Abstract

Vascular endothelial growth factor (VEGF) plays a critical role in ovarian folliculogenesis and normal reproductive function. So far, several studies focusing on association between VEGF gene polymorphisms and polycystic ovary syndrome (PCOS). However, above association between the VEGF gene polymorphisms and PCOS susceptibility is uncertain. Hence, we performed a timely meta-analysis containing all current publications to make clear this relationship. We searched articles from the PubMed, Embase and Chinese language (WanFang and CNKI) databases that were published up until May 10, 2019. Finally, we obtained 9 studies, containing 29 case–control studies and 11 different polymorphisms. The odds ratios (OR) and 95% confidence intervals (CI) were revealed association strengths. There were significantly decreased associations between rs2010963 (-634), +9812, +405 polymorphisms and PCOS risk. Nevertheless, there existed increased associations between rs699947 (-2578), rs833061, rs1570360 (-1154), rs3025020, rs3025039 polymorphisms and PCOS susceptibility. Our current analysis suggested VEGF gene polymorphisms may be associated with PCOS risk, which is possible to be expected to become biomarkers of early detection for women.

Introduction

Polycystic ovarian syndrome (PCOS) is a highly prevalent disorder affecting multiple aspects of a women’s overall health, with long-term effects that transcend well beyond the reproductive age [1–3]. Clinically, PCOS is characterized by hyperandrogenism manifested by hirsutism, acne and androgenic alopecia [4]. Patients with PCOS demonstrate reproductive abnormalities [5], marked insulin resistance [6], increased risk for Type 2 diabetes mellitus [7], coronary heart disease [8], atherogenic dyslipidemia [9], cerebrovascular morbidity [10], and anxiety and depression [11]. Although it was first reported in 1935 by Stein et al., the etiology remains unclear. Data from many studies suggest that genetics is very important in the development of PCOS [12].

Vascular endothelial growth factor (VEGF) gene, also known as VEGFA, VPF and MVCD1, locates at 6p21.1 and contains nine exon counts, and is a member of the PDGF/VEGF growth factor family [13]. It encodes a heparin-binding protein, which exists as a disulfide-linked homodimer. This growth factor induces proliferation and migration of vascular endothelial cells and is essential for both physiological and pathological angiogenesis [14].

VEGF plays a critical role in ovarian folliculogenesis and normal reproductive function [14], highlighted by the findings that women with PCOS had increased serum levels of VEGF, which paralleled increases in Doppler flow velocities within ovarian vessels [15,16]. High vascularization may result in abnormal growth of the theca interna, the site of androgen steroidogenesis, leading to hyperandrogenism, a hallmark of PCOS [17]. Several single-nucleotide polymorphisms (SNPs) were identified within the VEGF gene, of which some were functional, directly affecting VEGF secretion and its serum expression [18–20].

So far, many studies have investigated the association between VEGF polymorphisms and PCOS risk. However, the results were not conclusive or consistent. Considering the vital role of VEGF gene in the development of PCOS, we conducted a timely meta-analysis including 11 SNPs [21–29] to derive a more comprehensive estimation of the association between VEGF gene polymorphisms and PCOS susceptibility to identify some significant biomarkers.

Materials and methods

Identification and eligibility of relevant studies

We applied the PubMed, Embase, WanFang and CNKI databases using the key words ‘VEGF or VEGFA or VPF’, ‘PCOS or Polycystic ovarian syndrome’ and ‘polymorphism’ or ‘variant’ to identify including studies. The last search was updated on May 10, 2019. Finally, 29 case–control studies about 11 different SNPs were retrieved.

Inclusion criteria and exclusion criteria

Including studies had to meet following criteria: (1) address the correlation between PCOS risk and the VEGF gene SNPs; (2) be a case–control study, and (3) have sufficient genotype (wild-type and mutant type) numbers in each case and control group. 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 selected: the first author’s last name, the year of publication, the country of origin, the ethnicity of subjects, SNP type, total number of case and control groups, source of control (SOC), the number of each genotype frequency in the case/control groups, the Hardy–Weinberg equilibrium (HWE) in the control group, and the genotyping method. Ethnicity was categorized as Asian, European, Mixed and African.

