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Tumor Biology

, Volume 35, Issue 3, pp 2687–2692 | Cite as

Vascular endothelial growth factor +936C/T polymorphism and breast cancer risk: a meta-analysis of 13 case–control studies

  • Yulan Yan
  • Hongjie Liang
  • Taijie Li
  • Shihui Guo
  • Meng Li
  • Shan Li
  • Xue Qin
Open Access
Research Article

Abstract

The association between vascular endothelial growth factor (VEGF) +936C/T polymorphism and breast cancer risk has been widely reported, but results were inconsistent. In order to derive a more precise estimation of the relationship, a meta-analysis was performed. Eligible articles were identified through search of databases including PubMed, Embase, and Chinese Biomedical Literature Database (CBM). The association between the VEGF +936C/T polymorphism and breast cancer risk was conducted by odds ratios (ORs) and 95 % confidence intervals (95 % CIs). Finally, a total of 13 studies with 6,879 cases and 7,219 controls were included in our meta-analysis. Overall, a significant association was found between VEGF +936C/T polymorphisms and the risk of breast cancer in overall populations under five models (T vs. C: OR = 0.83, 95 % CI = 0.73–0.94, P = 0.002; TT vs. CC: OR = 0.74, 95 % CI = 0.61–0.91, P = 0.004, Fig. 1a; TC vs. CC: OR = 0.83, 95 % CI = 0.71–0.96, P = 0.014; TT vs. CC/CT: OR = 0.77, 95 % CI = 0.62–0.94, P = 0.010; TT/TC vs. CC: OR = 0.82, 95 % CI = 0.72–0.95, P = 0.006). In the subgroup analysis by ethnicity, there were also significant associations found between VEGF +936C/T polymorphism and breast cancer risk in Asians and Caucasians. In conclusion, the results of our meta-analysis suggest that the VEGF +936C/T polymorphism is significantly associated with breast cancer development and the VEGF 936T allele carriers may be associated with decreased breast cancer risk.

Keywords

VEGF Polymorphism Breast cancer Meta-analysis 

Introduction

Breast cancer is the second most common cancer in women after skin cancer, accounting for about one third of all cancers in women [1]. Global breast cancer incidence has been increasing by more than one million new cases every year, and the incidence is significantly higher in developed countries than in developing countries [2]. Incidence of breast cancer is increasing around the world, accounting for 23 % of the total cancer cases and 14 % of the cancer deaths in 2008. It is still the leading cause of cancer mortality in women [3]. Although the mechanism of breast carcinogenesis is still not fully understood, it is well known that tumorigenesis is a multistep process with multifactors involved [4, 5]. Besides environmental factors [6, 7], gene-based factors leading to individuals’ susceptibility to cancer development have been commonly studied [8, 9]. Gene polymorphisms, common risk factors for breast cancer, have been extensively studied recently [10, 11].

Previous studies have suggested that low-penetrance susceptibility genes combined with environmental factors may be important in the development of cancers, including breast cancer [7], while vascular endothelial growth factor (VEGF), which is located at 6p21.3, containing eight exons and seven introns, is one of the low-penetrance susceptibility genes [12]. At least 30 single-nucleotide polymorphisms (SNP) in this gene have been described in the publications. Among these, one of the most common gene, VEGF +936C/T (rs3025039) in the 3′-untranslated region, was found to be associated with variations in VEGF protein production [13, 14]. It was reported that the C-to-T change led to the loss of a potential binding site for transcription factor AP-4 and that VEGF plasma levels in 936T allele carriers were significantly lower than those in non-carriers; besides, the T allele has been found to be associated with a reduced uptake of 18F-fluorodeoxyglucose, used for detection and staging of breast cancer [15, 16, 17, 18].

Up to now, a number of studies have reported the association between the VEGF +936C/T polymorphism and breast cancer susceptibility, but the results remain inconsistent [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. In order to estimate the association between VEGF +936C/T polymorphism and breast cancer susceptibility, we conducted this meta-analysis.

Methods

Search strategy

We performed an electronic search of the PubMed, Embase, and Chinese Biomedical Literature Database (CBM) to retrieve articles linking VEGF +936C/T gene polymorphism and susceptibility to breast cancer available until August 2013 with keywords “breast cancer,” “breast neoplasm,” “vascular endothelial growth factor,” “VEGF,” “polymorphism,” and “variant,” and there were no limitations to the language of publications. Additional studies were identified by a hand search of the references of original studies, and review articles were also examined to find additional eligible studies.

