==== Front Medicine (Baltimore) MD Medicine 0025-7974 1536-5964 Lippincott Williams & Wilkins Hagerstown, MD 37390249 00021 10.1097/MD.0000000000034089 3 3500 Research Article Systematic Review and Meta-Analysis The relationship between VEGF-460(T>C) polymorphism and cancer risk: A systematic review and meta-analysis based on 46 reports Qin Haoran MD 974993086@qq.com a Xiao Qiang MD 15007091090@163.com a Xie Yufen MD 942239009@qq.com a Li Dan MD ltp823241573@126.com b Long Xiaozhou MD 352940983@qq.com a Li Taiping MD ltp823241573@126.com a Yi Siqing MD 1693830953@qq.com a Liu Yiqin MD 1442059738@qq.com a Chen Jian PhD drchenjian1996@163.com a https://orcid.org/0000-0003-1443-4654 Xu Foyan PhD c * a General Surgery Department, First Affiliated Hospital of Nanchang University, Nanchang, China b Department of Mammary Diseases, Zhuhai Hospital of Integrated Chinese and Western Medicine, Zhuhai, China c General Surgery Department, Zhuhai Hospital of integrated Traditional Chinese and Western Medicine, Guangdong, China. * Correspondence: Foyan Xu, Zhuhai Hospital of integrated Traditional Chinese and Western Medicine, 519020 Guangdong, China (e-mail: 18270881491@163.com). 30 6 2023 30 6 2023 102 26 e3408913 12 2022 31 5 2023 2 6 2023 Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. 2023 https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Extensive studies on the link between single nucleotide polymorphisms (SNPs) in vascular endothelial growth factor (VEGF) and various malignancy risks produced conflicting results, notably for VEGF-460(T/C). To evaluate this correlation more comprehensively and accurately, we perform a meta-analysis. Methods: Through retrieving 5 databases (Web of Science (WoS), Embase, Pubmed, Wanfang database (Wangfang), and China National Knowledge Infrastructure (CNKI)) and applying hand search, citation search, and gray literature search, 44 papers included 46 reports were enrolled. To evaluate the relationship between VEGF-460 and cancer risk, we pooled odds ratios (ORs) and 95% confidence intervals (CIs). Results: Our results indicated that the VEGF-460 polymorphism is not related to malignancy susceptibility (dominant model, OR = 0.98, 95% CI = 0.87–1.09; recessive model, OR = 0.95, 95% CI = 0.82–1.10; heterozygous model, OR = 0.99, 95% CI = 0.90–1.10; homozygous model, OR = 0.92, 95% CI = 0.76–1.10; additive model, OR = 0.98, 95% CI = 0.90–1.07). While, in subgroup analysis, this SNP may reduce the risk of hepatocellular carcinoma. Conclusion: this meta-analysis indicated that VEGF-460 was irrelevant to overall malignancy risk, but it might be a protective factor for hepatocellular carcinoma. meta-analysis neoplasms polymorphism single nucleotide VEGF-460(T>C) OPEN-ACCESSTRUE SDCT ==== Body pmc1. Introduction Worldwide, cancer imposes a massive burden on human health, second only to ischemic heart disease and stroke,[1] and is a critical barrier to increasing life expectancy.[2] According to statistics, 19.3 million newly diagnosed cancer cases and approximately 10 million cancer deaths occurred worldwide in 2020.[3] The World Health Organization (WHO) prediction model stated that cancer would replace ischemic heart disease as the major death cause over the next 4 decades.[1] Efforts on researching the pathogenesis of malignancies to administer effective interventions in carcinogenesis and progression are fundamental to cancer control worldwide. Molecular epidemiological findings have demonstrated that genetic factors, especially single nucleotide polymorphisms (SNPs), are vital in oncogenesis and progression.[4,5] The human VEGF gene is localized on chromosome 6p21.3 and spans over 16 kb, with a coding region consisting of 8 exons and 7 introns.[6,7] These exons selectively splice micro-RNA in different combinations to generate multiple coding products. Five homologous members, VEGF-A (commonly known as VEGF), VEGF-B, VEGF-C, VEGF-D, and placental growth factor, comprise the VEGF family.[8] VEGF receptors R1 and R2 were discovered successively as VEGF signaling receptors.[9] VEGF-R1 is a tyrosine kinase VEGF receptor with high affinity and displays weak ligand-dependent tyrosine autophosphorylation.[10] VEGF-R2 is the main signaling receptor associated with mitogenesis and permeability of vascular endothelial cells and thus is thought to be involved in tumorigenesis and progression.[11] Ligand bound to VEGF-R2 triggers autophosphorylation of intracellular tyrosine residues.