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. 2013 Dec 18;8(12):e83441. doi: 10.1371/journal.pone.0083441

The Association between Hypoxia-Inducible Factor-1 α Gene C1772T Polymorphism and Cancer Risk: A Meta-Analysis of 37 Case-Control Studies

Pengfei He 1,#, Qi Han 1,#, Jiajia Liu 1, Dongjuan Liu 1, Xin Zhao 1, Ting Hu 1, Lu Jiang 1, Hongxia Dan 1, Xin Zeng 1, Jing Li 1,*, Jiayi Wang 2,*, Qianming Chen 1
Editor: Hiromu Suzuki3
PMCID: PMC3867430  PMID: 24367595

Abstract

Background

The possible association between HIF-1α C1772T polymorphism and cancer risk has been studied extensively. However, the results were controversial. In order to get a more precise conclusion of this association, a meta-analysis was performed.

Methods

A total of 10186 cases and 10926 controls in 37 case-control studies were included in this meta-analysis. Allele and genotypic differences between cases and controls were evaluated. Subgroup analysis by cancer site, ethnicity, source of controls and gender was performed.

Results

The T allele of HIF-1α gene C1772T was significantly associated with increased cancer risk in three genetic models: TT+CT vs.CC (dominant model OR=1.23, 95%CI=1.03-1.47), TT vs. CT+CC (recessive model OR=2.51, 95%CI=1.54-4.09), TT vs. CC (homozygote comparison OR=2.02, 95%CI=1.21-3.39).In subgroup analysis, the frequency of the T variant was found to be significantly increased in cervical cancer, pancreatic cancer, head and neck cancer, renal cell carcinoma, Asian and female subgroups.

Conclusions

Our meta-analysis suggests that the substitution of C allele with T at HIF-1α gene C1772T polymorphism is a risk factor of cancer, especially for cervical, head and neck cancer, pancreatic cancer and renal cell carcinoma. It is also a risk factor of cancer in Asian group as well as in female group.

Introduction

Cancer is a multifactor disease which results from complex mutual effect between environmental and genetic factors. It has become one of the most challenging health issues today [1]. Hypoxia of cancer tissue is a hallmark of solid cancer. A hypoxic microenvironment initiates multiple cellular responses, such as proliferation and angiogenesis, resulting in the development and progression of cancer [2].

Hypoxia-inducible factor-1 (HIF-1) is a key transcription factor that regulates cellular reaction to hypoxia. It is over expressed in most solid tumors in response to low oxygen concentrations [3]. HIF-1 is a heterodimeric transcription factor consisting of two subunits, HIF-1α and HIF-1β. HIF-1α is the oxygen-regulating factor that determines HIF-1 activity. HIF-1α has the ability to induce the expression of genes whose products contribute to metabolic reprogramming, angiogenesis and metastasis. High HIF-1α levels have been associated with poor prognosis in most cancers [4–8], including epidermal carcinogenesis [9,10].

There is an important single nucleotide polymorphism (SNP) of human HIF-1α gene at 1772, the wild type cytosine (C) allele is replaced by a thymine (T) allele (named C1772T or P582S or rs11549465). This replacement leads to an amino acid substitution of proline with serine. The role of this polymorphism in various types of cancer has been widely investigated. It was found that C1772T status was associated with susceptibility to various cancer types. It was also suggested that genotypes with T allele (TT or CT) had significantly higher transcription activities of HIF-1α gene compared with wild CC genotype [3,11]. Moreover, this polymorphism may also be associated with lymph node or distal metastasis [12–14].

The possible association between HIF-1α C1772T and cancer risk has been investigated in several studies, but the results were inconclusive or even contradictory [15-39]. Although a meta-analysis on HIF-1α polymorphisms and cancer risk was conducted by Tongfeng Zhao et al [40] in 2009, it had several limitations. The sample size of this study was relatively limited (4131 cancer cases and 5387 controls). In the past 3 years, several studies on this topic have been published and a large number of cases have been accumulated. Furthermore, the genotype and allele analysis was incomplete. In order to get a more precise conclusion of the association between HIF-α C1772T and cancer risk, we conducted a meta-analysis of all eligible case-control studies.

