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Association between HIF-1α C1772T/G1790A polymorphisms and cancer susceptibility: an updated systematic review and meta-analysis based on 40 case-control studies

Contributed equally
BMC Cancer201414:950

https://doi.org/10.1186/1471-2407-14-950

Received: 31 March 2013

Accepted: 20 November 2014

Published: 15 December 2014

Abstract

Background

HIF-1 (hypoxia-inducible factor 1) is a transcriptional activator that functions as a critical regulator of oxygen homeostasis. Recently, a large number of epidemiological studies have investigated the relationship between HIF-1α C1772T/G1790A polymorphisms and cancer susceptibility. However, the results remain inconclusive. Therefore, we performed a meta-analysis on all of the available case-control studies to systematically summarize the possible association.

Methods

A literature search was performed using PubMed and the Web of Science database to obtain relevant published studies. Pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for the relationship between HIF-1α C1772T/G1790A polymorphisms and cancer susceptibility were calculated using fixed- and random-effects models when appropriate. Heterogeneity tests, sensitivity analyses and publication bias assessments were also performed in our meta-analysis.

Results

A total of 40 studies met the inclusion criteria were included in the meta-analysis: 40 studies comprised of 10869 cases and 14289 controls for the HIF-1α C1772T polymorphism and 30 studies comprised of 7117 cases and 10442 controls for the HIF-1α G1790A polymorphism. The results demonstrated that there were significant association between the HIF-1α C1772T polymorphism and cancer susceptibility under four genetic models (TT vs. CC: OR = 1.63, 95% CI = 1.02-2.60; CT + TT vs. CC: OR = 1.15, 95% CI = 1.01-1.34; TT vs. CT + CC: OR = 2.11, 95% CI = 1.32-3.77; T vs. C: OR = 1.21, 95% CI = 1.04-1.41). Similarly, the statistically significant association between the HIF-1α G1790A polymorphism and cancer susceptibility was found to be consistently strong in all of the genetic models. Moreover, increased cancer risk was observed when the data were stratified by cancer type, ethnicity and the source of controls.

Conclusions

This meta-analysis demonstrates that both the C1772T and G1790A polymorphisms in the HIF-1α gene likely contribute to increased cancer susceptibility, especially in the Asian population and in breast cancer, lung cancer, pancreatic cancer and oral cancer. However, further research is necessary to evaluate the relationship between these polymorphisms and cancer risk.

Keywords

HIF-1 gene Polymorphism Cancer Susceptibility Meta-analysis

Background

Human cancer is a major cause of death in the world, and it is estimated that the number of new cases will increase to more than 15 million in the coming decade, creating a substantial worldwide public health burden [1, 2]. Various factors, such as genetic and environmental influences, are associated with cancer prognosis. However, the exact etiology and mechanism of carcinogenesis have not yet been clearly elucidated. In recent years, it has become well-accepted that intrinsic factors, such as host genetic susceptibility, may play important roles in the process of cancer development [3, 4], and an increasing number of studies have focused on the association between genetic factors and cancer susceptibility.

Hypoxia-inducible factor 1 (HIF-1) is a transcriptional activator that functions as a critical regulator of oxygen homeostasis. It is a heterodimer composed of two subunits, HIF-1α and HIF-1β, which dimerize and bind to DNA via the basic helix-loop-helix Per/Arnt/Sim (bHLH-PAS) domain [5, 6]. HIF-1α expression is induced in hypoxic cells, and its level exponentially increase when the cells are exposed to O2 concentration of less than 6%. Under hypoxic condition, HIF-1α ubiquitination decreases dramatically, resulting in an accumulation of the protein, while under normoxic condition, HIF-1α is rapidly degraded through von Hippel-Lindau (VHL)-mediated ubiquitination and proteasomal degradation [710]. HIF-1 has also been suggested to play an important role in tumor development, progression and metastasis, and HIF-1 can activate the transcription of more than 60 target genes that are involved in crucial aspects of cancer establishment, including cell survival, glucose metabolism, angiogenesis and invasion [11, 12].