Inclusion and exclusion criteria

Eligible studies included in our meta-analysis had to meet the following criteria: (a) they should be case–control studies, (b) they should evaluate the VEGF +936C/T polymorphism and breast cancer risk, (c) they should supply the available genotype frequency in cases and controls, and (d) they should have sufficient published data for estimating an odds ratio (OR) with 95 % confidence interval (CI). The exclusion criteria were as follows: (a) not a case–control study, (b) no usable data reported, (c) duplicate data, (d) abstract, comment, review, and editorial. When multiple publications reported on the same or overlapping data, the most recent or largest population was selected.

Data extraction

Two investigators independently extracted the data, based on the inclusion criteria mentioned above. If conflicting evaluations were encountered, an agreement was reached following a discussion; if agreement could not be reached, then a third author was consulted to resolve the debate. The following information were extracted: (a) the name of the first author, (b) year of publication, (c) country of origin, (d) ethnicity, (e) genotyping methods, (f) source of the control group, (g) distribution of genotypes in case and control groups. We also evaluated whether the genotype distributions were in the Hardy–Weinberg equilibrium.

Statistical analysis

The odds ratio (OR) and 95 % confidence interval (CI) were used to evaluate the strength of associations between the VEGF +936C/T polymorphism and the risk of breast cancer according to five genetic models: allele contrast (T vs. C), homozygote (TT vs. CC), heterozygote (TC vs. CC), recessive (TT vs. TC/CC), and dominant (TT/TC vs. CC) models. The heterogeneity was tested by a chi-square-based Q statistic test. The effect of heterogeneity was quantified by using I 2 values as well as P values [30]. If I 2 value <50 % and P > 0.10, this suggests that obvious heterogeneity does not exist, ORs were pooled by a fixed-effects model (the Mantel–Haenszel method) [31]. Otherwise, ORs were pooled by a random-effects model (DerSimonian and Laird method) [32].

The Hardy–Weinberg equilibrium (HWE) [33] of controls was tested by using a professional web-based program (http://ihg2.helmholtz-muenchen.de/cgibin/hw/hwa1.pl); if P > 0.05, this suggests that the controls followed the HWE balance. Sensitivity analysis was performed to evaluate the stability of the results. A single study involved in the meta-analysis was omitted each time to reflect the influence of the individual data set on the pooled ORs [34]. When the Hardy–Weinberg equilibrium disequilibrium existed (P < 0.05 was considered statistically significant), the sensitivity analysis was also conducted. Possible publication bias was tested by Egger’s test (P < 0.05 was considered representative of statistically significant publication bias) [35] and visual observation of funnel plot [36] in the meta-analysis. All statistical tests were performed with STATA Software (version 9.2, Stata Corp). P < 0.05 for any test or model was considered to be statistically significant.

Results

Search results and study characteristics

After careful examination according to the inclusion criteria, a total of 11 publications with 13 studies including 6,879 cases and 7,219 controls were included in our meta-analysis [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. In the 13 studies, controls were mainly healthy populations and matched for age, and the genotype distribution in the controls of all studies was consistent with HWE (all P > 0.05). The main characteristics of the studies included in the present meta-analysis are listed in Table 1.
Table 1

Characteristics of case–control studies included in VEGF +C936T polymorphism and breast cancer risk

First author

Year

Country

Ethnicity

Genotyping methods

Source of control

Cases

Controls

CC

CT

TT

CC

CT

TT

Luo

2013

China

Asian

PCR-RFLP

HB

446

210

24

426

204

50

Rodrigues

2012

Span

Caucasian

PCR-RFLP

PB

366

76

5

332

99

11

Absenger

2013

Austria

Caucasian

TaqMan

PB

371

138

12

580

201

20

Oliveira

2011

Brazil

Mix

PCR-RFLP

PB

190

43

2

176

52

7

Lin

2009

China

Asian

PCR-RFLP

HB

155

59

6

211

117

6

Jakubowska

2008

Poland

Caucasian

PCR-RFLP

PB

245

67

7

202

81

7

Balasubramanian

2007

England

Caucasian

TaqMan

PB

624

204

20

531

165

12

Kataoka

2006

China

Asian

Taqman

PB

744

334

31

793

351

51

Jacobs

2006

USA

Mix

Taqman

PB

360

110

10

353

111

15

Jin(a)

2005

Polish

Caucasian

PCR-RFLP

PB

298

10

14

297

114

11

Jin(b)