Methods

Literature search and data extraction

The methods used for literature search and data extraction has been described elsewhere [41]. In brief, With the term “(cancer or carcinoma) and (Hypoxia-inducible factor-1α or HIF-1α) and (Polymorphism or mutation or variant)”, we found 35 reference in the PubMed, Embase and China National Knowledge Infrastructure (CNKI) database that were published after the first report on HIF-1α(last update: August 23 2013). Selection criteria of an eligible study were (a) investigation of the polymorphisms C1772T of HIF-1α and cancer risk; (b) use of a case-control design based on unrelated individuals and (c) sufficient genotype distributions for cases and controls so that an odds ratio (OR) with 95% confidence interval (CI) could be assessed. If more than one article was published using the same patient population, only the latest or the largest study would be used in our meta-analysis.

The data, including publish year, first author, original country, ethnicity, gender and cancer site (those cancer sites only exist in one article were divided into “other site group”), case number and control number, source of control (population-based or hospital-based) and genotyping method were independently extracted by two investigators and reached conformity on all items through consultation.

Statistical study

Crude odds ratios (ORs) and 95% confidence intervals (95% CIs) were used to estimate the strength of association between the C1772T polymorphism and cancer risk or lymph node metastasis and they were determined by Z-test. The pooled ORs were performed for the genetic models including dominant model (TT+CT vs.CC), recessive model (TT vs. CT+CC), homozygote comparison (TT vs. CC) and heterozygote comparison (CT vs. CC). The statistical significance of OR was analyzed by Z test, P < 0.05 was considered statistically significant.

In addition to the comparison among all subjects, we also performed stratification analyses by cancer site, ethnicity, source of controls and gender.

Heterogeneity was evaluated by a chi square-based Q statistic, and statistical significance was assumed for P value less than 0.05. A fixed-effect model was used when P heterogeneity < 0.05, otherwise a random effect model was used. The sensitivity analysis was performed by excluding those studies which did not follow HWE (Hardy-Weinberg equilibrium) to assess the stability of the current analysis [42].

The possible publication bias was examined visually by the Begg’s funnel plot and the degree of asymmetry was tested by Egger’s test.

All of the statistical tests were performed by STATA11.0.

Results

Characteristics of studies included

Basic characteristics of studies

463 relevant publications were identified after initial screening based on our search criteria (up to August 23 2013). Among these, 46 articles were subjected to further examination after reading the titles and abstract. 9 articles were excluded for not relevant to C1772T. 3 articles were excluded for not reporting allele frequency. (Figure 1 ) Finally, a total of 37 case-control studies from 35 articles that met the selection criteria (Table 1 ) and totally 10186 cases and 10926 controls were used in the pooled analysis. Out of those 37 studies, 6 studies focused on head and neck cancer, 6 on prostate cancer, 6 on breast cancer, 3 on cervical cancer, 3 on lung cancer, 3 on renal cell carcinoma, 2 on colorectal cancer, 2 on pancreatic cancer and one, respectively, on endometrial, gastric, hepatocellular, ovarian cancer, esophageal squamous cell carcinoma and glioma. Among the 35 articles, 1 article provided data of the C1772T polymorphism on three kinds of cancers (cervical cancer, endometrial cancer and ovarian cancer) (Konac et al., 2007）. Then, each type of cancer in the study was treated as a separate study in the meta-analysis. The ethnicities studied were Caucasian (13 articles), Asian (18 articles), mixed population (3 articles) and Brazilian (1 article). The gender studied were female (9 articles), male (6 articles) and mixed (20 articles).

Figure 1. Flow chart for the process of selecting associated publications.

