Abstract

Cytotoxic T-lymphocyte antigen 4 (CTLA-4) is expressed constitutively on regulatory T cells. So far, several studies have focused on association between CTLA-4 gene polymorphisms and recurrent pregnancy loss (RPL). However, above association between the CTLA-4 gene polymorphism and RPL susceptibility is uncertain. Therefore, we performed a timely meta-analysis of all current publications to clarify this relationship. We located articles from the PubMed and Chinese language (WanFang) databases that were published up until July 25, 2018. Finally, we obtained six case–control studies, containing 2405 total cases and 2607 total controls, based on search criteria for abortion susceptibility related to the CTLA-4 +49 G/A polymorphism. The odds ratios (OR) and 95% confidence intervals (CIs) revealed association strengths. There was significantly decreased association between this polymorphism and whole population risk (e.g. AA vs. GG: OR = 0.56, 95% CI = 0.38–0.81, P=0.002). Additionally, in ethnicity subgroups, similar association was found both in China (e.g. AA vs. GG: OR = 0.49, 95% CI = 0.39–0.63, P=0.002) and non-China (e.g. AG vs. GG: OR = 0.46, 95% CI = 0.34–0.63, P<0.001). Current analysis suggested CTLA-4 +49 G/A polymorphism may weakly decrease RPL risk for women of childbearing age.

Introduction

A pregnancy loss (PL) is defined as the spontaneous demise of a pregnancy before the fetus reaches viability. It includes all PLs [unexplained recurrent spontaneous abortion (RSA), RSA, recurrent miscarriage (RM), idiopathic RM] from the time of conception until 24 weeks of gestation [1,2]. Approximately 15% of pregnant women experience sporadic loss, 2% experience two consecutive PL and 0.4–1% have three consecutive PL [3]. Recurrent PL (RPL), also named as recurrent spontaneous abortion (RSA), is defined as the loss of two or more pregnancies [1,2,4]. In addition, RM is classically defined as the loss of three or more consecutive pregnancies before the 20th weeks of gestation with or without previous live births [5]. In the same time, the definition of RPL and RM exists some discrepancy in opinions, broadly speaking, both are classified as PL or abortion.

A series of pathogenic mechanisms associated with PL has been described, including uterine abnormalities, endocrine and metabolic problems, genetic anomalies, acquired and inherited thrombophilia and immunological factors [6]. More and more studies have focused on the genetic factors, especially the single nucleotide polymorphism (SNP) [7].

The cytotoxic T-lymphocyte antigen 4 (CTLA-4, Gene ID: 1493, MIM number: 123890 also known as GSE, ALPS5, CD152) gene maps to band q33 of human chromosome 2, spans about 6.2 kilobases, and contains four exons and three introns [8]. It is well known that CTLA-4 expressed on human placental regulatory T (Treg) cells in decidual and peripheral dendritic cells may induce the expression of an immune-suppressive enzyme indoleamine 2,3-dioxygenase (ID), particularly during early phases of pregnancy [9]. Furthermore, high expression of ID promotes maternal-fetal tolerance [9]. In addition, the expression of Treg cells and CTLA-4 in peripheral and decidual lymphocytes was down-regulated in human miscarriages in several in vivo studies [10]. We predicted CTLA-4 and its related Treg cells are protective factors for RPL. SNPs are known as the most common type of DNA variation in individuals [11], which may affect DNA promoter activity and influence the translation, and finally may be associated with the susceptibility about human diseases [11]. Therefore, we hypothesized that the reduced number and/or functional deficiency of Treg cells due to the genetic variations in CTLA-4 gene may increase the risk of RPL.

So far, many studies have investigated the association between CTLA-4 rs231775 G/A polymorphism (wild-type allele: A; polymorphic allele: G, 49A>G, Thr17Ala) and RPL risk. However, the results were not conclusive or consistent. Thus, we conducted this timely meta-analysis of six case–control studies to derive a more powerful estimation of the association between CTLA-4 rs231775 G/A polymorphism and RPL susceptibility [12–17].

Materials and methods

Identification and eligibility of relevant studies

Searches were conducted in PubMed and Chinese language (WanFang) databases using the key words ‘cytotoxic T-lymphocyte antigen 4 or CTLA-4’, ‘spontaneous abortion or miscarriage or pregnancy loss’ and ‘polymorphism’ or ‘variant’. The last search was updated on July 25, 2018. In total, 18 articles were retrieved using the abovementioned terms, and six articles contained the inclusion criteria.