2005

German

Caucasian

PCR-RFLP

PB

120

31

2

128

31

4

Jin(c)

2005

Swenden

Caucasian

PCR-RFLP

PB

708

204

12

720

203

11

Krippl

2003

Austria

Caucasian

Taqman

PB

412

79

9

353

137

10

PCR-RFLP polymerase chain reaction–restriction fragment length polymorphism, HB hospital based, PB population based

Meta-analysis results

The main results of this meta-analysis and the heterogeneity test were shown in Table 2. A significant association was found between VEGF +936C/T polymorphism and the risk of breast cancer in overall populations under five models (T vs. C: OR = 0.83, 95 % CI = 0.73–0.94, P = 0.002; TT vs. CC: OR = 0.74, 95 % CI = 0.61–0.91, P = 0.004, Fig. 1a; TC vs. CC: OR = 0.83, 95 % CI = 0.71–0.96, P = 0.014; TT vs. CC/CT: OR = 0.77, 95 % CI = 0.62–0.94, P = 0.010; TT/TC vs. CC: OR = 0.82, 95 % CI = 0.72–0.95, P = 0.006). In the subgroup analysis by ethnicity, there were also significant associations found between VEGF +936C/T polymorphisms and breast cancer risk in Asians (T vs. C: OR = 0.90, 95 % CI = 0.82–0.98, P = 0.014, Fig. 1b; TT vs. CC: OR = 0.61, 95 % CI = 0.45–0.83, P = 0.002; TT vs. CC/CT: OR = 0.61, 95 % CI = 0.45–0.83, P = 0.002) and Caucasians (T vs. C: OR = 0.79, 95 % CI = 0.64–0.99, P = 0.036, Fig. 1b; TC vs. CC: OR = 0.74, 95 % CI = 0.56–0.97, P = 0.027; TT/TC vs. CC: OR = 0.77, 95 % CI = 0.60–0.98, P = 0.031).
Table 2

Results of meta-analysis for VEGF +C936T polymorphism and breast cancer risk

Comparison

Population

Number

Test of association

Model

Test of heterogeneity

OR

95 % CI

P

P

I 2

T vs. C

Overall

13

0.83

0.73–0.94

0.002

R

0

76.7

Asian

3

0.90

0.82–0.98

0.014

F

0.488

0

Caucasians

8

0.79

0.64–0.99

0.036

R

0

85.5

Mix

2

0.85

0.71–1.03

0.097

F

0.226

31.7

TT vs. CC

Overall

13

0.74

0.61–0.91

0.004

F

0.316

12.8

Asian

3

0.61

0.45–0.83

0.002

F

0.225

32.9

Caucasians

8

0.96

0.71–1.29

0.767

F

0.679

0

Mix

2

0.54

0.27–1.07

0.078

F

0.316

0.5

TC vs. CC

Overall

13

0.83

0.71–0.96

0.014

R

0

83.3

Asian

3

0.97

0.88–1.06

0.498

F

0.186

40.5

Caucasians

8

0.74

0.56–0.97

0.027

R

0

89.4

Mix

2

0.92

0.76–1.12

0.426

F

0.384

0

TT vs. TC/CC

Overall

13

0.77

0.62–0.94

0.010

F

0.177

26.5

Asian

3

0.61

0.45–0.83

0.002

F

0.163

44.9

Caucasians

8

1.02

0.76–1.38

0.886

F

0.607

0

Mix

2

0.55

0.27–1.09

0.087

F

0.342

0

TT/TC vs. CC

Overall

13

0.82

0.72–0.95

0.006

R

0

81.8

Asian

3

0.94

0.86–1.02

0.121

F

0.350

4.6

Caucasians

8

0.77

0.60–0.98

0.031

R

0

88.6

Mix

2

0.89

0.74–1.07

0.213

F

0.287

11.7

OR odds ratio, CI confidence interval, F fixed-effects model, R random-effects model

Fig. 1

a The forest plot describing the meta-analysis under the homozygous model for the association between VEGF +936C/T polymorphism and the risk of breast cancer in overall population (TT vs. CC). b The forest plot describing the meta-analysis under the allele model for the association between VEGF +936C/T polymorphism and the risk of breast cancer in the subgroup analysis based on ethnicity (T vs. C)

Sensitivity analysis

Sensitivity analyses were conducted to determine whether modification of the inclusion criteria of the meta-analysis affected the final results. A single study involved in the meta-analysis was deleted each time to reflect the influence of the individual data set on the pooled OR, and the corresponding pooled OR was not materially altered (data not shown), indicating that our results were relatively stable and credible.

