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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Jun 15;8(6):8709–8719.

Overexpression of vascular endothelial growth factor indicates poor outcomes of glioma: a systematic review and meta-analysis

Wenjie Chen 1, Deshen He 2, Zuyun Li 1, Xin Zhang 1, Denghua Pan 1, Gang Chen 1
PMCID: PMC4538108  PMID: 26309522

Abstract

Background: Accumulated studies have revealed that vascular endothelial growth factor (VEGF) plays an essential role in the progression of glioma, but the prognostic significance of VEGF expression for patients with glioma remains unknown. Method and material: A literature search of public databases (PubMed, ISI Web of Science, Science Direct, Cochrane Central Register of Controlled Trials, Wiley Online Library, China National Knowledge Infrastructure, China Biology Medicine disc, Chongqing VIP and Wan Fang Data) was conducted. A meta-analysis was performed to evaluate the association between the overexpression of VEGF and the survival for the glioma patients. Subsequently we evaluated the impact of VEGF expression on the pathological grade of glioma. Results: A total of 32 articles with 2307 cases contributed to this analysis, of which 31 reported overall survival (OS) and 5 reported progression-free survival (PFS). In this meta-analysis, VEGF overexpression significantly identified the unfavorable outcome on OS (HR = 1.647, 95% CI: 1.324~2.048, P < 0.001, Z = 4.48) but not on PFS (HR = 1.021, 95% CI: 0.974~1.070, P = 0.393). Subgroup analyses also revealed that high level of VEGF was associated with the poor OS for the patients with glioma according to region, case number, specimen type, method to detect VEGF and statistical method. Furthermore, the significant correlation was achieved between VEGF expression and the pathological grade of glioma (r = 0.307, P < 0.001). Conclusion: This study suggests that VEGF expression is significantly correlated with the glioma progression and may be a valuable prognostic factor on OS for the glioma patients.

Keywords: Vascular endothelial growth factor, glioma, prognosis, meta-analysis

Introduction

Glioma, the most prevalent intracranial neoplasia in adults, is classified as grade I to grade IV for its differentiation, based on the World Health Organization (WHO) criteria. Studies have pointed to an increasing incidence of gliomas over the past few decades. Though diagnostic and therapeutic techniques have been improved tremendously in last ten years, the survival of patients with malignant glioma remains still poor. Several prognostic factors are well established for glioma patients, such as isocitrate dehydrogenase 1 (IDH1), P53, epidermal growth factor receptor (EGFR), and Ki-67 [1-4]. Nevertheless, there is a compelling demand to explore more prognostic markers to prolong the survival of glioma patients.

Vascular endothelial growth factor (VEGF) is an endothelial, cell-specific mitogen, which acts as a prime mediator in angiogenesis. As a critical pro-angiogenic factor, VEGF is also involved in carcinogenesis and metastasis in cancers [5,6]. Increasing evidence suggested that VEGF is a prognostic factor of many cancers, such as lung cancer, hepatocellular carcinoma, gastric cancer, colon cancer and osteosarcoma [7-11], but the predictive value of VEGF on the survival for glioma patients has not been clarified. Recent studies have focused on the interaction between glioblastoma cells and blood vessels. Bevacizumab, an angiogenesis inhibitor, has been observed to increase PFS in patients with glioblastoma multiforme, by inhibiting both VEGF and vascular permeability [12]. Evidence is accumulating that VEGF may play a vital role in the progression of glioma, but the potential of VEGF as a prognostic marker for glioma remains dismal. In the light of the previous studies, we performed a meta-analysis to explore the prognostic value of VEGF for glioma patients.