Inclusion criteria and exclusion criteria

Including studies had to meet following criteria: (1) address the correlation between RPL risk and the CTLA-4 rs231775 G/A SNP; (2) be a case–control study; and (3) have sufficient numbers of genotypes (AA, AG, and GG) for both the cases and controls. The following exclusion criteria were used: (1) lack of a control population; (2) lack of available genotype frequency data; and (3) duplicated studies.

Data extraction

The following items were collected: the last name of first author, the year of publication, the country of origin, the ethnicity of subjects, source of control (SOC), the total and number of each genotype frequency in the case–control groups, the Hardy–Weinberg equilibrium (HWE) of the controls, abortion type, control-type and the genotyping method. Ethnicity was categorized as Asian, China, and non-China.

Quality score assessment

The quality score assessment (Newcastle–Ottawa Scale, NOS) [18] was selected to assess the quality of each study. This measure assesses aspects of the methodologies used in observational studies, which are related to the study quality, including selection of cases, comparability of populations, and ascertainment of exposure to risks. The NOS rating ranges from zero stars (worst) to nine stars (best). Studies with a score of seven stars or greater was considered as a high quality.

Statistical analysis

Odds ratios (OR) with 95% confidence intervals (CI) were used to measure the strength of the association between the CTLA-4 rs231775 G/A SNP and RPL risk. The statistical significance of the summary OR was determined with the Z-test. A heterogeneity assumption was evaluated among studies using a chi-square-based Q-test. A P-value of more than 0.10 for the Q-test indicated a lack of heterogeneity among the studies [19]. If significant heterogeneity was detected, the random-effects model (DerSimonian–Laird method) was used. Otherwise, the fixed-effects model (Mantel–Haenszel method) was chosen [20,21].

We investigated the relationship between genetic variants of the CTLA-4 rs231775 G/A site and RPL risk by the allelic contrast (A-allele vs. G-allele), homozygote comparison (AA vs. GG), dominant genetic model (AA+AG vs. GG), heterozygote comparison (AG vs. GG), and recessive genetic model (AA vs. AG+GG). A sensitivity analysis was performed by omitting studies, one after another, to assess the stability of results. The departure of the CTLA-4 rs231775 G/A SNP from expected frequencies under HWE was assessed in controls using the Pearson chi-square test (P < 0.05 was considered significant). Funnel plot asymmetry was assessed using Begg’s test, and publication bias was assessed using Egger’s test [22], both of P-value less than is considered as significant. All statistical tests were performed using STATA Software (version 11.0; StataCorp LP, College Station, TX).

Network of gene interaction of CTLA-4 gene

The network of gene–gene interaction for CTLA-4 gene was utilized through String online server (http://string-db.org/) [23].

Results

Study characteristics

In total, 18 articles were collected from the PubMed and WanFang databases via a literature search using different combinations of key words. As shown in Figure 1, 12 articles were excluded (two were duplications, 10 were irrespective articles). Finally, six different articles were included in current meta-analysis (Figure 1). In total, there were 2405 cases and 2607 controls. Study characteristics from the published studies on the relationship between the CTLA-4 rs231775 G/A SNP and RPL risk are summarized in Table 1. In all the studies, the controls were women under normal pregnancy. Except one study, all studies were consistent with HWE. Finally, we checked the minor allele frequency (MAF) reported for the five main worldwide populations in the 1000 Genomes Browser [23]: East Asian (EAS), 0.3631; European (EUR), 0.3588; African (AFR), 0.3880; American (AMR), 0.4625; and South Asian (SAS), 0.3098 (Figure 2). The MAF in our analysis was 0.4033 and 0.5213 in the case and control group, respectively, both higher than the results in the EAS from1000 Genomes Browser database.

Flowchart illustrating the search strategy used to identify association studies for CTLA-4 gene rs231775 polymorphism and RPL risk

A-allele frequencies for the CTLA-4 gene rs231775 polymorphism among cases–controls stratified by ethnicity

Figure 2
A-allele frequencies for the CTLA-4 gene rs231775 polymorphism among cases–controls stratified by ethnicity

Vertical line, T-allele frequency; horizontal line, ethnicity type.