[12] VEGF binds specifically the VEGF receptor (VEGFR) in its extracellular domain, contributing to the formation of homo- or heterodimers of the VEGFR. Tyrosine residues 1214 (Y1214) and 1175 (Y1175) on VEGF-R2 are highly autophosphorylated in response to VEGF and Y1175 is essential for VEGF-dependent cell proliferation via the phospholipase Cγ (PLCγ)/PKC/mitogen-activated protein kinase pathway in cultured endothelial cells.[13] The dimerized VEGFR stimulates intracellular tyrosine kinases and induces autophosphorylation of tyrosine residues, which in turn activates a series of downstream signal transduction pathways, including PI3K/Akt and mitogen-activated protein kinases.[14] Thus, it exerts various biological functions, including promoting endothelial cell mitosis, inhibiting endothelial cell apoptosis, vasodilation, increasing vascular permeability.[9,15] As a crucial process of oncogenesis and development, angiogenesis is necessary for primary tumor cells’ growth, invasion, and metastasis.[16] Moreover, VEGF overexpression has been reported in many malignancies.[17] Over 30 SNPs were previously reported in the VEGF gene; parts have been demonstrated to modulate VEGF expression levels[18] and may be associated with cancer susceptibility. Numerous institutions have performed extensive studies to demonstrate the relationship between SNPs in VEGF and cancer, especially for VEGF-460(T/C).[19–78] However, different and even contradictory results were obtained. Several investigators have conducted meta-analyses to evaluate this link comprehensively.[79–92] Their meta-analysis was almost only for single cancer with small sample sizes. As far as we know, only one meta-analysis of VEGF-460 polymorphisms and overall cancer risk has been published,[79] but it was published earlier and included less studies. Therefore, we performed a meta-analysis with sufficient literature to investigate the strength of the link between VEGF-460 and overall malignancy. 2. Materials and methods The data in this meta-analysis was accessed from literature published or from other sources that did not directly involve patients. Consequently, this work did not require ethics committee approval and informed consent. This meta completes registration on the PROSPERO platform with the registration ID CRD42021241267. 2.1. Literature strategy We accessed related publications in 5 databases: WoS, Embase, Pubmed, Wanfang, and NCKI, until February 2022, without language restrictions. The complete search strategy is provided in Table S1, Supplemental Digital Content, http://links.lww.com/MD/J156 (See Table S1, Supplemental Digital Content, http://links.lww.com/MD/J156, which demonstrates the specific search strategies.). We also searched the reference of eligible studies to obtain as much of the available literature as possible. To reduce the bias risk possible, gray literature, such as these, and conference papers, was also considered. Two authors completed the above search independently and screened according to the following criteria. 2.1.1. Inclusion criteria. Studies related to VEGF-460(T/C) polymorphism and malignancy (no restriction on cancer type). Case-control studies: pathologically diagnosed malignant tumors in case groups. Studies with access to complete genotype frequencies. 2.1.2. Exclusion criteria. Repeated literature. Non-human trials. For multiple articles with data that overlap in whole or in part, we select articles published more recently or with more extensive data volumes. P value of the Hardy–Weinberg equilibrium (HWE) ≤ 0.05. If 2 authors reach different inclusion studies, discussion with a third author is essential. The 3 authors must agree on one point of view. 2.2. Data extraction Two authors separately extracted information below: author and year, nation, ethnicity, cancer type, control sources, genotype frequency, and P value of the HWE in control groups. If HWE were not reported in the original research, the chi-square test would be used to assess it; P > .05 was considered that the genotype distribution was conformed to HWE in the control group. If a dispute arises between 2 authors, a discussion with a third author is required to resolve the disagreement. 2.3. Quality assessment Since studies we included were case-control trials, quality assessment was performed with the Newcastle–Ottawa scale (NOS).