Publication bias

A funnel plot and Egger’s test were performed to assess publication bias. The funnel plot is relatively straightforward in observing whether the publication bias is present, and Egger’s test was used to provide statistical evidence of symmetries of the plots. The shape of the funnel plot did not reveal any evidence of obvious asymmetry (Fig. 2). Similarly, the results of Egger’s test still did not suggest any evidence of publication bias (all P > 0.05, data not shown).
Fig. 2

Begg funnel plot for publication bias test for the association between VEGF +936C/T polymorphism and the risk of breast cancer under the homozygous model (TT vs. CC). Each point represents a separate study for the indicated association. Log [OR], natural logarithm of OR. Horizontal line means effect size

Discussion

Angiogenesis is essential for the growth of microscopic cancers into larger tumors, while VEGF plays a central role in angiogenesis through a variety of mechanisms such as effects on endothelial cell proliferation, survival, and migration [37, 38]. Previous studies suggested that VEGF 936T allele causes lower VEGF plasma levels, mainly by the following two mechanisms. One is that the 936C/T mutation results in the loss of a potential binding site for AP-4. AP-4 is a helix–loop–helix transcription factor that enhances the expression of several viral and cellular genes by binding to specific enhancer sites. The other one is that the association between the 936C/T mutation and VEGF plasma levels could be due to linkage disequilibrium between this mutation and another yet unknown functional mutation elsewhere in the VEGF gene sequence [16, 39, 40, 41]. Thus, VEGF 936T allele carriers were considered to be associated with decreased breast cancer risk.

The present meta-analysis, including 6,879 cases and 7,219 controls, explored the association between the VEGF +936C/T polymorphism and breast cancer risk. The results show that the VEGF +936C/T polymorphism is significantly associated with breast cancer development. Actually, it might act as a protective factor for breast cancer. In the subgroup analyses based on ethnicity, a significant association was found in Asian and Caucasian populations. This may be explained by the fact that cancer is a complicated multigenetic disease and different genetic backgrounds may contribute to the discrepancy.

Although a comprehensive analysis was conducted to show the association between MTHFR polymorphism and risk of breast cancer, there are still some limitations that should be pointed out. First, the number of studies and the number of samples included in the meta-analysis were relatively small. Second, in the subgroup analyses, the number of Asians was relatively small, not having enough statistical power to explore the real association. Additionally, no data were available about Africans. Third, the controls were not uniformly defined. Some studies used controls that were population based, while others used hospital-based controls, which may not be representative of the general population. Finally, our results were based on unadjusted estimates, while a more precise analysis should be conducted if individual data were available, which would allow for the adjustment of other co-variants including age, menopausal status, environmental factors, and lifestyle.

In conclusion, the results of our meta-analysis suggest that the VEGF +936C/T polymorphism is significantly associated with breast cancer development and the VEGF 936T allele carriers may be associated with decreased breast cancer risk.

