Oncotarget

Meta-Analysis:

Vitamin D receptor Taq I polymorphism and the risk of prostate cancer: a meta-analysis

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Oncotarget. 2018; 9:7136-7147. https://doi.org/10.18632/oncotarget.23606

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Shaosan Kang _, Yansheng Zhao, Lei Wang, Jian Liu, Xi Chen, Xiaofeng Liu, Zhijie Shi, Weixing Gao and Fenghong Cao

Abstract

Shaosan Kang1,*, Yansheng Zhao2,*, Lei Wang1, Jian Liu1, Xi Chen1, Xiaofeng Liu3, Zhijie Shi4, Weixing Gao1 and Fenghong Cao1

1Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan 063000, China

2Department of Imaging, KaiLuan General Hospital, Tangshan 063000, China

3Department of Surgery, LaoTing Traditional Chinese Medicine Hospital, Tangshan 063600, China

4Department of Urology, TangShan Gongren Hospital, Tangshan 063000, China

*These authors contributed equally to this work

Correspondence to:

Shaosan Kang, email: kangshaosan@163.com

Keywords: Taq I; prostate cancer; vitamin D receptor; polymorphisms; meta-analysis

Received: September 11, 2017     Accepted: December 15, 2017     Published: December 22, 2017

ABSTRACT

Numerous previous studies reported the association of Vitamin D receptor gene Taq Ipolymorphism with prostate cancer risk, however these results were controversial. In order to provide a relatively comprehensive description of this relationship, we conducted this meta-analysis by searching PubMed, Embase, and China National Knowledge Infrastructure. Finally, 36 studies with 8,423 cases and 8,887 controls were included. Taq I polymorphism was found to marginally increase the prostate cancer risk in recessive genetic model (tt/Tt vs. TT: Odds Ratio (OR) = 0.89, 95% Confidence Interval (CI) = 0.80–1.00, p = 0.05) and allele genetic model (t vs. T allele: OR = 0.91, 95% CI = 0.84–0.99, p = 0.003) in the overall analysis. Subgroup analyses showed that significant increased risk was found in Asians in homozygote model (tt vs. TT: OR = 0.63, 95% CI = 0.41–0.95, p = 0.029) and allele genetic model (t vs. T: OR = 0.78, 95% CI = 0.67–0.90, p = 0.002), and in the subgroup of population-based controls in all the genetic models. These results suggest that Taq Ipolymorphism might be a risk factor of prostate cancer risk, especially in Asians. It could be considered as a promising target to predict the prostate cancer risk for clinical practice.


Vitamin D receptor <i>Taq</i> I polymorphism and the risk of prostate cancer: a meta-analysis | Kang | Oncotarget

INTRODUCTION

Prostate cancer (PCa) is second-most commonly diagnosed malignancy in males, and thought to be one of the leading causes of cancer-related death around the world. In 2014, approximately 233,000 newly diagnosed cases and 30,000 PCa-related deaths was reported in America [1]. Furthermore, the global incidence is rising rapidly. By 2030, the number of new PCa and PCa-related deaths annually will climb to 1,853,391 and 544,209, respectively [2]. The etiology of PCa has remained unclear. Several factors are considered to significant increase the risk of PCa, including ethnicity, hormonal status, environment, diet, aging, and genetic factors [3].

Low serum levels of vitamin D might be one of the risk factors for PCa [4]. Laboratory investigation demonstrated that vitamin D inhibits the growth and differentiation of PCa cells, decreases the invasion, metabolism and angiogenesis of tumor cell. It can also promote tumor cell apoptosis [4]. In 2007, a clinical trial suggested that calcitriol, a kind of analogue of vitamin D can significantly improve patients’ survival rate by decreasing serum level of prostate special antigen (PSA) [5]. The antineoplastic effect of vitamin D is activated when binding to vitamin D receptor (VDR) [6]. 1,25-Dihydroxy vitamin D3(1,25(OH)2D3) is the hormonally active form of vitamin D. It binds to VDR and forms a heterodimer complex, which subsequently binds to the vitamin D response element and reduces the transcription levels of many genes that stimulating the cell growth and differentiation [7, 8].

