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Open Access

Peer-reviewed

Research Article

HIF-1α 1772 C/T and 1790 G/A Polymorphisms Are Significantly Associated with Higher Cancer Risk: An Updated Meta-Analysis from 34 Case-Control Studies

  • Xi Yang ,

    Contributed equally to this work with: Xi Yang, Hong-Cheng Zhu

    Affiliation Department of Radiation Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    ⨯
  • Hong-Cheng Zhu ,

    Contributed equally to this work with: Xi Yang, Hong-Cheng Zhu

    Affiliation Department of Radiation Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    ⨯
  • Chi Zhang,

    Affiliation Department of Radiation Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    ⨯
  • Qin Qin,

    Affiliation Department of Radiation Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    ⨯
  • Jia Liu,

    Affiliation Department of Radiation Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    ⨯
  • Li-Ping Xu,

    Affiliation Department of Radiation Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    ⨯
  • Lian-Jun Zhao,

    Affiliation Comprehensive Cancer Center, Nanjing Drum Tower Hospital and Clinical College of Nanjing Medical University, Nanjing, China

    ⨯
  • Qu Zhang,

    Affiliation Department of Radiotherapy, Second People's Hospital of Lian Yungang, Lian Yungang, China

    ⨯
  • Jing Cai,

    Affiliation Tumor Institute, Nantong Tumor Hospital, Nantong, China

    ⨯
  • Jian-Xin Ma,

    Affiliation Department of Radiotherapy, Second People's Hospital of Lian Yungang, Lian Yungang, China

    ⨯
  • Hong-Yan Cheng,

    Affiliation Department of Synthetic Internal Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    ⨯
  • Xin-Chen Sun

    sxcnjmu@163.com

    Affiliation Department of Radiation Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    ⨯

Abstract

Background

HIF-1 activates various genes in cancer progression and metastasis. HIF-1α 1772 C/T and 1790 G/A polymorphisms are reportedly associated with cancer risk; however, the results are inconclusive.

Methodology/Principal Findings

A meta-analysis of 34 studies that involved 7522 cases and 9847 controls for 1772 C/T and 24 studies that involved 4884 cases and 8154 controls for 1790 G/A was conducted to identify the association of C/T and G/A polymorphisms with cancer risk. Odds ratio (OR) and 95% confidence intervals (95% CI) were used to assess the strength of association.

HIF-1α 1772 C/T and 1790 G/A polymorphisms were associated with higher cancer risk in homozygote comparison (1772C/T: TT vs. CC: OR = 2.45, 95% CI: 1.52, 3.96; Pheterogeneity = 0.028; 1790G/A: AA vs. GG: OR=4.74, 95% CI: 1.78, 12.6; Pheterogeneity < 0.01), dominant model (1772C/T: TT/CT vs. CC: OR = 1.27, 95% CI: 1.04, 1.55; Pheterogeneity < 0.01, 1790G/A: AA/GA vs. GG: OR = 1.65, 95% CI: 1.05, 2.60; Pheterogeneity < 0.01), T allele versus C allele (T vs. C: OR = 1.42, 95% CI: 1.18, 1.70; Pheterogeneity < 0.01), and A allele versus G allele (A vs. G: OR = 1.83, 95% CI: 1.13, 2.96; Pheterogeneity < 0.01). On a subgroup analysis, the 1772 C/T polymorphism was significantly linked to higher risks for breast cancer, lung cancer, prostate cancer, and cervical cancer, whereas the 1790 G/A polymorphism was significantly linked to higher risks for lung cancer and prostate cancer. A significantly increased cancer risk was found in both Asians and Caucasians for 1772C/T polymorphism, whereas a significantly increased cancer risk was found in Caucasians in the heterozygote comparison and recessive model for 1790G/A polymorphism.

Conclusions

HIF-1α 1772 C/T and 1790 G/A polymorphisms are significantly associated with higher cancer risk.