Notes

Conflicts of interest

None

References

  1. 1.
    Bray F, McCarron P, Parkin DM. The changing global patterns of female breast cancer incidence and mortality. Breast Cancer Res. 2004;6:229–39.PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Sturgeon SR, Schairer C, Grauman D, El Ghormli L, Devesa S. Trends in breast cancer mortality rates by region of the United States, 1950–1999. Cancer Causes Control. 2004;15:987–95.PubMedCrossRefGoogle Scholar
  3. 3.
    Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61:69–90.PubMedCrossRefGoogle Scholar
  4. 4.
    Meteoglu I, Dikicioglu E, Erkus M, Culhaci N, Kacar F, Ozkara E, et al. Breast carcinogenesis. Transition from hyperplasia to invasive lesions. Saudi Med J. 2005;26:1889–96.PubMedGoogle Scholar
  5. 5.
    Correa P. Human gastric carcinogenesis: a multistep and multifactorial process—First American Cancer Society Award Lecture on Cancer Epidemiology and Prevention. Cancer Res. 1992;52:6735–40.PubMedGoogle Scholar
  6. 6.
    O’Brien JM. Environmental and heritable factors in the causation of cancer: analyses of cohorts of twins from Sweden, Denmark, and Finland, by P. Lichtenstein, N.V. Holm, P.K. Verkasalo, A. Iliadou, J. Kaprio, M. Koskenvuo, E. Pukkala, A. Skytthe, and K. Hemminki. N Engl J Med 343:78–84, 2000. Surv Ophthalmol 2000;45:167–168.Google Scholar
  7. 7.
    Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M, et al. Environmental and heritable factors in the causation of cancer—analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000;343:78–85.PubMedCrossRefGoogle Scholar
  8. 8.
    Nathanson KL, Wooster R, Weber BL. Breast cancer genetics: what we know and what we need. Nat Med. 2001;7:552–6.PubMedCrossRefGoogle Scholar
  9. 9.
    Balmain A, Gray J, Ponder B. The genetics and genomics of cancer. Nat Genet. 2003;33(Suppl):238–44.PubMedCrossRefGoogle Scholar
  10. 10.
    Hu Z, Li X, Yuan R, Ring BZ, Su L. Three common tp53 polymorphisms in susceptibility to breast cancer, evidence from meta-analysis. Breast Cancer Res Treat. 2010;120:705–14.PubMedCrossRefGoogle Scholar
  11. 11.
    Qiu LX, Yao L, Zhang J, Zhu XD, Zhao XM, Xue K, et al. Xpd lys751gln polymorphism and breast cancer susceptibility: a meta-analysis involving 28,709 subjects. Breast Cancer Res Treat. 2010;124:229–35.PubMedCrossRefGoogle Scholar
  12. 12.
    Ferrara N. Vegf and the quest for tumour angiogenesis factors. Nat Rev Cancer. 2002;2:795–803.PubMedCrossRefGoogle Scholar
  13. 13.
    Stevens A, Soden J, Brenchley PE, Ralph S, Ray DW. Haplotype analysis of the polymorphic human vascular endothelial growth factor gene promoter. Cancer Res. 2003;63:812–6.PubMedGoogle Scholar
  14. 14.
    Chae YS, Kim JG, Sohn SK, Cho YY, Moon JH, Bae HI, et al. Investigation of vascular endothelial growth factor gene polymorphisms and its association with clinicopathologic characteristics in gastric cancer. Oncology. 2006;71:266–72.PubMedCrossRefGoogle Scholar
  15. 15.
    Dassoulas K, Gazouli M, Rizos S, Theodoropoulos G, Christoni Z, Nikiteas N, et al. Common polymorphisms in the vascular endothelial growth factor gene and colorectal cancer development, prognosis, and survival. Mol Carcinog. 2009;48:563–9.PubMedCrossRefGoogle Scholar
  16. 16.
    Renner W, Kotschan S, Hoffmann C, Obermayer-Pietsch B, Pilger E. A common 936 c/t mutation in the gene for vascular endothelial growth factor is associated with vascular endothelial growth factor plasma levels. J Vasc Res. 2000;37:443–8.PubMedCrossRefGoogle Scholar
  17. 17.
    Qiu LX, Wang K, Yang S, Mao C, Zhao L, Yao L, et al. Current evidences on vascular endothelial growth factor polymorphisms and breast cancer susceptibility. Mol Biol Rep. 2011;38:4491–4.PubMedCrossRefGoogle Scholar
  18. 18.
    Wolf G, Aigner RM, Schaffler G, Langsenlehner U, Renner W, Samonigg H, et al. The 936c > t polymorphism of the gene for vascular endothelial growth factor is associated with 18F-fluorodeoxyglucose uptake. Breast Cancer Res Treat. 2004;88:205–8.PubMedCrossRefGoogle Scholar
  19. 19.
    Luo T, Chen L, He P, Hu QC, Zhong XR, Sun Y, et al. Vascular endothelial growth factor (vegf) gene polymorphisms and breast cancer risk in a chinese population. Asian Pac J Cancer Prev. 2013;14:2433–7.PubMedCrossRefGoogle Scholar
  20. 20.
    Rodrigues P, Furriol J, Tormo E, Ballester S, Lluch A, Eroles P. The single-nucleotide polymorphisms +936 c/t vegf and −710 c/t vegfr1 are associated with breast cancer protection in a Spanish population. Breast Cancer Res Treat. 2012;133:769–78.PubMedCrossRefGoogle Scholar
