# Association between VEGF Gene Polymorphisms and the Susceptibility to Lung Cancer: An Updated Meta-Analysis

Authors: Fengming Yang; Zhiqiang Qin; Chuchu Shao; Weitao Liu; Ling Ma; Yongqian Shu; Hua Shen
Journal: BioMed Research International
 (2018)

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## Abstract

Background and Objective. The association between vascular endothelial growth factor (VEGF) gene polymorphisms (-2578C/A, +936C/T, and -460C/T) and lung cancer risk has been extensively studied in the last decades, but currently available results remain controversial or ambiguous. Therefore, we conducted a meta-analysis to assess whether the relationship between the VEGF gene and lung cancer susceptibility exists.Methods. The meta-analysis was conducted by searching the databases PubMed, Embase, and Web of Science covering all eligible studies published up to October 1, 2017. The pooled odds ratios (ORs) as well as their 95% confidence intervals (CIs) were utilized to evaluate the possible associations. Publication bias of relevant studies was examined via Begg’s funnel plots and Egger’s regression tests.Results. This meta-analysis included 13 published case–control studies covering 4477 patients with lung cancer and 4346 healthy controls, who had been accrued from December 1992 to July 2012. For the overall eligible data collected in our meta-analysis, it indicated that VEGF +936C/T, -460C/T, and -2578C/A polymorphisms did not correlate with the elevated lung cancer risk in all genetic comparison models. Moreover, VEGF +460T/C polymorphism was found to be significantly associated with susceptibility to lung cancer in these models (allele model: pooled OR = 1.12, 95% CI: 1.00–1.26,P = 0.184; homozygote model: pooled OR = 1.51, 95% CI: 1.12–2.03,P = 0.821), but no significant results were detected in Caucasian populations.Conclusions. VEGF +936C/T, -460C/T, and -2578C/A polymorphisms were not associated with the risk of lung cancer. The VEGF +460T/C polymorphism might be a risk factor for lung cancer only in Asian populations.


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## Body

## 1. Introduction

Lung cancer, as one of the most frequently occurring cancers in the world, is always the leading cause of cancer-related death among both men and women [1]. Approximately two million people are diagnosed with lung cancer each year and most of them diagnosed at an advanced stage [2]. The lack of effective treatment options and high mortality make lung cancer a major public health challenge all over the world [3, 4]. With the advent of next-generation genotyping and in-depth understanding of the molecular biology of lung cancer, genotyping of single-nucleotide polymorphisms (SNPs) may be pivotal in the personalized treatment for patients with lung cancer [5].Angiogenesis refers to the complex process of the formation of new networks of blood vessels. Increased angiogenesis has been proved to be associated with the process of tumor growth and metastasis [6]. Vascular endothelial growth factor (VEGF), also known as vascular permeability factor, is one of the most vital regulators of angiogenesis and vascular permeability. It is well known that VEGF plays a critical role in the progress and prognosis of malignancy [7, 8], which has been confirmedin vitro and in vivo[9, 10]. Serum VEGF levels of cancer patients were significantly higher than those of healthy controls [11]. Moreover, serum VEGF levels always can predict the effects of chemoradiotherapy sensitivity in cancer patients [12]. In lung cancer, VEGF is important in establishing a vascular supply within the tumor [13]. The VEGF/VEGF-receptor axis is composed of multiple ligands and receptors with overlapping and distinct ligand–receptor binding specificities, cell-type expression, and function [14]. Targeted inhibition of the VEGF signaling pathway can partially suppress tumor angiogenesis and growth. In recent years, the use of anti-VEGF antibodies, such as bevacizumab, has shown a favorable clinical efficacy in treating non-small cell lung cancer (NSCLC), especially when used in combination with chemotherapy [15, 16].The VEGF gene is located at chromosome 6p21.3, covering 14 kb in length with 8 exons and 7 introns [17]. Being highly polymorphic, at least 30 single SNPs have been identified and described [18]. Several SNPs of VEGF have been reported to be associated with individual susceptibility to cancer and can alter the VEGF expression and protein production [19, 20]. For example, −634G>C SNP in the 5′-UTR of VEGF affects the protein translation efficiency, and 936C>T SNP in the 3′-UTR influences the expression of VEGF in tumor tissues [21, 22]. SNPs, such as VEGF -2578C/A, +936C/T, and -460C/T, have been reported to be associated with cancer susceptibility, tumor growth, and radiotherapy sensitivity in patients with lung cancer [23, 24]. Recently, accumulating evidence has shown that VEGF aberrations may contribute to the development of lung cancer [25]. However, due to the limited sample size, the results of these studies remain controversial or inconclusive. In recent years, related studies were updated; in particular a study included in a previous meta-analysis was withdrawn [26]. Thus, this updated meta-analysis including all eligible case–control studies was performed to investigate whether VEGF polymorphisms were associated with the risk of lung cancer.