Figure 1

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Table 1. Characteristics of populations and cancer types of the studies included in the meta-analysis.
Author Year Country Ethnicity Cancer site Case-control Gendera Source ofcontrol Genotypingmethod HWE
Ribeiro AL 2013  Portugal Caucasian Breast cancer 96/74 Female HB PCR–RFLP 0
Alves LR 2012 Brazil Brazilian Oral squamous cell carcinoma 88/40 Mixed PB PCR–RFLP 0
Zagouri F 2012 Greece Caucasian Breast cancer 113/124 Female PB PCR–RFLP 0.41
Li P 2012 China Asian Prostate cancer 662/716 Male PB TaqMan 0.27
Mera-Menendez F 2012 Spain Caucasian Glottic cancer 121/154 Mixed PB PCR–RFLP 0.01
Ruiz-Tovar J 2012 Spain Caucasian Pancreatic cancer 59/159 Mixed PB PCR-RFLP 0.01
Kuo WH 2012 China Asian Non-small-cell lung cancer 285/300 Mixed PB PCR–RFLP 0.13
Qin C 2012 China Asian Renal cell carcinoma 620/623 Mixed PB TaqMan 0.22
Wang X 2011 China Asian Pancreatic cancer 263/271 Mixed PB PCR-SSCP 0.35
Putra AC 2011 Japan Asian Lung cancer 83/110 Mixed PB PCR-SSCP 0.55
Kim YH 2011 Korea Asian Cervical cancer 199/215 Female PB PCR-RFLP 0.32
Xu G 2011 China Asian Glioma 150/150 Mixed HB PCR-RFLP 0.35
Chai D 2010 China Asian Cervical cancer 97/117 Female HB PCR-RFLP 0.52
Shieh TM 2010 China Asian Oral squamous cell carcinoma 305/96 Mixed PB PCR-RFLP 0.71
Hsiao PC 2010 China Asian Hepatocellular carcinoma 102/347 Mixed HB PCR-RFLP 0.72
Chen MK 2009 China Asian Oral cancer 174/347 Mixed PB PCR-RFLP 0.72
Naidu R 2009 Malaysia Asian Breast cancer 410/275 Female PB PCR-RFLP 0.92
Foley R 2009 Ireland Caucasian Prostate cancer 95/196 Male PB Sequencing 0.62
Konac E 2009 Turkey Caucasian Lung Cancer 141/156 Mixed HB PCR-RFLP 0.34
Li K 2009 China Asian Gastric Cancer 87/106 Mixed PB PCR 0.51
Munoz-Guerra MF 2009 Spain Caucasian Oral Cancer 74/139 Mixed PB PCR-RFLP 0.01
Kim HO 2008 Korea Asian Breast cancer 90/102 Female PB PCR 0.64
Jacobs EJ 2008 USA Mixed Prostate cancer 1425/1453 Male PB TaqMan 0.04
Apaydin I 2008 Turkey Caucasian Breast Cancer 102/102 Female PB PCR-RFLP 0.42
Lee JY 2008 Korea Asian Breast cancer 1930/1677 Female PB PCR-RFLP 0.25
Li H 2007 USA Mixed Prostate Cancer 1072/1271 Male PB PCR-RFLP 0.16
Konac E-O 2007 Turkey Caucasian Ovarian 49/107 Female PB PCR-RFLP 0.23
Konac E-C 2007 Turkey Caucasian Cervical 32/107 Female PB PCR-RFLP 0.23
Konac E-E 2007 Turkey Caucasian Endometrial 21/107 Female PB PCR-RFLP 0.23
Orr-Urtreger A 2007 Israel Caucasian Prostate Cancer 402/300 Male PB PCR-SSCP 0.14
Fransen K 2006 Sweden Caucasian Colorectal Cancer 198/258 Mixed PB PCR-RFLP 0.92
Ling TS 2005 China Asian Esophageal squamous cell carcinoma 95/104 Mixed HB PCR-RFLP 0.57
Chau CH 2005 USA Mixed Prostate cancer 196/196 Male PB Sequencing 0.01
Kuwai T 2004 Japan Asian Colorectal cancer 100/100 Mixed PB PCR-SSCP 0.56
Ollerenshaw M 2004 UK Caucasian Renal cell carcinoma 160/288 Mixed PB PCR-SSCP 0
Tanimoto K 2003 Japan Asian Head and neck squamous cell carcinoma 55/110 Mixed PB Sequencing 0.55
Clifford SC 2001 UK Caucasian Renal cell carcinoma 35/143 Mixed HB PCR-SSCP 0.02
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a Mixed means samples contain both female and male;

PB, public based; HB, hospital based; HWE, Hardy-Weinberg equilibrium; PCR, polymerase chain reaction; RFLP, restriction fragment length polymorphism; TaqMan, TaqMan SNP Genotyping Assays.

Characteristics of allele average frequency

In the control group, T allele average frequency of HIF-1α C1772T among Asian populations (0.06) were lower than Caucasian populations (0.18) and it has statistical significance for P=0.004. (Figure 2 )

Figure 2. T allele frequency of HIF-1α C1772T among control subjects stratified by ethnicity.

Figure 2

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Meta-analysis

Aggregated ORs and heterogeneity test result for the association between C1772T and cancer risk was shown in Table 2 and the result of TT+CT vs.CC (dominant model) was shown in Figure 3 -Figure 7.

Table 2. Associations between the C1772T polymorphism and cancer risk.