Abbreviations: AFR, African; AMR, American; EAS, East Asian; EUR, European; SAS, South Asian.

Figure 2
A-allele frequencies for the CTLA-4 gene rs231775 polymorphism among cases–controls stratified by ethnicity

Vertical line, T-allele frequency; horizontal line, ethnicity type.

Abbreviations: AFR, African; AMR, American; EAS, East Asian; EUR, European; SAS, South Asian.

Table 1
Study characteristics from previous published studies on the association between CTLA-4 gene rs231775 polymorphism and RPL risk
Author Year Country Ethnicity SOC Case Control Case Control HWE Genotype Case type Control type NOS 
       AA AG GG AA AG GG      
Wang [172005 China Asian HB 168 117 20 66 82 17 61 39 0.38 PCR-RFLP Unexplained RSA Normal pregnancy 
Chai [122010 China Asian HB 233 224 28 101 104 35 95 94 0.18 PCR-RFLP RSA Normal pregnancy 
Fan [132018 China Asian HB 1284 1046 101 518 665 143 488 415 0.98 PCR-RFLP RSA Normal pregnancy 
Gupta [142012 India Asian HB 300 500 140 121 39 227 233 40 0.06 PCR-RFLP RM Normal pregnancy 
Nasiri [162016 Iran Asian HB 120 120 94 23 68 45 0.90 PCR-RFLP RPL Normal pregnancy 
Misra [152016 India Asian HB 300 600 105 135 60 264 288 48 0.01 PCR-RFLP Idiopathic RM Normal pregnancy 
Author Year Country Ethnicity SOC Case Control Case Control HWE Genotype Case type Control type NOS 
       AA AG GG AA AG GG      
Wang [172005 China Asian HB 168 117 20 66 82 17 61 39 0.38 PCR-RFLP Unexplained RSA Normal pregnancy 
Chai [122010 China Asian HB 233 224 28 101 104 35 95 94 0.18 PCR-RFLP RSA Normal pregnancy 
Fan [132018 China Asian HB 1284 1046 101 518 665 143 488 415 0.98 PCR-RFLP RSA Normal pregnancy 
Gupta [142012 India Asian HB 300 500 140 121 39 227 233 40 0.06 PCR-RFLP RM Normal pregnancy 
Nasiri [162016 Iran Asian HB 120 120 94 23 68 45 0.90 PCR-RFLP RPL Normal pregnancy 
Misra [152016 India Asian HB 300 600 105 135 60 264 288 48 0.01 PCR-RFLP Idiopathic RM Normal pregnancy 

Abbreviations: HB, hospital based; HWE: Hardy–Weinberg equilibrium of control group; NOS: Newcastle–Ottawa scale; PCR-RFLP: polymerase chain reaction followed by restriction fragment length polymorphism; SOC; source of control.

Quantitative synthesis

There was significantly decreased association between the CTLA-4 rs231775 G/A SNP and RPL risk susceptibility (AA vs. GG: OR = 0.56, 95% CI = 0.38–0.81, Pheterogeneity=0.014, P=0.002, Figure 3, AG vs. GG: OR = 0.61, 95% CI = 0.46–0.80, Pheterogeneity=0.034, P<0.001, and AA+AG vs. GG: OR = 0.61, 95% CI = 0.45–0.82, Pheterogeneity=0.008, P=0.001) (Table 2).

Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AA vs. GG) in the whole

Figure 3
Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AA vs. GG) in the whole

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.

Figure 3
Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AA vs. GG) in the whole

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.