## 2. Materials and Methods

### 2.1. Search Strategy and Selection Criteria

A systematic literature search was conducted on PubMed, Embase, and Web of Science databases until October 1, 2017, to identify studies for inclusion in the meta-analysis. The relevant key words and search strategies were as follows: “vascular endothelial growth factor” or “VEGF,” “polymorphisms” or “mutation,” “variants” and “lung cancer,” “non-small cell lung cancer,” or “NSCLC.” These terms were arranged into different combinations when used for the search. Besides, the reference lists of original studies were searched manually for additional literature. All the eligible studies were checked carefully to prevent overlapping datasets, and only previously published studies were included.The studies that fulfilled the following criteria were considered eligible: (1) studies focused on the association between VEGF and the risk of lung cancer; (2) studies used a case–control or cohort design; and (3) inclusion of sufficient data on the frequency of genotypes including ORs and their 95% CIs. In addition, the studies that did not meet the aforementioned inclusion criteria were excluded.


### 2.2. Data Extraction

The data were independently extracted from all the eligible studies by two investigators (Yang FM and Qin ZQ) according to the aforementioned inclusion criteria. When it came to conflicting evaluations, an agreement was settled after discussion with the third reviewer. For each study, the extracted information included: the surname of the first author, year of publication, ethnicity, source of controls, sample size of case and control groups, SNP genotyping methods, genotype distributions, and the results of the Hardy–Weinberg equilibrium (HWE) test.


### 2.3. Statistical Analysis

The pooled odds ratios (ORs) and their corresponding 95% CIs were calculated to evaluate the strength of association between VEGF gene polymorphisms and lung cancer risk under five genetic models: allele model, homozygous model, heterozygous model, dominant model, and recessive model. The genotypic distributions of different polymorphic sites in the subjects were compared with those expected under HWE using the chi-squared goodness-of-fit test, and aP value <0.05 was considered to indicate statistically significant heterogeneity.The pooled ORs were calculated using the fixed-effects model or the random-effects model according to the presence (P < 0.05) or absence (P > 0.05) of heterogeneity. When thePvalue was >0.05, the pooled ORs were calculated using the fixed-effects model based on the Mantel–Haenszel method. Otherwise, the random-effects model with the DerSimonian–Laird method was used for this meta-analysis. Then, the sources of heterogeneity were further analyzed according to ethnicity. The sensitivity analysis was performed by sequentially excluding individual studies to evaluate the stability and reliability of the results. In addition, publication bias between the studies was analyzed using Begg’s funnel plots and Egger’s linear regression tests. All the aforementioned statistical tests were performed using Stata12.0 software.


## 2.1. Search Strategy and Selection Criteria

A systematic literature search was conducted on PubMed, Embase, and Web of Science databases until October 1, 2017, to identify studies for inclusion in the meta-analysis. The relevant key words and search strategies were as follows: “vascular endothelial growth factor” or “VEGF,” “polymorphisms” or “mutation,” “variants” and “lung cancer,” “non-small cell lung cancer,” or “NSCLC.” These terms were arranged into different combinations when used for the search. Besides, the reference lists of original studies were searched manually for additional literature. All the eligible studies were checked carefully to prevent overlapping datasets, and only previously published studies were included.The studies that fulfilled the following criteria were considered eligible: (1) studies focused on the association between VEGF and the risk of lung cancer; (2) studies used a case–control or cohort design; and (3) inclusion of sufficient data on the frequency of genotypes including ORs and their 95% CIs. In addition, the studies that did not meet the aforementioned inclusion criteria were excluded.


## 2.2. Data Extraction

The data were independently extracted from all the eligible studies by two investigators (Yang FM and Qin ZQ) according to the aforementioned inclusion criteria. When it came to conflicting evaluations, an agreement was settled after discussion with the third reviewer. For each study, the extracted information included: the surname of the first author, year of publication, ethnicity, source of controls, sample size of case and control groups, SNP genotyping methods, genotype distributions, and the results of the Hardy–Weinberg equilibrium (HWE) test.


## 2.3. Statistical Analysis

The pooled odds ratios (ORs) and their corresponding 95% CIs were calculated to evaluate the strength of association between VEGF gene polymorphisms and lung cancer risk under five genetic models: allele model, homozygous model, heterozygous model, dominant model, and recessive model. The genotypic distributions of different polymorphic sites in the subjects were compared with those expected under HWE using the chi-squared goodness-of-fit test, and aP value <0.05 was considered to indicate statistically significant heterogeneity.The pooled ORs were calculated using the fixed-effects model or the random-effects model according to the presence (P < 0.05) or absence (P > 0.05) of heterogeneity. When thePvalue was >0.05, the pooled ORs were calculated using the fixed-effects model based on the Mantel–Haenszel method. Otherwise, the random-effects model with the DerSimonian–Laird method was used for this meta-analysis. Then, the sources of heterogeneity were further analyzed according to ethnicity. The sensitivity analysis was performed by sequentially excluding individual studies to evaluate the stability and reliability of the results. In addition, publication bias between the studies was analyzed using Begg’s funnel plots and Egger’s linear regression tests. All the aforementioned statistical tests were performed using Stata12.0 software.