[93] The NOS for case-control studies contained 3 aspects (selection, comparability, and exposure) included 8 items. Scoring criteria are provided for each item, except for comparability, which can get 2 stars, other items passed with 1 star. A total of 9 stars, 6 stars, and above are regarded as high quality. Two authors were evaluated separately and discussed with a third author. 2.4. Statistical analysis 2.4.1. Quantitative synthesis. We combined ORs with 95% CIs to estimate the link between VEGF-460 polymorphism and malignancy risk in 5 genetic models (dominant, recessive, heterozygous, homozygous, and additive models). The primary outcome was the effect of VEGF-460 polymorphism on cancer susceptibility; secondary outcomes were the relationship between the 2 in terms of ethnicity, cancer type, and control source. 2.4.2. Heterogeneity analysis. We used Cochran’s Q test to ascertain heterogeneity among the primary studies.[94] PHeterogeneity < 0.10 (not 0.05) represents heterogeneity as Cochran’s Q test has low statistical strength.[95] We also used I2 values to quantitatively assess heterogeneity, with I2 values of 20%, 50%, and 75% indicating low, medium, and high heterogeneity, respectively.[96] If there was no heterogeneity among the original researches, the ORs and 95% CIs were combined using fixed-effects models; otherwise, random-effects models were applied. Exploration of the sources of heterogeneity was conducted with meta-regression and subgroup analysis; the P value of meta-regression less than .05 represents the heterogeneity. 2.4.3. Publication bias. We used the contour-enhanced funnel plots, Begg’s, and Egger’s tests for the estimation of publication bias.[97] Moreover, asymmetry in the funnel plots or P values below .05 in either test suggested publication bias. 2.4.4. Sensitivity analysis. The leave-one-out method, which removes individual studies on a rotating basis to recombine ORs, was performed to determine the stability of the outcomes. This work was considered steady if there were no drastic changes or reversals in the recombined results. 2.4.5. Software. The above analysis is implemented on stata15.0. 3. Results 3.1. Characteristics of the studies 54 studies comprising 60 reports were enrolled initially, published literature[19–68] and gray literature.[69–72] We included 46 English-language articles and 8 Chinese-language articles. For the study population, we included 29 studies focused on Asian populations, 20 studies dedicated to Caucasian populations, and 5 studies of other ethnicities (including Omanis, Brazilians, and Tunisians). As for cancer types, there are 4 studies on VEGF-460 and gastric cancer susceptibility, 7 on breast cancer, 4 on prostate cancer, 3 on oral cancer, 3 on colorectal cancer, 2 on colon cancer, 2 on glioma, 3 on cervical cancer, 2 on ovarian cancer, 2 on bladder cancer, 5 on hepatocellular carcinoma, 11 on lung cancer, and 5 on renal cell carcinoma, in addition to studies on endometrial cancer, esophageal adenocarcinoma, pancreatic cancer, nasopharyngeal cancer, osteosarcoma, thyroid cancer, and cutaneous squamous cell carcinoma. We recalculated the HWE of the controls and found 12 studies in which the distribution of control genotype frequencies did not match the HWE (See Table 1).[19,20,28,32,43,48,49,53,56,58,63,65] The deviation from HWE may result from genotyping errors and selection bias in population stratification and control group recruitment.[98] To ensure the accuracy of the results, we excluded these 12 articles. After quality assessment by NOS, 7 studies, including 9 reports, were identified as low quality,[19,28,32,48,49,58,63] the results can be accessed in the Table S2, Supplemental Digital Content, http://links.lww.com/MD/J157 (See Table S2, Supplemental Digital Content, http://links.lww.com/MD/J157, which demonstrates the results of quality evaluation); notably, none of the control gene frequencies of these 7 studies conformed to HWE. All the above information is provided in Table 1. Ultimately, the present meta-analysis enrolled 42 studies comprised 46 reports involving 12868 cases and 14111 controls. The selection process of eligible studies is provided in Figure 1. Table 1 Characteristics of the included studies. Author and year Country Ethnicity Cancer-type Control source Genotype method Case Control TT TC CC TT TC CC HWE (controls) NOS Chae, 2007 Korea Asian Gastric Cancer PB PCR-RFLP 218 186 9 225 161 27 0.802 6 Al-Moundhri, 2009 Oman Omani Gastric Cancer Unstated PCR-RFLP 42 66 