Statistical analysis

Odds ratio (OR) with 95% confidence intervals (CI) were used to measure the strength of the association between the VEGF gene SNPs and PCOS 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. If a P-value of < 0.10 for the Q-test indicated heterogeneity among the studies. If significant heterogeneity was detected, the random-effects model (DerSimonian-Laird method) was used. Otherwise, the fixed-effects model (Mantel–Haenszel method) was applied [30,31].

We investigated the relationship between genetic variants of the VEGF gene SNPs and PCOS risk by the allelic contrast (1 vs. 2), homozygote comparison (1/1 vs. 2/2), dominant genetic model (1/1+1/2 vs. 2/2), heterozygote comparison (1/2 vs. 2/2) and recessive genetic model (1/1 vs. 1/2+2/2). A sensitivity analysis was performed by omitting studies, one after another, to assess the stability of results. The departure of the VEGF gene SNPs 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 [32], both the P-value < 0.05 is considered as significant. All statistical tests for this meta-analysis were performed with Stata software (version 11.0; StataCorp LP, College Station, TX).

Network of gene interaction of VEGF gene

To more complete understanding of the role of VEGF in PCOS, the network of gene–gene interactions for VEGF gene was utilized through String online server (http://string-db.org/) [33].

Results

Study characteristics

In total, 73 articles were collected from the PubMed, Embase, CNKI and WanFang databases via a literature search using different combinations of above keywords. As shown in Figure 1, 64 articles were excluded (such as duplications, irrelevant articles, reviews and other gene’s polymorphisms). Finally, 9 different articles including 11 SNPs were included in our current meta-analysis (Figure 1). Study characteristics from the published studies on the relationship between the VEGF gene SNPs and PCOS risk are summarized in Table 1. In all the studies, the controls were women under normal pregnancy. The detail of 11 SNPs were rs2010963 or -634 (three case–control studies including 632 cases and 622 controls), +9812 (two case–control studies including 212 cases and 183 controls), +13553 (two case–control studies including 208 cases and 184 controls), -460 (two case–control studies including 263 cases and 285 controls), +405 (two case–control studies including 263 cases and 285 controls), rs699947 or -2578 (four case–control studies including 724 cases and 782 controls), rs833061 (three case–control studies including 618 cases and 673 controls), rs1570360 or -1154 (four case–control studies including 697 cases and 737 controls), rs833068 (two case–control studies including 500 cases and 540 controls), rs3025020 (two case–control studies including 500 cases and 540 controls), and rs3025039 or +936 (three case–control studies including 586 cases and 628 controls).

Flowchart illustrating the search strategy used to identify association studies for VEGF gene polymorphisms and PCOS risk