## 3. Results

### 3.1. Studies Characteristics

According to the searching criteria, a total of 109 studies were initially identified through a primary search of PubMed, Embase, and Web of Science databases and reference lists. Among these studies, 13 full-text studies met the inclusion criteria and were included in the present meta-analysis for further evaluation, which were accrued between December 1992 and July 2012 [27–39]. Besides, all studies suggested that the distribution of genotypes in the controls was consistent under HWE. The flowchart of literature search and selection procedure is shown in Figure 1. The baseline characteristics of the studies associated with the risk of lung cancer are comprehensively listed in Tables 1 and 2. Among these 13 enrolled studies, 10 were based on Asian populations and 3 on Caucasian populations.Table 1
Baseline characteristics of studies associated with the risk of lung cancer included in the meta-analysis.
Year Surname Ethnicity Country SOC Genotyping Cases Controls Gene polymorphism 2017 Yamamoto Japan Asian HB TaqMan 462 379 -460C/T 2017 Naikoo India Asian PB PCR-RFLP 112 116 +936C/T 2017 Naykoo India Asian PB PCR-RFLP 199 401 +936C/T, -2578C/A 2015 Krupnova Belarus Caucasian HB PCR-RFLP 202 336 +936C/T, -2578C/A 2015 Liu China Asian PB PCR-RFLP 414 338 -460C/T, -2578C/A 2014 Deng China Asian HB PCR-RFLP 65 110 -2578C/A 2013 Sun China Asian PB PCR-RFLP 126 160 -460C/T 2013 de Mello Portugal Caucasian HB MassARRAY 144 144 +936C/T, -460C/T, -2578C/A 2012 Naik India Asian PB PCR-RFLP 100 150 +936C/T 2012 Li China Asian PB PCR-RFLP 150 150 +936C/T, -2578C/A 2009 Liang China Asian PB PCR-RFLP 171 172 +936C/T, -2578C/A 2008 Zhai America Caucasian HB TaqMan 1900 1458 +936C/T, -460C/T 2005 Lee Korea Asian PB PCR-RFLP 432 432 +936C/T, -460C/T SOC: source of control; HB: hospital-based; PB: population-based controls.Table 2
The genotype frequency data of individual studies included in the meta-analysis.
VEGF rs3025039 +936C/T  Case (n)  Control (n)  Year Surname Case Control CC CT TT CC CT TT HWE  2017 Naikoo 109 112 68 32 9 65 38 9 Y 2017 Naykoo 199 401 80 99 20 261 120 20 Y 2015 Krupnova 161 360 115 38 8 267 80 13 N 2013 de Mello 144 144 115 28 1 111 30 3 Y 2012 Naik 90 150 54 28 8 80 48 22 N 2012 Li 150 150 108 35 7 114 33 3 Y 2009 Liang 171 172 123 40 8 131 38 3 Y 2008 Zhai 1900 1458 1441 424 35 1125 308 25 Y 2005 Lee 432 432 281 137 14 266 154 12 Y  VEGF rs833061 -460C/T  2017 Yamamoto 462 379 32 197 233 35 157 187 Y 2015 Liu 414 338 21 164 229 23 138 177 Y 2013 Sun 126 160 22 43 61 38 69 53 Y 2013 de Mello 144 144 28 79 37 31 72 41 Y 2008 Zhai 1900 1458 439 922 539 342 694 422 Y 2005 Lee 430 432 18 184 228 27 168 237 Y  VEGF rs699947 -2578C/A  2017 Naykoo 199 401 24 170 5 200 145 56 N 2015 Krupnova 162 360 41 90 31 83 186 91 Y 2015 Liu 414 338 230 164 20 177 138 23 Y 2014 Deng 65 110 26 33 6 62 41 7 Y 2013 de Mello 144 144 43 75 26 44 73 27 Y 2012 Li 150 150 93 45 12 98 49 3 Y 2009 Liang 171 172 129 28 14 112 56 4 YFigure 1
The flowchart of literature search and selection procedure.