22 44 61 25 0.640 7 Jia, 2012 China Asian Gastric Cancer HB PCR-RFLP 98 56 5 71 78 13 0.178 6 Furuya, 2018 Brazil Brazilian Gastric Cancer PB Real-time PCR 72 79 27 91 121 41 0.942 7 Kataoka, 2006 Multi-center Asian Breast Cancer PB TaqMan 616 418 89 665 479 78 0.502 7 Balasubramanian, 2007 England Caucasian Breast Cancer Unstated TaqMan 111 248 134 106 251 141 0.771 7 Rahoui, 2014 Morocco Caucasian Breast Cancer HB Real-time PCR 27 25 18 16 39 15 0.337 6 Kapahi, 2015 India Asian Breast Cancer Unstated PCR-RFLP 61 92 51 72 105 27 0.240 6 Maryam, 2016 Iran Caucasian Breast Cancer PB PCR-RFLP 69 148 33 66 131 18 <0.001 6 Albalawi, 2020 Saudi Arabia Caucasian Breast Cancer PB ARMS-PCR 54 51 5 31 71 8 <0.001 5 Li, 2021 China Asian Breast Cancer Unstated PCR-LDR 146 98 15 152 103 18 0.922 7 Lin, 2003 China Asian Prostate Cancer HB PCR-RFLP 60 32 4 43 72 4 <0.001 5 Fukuda, 2007 Japan Asian Prostate Cancer HB PCR-RFLP 143 103 24 132 97 23 0.404 6 Onen, 2007 Turkey Caucasian Prostate Cancer HB PCR-RFLP 11 89 33 13 94 50 <0.001 5 Li, 2017 China Asian Prostate Cancer PB PCR-RFLP 9 9 10 11 9 10 0.027 4 Li, 2017 China Asian Bladder Cancer PB PCR-RFLP 11 10 9 11 9 10 0.027 4 Li, 2017 China Asian RCC PB PCR-RFLP 16 9 7 11 9 10 0.027 4 Ku, 2005 China Asian Oral Cancer HB PCR-RFLP 120 15 2 38 192 0 <0.001 6 Kammerer, 2010 Germany Caucasian Oral Cancer Unstated Real-time PCR 23 41 16 8 24 8 0.204 7 Borase, 2015 India Asian Oral Cancer Unstated PCR-RFLP 61 16 3 21 53 6 <0.001 5 Maltese, 2009 Italy Caucasian Colorectal Cancer Unstated PCR-RFLP 70 153 76 47 54 10 0.314 6 Dassoulas, 2009 Greece Caucasian Colorectal Cancer HB PCR-RFLP 161 104 47 199 121 42 <0.001 5 Ehsan, 2021 Iran Caucasian Colorectal Cancer HB Mass ARRAY 99 116 62 120 178 77 0.462 6 Cacev, 2008 Croatia Caucasian Colon Cancer PB Real-time PCR 31 84 40 32 83 45 0.574 7 Jannuzzi, 2015 Turkey Caucasian Colon Cancer HB PCR-RFLP 19 83 1 15 114 0 <0.001 5 Linhares, 2018 Portugal Caucasian Glioma HB Mass ARRAY 3 77 27 41 74 28 0.602 7 Vasconcelos, 2019 Brazil Brazilian Glioma PB Real-time PCR 83 95 27 72 88 45 0.071 7 Kim, 2010 Korea Asian Cervical Cancer HB PCR-RFLP 109 83 7 120 77 17 0.359 7 Zidi, 2014 Tunisia Tunisian Cervical Cancer Unstated Real-time PCR 20 53 13 46 58 20 0.811 6 Konac, 2007 Turkey Caucasian Cervical Cancer HB PCR-RFLP 15 13 4 13 58 35 0.137 6 Konac, 2007 Turkey Caucasian Ovarian Cancer HB PCR-RFLP 21 21 5 13 58 35 0.137 6 Konac, 2007 Turkey Caucasian Endometrial Cancer HB PCR-RFLP 8 8 5 13 58 35 0.137 6 Kazimi, 2010 Turkey Caucasian HCC Unstated PCR-RFLP 27 28 18 18 24 20 0.075 6 Wu, 2013 China Asian HCC HB TaqMan 240 146 16 559 406 78 0.719 6 Wu, 2013 China Asian HCC HB TaqMan 181 135 21 143 132 35 0.589 6 Wu, 2013 China Asian HCC HB TaqMan 223 126 18 203 136 36 0.069 6 Carvalho, 2021 Brazil Brazilian HCC HB Real-time PCR 44 59 16 53 54 21 0.260 6 Lee, 2005 Korea Asian Lung Cancer HB PCR-RFLP 228 184 18 237 168 27 0.700 7 Zhai, 2008 Multi-center Caucasian Lung Cancer HB TaqMan 539 922 439 422 694 342 0.085 6 Gao, 2012 China Asian Lung Cancer HB PCR-RFLP 105 80 15 129 68 7 0.583 6 de Mello, 2013 Multi-center Caucasian Lung Cancer HB MassARRAY 37 79 28 41 72 31 0.954 6 Sun, 2013 China Asian Lung Cancer Unstated PCR-RFLP 61 43 22 53 69 38 0.100 7 Liu, 2015 China Asian Lung Cancer HB PCR-RFLP 229 164 21 177 138 23 0.573 7 Yamamoto, 2016 Japan Asian Lung Cancer HB Real-time PCR 233 197 32 187 157 35 0.825 6 Yu, 2019 China Asian Lung Cancer PB TaqMan 213 191 29 217 127 36 0.010 6 Li, 2014 China Asian Lung Cancer HB PCR-RFLP 227 159 21 152 103 15 0.649 7 Liu, 2012 China Asian Lung Cancer HB PCR-RFLP 103 96 61 90 131 39 0.438 7 Yuan, 2011 China Asian Lung Cancer HB PCR-RFLP 131 101 19 156 90 9 0.351 7 Bruyère, 2010 France Caucasian RCC PB PCR-RFLP 19 29 1 47 109 46 0.260 7 Sáenz-López, 2013 Spain Caucasian RCC PB TaqMan 56 111 49 77 138 58 0.793 7 Lu, 2015 China Asian RCC HB PCR-RFLP 228 93 91 513 168 143 <0.001 6 Liu, 2020 China Asian RCC HB PCR-RFLP 204 168 48 542 258 42 0.128 7 Zhai, 2008 Multi-center Caucasian EA HB TaqMan 72 155 81 149 257 122 0.582 7 Li, 2010 China Asian Ovarian Cancer HB PCR-RFLP 198 93 12 191 95 17 0.271 7 Sivaprasad, 2013 India Asian Pancreatic Cancer HB PCR-RFLP 23 40 17 19 56 12 0.005 6 Cheng, 2014 China Asian NPC HB PCR-RFLP 127 94 19 155 79 11 0.818 7 Zhao, 2015 China Asian Osteosarcoma HB PCR-RFLP 48 89 39 66 85 25 0.777 6 Bingül, 2016 Turkey Caucasian Thyroid Carcinoma HB Real-time PCR 49 52 26 75 90 38 0.238 7 Nie, 2016 China Asian CSCC HB PCR-RFLP 4 31 65 10 63 51 0.111 6 Ai, 2020 China Asian Bladder Cancer HB Real-time PCR 115 99 16 118 91 21 0.571 7 Bold text indicates statistically significant data. CSCC = cutaneous squamous cell carcinoma, EA = esophageal adenocarcinoma, HB = hospital-based, HCC = hepatocellular carcinoma, HWE = Hardy-Weinberg equilibrium, NOS = Newcastle–Ottawa scale, NPC = nasopharyngeal carcinoma, PB = population-based, RCC = renal cell carcinoma. Figure 1. Flow diagram for screening of qualified reports. 