The HIF-1α gene is located on chromosome 14q21-24, and recent studies have shown that there are a total of 35 common single nucleotide polymorphisms (SNPs) throughout the HIF-1α gene in Caucasian and Asian population [1315]. Two important SNPs in exon 12 of the HIF-1 gene, HIF-1α C1772T (rs11549465) and HIF-1α G1790A (rs11549467), lead to amino acid substitution of proline to serine at position 582 and alanine to threonine at position 588 of the protein, respectively [8, 16, 17]. These two polymorphisms have been demonstrated to be functionally meaningful, resulting in increased transcriptional activity of HIF-1α [14, 18]. Previous studies have shown that the overexpression of HIF-1α is significantly associated with cell proliferation, increased tumor susceptibility, tumor size, lymph node metastasis and prognosis [19, 20].

In recent years, the HIF-1α gene has been a research focus in the scientific community, and many epidemiological studies have been performed to assess the association between HIF-1α C1772T/G1790A polymorphisms and cancer susceptibility. However, the results of the different studies are conflicting. Hence, we performed a meta-analysis of all of the eligible studies to clarify the role of HIF-1α C1772T/G1790A polymorphisms in cancer development.

Methods

Study eligibility and validity assessment

We performed a computerized literature search of the PubMed and Web of Science databases to identify all of the relevant studies of cancer that contained sufficient genotyping data for at least one of the two polymorphisms, HIF-1α C1772T or HIF-1α G1790A. The search strategy was designed by two researchers and included the following keywords: “HIF-1 OR hypoxia-inducible factor-1” and “polymorphism”, and the last search was updated on September 20th, 2013. To obtain all eligible publications, we also manually reviewed the references of the selected articles to identify other potential eligible publications. Articles investigating the association between cancer risk and the HIF-1α polymorphisms were identified with no language restriction.

Inclusion criteria

The studies selected were required to meet the following criteria: 1) evaluate the association between the HIF-1α C1772T and/or HIF-1α G1790A polymorphisms and cancer risk; 2) use a human case-control design; 3) contain sufficient published data for the estimation of an odds ratio (OR) with a 95% confidence interval (CI).

Data extraction

Data were extracted from all of the eligible publications by two investigators (Yan and Chen) independently, according to the inclusion criteria listed above. Disagreements between the two investigators were resolved by discussion until a consensus was reached. The following information was extracted from each of the included publications: the first author’s name, publication data, country of origin, ethnicities of the sample population (categorised as Asians, Caucasians and Mixed), cancer type, source of control group (population- or hospital-based controls), total number of cases and controls, and the number of cases and controls with the HIF-1α C1772T/G1790A polymorphisms.

Statistical methods

The strength of the association between the HIF-1α C1772T/HIF-1α G1790A polymorphisms and cancer risk was measured by ORs with 95% CIs. The statistical significance of the pooled OR was calculated by the Z test, a P < 0.05 was considered to be statistically significant (P-values were two sided). For HIF-1α C1772T polymorphism, we examined the overall ORs and compared the cancer incidence using the allelic model (T versus C), homozygote model (TT versus CC), heterozygote model (TC versus CC), dominant model (TT + TC versus CC), recessive model (TT versus TC + CC). For HIF-1α G1790A polymorphism, we evaluated the risk in the allelic model (A versus G), homozygote model (AA versus GG), heterozygote comparison model (GA versus GG), dominant models (AA + AG versus GG), and recessive model (AA versus AG + GG). Subgroup analyses were also conducted by ethnicity, cancer type (“other cancer groups” means any cancer types with less than two separate publications) and source of controls. Statistical heterogeneity was estimated by a chi-square based Q-test, and when P < 0.05, the heterogeneity was considered to be significant. We combined all of the values from each individual study using the fixed-effect model and the random-effect model. When P > 0.05, the effects were assumed to be homogenous, and the fixed-effect model (the Mantel-Haenszel method) was used [21]. When P < 0.05, the random-effect model (the DerSimonian and Laird method) was more appropriate [22]. The inter-study variance I2 (I2 = 100% × (Q-df)/Q) was used to quantitatively estimate the heterogeneity, and the percentage of I2 was used to describe the extent of the heterogeneity, I2 < 25%, 25-75% and >75% represent low, moderate and high inconsistency, respectively [23, 24]. In addition, we performed sensitivity analyses to evaluate the potential biases of the results in our meta-analyses. The Hardy-Weinberg equilibrium (HWE) of the controls for each study was also calculated using a goodness-of-fit test (chi-square or Fisher’s exact test) and P < 0.05 was considered to be statistically significant. Sensitivity analyses were carried out to assess the stability of the results by conducting analysis of studies with controls in HWE. Finally, the Begg’s funnel plot and Egger’s test were utilised to estimate the publication bias [25]. All analyses were conducted by the software Stata (Version 11; Stata Corporation, College Station, Texas, USA). All P-values were two-sided and a P of < 0.05 was considered to be statistically significant.