Citation: Yang X, Zhu H-C, Zhang C, Qin Q, Liu J, Xu L-P, et al. (2013) HIF-1α 1772 C/T and 1790 G/A Polymorphisms Are Significantly Associated with Higher Cancer Risk: An Updated Meta-Analysis from 34 Case-Control Studies. PLoS ONE 8(11): e80396. https://doi.org/10.1371/journal.pone.0080396

Editor: Qing-Yi Wei, Duke Cancer Institute, United States of America

Received: June 27, 2013; Accepted: October 2, 2013; Published: November 18, 2013

Copyright: © 2013 Yang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by the Natural Science Foundation of China (number 81272504), the Innovation Team (number LJ201123 (EH11)), and Jiangsu Provincial Science and Technology Projects (BK2011854 (DA11)), and “333” Project of Jiangsu Province (BRA2012210 (RS12)), and research grants from Chinese Society of Clinical Oncology (T-H2010-033 (KA10)). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

INTRODUCTION

Cancer, which results from complex interactions between genetic and environmental factors, has become a challenging health problem. An increasing number of studies have been performed in the past few years to assess the relationship between genetic variation and cancer risk [1].

Oxygen (O2) concentration in tumor tissues is significantly lower than that in the surrounding normal tissues. Many studies have focused on hypoxia because of its function in maintaining tumor microenvironments [2]. Hypoxic tumor microenvironment initiates multiple cellular responses, such as proliferation and angiogenesis, triggering the development and progression of cancer. In general, hypoxia may regulate tumor cell phenotypes by altering genes that are sensitive to O2 pressure [3]. Studies have demonstrated that HIF-1 has an important function in the development and progression of cancer by activating various genes associated with angiogenesis, cell adhesion, erythropoiesis, and glucose transportation [4]. HIF-1 is a heterodimer consisting of an oxygen-sensitive subunit HIF-1α and a constitutively expressed subunit HIF-1β; it is degraded rapidly through the von Hippel–Lindau-mediated ubiquitin–proteasome pathway under normoxia conditions [5]. Recent studies have shown that HIF-1α is overexpressed in many human cancers with advanced tumor grade, suggesting that HIF-1α acts as an independent factor of cancer prognosis [6].

The human HIF-1α gene, which is located at chromosome 14q21–24, is composed of 15 exons. It codes for a 3919 bp cDNA and produces an 826 amino acid protein. Single nucleotide polymorphisms (SNPs) in coding regions can mediate amino acid changes and affect the structure and biological activity of the translated protein [7]. The most widely studied HIF-1α polymorphisms are 1772 C/T (Pro582Ser, rs11549465) and 1790 G/A (Ala588Thr, G1790A, rs11549467), which induce proline-to-serine and alanine-to-threonine amino acid substitutions, respectively. Both polymorphic variants can significantly elevate transcriptional activity than the wild type under both hypoxic and normoxic conditions in in vitro studies [8]. Moreover, both polymorphisms are associated with increased tumor microvessel density, thereby contributing to the development and progression of cancer [9].

HIF-1α 1772 C/T and 1790 G/A genetic polymorphisms were previously suggested to be responsible for the risk for various types of cancer. However, the results of epidemiological studies are inconsistent [10–12]. Thus, the relationship between HIF-1α polymorphisms and cancers requires further investigation. Accordingly, we performed a meta-analysis on eligible case–control studies to produce a more powerful estimation of the association of HIF-1α 1772 C/T and 1790 G/A polymorphisms with cancer risk.

Methods

Identification and eligibility of relevant studies

All studies published before June 26, 2013 that investigated the association of HIF-1α 1772 C/T and 1790 G/A polymorphisms with cancer risk were considered in this meta-analysis. A systematic search of literature was carried out using PubMed and Embase. The keywords used for the search were “hypoxia-inducible factor-1” or “HIF-1” concatenated with “SNP,” “polymorphism,” “mutation,” or “variant” and “tumor,” “cancer,” “carcinoma,” or “malignancy.” Only studies with complete data on the comparison of HIF-1α 1772 C/T or 1790 G/A polymorphisms between cancer patients and controls were selected. Case reports, animal studies, review articles, editorials, abstracts, reports with incomplete data, and studies based on pedigree data were excluded.

Data extraction

Two investigators (Yang and Zhu) independently reviewed the articles to exclude irrelevant and overlapping studies. The results were compared, and disagreements were resolved by discussion and consensus. We only included the publication that reported the most extensive information when overlapping articles were found. The following data were extracted for each study: first author, year of publication, country, ethnicity, control source, cancer type, Hardy–Weinberg equilibrium, and the number of cases and controls for each genotype.