3.2. Meta-analysis findings These meta-analysis findings are provided in Tables 2, 3, and Figures 2–5. Table 2 Association of VEGF-460 (T/C) and cancer risk in the cancer type subgroup. Cancer type N Dominant model (TC + CC vs TT) Recessive model (CC vs TC + TT) Heterozygous model (TC vs TT) Homozygous model (CC vs TT) Additive model (C vs T) OR (95% CI) P/I2 (%) OR (95% CI) P/I2 (%) OR (95% CI) P/I2 (%) OR (95% CI) P/I2 (%) OR (95% CI) P/I2 (%) Overall 46 0.98 (0.87, 1.09) <.001/74.6% 0.95 (0.82, 1.10) <.001/68.1% 0.99 (0.90, 1.10) <.001/65.9% 0.92 (0.76, 1.10) <.001/76.1% 0.98 (0.90, 1.07) <.001/78.7% Gastric Cancer 4 0.83 (0.59, 1.19) .024/68.3% 0.60 (0.35, 1.04) .078/56.1% 0.88 (0.61, 1.29) .019/69.8% 0.57 (0.33, 1.00) .088/54.2% 0.84 (0.67, 1.05) .074/56.8% Breast Cancer 5 0.97 (0.82, 1.14) .243/26.8% 1.22 (0.90, 1.66) .065/54.9% 0.93 (0.78, 1.10) .265/23.4% 1.14 (0.81, 1.59) .074/53.1% 1.03 (0.90, 1.18) .123/44.8% Colorectal Cancer 2 1.41 (0.51, 3.91) <.001/92.3% 1.88 (0.62, 5.72) .005/87.2% 1.21 (0.51, 2.85) .004/87.9% 2.16 (0.43, 11.01) <.001/92.9% 1.40 (0.66, 2.99) <.001/93.3% Gliomas 2 3.16 (0.17, 58.89) <.001/95.3% 0.85 (0.34, 2.15) .020/81.5% 3.45 (0.22, 54.60) <.001/94.6% 2.49 (0.10, 63.15) <.001/95.3% 1.18 (0.47, 2.94) <.001/93.8% Cervical Cancer 3 0.73 (0.24, 2.25) <.001/90.2% 0.53 (0.27, 1.05) .183/41.2% 0.84 (0.29, 2.45) <.001/88.0% 0.43 (0.10, 1.82) .002/83.6% 0.76 (0.39, 1.47) .001/86.8% Ovarian Cancer 2 0.41 (0.08, 2.08) <.001/92.6% 0.43 (0.15, 1.22) .100/63.1% 0.49 (0.12, 1.98) .002/89.3% 0.26 (0.03, 1.90) .004/87.9% 0.55 (0.20, 1.47) .001/91.3% HCC 5 0.79 (0.68, 0.92) .562/0.0% 0.57 (0.43, 0.75) .810/0.0% 0.86 (0.74, 1.00) .625/0.0% 0.53 (0.40, 0.71) .629/0.0% 0.78 (0.69, 0.87) .598/0.0% Lung Cancer 10 1.01 (0.88, 1.17) .031/51.1% 1.00 (0.79, 1.27) .025/52.8% 1.01 (0.88, 1.17) .043/48.3% 0.98 (0.76, 1.26) .027/52.0% 1.01 (0.91, 1.13) .015/56.2% RCC 3 1.07 (0.54, 2.14) <.001/88.6% 0.94 (0.32, 2.78) <.001/88.4% 1.16 (0.70, 1.94) .013/77.0% 0.91 (0.25, 3.33) <.001/90.4% 1.00 (0.53, 1.90) <.001/93.7% Others 10 1.09 (0.87, 1.37) .026/52.4% 1.22 (0.94, 1.57) .062/44.6% 1.08 (0.88, 1.32) .125/35.4% 1.16 (0.84, 1.60) .034/50.3% 1.12 (0.94, 1.32) .004/62.3% Bold text indicates statistically significant data. HCC = hepatocellular carcinoma, Others = prostate cancer, oral cancer, colon cancer, endometrial cancer, esophageal adenocarcinoma, nasopharyngeal carcinoma, osteosarcoma, thyroid carcinoma, cutaneous squamous cell carcinoma, and bladder cancer, RCC = renal cell carcinoma, VEGF = vascular endothelial growth factor. Table 3 Association between VEGF-460 (T/C) and cancer risk in other subgroups. Subgroups N Dominant model (TC + CC vs TT) Recessive model (CC vs TC + TT) Heterozygous model (TC vs TT) Homozygous model (CC vs TT) Additive model (C vs T) OR (95% CI) P/I2 (%) OR (95% CI) P/I2 (%) OR (95% CI) P/I2 (%) OR (95% CI) P/I2 (%) OR (95% CI) P/I2 (%) Overall 46 0.98 (0.87, 1.09) <.001/74.6% 0.95 (0.82, 1.10) <.001/68.1% 0.99 (0.90, 1.10) <.001/65.9% 0.92 (0.76, 1.10) <.001/76.1% 0.98 (0.90, 1.07) <.001/78.7% Ethnicity  Asian 24 1.02 (0.90, 1.15) <.001/71.6% 0.98 (0.76, 1.26) <.001/76.4% 1.02 (0.91, 1.13) <.001/59.3% 0.96 (0.73, 1.26) <.001/77.3% 1.03 (0.92, 1.15) <.001/80.5%  Caucasian 17 0.84 (0.64, 1.09) <.001/81.9% 0.98 (0.81, 1.19) .003/56.1% 0.86 (0.67, 1.11) <.001/76.5% 0.86 (0.62, 1.20) <.001/80.2% 0.90 (0.77, 1.06) <.001/81.4%  Others 5 1.04 (0.78, 1.39) .127/44.2% 0.77 (0.59, 1.01) .630/0.0% 1.13 (0.84, 1.51) .156/39.9% 0.82 (0.59, 1.12) .352/9.6% 0.94 (0.76, 1.12) .220/30.2% Control source  PB 7 0.95 (0.83, 1.08) .327/13.5% 0.74 (0.50, 1.11) .001/73.3% 0.98 (0.87, 1.10) .614/0.0% 0.72 (0.47, 1.11) .001/72.2% 0.90 (0.77, 1.04) .026/58.2%  HB 30 0.97 (0.84, 1.12) <.001/79.4% 0.96 (0.80, 1.16) <.001/69.1% 0.98 (0.85, 1.12) <.001/73.2% 0.91 (0.72, 1.17) <.001/78.3% 0.99 (0.88, 1.11) <.001/81.6%  Unstated 9 1.06 (0.78, 1.43) <.001/71.6% 