’s test and Egger’s test. A T test was performed to determine the significance of the asymmetry. An asymmetric plot suggested possible publication bias (PE ≥ 0.05 suggests no bias).

ACKNOWLEDGMENTS

This study was supported by grants from the National Natural Science Foundation of China (Grant No. 31201050 and 81372201).

CONFLICTS OF INTEREST

The authors declare no competing financial interests.

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