Statistical analysis

STATA (version 11.0; StataCorp, College Station, Texas, USA) was used for the meta-analysis. All genotype models for the two HIF-1α polymorphisms were evaluated. We also conducted subgroup analyses by cancer type, ethnicity, and source of control. For cancer type subgroups, we included the subgroup that contained more than three studies.

The existence of heterogeneity between studies was ascertained by Q-statistic. The pooled odds ratio (OR) was estimated with models based on fixed-effects or random-effects assumptions. A random-effects model was used when the significant Q statistic (P < 0.1) indicated the presence of heterogeneity in the studies. Otherwise, a fixed-effects model was selected. The 95% confidence interval (CI) of OR was also calculated. The distribution of genotypes in the controls was checked for Hardy–Weinberg equilibrium. Studies with controls not in the Hardy–Weinberg equilibrium were subjected to sensitivity analysis.

The publication bias among the studies was assayed. Funnel plots of the HIF-1α 1772 C/T polymorphism for T allele versus C allele and the HIF-1α 1790 G/A polymorphism for A allele versus G allele were built to search for any evidence of publication bias. An assymetric funnel plot is indicative of publication bias, whereas a symmetric funnel plot implies the absence of publication bias. Egger’s test, estimated by MIX 1.7 software (Kitasato Clinical Research Center, Kitasato University, Japan), was performed to measure funnel plot asymmetry.

Results

Characteristics of eligible studies

The flow diagram illustrates the main reasons for study exclusion (Figure 1). The selected study characteristics are summarized in Tables 1 and 2. Thirty-four relevant case–control studies concerning the 1772 C/T and 1790 G/A polymorphisms and cancer risk were included in the meta-analysis. Of the 34 studies, 5 concentrated on prostate cancer [13–17], 5 on breast cancer [18–22], 4 on colorectal cancer [23–26], 3 on oral cancer [27–29], 3 on lung cancer [30–32], 2 on pancreatic cancer [33,34], 2 on renal cell carcinoma [35,36], 2 on cervical cancer [37,38], 1 on ovarian cancer, endometrial cancer, and cervical cancer [39], and 7 concentrated separately on esophageal squamous cell carcinoma [40], endometrial cancer [9], liver cancer [41], gastric cancer [42], glioma [43], bladder cancer [44], and head and neck squamous cell carcinoma [8]. Among the eligible studies, 34 presented data on 1772 C/T polymorphism and 24 presented data on 1790 G/A polymorphism. For the 1772 C/T polymorphism, the distribution of the genotypes in the control groups in 5 studies was not in Hardy–Weinberg equilibrium [9,13,28,35,36]. For the 1790 G/A polymorphism, the distribution of the genotypes in the control groups in one study was not in Hardy–Weinberg equilibrium [36]. Among the eligible studies, 1 study provided data on three types of cancer (endometrial cancer, ovarian cancer, and cervical cancer) for both polymorphisms [39].

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Figure 1. Reference search and selection of studies in the meta-analysis.