Variables TT+CT VS CC
 TT VS CT +CC
 TT VS CC
 CT VS CC
na OR(95%CI) P b na OR(95%CI) P b na OR(95%CI) P b na OR(95%CI) P b
Overall 36 1.23(1.03-1.47) 0 26 2.51(1.54-4.09) 0 25 2.02(1.21-3.39) 0 36 1.16(0.97-1.38) 0
Overall for HWEd 28 1.31(1.08-1.60) 0 17 2.77(1.59-4.82) 0.01 17 3.04(1.62-5.69) 0 28 1.24(1.03-1.49) 0
cancer cite
Breast cancer 6 1.12(0.87-1.52) 0 5 1.64(0.56-4.77) 0.11 5 1.69(0.56-5.14) 0.09 6 1.10(0.83-1.46) c 0.16
Cervical cancer 3 1.81(0.79-4.10) 0.01 2 8.80(2.31-33.52) c 0.24 2 11.49(2.21-59.67) c 0.16 3 1.47(0.79-2.74) c 0.29
Colorectal cancer 2 0.26(0.01-5.09) 0.03 1 1.97(0.33-11.90) 0 1 1.91(0.32-11.58) 0 2 0.25(0.01-4.69) 0
Head and neck cancer 5 1.20(0.87-1.67) c 0.48 4 11.29(1.24-103.02) c 0.79 3 2.24(1.14-4.39) c 0.72 5 1.03(0.69-1.62) c 0.92
Lung cancer 3 1.19(0.51-2.76) 0 2 1.39(0.09-21.85) 0.07 2 1.42(0.07-29.73) c 0.05 3 1.13(0.59-2.19) c 0.16
Pancreatic cancer 2 1.39(0.54 -3.56) 0.03 1 4.13(1.57-10.86) 0 1 3.39(1.28-8.97) 0 2 0.51(0.02-11.53) 0
Prostate cancer 6 1.36(0.95-1.96) 0 5 1.31(0.54-3.18) 0.01 5 1.34(0.54-3.30) 0.01 6 1.34(0.93-1.92) 0.03
Renal cell carcinoma 3 0.46(0.13-1.60) 0.01 3 1.55(1.02-2.37) c 0.47 3 0.29(0.06-1.45) c 0.21 3 0.44(0.11-1.69) c 0.42
Other cancer 6 1.46(0.72-2.96) 0 3 3.98(0.69-22.67) 0.14 3 5.04(0.47-54.13) 0.04 6 1.39(0.72-2.68) c 0.33
Subgroup by ethnicity
Caucasian 15 1.13(0.78-1.65) 0 13 2.20(1.28-3.78) 0.01 13 1.78(0.80-3.94) c 0.95 15 0.98(0.65-1.48) 0.01
Asian 18 1.34(1.08-1.67) 0 9 3.71(2.26-6.08) c 0.97 9 4.20(2.55-6.92) c 0.95 18 1.27(1.04-1.56) 0.01
control source
PB 29 1.21(0.99-1.47) 0 21 2.92(1.71-4.99) 0 20 2.26(1.27-4.03) 0 29 1.12(0.92-1.36) 0.01
HB 7 1.33(0.87- 2.03) 0.03 5 1.15(0.39-3.34) c 0.26 5 1.21(0.39-3.74) c 0.21 7 1.35(0.91-2.00) c 0.08
Gender
Female 11 1.34(0.97-1.86) 0 9 3.17(1.27-7.91) 0.01 9 3.72(1.29-10.74) 0 11 1.23(0.93-1.62) 0.02
Male 6 1.36(0.95-1.96) 0 5 1.31(0.54-3.18) 0.01 5 1.34(0.54-3.30) 0.01 6 1.34(0.93-1.92) 0.03
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a Number of comparisons.

b P value of Q-test for heterogeneity test.

c Fixed-effects model was used when P value for heterogeneity test≥0.05; Otherwise, Random-effects model was used.

d data after excluding those studies’ controls not in Hardy-Weinberg equilibrium.

Figure 3. Forest plot of cancer risk associated with C1772T (TT +CT vs. CC). The squares and horizontal lines correspond to the OR and 95%CI. The area of the squares indicates the study-specific weight.

Figure 3

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Figure 4. Forest plot of cancer risk associated with C1772T (TT +CT vs. CC) stratified by cancer site.

Figure 4

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Figure 5. Forest plot of cancer risk associated with C1772T (TT +CT vs. CC) stratified by ethnicity.

