Oncotarget

Meta-Analysis:

Associations between VDR gene polymorphisms and colorectal cancer susceptibility: an updated meta-analysis based on 39 case-control studies

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Oncotarget. 2018; 9:13068-13076. https://doi.org/10.18632/oncotarget.23964

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Zhipeng Pan, _ Mengya Chen, Xingxing Hu, Hua Wang, Jiajia Yang, Congjun Zhang, Faming Pan, Guoping Sun

Abstract

Zhipeng Pan1,*, Mengya Chen2,3,*, Xingxing Hu2,3, Hua Wang1, Jiajia Yang2,3, Congjun Zhang1, Faming Pan2,3 and Guoping Sun1

1Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China

2Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China

3The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, Hefei, Anhui, 230032, China

*These authors contributed equally to this work and should be considered co-first authors

Correspondence to:

Guoping Sun, email: sunguoping@ ahmu.edu.cn

Faming Pan, email: famingpan@ahmu.edu.cn

Keywords: vitamin D receptor; colorectal cancer; VDR; meta-analysis; polymorphisms

Received: July 26, 2017     Accepted: November 14, 2017     Published: January 04, 2018

ABSTRACT

Background: Recent studies have reported the associations between vitamin D receptor (VDR) polymorphisms and colorectal cancer (CRC), but the results were not always consistent. This meta-analysis aims to evaluate whether VDR polymorphisms are associated with CRC susceptibility.

Materials And Methods: Studies on the associations between VDR polymorphisms and CRC were retrieved from the Web of Science, PubMed, the Chinese Biomedical Database (CBM), Chinese National Knowledge Infrastructure (CNKI) and Wanfang (Chinese) databases. The odds ratio (OR) with 95% confidence intervals (CIs) was obtained.

Results: Thirty-nine articles met all inclusion criteria and were included in the meta-analysis including 22101 CRC cases and 23696 healthy controls. The 39 articles consisted of five VDR gene polymorphisms including ApaI, FokI, BsmI, TaqI and Cdx2. The results of meta-analysis showed that the FokI polymorphism was on the fringe of statistically significant in the comparisons of F allele vs. f allele in fixed model (OR = 1.029, 95% CI = 0.999–1.059, Praw = 0.057, PFDR = 0.057). Moreover, for the associations between BsmI polymorphism with CRC, We observed significant differences in allele frequencies, the homozygous model and the dominant model between CRC patients and healthy controls (B vs. b: OR = 0.862, 95% CI = 0.761–0.976, Praw = 0.019, PFDR = 0.019; BB vs. bb: OR = 0.786, 95% CI = 0.636–0.972, Praw = 0.026, PFDR = 0.039; BB + Bb vs. bb: OR = 0.934, 95% CI = 0.888-0.982, Praw = 0.008, PFDR = 0.024, respectively).

Conclusions: This meta-analysis suggests that BsmI is associated with CRC risk and FokI might be a risk factor for CRC. However, these associations with CRC need further studied.


Associations between VDR gene polymorphisms and colorectal cancer susceptibility: an updated meta-analysis based on 39 case-control studies | Pan | Oncotarget

INTRODUCTION

Colorectal cancer (CRC) is a major health problem and is currently ranked third for both cancer incidence and mortality [1]. In spite of the revised treatment patterns, CRC remains a major cause of cancer mortality. At an estimated 1.2 million new cancer cases and 608,700 deaths worldwide each year, people who die from CRC account for 8% of all cancer related deaths [2]. The incidence rate of CRC has been increasing greatly in China in the past few years, which accounts for about 6.5% of total cancers in urban areas and 4.6% in rural areas [3]. CRC is a multifactorial disease, involving the complex interactions between environmental and genetic factors [4]. However, the exact mechanisms which result in the development of colorectal cancer remain unclear. Nowadays, a large number of candidate genes responsible for the genesis of colorectal cancer have been identified.