https://doi.org/10.1371/journal.pone.0080396.g001

First Author (Reference)   YearCountryEthnicityControl Source   Cancer TypeCasesControlsHWE
CCCTTTCCCTTT
Tanimoto2003JapanAsianPBHead and neck squamous cell carcinoma45100981200.545
Kuwai2004JapanAsianPBColorectal cancer10000891100.561
Ling2005ChinaAsianPBEsophogeal sqaumous cell carcinoma84110931100.569
Kim2008KoreaAsianHBBreast cancer818193900.641
Lee2008KoreaAsianPBBreast cancer12071196124512310.25
Nadaoka2008JapanAsianPBTransitional cell carcinoma of bladder19722419420.35
Chen2009ChinaAsianPBOral cancer1631013341300.722
Li2009ChinaAsianPBGastric cancer8340931300.501
Naidu2009MalaysiaAsianPBBreast cancer294100162225030.922
Chai2010ChinaAsianHBCervical cancer65257942120.52
Hsiao2010ChinaAsianHBHepatocellular carcinoma94803341300.722
Kang2011KoreaAsianPBColorectal cancer3812464
Kim2011KoreaAsianHBCervical cancer1772201872700.325
Putra2011JapanAsianHBLung cancer7490981200.545
Wang2011ChinaAsianHBPancreatic cancer209, (198)5402422900.352
Xu2011ChinaAsianHBGlioma1212721351410.354
Li2012ChinaAsianHBProstate cancer6124826595700.267
Clifford2001UKCaucasianPBRenal cell carcinoma30501102760.018
Ollerenshaw2004UKCaucasianPBRenal cell carcinoma16549019071<0.001,
Fransen2006SwedenCaucasianPBColorectal cancer1672832134320.916
Konac2007TurkeyCaucasianHBEndometrial, ovarian, and cervical cancer484014683720.229
Orr-Urtreger2007IsraelCaucasianPBProstate cancer28799162178030.137
Horre´e2008NetherlandsCaucasianPBEndometrial cancer505346384120.001
Apaydin2008TurkeyCaucasianPBBreast cancer79212682950.415
Foley2009IrelandCaucasianPBProstate cancer653001751300.623
Muñoz-Guerra2009SpainCaucasianPBOral cancer57,671132780.001
Konac2009TurkeyCaucasianHBLung cancer110,3101114320.335
Knechtel2010AustrilaCaucasianHBColorectal cancer291771773383>0.05
Ruiz-Tovar2012SpianCaucasianPBPancreatic cancer471111162880.002
Kuo2012ChinaCaucasianHBLung cancer153943821673110.132
Alves2012BrazilCaucasianPBOral cancer01390853<0.001
Zagouri2012GreeceCaucasianHBBreast cancer981501071700.413
Chau2005USAMixedPBProstate cancer161296179143<0.001
Li2007USAMixedPBProstate cancer818209141751300.623

Table 1. Characteristics of eligible studies for the association between the 1772 C/T polymorphism and cancer risk.

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First Author (Reference)   YearCountryEthnicityControl Source   Cancer TypeCasesControlsHWE
GGGAAAGGGAAA
Tanimoto2003JapanAsianPBHead and neck squamous cell carcinoma5140101900.655
Kim2008KoreaAsianHBBreast cancer873094710.06
Nadaoka2008JapanAsianPBTransitional cell carcinoma of bladder20415421400.25
Chen2009ChinaAsianPBOral cancer3331401532010.697
Li2009ChinaAsianPBGastric cancer7413010060 0.764
Naidu2009MalaysiaAsianPBBreast cancer3327262324120.898
Hsiao2010ChinaAsianHBHepatocellular carcinoma2780200700.805
Kim2011KoreaAsianHBCervical cancer1871202001310.136
Putra2011JapanAsianHBLung cancer7292101900.655
Wang2011ChinaAsianHBPancreatic cancer1986412492200.486
Li2012ChinaAsianHBProstate cancer6144716853100.554
Clifford2001UKCaucasianPBRenal cell carcinoma3500140400.866
Ollerenshaw2004UKCaucasianPBRenal cell carcinoma6567142393910<0.001
Fransen2006SwedenCaucasianPBColorectal cancer189890247900.775
Konac2007TurkeyCaucasianHBEndometrial, ovarian, and cervical cancer10020107001
Orr-Urtreger2007IsraelCaucasianPBProstate cancer19820298200.954
Apaydin2008TurkeyCaucasianPBBreast cancer1020094400.837
Muñoz-Guerra2009SpainCaucasianPBOral cancer40213130900.693
Konac2009TurkeyCaucasianHBLung cancer14010152 200.936
Knechtel2010AustrilaCaucasianHBColorectal cancer35611208076>0.05
Ruiz-Tovar2012SpianCaucasianPBPancreatic cancer542314210 00.675
Kuo2012ChinaCaucasianHBLung cancer15014121574110.154
Alves2012BrazilCaucasianPBOral cancer213781700.698
Li2007USAMixedPBProstate cancer105313012471700.81

Table 2. Characteristics of eligible studies for the association between the 1790 A/G polymorphism and cancer risk.