Figure 5

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Figure 6. Forest plot of cancer risk associated with C1772T (TT +CT vs. CC) stratified by source of control.

Figure 6

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Figure 7. Forest plot of cancer risk associated with C1772T (TT +CT vs. CC) stratified by gender.

Figure 7

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Overall, the C1772T was found to be significantly associated with increased cancer risk for three genetic models: TT+CT vs.CC (dominant model OR=1.23, 95%CI=1.03-1.47), TT vs. CT+CC (recessive model OR=2.51, 95%CI=1.54-4.09), TT vs. CC (homozygote comparison OR=2.02, 95%CI=1.21-3.39).

As for cancer site subgroup, significant association exists in the following four cancer sites: Cervical cancer, for TT vs. CT+CC (OR=8.80, 95%CI=2.31-33.52) and TT vs. CC (OR=11.49, 95%CI=2.21-59.67). Pancreatic cancer, for TT vs. CT+CC (OR= 4.13, 95%CI= 1.57-10.86) and TT vs. CC (OR= 3.39, 95%CI= 1.28-8.97). Head and neck cancer, for TT vs. CT+CC (OR= 11.29, 95%CI= 1.24-103.02) and TT vs. CC (OR= 2.24, 95%CI= 1.14-4.39). Renal cell carcinoma for TT VS CT +CC (OR=1.55, 95%CI=1.02-2.37).

As for ethnicity subgroup, significant association exists among Asian population for TT+CT vs.CC (OR=1.34, 95%CI=1.08-1.67), TT vs. CT+CC (OR=3.71, 95%CI=2.26-6.08), TT vs. CC (OR=4.20, 95%CI=2.55-6.92) and CT vs. CC (OR=1.27, 95%CI=1.04-1.56). However, in Caucasian subgroup, significant increased risk only exist for TT vs. CT+CC (OR=2.20, 95%CI=1.28-3.78) and not for other genetic models.

As for gender subgroup, significant association exists in female group for TT vs. CT+CC (OR=3.17, 95%CI=1.27-7.91) and TT vs. CC (OR=3.72, 95%CI=1.29-10.74).

Heterogeneity analysis and Sensitivity analysis

Significant heterogeneity was found in overall comparisons under all four genetic models (dominant model P<0.01, recessive model P<0.01, homozygote comparison P<0.01 and heterozygote comparison P<0.01).

As for eight studies included in our meta-analysis which did follow HWE (Table 1 ), we performed the sensitivity analysis by excluding those studies to assess the stability of the current analysis. All the obtained results were similar except for CT vs. CC genetic model (OR=1.24, 95%CI=1.03-1.49). (Table 2 )

Publication bias

The shape of the funnel plots did not reveal any evidence of obvious asymmetry (Figure 8 ), suggesting that there was no obvious publication bias. Egger’s test was used to provide further statistical evidence; similarly, the results showed no significant publication bias in this meta-analysis (t =0.69, P = 0.50 for TT+CT vs. CC).

Figure 8. Funnel Plot for Publication Bias of the C1772T Polymorphism.

Figure 8

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Each point represents a separate study for the indicated association. Log (OR): natural logarithm of OR. Horizontal line: mean effect size.

Discussion

HIF-1 is a key transcription factor that regulates cellular reaction to hypoxia and is over expressed in most solid tumors in response to low oxygen concentrations [3]. It has the ability to influence metabolic reprogramming, angiogenesis, metastasis and recent studies found that HIF-1α was associated with many kinds of cancer [3,6,7].

HIF-1 gene polymorphisms have been investigated for a possible role in mediating genetic predisposition to cancer[34,43,44] and C1772T is one of important SNP of human HIF-1α gene [3]. The possible association between C1772T and cancer risk has been studied by several investigators, but the results were inconsistent. Meta-analysis is a powerful tool for summarizing the results from different studies which provide more reliable results than a single case-control study [34].

In this meta-analysis we investigated the association between C1772T and cancer risk. Totally 10186 cases and 10926 controls were included and subgroup analysis by cancer site, ethnicity, source of controls and gender was performed.

In overall meta-analysis, the T allele of C1772T was found to be significantly associated with an increased cancer risk in all four genetic models. The T allele of C1772T may influence the development of cancer through the following mechanisms. First, it may increase the transactivation capacity of HIF-1α. As a result, HIF-1α-regulated genes expression increased, leading to enhanced angiogenesis [3]. Also, Fu et al found that T allele of C1772T may enhance the HIF-1α stability that increases tumor susceptibility [45].

