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The association between the TP53 Arg72Pro polymorphism and colorectal cancer: An updated meta-analysis based on 32 studies

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Oncotarget. 2017; 8:1156-1165. https://doi.org/10.18632/oncotarget.13589

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Xin Tian _, Shundong Dai, Jing Sun, Shenyi Jiang and Youhong Jiang

Abstract

Xin Tian1, Shundong Dai2,3, Jing Sun4, Shenyi Jiang5, Youhong Jiang1

1Molecular Oncology Laboratory of Cancer Research Institute, The First Affiliated Hospital of China Medical University, Shenyang, 110001, PR China

2Department of Pathology, The First Affiliated Hospital and College of Basic Medical Sciences of China Medical University, Shenyang, 110001, PR China

3Institute of Pathology and Pathophysiology, Shenyang, 110001, PR China

4Department of Immunology and Biotherapy, Liaoning Cancer Hospital and Institute, Shenyang, 110042, PR China

5Department of Rheumatology, The First Affiliated Hospital of China Medical University, Shenyang, 110001, PR China

Correspondence to:

Xin Tian, email: [email protected]

Keywords: TP53, colorectal cancer, polymorphism, meta-analysis

Received: July 05, 2016    Accepted: November 08, 2016    Published: November 25, 2016

ABSTRACT

Several previous studies evaluated the association between the Arg72Pro (rs1042522) polymorphism in the TP53 tumor suppressor gene and colorectal cancer (CRC). However, the results are conflicting. This meta-analysis aimed to shed new light on the precise association between TP53 variants and CRC. We analyzed 32 published case-control studies involving 8,586 cases and 10,275 controls using crude odd ratios (ORs) with 95% confidence intervals (CIs). The meta-analysis was performed using a fixed-effect or random-effects model, as appropriate. We found that the TP53 Arg72Pro polymorphism was not significantly associated with CRC risk in the overall population. However, subgroup analysis based on ethnicity revealed an increased risk of CRC among Asians (CC vs. GC+GG: OR=1.22, 95% CI: 1.02-1.45), and similar results were found for rectal cancer (CC vs. GC+GG: OR=1.34, 95% CI: 1.120-1.62). These results suggest that the TP53 Arg72Pro polymorphism CC genotype may contribute to an increased risk of CRC, especially for rectal cancer and among Asians.


INTRODUCTION

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second most commonly diagnosed cancer in females. CRC is also the leading cause of cancer-related death in the Western world, and has exhibited a striking rise in incidence in Asian countries [13]. The etiology of CRC is multifactorial, though it is widely accepted that CRC can be caused by an accumulation of mutations in various genes [4]. The identification of CRC-related genes may help facilitate the early diagnosis, prevention and treatment of the disease [5].

The TP53 tumor suppressor gene, which is located on chromosome 17p13, is one of the most frequently mutated in human carcinogenesis [6]. The encoded TP53 protein is a key mediator in many cellular processes, including cell cycle arrest, apoptosis, senescence, DNA repair, and changes in metabolism [7]. Consequently, TP53 mutations may result in a loss of the protein’s tumor suppressor function and thus contribute to the development of malignant tumors. The common TP53 Arg72Pro polymorphism (rs1042522) at codon 72 of exon 4 is the most studied polymorphism in cancer [8]. The guanine to cytosine (G>C) nucleotide exchange associated with this polymorphism leads to a nonsynonymous amino acid change from arginine to proline. The 72Arg variant of TP53 exhibits enhanced ability to localize to the mitochondria and induce apoptosis, whereas the 72Pro variant more efficiently induces cell cycle arrest [9].

Several studies have been conducted to investigate the association between the TP53 Arg72Pro polymorphism and CRC. However, the results are inconsistent and conflicting. The present meta-analysis was performed to provide a more precise estimation of this association.

RESULTS

Study characteristics

Our search strategy yielded a total of 545 records, which were screened to identify original research articles pertaining to TP53 and CRC. The literature search and detailed selection procedures are summarized in Figure 1. After the primary screening, the full text of 40 articles was retrieved for further assessment [1049]. Ten of those articles were then excluded from further analysis: 6 were not case-control studies [4045], 1 was based on duplicate data from another eligible study [46], and 3 reported a genotype distribution among the controls that was not in Hardy-Weinberg equilibrium (HWE) [4749]. Two of the articles reported 2 studies each [19, 24]. Thus, 1 study in each of 28 articles and 2 studies in each of 2 articles, adds up to a total of 32 studies in 30 articles [1039]. In these 32 studies that conformed to our inclusion criteria, there were 8586 CRC cases and 10275 controls. Fourteen studies involved Asian participants, 12 involved Caucasians, and 6 involved mixed populations. The population characteristics of the included studies are shown in Table 1.