  21. 21.
    Absenger G, Szkandera J, Stotz M, Pichler M, Winder T, Langsenlehner T, et al. A common and functional gene variant in the vascular endothelial growth factor a predicts clinical outcome in early-stage breast cancer. Mol Carcinog 2013Google Scholar
  22. 22.
    Oliveira C, Lourenco GJ, Silva PM, Cardoso-Filho C, Favarelli MH, Goncales NS, et al. Polymorphisms in the 5′- and 3′-untranslated region of the vegf gene and sporadic breast cancer risk and clinicopathologic characteristics. Tumour Biol. 2011;32:295–300.PubMedCrossRefGoogle Scholar
  23. 23.
    Jakubowska A, Gronwald J, Menkiszak J, Gorski B, Huzarski T, Byrski T, et al. The VEGF_936_C > T 3′UTR polymorphism reduces BRCA1-associated breast cancer risk in Polish women. Cancer Lett. 2008;262:71–6.PubMedCrossRefGoogle Scholar
  24. 24.
    Balasubramanian SP, Cox A, Cross SS, Higham SE, Brown NJ, Reed MW. Influence of vegf-a gene variation and protein levels in breast cancer susceptibility and severity. Int J Cancer. 2007;121:1009–16.PubMedCrossRefGoogle Scholar
  25. 25.
    Kataoka N, Cai Q, Wen W, Shu XO, Jin F, Gao YT, et al. Population-based case–control study of VEGF gene polymorphisms and breast cancer risk among Chinese women. Cancer Epidemiol Biomarkers Prev. 2006;15:1148–52.PubMedCrossRefGoogle Scholar
  26. 26.
    Jacobs EJ, Feigelson HS, Bain EB, Brady KA, Rodriguez C, Stevens VL, et al. Polymorphisms in the vascular endothelial growth factor gene and breast cancer in the Cancer Prevention Study II cohort. Breast Cancer Res. 2006;8:R22.PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Jin Q, Hemminki K, Enquist K, Lenner P, Grzybowska E, Klaes R, et al. Vascular endothelial growth factor polymorphisms in relation to breast cancer development and prognosis. Clin Cancer Res. 2005;11:3647–53.PubMedCrossRefGoogle Scholar
  28. 28.
    Krippl P, Langsenlehner U, Renner W, Yazdani-Biuki B, Wolf G, Wascher TC, et al. A common 936 c/t gene polymorphism of vascular endothelial growth factor is associated with decreased breast cancer risk. Int J Cancer. 2003;106:468–71.PubMedCrossRefGoogle Scholar
  29. 29.
    Lin GT, Tseng HF, Yang CH, Hou MF, Chuang LY, Tai HT, et al. Combinational polymorphisms of seven CXCL12-related genes are protective against breast cancer in Taiwan. OMICS. 2009;13:165–72.PubMedCrossRefGoogle Scholar
  30. 30.
    Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–58.PubMedCrossRefGoogle Scholar
  31. 31.
    Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22:719–48.PubMedGoogle Scholar
  32. 32.
    DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–88.PubMedCrossRefGoogle Scholar
  33. 33.
    Zamora-Ros R, Rothwell JA, Scalbert A, Knaze V, Romieu I, Slimani N, et al. Dietary intakes and food sources of phenolic acids in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Br J Nutr 2013:1–12.Google Scholar
  34. 34.
    Tobias A, Campbell MJ. Modelling influenza epidemics in the relation between black smoke and total mortality. A sensitivity analysis. J Epidemiol Community Health. 1999;53:583–4.PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–34.PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–101.PubMedCrossRefGoogle Scholar
  37. 37.
    Carmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature. 2000;407:249–57.PubMedCrossRefGoogle Scholar
  38. 38.
    Ferrara N. Vascular endothelial growth factor: basic science and clinical progress. Endocr Rev. 2004;25:581–611.PubMedCrossRefGoogle Scholar
  39. 39.
    Hu YF, Luscher B, Admon A, Mermod N, Tjian R. Transcription factor ap-4 contains multiple dimerization domains that regulate dimer specificity. Genes Dev. 1990;4:1741–52.PubMedCrossRefGoogle Scholar
  40. 40.
    Mermod N, Williams TJ, Tjian R. Enhancer binding factors ap-4 and ap-1 act in concert to activate sv40 late transcription in vitro. Nature. 1988;332:557–61.PubMedCrossRefGoogle Scholar
  41. 41.
    Yang DS, Park KH, Woo OH, Woo SU, Kim AR, Lee ES, et al. Association of a vascular endothelial growth factor gene 936 c/t polymorphism with breast cancer risk: a meta-analysis. Breast Cancer Res Treat. 2011;125:849–53.PubMedCrossRefGoogle Scholar

Copyright information

© The Author(s) 2014

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  1. 1.Department of Clinical LaboratoryFirst Affiliated Hospital of Guangxi Medical UniversityNanningPeople’s Republic of China

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