Table 2
Total and stratified analysis of CTLA-4 gene rs231775 polymorphism and RPL risk
Total OR(95% CI) PhP genetic model 
A-allele vs. G-allele 0.85(0.66–1.09)0.000 0.193 random model 
AA vs. GG 0.56(0.38–0.81)0.014 0.002 random model 
AG vs. GG 0.61(0.46–0.80)0.034 0.000 random model 
AA+AG vs. GG 0.61(0.45–0.82)0.008 0.001 random model 
AA VS. AG+GG 0.90(0.61–1.33)0.000 0.597 random model 
Ethnicity subgroup  
China OR(95% CI) PhP genetic model 
A-allele vs. G-allele 0.69(0.62–0.77)0.000 0.000 fixed model 
AA vs. GG 0.49(0.39–0.63)0.294 0.002 fixed model 
AG vs. GG 0.68(0.59–0.80)0.126 0.000 fixed model 
AA+AG vs. GG 0.64(0.55–0.74)0.128 0.000 fixed model 
AA VS. AG+GG 0.59(0.47–0.75)0.406 0.000 fixed model 
Ethnicity subgroup  
Non-China OR(95% CI) PhP genetic model 
A-allele vs. G-allele 1.06(0.61–1.84)0.000 0.834 random model 
AA vs. GG 0.68(0.28–1.68)0.003 0.405 random model 
AG vs. GG 0.46(0.34–0.63)0.237 0.000 fixed model 
AA+AG vs. GG 0.60(0.29–1.23)0.015 0.166 random model 
AA VS. AG+GG 1.20(0.64–2.26)0.000 0.573 random model 
Total OR(95% CI) PhP genetic model 
A-allele vs. G-allele 0.85(0.66–1.09)0.000 0.193 random model 
AA vs. GG 0.56(0.38–0.81)0.014 0.002 random model 
AG vs. GG 0.61(0.46–0.80)0.034 0.000 random model 
AA+AG vs. GG 0.61(0.45–0.82)0.008 0.001 random model 
AA VS. AG+GG 0.90(0.61–1.33)0.000 0.597 random model 
Ethnicity subgroup  
China OR(95% CI) PhP genetic model 
A-allele vs. G-allele 0.69(0.62–0.77)0.000 0.000 fixed model 
AA vs. GG 0.49(0.39–0.63)0.294 0.002 fixed model 
AG vs. GG 0.68(0.59–0.80)0.126 0.000 fixed model 
AA+AG vs. GG 0.64(0.55–0.74)0.128 0.000 fixed model 
AA VS. AG+GG 0.59(0.47–0.75)0.406 0.000 fixed model 
Ethnicity subgroup  
Non-China OR(95% CI) PhP genetic model 
A-allele vs. G-allele 1.06(0.61–1.84)0.000 0.834 random model 
AA vs. GG 0.68(0.28–1.68)0.003 0.405 random model 
AG vs. GG 0.46(0.34–0.63)0.237 0.000 fixed model 
AA+AG vs. GG 0.60(0.29–1.23)0.015 0.166 random model 
AA VS. AG+GG 1.20(0.64–2.26)0.000 0.573 random model 

Ph: value of Q-test for heterogeneity test; P: Z-test for the statistical significance of the OR

When studies were stratified according to ethnicity, there was also similar association found both in China (e.g. A-allele vs. G-allele: OR = 0.69, 95% CI = 0.62–0.77, Pheterogeneity<0.001, P<0.001, AG vs. GG: OR = 0.68, 95% CI = 0.59–0.80, Pheterogeneity=0.126, P<0.001, Figure 4, and AA vs. AG+GG: OR = 0.59, 95% CI = 0.47–0.75, Pheterogeneity=0.406, P<0.001) and non-China risk (AG vs. GG: OR = 0.46, 95% CI = 0.34–0.63, Pheterogeneity=0.237, P<0.001, Figure 5, Table 2).

Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AG vs. GG) in China population

Figure 4
Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AG vs. GG) in China population

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.

Figure 4
Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AG vs. GG) in China population

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.

Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AG vs. GG) in non-China population

Figure 5
Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AG vs. GG) in non-China population

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.

Figure 5
Forest plot of RPL risk associated with CTLA-4 gene rs231775 polymorphism (AG vs. GG) in non-China population

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.

Sensitivity analysis and bias diagnosis

We used a sensitivity analysis to determine whether modifying the meta-analysis inclusion criteria affected the results. No other single study influenced the summary OR qualitatively (Figure 6). Egger’s and Begg’s tests were performed to assess publication bias and the funnel plot symmetry was examined. Finally, no proof of publication bias was obtained (e.g. AG vs. GG: t = −0.23, P=0.892 for Egger’s test; and z = −0.19, P=0.851 for Begg’s test; Figures 7 and 8, Table 3).

