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. 2013 Nov 18;8(11):e80396. doi: 10.1371/journal.pone.0080396

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 1,#, Hong-Cheng Zhu 1,#, Chi Zhang 1, Qin Qin 1, Jia Liu 1, Li-Ping Xu 1, Lian-Jun Zhao 2, Qu Zhang 3, Jing Cai 4, Jian-Xin Ma 3, Hong-Yan Cheng 5, Xin-Chen Sun 1,*
Editor: Qing-Yi Wei6
PMCID: PMC3832403  PMID: 24260383

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; P heterogeneity = 0.028; 1790G/A: AA vs. GG: OR=4.74, 95% CI: 1.78, 12.6; P heterogeneity < 0.01), dominant model (1772C/T: TT/CT vs. CC: OR = 1.27, 95% CI: 1.04, 1.55; P heterogeneity < 0.01, 1790G/A: AA/GA vs. GG: OR = 1.65, 95% CI: 1.05, 2.60; P heterogeneity < 0.01), T allele versus C allele (T vs. C: OR = 1.42, 95% CI: 1.18, 1.70; P heterogeneity < 0.01), and A allele versus G allele (A vs. G: OR = 1.83, 95% CI: 1.13, 2.96; P heterogeneity < 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.

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].

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

Figure 1

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

First Author (Reference)    Year Country Ethnicity Control Source    Cancer Type Cases
 Controls
 HWE
CC CT TT CC CT TT
Tanimoto 2003 Japan Asian PB Head and neck squamous cell carcinoma 45 10 0 98 12 0 0.545
Kuwai 2004 Japan Asian PB Colorectal cancer 100 0 0 89 11 0 0.561
Ling 2005 China Asian PB Esophogeal sqaumous cell carcinoma 84 11 0 93 11 0 0.569
Kim 2008 Korea Asian HB Breast cancer 81 8 1 93 9 0 0.641
Lee 2008 Korea Asian PB Breast cancer 1207 119 6 1245 123 1 0.25
Nadaoka 2008 Japan Asian PB Transitional cell carcinoma of bladder 197 22 419 42 0.35
Chen 2009 China Asian PB Oral cancer 163 10 1 334 13 0 0.722
Li 2009 China Asian PB Gastric cancer 83 4 0 93 13 0 0.501
Naidu 2009 Malaysia Asian PB Breast cancer 294 100 16 222 50 3 0.922
Chai 2010 China Asian HB Cervical cancer 65 25 7 94 21 2 0.52
Hsiao 2010 China Asian HB Hepatocellular carcinoma 94 8 0 334 13 0 0.722
Kang 2011 Korea Asian PB Colorectal cancer 38 12 46 4
Kim 2011 Korea Asian HB Cervical cancer 177 22 0 187 27 0 0.325
Putra 2011 Japan Asian HB Lung cancer 74 9 0 98 12 0 0.545
Wang 2011 China Asian HB Pancreatic cancer 209, (198) 54 0 242 29 0 0.352
Xu 2011 China Asian HB Glioma 121 27 2 135 14 1 0.354
Li 2012 China Asian HB Prostate cancer 612 48 2 659 57 0 0.267
Clifford 2001 UK Caucasian PB Renal cell carcinoma 30 5 0 110 27 6 0.018
Ollerenshaw 2004 UK Caucasian PB Renal cell carcinoma 16 54 90 1 90 71 <0.001,
Fransen 2006 Sweden Caucasian PB Colorectal cancer 167 28 3 213 43 2 0.916
Konac 2007 Turkey Caucasian HB Endometrial, ovarian, and cervical cancer 48 40 14 68 37 2 0.229
Orr-Urtreger 2007 Israel Caucasian PB Prostate cancer 287 99 16 217 80 3 0.137
Horre´e 2008 Netherlands Caucasian PB Endometrial cancer 50 5 3 463 84 12 0.001
Apaydin 2008 Turkey Caucasian PB Breast cancer 79 21 2 68 29 5 0.415
Foley 2009 Ireland Caucasian PB Prostate cancer 65 30 0 175 13 0 0.623
Muñoz-Guerra 2009 Spain Caucasian PB Oral cancer 57, 6 7 113 27 8 0.001
Konac 2009 Turkey Caucasian HB Lung cancer 110, 31 0 111 43 2 0.335
Knechtel 2010 Austrila Caucasian HB Colorectal cancer 291 77 1773 383 >0.05
Ruiz-Tovar 2012 Spian Caucasian PB Pancreatic cancer 47 1 11 116 28 8 0.002
Kuo 2012 China Caucasian HB Lung cancer 153 94 38 216 73 11 0.132
Alves 2012 Brazil Caucasian PB Oral cancer 0 1 39 0 85 3 <0.001
Zagouri 2012 Greece Caucasian HB Breast cancer 98 15 0 107 17 0 0.413
Chau 2005 USA Mixed PB Prostate cancer 161 29 6 179 14 3 <0.001
Li 2007 USA Mixed PB Prostate cancer 818 209 14 175 13 0 0.623
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Table 2. Characteristics of eligible studies for the association between the 1790 A/G polymorphism and cancer risk.

