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Research Papers:

Association of a common genetic variant in RNASEL and prostate cancer susceptibility

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Oncotarget. 2017; 8:75141-75150. https://doi.org/10.18632/oncotarget.20646

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Li Zuo, Ke-Wei Ren, Yu Bai, Li-Feng Zhang, Jian-Gang Zou, Xi-Hu Qin _, Yuan-Yuan Mi, Atsushi Okada and Takahiro Yasui

Abstract

Li Zuo1,*, Ke-Wei Ren5,*, Yu Bai1,*, Li-Feng Zhang1,*, Jian-Gang Zou1,*, Xi-Hu Qin2, Yuan-Yuan Mi3, Atsushi Okada4 and Takahiro Yasui4

1Department of Urology, Changzhou No. 2 People’s Hospital Affiliated to Nanjing Medical University, Changzhou 213003, China

2Department of General Surgery, Changzhou No. 2 People’s Hospital Affiliated to Nanjing Medical University, Changzhou 213003, China

3Department of Urology, Third Affiliated Hospital of Nantong University, Wuxi 214041, China

4Department of Nephrourology, Nagoya City University Graduate School of Medical Sciences, Aichi 4678601, Japan

5Department of Orthopedics, The Affiliated Jiangyin Hospital of Southeast University Medical School, Jiangyin 214400, China

*These authors have contributed equally to this work

Correspondence to:

Xi-Hu Qin, email: nj-likky@163.com

Keywords: RNASEL, polymorphism, prostate cancer, meta-analysis

Received: April 26, 2017    Accepted: August 07, 2017    Published: September 05, 2017

ABSTRACT

The RNASEL gene (2’, 5’-oligoisoadenylate synthetase-dependent) encodes a ribonuclease that plays a significant role in the apoptotic and antiviral activities of interferons. Various studies have used polymorphisms in the RNASEL gene to evaluate prostate cancer risk but studies that show an association between RNASEL Arg462Gln (1385G>A, R462Q, rs486907) polymorphism and prostate cancer risk are somewhat inconclusive. To assess the impact of RNASEL Arg462Gln polymorphism on prostate cancer risk, we conducted a meta-analysis of all available studies including 11,522 patients and 10,976 control subjects. The overall results indicated no positive association between the variant and prostate cancer risk. However, in a subgroup analysis by ethnicity, obvious associations were observed in Hispanic Caucasians for allelic contrast (OR = 1.18, 95% CI = 1.00 - 1.39, Pheterogeneity = 0.010), homozygote comparison (OR = 1.50, 95% CI = 1.02 – 2.20, Pheterogeneity = 0.001), and the recessive genetic model (OR = 1.44, 95% CI = 1.01 - 2.05, Pheterogeneity = 0.002) ; and in African descendants for homozygote comparison (OR = 2.59, 95% CI = 1.29 – 5.19, Pheterogeneity = 0.194) and the recessive genetic model (OR = 2.61, 95% CI = 1.30 – 5.23, Pheterogeneity = 0.195). In conclusion, the RNASEL Arg462Gln polymorphism may contribute to the risk of developing prostate cancer in African descendants and Hispanic Caucasians. Further larger and well-designed studies are warranted to evaluate this association in detail.


INTRODUCTION

Prostate cancer (PCa) is one of the most common types of neoplasm in the Western world. In United States, prostate cancer is the most prevalent cancer, with 217,730 new cases predicted to occur in 2010 [1]. The etiology of PCa is still poorly understood, but exposure to hormones, infectious agents, or dietary carcinogens may contribute to inflammation of the prostate [2, 3]. Intraprostatic inflammation may affect the tissue microenvironment, promoting genetic damage, and driving cellular proliferation, which may lead to prostate carcinogenesis [4, 5]. Prior studies have suggested that family history is the most reproducible and significant risk factor. Men with a brother or father diagnosed with PCa were twice as likely to develop this cancer as men with no relatives affected [6].

Ribonuclease L (RNASEL), which is considered to be a tumor-suppressor gene, plays a significant role in the pathogenesis of prostate cancer through inflammation and infection. RNASEL is on chromosome 1q24-25 and encodes Ribonuclease L, a significant enzyme of the interferon-induced antiviral 2-5A system [7]. Mutation of RNASEL can lead to dysfunction of Ribonuclease L in regulating single-stranded RNA cleavage, cellular viral defense, and tumor suppressor activities, such as stress-mediated apoptosis and regulation of protein synthesis [89].