### 3.2. Quantitative Synthesis Results

Overall, the strength of association between VEGF genetic polymorphisms and lung cancer risk was evaluated using the pooled ORs with 95% CIs based on five genetic comparison models. A summary of all the meta-analysis results for the 13 studied VEGF polymorphisms and lung cancer susceptibility is provided in Table3.Table 3
Meta-analysis results for the included studies of the association between rs3025039, rs833061, and rs699947 polymorphisms and risk of lung cancer.
Variables No. of studies Dominant model Recessive model Homozygous model Heterozygous model Allele model  OR (95% CI) P values I-squared (%) OR (95% CI) P values I-squared (%) OR (95% CI) P values I-squared (%) OR (95% CI) P values I-squared (%) OR (95% CI) P values I-squared (%)  rs3025039 C>T +936C/T (CT + TT) vs. CC TT vs. (CT + CC) TT vs. CC CT vs. CC T vs. C All 9 1.12 (0.87-1.46) <0.001 76.4 1.25 (0.94-1.65) 0.266 19.9 1.32(0.86-2.04) 0.047 48.9 1.11 (0.86-1.42) <0.001 72.1 1.11 (0.89-1.38) <0.001 76.2 Ethnicity Asian 6 1.17 (0.75-1.81) <0.001 84.4 1.37 (0.95-1.97) 0.154 37.9 1.47 (0.78-2.77) 0.022 61.9 1.14 (0.74-1.75) <0.001 82 1.15(0.81-1.65) <0.001 83.8 Caucasian 3 1.07 (0.92-1.23) 0.681 0 1.10 (0.70-1.70) 0.507 0 1.11 (0.71-1.73) 0.488 0 1.07 (0.92-1.24) 0.836 0 1.06 (0.93-1.21) 0.522 0 Source of control HB 3 1.07 (0.92-1.23) 0.681 0 1.10 (0.70-1.70) 0.507 0 1.11 (0.71-1.73) 0.488 0 1.07(0.92-1.24) 0.836 0 1.06 (0.93-1.21) 0.522 0 PB 6 1.17 (0.75-1.81) <0.001 84.4 1.37 (0.95-1.97) 0.154 37.9 1.47 (0.78-2.77) 0.022 61.9 1.14 (0.74-1.75) <0.001 82 1.15(0.81-1.65) <0.001 83.8 rs833061 C>T -460C/T (CT + TT) vs. CC TT vs. (CT + CC) TT vs. CC CT vs. CC T vs. C All 6 1.11(0.97-1.27) 0.534 0 1.02(0.92-1.13) 0.151 38.2 1.12 (0.96-1.30) 0.269 21.9 1.11 (0.96-1.28) 0.687 0 1.04 (0.97-1.12) 0.201 31.2 Ethnicity Asian 4 1.42 (1.07-1.90) 0.991 0 1.09 (0.94-1.27) 0.085 54.6 1.51 (1.12-2.03) 0.821 0 1.34 (0.99-1.81) 0.837 0 1.12 (1.00-1.26) 0.184 38 Caucasian 2 1.03 (0.88-1.20) 0.721 0 0.96 (0.83-1.11) 0.684 0 1.00 (0.83-1.20) 0.991 0 1.05 (0.89-1.24) 0.616 0 0.99 (0.91-1.09) 0.957 0 Source of control HB 3 1.05 (0.91-1.22) 0.531 0 0.98 (0.86-1.11) 0.807 0 1.03 (0.87-1.23) 0.532 0 1.07 (0.92-1.26) 0.554 0 1.01 (0.93-1.10) 0.757 0 PB 3 1.45 (1.03-2.06) 0.968 0 1.11 (0.93-1.33) 0.039 69.1 1.59 (1.11-2.29) 0.708 0 1.33 (0.92-1.91) 0.656 0 1.14 (0.99-1.31) 0.095 57.5 rsrs699947 C>A -2578C/A (CA + AA) vs. CC AA vs. (AA + CC) AA vs. CC AA vs. CC A vs. C All 7 1.33(0.74-2.40) <0.001 91.8 0.98(0.52-1.84) <0.001 78 1.15(0.71-1.86) 0.037 55.1 1.33 (0.66-2.68) <0.001 93.7 1.12 (0.87-1.43) <0.001 77.2 Ethnicity Asian 5 1.53 (0.67-3.51) <0.001 94.3 1.15(0.37-3.52) <0.001 84.9 1.50(0.71-3.20) 0.026 63.9 1.48(0.54-4.04) <0.001 95.7 1.22 (0.87-1.70) <0.001 80.6 Caucasian 2 0.94(0.68-1.31) 0.654 0 0.79(0.55-1.13) 0.417 0 0.79 (0.52-1.22) 0.426 0 1.01(0.72-1.42) 0.842 0 0.90 (0.74-1.11) 0.434 0 Source of control HB 3 1.15 (0.75-1.77) 0.122 52.6 0.83 (0.59-1.18) 0.411 0 0.93 (0.56-1.54) 0.247 28.6 1.19(0.82-1.73) 0.224 33.2 1.05 (0.76-1.45) 0.073 61.8 PB 4 1.45(0.54-3.90) <0.001 95.6 1.09 (0.27-4.34) <0.001 88.2 1.43 (0.58-3.54) 0.017 70.4 1.39 (0.42-4.65) <0.001 96.7 1.15 (0.79-1.69) <0.001 84.3


### 3.3. +936C/T and Lung Cancer Risk

In the present meta-analysis, the combined results of all analyses showed that the pooled OR of nine studies was 1.11 (95% CI: 0.89–1.38,P < 0.001) for the allele model, 1.32 (95% CI: 0.86–2.04,P = 0.047) for the homozygote model, 1.11 (95% CI: 0.86–1.42,P < 0.001) for the heterozygote model, 1.12 (95% CI: 0.87–1.46,P < 0.001) for the dominant model, and 1.25 (95% CI: 0.94–1.65,P = 0.266) for the recessive model, indicating no association between VEGF +936C/T mutation and lung cancer susceptibility (Figure 2). Furthermore, subgroup analyses by ethnicity and source of control were performed to establish the effects of heterogeneity on the results. In the subgroup analyses by ethnicity and source of control, no significant results were found in all genetic comparison models (Table 3).Figure 2
Forest plot of the association between the +936C/T polymorphism and lung cancer risk. (a) Allele model; (b) homozygote model; (c) heterozygote model; (d) dominant model; (e) recessive model.
(a) (b) (c)  (d) (e)