Results

Studies selected

Through the literature search and selection, a total of 40 eligible studies met the inclusion criteria and were included in our meta-analysis. One study (Konac et al.) [26] provided data on three types of cancer (cervical cancer, ovarian cancer, and endometrial cancer) and both polymorphisms; therefore, we have grouped them as one in the meta-analyses of all subjects except when stratified by cancer type. Thus, each type of cancer in this study was treated as a separated study in sub-group analyses. Among the 40 eligible studies, 40 studies, representing 10869 cases and 14289 controls, were ultimately analyzed for the HIF-1α C1772T polymorphism [8, 17, 2663], and 30 studies, representing 7177 cases and 10442 controls, were analyzed for the HIF-1α G1790A polymorphism [8, 17, 26, 2931, 3335, 3743, 4548, 50, 5257, 59, 62, 63]. The literature search and study selection procedure are shown in Figure 1. Of the 40 studies on the HIF-1α C1772T polymorphism, 6 studies were conducted on prostate cancer, 6 studies on breast cancer, 3 studies on lung cancer, 4 studies on colorectal cancer, 4 studies on renal cancer, 4 studies on oral cancer and 12 studies on other cancers. Among these eligible studies, 20 were studies on Asians, 16 were studies on Caucasians and 4 studies were performed on a population of mixed ethnicity. The control sources were population-based in 17 studies and hospital-based in 23 studies. For the HIF-1α G1790A polymorphism, 15 of the 30 eligible studies were performed in Asian populations, 13 studies were performed in Caucasian populations and 2 studies were performed in a mixed ethnicity population. Of these studies, 4 studies were conducted on breast cancer, 3 studies on lung cancer, 4 studies on oral cancer, 3 studies on prostate cancer, 3 studies on cervical cancer, 2 studies on pancreatic cancer, 2 studies on colorectal cancer, 4 studies on renal cancer and 7 studies on other cancers. The control sources were population-based in 17 studies and hospital-based in 13 studies. The genotype frequency data of the HIF-1α C1772T and HIF-1α G1790A polymorphisms were extracted from all of these eligible publications. For the HIF-1α C1772T polymorphism, the distributions of the genotypes in the control groups in 11 studies were not in HWE [17, 50, 51, 53, 54, 5658, 6062]. For the HIF-1α G1790A polymorphism there was 1 study not in HWE [62]. The main characteristics of the eligible studies in the meta-analysis are listed in Table 1.
Figure 1

Study flow-chart illustrating the literature search and eligible study selection process.