Figure 1
Flowchart illustrating the search strategy used to identify association studies for VEGF gene polymorphisms and PCOS risk
Figure 1
Flowchart illustrating the search strategy used to identify association studies for VEGF gene polymorphisms and PCOS risk
Table 1
Basic information for included studies of the association between polymorphisms in VEGF gene and PCOS susceptibility
AuthorYearCountryEthnicitySNPsCaseControlSOCCasesControlsHWE
1/1*1/2*2/2*1/1*1/2*2/2*Genotype
Lee 2008 Korea Asian rs2010963 (-634) 132 99 HB 26 60 46 20 45 34 0.47 TaqMan 
Almawi 2016 Bahrain Asian rs2010963 (-634) 382 393 PB 57 142 183 42 190 161 0.01 TaqMan 
Huang 2018 China Asian rs2010963 (-634) 118 130 HB 13 45 60 19 64 47 0.71 PCR-LDR 
Lee 2008 Korea Asian +9812 132 99 HB 36 90 12 29 58 0.01 TaqMan 
Ding 2009 China Asian +9812 80 84 HB 24 50 16 24 44 0.001 sequencing 
Lee 2008 Korea Asian +13553 128 100 HB 35 89 10 31 59 0.06 TaqMan 
Ding 2009 China Asian +13553 80 84 HB 35 45 30 54 0.046 sequencing 
Vural 2009 Turkey European -460 137 155 HB 18 64 55 29 74 52 0.76 F-LHPLC 
Guruvaiah 2014 India Asian -460 126 130 HB 27 59 40 25 72 33 0.2 sequencing 
Vural 2009 Turkey European +405 137 155 HB 44 90 39 112 0.78 F-LHPLC 
Guruvaiah 2014 India Asian +405 126 130 HB 10 46 70 19 59 52 0.73 sequencing 
Salem 2016 Tunisia African rs699947(-2578) 118 150 HB 20 63 35 29 76 45 0.76 TaqMan 
Almawi 2016 Bahrain Asian rs699947(-2578) 382 393 PB 64 183 135 50 178 165 0.85 TaqMan 
Gomes 2019 Brazil Mixed rs699947(-2578) 87 84 HB 27 38 22 18 41 25 <0.001 PCR-RFLP 
Vural 2009 Turkey European rs699947 (-2578) 137 155 HB 22 63 52 25 78 52 <0.001 F-LHPLC 
Salem 2016 Tunisia African rs833061 118 150 HB 30 55 33 32 76 42 0.82 TaqMan 
Almawi 2016 Bahrain Asian rs833061 382 393 PB 78 174 130 71 190 132 0.85 TaqMan 
Huang 2018 China Asian rs833061 118 130 HB 10 45 63 42 80 0.44 PCR-LDR 
Salem 2016 Tunisia African rs1570360 (-1154) 118 150 HB 19 42 57 18 57 75 0.17 TaqMan 
Almawi 2016 Bahrain Asian rs1570360 (-1154) 382 393 PB 45 140 197 44 131 218 <0.001 TaqMan 
Li 2014 China Asian rs1570360 (-1154) 110 110 HB 29 78 30 65 0.53 PCR-RFLP 
Gomes 2019 Brazil Mixed rs1570360 (-1154) 87 84 HB 24 56 31 52 <0.001 TaqMan 
Salem 2016 Tunisia African rs833068 118 150 HB 13 63 42 23 63 64 0.26 TaqMan 
Almawi 2016 Bahrain Asian rs833068 382 390 PB 51 175 156 34 200 156 0.006 TaqMan 
Salem 2016 Tunisia African rs3025020 (-583) 118 150 HB 10 40 68 52 90 0.89 TaqMan 
Almawi 2016 Bahrain Asian rs3025020 (-583) 382 393 PB 54 149 179 35 155 203 0.49 TaqMan 
Salem 2016 Tunisia African rs3025039 (+936) 118 150 HB 27 88 19 127 0.005 TaqMan 
Almawi 2016 Bahrain Asian rs3025039 (+936) 382 393 PB 81 296 68 318 0.141 TaqMan 
Gomes 2019 Brazil Mixed rs3025039 (+936) 86 85 HB 70 16  25 60   PCR-RFLP 
AuthorYearCountryEthnicitySNPsCaseControlSOCCasesControlsHWE
1/1*1/2*2/2*1/1*1/2*2/2*Genotype
Lee 2008 Korea Asian rs2010963 (-634) 132 99 HB 26 60 46 20 45 34 0.47 TaqMan 
Almawi 2016 Bahrain Asian rs2010963 (-634) 382 393 PB 57 142 183 42 190 161 0.01 TaqMan 
Huang 2018 China Asian rs2010963 (-634) 118 130 HB 13 45 60 19 64 47 0.71 PCR-LDR 
Lee 2008 Korea Asian +9812 132 99 HB 36 90 12 29 58 0.01 TaqMan 
Ding 2009 China Asian +9812 80 84 HB 24 50 16 24 44 0.001 sequencing 
Lee 2008 Korea Asian +13553 128 100 HB 35 89 10 31 59 0.06 TaqMan 
Ding 2009 China Asian +13553 80 84 HB 35 45 30 54 0.046 sequencing 
Vural 2009 Turkey European -460 137 155 HB 18 64 55 29 74 52 0.76 F-LHPLC 
Guruvaiah 2014 India Asian -460 126 130 HB 27 59 40 25 72 33 0.2 sequencing 
Vural 2009 Turkey European +405 137 155 HB 44 90 39 112 0.78 F-LHPLC 
Guruvaiah 2014 India Asian +405 126 130 HB 10 46 70 19 59 52 0.73 sequencing 
Salem 2016 Tunisia African rs699947(-2578) 118 150 HB 20 63 35 29 76 45 0.76 TaqMan 
Almawi 2016 Bahrain Asian rs699947(-2578) 382 393 PB 64 183 135 50 178 165 0.85 TaqMan 
Gomes 2019 Brazil Mixed rs699947(-2578) 87 84 HB 27 38 22 18 41 25 <0.001 PCR-RFLP 
Vural 2009 Turkey European rs699947 (-2578) 137 155 HB 22 63 52 25 78 52 <0.001 F-LHPLC 
Salem 2016 Tunisia African rs833061 118 150 HB 30 55 33 32 76 42 0.82 TaqMan 
Almawi 2016 Bahrain Asian rs833061 382 393 PB 78 174 130 71 190 132 0.85 TaqMan 
Huang 2018 China Asian rs833061 118 130 HB 10 45 63 42 80 0.44 PCR-LDR 
Salem 2016 Tunisia African rs1570360 (-1154) 118 150 HB 19 42 57 18 57 75 0.17 TaqMan 
Almawi 2016 Bahrain Asian rs1570360 (-1154) 382 393 PB 45 140 197 44 131 218 <0.001 TaqMan 
Li 2014 China Asian rs1570360 (-1154) 110 110 HB 29 78 30 65 0.53 PCR-RFLP 
Gomes 2019 Brazil Mixed rs1570360 (-1154) 87 84 HB 24 56 31 52 <0.001 TaqMan 
Salem 2016 Tunisia African rs833068 118 150 HB 13 63 42 23 63 64 0.26 TaqMan 
Almawi 2016 Bahrain Asian rs833068 382 390 PB 51 175 156 34 200 156 0.006 TaqMan 
Salem 2016 Tunisia African rs3025020 (-583) 118 150 HB 10 40 68 52 90 0.89 TaqMan 
Almawi 2016 Bahrain Asian rs3025020 (-583) 382 393 PB 54 149 179 35 155 203 0.49 TaqMan 
Salem 2016 Tunisia African rs3025039 (+936) 118 150 HB 27 88 19 127 0.005 TaqMan 
Almawi 2016 Bahrain Asian rs3025039 (+936) 382 393 PB 81 296 68 318 0.141 TaqMan 
Gomes 2019 Brazil Mixed rs3025039 (+936) 86 85 HB 70 16  25 60   PCR-RFLP 