### 3.4. -460C/T and Lung Cancer Risk

The results demonstrated that the VEGF -460C/T polymorphism was not significantly correlated with the risk of lung cancer only in the allele model (pooled OR = 1.04, 95% CI: 0.97–1.12,P = 0.201), homozygote model (pooled OR = 1.12, 95% CI: 0.96–1.30,P = 0.269), heterozygote model (pooled OR = 1.11, 95% CI: 0.96–1.28,P = 0.687), dominant model (pooled OR = 1.11, 95% CI: 0.97–1.27,P = 0.534), and recessive model (pooled OR = 1.02, 95% CI: 0.92–1.13,P = 0.151) (Figure 3). When the studies were stratified by ethnicity, significant differences were observed in Asian populations in these models (allele model: pooled OR = 1.12, 95% CI: 1.00–1.26; homozygote model: pooled OR = 1.51, 95% CI: 1.12–2.03), but no significant results were detected in Caucasian populations. Furthermore, significant results were found in the homozygote model (pooled OR = 1.59, 95% CI: 1.11–2.29) in the subgroup analysis by the source of control (Table 3).Figure 3
Forest plot of the association between the -460C/T polymorphism and lung cancer risk. (a) Allele model; (b) homozygote model; (c) heterozygote model; (d) dominant model; (e) recessive model.
(a) (b) (c)  (d) (e)


### 3.5. -2578C/A and Lung Cancer Risk

The combined results of all analyses showed that the pooled OR of these studies was 1.12 (95% CI: 0.87–1.43,P < 0.001) for the allele model, 1.15 (95% CI: 0.71–1.86,P = 0.037) for the homozygote model, 1.33 (95% CI: 0.66–2.68,P < 0.001) for the heterozygote model, 1.33 (95% CI: 0.74–2.40,P < 0.001) for the dominant model, and 0.98 (95% CI: 0.52–1.84,P < 0.001) for the recessive model, indicating no significant association between VEGF -2578C/A polymorphism and lung cancer risk (Figure 4). In addition, when the studies were stratified by ethnicity and source of control, no significant differences were found in all genetic models (Table 3).Figure 4
Forest plot of the association between the -2578C/A polymorphism and lung cancer risk. (a) Allele model; (b) homozygote model; (c) heterozygote model; (d) dominant model; (e) recessive model.
(a) (b) (c)  (d) (e)


### 3.6. Sensitivity Analysis

Individual studies were consecutively omitted in the sensitivity analysis to detect the influence of each study on the pooled OR. The sensitivity analysis for the results of VEGF genetic polymorphisms and lung cancer risk demonstrated that the obtained results were statistically robust, and no individual study affected the pooled OR significantly (Figure5).Figure 5
Sensitivity analysis in the homozygote model. (a) +936C/T polymorphism; (b) -460C/T polymorphism; (c) -2578C/A polymorphism.
(a) (b) (c)


### 3.7. Publication Bias

The possible publication bias of the studies involved in the present meta-analysis was examined via Begg’s funnel plot and Egger’s test. As shown in Figure6, the shapes of funnel plots showed no evidence of publication bias in the dominant model (+936C/T,P = 0.754; -460C/T,P = 0.133; -2578C/A,P = 0.230). Result from Begg’s test and Egger’s test also confirmed the absence of publication bias. Considering the above tests, the results of our meta-analysis were reliable.Figure 6
Begg’s funnel plot of publication bias test in the homozygote model. (a) +936C/T polymorphism; (b) -460C/T polymorphism; (c) -2578C/A polymorphism.
(a) (b) (c)


## 3.1. Studies Characteristics

According to the searching criteria, a total of 109 studies were initially identified through a primary search of PubMed, Embase, and Web of Science databases and reference lists. Among these studies, 13 full-text studies met the inclusion criteria and were included in the present meta-analysis for further evaluation, which were accrued between December 1992 and July 2012 [27–39]. Besides, all studies suggested that the distribution of genotypes in the controls was consistent under HWE. The flowchart of literature search and selection procedure is shown in Figure 1. The baseline characteristics of the studies associated with the risk of lung cancer are comprehensively listed in Tables 1 and 2. Among these 13 enrolled studies, 10 were based on Asian populations and 3 on Caucasian populations.Table 1
Baseline characteristics of studies associated with the risk of lung cancer included in the meta-analysis.
Year Surname Ethnicity Country SOC Genotyping Cases Controls Gene polymorphism 2017 Yamamoto Japan Asian HB TaqMan 462 379 -460C/T 2017 Naikoo India Asian PB PCR-RFLP 112 116 +936C/T 2017 Naykoo India Asian PB PCR-RFLP 199 401 +936C/T, -2578C/A 2015 Krupnova Belarus Caucasian HB PCR-RFLP 202 336 +936C/T, -2578C/A 2015 Liu China Asian PB PCR-RFLP 414 338 -460C/T, -2578C/A 2014 Deng China Asian HB PCR-RFLP 65 110 -2578C/A 2013 Sun China Asian PB PCR-RFLP 126 160 -460C/T 2013 de Mello Portugal Caucasian HB MassARRAY 144 144 +936C/T, -460C/T, -2578C/A 2012 Naik India Asian PB PCR-RFLP 100 150 +936C/T 2012 Li China Asian PB PCR-RFLP 150 150 +936C/T, -2578C/A 2009 Liang China Asian PB PCR-RFLP 171 172 +936C/T, -2578C/A 2008 Zhai America Caucasian HB TaqMan 1900 1458 +936C/T, -460C/T 2005 Lee Korea Asian PB PCR-RFLP 432 432 +936C/T, -460C/T SOC: source of control; HB: hospital-based; PB: population-based controls.Table 2
The genotype frequency data of individual studies included in the meta-analysis.
VEGF rs3025039 +936C/T  Case (n)  Control (n)  Year Surname Case Control CC CT TT CC CT TT HWE  2017 Naikoo 109 112 68 32 9 65 38 9 Y 2017 Naykoo 199 401 80 99 20 261 120 20 Y 2015 Krupnova 161 360 115 38 8 267 80 13 N 2013 de Mello 144 144 115 28 1 111 30 3 Y 2012 Naik 90 150 54 28 8 80 48 22 N 2012 Li 150 150 108 35 7 114 33 3 Y 2009 Liang 171 172 123 40 8 131 38 3 Y 2008 Zhai 1900 1458 1441 424 35 1125 308 25 Y 2005 Lee 432 432 281 137 14 266 154 12 Y  VEGF rs833061 -460C/T  2017 Yamamoto 462 379 32 197 233 35 157 187 Y 2015 Liu 414 338 21 164 229 23 138 177 Y 2013 Sun 126 160 22 43 61 38 69 53 Y 2013 de Mello 144 144 28 79 37 31 72 41 Y 2008 Zhai 1900 1458 439 922 539 342 694 422 Y 2005 Lee 430 432 18 184 228 27 168 237 Y  VEGF rs699947 -2578C/A  2017 Naykoo 199 401 24 170 5 200 145 56 N 2015 Krupnova 162 360 41 90 31 83 186 91 Y 2015 Liu 414 338 230 164 20 177 138 23 Y 2014 Deng 65 110 26 33 6 62 41 7 Y 2013 de Mello 144 144 43 75 26 44 73 27 Y 2012 Li 150 150 93 45 12 98 49 3 Y 2009 Liang 171 172 129 28 14 112 56 4 YFigure 1
The flowchart of literature search and selection procedure.