Table 1

Characteristics of studies included in the meta-analysis

First author

Year

Country

Ethnicity

Cancer type

Gene type

Source of controls

Cases

Controls

Case

Control

HWE

         

MM

MW

WW

MM

MW

WW

 

Clifford

2001

UK

Caucasian

Renal

C1772T

PB

48

143

42

6

0

110

27

6

0.02

     

G1790A

PB

48

144

47

1

0

140

4

0

0.87

Tanimoto

2003

Japan

Asian

HNSCC

C1772T

PB

55

110

45

10

0

98

12

0

0.55

     

G1790A

PB

55

110

51

4

0

101

9

0

0.65

Kuwai

2004

Japan

Asian

Colorectal

C1772T

PB

100

100

100

0

0

89

11

0

0.56

Ollerenshaw

2004

UK

Caucasian

Renal

C1772T

PB

160

162

16

54

90

1

90

71

0.001

     

G1790A

PB

146

288

65

67

14

239

39

10

0.001

Ling

2005

China

Asian

Esophageal

C1772T

HB

95

104

84

11

0

93

11

0

0.57

Chau

2005

USA

Caucasian

Prostate

C1772T

PB

196

196

161

29

6

179

14

3

0.002

Fransen

2006

Sweden

Caucasian

Colorectal

C1772T

PB

198

258

167

28

3

213

43

2

0.92

Fransen

2006

Sweden

Caucasian

Colorectal

G1790A

PB

198

256

189

9

0

247

9

0

0.77

Konac

2007

Turkey

Caucasian

Cervical

C1772T

HB

32

107

10

14

8

68

37

2

0.23

     

G1790A

HB

32

107

32

0

0

107

0

0

0.99

   

Caucasian

Ovarian

C1772T

HB

49

107

34

14

1

68

37

2

0.23

     

G1790A

HB

49

107

47

2

0

107

0

0

0.99

   

Caucasian

Endometrial

C1772T

HB

21

107

4

12

5

68

37

2

0.23

     

G1790A

HB

21

107

21

0

0

107

0

0

0.99

Li

2007

USA

mixed

Prostate

C1772T

PB

1041

1234

818

209

14

995

221

18

0.16

     

G1790A

PB

1066

1264

1053

13

0

1247

17

0

0.81

Orr-Urtreger

2007

Israel

Caucasian

Prostate

C1772T

PB

402

300

287

99

16

217

80

3

0.14

     

G1790A

PB

200

300

198

2

0

298

2

0

0.95

Apaydin

2008

Turkey

Caucasian

Breast

C1772T

PB

102

102

79

21

2

68

29

5

0.42

     

G1790A

PB

102

102

102

0

0

98

4

0

0.84

Lee

2008

Korea

Asian

Breast

C1772T

PB

1332

1369

1207

119

6

1245

123

1

0.25

Kim

2008

Korea

Asian

Breast

C1772T

HB

90

102

81

8

1

93

9

0

0.64

     

G1790A

HB

90

102

87

3

0

94

7

1

0.06

Nadaoka

2008

Japan

Asian

Bladder

C1772T

HB

219

461

197

21

1

419

42

0

0.35

     

G1790A

HB

219

461

204

13

2

421

40*

-

0.46

Jacobs

2008

USA

mixed

Prostate

C1772T

HB

1420

1450

1156

252

12

1138

284

28

0.04

Horree

2008

Netherland

Caucasian

Endometrial

C1772T

PB

58

559

50

5

3

463

84

12

0.01

Naidu

2009

Malaysia

Asian

Breast

C1772T

PB

410

275

294

100

16

222

50

3

0.92

     

G1790A

PB

410

275

332

72

6

232

41

2

0.90

Chen

2009

Taiwan

Asian

Oral

C1772T

PB

174

347

163

10

1

334

13

0

0.72

     

G1790A

PB

174

347

153

20

1

333

14

0

0.70

Konac

2009

Turkey

Caucasian

Lung

C1772T

PB

141

156

110

31

0

111

43

2

0.34

     

G1790A

PB

141

156

141

1

0

154

2

0

0.94

Morris

2009

UK

Caucasian

Renal

C1772T

PB

332

313

290

39

3

262

46

5

0.08

     