Extensive epidemiological studies had been conducted to explore the association between RNASEL polymorphism and prostate cancer risk. A G-to-A transversion at nucleotide position 1385 (rs486907), which results in a glutamine instead of arginine at amino acid position 462 (R462Q), is one of the most widely investigated polymorphisms in RNASEL. Nevertheless, the association between the RNASEL R462Q polymorphism and prostate cancer risk is controversial because of conflicting case–control studies. Therefore, in this meta-analysis from all eligible studies published to date [1032], we used enhance statistical power to understand the effect of this variant.

RESULTS

Study characteristics

A total of 22 articles (including 26 case–control studies) met all the inclusion criteria and were included (Figure 1). The genotype distribution of the control population was consistent with Hardy-Weinberg equilibrium (HWE) in 19 of the publications. Characteristics of the eligible studies are summarized in Table 1. In general, 11,522 prostate cancer patients and 10,976 control subjects with the RNASEL Arg462Gln polymorphism were evaluated. In the ethnic subgroups, 17 case–control studies were performed with European descendants, three with Asian descendants, and four with African descendants. We checked the Minor Allele Frequency (MAF) reported for the five main worldwide populations in the 1000 Genomes Browser: East Asian, 0.2421; European, 0.3708; African, 0.0666; American, 0.2233; and South Asian, 0.3016. The MAF in our analysis was 0.3034 and 0.2900 in the case and control group, respectively (Figure 2). Hospital-based controls were carried out in 15 of the studies. TaqMan real-time polymerase chain reaction (PCR), the classical genotyping method, was utilized in 10 comparisons. Five studies used the GoldenGate platform or Sequenom MassARRAY platform genotyping method. Six publications had genotype frequency information for familial and sporadic prostate cancer cases.

Table 1: Study characteristics of RNASEL Arg462Gln (1385G>A) polymorphism included in this meta-analysis