Flow chart of study selection process.

Figure 1: Flow chart of study selection process.

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

First author

Year

Country

Ethnicity

Source of control

Type of CRC

Cases

Controls

HWE

Methods

GG

GC

CC

GG

GC

CC

Olschwang10

1991

France

Caucasian

Population-based

Sporadic

32

34

5

49

52

14

0.97

PCR-RFLP

Kawajiri11

1993

Japan

Asian

Population-based

Sporadic

36

32

16

144

165

38

0.36

Allele specific PCR

Murata12

1996

Japan

Asian

Hospital-based

Sporadic

46

55

14

53

76

23

0.62

Allele specific PCR

Wang13

1999

China

Asian

Hospital-based

Sporadic

18

33

10

43

70

27

0.86

PCR-RFLP

Sayhan14

2001

Turkey

Mix

Population-based

Sporadic

26

30

11

21

43

12

0.20

PCR-RFLP

Hamajima15

2002

Japan

Asian

Hospital-based

Sporadic

58

72

17

91

107

43

0.24

Allele specific PCR

Gemignani16

2004

Spain

Caucasian

Hospital-based

Sporadic

201

133

18

202

95

19

0.09

Allele specific PCR

Schneider17

2004

Germany

Caucasian

Population-based

Sporadic

26

26

5

38

41

6

0.25

PCR-SSCP

Krüger18

2005

Germany

Caucasian

Population-based

Hereditary

180

95

18

150

78

17

0.13

PCR-RFLP

Sotamaa19

2005

Finland

Caucasian

Population-based

Hereditary, Sporadic

231

129

19

172

125

26

0.62

PCR-SSCP

USA

Mix

Population-based

Hereditary

21

7

2

64

41

13

0.11

PCR-SSCP

Koushik20

2006

USA

Mix

Population-based

Sporadic

228

186

28

498

351

55

0.51

Allele specific PCR

Lima21

2006

Brazil

Mix

Hospital-based

Sporadic

56

38

6

58

36

6

0.90

Allele specific PCR

Pérez22

2006

Argentina

Mix

Population-based

Sporadic

31

20

2

44

53

12

0.50

Allele specific PCR

Perfumo23

2006

Italy

Caucasian

Hospital-based

Sporadic

28

30

2

90

49

7

0.92

PCR-RFLP

Talseth24

2006

Australia

Caucasian

Population-based

Hereditary

39

19

3

10

11

0

0.10

Sequencing

Poland

Caucasian

Population-based

Hereditary

33

19

4

45

28

5

0.82

Sequencing

Tan25

2007

Germany

Caucasian

Population-based

Sporadic

312

131

24

343

193

27

0.98

Allele specific PCR

Zhu26

2007

China

Asian

Population-based

Sporadic

83

117

85

244

321

105

0.97

PCR-RFLP

Grünhage27

2008

Germany

Caucasian

Hospital-based

Hereditary, Sporadic

105

72

14

123

78

19

0.20

PCR-RFLP

Csejtei28

2008

Hungary

Caucasian

Population-based

Sporadic

66

32

4

62

29

6

0.31

Allele specific PCR

Cao29

2009

Korean

Asian

Population-based

Sporadic

54

67

35

114

140

39

0.70

PCR-RFLP

Polakova30

2009

Germany

Caucasian

Hospital-based

Sporadic

327

225

60

326

237

49

0.52

PCR-RFLP

Mojtahedi31

2010

Iran

Asian

Population-based

Sporadic

46

63

23

58

77

28

0.78

Allele specific PCR

Aizat32

2011

Malaysia

Asian

Hospital-based

Sporadic

70

88

44

75

101

25

0.31

PCR-RFLP

Dastjerdi33

2011

Iran

Asian

Population-based

Sporadic

97

101

52

76

113

61

0.14

PCR-RFLP

Engin34

2011

Turkey

Mix

Hospital-based

Sporadic

50

41

5

52

42

14

0.24

PCR-RFLP

Joshi35

2011

Japan

Asian

Population-based

Sporadic

239

342

104

310

361

107

0.90

PCR-RFLP

Song36

2011

Korea

Asian

Population-based

Sporadic

740

844

244

734

776