HWE: Hardy–Weinberg equilibrium; HB: hospital-based; SOC: source of control; SNPs: single-nucleotide polymorphism; PCR-RFLP: polymerase chain reaction and restrictive fragment length polymorphism; PCR-LDR: polymerase chain reaction-ligase detection reaction; F-LHPLC: fluorescence-labeled hybridization probes in a Light-Cycler; * 1/1: mutant genotype, 1/2: heterozygous, 2/2: wide type.

Quantitative synthesis

Significantly increased association were detected between five VEGF gene SNPs and PCOS susceptibility: rs699947 (Recessive model: OR = 1.74, 95% CI = 1.33–2.27, P = 0.346 for heterogeneity, P < 0.001, Figure 2, Table 2); rs833061 (Recessive model: OR = 1.71, 95% CI = 1.28–2.21, P = 0.794 for heterogeneity, P < 0.001, Figure 2, Table 2); rs1570360 (Recessive model: OR = 1.92, 95% CI = 1.36–2.72, P = 0.231 for heterogeneity, P < 0.001, Figure 2, Table 2); rs3025020 (Homozygote comparison: OR = 1.73, 95% CI = 1.13–2.65, P = 0.920 for heterogeneity, P = 0.011, Figure 3, Table 2); rs3025039 (Dominant model: OR = 1.37, 95% CI = 1.01–1.85, P = 0.235 for heterogeneity, P = 0.042, Figure 4, Table 2).