First author

Year

Country

Ethnicity

Source of Genotype method

Sample size of case

Sample size of control

control

GG

GA

AA

Total

MAF

HWE

GG

GA

AA

Total

MAF

HWE

Babaei

2015

Iran

Asian

HB

PCR

20

15

5

40

0.313

0.421

44

32

4

80

0.250

0.551

Alvarez-Cubero

2015

Spain

Hispanic

HB

Goldengate assay

80

120

37

237

0.409

0.468

61

114

41

216

0.454

0.342

Winchester

2015

USA

Non-Hispanic

PB

Goldengate assay

352

407

105

864

0.357

0.445

330

372

129

831

0.379

0.157

San Francisco

2014

Chile

Hispanic

PB

Taqman

43

31

9

83

0.295

0.351

28

14

4

46

0.239

0.267

Reza

2012

Iran

Asian

HB

Taqman

64

73

44

181

0.445

0.014

14

4

1

19

0.158

0.364

Sakuma

2011

USA

Caucasian

HB

Real-time PCR

43

55

12

110

0.359

0.366

11

21

8

40

0.463

0.723

Beuten

2010

USA

Hispanic

HB

Goldengate assay

75

64

17

156

0.314

0.550

126

91

7

224

0.234

0.048

Meyer

2010

USA

Caucasian

PB

Sequenom MassARRAY

529

547

159

1235

0.350

0.346

505

546

159

1210

0.357

0.551

Martinez-Fierro

2010

Mexico

Mixed

HB

Taqman

9

2

0

11

0.091

0.041

8

2

1

11

0.182

0.197

Agalliu

2010

USA

Non-Hispanic

PB

Pyrosequencing

467

414

84

965

0.302

0.566

572

556

109

1237

0.313

0.110

Wang

2009

USA

Caucasian

PB

Taqman

100

121

27

248

0.353

0.282

88

132

33

253

0.391

0.130

Fischer

2008

Germany

Non-Hispanic

HB

Real time PCR

51

29

7

87

0.247

0.331

42

24

4

70

0.229

0.816

Robbins

2008

USA

African

HB

Sequenom MassARRAY

183

55

5

243

0.134

0.718

225

66

5

296

0.128

0.950

Shea

2008

USA

African

PB

PCR

187

41

2

230

0.098

0.881

362

88

2

452

0.102

0.168

Shook

2007

USA

African

HB

Taqman

45

13

10

68

0.243

<0.001

111

31

3

145

0.128

0.633

Shook

2007

USA

Hispanic

HB

Taqman

72

62

16

150

0.313

0.629

136

96

7

239

0.230

0.039

Shook

2007

USA

Non-Hispanic

HB

Taqman

187

183

60

430

0.352

0.162

221

225

57

503

0.337

0.981

Cybulski

2007

Poland

Non-Hispanic

PB

PCR-RFLP

245

376

116

737

0.412

0.153

177

252

82

511

0.407

0.625

Daugherty

2007

USA

Non-Hispanic

HB

TaqMan

463

505

148

1116

0.359

0.578

554

602

188

1344

0.364

0.235

Daugherty

2007

USA

African

HB

TaqMan

73

23

2

98

0.138

0.905

277

98

5

380

0.142

0.261

Maier

2005

Germany

Non-Hispanic

PB

PCR

133

171

59

363

0.398

0.746

73

97

37

207

0.413

0.629

Nam

2005

Canada

Mixed

PB

Mass spectrometry

477

409

110

996

0.316

0.117

521

459

112

1092

0.313

0.464

Wiklund

2004

Sweden

Non-Hispanic

PB

TaqMan

597

778

247

1622

0.392

0.804

297

384

115

796

0.386

0.611

Nakazato

2003

Japan

Asian

HB

PCR

69

32

0

101

0.158

0.059

71

26

8

105

0.200

0.020

Rokman

2002

Finland

Non-Hispanic

HB

PCR

88

106

39

233

0.395

0.464

69

84

23

176

0.369

0.745

Wang

2002

USA

Caucasian

PB

PCR

389

427

102

918

0.344

0.347

193

233

67

493

0.372

0.802

HWE: Hardy-Weinberg equilibrium of controls, HB: Hospital-based; PB: Population-based; MAF: Minor Allele Frequency.

Flowchart illustrating the search strategy used to identify association studies for RNASEL Arg462Gln polymorphism and prostate cancer risk.

Figure 1: Flowchart illustrating the search strategy used to identify association studies for RNASEL Arg462Gln polymorphism and prostate cancer risk.

A-allele frequencies for the RNASEL Arg462Gln polymorphism in the controls stratified by ethnicity.

Figure 2: A-allele frequencies for the RNASEL Arg462Gln polymorphism in the controls stratified by ethnicity. Vertical line, A-allele frequency; Horizontal line, ethnicity type.