Recently, the associations between vitamin D and colorectal cancer has aroused a great deal of attention, and genetic variation in metabolic pathways for these nutrients may play a role in colorectal carcinogenesis [5]. It’s known to us that Vitamin D plays an important role in calcium absorption, cellular proliferation and differentiation, as well as carcinogenesis. Animal studies and case–control studies in humans have provided strong evidence that vitamin D protects against colorectal cancer [6, 7]. Genomic actions of the active metabolite of vitamin D [1, 25(OH)2D3] are mediated by the vitamin D receptor (VDR) which maps to a region on chromosome 12 [8, 9]. The active form of vitamin D [1,25(OH)2D3] is bound by the intracellular VDR. This complex bindings and interactions with target-cell nuclei (at VDR elements) produce varieties of biological effects [10]. Recently, the VDR gene polymorphisms [1149] including FokI [12, 13, 15, 18, 19, 2126, 2932, 3539, 4143, 4547, 49], BsmI [1113, 1518, 20, 23, 26, 27, 29, 30, 32, 33, 36, 37, 4042, 44, 48, 49], ApaI [1113, 15, 16, 18, 23, 27, 28, 30, 36, 41], TaqI [1215, 17, 18, 2024, 27, 28, 31, 36, 38, 41, 49] and Cdx2 [21, 30, 31, 36] have been assessed in genetic associations studies of CRC, but the results from these studies are still inconsistent. Three meta-analyses [3, 50, 51] had been published assessing the associations between VDR polymorphisms and CRC risk in recent years. However, there are some limitations in the three studies, such as relatively small sample size. Moreover, a number of studies that assessed the associations between VDR polymorphisms and CRC risk were published after that period. In order to derive a more comprehensive estimation of the associations between VDR polymorphisms and CRC risk, we conducted a meta-analysis from 39 eligible case-control studies to evaluate the associations.

RESULTS

Data source

Figure 1 summarizes the selection process of study. According to the strategy, 139 published studies relevant to the VDR genes and the risk of CRC were reviewed including 28 from The Web of Science; 96 from PubMed; five from CBM and 10 from CNKI. 52 articles were selected for full-text review on the basis of their titles and abstracts. Finally, 39 articles met all inclusion criteria and were included in the meta-analysis including 22101 CRC cases and 23696 healthy controls. The 39 articles [1149] consisted of five VDR gene polymorphisms including FokI [12, 13, 15, 18, 19, 2126, 2932, 3439, 4143, 4547, 49], BsmI [1113, 1518, 20, 23, 26, 27, 29, 30, 32, 33, 36, 37, 4042, 44, 48, 49], ApaI [1113, 15, 16, 18, 23, 27, 28, 30, 36, 41], TaqI [1215, 17, 18, 2024, 27, 28, 31, 36, 38, 41, 49] and Cdx2 [21, 30, 31, 36]. Selected characteristics on the relationships between VDR polymorphisms and CRC were listed in Table 1.

Heterogeneity and publication bias

Flow diagram of the study selection process.

Figure 1: Flow diagram of the study selection process.

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

First Author

Year

Country

Ethnicity

Case/ Control

age

Control Methods

HWE

VDR polymorphisms

N

case

control

Vigidal [11]

2016

Brazil

Caucasian

152/321

62.8 ± 13.02

62.7 ± 10.42

PCR-RFLP

Yes

BsmI, AapI

Alkhayal [12]

2016

Saudi Arabia

Caucasian

100/100

57.5 (20–80)

57.5 (21–81)

PCR

No

FokI, BsmI, AapI, TaqI

Takeshige [13]

2015

Japan

Asian

685/778

60.2 ± 9.1

58.6 ± 10.7

PCR-RFLP

Yes

FokI, BsmI, AapI, TaqI

Atoum [14]

2014

Jordan

Asian

93/102

NA

NA

PCR

Yes

TaqI

Laczmanska [15]

2014

Poland

Caucasian

179/180

65.7 (32–87)

NA

PCR

No

FokI, BsmI, AapI, TaqI

Rasool [16]

2014

India

Asian

180/188

52.05

51.06

PCR-RFLP

No

BsmI, AapI

Pibiri [17]

2014

United States

African American

961/838

62.0 ± 10

65.0 ± 6

PCR

Yes

BsmI, TaqI

Sarkissyan [18]

2014

American

Mixed

78/230

55.2 ± 9.9

54.9 ± 9.8

PCR-RFLP

Yes

FokI, BsmI, AapI, TaqI

Rasool [19]

2013

India

Asian

312/305

52.05

51.06

PCR

Yes

FokI

Gunduz [20]

2012

Turkey

Caucasian

43/42

54.8

48.8

PCR-RFLP

No

BsmI, TaqI

Bentley [21]