1.10 (0.78, 1.56) .003/66.1% 1.04 (0.80, 1.35) .016/57.4% 1.13 (0.71, 1.78) <.001/75.0% 1.05 (0.84, 1.32) <.001/77.7% Genotype method  PCR-RFLP 24 0.94 (0.77, 1.15) <.001/81.9% 0.97 (0.72, 1.32) <.001/78.1% 0.96 (0.81, 1.14) <.001/73.6% 0.87 (0.59, 1.27) <.001/83.3% 0.98 (0.83, 1.15) <.001/85.8%  Real-time PCR 10 0.95 (0.80, 1.12) .221/24.2% 0.84 (0.69, 1.02) .774/0.0% 0.98 (0.81, 1.20) .109/37.4% 0.81 (0.65, 1.00) .798/0.0% 0.93 (0.84, 1.03) .682/0.0%  TaqMan 8 0.93 (0.83, 1.05) .077/45.3% 0.89 (0.72, 1.10) .006/64.3% 0.96 (0.88, 1.05) .467/0.0% 0.86 (0.67, 1.12) .001/70.7% 0.93 (0.84, 1.04) .004/66.8%  PCR-LDR 1 0.97 (0.69, 1.37) –/– 0.87 (0.43, 1.77) –/– 0.99 (0.69, 1.42) –/– 0.87 (0.42, 1.79) –/– 0.96 (0.73, 1.27) –/–  Mass ARRAY 3 1.97 (0.67, 5.77) <.001/90.3% 1.11 (0.84, 1.46) .561/0.0% 2.01 (0.64, 6.34) <.001/90.7% 1.97 (0.63, 6.14) .001/86.5% 1.21 (0.81, 1.79) .006/80.6% Literature type  Published literature 42 0.97 (0.86, 1.09) <.001/76.1% 0.93 (0.80, 1.08) <.001/68.7% 0.99 (0.89, 1.10) <.001/67.0% 0.89 (0.73, 1.08) <.001/77.4% 0.97 (0.88, 1.06) <.001/80.0%  Grey literature 4 1.06 (0.84, 1.33) .155/42.7% 1.28 (0.79, 2.08) .076/56.3% 1.00 (0.75, 1.34) .049/61.8% 1.23 (0.79, 1.91) .154/42.9% 1.10 (0.94, 1.29) .223/29.9% Others = Omani, Brazilian, and Tunisian, VEGF = vascular endothelial growth factor. Figure 2. Forest plot of the relationship between VEGF-460(T/C) and cancer risk in the dominant model (TC + CC vs TT). VEGF = vascular endothelial growth factor. Figure 3. Forest plot of the relationship between VEGF-460(T/C) and cancer risk in the recessive mode (CC vs TC + TT). VEGF = vascular endothelial growth factor. Figure 4. Forest plot of the relationship between VEGF-460(T/C) and cancer risk in the homozygous model (CC vs TT). VEGF = vascular endothelial growth factor. Figure 5. Forest plot of the relationship between VEGF-460(T/C) and cancer risk in the additive model (C vs T). VEGF = vascular endothelial growth factor. 3.2.1. Primary outcome. We identified no association between polymorphisms and malignancy risk (dominant model, OR = 0.98, 95% CI = 0.87–1.09; recessive model, OR = 0.95, 95% CI = 0.82–1.10; heterozygous model, OR = 0.99, 95% CI = 0.90–1.10; homozygous model, OR = 0.92, 95% CI = 0.76–1.10; additive model, OR = 0.98, 95% CI = 0.90–1.07). 3.2.2. Secondary outcomes. To further investigate the correlation between VEGF-460 polymorphism and cancers, we performed stratification based on cancer type, ethnicity, control sources, genotyping methods, and literature type. In the cancer type subgroup, we found that this SNP reduced the risk of hepatocellular carcinoma (HCC; dominant model, OR = 0.79, 95% CI = 0.68–0.92; recessive model, OR = 0.57, 95% CI = 0.43–0.75; homozygous model, OR = 0.53, 95% CI = 0.40–0.71; additive model, OR = 0.78, 95% CI = 0.69–0.87). Notably, the HCC subgroup contained 5 reports, 3 of which had controls who were chronic hepatitis B (CHB) patients (a risk factor for HCC), yet concluded that the SNP was a protective factor for HCC, which strengthens the credibility of the conclusion. VEGF-460 polymorphism was not associated with other types of malignancies. In all other subgroups, the SNP was not associated with cancer. Overall, the SNP is not relevant to overall cancer but may lower the risk of HCC. 3.3. Heterogeneity analysis High heterogeneity was present in 5 models (Tables 2 and 3 provide PHeterogeneity and I2). We performed meta-regression based on cancer type, ethnicity, the sources of controls, genotyping methods, and literature publication status, and the results were not statistically significant (Table 4). Therefore, we further explored the origins of heterogeneity using subgroup analysis. All showed heterogeneity in the cancer type subgroup except for the breast cancer and HCC groups. Among ethnicity, sources of controls, genotyping methods, and literature type subgroups, heterogeneity was detected in all subgroups except the real-time PCR and gray literature groups. Table 4 Results of meta-regression analysis of VEGF-460 (T/C) and cancer risk in 5 genetic models. Covariates Number of dummy variables Dominant model Recessive model Heterozygous model Homozygous model Additive model Year – 0.327 0.190 0422 0.195 0.217 Ethnicity 3 0.279 0.709 0.321 0.749 0.391 Cancer type 10 0.898 0.279 0.765 0.478 0.450 Genotyping methods 5 0.342 0.619 0.394 0.538 0.695 Source of controls 3 0.842 0.500 0.682 0.732 0.770 Literature type 2 0.872 0.358 0.926 0.498 0.667 VEGF = vascular endothelial growth factor. Overall, this work had high heterogeneity due to multifactorial factors, including differences in cancer type, the population, source of controls, genotyping methods, and literature type in the included studies. 