Quantitative synthesis

When all the eligible studies were pooled into the analysis (Table 2), no positive association was observed for allelic contrast (fixed-effects OR = 0.99, 95% CI = 0.95 - 1.03, Pheterogeneity = 0.004, P = 0.758, I2 = 47.9), homozygote comparison (fixed-effects OR = 1.00, 95% CI = 0.91 - 1.09, Pheterogeneity = 0.001, P = 0.968, I2 = 54.2), heterozygote comparison (fixed-effects OR = 1.01, 95% CI = 0.92 - 1.10, Pheterogeneity = 0.029, P = 0.861, I2 = 37.6), the dominant genetic model (fixed-effects OR = 0.99, 95% CI = 0.93 - 1.04, Pheterogeneity = 0.361, P = 0.653, I2 = 7.0), and the recessive genetic model(fixed-effects OR = 1.00, 95% CI = 0.92 – 1.09, Pheterogeneity = 0.002, P = 0.960, I2 = 50.3). However, in the subgroup analysis by ethnicity, obvious associations between the RNASEL Arg462Gln polymorphism and prostate cancer risk were observed in African descendants for homozygote comparison (fixed-effects OR = 2.59, 95% CI = 1.29 – 5.19, Pheterogeneity = 0.194, P = 0.008, I2 = 36.3), and the recessive genetic model (fixed-effects OR = 2.61, 95% CI = 1.30 – 5.23, Pheterogeneity = 0.195, P = 0.007, I2 = 36.1); and for Hispanic Caucasians for the recessive genetic model (fixed-effects OR = 1.44, 95% CI = 1.01 - 2.05, Pheterogeneity = 0.002, P = 0.046, I2 = 79.9), homozygote comparison (fixed-effects OR = 1.50, 95% CI = 1.02 – 2.20, Pheterogeneity = 0.001, P = 0.039, I2 = 82.3), and allelic contrast (fixed-effects OR = 1.18, 95% CI = 1.00 - 1.39, Pheterogeneity = 0.010, P = 0.050, I2 = 73.5). No association was observed in Asian descendants for allelic contrast (fixed-effects OR = 1.30, 95% CI = 0.93 – 1.83, Pheterogeneity = 0.004, P = 0.126, I2 = 82.2), homozygote comparison (fixed-effects OR = 1.49, 95% CI = 0.70 – 3.17, Pheterogeneity = 0.013, P = 0.303, I2 = 76.9), and the recessive genetic model (fixed-effects OR = 1.23, 95% CI = 0.57 – 2.63, Pheterogeneity = 0.019, P = 0.600, I2 = 74.8); non-Hispanic Caucasians for allelic contrast (fixed-effects OR = 0.99, 95% CI = 0.94 – 1.04, Pheterogeneity = 0.856, P = 0.641, I2 = 0) homozygote comparison (fixed-effects OR = 0.98, 95% CI = 0.87 – 1.10, Pheterogeneity = 0.631, P = 0.701, I2 = 0), and the recessive genetic model (fixed-effects OR = 0.98, 95% CI = 0.88 – 1.09, Pheterogeneity = 0.487, P = 0.680, I2 = 0); and mixed descendants for allelic contrast (fixed-effects OR = 1.01, 95% CI = 0.89 – 1.15, Pheterogeneity = 0.381, P = 0.886, I2 = 0), homozygote comparison (fixed-effects OR = 1.06, 95% CI = 0.79 – 1.42, Pheterogeneity = 0.453, P = 0.692, I2 = 0), and the recessive genetic model (fixed-effects OR = 1.07, 95% CI = 0.81 – 1.42, Pheterogeneity = 0.452, P = 0.610, I2 = 0) (Figure 3). Furthermore, a significant association was also observed under the recessive genetic model (random-effects OR = 1.45, 95% CI = 1.01 – 2.08, Pheterogeneity < 0.001, P = 0.046, I2 = 64.8) between the RNASEL polymorphism and hospital-based controls (Figure 4). Interestingly, in a stratified analysis by the type of prostate cancer, a positive association was observed in familial prostate cancer for allelic contrast (fixed-effects OR = 0.89, 95% CI = 0.79 – 0.99, Pheterogeneity = 0.209, P = 0.028, I2 = 31.8) and homozygote comparison (fixed-effects OR = 0.77, 95% CI = 0.61 – 0.98, Pheterogeneity = 0.136, P = 0.037, I2 = 42.9), but not in sporadic prostate cancer for allelic contrast (fixed-effects OR = 1.02, 95% CI = 0.94 – 1.10, Pheterogeneity = 0.774, P = 0.671, I2 = 0) and homozygote comparison (fixed-effects OR = 1.06, 95% CI = 0.89 – 1.27, Pheterogeneity = 0.640, P = 0.503, I2 = 0).

Table 2: Stratified analyses of the RNASEL Arg462Gln polymorphism on prostate cancer risk

Variables

Na

Cases/

A-allele vs. G-allele

AA vs. GG

AA vs. GA+GG

Controls

OR(95%CI)

P

Pheterb

I2

OR(95%CI)

P

Pheterb

I2

OR(95%CI)