2102

New Zealand

Asian

200/200

69.5 ± 0.4

69.5 ± 0.4

Taqman

Yes

FokI, TaqI, Cdx2

Yamaji [22]

2012

Japan

Asian

684/641

NA

NA

Taqman

Yes

FokI, TaqI

Kupfer [23]

2011

United States

Mixed

2119/1975

64.5 ± 11.7

62.3 ± 13.2

Taqman

Yes

FokI, BsmI, AapI, TaqI

Ashktorab [24]

2011

United States

Caucasian

93/187

59

60

PCR

Yes

FokI, TaqI

Abulí [25]

2011

Spain

Caucasian

515/515

NA

NA

Taqman

Yes

FokI

Mahmoudi [26]

2010

Iran

Asian

452/452

44.3 ± 17.2

53.7 ± 13.3

PCR-RFLP

Yes

FokI, BsmI

Hughes [27]

2010

Czech Republic

Caucasian

754/627

61 (27–85)

53 (29–91)

ASM-PCR

Yes

BsmI, AapI, TaqI

Mahmoudi [28]

2010

Iran

Asian

160/180

52.6 ± 14.0

44.4 ± 17.6

PCR-RFLP

No

AapI, TaqI

Jenab [29]

2009

United Kingdom

Caucasian

1248/1248

58.5 ± 7.2

58.6 ± 7.2

Taqman

Yes

FokI, BsmI

Theodoratou [30]

2008

United Kingdom

Caucasian

3005/3072

62.0 ± 10.7

62.4 ± 10.5

Microarray

No

FokI, BsmI, AapI, Cdx2

Ochs-Balcom [31]

2008

United States

Mixed

250/246

62.7 ± 10.2

58.4 ± 12.1

Taqman

Yes

FokI, TaqI, Cdx2

Li [32]

2008

China

Asian

200/200

61.5 ± 12.6

61.3 ± 12.5

PCR-RFLP

Yes

FokI, BsmI

Parisi [33]

2008

Spain

Caucasian

50/32

NA

NA

PCR-RFLP

Yes

BsmI

Wang [34]

2008

China

Asian

60/218

38–78

19.6 ± 1.3

PCR-RFLP

Yes

FokI

Grünhage [35]

2008

Germany

Caucasian

194/220

65 ± 9

63 ± 8

PCR-RFLP

Yes

FokI

Flügge [36]

2007

Germany

Caucasian

256/256

61.9 ± 10.0

62.2 ± 11.2

PCR-RFLP

Yes

FokI, BsmI, AapI, TaqI, Cdx2

Slattery [37]

2007

United States

Caucasian

2380/2990

NA

NA

Taqman

Yes

FokI, BsmI

Yaylim-Eraltan [38]

2007

Turkey

Caucasian

26/52

59.1 ± 4.0

52.0 ± 0.8

PCR-RFLP

No

FokI, TaqI

Murtaugh [39]

2006

United States

Caucasian

1820/2821

NA

NA

PCR-RFLP

Yes

FokI

Kadiyska [40]

2006

Bulgaria

Caucasian

140/94

59 (22–83)

NA

PCR-RFLP

Yes

BsmI

Park [41]

2006

South Korea

Asian

190/318

55 (32–81)

NA

PCR-RFLP

Yes

FokI, BsmI, AapI, TaqI

Slattery [42]

2004

United States

Caucasian

1936/2130

NA

NA

PCR-RFLP

No

FokI, BsmI

Peters [43]

2004

United States

Caucasian

763/774

62.9

62.3

PCR-RFLP

Yes

FokI

Boyapati [44]

2003

United States

Caucasian

177/228

58.4 ± 8.4

56.0 ± 10.0

PCR-RFLP

No

BsmI

Wong [45]

2003

China

Asian

217/890

56.5

NA

PCR-RFLP

Yes

FokI

Peters [46]

2001

United States

Caucasian

239/228

NA

NA

PCR-RFLP

Yes

FokI

Ingles [47]

2001

United States

Caucasian

373/394

62.3

62.2

PCR-RFLP

Yes

FokI

Kim [48]

2001

United States

Caucasian

393/406

57.9 ± 9.7

53.0 ± 10.9

Taqman

Yes

BsmI

Slattery [49]

2001

United States

Caucasian

424/266

NA

NA

PCR-RFLP

Yes

FokI, BsmI, TaqI

VDR, vitamin D receptor; PCR, polymerase chain reaction; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; ASM-PCR, allele specific multiple-PCR; HWE, Hardy–Weinberg equilibrium; NA, Not available.