## 3.2. Quantitative Synthesis Results

Overall, the strength of association between VEGF genetic polymorphisms and lung cancer risk was evaluated using the pooled ORs with 95% CIs based on five genetic comparison models. A summary of all the meta-analysis results for the 13 studied VEGF polymorphisms and lung cancer susceptibility is provided in Table3.Table 3
Meta-analysis results for the included studies of the association between rs3025039, rs833061, and rs699947 polymorphisms and risk of lung cancer.
Variables No. of studies Dominant model Recessive model Homozygous model Heterozygous model Allele model  OR (95% CI) P values I-squared (%) OR (95% CI) P values I-squared (%) OR (95% CI) P values I-squared (%) OR (95% CI) P values I-squared (%) OR (95% CI) P values I-squared (%)  rs3025039 C>T +936C/T (CT + TT) vs. CC TT vs. (CT + CC) TT vs. CC CT vs. CC T vs. C All 9 1.12 (0.87-1.46) <0.001 76.4 1.25 (0.94-1.65) 0.266 19.9 1.32(0.86-2.04) 0.047 48.9 1.11 (0.86-1.42) <0.001 72.1 1.11 (0.89-1.38) <0.001 76.2 Ethnicity Asian 6 1.17 (0.75-1.81) <0.001 84.4 1.37 (0.95-1.97) 0.154 37.9 1.47 (0.78-2.77) 0.022 61.9 1.14 (0.74-1.75) <0.001 82 1.15(0.81-1.65) <0.001 83.8 Caucasian 3 1.07 (0.92-1.23) 0.681 0 1.10 (0.70-1.70) 0.507 0 1.11 (0.71-1.73) 0.488 0 1.07 (0.92-1.24) 0.836 0 1.06 (0.93-1.21) 0.522 0 Source of control HB 3 1.07 (0.92-1.23) 0.681 0 1.10 (0.70-1.70) 0.507 0 1.11 (0.71-1.73) 0.488 0 1.07(0.92-1.24) 0.836 0 1.06 (0.93-1.21) 0.522 0 PB 6 1.17 (0.75-1.81) <0.001 84.4 1.37 (0.95-1.97) 0.154 37.9 1.47 (0.78-2.77) 0.022 61.9 1.14 (0.74-1.75) <0.001 82 1.15(0.81-1.65) <0.001 83.8 rs833061 C>T -460C/T (CT + TT) vs. CC TT vs. (CT + CC) TT vs. CC CT vs. CC T vs. C All 6 1.11(0.97-1.27) 0.534 0 1.02(0.92-1.13) 0.151 38.2 1.12 (0.96-1.30) 0.269 21.9 1.11 (0.96-1.28) 0.687 0 1.04 (0.97-1.12) 0.201 31.2 Ethnicity Asian 4 1.42 (1.07-1.90) 0.991 0 1.09 (0.94-1.27) 0.085 54.6 1.51 (1.12-2.03) 0.821 0 1.34 (0.99-1.81) 0.837 0 1.12 (1.00-1.26) 0.184 38 Caucasian 2 1.03 (0.88-1.20) 0.721 0 0.96 (0.83-1.11) 0.684 0 1.00 (0.83-1.20) 0.991 0 1.05 (0.89-1.24) 0.616 0 0.99 (0.91-1.09) 0.957 0 Source of control HB 3 1.05 (0.91-1.22) 0.531 0 0.98 (0.86-1.11) 0.807 0 1.03 (0.87-1.23) 0.532 0 1.07 (0.92-1.26) 0.554 0 1.01 (0.93-1.10) 0.757 0 PB 3 1.45 (1.03-2.06) 0.968 0 1.11 (0.93-1.33) 0.039 69.1 1.59 (1.11-2.29) 0.708 0 1.33 (0.92-1.91) 0.656 0 1.14 (0.99-1.31) 0.095 57.5 rsrs699947 C>A -2578C/A (CA + AA) vs. CC AA vs. (AA + CC) AA vs. CC AA vs. CC A vs. C All 7 1.33(0.74-2.40) <0.001 91.8 0.98(0.52-1.84) <0.001 78 1.15(0.71-1.86) 0.037 55.1 1.33 (0.66-2.68) <0.001 93.7 1.12 (0.87-1.43) <0.001 77.2 Ethnicity Asian 5 1.53 (0.67-3.51) <0.001 94.3 1.15(0.37-3.52) <0.001 84.9 1.50(0.71-3.20) 0.026 63.9 1.48(0.54-4.04) <0.001 95.7 1.22 (0.87-1.70) <0.001 80.6 Caucasian 2 0.94(0.68-1.31) 0.654 0 0.79(0.55-1.13) 0.417 0 0.79 (0.52-1.22) 0.426 0 1.01(0.72-1.42) 0.842 0 0.90 (0.74-1.11) 0.434 0 Source of control HB 3 1.15 (0.75-1.77) 0.122 52.6 0.83 (0.59-1.18) 0.411 0 0.93 (0.56-1.54) 0.247 28.6 1.19(0.82-1.73) 0.224 33.2 1.05 (0.76-1.45) 0.073 61.8 PB 4 1.45(0.54-3.90) <0.001 95.6 1.09 (0.27-4.34) <0.001 88.2 1.43 (0.58-3.54) 0.017 70.4 1.39 (0.42-4.65) <0.001 96.7 1.15 (0.79-1.69) <0.001 84.3