Recently, the relationship of several single nucleotide polymorphisms (SNPs) of VDR gene and PCa risk has been the focus of research attention [8, 9]. Taq I polymorphism (rs731236) is one of the most widely-studied SNPs. It is a synonymous mutation located in exon 9 of VDR gene [10]. This mutation could reduce the mRNA stability and therefore decrease the mRNA levels of VDR gene [11]. Recently, some studies have suggested that Taq I variation might increase the susceptibility of PCa [12, 13]. However, these results are debatable and inconsistent in the effect of Taq I polymorphism on PCa risk. Numerous studies in favor of the association of Taq I polymorphisms and PCa risk [1419]. Meanwhile, some studies disapprove of the relationship [2022]. The difference might be due to under-power for individual study. Moreover, previous meta-analyses [10, 23, 24] seem to be outdated since new data appeared [17, 2527]. Therefore, we conduct this meta-analysis to get more accurate results.

RESULTS

Characteristics of studies

We identified 288 potentially relevant studies following the retrieval strategy. Based on the inclusion criteria, 36 studies [3, 7, 9, 12, 1419, 22, 2549] between 1996 to 2017 were finally included (Figure 1). The number of cases and controls varied from 28 to 1,617, and 41 to 1,072, respectively (Table 1). The genotype distribution frequency in the control groups was consistent with Hardy-Weinberg equilibrium (HWE) for most studies, except for four studies [12, 19, 25, 49]. Each individual study scored more than 4 by Newcastle-Ottawa Scale (NOS), and was considered to be of high quality (Table 1). The percentages of tt, Tt and TT genotype in case group and control group were 11.9%, 40.4%, 47.7% and 12.1%, 41.3%, 46.6%, respectively in overall population.

Study flowchart for the process of selecting the final 36 studies.

Figure 1: Study flowchart for the process of selecting the final 36 studies.

Table 1: Characteristics and quality assessment of the studies included in this meta-analysis

Author

Year

Country

Ethnicity

Genotyping method

Sample size

(cases/controls)