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Summary statistics

The meta-analysis for the HIF-1α 1772 C/T polymorphism included 7522 cases and 9847 controls. The prevalence of the CC genotype was the highest, allele C was the most frequent, and the prevalence of the TT genotype was the lowest in both case and control groups.

The meta-analysis for the HIF-1α 1790 G/A polymorphism included 4884 cancer cases and 8154 controls. The prevalence of the GG genotype was the highest, allele G was the most frequent, and the prevalence of the AA genotype was the lowest in both case and control groups.

Overall analysis

Upon pooling of all eligible studies, we observed that both 1772 C/T and 1790 G/A polymorphisms were significantly associated with cancer risk in homozygote comparison (1772C/T: TT vs. CC: OR=2.45, 95% CI: 1.52, 3.96; Pheterogeneity = 0.028; 1790G/A: AA vs. GG: OR = 4.74, 95% CI: 1.78, 12.6; Pheterogeneity < 0.01), dominant model (1772C/T: TT/CT vs. CC: OR=1.27, 95% CI: 1.04, 1.55; Pheterogeneity < 0.01, 1790G/A: AA/GA vs. GG: OR = 1.65, 95% CI: 1.05, 2.60; Pheterogeneity < 0.01) (Figures 2 and 3), recessive model (1772C/T: TT vs. CC/CT: OR = 3.18, 95% CI: 1.92, 5.29; Pheterogeneity < 0.01, 1790G/A: AA vs. GG/GA: OR = 4.39, 95% CI: 1.61,11.9; Pheterogeneity < 0.01), T allele versus C allele (T vs. C: OR = 1.42, 95% CI: 1.18, 1.70; Pheterogeneity < 0.01), and A allele versus G allele (A vs. G: OR = 1.83, 95% CI: 1.13,2.96; Pheterogeneity < 0.01) (Figures 4 and 5). The association strength between HIF-1α polymorphism and cancer risk is shown in Table 3. No significant association was found in heterozygote comparison (1772C/T: CT vs. CC: OR = 1.15, 95% CI: 0.92, 1.45; Pheterogeneity < 0.01, 1790G/A: GA vs. GG: OR = 1.35, 95% CI: 0.82, 2.21; Pheterogeneity < 0.01). However, the 1772C/T polymorphism was significantly associated with cancer in the heterozygote model (CT vs. CC: OR = 1.29, 95% CI: 1.04, 1.62; Pheterogeneity < 0.01) when studies not in the Hardy–Weinberg equilibrium were excluded.

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Figure 2. Forest plot of dominant model for overall comparison (1772 C/T, TT/CT vs. CC).

https://doi.org/10.1371/journal.pone.0080396.g002

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Figure 3. Forest plot of dominant model for overall comparison (1790 G/A, AA/GA vs. GG).

https://doi.org/10.1371/journal.pone.0080396.g003

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Figure 4. Forest plot of overall comparison (1772 C/T, T allele vs. C allele).

https://doi.org/10.1371/journal.pone.0080396.g004

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Figure 5. Forest plot of overall comparison (1790 G/A, A allele vs. G allele).