## 3.3. +936C/T and Lung Cancer Risk

In the present meta-analysis, the combined results of all analyses showed that the pooled OR of nine studies was 1.11 (95% CI: 0.89–1.38,P < 0.001) for the allele model, 1.32 (95% CI: 0.86–2.04,P = 0.047) for the homozygote model, 1.11 (95% CI: 0.86–1.42,P < 0.001) for the heterozygote model, 1.12 (95% CI: 0.87–1.46,P < 0.001) for the dominant model, and 1.25 (95% CI: 0.94–1.65,P = 0.266) for the recessive model, indicating no association between VEGF +936C/T mutation and lung cancer susceptibility (Figure 2). Furthermore, subgroup analyses by ethnicity and source of control were performed to establish the effects of heterogeneity on the results. In the subgroup analyses by ethnicity and source of control, no significant results were found in all genetic comparison models (Table 3).Figure 2
Forest plot of the association between the +936C/T polymorphism and lung cancer risk. (a) Allele model; (b) homozygote model; (c) heterozygote model; (d) dominant model; (e) recessive model.
(a) (b) (c)  (d) (e)


## 3.4. -460C/T and Lung Cancer Risk

The results demonstrated that the VEGF -460C/T polymorphism was not significantly correlated with the risk of lung cancer only in the allele model (pooled OR = 1.04, 95% CI: 0.97–1.12,P = 0.201), homozygote model (pooled OR = 1.12, 95% CI: 0.96–1.30,P = 0.269), heterozygote model (pooled OR = 1.11, 95% CI: 0.96–1.28,P = 0.687), dominant model (pooled OR = 1.11, 95% CI: 0.97–1.27,P = 0.534), and recessive model (pooled OR = 1.02, 95% CI: 0.92–1.13,P = 0.151) (Figure 3). When the studies were stratified by ethnicity, significant differences were observed in Asian populations in these models (allele model: pooled OR = 1.12, 95% CI: 1.00–1.26; homozygote model: pooled OR = 1.51, 95% CI: 1.12–2.03), but no significant results were detected in Caucasian populations. Furthermore, significant results were found in the homozygote model (pooled OR = 1.59, 95% CI: 1.11–2.29) in the subgroup analysis by the source of control (Table 3).Figure 3
Forest plot of the association between the -460C/T polymorphism and lung cancer risk. (a) Allele model; (b) homozygote model; (c) heterozygote model; (d) dominant model; (e) recessive model.
(a) (b) (c)  (d) (e)


## 3.5. -2578C/A and Lung Cancer Risk

The combined results of all analyses showed that the pooled OR of these studies was 1.12 (95% CI: 0.87–1.43,P < 0.001) for the allele model, 1.15 (95% CI: 0.71–1.86,P = 0.037) for the homozygote model, 1.33 (95% CI: 0.66–2.68,P < 0.001) for the heterozygote model, 1.33 (95% CI: 0.74–2.40,P < 0.001) for the dominant model, and 0.98 (95% CI: 0.52–1.84,P < 0.001) for the recessive model, indicating no significant association between VEGF -2578C/A polymorphism and lung cancer risk (Figure 4). In addition, when the studies were stratified by ethnicity and source of control, no significant differences were found in all genetic models (Table 3).Figure 4
Forest plot of the association between the -2578C/A polymorphism and lung cancer risk. (a) Allele model; (b) homozygote model; (c) heterozygote model; (d) dominant model; (e) recessive model.
(a) (b) (c)  (d) (e)