Materials and methods

Search strategy

An electronic literature search was conducted in PubMed, ISI Web of Science, Science Direct, Cochrane Central Register of Controlled Trials and Wiley Online Library, which are English Databases up to 28th March 2015. For Chinese Databases, the search identified the eligible studies in China National Knowledge Infrastructure (CNKI), China Biology Medicine disc (CBM), Chongqing VIP and Wan Fang Data. The search was based on the keywords as follows: (“vascular endothelial growth factor” or “VEGF” or “VEGFA” or “VEGFB” or “VEGFC” or “VEGFD” or “angiogenesis” or “bevacizumab” or “endostatin”) and (“glioma” or “astrocytoma” or “oligodendroglioma” or “oligoastrocytoma” or “ependymoma” or “glioblastoma” or “gliomatosis cerebri” or “brain cancer” or “brain neoplasm” or “brain tumor” or “GBM” or “AA” or “AO” or “DIPG” ) and (“prognos*” or “surviv*” or “follow-up studies” or “mortality” or “incidence” or “predict” or “outcome”).

Selection criteria

All eligible studies were included by the following criteria: (1) glioma patients should be affirmed pathologically; (2) the association between VEGF expression and OS or PFS should be evaluated for glioma patients; (3) a hazard ratio (HR) should be provided or the sufficient data should be available to calculate a HR for OS or PFS; (4) it should be the most recent or complete study if the same patient cohort were reported more than once by the same authors or research group; (5) it should be written in either Chinese or English in full text.

The studies were considered ineligible by the following exclusion criteria: (1) review, experimental studies, conference abstracts, expert opinion or case report; or (2) no sufficient data for calculating the HR.

Data extraction and assessment of study quality

Two authors (WJ Chen and Xin Zhang) reviewed all of the included studies independently and extracted the following data: first author’s name, publish year, region, case number, WHO grade, test method, specimen type, survival, HR and statistical method. We labeled the data without reporting the above contents as “not applicable”. Each discrepancy was resolved by discussion and consensus among the authors. Newcastle-Ottawa quality assessment scale was used to assess the quality of each study.

Statistical analysis

The relationship of VEGF expression with the pathological grade of glioma was analyzed by using a two-sided Chi-square test and spearman’s rank correlation. HRs and 95% CIs were used to estimate overall effects for survival outcomes. Study region, case number, specimen type, VEGF test method, statistical method were analyzed for subgroup analyses. HR greater than 1 with 95% CI not overlapping 1 indicates a poor prognosis for the VEGF-positive group. The Z test was used to determine the significance of the combined HR (P < 0.05 was considered as statistically significant).We used the methods described by Parmar et al. and the software Engauge Digitizer Version4.1 (http://digitizer.sourceforge.net/) when the studies provided Kaplan-Meier survival curves but no HRs with 95% CIs [13]. Moreover, the multivariate HRs and 95% CIs were combined when multivariate and univariate analyses of OS and/or PFS were available in the same study, which could better reveal the influence of multiple factors on the survival response. The Q-statistic was selected to test the statistical heterogeneity. The random-effects model was used when the Q-test reported a P value < 0.05 by using the method described previously [14]. Otherwise, the fixed-effects model (Mantel-Haenszel method) [15] was selected. We also used the I2-statistic to calculate heterogeneity (I2 less than 25%, no heterogeneity; I2 = 25-50%, moderate heterogeneity; and I2 greater than 50%, large or extreme heterogeneity). Publication bias was estimated by a funnel plot and Egger’ test [16,17]. All two-sided P values less than 0.05 were considered to be significant. SPSS20 and STATA version 12.0 software were used for the statistical calculation.

Results

Literature search and characteristics of included studies

The flow diagram for the study selection process was depicted in Figure 1. In total, 32 studies were included in the analysis, of which 31 reported the OS and 5 reported the PFS for glioma patients [18-49]. These 32 studies published between 2000 and 2015 include 2307 cases, among which 5 studies [19,22,25,36,49] were in Chinese. In the included studies, ten studies [19,25,29,30,36,40,41,45,46,49] with 713 cases reported the of VEGF overexpression with the pathological grade of gliomas. In total, 17 Asian studies, 8 European studies, 5 American studies and 1 African study were included in the current meta-analysis. The characteristics of included studies were presented in Table 1.