https://doi.org/10.1371/journal.pone.0080396.g005

1772 C/T polymorphisms (rs11549465)TT VS CCCT VS CCTT/CT VS CCTT VS CT/CCT allele VS C allele
NSample sizeORaP bORaP bORaP bORaP bORaP b
Total3475222.45 (1.52–3.96)0.0281.15 (0.92–1.45)<0.0011.27 (1.05–1.55)<0.0013.18 (1.92–5.29)<0.0011.42 (1.18–1.70)<0.001
Total in HWE2565753.65 (2.47–5.40)0.3181.29 (1.04–1.62)<0.0011.35 (1.10–1.65)<0.0013.38 (2.29–5.00)0.4761.40 (1.15–1.71)<0.001
Cancer types
Breast cancer520472.30 (1.08–4.91)0.0841.07 (0.88–1.29)0.1881.12 (0.92–1.35)0.7112.27 (1.06–4.87)0.1201.09 (0.76–1.55)0.022
Lung cancer35091.41 (0.07–30.4)0.0441.13 (0.59–2.19)0.0181.50 (1.15–1.96)0.6883.27 (1.73–6.17)0.0651.19 (0.50–2.86)<0.001
Oral cancer32842.01 (0.75–5.41)0.4630.85 (0.24–2.97)0.0471.04 (0.61–1.78)0.82322.8 (0.28–1888)<0.0013.93 (0.61–25.4)<0.001
Colorectal cancer46271.91 (0.32–11.6)0.24 (0.01–5.51)0.0271.10 (0.87–1.38)0.7441.97 (0.33–11.9)1.36 (0.68–2.70)0.002
Prostate cancer523963.68 (1.58–8.55)0.8712.02 (1.01–4.07)<0.0012.10 (1.08–4.09)0.0283.52 (1.52–8.16)0.8472.06 (1.15–3.68)<0.001
Cervical cancer332810.1 (3.12–32.6)0.1531.37 (0.92–2.02)0.0991.63 (1.12–2.37)0.1588.26 (2.64–25.9)0.2361.89 (0.84–4.26)0.002
Others1313311.68 (0.42–6.80)<0.0010.97 (0.56–1.68)<0.0011.20 (0.98–1.47)0.5121.99 (1.40–2.84)0.1001.37 (0.96–1.97)<0.001
Ethnicities
Caucasian1521511.70 (0.81–3.55)0.0010.86 (0.57–1.31)<0.0011.05 (0.76–1.46)<0.0012.97 (1.44–6.14)<0.0011.32 (0.99–1.75)<0.001
Asian1741344.42 (2.07–9.43)0.9971.25 (0.98–1.60)0.0101.33 (1.06–1.68)0.0064.12 (1.93–8.77)0.9551.40 (1.11–1.78)0.002
Mixed212373.13 (0.90–10.8)0.5002.98 (1.92–4.63)0.3723.05 (2.00–4.66)0.2692.77 (0.80–9.54)0.6462.91 (1.96–4.32)0.208
Source of control
PB2149441.92 (1.05–3.50)0.0370.99 (0.69–1.41)<0.0011.17 (0.87–1.57)<0.0013.14 (1.60–6.16)<0.0011.40 (1.06–1.84)<0.001
HB1325784.38 (2.64–7.47)0.4861.32 (1.13–1.57)0.0231.39 (1.09–1.77)0.0023.88 (2.32–6.51)0.5691.46 (1.16–1.85)<0.001
1790 G/A polymorphisms (rs11549465)AA VS GGGA VS GGAA/GA VS GGAA VS GA/GGA allele VS G allele
NSample sizeORaP bORaP bORaP bORaP bORaP b
Total2451364.74 (1.78–12.6)0.0021.35 (0.82–2.21)<0.0011.65 (1.05–2.60)<0.0014.39 (1.61–11.9)0.0011.83 (1.13–2.96)<0.001
Total in HWE2350904.68 (1.34–16.3)0.0011.23 (0.77–1.98)<0.0011.53 (0.99–2.36)<0.0014.65 (1.35–16.0)0.0011.83 (1.13–2.96)<0.001
Cancer types
Breast cancer35211.44 (0.38–5.44)0.3361.03 (0.70–1.52)0.1151.05 (0.72–1.53)0.0771.41 (0.37-5.37)0.3561.07 (0.75-1.52)0.055
Lung cancer33625.42 (2.75–10.7)0.8660.26 (0.01–7.10)<0.0010.82 (0.56–1.19)0.2267.11 (3.61–14.0)0.9751.48 (1.09-2.00)0.575
Oral cancer337520.7(0.10–4519)<0.0012.21 (0.18–26.9)<0.0017.81 (0.27–224)<0.00117.5 (0.10–3257)<0.0019.34 (0.23-388)<0.001
Prostate cancer318653.35 (0.14–82.3)1.41 (0.97–2.07)0.3651.44 (0.98–2.10)0.3403.25 (0.13–79.9)1.45 (1.00-2.11)0.330
Others1415424.81 (2.34–9.87)0.4601.70 (0.99–2.90)<0.0011.80 (0.99–3.26)<0.0013.01 (1.47–6.21)0.3671.91 (1.01-3.58)<0.001
Ethnicities
Caucasian12163517.4 (4.01-75.3)0.0011.09 (0.33–3.58)<0.0012.19 (0.90–5.34)<0.00115.8(3.42–72.9)<0.0012.27 (0.92-5.58)<0.001
Asian1124351.44 (0.60-3.46)0.5221.45 (0.85–2.46)<0.0011.36 (0.83–2.24)<0.0011.41 (0.58–3.39)0.5081.42 (0.84-2.40)<0.001
Source of control
PB1430139.69 (1.41-66.7)<0.0011.40 (0.71–2.74)<0.0011.80 (0.89–3.64)<0.0018.08 (1.12–58.1)<0.0012.10 (0.95-4.68)<0.001
HB1021234.08 (2.26-7.37)0.4011.23 (0.53–2.86)<0.0011.47 (0.85–2.55)<0.0015.02 (2.79–9.02)0.2781.50 (0.86-2.62)<0.001