Sensitivity analysis between CTLA-4 gene rs231775 polymorphism and tuberculosis risk (AG vs. GG)

Begg’s funnel plot for publication bias test (AG vs. GG)

Figure 7
Begg’s funnel plot for publication bias test (AG vs. GG)

Each point represents a separate study for the indicated association. Log [OR], natural logarithm of OR. Horizontal line, mean effect size.

Figure 7
Begg’s funnel plot for publication bias test (AG vs. GG)

Each point represents a separate study for the indicated association. Log [OR], natural logarithm of OR. Horizontal line, mean effect size.

Egger’s publication bias plot (AG vs. GG)

Table 3
Publication bias tests (Begg’s funnel plot and Egger’s test for publication bias test) for CTLA-4 gene rs231775 polymorphism
Egger’s test      Begg’s test 
Genetic type Coefficient Standard error t P-value 95% CI of intercept z P-value 
A-allele vs. G-allele 1.093 3.328 0.33 0.759 (−8.147,10.334) 0.75 0.452 
AG vs. GG −0.312 1.354 −0.23 0.892 (−4.072,3.446) −0.19 0.851 
AA vs. GG 0.422 1.461 0.29 0.787 (−3.634,4.479) 0.94 0.348 
AA+AG vs. GG −0.339 1.436 −0.24 0.825 (−4.327,3.647) 
AA vs. AG+GG 2.753 2.308 1.19 0.299 (−3.655,9.162) 0.75 0.452 
Egger’s test      Begg’s test 
Genetic type Coefficient Standard error t P-value 95% CI of intercept z P-value 
A-allele vs. G-allele 1.093 3.328 0.33 0.759 (−8.147,10.334) 0.75 0.452 
AG vs. GG −0.312 1.354 −0.23 0.892 (−4.072,3.446) −0.19 0.851 
AA vs. GG 0.422 1.461 0.29 0.787 (−3.634,4.479) 0.94 0.348 
AA+AG vs. GG −0.339 1.436 −0.24 0.825 (−4.327,3.647) 
AA vs. AG+GG 2.753 2.308 1.19 0.299 (−3.655,9.162) 0.75 0.452 

Gene–gene interaction of online analysis

String online server indicated that MTR gene interacts with numerous genes. The network of gene–gene interaction has been illustrated in Figure 9.

Human CTLA-4 interactions network with other genes obtained from String server

Figure 9
Human CTLA-4 interactions network with other genes obtained from String server

At least 10 genes have been indicated to correlate with CTLA-4 gene.

Abbreviations: AKT1, V-akt murine thymoma viral oncogene homolog 1; CD80, CD80 molecule; CD86, CD86 molecule; FOXP3, Forkhead box p3; FYN, FYN oncogene related to SRC, FGR, YES; ICOSLG, inducible T-cell co-stimulator ligand; LCK, lymphocyte-specific protein tyrpsine kinase; LYN, V-yes-1 Yamaguchi sarcoma viral-related oncogene homolog; NFATC2, nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 2; PTPN11, protein tyrosine phosphatase, non-reveptor type 11.

Figure 9
Human CTLA-4 interactions network with other genes obtained from String server

At least 10 genes have been indicated to correlate with CTLA-4 gene.

Abbreviations: AKT1, V-akt murine thymoma viral oncogene homolog 1; CD80, CD80 molecule; CD86, CD86 molecule; FOXP3, Forkhead box p3; FYN, FYN oncogene related to SRC, FGR, YES; ICOSLG, inducible T-cell co-stimulator ligand; LCK, lymphocyte-specific protein tyrpsine kinase; LYN, V-yes-1 Yamaguchi sarcoma viral-related oncogene homolog; NFATC2, nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 2; PTPN11, protein tyrosine phosphatase, non-reveptor type 11.

Discussion

RPL is a common pregnancy complication affecting 1–3% of couples trying to conceive. Successful pregnancy is a result of maintaining the semi-allograft fetus from maternal immune responses [24]. The decision between tolerance and immunity determines the fate of each pregnancy. A network composed of different immune cells, numerous cytokines, growth factors, and adhesion molecules collaborate to reach the best outcome [25]. Treg cells are cellular components of natural self-tolerance and seem to reduce the chance of pregnancy failure by providing the tolerant environment in the endometrium, where a successful implantation can occur [26]. Confirming evidence, in this regard, came from a significant reduction in circulating and deciduas Treg cells among women with RPL [10,27]. One of the proposed mechanisms is the CTLA-4-dependent pathway using anti-CTLA-4-mAb disrupts the Treg activity in vivo, in which Treg provide this tolerance against the fetus [28].