Source of

Controls

HWE

NOS

Andersson

2006

Sweden

Caucasian

PCR-RFLP

137/176

PB

Y

6

Bai

2009

China

Asian

PCR-RFLP

122/130

HB

Y

6

Blazer

2000

USA

Caucasian

PCR-RFLP

77/183

PB

Y

6

Bodiwala

2004

UK

Caucasian

PCR-RFLP

368/243

BPH

N

6

Chaimuangraj

2006

Thailand

Asian

PCR-RFLP

28/30/44

HB/BPH

Y

5

Cicek

2006

USA

Mixed

PCR-RFLP

439/478

PB

Y

6

Correa-Cerro

1999

Germany/France

Caucasian

PCR-RFLP

106/95

HB

Y

6

Forrest

2005

UK

Caucasian

PCR-RFLP

262/444

HB

Y

6

Furuya

1999

Japan

Asian

PCR-RFLP

66/60

HB

Y

5

Gsur

2002

Austria

Caucasian

PCR-RFLP

190/190

BPH

Y

6

Habuchi

2000

Japan

Asian

PCR-RFLP

222/128/209

HB/BPH

Y

6

Hamasaki

2001

Japan

Asian

PCR-RFLP

115/133

HB

Y

6

Hamasaki

2002

Japan

Asian

PCR-RFLP

110/90/83

HB/BPH

Y

6

Holick

2007

USA

Caucasian

SNPlex

586/541

PB

Y

6

Holt

2009

USA

Caucasian

SNPlex

697/697

PB

Y

6

Hu

2014

China

Asian

TaqMan

108/242

PB

Y

6

Huang

2004

China

Asian

PCR-RFLP

160/205

PB

Y

6

Jingwi

2015

USA

African

TaqMan

306/251

PB

Y

6

John

2005

USA

African/Asian

TaqMan

424/436

PB

Y

6

Kambale

2017

India

Asian

PCR-RFLP

120/240

PB

N

5

Kibel

1998

USA

Mixed

PCR-RFLP

41/41

PB

Y

5

Luscombe

2001

UK

Caucasian

PCR-RFLP

209/154

BPH

Y

6

Ma

1998

USA

Caucasian

PCR-RFLP

354/589

HB

Y

7

Maistro

2004

Brazil

African

PCR-RFLP

165/200

HB

Y

6

Medeiros

2002

Portugal

Caucasian

PCR-RFLP

162/206

PB

Y

6

Oakley-Grivan

2004

USA

Mixed

PCR-RFLP

345/292

PB

Y

6

Oh

2013

Korea

Asian

IGGGS

272/173

BPH

Y

6

Onen

2008

Turkey

Caucasian

PCR-RFLP

133/157

PB

Y

6

Onsory

2008

India

Asian

PCR-RFLP

100/100

PB

Y

6

Rowland

2013

USA

Mixed

TaqMan

1617/1072

PB

Y

7

Suzuki

2003

Japan

Asian

PCR-RFLP

81/105

HB

Y

5

Tayeb

2003

UK

Caucasian

PCR-RFLP

21/379

BPH

Y

5

Tayeb

2004

UK

Caucasian

PCR-RFLP

28/56

BPH

Y

5

Taylor

1996

USA

Mixed

PCR-RFLP

108/170

BPH

Y

6

Watanabe

1999

Japan

Asian

PCR-RFLP

100/202

BPH

N

5

Yousaf

2014

Pakistani

Asian

PCR-RFLP

44/119

HB

N

5

Abbreviations: HWE, Hardy-Weinberg equilibrium; PB, population-based; HB, hospital-based; BPH, Benign Prostate Hyperplasia; RFLP, restriction fragment length polymorphism; NOS, Newcastle-Ottawa Scale.

Pooled results

As shown in Figure 2 and Table 2. Our results indicated that Taq I polymorphism marginally increase the PCa risk in the overall populations carrying TT genotype or T allele genotype (tt/Tt vs. TT: OR = 0.89, 95% CI = 0.80–1.00, p = 0.05; t vs. T allele: OR = 0.91, 95% CI = 0.84–0.99, p = 0.003), but not in other comparison models (tt vs. TT: OR = 0.86, 95% CI = 0.73−1.01, p = 0.069; Tt vs. TT: OR = 0.92, 95% CI = 0.81–1.10, p = 1.04; tt vs. TT/Tt: OR = 0.90, 95% CI = 0.76−1.06, p = 0.197) (Table 2).

Figure 2:

Figure 2: Forest plots to estimate the association of VDR Taq I polymorphism with PCa in the overall analysis. (A) Dominant model (tt/Tt vs. TT). (B) Allelic frequency model (t vs. T allele).

Table 2: Results of the association between Taq I polymorphism and PCa risk in the whole population

Comparison

Studies

Overall effect

Heterogeneity

Publication bias

OR

Z-score

p-value

I2

P-value

Begg’s test

Egger’s test

tt vs TT

36

0.86 [0.73–1.01]

1.82

0.069

44.10%

0.004

0.382

0.363

Tt vs TT

36

0.92 [0.81–1.04]

1.35

0.176

60%

0.000

0.955

0.891

tt/Tt vs TT

36

0.89 [0.80–1.00]

1.96

0.05

56.20%

0.000

0.808

0.914

tt vs TT/Tt

36

0.90 [0.76–1.06]

1.29

0.197

54.20%

0.000

0.318

0.496

t vs T

36

0.91 [0.84–0.99]

2.18

0.03

56.90%

0.000

0.465

0.472

For the stratified analysis of different ethnicities, significantly increased risk was found in Asians in T allele genotype carriers (t vs. T: OR = 0.79, 95% CI = 0.68–0.91, p = 0.002) (Table 3 and Figure 2). However, when 15 studies performed in Caucasians and 3 studies in Africans were analyzed, no significant associations were found in any comparison models (Table 3).

Table 3: Results of the association between Taq I polymorphism and PCa risk in different ethnicities

Comparison

Studies

Overall effect

Heterogeneity

Publication bias

OR

Z-score

p-value

I2

P-value

Begg’s test

Egger’s test

Asian

tt vs TT

14

0.63 [0.41–0.95]

2.18

0.029

0%

0.504

0.312

0.981

Tt vs TT

14

0.87 [0.63–1.21]

0.82

0.413

69%

0.000

0.033

0.022

tt/Tt vs TT

14

0.80 [0.63–1.03]

1.71

0.087

53%

0.010

0.055

0.023

tt vs TT/Tt

14

0.73 [0.38–1.39]