Table 3. Main results of the meta-analysis for the association of HIF1A gene 1772 C/T and 1790 G/A polymorphisms with cancer risk.

a Random-effects model was used when the P value for the heterogeneity test was < 0.05; otherwise, fixed-effects model was used.
b P Value of Q-Test for the Heterogeneity Test
N: number of studies included; OR: odds ratio; PB: population-based; HB: hospital-based; HWE= Hardy–Weinberg equilibrium.
One study contained detailed data on ovarian cancer, endometrial cancer, and cervical cancer. We used the combined data for the overall analysis and the separate data for the subgroup analysis by cancer type.
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Subgroup analyses

Subgroup analyses were performed to investigate the effect of cancer type, ethnicity, and source of control. For cancer type, the 1772C/T polymorphism demonstrated an increased risk for breast cancer, lung cancer, prostate cancer, cervical cancer, and other cancers in various models. In the subgroup analyses of “oral cancer” and “colorectal cancer,” we did not find any significant association between the 1772C/T polymorphism and cancer risk. The 1790G/A polymorphism exhibited an increased cancer risk for lung cancer in the homozygote and recessive models (AA vs. GG: OR = 5.42, 95% CI: 2.75, 10.7; Pheterogeneity = 0.866; AA vs. GG/GA: OR = 7.11, 95% CI: 3.61, 14.0; Pheterogeneity = 0.975; A vs. G: OR = 1.48, 95% CI: 1.09, 2.00; Pheterogeneity = 0.575) and for prostate cancer (A vs. G: OR = 1.45, 95% CI: 1.00, 2.11; Pheterogeneity = 0.330). We found a significant association between 1772C/T and 1790G/A polymorphisms and cancer risk in both population-based and hospital-based studies.

However, ethnicity significantly affected cancer susceptibility. For the 1772C/T polymorphism, a significantly increased cancer risk was found in both Asians and Caucasians. For the 1790G/A polymorphism, a significantly increased cancer risk was found in Caucasians in the heterozygote comparison (AA vs. GG: OR = 17.4, 95% CI: 4.01, 75.3; Pheterogeneity < 0.01) and recessive model (GG/GA: OR = 15.8, 95% CI: 3.42, 72.9; Pheterogeneity < 0.01). However, no significant association between these polymorphisms and cancer risk was found in Asians. These results revealed that the effect of HIF-1α polymorphisms on cancer was associated with ethnicity.

Sensitivity analysis

Sensitivity analysis was performed to explore the influence of an individual study on the pooled results by deleting a single study each time from the pooled analysis. The results showed that no individual study significantly affected the pooled OR because no substantial change was found (figure not shown).

Publication bias

Publication bias was assessed by Begg’s funnel plot and Egger’s test. Begg’s funnel plot for the 1772 C/T polymorphism is shown in Figure 6 (P = 0.589 for T allele vs. C allele). Egger’s test was performed for statistical analysis, and no publication bias was detected (P =0.481 for T allele vs. C allele). The results of Begg’s and Egger’s tests for the 1790 G/A polymorphism were P = 0.785 and P = 0.870, respectively, for A allele versus G allele (Figure 7). Overall, no publication bias was detected in the data.