To combine the importance of genetic etiology of RPL, it makes sense to deep study the CTLA-4 gene polymorphisms. Rs231775 variant is one of common polymorphisms in CTLA-4 gene. To our best of knowledge, it is the first time to select all published articles to analyze the association between CTLA-4 gene rs231775 polymorphism and RPL susceptibility. In current study, the major discover is that rs231775 may decrease RPL risk, in other words, individuals carrying A-allele may have a decrease association for RPL, or A-allele is a protective factor for RPL risk, on the other hand, the G-allele is a potential risk factor. We boldly guess that the A-allele may increase the expression of CTLA-4 protein, because CTLA-4 is a protective factor in promoting fetus toleration and RPL [17].

In addition, we used the online analysis system String to predict potential and functional partners (Figure 9). Finally, ten genes were predicted. The highest score of association was CD86 and CD80 (score = 0.999); however, ICOSLG and LYN had the lowest scores (0.963 and 0.949, respectively). CD86 and CD80 are both the natural B7 family ligands of CTLA-4, the level of CD86(+) was significantly higher in the RPL group than the normal pregnancy group [29]. Several observations have indicated that CD28/CTLA-4 and CD86/CD80 are involved in the maternal–fetal immune regulation, which might be potentially useful to immunotherapy for human RPL [30]. Most studies were focused on the association between FOXP3 gene and RPL, including SNPs and immune regulation (such as Treg, CD4+CD25+, Th17) [13]. Karim et al. reported that several novel CNVs/genes (such as 14q32.33/AKT1) in chromosomal abnormalities were associated with RPL risk [31]. Above information predicted that CD86, CD80, FOXP3, and AKT1 may influence CTLA-4 and regulate the RPL development, which may become intervention and treatment target genes in the future.

Limitations in the present meta-analysis include the suboptimal number of published studies for a comprehensive analysis. Second, interactions between different polymorphic loci of the same CTLA-4 may modulate RPL risk, which should be included in future research and analysis. In addition, our meta-analysis was based on unadjusted estimates. A more precise analysis should be conducted if individual data are available to adjust for other covariates including age, sex, family history, environmental factors, endocrine abnormalities, each type of PL, and lifestyle.

In summary, in the present meta-analysis, a significant decreased association was found between the CTLA-4 gene rs231775 SNP and RPL risk. To further confirm the results, larger scale case–control studies with different ethnic groups and multiple PL types are needed.

Author Contribution

Y.S. and Y.C. conceived the study. Y.C. and Q.X. searched the databases and extracted the data. Y.S. and Q.X. analyzed the data. Q.X. wrote the draft of the paper. Y.S. and Y.C. reviewed the manuscript.

Funding

The authors declare that there are no sources of funding to be acknowledged.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Abbreviations