3.4. Publication bias We used contour-enhanced funnel plots to estimate the publication bias in 5 gene models and found that all 5 funnel plots were approximately symmetrical (Fig. 6). The funnel plots had studies distributed in statistically significant places outside the white areas, indicating study heterogeneity, consistent with PHeterogeneity and I2 value. And the results of Begg’s and Egger’s tests showed the presence of publication bias in the recessive model (dominant model: PBegg = 0.379, PEgger = 0.512; recessive model: PBegg < 0.001, PEgger < 0.001; heterozygous model: PBegg = 0.622, PEgger = 0.077; homozygous model: PBegg = 0.214, PEgger = 0.548; additive model: PBegg = 0.520, PEgger = 0.967). The effects of publication bias were assessed by the trim and fill method for the recessive model, and we found no significant alteration in the results, suggesting that the results were steady and the publication bias was acceptable. Figure 6. Funnel plot of the relationship between VEGF-460 and cancer risk. (A) dominant model, (B) recessive model, (C) heterozygous model, (D) homozygous model, (E) additive model. VEGF = vascular endothelial growth factor. 3.5. Sensitivity analysis After we rotated out individual studies to recombine ORs, we found no significant alteration in results, much less reversal. The sensitivity analysis results on the 5 models are provided in Figure S1, Supplemental Digital Content, http://links.lww.com/MD/J158 (See Figure S1, Supplemental Digital Content, http://links.lww.com/MD/J158, which demonstrates the results of sensitivity analysis in 5 model). The above results indicated the stability of the results of this paper. 4. Discussion The present meta-analysis included 44 articles comprising 46 case-control trials with 12,868 cases and 14,111 controls. We found that VEGF-460(T/C) polymorphism was unrelated to overall malignancy but may reduce the risk of HCC. Four previous meta-analyses were performed on this SNP and lung cancer risk; Junwei Tu et al, Fengming Yang et al and Junli Fan et al concluded that this SNP increases the lung cancer risk among Asians[81–83]; nevertheless, Ning Song et al identified VEGF-460(T/C) polymorphism as a defensive factor in nonsmokers and patients with squamous lung cancer.[80] Zhou et al[84] concluded that the SNP was not relevant to colorectal cancer, whereas Zigang Zhao et al concluded that it could result in colorectal cancer.[85] Both meta-findings demonstrate that this SNP is unrelated to renal cell carcinoma.[87,88] And J. Zhao et al conclude that VEGF-460 enhances the risk of osteosarcoma.[91] As for ovarian, gastric, oral, and prostate cancers, there is no evidence of VEGF-460 being related to them.[86,89,90,92] A meta-analysis of VEGF-460 and overall cancer susceptibility was reported in 2009 by Xu et al.[79] They concluded that this SNP could contribute to elevated cancer risk in Asian populations. Most of the above meta-analysis conclusions were consistent with ours, but only for single cancer and less included literature. This meta-analysis included 30 more eligible papers than the previous meta-analysis examining VEGF-460 and overall cancer risk. This meta-analysis incorporated a large body of case-control studies targeting different malignancies, ethnic populations, recruitment methods, etc. Therefore, heterogeneity detection is inevitable. In order to minimize the effect of heterogeneity, random-effects models were taken once the heterogeneity was detected. Meta-regression based on year (year of publication or graduation), ethnicity, cancer type, genotyping method, control sources, and literature type was performed to probe sources of heterogeneity. However, P values for all covariates exceeded .05, indicating that no sources of heterogeneity were detected. We then performed a detailed subgroup analysis and found heterogeneity in almost all subgroups, suggesting multiple causes of heterogeneity, including differences in tumor type, race, participant recruitment methods, and genotyping methods. We included gray literature to reduce publication bias as much as possible. We initially used contour-enhanced funnel plots to detect publication