0.95

0.34

46%

0.046

0.243

0.414

t vs T

14

0.78 [0.67-0.90]

3.14

0.002

7%

0.373

0.033

0.026

Caucasian

tt vs TT

15

0.99 [0.86–1.14]

0.08

0.935

56%

0.005

0.961

0.688

Tt vs TT

15

0.99 [0.85–1.16]

0.08

0.933

50%

0.014

0.961

0.878

tt/Tt vs TT

15

1.00 [0.85–1.17]

0.03

0.974

55%

0.05

0.961

0.762

tt vs TT/Tt

15

1.01 [0.81–1.26]

0.08

0.938

62%

0.001

0.656

0.913

t vs T

15

1.01 [0.89–1.14]

0.12

0.905

67%

0.000

0.729

0.884

African

tt vs TT

3

0.96 [0.45–2.08]

0.1

0.922

72%

0.027

0.602

0.603

Tt vs TT

3

0.94 [0.51–1.72]

0.22

0.829

82%

0.004

0.602

0.632

tt/Tt vs TT

3

0.94 [0.50–1.78]

0.18

0.855

85%

0.001

0.602

0.581

tt vs TT/Tt

3

0.96 [0.59–1.56]

0.17

0.86

40%

0.189

0.602

0.515

t vs T

3

0.96 [0.61–1.52]

0.18

0.86

85%

0.002

0.602

0.597

Taq I polymorphism could significantly increase PCa risk in the subgroup of population-based controls when patients carrying TT genotype or T allele genotype in all the genetic models (tt vs. TT: OR = 0.83, 95% CI = 0.73–0.94, p = 0.004; Tt vs. TT: OR = 0.83, 95% CI = 0.69–1.00, p = 0.049; tt/Tt vs. TT: OR = 0.82, 95% CI = 0.70–0.96, p = 0.016; tt vs. TT/Tt: OR = 0.88, 95% CI = 0.78–0.98, p = 0.023; t vs. T allele: OR = 0.89, 95% CI = 0.84–0.95, p = 0.000) (Table 4 and Figure 3). Meanwhile, results for the subgroups of hospital-based and BPH controls revealed no significantly increased risk (Table 4).

Figure 3:

Figure 3: Forest plots to estimate the association of VDR Taq I polymorphism with PCa in Asians. (A) Homozygote model (tt vs. TT). (B) Allelic frequency model (t vs. T allele).

Table 4: Results of the association between Taq I polymorphism and PCa risk in different source of controls

Comparison

Studies

Overall effect

Heterogeneity

Publication bias

OR

Z-score

p-value

I2

P-value

Begg’s test

Egger’s test

Population-based

tt vs TT

16

0.83 [0.73–0.94]

2.98

0.003

2%

0.429

0.882

0.843

Tt vs TT

16

0.83 [0.69–1.00]

1.97

0.049

73%

0.000

0.471

0.437

tt/Tt vs TT

16

0.82 [0.70–0.96]

2.41

0.016

68%

0.000

0.719

0.419

tt vs TT/Tt

16

0.88 [0.78–0.98]

2.28

0.023

27%

0.155

0.961

0.862

t vs T

16

0.89 [0.84–0.95]

3.89

0.000

39%

0.057

0.418

0.297

Hospital-based

tt vs TT

12

0.90 [0.51–1.59]

0.37

0.710

70%

0.000

0.815

0.481

Tt vs TT

12

1.02 [0.81–1.30]

0.19

0.851

50%

0.025

0.411

0.406

tt/Tt vs TT

12

0.99 [0.78–1.27]

0.07

0.946

57%

0.008

0.681

0.752

tt vs TT/Tt

12

0.89 [0.51–1.54]

0.42

0.675

72%

0.000

0.484

0.390

t vs T

12

0.97 [0.76–1.25]

0.21

0.832

77%

0.000

0.681

0.767

BPH

tt vs TT

11

0.90 [0.68–1.19]

0.75

0.451

20%

0.267

0.677

0.476

Tt vs TT

11

1.01 [0.85–1.20]

0.11

0.911

25%

0.208

0.938

0.715

tt/Tt vs TT

11

0.98 [0.83–1.16]

0.22

0.823

17%

0.282

0.586

0.586

tt vs TT/Tt

11

0.85 [0.66–1.10]

1.23

0.217

43%

0.083

0.677

0.585

t vs T

11

0.95 [0.85–1.08]

0.76

0.447

24%

0.219

0.586

0.501

Studies were stratified into TaqMan, PCR-RFLP, and SNPlex groups by genotyping methods. No significant association was found in almost subgroups, except TaqMan group in tt vs. TT/Tt comparison (Table 5). The pooled outcome indicated that the genotyping methods included in these studies are both available and did not alter the outcomes.