First Author (Reference)    Year Country Ethnicity Control Source    Cancer Type Cases
 Controls
 HWE
GG GA AA GG GA AA
Tanimoto 2003 Japan Asian PB Head and neck squamous cell carcinoma 51 4 0 101 9 0 0.655
Kim 2008 Korea Asian HB Breast cancer 87 3 0 94 7 1 0.06
Nadaoka 2008 Japan Asian PB Transitional cell carcinoma of bladder 204 15 421 40 0.25
Chen 2009 China Asian PB Oral cancer 333 14 0 153 20 1 0.697
Li 2009 China Asian PB Gastric cancer 74 13 0 100 6 0 0.764
Naidu 2009 Malaysia Asian PB Breast cancer 332 72 6 232 41 2 0.898
Hsiao 2010 China Asian HB Hepatocellular carcinoma 27 8 0 200 7 0 0.805
Kim 2011 Korea Asian HB Cervical cancer 187 12 0 200 13 1 0.136
Putra 2011 Japan Asian HB Lung cancer 72 9 2 101 9 0 0.655
Wang 2011 China Asian HB Pancreatic cancer 198 64 1 249 22 0 0.486
Li 2012 China Asian HB Prostate cancer 614 47 1 685 31 0 0.554
Clifford 2001 UK Caucasian PB Renal cell carcinoma 35 0 0 140 4 0 0.866
Ollerenshaw 2004 UK Caucasian PB Renal cell carcinoma 65 67 14 239 39 10 <0.001
Fransen 2006 Sweden Caucasian PB Colorectal cancer 189 89 0 247 9 0 0.775
Konac 2007 Turkey Caucasian HB Endometrial, ovarian, and cervical cancer 100 2 0 107 0 0 1
Orr-Urtreger 2007 Israel Caucasian PB Prostate cancer 198 2 0 298 2 0 0.954
Apaydin 2008 Turkey Caucasian PB Breast cancer 102 0 0 94 4 0 0.837
Muñoz-Guerra 2009 Spain Caucasian PB Oral cancer 40 21 3 130 9 0 0.693
Konac 2009 Turkey Caucasian HB Lung cancer 140 1 0 152 2 0 0.936
Knechtel 2010 Austrila Caucasian HB Colorectal cancer 356 11 2080 76 >0.05
Ruiz-Tovar 2012 Spian Caucasian PB Pancreatic cancer 54 2 3 142 10 0 0.675
Kuo 2012 China Caucasian HB Lung cancer 150 1 41 215 74 11 0.154
Alves 2012 Brazil Caucasian PB Oral cancer 2 1 37 81 7 0 0.698
Li 2007 USA Mixed PB Prostate cancer 1053 13 0 1247 17 0 0.81
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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; P heterogeneity = 0.028; 1790G/A: AA vs. GG: OR = 4.74, 95% CI: 1.78, 12.6; P heterogeneity < 0.01), dominant model (1772C/T: TT/CT vs. CC: OR=1.27, 95% CI: 1.04, 1.55; P heterogeneity < 0.01, 1790G/A: AA/GA vs. GG: OR = 1.65, 95% CI: 1.05, 2.60; P heterogeneity < 0.01) (Figures 2 and 3), recessive model (1772C/T: TT vs. CC/CT: OR = 3.18, 95% CI: 1.92, 5.29; P heterogeneity < 0.01, 1790G/A: AA vs. GG/GA: OR = 4.39, 95% CI: 1.61,11.9; P heterogeneity < 0.01), T allele versus C allele (T vs. C: OR = 1.42, 95% CI: 1.18, 1.70; P heterogeneity < 0.01), and A allele versus G allele (A vs. G: OR = 1.83, 95% CI: 1.13,2.96; P heterogeneity < 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; P heterogeneity < 0.01, 1790G/A: GA vs. GG: OR = 1.35, 95% CI: 0.82, 2.21; P heterogeneity < 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.