P

Pheterb

I2

Total

26

11522/10976

0.99(0.95-1.03

0.758

0.004

47.9

1.00(0.91-1.09

0.968

0.001

54.2

1.00(0.92-1.09

0.960

0.002

50.3

Ethnicity

Asian

3

322/204

1.30(0.93-1.83

0.126

0.004

82.2

1.49(0.70-3.17

0.303

0.013

76.9

1.23(0.57-2.63

0.600

0.019

74.8

African

4

639/1273

1.12(0.91-1.37

0.281

0.056

60.3

2.59(1.29-5.19

0.008

0.194

36.3

2.61(1.30-5.23

0.007

0.195

36.1

Caucasian

4

2511/1996

0.92(0.84-1.00

0.058

0.371

4.4

0.84(0.70-1.02

0.081

0.319

14.6

0.88(0.74-1.06

0.173

0.462

0

Hispanic Caucasians

4

626/725

1.18(1.00-1.35

0.050

0.010

73.5

1.50(1.02-2.20

0.039

0.001

82.3

1.44(1.01-2.05

0.046

0.002

79.9

Non-Hispanic Caucasians

9

6417/5675

0.99(0.94-1.04

0.641

0.856

0

0.98(0.87-1.10

0.701

0.631

0

0.98(0.88-1.09

0.680

0.487

0

Mixed

2

1007/1103

1.01(0.89-1.15

0.886

0.381

0

1.06(0.79-1.42

0.692

0.453

0

1.07(0.81-1.42

0.610

0.452

0

Source of control

Hospital-based

15

3261/3848

1.06(0.98-1.14

0.120

0.001

63.1

1.47(0.99-2.20

0.059

<0.001c

67.9

1.45(1.01-2.08

0.046

<0.001c

64.8

Population-based

11

8261/7128

0.97(0.92-1.01

0.169

0.802

0

0.94(0.84-1.04

0.235

0.709

0

0.95(0.86-1.05

0.296

0.739

0

Type of prostate cancer

Sporadic Pca

6

2838/2934

1.02(0.94-1.10

0.671

0.774

0

1.06(0.89-1.27

0.503

0.640

0

1.07(0.91-1.26

0.441

0.679

0

Familial Pca

5

1313/1967

0.89(0.79-0.99

0.028

0.209

31.8

0.77(0.61-0.98

0.037

0.145

31.8

0.81(0.65-1.02

0.070

0.210

31.7

a Number of comparisons

b P value of Q-test for heterogeneity test(Pheter).

c Random effects model was performed when Pheter <0.001; otherwise, fixed effects model was used.

Forest plot of prostate cancer risk associated with RNASEL Arg462Gln polymorphism (recessive genetic model) in the stratified analysis by ethnicity.

Figure 3: Forest plot of prostate cancer risk associated with RNASEL Arg462Gln polymorphism (recessive genetic model) in the stratified analysis by ethnicity. The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI. Separate details were summarized in Table 1.

Forest plot of prostate cancer risk associated with RNASEL Arg462Gln polymorphism (recessive genetic model of AA vs. GA &#x002B; GG) by source of control.

Figure 4: Forest plot of prostate cancer risk associated with RNASEL Arg462Gln polymorphism (recessive genetic model of AA vs. GA + GG) by source of control. The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.

Publication bias

The Egger’s test and Begg’s funnel plot were carried out to assess the publication bias of the literature. No obvious evidence of publication bias was found (A-allele vs. G-allele, t = 1.72, P = 0.098; AA vs. GG, t = 1.77, P = 0.089; GA vs. GG, t = 1.75, P = 0.094; AA + GA vs. AA, t = 1.23, P = 0.231; AA vs. GA + GG, t = 1.77, P = 0.090)

DISCUSSION

Published studies have shown evidence that RNASEL is a constitutively expressed latent endo-ribonuclease that mediates proapoptotic and antiviral activities of the IFN-inducible 2-5A system [79]. Mutation carriers in the RNASEL gene have loss of heterozygosity and are deficient in functional RNase L activity [33]. However, previous reports showing association between RNASEL polymorphism and prostate cancer susceptibility are contradictory. The general goal of this pooled analysis is to quantitatively analyze previous studies to understand the true relationship between RNASEL polymorphism and prostate cancer. Here, previous case-control studies with information between the RNASEL polymorphism and different types of Caucasians (Hispanic and Non-Hispanic) were included. As a result, some new findings were observed in our meta-analysis.

Our results indicated that the RNASEL Arg462Gln polymorphism may be associated with increased prostate cancer in African descendants (under homozygote comparison and the recessive genetic model) and Hispanic Caucasians (under allelic contrast, homozygote comparison, and the recessive genetic model), but not in Asian descendants and Non-Hispanic Caucasians. Furthermore, in a stratified analysis by source of control, the RNASEL Arg462Gln variant was found to increase prostate cancer risk in hospital-based studies (under the recessive genetic model). Nevertheless, several caveats limit generalization of these results. First, detailed information, such as age, prognostic parameters, environmental factors, and life-style, were not considered. Second, because different types of prostate cancer influence susceptibility, we tried to assess the effect of this polymorphism to different types of prostate cancer but not all data was compatible. Third, positive findings may be published faster than that those with “negative” results, which may result in a time-lag bias [34]. In addition, more environmental interactions, such as smoking habits, dietary factors, hormone exposure, toxins, and infectious agent, need to be added to the meta-analysis in the future.