Table 5: Results of the association between Taq I polymorphism and PCa risk in different genotyping method

Comparison

Studies

Overall effect

Heterogeneity

Publication bias

OR

Z-score

p-value

I2

P-value

Begg’s test

Egger’s test

PCR-RFLP

tt vs TT

29

0.88 [0.71–1.01]

1.13

0.258

46%

0.006

0.393

0.283

Tt vs TT

29

0.94 [0.80–1.10]

0.77

0.441

62%

0.000

0.970

0.702

tt/Tt vs TT

29

0.91 [0.79–1.05]

1.31

0.19

57%

0.000

0.851

0.995

tt vs TT/Tt

29

0.90 [0.71–1.13]

0.93

0.35

58%

0.000

0.307

0.277

t vs T

29

0.92 [0.82–1.03]

1.51

0.13

59%

0.000

0.476

0.424

TaqMan

tt vs TT

4

0.71 [0.53–0.94]

2.38

0.017

32%

0.219

1.000

0.794

Tt vs TT

4

0.85 [0.69–1.05]

1.50

0.134

44%

0.147

0.174

0.691

tt/Tt vs TT

4

0.82 [0.65–1.02]

1.79

0.074

53%

0.093

0.497

0.812

tt vs TT/Tt

4

0.77 [0.64–0.93]

2.68

0.007

3%

0.378

1.000

0.618

t vs T

4

0.83 [0.71–0.97]

2.36

0.018

49%

0.117

1.000

0.995

SNPlex

tt vs TT

3

0.95 [0.75–1.20]

0.43

0.669

0%

0.322

0.317

-

Tt vs TT

3

0.91 [0.60–1.39]

0.43

0.664

78%

0.010

0.602

0.999

tt/Tt vs TT

3

0.92 [0.64–1.33]

0.45

0.656

74%

0.022

0.602

0.997

tt vs TT/Tt

3

1.01 [0.81–1.25]

0.05

0.961

0%

0.783

0.317

-

t vs T

3

0.96 [0.82–1.23]

0.52

0.603

37%

0.203

0.602

0.987

As shown in Figure 4 and Table 6, we also performed a stratified analysis based on the clinical stages by Gleason Score to describe the relationship in more detail. The pooled results from 9 studies for advanced tumor group and 8 studies for localized tumor group did not reveal any association of Taq I polymorphism with the PCa risk in various genetic models. When 4 studies deviated from HWE in the controls were excluded, similar results were obtained (The results were not given).

Figure 4:

Figure 4: Forest plots to estimate the association of VDR Taq I polymorphism with PCa in the subgroup of population-Based controls. (A) Homozygote model (tt vs. TT). (B) Allelic frequency model (t vs. T allele).

Table 6: Results of the association between Taq I polymorphism and PCa risk in different tumor stage

Comparison

Studies

Overall effect

Heterogeneity

Publication bias

OR

Z-score

p-value

I2

P-value

Begg’s test

Egger’s test

Advanced

tt vs TT

9

0.87 [0.66–1.14]

1.02

0.307

23%

0.243

0.621

0.763

Tt vs TT

9

0.85 [0.65–1.11]

1.18

0.237

53%

0.030

0.404

0.357

tt/Tt vs TT

9

0.84 [0.64–1.10]

1.28

0.200

59%

0.012

0.532

0.347

tt vs TT/Tt

9

0.92 [0.69–1.22]

0.59

0.552

34%

0.155

0.621

0.686

t vs T

9

0.88 [0.70–1.10]

1.14

0.252

66%

0.003

0.211

0.301

Localized

tt vs TT

8

0.63 [0.27–1.45]

1.10

0.273

85%

0.000

0.453

0.966

Tt vs TT

8

0.90 [0.66–1.24]