The heterogeneity was assessed for each study using the Q statistic. Significant heterogeneity (P for heterogeneity < 0.10 or I2 > 50%) between studies were observed in BsmI and ApaI, but no heterogeneity was found in FokI, TaqI and Cdx2 polymorphisms.

Funnel plot and Egger’s test were performed to evaluate the publication bias of literatures on CRC, and no statistically significant publication biases were found in all genetic models.

Meta-analysis results

FokI polymorphism and CRC

A total of 29 studies examined the association between CRC and the FokI polymorphism. The result of meta-analysis showed that the FokI polymorphism was on the fringe of statistically significant in the comparison of F allele vs. f allele in fixed model (OR = 1.029, 95%CI = 0.999–1.059, Praw = 0.057, PFDR = 0.057). The homozygous model, the dominant model and the recessive model were no significant associated with CRC risk (Table 2).

Table 2: Meta-analysis of the association between VDR polymorphisms and CRC

SNP

Comparison

Qualified studies

OR (95%CI)

P-value

FDR

Heterogeneity test

Effect model

FokI

F vs. f

29

1.029 (0.999–1.059)

0.057

0.057

P = 0.003, I2 = 46.8%

F

FF vs. ff

1.055 (0.990–1.123)

0.097

0.211

P = 0.015, I2 = 39.8%

F

FF + Ff vs. ff

1.045 (0.986–1.107)

0.141

0.211

P = 0.124, I2 = 23.9%

F

Ff + ff vs. FF

0.974 (0.876–1.083)

0.625

0.625

P < 0.001, I2 = 81.5%

R

BsmI

B vs. b

23

0.862 (0.761–0.976)

0.019*

0.019*

P < 0.001, I2 = 91.4%

R

BB vs.bb

0.786 (0.636–0.972)

0.026*

0.039*

P < 0.001, I2 = 85.5%

R

BB + Bb vs. bb

0.824 (0.705–0.964)

0.015*

0.039*

P < 0.001, I2 = 88.0%

R

Bb + bb vs. BB

0.887 (0.759–1.036)

0.129

0.129

P < 0.001, I2 = 78.8%

R

ApaI

A vs. a

12

1.025 (0.928–1.132)

0.631

0.631

P < 0.001, I2 = 68.9%

R

AA vs. aa

0.953 (0.775–1.172)

0.650

0.900

P < 0.001, I2 = 67.9%

R

AA + Aa vs. aa

1.009 (0.875–1.163)

0.900

0.900

P = 0.003, I2 = 59.8%

R

Aa + aa vs. AA

0.901 (0.770–1.055)

0.197

0.591

P = 0.001, I2 = 65.5%

R

TaqI

T vs. t

18

1.011 (0.960–1.066)

0.673

0.673

P = 0.081, I2 = 33.1%

F

TT vs. tt

1.027 (0.912–1.157)

0.656

0.746

P = 0.091, I2 = 32.5%

F

TT +Tt vs. tt

1.018 (0.913–1.136)

0.746

0.746

P = 0.069, I2 = 35.4%

F

Tt + tt vs. TT

1.013 (0.944–1.086)

0.724

0.746

P = 0.310, I2 = 11.8%

F

Cdx2

C vs. c

4

0.936 (0.828–1.058)

0.287

0.287

P = 0.352, I2 = 8.2%

F

CC vs. cc

0.862 (0.627–1.186)

0.363

0.544

P = 0.193, I2 = 36.6%

F

CC + Cc vs. cc

0.933 (0.723–1.204)

0.594

0.594

P = 0.176, I2 = 39.3%

F

Cc + cc vs. CC

0.918 (0.783–1.077)

0.293

0.544

P = 0.777, I2 = 0.0%

F

VDR, vitamin D receptor; CRC, Colorectal cancer; OR, odds ratios; 95% CI, 95% confidence interval; FDR: p value from Benjamini–Hochberg method control for false discovery rate (FDR); R, random-effects model; F, fixed-effects model; *statistical significance.