Figure 1.

Figure 1

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A Flow Diagram for the Study Selection Process.

Table 1.

The characteristics of included studies in the meta-analysis

Author and year Region Case Grade Specimen type Assay HR (95% CI) Outcome Survival Method quality score
Bian 2000 China 48 I-IV Tumor tissues IHC 6.625 (0.875, 50.230) NS* OS Survival Curve 6
Zhong 2001 China 94 I-IV Tumor tissues IHC 3.876 (1.408, 10.750) Poor OS HR (multivariate) 7
Hara 2004 Japan 100 II-IV Tumor tissues IHC 0.904 (0.463, 1.765) NS OS HR (multivariate) 7
Liu 2004 China 50 I-IV Tumor tissues IHC 4.275 (0.816, 22.390) NS OS HR (univariate) 6
Nam 2004 Korea 26 IV Tumor tissues RT-PCR 3.175 (0.858, 11.740) NS OS Original Data 7
Zhou 2005 China 87 III-IV Tumor tissues qRT-PCR 1.226 (0.390, 3.595) NS OS HR (multivariate) 3
Buccoliero 2006 Italy 43 IV Tumor tissues IHC 1.562 (0.717, 3.405) NS OS Original Data 7
Cheng 2006 China 60 I-IV Tumor tissues IHC 2.114 (1.054, 4.255) Poor OS HR (univariate) 6
Carlson 2007 USA 71 III-IV Tumor tissues RT-PCR 4.340 (2.240, 8.430) Poor OS HR (univariate) 6
Sathornsumetee 2008 USA 68 III-IV Tumor tissues IHC 1.180 (0.500, 2.830) NS OS HR (multivariate) 7
Flynn 2008 USA 62 IV Tumor tissues IHC 1.840 (1.060, 3.210) Poor OS HR (multivariate) 7
Zeng 2009 China 56 I-IV Tumor tissues IHC 1.070 (0.540, 1.770 NS OS Survival Curve 6
Yoo 2010 Korea 76 I-IV Tumor tissues IHC 1.021 (0.574, 1.815) NS OS HR (multivariate) 6
Piperi 2011 Greece 97 II-IV Tumor tissues IHC 0.974 (0.543, 1.749) NS OS HR (multivariate) 7
Saetta 2011 Greece 60 II-IV Tumor tissues IHC 1.007 (0.991, 1.023) NS OS HR (multivariate) 5
El-Sayed 2011 Egypt 26 I-IV Tumor tissues IHC 17.074 (3.491, 83.520) Poor OS Original Data 7
BeriNAan-Neagoe 2012 Romania 14 IV Tumor tissues RT-PCR 0.910 (0.180, 4.640) NS OS HR (NA*) 6
Castells 2012 Spain 71 IV Tumor tissues RT-PCR 1.631 (0.955, 1.663) NS OS HR (multivariate) 6
Fan 2012 China 62 II-IV Tumor tissues IHC 1.710 (0.770, 3.783) NS OS Survival Curve 6
Smith 2012 UK 79 III-IV Tumor tissues IHC 0.559 (0.291, 1.077) NS OS HR (multivariate) 7
Cao 2013 Japan 22 I-IV Tumor tissues IHC 2.748 (0.321, 23.560) NS OS Original Data 7
Shin 2013 Korea 67 IV Tumor tissues IHC 1.010 (0.500, 2.040) NS OS HR (multivariate) 6
Xu 2013 China 88 I-IV Tumor tissues IHC 0.560 (0.191, 1.641) NS OS HR (multivariate) 6
Xu 2013 China 80 NA Tumor tissues IHC 1.830 (0.903, 3.713) NS OS Survival Curve 6
Xu 2013 China 36 NA Tumor tissues IHC 3.310 (0.560, 19.500) NS OS Survival Curve 6
Jensen 2013 USA 18 III-IV Tumor tissues ELISA 8.727 (1.375,55.350) Poor OS HR (univariate) 6
Tabouret 2013 France 26 II-IV Blood ELISA 3.170 (1.193, 8.422) Poor OS HR (multivariate) 7
Jensen 2013 USA 18 III-IV Tumor tissues ELISA 0.460 (0.160, 1.373) NS PFS HR (univariate) 6
Krauze 2013 USA 202 IV Urine ELISA 1.001 (0.998, 1.005) NS PFS HR (multivariate) 3
Shin 2013 Korea 67 IV Tumor tissues IHC 1.550 (0.790, 3.020) NS PFS HR (multivariate) 5
Tabouret 2013 France 26 II-IV Blood ELISA 2.822 (1.088, 7.321) Poor PFS HR (multivariate) 5
Chinorean 2014 Romania 14 IV Blood ELISA 2.340 (0.580, 9.440) NS OS HR (NA) 6
Nambirajan 2014 India 126 I-III Tumor tissues IHC 1.200 (0.300, 4.200) NS OS HR (multivariate) 6
Clara 2014 Brazil 208 IV Tumor tissues IHC 1.940 (1.223, 3.078) Poor OS HR (multivariate) 7
Takano 2014 Japan 37 III-IV Blood ELISA 3.480 (1.546, 7.840) Poor OS Survival Curve 6
McLeNAon 2015 USA 22 I-III Tumor tissues IHC 1.038 (1.010, 1.068) Poor PFS HR (univariate) 4
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*