Forest plot of PCOS risk associated with VEGF gene polymorphisms (rs699947, rs833061, rs1570360) (Recessive model) in the whole

Figure 2
Forest plot of PCOS risk associated with VEGF gene polymorphisms (rs699947, rs833061, rs1570360) (Recessive model) 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 2
Forest plot of PCOS risk associated with VEGF gene polymorphisms (rs699947, rs833061, rs1570360) (Recessive model) 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.

Forest plot of PCOS risk associated with VEGF gene polymorphisms (rs3025020 and +9812) (Homozygote comparison) in the whole

Figure 3
Forest plot of PCOS risk associated with VEGF gene polymorphisms (rs3025020 and +9812) (Homozygote comparison) 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 PCOS risk associated with VEGF gene polymorphisms (rs3025020 and +9812) (Homozygote comparison) 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.

Forest plot of PCOS risk associated with VEGF gene polymorphism (rs3025039) (Dominant model) in the whole. The squares and horizontal lines correspond to the study-specific OR and 95% CI

Figure 4
Forest plot of PCOS risk associated with VEGF gene polymorphism (rs3025039) (Dominant model) 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 4
Forest plot of PCOS risk associated with VEGF gene polymorphism (rs3025039) (Dominant model) 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 subgroup analysis for VEGF gene polymorphisms and PCOS susceptibility
VariablesNCase/Allelic contrastHomozygote comparisonHeterozygote comparisonDominant modelRecessive model
ControlOR(95%CI)PhPOR(95%CI)PhPOR(95%CI)PhPOR(95%CI)PhPOR(95%CI)PhP
rs2010963 (-634) 632/622 0.89(0.75–1.05) 0.242 0.152 0.98(0.69–1.38) 0.233 0.899 0.68(0.53–0.86) 0.339 0.002 0.74(0.59–0.93) 0.307 0.009 1.68(1.22–2.30) 0.197 0.001 
+9812 212/183 0.60(0.43–0.83) 0.892 0.002 0.33(0.16–0.68) 0.974 0.003 0.83(0.53–1.31) 0.838 0.425 0.66(0.44–0.99) 1.000 0.047 0.57(0.28–1.16) 0.985 0.119 
+13553 208/184 0.86(0.40–1.85) 0.031 0.697   – 1.00(0.65–1.51) 0.153 0.991 0.93(0.42–2.02) 0.061 0.846   – 
-460 263/285 0.84(0.66–1.07) 0.487 0.158 0.72(0.44–1.18) 0.413 0.194 0.75(0.51–1.10) 0.626 0.142 0.74(0.52–1.06) 0.938 0.105 1.19(0.77–1.84) 0.278 0.422 
+405 263/285 0.86(0.41–1.77) 0.012 0.673 0.48(0.23–1.00) 0.327 0.050 0.90(0.38–2.15) 0.018 0.819 0.86(0.34–2.13) 0.009 0.731 0.85(0.42–1.73) 0.430 0.661 
rs699947 (-2578) 724/782 1.13(0.98–1.31) 0.258 0.100 1.28(0.94–1.73) 0.333 0.112 1.17(0.88–1.56) 0.596 0.288 1.15(0.93–1.42) 0.389 0.201 1.74(1.33–2.27) 0.346 0.000 
rs833061 618/673 1.09(0.93–1.28) 0.582 0.294 1.18(0.85–1.63) 0.809 0.325 1.01(0.79–1.29) 0.460 0.958 1.06(0.84–1.34) 0.484 0.620 1.71(1.28–2.21) 0.794 0.000 
rs1570360 (-1154) 697/737 1.08(0.91–1.27) 0.541 0.390 1.23(0.85–1.76) 0.259 0.268 1.15(0.79–1.68) 0.170 0.453 1.05(0.85–1.30) 0.578 0.626 1.92(1.36–2.72) 0.231 0.000 