     
  • CI

    confidence interval

  •  
  • CTLA4

    cytotoxic T-lymphocyte antigen 4

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • ID

    indoleamine 2,3-dioxygenase

  •  
  • MAF

    minor allele frequency

  •  
  • NOS

    Newcastle–Ottawa Scale

  •  
  • PL

    pregnancy loss

  •  
  • RM

    recurrent miscarriage

  •  
  • RSA

    recurrent spontaneous abortion

  •  
  • SNP

    single nucleotide polymorphism

References

References
1.
Goddijn
M.
,
Elson
J.
,
Peramo
B.
,
Bender Atik
R.
,
Christiansen
O.
,
Kolte
A.
et al. .
(
2017
)
Guideline on the management of recurrent pregnancy loss
.
Eur. Soc. Human Reprod. Embryol.
2
,
1
10
2.
Royal College Obstetricians and Gynaecologists (RCOG)
(
2011
)
The investigation and treatment of couples with recurrent first-trimester and second-trimester miscarriage
.
R. Coll. Obstet. Gynaecol.
17
,
1
18
3.
Salat-Baroux
J.
(
1988
)
[Recurrent spontaneous abortions]
.
Reprod. Nutr. Dev.
28
,
1555
1568
[PubMed]
4.
(
2013
)
Definitions of infertility and recurrent pregnancy loss: a committee opinion
.
Fertil. Steril.
99
,
63
,
Practice Committee of American Society for Reproductive Medicine
[PubMed]
5.
Jauniaux
E.
,
Farquharson
R.G.
,
Christiansen
O.B.
and
Exalto
N.
(
2006
)
Evidence-based guidelines for the investigation and medical treatment of recurrent miscarriage
.
Hum. Reprod.
21
,
2216
2222
[PubMed]
6.
Toth
B.
,
Wurfel
W.
,
Bohlmann
M.K.
,
Gillessen-Kaesbach
G.
,
Nawroth
F.
,
Rogenhofer
N.
et al. .
(
2015
)
Recurrent miscarriage: diagnostic and therapeutic procedures. Guideline of the DGGG (S1-Level, AWMF Registry No. 015/050, December 2013)
.
Geburtshilfe Frauenheilkd
75
,
1117
1129
[PubMed]
7.
Garrido-Gimenez
C.
and
Alijotas-Reig
J.
(
2015
)
Recurrent miscarriage: causes, evaluation and management
.
Postgrad. Med. J.
91
,
151
162
[PubMed]
8.
Kucharska
A.M.
,
Gorska
E.
,
Wasik
M.
,
Pyrzak
B.
and
Demkow
U.
(
2009
)
Expression of CD152 (CTLA-4) in children with autoimmune thyroiditis and +49 A/G polymorphism of exon 1 of the CTLA-4 gene
.
J. Physiol. Pharmacol.
60 Suppl 5
,
77
80
[PubMed]
9.
Sasaki
Y.
,
Sakai
M.
,
Miyazaki
S.
,
Higuma
S.
,
Shiozaki
A.
and
Saito
S.
(
2004
)
Decidual and peripheral blood CD4+CD25+ regulatory T cells in early pregnancy subjects and spontaneous abortion cases
.
Mol. Hum. Reprod.
10
,
347
353
[PubMed]
10.
Jin
L.P.
,
Chen
Q.Y.
,
Zhang
T.
,
Guo
P.F.
and
Li
D.J.
(
2009
)
The CD4+CD25 bright regulatory T cells and CTLA-4 expression in peripheral and decidual lymphocytes are down-regulated in human miscarriage
.
Clin. Immunol.
133
,
402
410
[PubMed]
11.
Shastry
B.S.
(
2009
)
SNPs: impact on gene function and phenotype
.
Methods Mol. Biol.
578
,
3
22
[PubMed]
12.
Chai
L.
(
2010
)
Association of the CTLA-4 Gene Polymorphism with the Recurrent Spontaneous Abortion
.
Master’s Degree Thesis, Nixia Medical University
13.
Fan
Q.
,
Zhang
J.
,
Cui
Y.
,
Wang
C.
,
Xie
Y.
,
Wang
Q.
et al. .
(
2018
)
The synergic effects of CTLA-4/Foxp3-related genotypes and chromosomal aberrations on the risk of recurrent spontaneous abortion among a Chinese Han population
.
J. Hum. Genet.
63
,
579
587
[PubMed]
14.
Gupta
R.
,
Prakash
S.
,
Parveen
F.
and
Agrawal
S.
(
2012
)
Association of CTLA-4 and TNF-alpha polymorphism with recurrent miscarriage among North Indian women
.
Cytokine
60
,
456
462
[PubMed]
15.
Misra
M.K.
,
Mishra
A.
,
Phadke
S.R.
and
Agrawal
S.
(
2016
)