NA for not applicable, NS for not significant.

Meta-analysis

Thirty-one studies provided the sufficient data evaluable for OS in this meta-analysis. VEGF positive expression conferred the poor OS for glioma patients with HR = 1.647 (Z=4.48; Figure 2). The heterogeneity was found for the pooled HR for OS (I2 = 72.3%, P < 0.001). However, the prognostic effect of VEGF positive expression for glioma patients was not significant in PFS analysis group with the pooled HR of 1.021 (95% CI: 0.974~1.070, P = 0.393, Z = 0.860; I2 = 72.6%, P=0.006; Figure 3) with 5 studies included. Additionally, we divided patients into different subgroups for OS classified by region, case number, specimen type, VEGF test method and statistical method. The combined HR was 1.492 (95% CI: 1.206~1.844, P < 0.001) and 2.191 (95% CI: 1.638~2.929, P < 0.001) in the Asian studies and American studies, respectively. Next, we divided the studies into two groups depending on the case number less or more than sixty. VEGF positive expression was a valuable prognostic marker in both small sample sizes (N ≤ 60, HR = 2.398, 95% CI: 1.547~3.716, P < 0.001) and large sample sizes (N > 60, HR = 1.395, 95% CI: 1.081~1.799, P = 0.011). The subgroup analyses were proceeded among the specimen types. In the tumor tissues and blood sample group, the combined HRs were 1.547 (95% CI: 1.242~1.928, P < 0.001) and 3.155 (95% CI: 1.784~5.579, P < 0.001), respectively. VEGF expression was also considered as a prognostic factor according to different VEGF test methods. Significant impacts of VEGF expression were observed in IHC group (HR = 1.394, 95% CI: 1.112~1.747, P = 0.004), RT-PCR group (HR = 2.286, 95% CI: 1.175~4.446, P = 0.015) and ELISA group (HR = 3.447, 95% CI, 1.999~5.942, P < 0.001), respectively. Finally, we also found an inverse effect of VEGF expression on OS according to the statistical methods. The pooled HRs were 1.797 (95% CI: 1.275~2.533, P = 0.001), 2.633 (95% CI: 1.455~4.763, P = 0.001) and 3.359 (95% CI: 2.147~5.256, P < 0.001) in the group providing survival curve, original data and univariate HR, respectively (Table 2).