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Figure 6. Funnel plot of heterozygote comparison (1772 C/T, T allele vs. C allele).

https://doi.org/10.1371/journal.pone.0080396.g006

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Figure 7. Funnel plot of heterozygote comparison (1790 G/A, A allele vs. G allele).

https://doi.org/10.1371/journal.pone.0080396.g007

Discussion

HIF-1 has an important function in cancer progression and metastasis by activating various genes that are linked to the regulation of angiogenesis, cell survival, and energy metabolism [3]. The presence of T and A variant alleles of HIF-1α 1772, namely, C/T and 1790 G/A polymorphisms, are associated with high transcriptional abilities and protein synthesis in vitro [8]. In vivo studies related these genetic variations to many aggressive clinical features of cancer, such as the ulcerative growth pattern in colorectal tumors, suggesting that HIF-1α polymorphism is associated with cancer [45]. However, studies on the association of HIF-1α 1772 C/T and 1790 G/A polymorphisms with cancer are conflicting. In 2009, Zhao [10] conducted a meta-analysis using 16 case–control studies and concluded that 1772 C/T is significantly associated with higher cancer risk and that 1790 G/A is only significantly associated with breast cancer. Liu [12] performed a similar meta-analysis from 22 case–control studies, including 5552 cases and 8044 controls for 1772 C/T and 3381 cases and 5830 controls for 1790 G/A, and one study evaluated cancer prognosis by polymorphism [45]. This previous study concluded that the 1790 G/A polymorphism and not the 1772 C/T polymorphism is significantly associated with cancer risk. In the present study, we performed an updated meta-analysis from 34 case–control studies that involved 7522 cases and 9847 controls for 1772 C/T polymorphism and 4884 cases and 8154 controls for 1790 G/A polymorphism.

In the present meta-analysis, we investigated the association of HIF-1α 1772 C/T and 1790 G/A polymorphisms with cancer risk. Subgroup analyses by cancer type and ethnicity were also performed. Our analyses showed that both 1772C/T and 1790G/A polymorphisms were significantly associated with cancer risk. In the subgroup study, various types of cancers, such as breast cancer, lung cancer, prostate cancer, and cervical cancer, were associated with 1772C/T, whereas only lung cancer was linked with 1790G/A. However, the odds ratio values in some of the subgroup analyses were large and lacked statistical power because of the significant heterogeneity. Ethnicity may also significantly affect cancer susceptibility. For the 1790G/A polymorphism, we did not find any association between the 1790G/A polymorphism and cancer risk in Asians. This finding can be explained by the difference in genetic background, environmental exposure, and risk factors relating to lifestyle between Asian and Caucasian populations.

Some limitations of this meta-analysis should be addressed. First, the lack of the the detailed information about environment risk factors for cancer risk from included studies limited our further evaluation of potential gene–gene and gene–environment interactions. Second, the P value of the Hardy–Weinberg equilibrium of three included studies was less than 0.05, suggesting that these study populations were not representative of the broader target population. Despite these limitations, our meta-analysis had some strong advantages. This meta-analysis shed light on the association between HIF-1α polymorphisms and increased risk for various cancers. In addition, the quality of the included studies was satisfactory and met our inclusion criterion. Moreover, substantial numbers of cases and controls were pooled from different studies, which significantly increased the statistical power of the analysis. No publication bias was also found in the collected data.

In summary, this meta-analysis provided insights into the association of HIF-1α 1772 C/T and 1790 G/A gene polymorphisms with cancer risk, supporting the hypothesis that HIF-1α polymorphisms are a susceptibility marker of cancer. However, large sample studies are warranted to validate our findings, especially in some types of cancer, such as breast cancer and cervical cancer. More studies on gene–gene and gene–environment interactions should also be considered in the future to obtain a more comprehensive understanding of the association between HIF-1α polymorphisms and cancer risk.

Supporting Information

Checklist S1.

PRISMA checklist.

https://doi.org/10.1371/journal.pone.0080396.s001

(DOC)

Author Contributions

Conceived and designed the experiments: XCS HYC JXM JC. Performed the experiments: XY HCZ CZ. Analyzed the data: QQ JL LPX. Contributed reagents/materials/analysis tools: LJZ QZ. Wrote the manuscript: XY HCZ.

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