0.63

0.531

61%

0.013

0.458

0.901

tt/Tt vs TT

8

0.84 [0.56–1.27]

0.83

0.406

79%

0.000

0.458

0.933

tt vs TT/Tt

8

0.66 [0.35–1.22]

1.33

0.182

76%

0.000

0.652

0.891

tvs T

8

0.84 [0.69–1.01]

0.95

0.344

86%

0.000

0.621

0.903

Heterogeneity

Significant between-study heterogeneity was detected in the overall analysis for all the comparison models (tt vs. TT: p = 0.004, I2 = 44%) Tt vs. TT: p = 0.000, I2 = 60%; tt/Tt vs. TT: p = 0.000, I2 = 56%; t vs. TT/Tt: p = 0.000, I2 = 54%; and t vs. T allele: p = 0.000, I2 = 57%) (Table 2). Therefore, random-effects estimates would be more suitable for data analysis. In the subgroup analyses of ethnicity, no heterogeneity was detected in homozygosis genetic model (p = 0.504, I2 = 0%) or allele-frequency model (p = 0.373, I2 = 7%) (Table 3). Similarly, subgroup analysis of population-based controls reported no heterogeneity in homozygosis model, recessive model or allele-frequency model (Table 4). Fix-effect model was applied in these comparison models.

Publication bias and sensitivity analysis

As shown in Figure 5, funnel plots did not reveal any obvious asymmetry. Moreover, the Egger’s test also showed that there was no publication bias in the overall analysis (Table 2) and almost the subgroup analyses (Table 3-6). Sensitivity analyses suggested that the pooled results had not changed significantly by omitting each individual study from all the analyses (Figure 6).

Figure 5:

Figure 5: Begg’s funnel plots to examine publication bias for reported comparisons of VDR gene Taq I polymorphism for the homozygote in (A) Subgroup of Asians. (B) Subgroup of Population-Based controls.

Figure 6:

Figure 6: Sensitivity analysis of the comparison in Allelic frequency model (t vs. T allele) in Asians.

DISCUSSION

In recent years, polymorphism of VDR gene has drawn great attention, because more and more studies have shown that the mutations of VDR gene were related to the PCa risk [1419]. However, these results have been disputable [2022]. Previous meta-analyses were reported by Yin et al. in 2009, Fei et al. in 2016 and Liu et al. in 2017 [10, 23, 24], in which the number of included studies was 23, 27 and 8, respectively. However, some new data was reported, which is not consistent with the results of the former three studies [17, 2527]. 8,423 cases and 8,867 controls were included in our analysis from 36 independent studies. The cases included were much more than the previous meta-analyses. Therefore, our results might be more convincing and stringent.

Our meta-analysis showed that Taq I polymorphism might increase the PCa risk in overall population in recessive genetic model and allele-frequency genetic model. It is not consistent with the results of previous report by Liu et al. [10]. But for the stratified analysis of ethnicity, significant increased risk was found to be associated with Taq I polymorphism in Asians, which is consistent with the results of the report of Fei et al [24]. Ethnicity is an important biological factor for the decline of VDR function [50]. The difference in outcome among ethnicities might result from racial backgrounds and geographic discrepancies [51]. In addition, different dietary patterns could also contribute to the difference [52]. Our results suggested that the Taq I variation might be one of the valuable biomarkers for predicting the susceptibility of PCa. Further studies of Caucasian and African are required.

For the subgroup analysis by the source of controls, increased risk of PCa was found to be associated with Taq I polymorphism in population-based controls in all the comparisons. Possibly some sick population were included in the groups of HBP or hospital-based controls, these groups were special and could not represent all the population [53]. Therefore, the results of these groups might be lack of credibility. Our results revealed some discrepancies between the genotyping methods. It suggested that Taq I polymorphism in the subgroup of TaqMan, Taq I was associated with PCa risk, which may be the cause of heterogeneity. According to a report in 2004, clinical tumor stage of PCa would be accelerated by VDR gene polymorphism [54]. Hence, we performed a subgroup analysis by clinical stage. Our results indicated no association between Taq I polymorphism and susceptibility of PCa, which were different from the previous meta-analyses [24].