190

0.48

TaqMan

Zhang37

2012

China

Asian

Hospital-based

Sporadic

147

199

98

196

271

102

0.62

MALDI-TOF

Oh38

2014

Korea

Asian

Hospital-based

Sporadic

222

247

76

145

218

65

0.25

PCR-RFLP

Singamsetty39

2014

India

Asian

Population-based

Sporadic

16

48

39

37

45

25

0.13

Sequencing

HWE, Hardy-Weinberg equilibrium; PCR, polymerase chain reaction; RFLP, restriction fragment length polymorphism; MALDI-TOF, Matrix-assisted laser desorption/ionization time-of-flight.

Meta-analysis results

We assessed the association between the TP53 Arg72Pro polymorphism and CRC susceptibility by calculating an odds ratio (OR) and its 95% confidence interval (CI) under the following four genetic models: the allele model (C vs. G), the homozygote model (CC vs. GG), the dominant model (CC+GC vs. GG), and the recessive model (CC vs. GC+GG). A summary of our meta-analysis of the association between the TP53 Arg72Pro polymorphism and CRC is shown in Table 2. Overall, we observed no significant associations in any of the genetic models (C vs. G: OR =1.02, 95%CI 0.94-1.10; CC vs. GG: OR=1.06, 95%CI 0.90-1.25; CC+GC vs. GG: OR=1.01, 95%CI 0.91-1.11; CC vs. GC+GG: OR=1.09, 95%CI 0.95-1.24) (Figure 2). Further subgroup analyses were conducted to assess the effects of potential confounding factors. There was no evidence for an association between TP53 Arg72Pro polymorphism and CRC risk in subgroup analyses based on the source of the controls or the type of CRC (Table 2). However, when stratified based on tumor location, we found that the CC genotype increased the risk of rectal cancer (CC vs. GC+GG: OR=1.34, 95%CI 1.12-1.62), but did not alter the risk of colon cancer (CC vs. GC+GG: OR=1.14, 95%CI 0.94-1.39). When the data for rectal cancer were stratified based on ethnicity, no significant associations were observed between TP53 Arg72Pro polymorphism and CRC risk. Similarly, no associations were found for colon cancer (Table 2). Nonetheless, after stratification based on ethnicity, a significant risk was observed among subjects in Asian populations who carried the CC genotype (CC vs. GC+GG: OR=1.22, 95%CI 1.02-1.45), whereas no risk was observed in Caucasian and mixed populations (CC vs. GC+GG: OR=0.94, 95%CI 0.76-1.16 and OR=0.82, 95%CI 0.5-1.16, respectively). Subgroup analyses based on ethnicity revealed no significant association between TP53 Arg72Pro polymorphism and CRC risk in Caucasian and Mixed populations.

Table 2: Meta-analysis of the association between TP53 Arg72Pro polymorphism and colorectal cancer risk

Subgroup

NO.

C vs. G

CC vs. GG

CC+GC vs. GG

CC vs. GC+GG

OR(95%CI)

Ph

POR

OR (95%CI)

Ph

POR

OR(95%CI)

Ph

POR

OR(95%CI)

Ph

POR

Overall

32

1.02 (0.94-1.10)

0.000

0.678*

1.06 (0.90-1.25)

0.000

0.489*

1.01 (0.91-1.11)

0.000

0.912*

1.09 (0.95-1.24)

0.017

0.223*

Ethnicity

 Caucasian

12

0.96 (0.88-1.05)

0.338

0.359

0.92 (0.74-1.15)

0.854

0.472

0.96 (0.86-1.06)

0.130

0.399

0.94 (0.76-1.16)

0.820

0.555

 Asian

14

1.10 (0.98-1.23)

0.000

0.102*

1.25 (0.99-1.58)

0.000

0.060*

1.08 (0.93-1.26)

0.000

0.300*

1.22 (1.02-1.45)

0.005*

0.026*

 Mixed

6

0.94 (0.82-1.09)

0.056

0.416

0.79 (0.55-1.12)

0.244

0.181

0.96 (0.81-1.15)

0.057

0.663

0.82 (0.58-1.16)