rs833068 500/540 1.08(0.90–1.28) 0.909 0.402 1.28(0.85–1.94) 0.239 0.232 1.11(0.65–1.89) 0.071 0.713 1.05(0.82–1.35) 0.256 0.699 1.62(0.74–3.58) 0.061 0.230 
rs3025020 (-583) 500/540 1.24(1.03–1.50) 0.719 0.026 1.73(1.13–2.65) 0.920 0.011 1.07(0.83–1.39) 0.823 0.604 1.18(0.93–1.51) 0.744 0.176 2.65(1.77–3.97) 0.974 0.000 
rs3025039 (+936) 586/628 1.27(0.97–1.27) 0.276 0.087 0.87(0.35–2.18) 0.724 0.766 1.43(1.05–1.96) 0.212 0.025 1.37(1.01–1.85) 0.235 0.042 3.17(0.72–13.97) 0.004 0.128 
VariablesNCase/Allelic contrastHomozygote comparisonHeterozygote comparisonDominant modelRecessive model
ControlOR(95%CI)PhPOR(95%CI)PhPOR(95%CI)PhPOR(95%CI)PhPOR(95%CI)PhP
rs2010963 (-634) 632/622 0.89(0.75–1.05) 0.242 0.152 0.98(0.69–1.38) 0.233 0.899 0.68(0.53–0.86) 0.339 0.002 0.74(0.59–0.93) 0.307 0.009 1.68(1.22–2.30) 0.197 0.001 
+9812 212/183 0.60(0.43–0.83) 0.892 0.002 0.33(0.16–0.68) 0.974 0.003 0.83(0.53–1.31) 0.838 0.425 0.66(0.44–0.99) 1.000 0.047 0.57(0.28–1.16) 0.985 0.119 
+13553 208/184 0.86(0.40–1.85) 0.031 0.697   – 1.00(0.65–1.51) 0.153 0.991 0.93(0.42–2.02) 0.061 0.846   – 
-460 263/285 0.84(0.66–1.07) 0.487 0.158 0.72(0.44–1.18) 0.413 0.194 0.75(0.51–1.10) 0.626 0.142 0.74(0.52–1.06) 0.938 0.105 1.19(0.77–1.84) 0.278 0.422 
+405 263/285 0.86(0.41–1.77) 0.012 0.673 0.48(0.23–1.00) 0.327 0.050 0.90(0.38–2.15) 0.018 0.819 0.86(0.34–2.13) 0.009 0.731 0.85(0.42–1.73) 0.430 0.661 
rs699947 (-2578) 724/782 1.13(0.98–1.31) 0.258 0.100 1.28(0.94–1.73) 0.333 0.112 1.17(0.88–1.56) 0.596 0.288 1.15(0.93–1.42) 0.389 0.201 1.74(1.33–2.27) 0.346 0.000 
rs833061 618/673 1.09(0.93–1.28) 0.582 0.294 1.18(0.85–1.63) 0.809 0.325 1.01(0.79–1.29) 0.460 0.958 1.06(0.84–1.34) 0.484 0.620 1.71(1.28–2.21) 0.794 0.000 
rs1570360 (-1154) 697/737 1.08(0.91–1.27) 0.541 0.390 1.23(0.85–1.76) 0.259 0.268 1.15(0.79–1.68) 0.170 0.453 1.05(0.85–1.30) 0.578 0.626 1.92(1.36–2.72) 0.231 0.000 
rs833068 500/540 1.08(0.90–1.28) 0.909 0.402 1.28(0.85–1.94) 0.239 0.232 1.11(0.65–1.89) 0.071 0.713 1.05(0.82–1.35) 0.256 0.699 1.62(0.74–3.58) 0.061 0.230 
rs3025020 (-583) 500/540 1.24(1.03–1.50) 0.719 0.026 1.73(1.13–2.65) 0.920 0.011 1.07(0.83–1.39) 0.823 0.604 1.18(0.93–1.51) 0.744 0.176 2.65(1.77–3.97) 0.974 0.000 
rs3025039 (+936) 586/628 1.27(0.97–1.27) 0.276 0.087 0.87(0.35–2.18) 0.724 0.766 1.43(1.05–1.96) 0.212 0.025 1.37(1.01–1.85) 0.235 0.042 3.17(0.72–13.97) 0.004 0.128 

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

In opposite, several VEGF gene SNPs acts as a decreased association or protective effect for PCOS risk: rs2010963 (Heterozygote comparison: OR = 0.68, 95% CI = 0.53–0.86, P = 0.339 for heterogeneity, P = 0.002, Figure 5, Table 2); +9812 (Allelic contrast: OR = 0.60, 95% CI = 0.43–0.83, P = 0.892 for heterogeneity, P = 0.002, Figure 3, Table 2); +405 (Homozygote comparison: OR = 0.48, 95% CI = 0.23–1.00, P = 0.327 for heterogeneity, P = 0.050, Figure 6, Table 2).