Association of functional genetic variants of CTLA4 with reduced serum CTLA4 protein levels and increased risk of idiopathic recurrent miscarriages
.
Fertil. Steril.
106
,
1115
1123.e1116
[PubMed]
16.
Nasiri
M.
and
Rasti
Z.
(
2016
)
CTLA-4 and IL-6 gene polymorphisms: risk factors for recurrent pregnancy loss
.
Hum. Immunol.
77
,
1271
1274
[PubMed]
17.
Wang
X.
,
Lin
Q.
,
Ma
Z.
,
Hong
Y.
,
Zhao
A.
,
Di
W.
et al. .
(
2005
)
Association of the A/G polymorphism at position 49 in exon 1 of CTLA-4 with the susceptibility to unexplained recurrent spontaneous abortion in the Chinese population
.
Am. J. Reprod. Immunol.
53
,
100
105
[PubMed]
18.
Wells
G.
,
Shea
B.
,
O’Connell
D.
,
Robertson
J.
,
Peterson
J.
,
Welch
V.
et al. .
(
2011
)
The Newcastle–Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-analyses.
Ottawa Health Research Institute
19.
Higgins
J.P.
and
Thompson
S.G.
(
2002
)
Quantifying heterogeneity in a meta-analysis
.
Stat. Med.
21
,
1539
1558
[PubMed]
20.
DerSimonian
R.
and
Laird
N.
(
1986
)
Meta-analysis in clinical trials
.
Control. Clin. Trials
7
,
177
188
[PubMed]
21.
Mantel
N.
and
Haenszel
W.
(
1959
)
Statistical aspects of the analysis of data from retrospective studies of disease
.
J. Natl. Cancer Inst.
22
,
719
748
[PubMed]
22.
Egger
M.
,
Davey Smith
G.
,
Schneider
M.
and
Minder
C.
(
1997
)
Bias in meta-analysis detected by a simple, graphical test
.
BMJ
315
,
629
634
23.
Shao
H.B.
,
Ren
K.
,
Gao
S.L.
,
Zou
J.G.
,
Mi
Y.Y.
,
Zhang
L.F.
et al. .
(
2018
)
Human methionine synthase A2756G polymorphism increases susceptibility to prostate cancer
.
Aging
10
,
1776
1788
[PubMed]
24.
Guerin
L.R.
,
Prins
J.R.
and
Robertson
S.A.
(
2009
)
Regulatory T-cells and immune tolerance in pregnancy: a new target for infertility treatment
.
Hum. Reprod. Update
15
,
517
535
[PubMed]
25.
van Mourik
M.S.
,
Macklon
N.S.
and
Heijnen
C.J.
(
2009
)
Embryonic implantation: cytokines, adhesion molecules, and immune cells in establishing an implantation environment
.
J. Leukoc. Biol.
85
,
4
19
[PubMed]
26.
Zenclussen
A.C.
(
2006
)
Regulatory T cells in pregnancy
.
Springer Semin. Immunopathol.
28
,
31
39
[PubMed]
27.
Yang
H.
,
Qiu
L.
,
Chen
G.
,
Ye
Z.
,
Lu
C.
and
Lin
Q.
(
2008
)
Proportional change of CD4+CD25+ regulatory T cells in decidua and peripheral blood in unexplained recurrent spontaneous abortion patients
.
Fertil. Steril.
89
,
656
661
[PubMed]
28.
Read
S.
,
Greenwald
R.
,
Izcue
A.
,
Robinson
N.
,
Mandelbrot
D.
,
Francisco
L.
et al. .
(
2006
)
Blockade of CTLA-4 on CD4+CD25+ regulatory T cells abrogates their function in vivo
.
J. Immunol.
177
,
4376
4383
[PubMed]
29.
Toldi
G.
,
Vasarhelyi
B.
,
Biro
E.
,
Fugedi
G.
,
Rigo
J.
Jr.
and
Molvarec
A.
(
2013
)
B7 costimulation and intracellular indoleamine-2,3-dioxygenase expression in peripheral blood of healthy pregnant and pre-eclamptic women
.
Am. J. Reprod. Immunol.
69
,
264
271
[PubMed]
30.
Wang
X.
,
Ma
Z.
,
Hong
Y.
,
Lu
P.
and
Lin
Q.
(
2006
)
Expression of CD28 and cytotoxic T lymphocyte antigen 4 at the maternal-fetal interface in women with unexplained pregnancy loss
.
Internat. J. Gynaecol. Obstet.
93
,
123
129
[PubMed]
31.
Karim
S.
,
Jamal
H.S.
,
Rouzi
A.
,
Ardawi
M.S.M.
,
Schulten
H.J.
,
Mirza
Z.
et al. .
(
2017
)
Genomic answers for recurrent spontaneous abortion in Saudi Arabia: an array comparative genomic hybridization approach
.
Reprod. Biol.
17
,
133
143
[PubMed]
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