0.385

0.261

Source of controls

 Population-based

20

1.01 (0.90-1.14)

0.000

0.825*

1.12 (0.89-1.41)

0.000

0.319*

0.99 (0.85-1.14)

0.000

0.843*

1.15 (0.97-1.36)

0.046

0.102*

 Hospital-based

12

1.01 (0.94-1.09)

0.155

0.744

1.00 (0.85-1.19)

0.165

0.974

1.04 (0.98-1.10)

0.251

0.900

1.04 (0.89-1.21)

0.093

0.636

Tumor location

Colon cancer

8

1.12 (0.96-1.32)

0.020

0.159*

1.23 (0.88-1.73)

0.041

0.228*

1.21 (0.94-1.56)

0.005

0.145*

1.14 (0.94-1.39)

0.421

0.185

  (Caucasian)

2

1.09 (0.90-1.32)

0.456

0.365

1.19 (0.77-1.84)

0.160

0.432

1.11 (0.87-1.41)

0.942

0.410

1.14 (0.75-1.73)

0.104

0.551

  (Asian)

5

1.16 (0.86-1.55)

0.003

0.332*

1.35 (0.79-2.30)

0.015

0.275*

1.29 (0.78-2.13)

0.001

0.319*

1.18 (0.93-1.50)

0.414

0.174

  (Mixed)

1

1.15 (0.93-1.41)

0.192

1.07 (0.61-1.88)

0.808

1.25 (0.97-1.62)

0.091

0.96 (0.56-1.66)

0.888

Rectum cancer

8

1.13 (0.92-1.38)

0.001

0.257*

1.36 (0.93-1.99)

0.010

0.108*

1.07 (0.83-1.36)

0.018

0.615*

1.34 (1.12-1.62)

0.125

0.002

  (Caucasian)

2

0.90 (0.72-1.13)

0.549

0.359

1.00 (0.61-1.65)

0.581

0.998

0.82 (0.62-1.09)

0.549

0.161

1.11 (0.68-1.81)

0.687

0.671

  (Asian)

5

1.24 (0.93-1.67)

0.001

0.142*

1.53 (0.88-2.66)

0.002

0.128*

1.24 (0.85-1.79)

0.010

0.264*

1.41 (0.97-2.05)

0.034

0.071*

  (Mixed)

1

1.09 (0.78-1.53)

0.626

1.42 (0.64-3.15)

0.387

1.03 (0.68-1.58)

0.877

1.44 (0.66-3.11)

0.360

Type of CRC

Sporadic

28

1.03 (0.95-1.12)

0.000

0.459*

1.09 (0.92-1.29)

0.000

0.323*

1.02 (0.92-1.14)

0.000

0.695*

1.11 (0.97-1.27)

0.018

0.122*

  (Caucasian)

9

0.97 (0.88-1.07)

0.069

0.594

0.97 (0.76-1.24)

0.904

0.803

0.96 (0.85-1.09)

0.077

0.540

0.99 (0.78-1.25)

0.838

0.928

  (Asian)

14

1.10 (0.98-1.23)

0.000

0.102*

1.25 (0.99-1.58)

0.000

0.060*

1.08 (0.93-1.26)

0.000

0.300*

1.22 (1.02-1.45)

0.005*

0.026*

  (Mixed)

5

0.97 (0.84-1.11)

0.299

0.072

0.81 (0.56-1.17)

0.186

0.262

0.99 (0.83-1.19)

0.075

0.923

0.84 (0.59-1.19)

0.287

0.328

Hereditary

6

0.86 (0.73-1.01)

0.422

0.072

0.69 (0.45-1.04)

0.374

0.078

0.87 (0.71-1.06)

0.465

0.158

0.71 (0.47-1.07)

0.417

0.106

  (Caucasian)

5

0.88 (0.75-1.05)

0.474

0.148

0.71 (0.46-1.10)

0.284

0.124

0.89 (0.73-1.10)

0.551

0.290

0.73 (0.48-1.11)

0.300

0.141

  (Mixed)

1

0.57 (0.28-1.16)

0.118

0.47 (0.10-2.25)

0.344

0.51 (0.22-1.20)

0.123

0.58 (0.12-2.71)

0.486

Genotype methods

 PCR-RFLP

14

1.04 (0.92-1.18)

0.000

0.519*

1.07 (0.81-1.40)