Forest plot of PCOS risk associated with VEGF gene polymorphism (rs2010963) (Heterozygote comparison) in the whole

Figure 5
Forest plot of PCOS risk associated with VEGF gene polymorphism (rs2010963) (Heterozygote comparison) 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 5
Forest plot of PCOS risk associated with VEGF gene polymorphism (rs2010963) (Heterozygote comparison) 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.

Forest plot of PCOS risk associated with VEGF gene polymorphism (+405) (Allelic contrast) in the whole

Figure 6
Forest plot of PCOS risk associated with VEGF gene polymorphism (+405) (Allelic contrast) 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 6
Forest plot of PCOS risk associated with VEGF gene polymorphism (+405) (Allelic contrast) 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.

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 (data not shown). Egger and Begg’s tests were performed to assess publication bias and the funnel plot symmetry was examined. Finally, no publication bias was observed (data not shown).

Gene–gene network diagram and interaction of online website

String online server indicated that VEGF gene interacts with numerous genes. The network of gene–gene interaction has been illustrated in Figure 7.

Human VEGF interactions network with other genes obtained from String server

Figure 7
Human VEGF interactions network with other genes obtained from String server

At least 10 genes have been indicated to correlate with VEGF gene. KDR: vascular endothelial growth factor receptor 2; FLT1: vascular endothelial growth factor receptor 1; NRP2: neuropilin-2; HIF1A: hypoxia-inducible factor 1-α; NRP1: neuropilin-1; STAT3: signal transducer and activator of transcription 3; TGFB1: transforming growth factor β-1; EGF: pro-epidermal growth factor; IGF1: insulin-like growth factor 1; NOS3: nitric oxide synthase, endothelial.

Figure 7
Human VEGF interactions network with other genes obtained from String server

At least 10 genes have been indicated to correlate with VEGF gene. KDR: vascular endothelial growth factor receptor 2; FLT1: vascular endothelial growth factor receptor 1; NRP2: neuropilin-2; HIF1A: hypoxia-inducible factor 1-α; NRP1: neuropilin-1; STAT3: signal transducer and activator of transcription 3; TGFB1: transforming growth factor β-1; EGF: pro-epidermal growth factor; IGF1: insulin-like growth factor 1; NOS3: nitric oxide synthase, endothelial.

Discussion

A strong association between increased serum VEGF levels and PCOS was previously reported, and a correlation between serum VEGF levels and increased ovarian stromal blood flow in women with polycystic ovaries was suggested [15,16,34,35]. On the other hand, polymorphisms in the VEGF gene may lead to alterations in the production of this protein and may play an important role in the pathophysiology of PCOS, contributing to ovulatory dysfunction, infertility, and ovarian hyperstimulation syndrome, which are commonly observed in women with PCOS [36].

To combine the important of genetic etiology of PCOS, it makes sense to deep study the VEGF gene polymorphisms. There are at least 80 SNPs places in this gene (NCBI Gene association no: NT 007592). Among them, we searched several popular databases to select more comprehensive case–control studies about SNPs in VEGF gene, which have been reported more than once about PCOS disease. Finally, 11 SNPs [rs2010963 (-634), +9812, +13553, -460, +405, rs699947 (-2578), rs833061, rs1570360 (-1154), rs833068, rs3025020 (-583), rs3025039 (+936)] were identified.

Polymorphisms in the promoter region (loci: -2578, -1154 and -460) or intron 6 (loci: -583) or 5′-untranslated region (loci: +405, +963 and -634) or +534 have been associated with different levels of VEGF expression. It was reported that -2578 C, -460 T and +405 G alleles appear to correlate with altered VEGF expression levels [18,20,37–40]. In addition, a strong association between increased serum VEGF levels and PCOS was previously reported, and a correlation between serum VEGF levels and increased ovarian stromal blood flow in women with polycystic ovaries was suggested [15,16,34,35]. Due to above items, these four SNPs have been widely reported in PCOS.