BsmI polymorphism and CRC

There were 23 articles on the relationship between BsmI polymorphism and CRC. We observed significant differences in allele frequencies, the homozygous model and the dominant model between CRC patients and healthy controls (B vs. b: OR = 0.862, 95% CI = 0.761–0.976, Praw = 0.019, PFDR= 0.019; BB vs. bb: OR = 0.786, 95% CI = 0.636–0.972, Praw = 0.026, PFDR = 0.039; BB + Bb vs. bb: OR = 0.824, 95% CI = 0.705–0.964, Praw = 0.015, PFDR = 0.039, respectively). There was little evidence of significant differences that investigated an association between BsmI polymorphism and CRC in the recessive model (Table 2, Figure 2).

Forest plots for BsmI gene polymorphism and CRC.

Figure 2: Forest plots for BsmI gene polymorphism and CRC. (A) Allelic mode (B) Dominant mode (C) Recessive model (D) Homozygous model.

Other polymorphisms and CRC

Other three polymorphisms including ApaI, TaqI, Cdx2 were not associated with CRC in all genetic models.

Sensitivity analysis

Sensitivity analysis was performed by sequential omission of individual studies. The pooled ORs of the polymorphisms were not altered after omission, indicating that our results were statistically robust.

DISCUSSION

The pathogenesis of CRC remains unknown. Gene-environment interactions, gene-gene interactions and life-style have an important impact on the development of CRC. There is consistent epidemiologic evidence that increased vitamin D intake is associated with reduced risk of colorectal. VDR mediate the biological activity of vitamin D and plays a crucial role in the etiology and development of cancer. A number of genetic associations studies were carried out to investigate the association of VDR polymorphisms with CRC risk, but the results are conflictive and the effect of VDR polymorphisms on CRC remains unclear. Therefore, in order to overcome the limitations of individual studies, we performed meta-analysis to evaluate the associations of VDR polymorphisms with CRC risk. Meta-analysis increases statistical power and resolution by pooling the results of independent analyses. A total of 52 reports had predicted a potential genetic association, and only 39 articles were included in this meta-analysis based on the selection criteria. The meta-analysis showed that the FokI polymorphism was on the fringe of statistically significant in the comparisons of F vs. f (OR = 1.029, 95% CI = 0.999–1.059, Praw = 0.057, PFDR = 0.057) and the BsmI B allele was associated with a lower CRC risk (B vs. b: OR = 0.862, 95% CI = 0.761–0.976, Praw = 0.019, PFDR = 0.019). Similarly, a decreased CRC risk was also found in the homozygous model and the dominant model of BsmI (BB vs. bb: OR = 0.786, 95% CI = 0.636–0.972, Praw = 0.026, PFDR = 0.039; BB + Bb vs. bb: OR = 0.824, 95% CI = 0.705–0.964, Praw = 0.015, PFDR = 0.039, respectively). The results are consistent with the previous meta-analysis, which further confirmed the conclusions of the previous meta-analysis. However, our results were not consistent with the previous meta-analysis in the recessive model of BsmI and CRC. Yu et al. [3] and Bai et al. [51] draw the conclusion that the recessive model of BsmI was associated with a decreased CRC risk. The reasons for different results are as follows: first, our study is an updated and more carefully selected study than Yu et al and Bai et al. Second, our study included more Asian population. The estimated VDR polymorphisms including FokI, ApaI, TaqI and Cdx2 showed no significant associations between CRC. Previous meta-analysis’s pooled ORs were similar to ours. In addition, we found significant heterogeneities between studies in BsmI and ApaI. But the reasons for the heterogeneity were unclear. The heterogeneity may be explained by the following factors: the study design, clinical characteristics, year of publication, and especially the different genetic backgrounds.

As in any study, some limitations of this study should be considered. First, only published studies in English and Chinese were included in this meta-analysis, so publication bias may have occurred. Second, significant heterogeneity was observed in overall comparisons. Although no publication bias was observed, different background and variant adjusted factors of controls were possible major source of heterogeneity. Third, although environment and diet may partially contribute to CRC, gene-gene and gene-environment interactions could not be investigated. Fourth, meta-analysis was still an observational study that subjected to the methodological deficiencies of the included studies.

In conclusion, this meta-analysis suggests that BsmI was associated with CRC risk and FokI might be risk factors for CRC. However, these associations with CRC need further studied.