0.000

0.634*

1.04 (0.88-1.23)

0.001

0.628*

1.10 (0.89-1.36)

0.011

0.381*

 Allele specific PCR

10

0.98 (0.89-1.07)

0.153

0.636

0.93 (0.74-1.16)

0.453

0.518

0.98 (0.88-1.11)

0.119

0.791

0.94 (0.76-1.169)

0.348

0.543

 PCR-SSCP

3

0.77 (0.62-0.95)

0.383

0.013

0.61 (0.36-1.03)

0.510

0.065

0.74 (0.57-0.95)

0.514

0.020

0.68 (0.41-1.14)

0.559

0.142

 Sequencing

3

1.20 (0.65-2.22)

0.031

0.554*

2.66 (1.39-5.08)

0.328

0.003

1.18 (0.44-3.12)

0.009

0.745*

1.85 (1.08-3.16)

0.731

0.025

OR odds ratio; 95%CI 95% confidence interval; POR, pool P value; Ph, P value of heterogeneity test;

* Estimates for random-effects model; otherwise, fixed-effects model was used.

Forest plots of TP53 Arg72Pro polymorphism and CRC risk.

Figure 2: Forest plots of TP53 Arg72Pro polymorphism and CRC risk. a. allele model, b. homozygous model, c. dominant models, d. recessive models.

Publication bias and sensitivity analysis

We used Begg’s funnel plot and Egger’s test to assess the publication bias of the published articles. The symmetrical funnel plot for the allele model shown in Figure 3 suggests the findings of our meta-analysis were not affected by publication bias. The Egger’s test results also did not suggest the existence of publication bias, as indicated by P values greater than 0.05 (P=0.098 for the allele model). The influence of each individual study on the pooled OR was assessed by performing the analysis while deleting one study at a time. Because the OR was not significantly influenced by omitting any single study (data not shown), we conclude our data are relatively stable and credible.

Beggar’s funnel plot of TP53 Arg72Pro polymorphism and CRC risk under the allele model.

Figure 3: Beggar’s funnel plot of TP53 Arg72Pro polymorphism and CRC risk under the allele model.

DISCUSSION

The mechanisms that underlie the development of CRC are complex, and both environmental and genetic factors play important roles in the occurrence and progression of this disease [50]. TP53 is crucial for proper control of gene transcription, DNA synthesis and repair, cell cycle arrest, senescence and apoptosis. Mutations in TP53 can disrupt these functions, leading to genetic instability and the progression to cancer.

In this meta-analysis, we found that the TP53 Arg72Pro polymorphism was not associated with CRC in patients stratified based on type of CRC, genotype method or source of controls. When stratified based on ethnicity, there was a positive association between the TP53 Arg72Pro polymorphism and CRC risk in Asian populations, but not Caucasian or mixed populations. These differences may reflect differences in genetic background and/or environmental factors. The Arg72 variant of the TP53 Arg72Pro polymorphism is more efficient with respect to mitochondrial localization than the Pro72 variant and has a stronger capacity to induce apoptosis [51]. Researchers observed that the Arg72 form induced apoptosis with faster kinetics than did the Pro72 variant [52]. The greater apoptotic potential of the Arg72 protein stems from the greater interaction of this protein with MDM2, which facilitates nuclear export [53]. The two polymorphic variants of TP53 are functionally distinct, and these differences may influence cancer risk or treatment. Our result is does not confirm the findings of 2 earlier meta-analyses [54, 55]. These differences may be the result of the rigid inclusion criteria of our study. We excluded two studies with control genotypic distributions that deviated from the HWE [47, 48] and 2 studies with overlapping populations [18, 46]. We also identified 8 studies as eligible [3239] that were not included in earlier meta-analyses. Thus, our meta-analysis likely provides a more precise estimate of the relationship between the TP53 Arg72Pro polymorphism and CRC risk.

Several studies have indicated that there are multiple differences in the epidemiological, pathological and molecular features of CRCs [5658]. Kapiteijn et al. indicated that rectal cancer may involve more nuclear β-catenin in the APC/β-catenin pathway than colon cancer and reported that the p53-pathway also appears to be more important in rectal cancer [57]. In another study, Slattery et al. found that rectal and distal colon tumors are more likely to have a p53 mutation than proximal colon tumors [58]. When we stratified based on tumor location, we observed a significant association between the TP53 Arg72Pro CC genotype and rectal cancer, but no association was observed between this genotype and colon cancer. One possible explanation for this finding could be that different bacterial flora and a longer transit time in the rectum might change the contact between intestinal cells and potential carcinogens or promoters in the fecal stream, which may lead to more (exogenous) mutations of p53.