It is the first time to collect such more SNPs at one time, 11 SNPs containing 5203 cases and 5462 controls. The meaningful of our current analysis was that we found five SNPs (rs699947, rs833061, rs1570360, rs3025020, rs3025039) may act as a risk effect for the development of PCOS; moreover, three SNPs (rs2010963, +9812, +405) may have a protective influence for PCOS. Among above results, rs699947, rs3025020 and +405 polymorphisms were consistent with abnormal expression of VEGF gene in serum, and may be associated with PCOS risk through the serum VEGF levels. Some factors may be explained: First, different polymorphisms in the same gene may exert different effects on gene expression and function, leading to vary PCOS risks. Second, single genes or single environmental factors may not be likely to have direct effects on PCOS susceptibility, but complex interactions between genetic and environmental factors may be involved in the disease development. The last but not the least, if numbers of included studies were small, false-negative results may be detected for each polymorphism [41].

If one woman exists one or more significant following five SNPs (rs699947, rs833061, rs1570360, rs3025020, rs3025039) for VEGF from peripheral blood test, which may indicate that it is possible to increase the occurrence of PCOS for her in present time or at some point in the future. Therefore, it can be offer us some targets to intervene, such as lifestyle modification (reducing the BMI, obesity, high blood pressure, high blood fat and cardiovascular disease) for prevention status, regular gynecological examination (vaginal ultrasound or CT scans or endocrinology) to identify or rule out this disease and carry out treatments as soon as possible (oral contraceptive therapy, ovulation induction, high testosterone therapy, insulin sensitizer, GLP-I receptor agonist therapy, surgical treatment) [42]. To sum up, we wish to use this method to reduce the incidence of PCOS and improve the cure rate of early treatment. In addition, for another three decreased association SNPs (rs2010963, +9812, +405) and these no associated SNPs, it is not necessary to take corresponding monitoring measures at current moment.

In addition, we used the online analysis system-String to predict potential and functional partners (Figure 7). Finally, ten genes were predicted. The average score was very high. Among them, the highest score of association was KDR and FLT1 (score = 0.999); however, TGFB1, EGF, IGF1 and NOS3 had the lowest scores (0.993). The action of VEGF is mediated by binding to tyrosine kinase receptors, VEGFR-1 (Fms-like tyrosine kinase: FLT1) and VEGFR-2 (kinase domain-containing receptor: KDR) [43]. Pan et al. demonstrated that HIF1A-mediated VEGF expression might be an important mechanism regulating ovarian luteal development in mammals in vivo, which may provide new strategies for fertility control and for treating PCOS [44]. Additional, IGF-1, TGFB1, STAT3 and NOS3 were just suggested to participate in the development of PCOS [45–48], rather than combined with VEGF. Above information predicted FLT1, KDR, and HIF1A may influence VEGF and regulate the PCOS development, which maybe become intervention and treatment target genes in the future.

Several limitations in our current analysis should be considered. First, unadjusted OR was used. Second, control sources were not all health women. Third, only four databases were searched for study retrieval, few relevant studies may be omitted. Fourthly, gene–gene interaction was missing. Fifth, other confounding factors such as age, BMI, lifestyle, total cholesterol, free androgen index, triglycerides and environment were not included and analyzed.

In summary, in the present meta-analysis, VEGF gene polymorphisms may be associated with PCOS susceptibility.

Competing Interests

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

Funding

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

Author Contribution

L.W. conceived the study. L.H. searched the databases and extracted the data. L.H. analyzed the data. L.H. wrote the draft of the paper. L.W. reviewed the manuscript.

Abbreviations

     
  • CI

    confidence intervals

  •  
  • OR

    odds ratios

  •  
  • PCOS

    polycystic ovary syndrome

  •  
  • SNP

    single-nucleotide polymorphism

  •  
  • VEGF

    vascular endothelial growth factor

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