MATERIALS AND METHODS

Literature search strategy

All genetic association studies that assessed the associations of the FokI, BsmI, ApaI, TaqI and Cdx2 polymorphisms in the VDR genes with CRC susceptibility were included/enrolled in the meta-analysis. The studies were identified by extended computer based search of The PubMed, Web of Science, the Chinese Biomedical Database (CBM) and Chinese National Knowledge Infrastructure (CNKI) and Wanfang (Chinese) databases (published until April 2017). The keywords “Colorectal cancer” or “CRC” or “Colorectal carcinoma” or “Colorectal tumor”, “polymorphism” or “variant” or “genes” or “genotypes” or “genotyping”, “vitamin D receptor” or “VDR” were used. All references cited in the publications were also reviewed to identify other relevant publications. Finally, only published studies with full text were included.

Inclusion and exclusion criteria

Regarding CRC susceptibility and VDR gene polymorphisms, studies which satisfy all the following criteria were identified: (1) articles investigate the associations of the FokI, BsmI, ApaI, TaqI and Cdx2 polymorphisms in the VDR genes with the development of CRC; (2) a case–control study; (3) articles reported the number of individual genotypes and/or alleles for VDR polymorphisms in cases and controls; (4) the paper should clearly describe CRC diagnoses; (5) the control’ ethnic background and geographic area were the same with case’; (6) the language of articles was restricted to English or Chinese; (7) full text was available. Exclusion criteria: (1) the study was conducted on animals; (2) abstracts, case reports, editorials and review articles were excluded; (3) studies that did not met the inclusion criteria; (4) study with no detailed data.

Data extraction

According to the selection criteria, data from relevant studies were carefully and independently extracted by two authors (Zhipeng Pan and Mengya Chen). Disagreement was resolved by discussion and consultation with the third researcher (Xingxing Hu). The following data were extracted if available: first author, year of publication, country, ethnicity of study population, the genotyping method, sample size, number of each genotype in cases and controls.

Statistical analysis

The strength of the associations between the VDR polymorphisms and CRC susceptibility were evaluated by Odds ratios (ORs) with 95% confidence intervals (95% CIs) under the appropriate genetic model. The pooled ORs were calculated for the allele contrasts, homozygous model, recessive genetic model and dominant genetic model. P value < 0.05 was considered to be statistically significant comparing CRC cases with controls. Considering the possibility of heterogeneity in the studies, heterogeneity assumption was measured by the chi-square based Q test (P < 0.1 indicates heterogeneity) [52]. In addition, the presence of heterogeneity between studies was tested by the I2. I2 values of 25, 50, and 75% are defined as low, moderate, and high estimates, respectively. The pooled effect was calculated by a fixed effect model when there is no heterogeneity (I2 < 50% or P > 0.1), otherwise, a random effects model was used. HWE was assessed by the Chi-square test in the control group of each study in all the included studies (P < 0.05 was considered significant). The funnel plot and Egger’s regression test were used to search for publication bias, and an asymmetric Funnel plot or P < 0.05 in Egger weighted regression suggested possible publication bias. In consideration of multiple comparisons, Benjamini–Hochberg (BH) method was applied to control the false discovery rate (FDR). All the statistical manipulations were performed using the STATA statistical software 11.0 (StataCorp, College Station, TX, USA) and Review Manager Software 5.1 (Cochrane Collaboration, Oxford, UK). All P values tested were two-tailed.

Abbreviations

CRC: Colorectal cancer; VDR: vitamin D receptor; CBM: the Chinese Biomedical Database; CNKI: Chinese National Knowledge Infrastructure; OR: odds ratio; 95% CIs: 95% confidence intervals; FDR: false discovery rate.

Author contributions

ZPP: Search the literature, Data extraction, Manuscript writing. MYC: Data extraction. XXH: Search the literature. JJY: Search the literature. HW: Software analysis. CJZ: Software analysis. FMP: Review the manuscript. GPS: Review the manuscript.

ACKNOWLEDGMENTS

Thanks for the people who participated in this study.

CONFLICTS OF INTEREST

All the authors declare that they have no conflicts of interest.

FUNDING

The study was supported by grants from the National Natural Science Foundation of China (30771849, 30972530, 81273169, 81573218 and 81773514).

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