As for the subgroup analysis by cancer site, the T allele of C1772T influence outcome varied in different cancer sites. Obvious association between T allele of C1772T and increased cancer risk exists in cervical cancer, pancreatic cancer and head and neck cancer, while not associated with colorectal cancer, lung cancer, breast cancer, prostate cancer and other cancers. Cancer of different sites has variant tumor microenvironment that regulates or influences the gene expression profiles. The same polymorphism may exert different effects in variant cancers [46,47]. However, all those results should be treated with reservation as there were only 2 or 3 studies included for some cancer site subgroup which may reduce the results’ reliability.

In the subgroup of ethnicity, significantly increased cancer risks with T allele of C1772T were found in Asian population under all four genetic models. As for Caucasian subgroup, significant increased risk only exists under recessive model. Possible explanations were as follows, First, genetic background varies among different ethnicities [48], as listed in the result part, the T allele of C1772T between Asian and Caucasian was statistically different (P=0.004). Second, the outer environment and life-styles were different between Asian and Caucasian which may influence the causal and development of cancer. Third, relatively deficient studies and sample size may limit the districts and kinds of ethnicities in our analysis and could enhance the bias in our analysis.

As for subgroup of gender, we found significantly increased cancer risk with T allele of C1772T in female subgroup instead of male subgroup. This may be explained by the following reasons. First, estrogen and progestin can enhance the expression of HIF-1α via Akt signaling pathway which could cause cancer cell angiogenesis or invasion [49,50]. Second, only female-specific cancer (breast cancer, ovarian cancer, cervical cancer and endometrial cancer) were included in female subgroup, while no data reported non-female-specific cancer such as lung cancer. Third, female subgroup contains 5932 samples, while totally 21112 samples were included in this meta-analysis. As a result, the overall results may be influenced by gender difference. Therefore, more studies on C1772T and non-gender-specific cancer risk among different genders are called for in the future.

In this study, obvious heterogeneity was found in all four genetic models under both overall and subgroup analysis. Totally eight studies included in our meta-analysis did not follow HWE. Deviation from HWE reflects potential mistakes existing in those eight studies, such as laboratory or genotyping errors, population stratification or selection bias in the choice of controls [42,51]. The sensitivity analysis was performed by excluding those 9 studies deviated from HWE to assess the stability of the current analysis. All the obtained results were similar except for the CT vs. CC genetic model. The varying result of association between C1772T heterozygote and cancer risk calls for more related case-control studies.

The current meta-analysis has several limitations which should be noted. First, although 34 studies and totally 21112 samples were included in our study, the sample size for some subgroup analysis was limited which could increase the likelihood of type I and type II errors. Second, cancer is the result of the influence of genetic and environmental factors and the latter was not analyzed in this study. Therefore, it is necessary to assess the roles of some environment factors such as diet, life style, tobacco and so on. Third, publication bias was not avoidable although our funnel plot and Egger’s test did not show any bias, as for positive results were much more likely to be published.

Despite those limitations above, our meta-analysis also has some advantages. First, it contains the latest data about association between C1772T of HI F-1α gene polymorphism and cancer risk. Second, we conducted four types of genotype analysis and subgroup analysis by cancer site, ethnicity, source of controls and gender.

Conclusions

Our meta-analysis suggests that the substitution of C to T of HIF-1α gene C1772T polymorphism is a risk factor of cancer, especially for cervical, head and neck cancer, pancreatic cancer and renal cell carcinoma. Also, a significant increase in cancer was observed in Asian group as well as in female group.

Supporting Information

Figure S1

Preferred reporting items for systemic reviews and meta-analysis (PRISMA) flow chart.

(DOC)

Click here for additional data file. (39.5KB, doc)
Checklist S1

PRISMA check list.

(DOC)

Click here for additional data file. (68.5KB, doc)

Funding Statement

This work was supported by grants from the National Natural Science Foundation of China (No. 81061120531, 81200791, 30930100, 81102060, and 81001208), the International Science and Technology Cooperation Program of China (No. 2012DFA31370), and Doctoral Program of the Ministry of Education of China (No. 20110181110055, 20100181120057). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Associated Data

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Supplementary Materials

Figure S1

Preferred reporting items for systemic reviews and meta-analysis (PRISMA) flow chart.

(DOC)

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Checklist S1

PRISMA check list.

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