## 3.6. Sensitivity Analysis

Individual studies were consecutively omitted in the sensitivity analysis to detect the influence of each study on the pooled OR. The sensitivity analysis for the results of VEGF genetic polymorphisms and lung cancer risk demonstrated that the obtained results were statistically robust, and no individual study affected the pooled OR significantly (Figure5).Figure 5
Sensitivity analysis in the homozygote model. (a) +936C/T polymorphism; (b) -460C/T polymorphism; (c) -2578C/A polymorphism.
(a) (b) (c)


## 3.7. Publication Bias

The possible publication bias of the studies involved in the present meta-analysis was examined via Begg’s funnel plot and Egger’s test. As shown in Figure6, the shapes of funnel plots showed no evidence of publication bias in the dominant model (+936C/T,P = 0.754; -460C/T,P = 0.133; -2578C/A,P = 0.230). Result from Begg’s test and Egger’s test also confirmed the absence of publication bias. Considering the above tests, the results of our meta-analysis were reliable.Figure 6
Begg’s funnel plot of publication bias test in the homozygote model. (a) +936C/T polymorphism; (b) -460C/T polymorphism; (c) -2578C/A polymorphism.
(a) (b) (c)


## 4. Discussion

As an important regulator of various malignancy angiogenesis, VEGF is always involved in the development and progression of multiple cancers [25]. The process of carcinogenesis is accelerated when the VEGF gene expression is influenced by some molecular biology variations [40]. Previous studies have identified at least 30 SNPs of VEGF [18]. Some of these have been confirmed to be associated with individual susceptibility to various types of cancers, including lung cancer [41, 42]. In the last two decades, many case–control studies have assessed the association of VEGF polymorphisms with the risk of lung cancer. However, due to the limited sample size and other reasons, the findings are controversial or ambiguous [27–39]. In addition, a recent meta-analysis suggested that VEGF -2578C/A polymorphism was capable of increasing the susceptibility to lung cancer, especially among smokers and patients with lung squamous cell carcinoma. Additionally, for +936C/T polymorphism, increased lung cancer susceptibility was observed only among patients with lung adenocarcinoma. In contrast, VEGF -460C/T polymorphism might be a protective factor among nonsmokers and patients with SCC [43]. However, several other studies showed that the -460C/T and -2578C/A polymorphisms of VEGF were not associated with an increased risk of lung cancer [44–46]. The conclusion in previous studies was still inaccurate due to limited published data and lack of subgroup analysis. Therefore, this updated meta-analysis including all eligible case–control studies was performed to investigate these associations.This meta-analysis included 13 independent case–control studies with a total of 4477 patients with lung cancer and 4346 healthy controls to investigate the correlation between VEGF variants and the risk of lung cancer. The results of our meta-analysis showed that VEGF +936C/T, -460C/T, and -2578C/A gene polymorphisms were not associated with the risk of lung cancer. However, the conclusion was still inaccurate due to limited published data. Furthermore, the predictive value of VEGF polymorphisms in the prognosis of lung cancer could not be assessed. The sample size in the present meta-analysis was larger than that in any individual study, leading to more precise and robust results. Thus, the present meta-analysis aimed to provide a more powerful and reliable conclusion about the relationship between VEGF genetic polymorphisms and lung cancer risk. Moreover, no statistically significant association was detected between VEGF +936C/T, -460C/T, and -2578C/A polymorphisms and lung cancer risk in all genetic comparison models. Interestingly, the VEGF +460T/C polymorphism was found to be significantly associated with the susceptibility to lung cancer only in Asian populations. Although the exact mechanism underlying ethnic differences was unknown, the possible reasons could be the natural selection pressures or a balance by other related functional genetic variants and environmental exposures, resulting in gene polymorphisms [47]. Each polymorphism alone may be insufficient to influence the susceptibility to lung cancer.However, the present meta-analysis had several limitations. First, the number of included studies in our meta-analysis was not so abundant, leading to limited statistical precision, especially in subgroup analysis. Hence, more studies using standardized unbiased methods are needed to offer more detailed individual data. Second, the population included in this meta-analysis is mainly limited to Asian and Caucasian ethnicities, and more data from other ethnic groups will be required. Third, since only data from previously published studies were included in the meta-analysis, it was possible that some related published studies or unpublished studies had null results, resulting in a deviation from the expected results. Finally, tumorigenesis is a complex process involved in the regulation of a series of genetic factors besides VEGF. As a multifactorial disease, the risk of developing lung cancer was closely related to the environment, smoking, occupational exposure, and interaction among various genetic factors, and not just a single factor. Therefore, more raw data are needed to adjust other variables, such as age, sex, and smoking. Consequently, further high-quality studies on the risk of lung cancer should be conducted over the next few years to achieve more accurate results.


## 5. Conclusions

In sum, this meta-analysis found that VEGF +936C/T, -460C/T, and -2578C/A polymorphisms were not associated with the susceptibility to lung cancer. Interestingly, the VEGF +460T/C polymorphism might be a risk factor for lung cancer only in Asian populations. Based on our discovery, additional large population-based multicenter prospective studies are needed to confirm the association of VEGFgene polymorphisms and lung cancer risk.



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*Source: 9271215-2018-06-14.xml*