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

Figure 2

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

Figure 3

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

Figure 4

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

Figure 5

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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.

1772 C/T polymorphisms (rs11549465)
 TT VS CC
 CT VS CC
 TT/CT VS CC
 TT VS CT/CC
T allele VS C allele
N Sample size ORa P b ORa P b ORa P b ORa P b ORa P b
Total 34 7522 2.45 (1.52–3.96) 0.028 1.15 (0.92–1.45) <0.001 1.27 (1.05–1.55) <0.001 3.18 (1.92–5.29) <0.001 1.42 (1.18–1.70) <0.001
Total in HWE 25 6575 3.65 (2.47–5.40) 0.318 1.29 (1.04–1.62) <0.001 1.35 (1.10–1.65) <0.001 3.38 (2.29–5.00) 0.476 1.40 (1.15–1.71) <0.001
Cancer types
Breast cancer 5 2047 2.30 (1.08–4.91) 0.084 1.07 (0.88–1.29) 0.188 1.12 (0.92–1.35) 0.711 2.27 (1.06–4.87) 0.120 1.09 (0.76–1.55) 0.022
Lung cancer 3 509 1.41 (0.07–30.4) 0.044 1.13 (0.59–2.19) 0.018 1.50 (1.15–1.96) 0.688 3.27 (1.73–6.17) 0.065 1.19 (0.50–2.86) <0.001
Oral cancer 3 284 2.01 (0.75–5.41) 0.463 0.85 (0.24–2.97) 0.047 1.04 (0.61–1.78) 0.823 22.8 (0.28–1888) <0.001 3.93 (0.61–25.4) <0.001
Colorectal cancer 4 627 1.91 (0.32–11.6) 0.24 (0.01–5.51) 0.027 1.10 (0.87–1.38) 0.744 1.97 (0.33–11.9) 1.36 (0.68–2.70) 0.002
Prostate cancer 5 2396 3.68 (1.58–8.55) 0.871 2.02 (1.01–4.07) <0.001 2.10 (1.08–4.09) 0.028 3.52 (1.52–8.16) 0.847 2.06 (1.15–3.68) <0.001
Cervical cancer 3 328 10.1 (3.12–32.6) 0.153 1.37 (0.92–2.02) 0.099 1.63 (1.12–2.37) 0.158 8.26 (2.64–25.9) 0.236 1.89 (0.84–4.26) 0.002
Others 13 1331 1.68 (0.42–6.80) <0.001 0.97 (0.56–1.68) <0.001 1.20 (0.98–1.47) 0.512 1.99 (1.40–2.84) 0.100 1.37 (0.96–1.97) <0.001
Ethnicities
Caucasian 15 2151 1.70 (0.81–3.55) 0.001 0.86 (0.57–1.31) <0.001 1.05 (0.76–1.46) <0.001 2.97 (1.44–6.14) <0.001 1.32 (0.99–1.75) <0.001
Asian 17 4134 4.42 (2.07–9.43) 0.997 1.25 (0.98–1.60) 0.010 1.33 (1.06–1.68) 0.006 4.12 (1.93–8.77) 0.955 1.40 (1.11–1.78) 0.002
Mixed 2 1237 3.13 (0.90–10.8) 0.500 2.98 (1.92–4.63) 0.372 3.05 (2.00–4.66) 0.269 2.77 (0.80–9.54) 0.646 2.91 (1.96–4.32) 0.208
Source of control
PB 21 4944 1.92 (1.05–3.50) 0.037 0.99 (0.69–1.41) <0.001 1.17 (0.87–1.57) <0.001 3.14 (1.60–6.16) <0.001 1.40 (1.06–1.84) <0.001
HB 13 2578 4.38 (2.64–7.47) 0.486 1.32 (1.13–1.57) 0.023 1.39 (1.09–1.77) 0.002 3.88 (2.32–6.51) 0.569 1.46 (1.16–1.85) <0.001
1790 G/A polymorphisms (rs11549465) AA VS GG GA VS GG AA/GA VS GG AA VS GA/GG A allele VS G allele
N Sample size ORa P b ORa P b ORa P b ORa P b ORa P b