Factors known to affect the risk of CRC include gender, age, environmental factors and chronic inflammation. Joshi et al. found that men with the CC genotype and C allele had significantly higher risk for CRC than women with the same genotype [35]. Aizat et al. found that carriers of CC genotype aged 50 years and older were also at significantly greater risk for CRC [32]. However, no significant associations were found between these two confounding factors and CRC susceptibility in other studies [26, 29]. The difference may be explained by differences in the groups studied or populations and/or by differences in environmental exposure and lifestyle factors. Additional studies with a large patient cohort are needed to verify these initial observations.

Our meta-analysis had several limitations. First, we did not calculate an adjusted estimate for the association between the TP53 Arg72Pro polymorphism and CRC risk because not all studies reported adjusted ORs. Second, because heterogeneity was obvious, even in some sub-analyses, other potential confounding factors appeared to be present in the included studies; we did not take these confounding factors into account. Third, due to an absence of information, we were unable to assess other factors such as gender, age, alcohol consumption and smoking status, which may have modified the association. Finally, potential gene-gene and gene-environment interactions were not analyzed due to a lack of relevant data.

In summary, our updated meta-analysis demonstrated that the TP53 Arg72Pro polymorphism CC genotype may contribute to an increased risk of CRC, especially for rectal cancer and among Asians. Future well-designed studies with larger samples are needed to confirm our findings.

MATERIALS AND METHODS

Identification of eligible studies

Potentially relevant articles published prior to December 2014 were identified in the PubMed, EMBASE, Web of Knowledge, and Chinese National Knowledge Infrastructure databases using the following key words: “TP53 or P53,” “polymorphism or variant,” and “colorectal cancer, colon or CRC.” Additional studies on the topic of interest were identified by hand-searching the reference lists of the retrieved articles. When multiple publications reported on the same or overlapping data, the most recent study with the largest sample size was selected.

Inclusion and exclusion criteria

The studies included in our meta-analysis were required to meet the following criteria: 1) the study was a case-control or cohort study; 2) the study investigated the association between the TP53 Arg72Pro polymorphism and CRC risk; 3) the study provided sufficient information to estimate ORs and 95% CIs; and 4) the study had a control genotype distribution in HWE. Studies were excluded for the following reasons: 1) the study was not a case-control study; 2) the publication contained incomplete data; and 3) the study was a duplicate of a previous publication.

Data extraction

Data were independently extracted by two reviewers (Dai and Sun) using a standardized data extraction form. Disagreements were resolved through discussion. The extracted data included the following items: first author, publication year, country of origin, ethnicity, source of control, sample sizes, genotype distribution in cases and controls, P-value for HWE, and genotyping methods.

Statistical analysis

Pooled ORs with corresponding 95% CIs were used to evaluate the strength of the observed associations. Four genetic contrast models, including allelic contrast (C vs. G), homozygote comparisons (CC vs. GG), dominant models (CC+GC vs. GG), and recessive models (CC vs. GC+GG), were applied. HWE was evaluated in the control group for each study using the χ2 test, and the significance level was set at P<0.05. Between-study heterogeneity was assessed by calculating the Q-statistic and quantified using the I2 value. A fixed effect model that used the Mantel-Haenszel approach was applied to calculate the pooled ORs if the between-study heterogeneity was not significant [59]. A random effect model that used DerSimonian and Laird’s method was adopted when the between-study heterogeneity was obvious [60]. When the Q test P>0.05 and I2<50%, the fixed-effects model was used; otherwise, the random-effects model was used. Subgroup analyses were performed based on ethnicity, source of controls, tumor location and genotype method. Sensitivity analysis was performed to determine the influence of single datasets on the combined estimates. Begg’s funnel plot and Egger’s test were used to assess publication bias [61, 62]. All analyses were performed using Stata software version 12.0 (Stata Corp., College Station, TX), and all P values were two-sided.

ACKNOWLEDGMENTS AND FUNDING

This study was supported by grants from the National Natural Science Foundation of China (No. 81573654).

CONFLICTS OF INTEREST

The authors indicated no financial relationships.

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