Figure 2.

Figure 2

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A Forest Plot of the Combined Relative HR form Random-effect OS.

Figure 3.

Figure 3

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A Forest Plot of the Combined Relative HR form Random-effect PFS.

Table 2.

Summarized HRs of subgroup analyses for OS in the meta-analysis

Stratified analysis Study (N) HR (95% CI) P Z Heterogeneity
I2 P Statistical model
Region
    Asia 17 1.492 (1.206, 1.844) 0.000 3.690 34.9% 0.077 Fixed-effects model
    Europe 8 1.218 (0.893, 1.663) 0.213 1.250 69.0% 0.002 Random-effects model
    America 5 2.191 (1.638, 2.929) 0.000 5.290 54.8% 0.065 Fixed-effects model
Case number
    Small 15 2.398 (1.547, 3.716) 0.000 3.910 72.2% 0.000 Random-effects model
    Large 16 1.395 (1.081, 1.799) 0.011 2.560 57.5% 0.002 Random-effects model
Specimen type
    Tumor tissues 28 1.547 (1.242, 1.928) 0.000 3.890 70.9% 0.000 Random-effects model
    Blood 3 3.155 (1.784, 5.579) 0.000 3.950 0.0% 0.890 Fixed-effects model
Assay
    IHC 22 1.394 (1.112, 1.747) 2.880 0.004 61.5% 0.000 Random-effects model
    RT-PCR 4 2.286 (1.175, 4.446) 2.440 0.015 64.8% 0.037 Random-effects model
    ELISA 4 3.447 (1.999, 5.942) 4.450 0.000 0.0% 0.730 Fixed-effects model
Method
    Survival curve 6 1.797 (1.275, 2.533) 0.001 3.350 33.7% 0.183 Fixed-effects model
    Original data 4 2.633 (1.455, 4.763) 0.001 3.200 57.9% 0.068 Fixed-effects model
    HR (multivariate) 15 1.246 (0.994, 1.562) 0.057 1.910 65.4% 0.000 Random-effects model
    HR (univariate) 4 3.359 (2.147, 5.256) 0.000 5.300 11.1% 0.338 Fixed-effects model
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Heterogeneity analysis results

Significant heterogeneities were found in the analysis between VEGF and OS and PFS (I2 = 72.30%, P < 0.001 and I2 = 72.60%, P = 0.006, respectively). In the subgroup analyses for OS, the heterogeneities were reduced in the Asian and American studies (I2 = 34.9%, P = 0.077 and I2 = 54.8%, P = 0.065). The pooled HR in blood tissue group did not show obvious heterogeneity (I2 = 0.0%, P = 0.890). The pooled HR also reached low heterogeneity in ELISA group (I2 = 0.0%, P = 0.730). Moreover, heterogeneity was not noticeable in the group of survival curve group, original data and univariate HR (I2 = 33.7%, P = 0.183; I2 = 57.9%, P = 0.068 and I2 = 11.1%, P = 0.338) (Table 2).

Publication bias

The Funnel plot and Begg’s test did not show any evidence of publication bias (P = 0.507 for OS, P = 1 for PFS; Figures 4 and 5).

Figure 4.

Figure 4

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A Funnel Blot for the publication bias test of OS studies.

Figure 5.

Figure 5

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A Funnel Blot for the publication bias test of PFS studies.

Correlation of VEGF overexpression with the pathological grade of gliomas

The Chi-square test was applied to analyze the relationship between VEGF expression and glioma pathological grade. The results revealed that VEGF positive rate was higher in high-grade glioma (70.27%) than that in low-grade glioma (39.54%, Z = -8.199, P < 0.005). VEGF expression was shown to be positively relevant with pathological grade of gliomas by spearman’s rank correlation (r = 0.307, P < 0.005) (Table 3).

Table 3.