Total 24 5136 4.74 (1.78–12.6) 0.002 1.35 (0.82–2.21) <0.001 1.65 (1.05–2.60) <0.001 4.39 (1.61–11.9) 0.001 1.83 (1.13–2.96) <0.001
Total in HWE 23 5090 4.68 (1.34–16.3) 0.001 1.23 (0.77–1.98) <0.001 1.53 (0.99–2.36) <0.001 4.65 (1.35–16.0) 0.001 1.83 (1.13–2.96) <0.001
Cancer types
Breast cancer 3 521 1.44 (0.38–5.44) 0.336 1.03 (0.70–1.52) 0.115 1.05 (0.72–1.53) 0.077 1.41 (0.37-5.37) 0.356 1.07 (0.75-1.52) 0.055
Lung cancer 3 362 5.42 (2.75–10.7) 0.866 0.26 (0.01–7.10) <0.001 0.82 (0.56–1.19) 0.226 7.11 (3.61–14.0) 0.975 1.48 (1.09-2.00) 0.575
Oral cancer 3 375 20.7(0.10–4519) <0.001 2.21 (0.18–26.9) <0.001 7.81 (0.27–224) <0.001 17.5 (0.10–3257) <0.001 9.34 (0.23-388) <0.001
Prostate cancer 3 1865 3.35 (0.14–82.3) 1.41 (0.97–2.07) 0.365 1.44 (0.98–2.10) 0.340 3.25 (0.13–79.9) 1.45 (1.00-2.11) 0.330
Others 14 1542 4.81 (2.34–9.87) 0.460 1.70 (0.99–2.90) <0.001 1.80 (0.99–3.26) <0.001 3.01 (1.47–6.21) 0.367 1.91 (1.01-3.58) <0.001
Ethnicities
Caucasian 12 1635 17.4 (4.01-75.3) 0.001 1.09 (0.33–3.58) <0.001 2.19 (0.90–5.34) <0.001 15.8(3.42–72.9) <0.001 2.27 (0.92-5.58) <0.001
Asian 11 2435 1.44 (0.60-3.46) 0.522 1.45 (0.85–2.46) <0.001 1.36 (0.83–2.24) <0.001 1.41 (0.58–3.39) 0.508 1.42 (0.84-2.40) <0.001
Source of control
PB 14 3013 9.69 (1.41-66.7) <0.001 1.40 (0.71–2.74) <0.001 1.80 (0.89–3.64) <0.001 8.08 (1.12–58.1) <0.001 2.10 (0.95-4.68) <0.001
HB 10 2123 4.08 (2.26-7.37) 0.401 1.23 (0.53–2.86) <0.001 1.47 (0.85–2.55) <0.001 5.02 (2.79–9.02) 0.278 1.50 (0.86-2.62) <0.001
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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.

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; P heterogeneity = 0.866; AA vs. GG/GA: OR = 7.11, 95% CI: 3.61, 14.0; P heterogeneity = 0.975; A vs. G: OR = 1.48, 95% CI: 1.09, 2.00; P heterogeneity = 0.575) and for prostate cancer (A vs. G: OR = 1.45, 95% CI: 1.00, 2.11; P heterogeneity = 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; P heterogeneity < 0.01) and recessive model (GG/GA: OR = 15.8, 95% CI: 3.42, 72.9; P heterogeneity < 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.

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

Figure 6

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

Figure 7

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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.

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Funding Statement

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.

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(DOC)

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