Other limitations of the meta-analysis need to be addressed. First, while it is possible that the Arg462Gln polymorphism contributes to cancer, the combined effects of multiple environmental or genetic components predominate in the development of carcinoma, and may mask the effect of the polymorphism [35]. Second, the present analysis was based on unadjusted estimates. A more precise analysis with individual data is needed to evaluate combinatorial effects of the polymorphism [36]. Despite these concerns, the current analysis has some advantages compared with the individual studies. First, a substantial number of cases and controls were pooled from different studies, which significantly enhance the statistical power of this analysis. Second, the quality of case-control studies enrolled in our analysis was satisfactory based on the selection criteria. Third, no obvious publication bias was observed, which indicates that the conclusions were relatively stable and the publication bias might not influence the conclusions of the present meta-analysis.

In conclusion, this meta-analysis showed evidence that the RNASEL Arg462Gln polymorphism may contribute to the risk for developing prostate cancer in African descendants and Hispanic Caucasians, but not for other descendants. Further well-designed and prospective studies, particularly focused on gene-environment interactions, are warranted. These future studies should lead to a comprehensive understanding of the association between the RNASEL Arg462Gln polymorphism and prostate cancer risk.

MATERIALS AND METHODS

Search strategy and identification of relevant studies

PubMed database searches were conducted using the following keywords: “ribonuclease L” or “RNASEL”, “prostate cancer”, and “polymorphism” (last search updated on March 01, 2017). References of the relevant articles and retrieved paper were also screened by hand search. Eligible studies had to meet all the following criteria: (a) used an unrelated case–control design; (b) contained information about available genotype frequency; (c) published in English; and (d) included the full-text article.

Data extraction and quality assessment

Data were collected on the genotype of rs486907 G/A (R462Q) according to prostate cancer. For each publication, the data extraction and methodological quality assessment were conducted by two of the investigators independently to ensure accuracy of the data. Disagreement was resolved by discussion between the two investigators. If they could not reach a consensus, the problem was discussed by all investigators to reach a consensus. The following parameters from each study were recorded: first author’s name, publication date, ethnicity, sources of cancer cases and controls, sample size in cases and controls, and the number of cases and controls with wild-type and variant allele, respectively.

Statistical analysis

Crude ORs with 95% CIs were utilized to evaluate the strength of association between the RNASEL polymorphism and prostate cancer based on genotype frequencies in cases and controls. Five genetic contrasts were used to assess the association: allelic contrast (A-allele vs. G-allele), homozygote comparison (AA vs. GG), heterozygote comparison (GA vs. GG), the dominant genetic model (AA + GA vs. GG), and the recessive genetic model (AA vs. GA + GG). Subgroup analysis was stratified by ethnicity, source of control (hospital-based and population-based), and smoking exposure. We utilized the random effects model and fixed effects model to calculate the pooled OR. Heterogeneity assumption was evaluated by a chi-square-based Q test. P value lower than 0.001 for the Q-test indicates lack of heterogeneity among studies, hence the pooled OR was utilized by the random effects model (DerSimoniane and Laird method [37] or by the fixed-effects model (the Mantel-Haenszel method [38]). HWE was checked by the Pearson chi-square test for goodness of fit. A Z-test was used to evaluate statistical significance of the summary OR, and P value of ≤ 0.05 was considered significant. We also utilized the I2 statistic to test heterogeneity, with I2 >75%, 25–75%, and < 25% to represent high, moderate, and low degree of inconsistency, respectively [39]. We determined significance of the intercept by t-test suggested by Egger (P < 0.01 was considered representative of significant publication bias) [40]. All statistical analyses were performed with STATA version 11.0 (Stata Corporation, College Station, TX), utilizing two-sided P values.

CONFLICTS OF INTEREST

The authors declare that they have no competing interests.

FUNDING

This study was supported by the foundation of High-Level Medical Talents Training Project (No.2016CZBJ035) and Changzhou 23rd Science and Technology Project (International Science and Technology Collaboration), Project No. CZ20160017.

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