Correlation of VEGF expression with the pathological grade of gliomas from the available included studies

Study Positive Negative Chi-square test Spearman
Low-grade High-grade Low-grade High-grade
Zhong 2001 24 15 11 0
Cheng 2006 8 37 8 7
Zeng 2009 2 38 6 10
Yoo 2010 2 31 12 31
El-Sayed 2010 8 14 3 1
Fan 2012 2 27 7 16
Cao 2013 3 18 2 4
Xu 2013 36 56 20 4
Nambirajan 2014 18 25 58 25
Tabouret 2014 18 25 58 23
Total 121 286 185 121 Z = -8.199, P < 0.001 r = 0.307, P < 0.001
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Discussion

VEGF, with a molecular weight of 38.2 kDa, is involved in triggering the process of angiogenesis in neoplasia. VEGF also stimulates capillary permeability, angiogenesis and endothelial cell growth [50]. Glioma, a highly vascularized tumor, develops with progressive angiogenesis. VEGF has been indicated to be a potential biomarker in serum/plasma and cerebrospinal fluid of glioma patients [51,52]. VEGF upregulation also increases cell density, leading to the tumor hypoxia [53]. VEGF, induced by hypoxia via HIF-1a, acts as a central proangiogenic factor in blood vessel formation by stimulating VEGFR-2/KDR in glioma [54]. Additionally, the prognostic value of VEGF upregulation for survival in glioma patients was described previously [55,56]. However, other researchers argued that VEGF could not be an independent prognostic factor for the survival of glioma patients [57,58]. Hence, well-designed studies with large sample size are still needed to provide strong evidence to explore the prognostic value of VFGF for the glioma patients.

Our study suggested that higher positive rate of VEGF expression was found in the group of high-grade glioma compared to the low-grade glioma. Previous studies also reported that VEGF upregulation was involved in the angiogenesis and progression of glioma, suggesting the participation of VEGF in positive regulation of neoangiogenesis and proliferation during gliomagenesis [59,60]. Consistent with the previous researches, our study revealed that VEGF expression was significantly related to glioma pathological grade by analyzing the 713 cases available in the included studies.

In this meta-analysis, our result implied that VEGF overexpression was notably associated with OS of glioma patients. Of note, the subgroup analyses also proved the strong prognostic relevance of VEGF overexpression on OS of glioma patients. Contrary to our expectation, no significance was found between VEGF overexpression and PFS of glioma patients. The main reason might probably be that too few studies were available reporting the relation between VEGF expression and PFS of glioma patients. Hence, larger cohort would be required to investigate the impact of VEGF on PFS of gliomas.

Nevertheless, several limitations did exist in the present meta-analysis. Firstly, we only included English and Chinese studies in this meta-analysis while the coincident studies in other languages were not included. Secondly, another key bias might be extrapolating the HRs and the 95% CI by different methods. When the studies did not provide the HRs, we extrapolated HRs from the survival curves or estimated them from the sufficient data, which might be less reliable than the ones obtained from published statistics. Thirdly, journals often favor studies with positive results, whereas the negative results might not be showed in publications. Fourthly, we did not exclude the studies with limited numbers of cases, which might be an important bias since the small scale probably did not provide reliable evidences for this analysis.

In conclusion, this meta-analysis was the first one to yield the association between VEGF expression and the survival of glioma patients. The VEGF overexpression is significantly associated with the OS of the glioma patients but not with PFS. Moreover, we infer the significant association between VEGF expression and glioma pathological grade in a large number of cases included. Our study suggests that VEGF shows the significant relevance to glioma pathological grade and might be a valuable prognostic factor for the OS of the patients with glioma. Nevertheless, well-designed studies with larger cohort are needed to explore the valuable role of VEGF in glioma.

Acknowledgements

The study was supported by the fund of Guangxi Zhuang Autonomous Region University Student Innovative Plan (No. 201410598026), China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper.

Disclosure of conflict of interest

None.

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