Although the between-study heterogeneity was detected, sensitivity analysis did not reveal any significant change in our results by omitting the studies contribute to the heterogeneity. It suggested that our results were credible and statistically robust.

Some limitations should be acknowledged. First, several studies with too little number of patients were included in our analysis, they may introduce potential bias. Second, our results were based on unadjusted parameters, a more accurate analysis are needed, in which some related parameters should be included to adjust the outcome, including age, diet, and other important lifestyle factors.

In conclusion, our meta-analysis might be the largest meta-analysis to estimate the association of VDR gene Taq I polymorphism with the risk of PCa. Marginally increase of PCa risk was found to be related with Taq I polymorphism in overall population, especially in Asians and in population-based controls subgroup. In the future, large and well-designed researches are required to demonstrate the increased effect of Taq I polymorphism on PCa risk.

MATERIALS AND METHODS

Literature and search strategy

The PubMed, Embase, and Chinese National Knowledge Infrastructure (CNKI) database searches were carried out for all the eligible papers. The following search terms were included: “VDR/vitamin D receptor”, “prostate cancer/tumor/carcinoma” and “polymorphism/mutation/variant”. The literature search was updated to August, 2017. In addition, manually searching for the additional studies was conducted according to the references of the original and review reports.

Study selection

Retrieved studies were deemed eligible provided that they met all of the following criteria: (a) studies on human beings; (b) in a case-control or nested case-control design; (c) investigated the relationship of Taq I polymorphism with PCa risk; (d) distribution of genotype frequency for cases and controls could be obtained or calculated; (e) and received more than 4 points in the NOS, which was considered to be high quality; (f) the difference of baseline characters and clinical information was not significant between PCa patients and controls.

Data extraction

The studies meeting the inclusion criteria were read carefully by two investigators independently (Yansheng Zhao and Xiaofeng Liu). We collected the following information: author, year, country, ethnicity, genotyping methods, source of controls, sample size, and genotype and allele frequencies. The subjects were divided into different subgroups: Asians, Africans and Caucasians for ethnicity; hospital-based, population-based, and Benign Prostate Hyperplasia (BPH) for the source of controls; TaqMan, PCR-RFLP and SNPlex for genotyping method. The clinical stages were categorized as localized group (Gleason < 7) and advanced group (Gleason ≥ 7). In order to reach consensus on all of the items, any disagreement was resolved by a third reviewer (Lei Wang).

Statistical analysis

A χ2-test based on the Q statistic was conducted to evaluate the heterogeneity. The between-study heterogeneity was considered to be significant when I2 > 50% and p < 0.05, and the random effects model was used to combine values from studies [55]. Otherwise, for homogeneous studies, the fixed effects model was chosen.

The pooled odds ratios (ORs) together with its 95% confidence intervals (95% CIs) were calculated to evaluate the strength of the association. The statistical significance of ORs was determined with Z-test. To get a more reasonable result, five genetic models were adopted in our analysis: homozygote model (tt vs. TT), heterozygous model (Tt vs. TT), dominant model (tt vs. TT/Tt), recessive model (tt/Tt vs. TT) and allele genetic model (t vs. T).

To assess the potential publication bias, Begg’s Funnel plot was generated based on the analysis result and database size. The more asymmetry the funnel plot looked, the more publication bias was introduced. Meanwhile, Egger’s test was also performed for further investigation. For the Egger’s test, the significance level was set as p value < 0.05. Moreover, HWE of controls was recalculated with the goodness-of-fit χ2-test, P values of > 0.05 was considered as significant equilibrium.

For each outcome, we also performed subgroup analyses according to ethnicity, source of controls, genotyping method and clinic stages. Sensitivity analysis was performed to assess the stability of pooled results.

All analyses were performed using STATA package version 12.0 (Stata Corp, College Station, TX, USA). Two-sided P values of < 0.05 was considered to be statistically significant.

ACKNOWLEDGMENTS

Conceived and designed the experiments: Shaosan Kang and Yansheng Zhao. Extracted data: Yansheng Zhao, Xiaofeng Liu and Lei Wang. Performed the data analysis: Jian Liu, Xi Chen and Zhijie Shi. Wrote the paper: Weixing Gao and Fenghong Cao.

CONFLICTS OF INTEREST

The authors report no conflicts of interest.

GRANT SUPPORT

None.

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