bias, roughly symmetrical for the 5 models. However, a few studies were distributed in statistically significant regions outside the white areas, which is not the result of publication bias but most likely due to heterogeneity. The Begg’s and Egger’s tests were employed to quantify publication bias, and the results indicated that publication bias was mainly present in the recessive model. We performed the trim and fill method for the recessive model and found no change in the results after trim and fill, indicating that the publication bias was acceptable. The sensitivity analysis was conducted by eliminating single reports in turn, then recombining the ORs to test the stability of the results. The ORs and 95% CIs showed no significant changes and no reversal, indicating that these meta-analysis findings were steady. Since Judah Folkman first emphasized the involvement of angiogenesis in the growth and multiplication of solid tumors in 1971,[99] the concept of anti-angiogenesis as a potential means of cancer therapy has been repeatedly proposed.[100,101] With the follow-up of related studies, anti-angiogenic drugs, especially those targeting VEGF, like bevacizumab, sunitinib, and pazopanib, have been used for oncology therapy.[102] Despite the effectiveness of these clinically approved anti-angiogenic drugs in decreasing cancer angiogenesis through normalization of hyperpermeable tumor vessels, metastasis and death still follow therapy.[102] This is due to the unclear mechanism of VEGF involvement in oncogenesis and proliferation and the resistance to anti-angiogenic drugs. Future research may be multi-directional, such as anti-angiogenic drugs combined with immunotherapy or nanoparticles.[103–105] However, research on VEGF mechanisms and other pro-angiogenic factors involved in tumor formation and proliferation must be fundamental and essential. This Meta-analysis incorporated more studies with different types of cancer, including unpublished literature, compared to previous meta-analyses of the same type, and the results were more comprehensive and reliable. Although it was concluded that VEGF-460 did not correlate with malignancy risk, the SNP might reduce HCC susceptibility in the subgroup of cancer types. It is noteworthy that a large proportion of controls in this subgroup were patients with chronic hepatitis B, one of the factors contributing to HCC; if the result is this SNP causing HCC, then the phenomenon reduces the validity of the result, and vice versa increases the validity of the opposite result. This may be a breakthrough for related research in the future. However, some limitations remain; first, there was high heterogeneity due to multiple factors. Secondly, the African population was not analyzed. Third, because of the limitations of the original study, it was not possible to analyze on more detailed stratification, such as gender, BMI, whether or not to smoke. In conclusion, VEGF-460(T/C) was not associated with malignancy. However, in the cancer type subgroup, this SNP reduced the risk of HCC. Future studies with more rigorous trial designs and larger sample sizes are required to update and refine our conclusions. 5. Conclusion This meta-analysis indicated that VEGF-460 was irrelevant to overall malignancy risk, but it might be a protective factor for HCC. This work was funded by the Youth Science Fund Project of Jiangxi Provincial Department of Science and Technology (20192BAB215037). Author contributions Conceptualization: Foyan Xu. Data curation: Haoran Qin, Taiping Li. Formal analysis: Haoran Qin, Qiang Xiao, Yufen Xie, Dan Li. Funding acquisition: Foyan Xu. Investigation: Haoran Qin, Qiang Xiao, Yufen Xie, Xiaozhou Long, Yiqin Liu. Methodology: Haoran Qin, Dan Li, Xiaozhou Long, Taiping Li. Supervision: Foyan Xu. Validation: Qiang Xiao, Yufen Xie, Siqing Yi, Jian Chen. Writing – original draft: Haoran Qin, Qiang Xiao. Writing – review & editing: Jian Chen. Supplementary Material Abbreviations: CI confidence interval HCC hepatocellular carcinoma HWE Hardy–Weinberg equilibrium NOS Newcastle–Ottawa scale ORs odd ratios SNPs single nucleotide polymorphisms VEGF vascular endothelial growth factor HQ, QX, YX, and DL contributed equally to this work. The authors have no conflicts of interest to disclose. All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Supplemental Digital Content is available for this article. 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