G1790A

PB

325

309

313

10

2

294

15

0

0.66

Foley

2009

Ireland

Caucasian

Prostate

C1772T

PB

95

188

65

30

0

175

13

0

0.62

Li

2009

China

Asian

Gastric

C1772T

HB

87

106

83

4

0

93

13

0

0.50

     

G1790A

HB

87

106

74

13

0

100

6

0

0.76

Munoz-

               

Guerra

2009

Spain

Caucasian

Oral

C1772T

PB

70

148

57

6

7

113

27

8

<0.01

     

G1790A

PB

64

139

40

21

3

130

9

0

0.69

Kim

2010

Korea

Asian

Cervical

C1772T

HB

199

214

177

22

0

187

27

0

0.32

     

G1790A

HB

199

213

187

12

0

200

12

1

0.10

Shieh

2010

Taiwan

Asian

Oral

C1772T

HB

305

96

282

23

0

89

7

0

0.71

     

G1790A

HB

305

96

281

24

0

89

7

0

0.71

Knechtal

2010

Austria

Caucasian

Colorectal

C1772T

PB

368

2156

291

77**

-

1773

383*

-

>0.05

     

G1790A

PB

367

2156

356

11*

-

2080

76*

-

>0.05

Hsiao

2010

Taiwan

Asian

Hepatocellul-ar

C1772T

HB

102

347

94

8

0

334

13

0

0.72

     

G1790A

HB

102

347

87

15

0

333

14

0

0.70

Xu

2011

China

Asian

Glioma

C1772T

HB

150

150

121

27

2

135

14

1

0.35

Putra

2011

Japan

Asian

Lung

C1772T

PB

83

110

74

9

0

98

12

0

0.55

     

G1790A

PB

83

110

72

9

2

101

9

0

0.65

Kang

2011

Korea

Asian

Colorectal

C1772T

PB

50

50

46

4**

-

38

12**

-

<0.01

Wang

2011

China

Asian

Pancreatic

C1772T

HB

263

271

209

54

0

242

29

0

0.35

     

G1790A

HB

263

271

198

65

0

249

22

0

0.49

Zagouri

2012

Greece

Caucasian

Breast

C1772T

HB

113

124

98

15

0

107

17

0

0.41

Kuo

2012

Taiwan

Asian

Lung

C1772T

HB

285

300

153

94

38

216

73

11

0.13

     

G1790A

HB

285

300

150

94

41

215

74

11

0.15

Qin

2012

China

Asian

Renal

C1772T

HB

620

623

572

46

2

578

43

2

0.22

     

G1790A

HB

620

623

575

45

0

584

39

0

0.42

Li

2012

China

Asian

Prostate

C1772T

HB

662

716

612

48

2

659

57

0

0.27

     

G1790A

HB

662

716

614

47

1

685

31

0

0.55

Alves

2012

Brazil

mixed

Oral

C1772T

PB

40

88

0

1

39

0

85

3

<0.01

     

G1790A

PB

40

88

2

1

37

81

7

0

0.70

Ruiz-Tovar

2012

Spain

Caucasian

Pancreatic

C1772T

PB

59

152

47

1

11

116

28

8

0.002

     

G1790A

PB

59

152

54

2

3

142

10

0

0.68

Fu

2013

China

Asian

Cervical

C1772T

HB

518

553

467

49

2

492

60

1

0.55

     

G1790A

HB

509

553

489

20

0

510

42

1

0.89

Ribeiro

2013

Portugal

Caucasian

Breast

C1772T

PB

96

74

74

21

1

61

7

4

0.001

     

G1790A

PB

96

74

96

0

0

74

0

0

0.99

Mera-

               

Menendez

2013

Spain

Caucasian

Glottic

           

larynx

C1772T

HB

118

148

85

18

15

113

27

8

0.001

    
     

G1790A

HB

111

139

107

4

0

130

9

0

0.69

Total

    

C1772T

 

10869

14289

8994

1568

307

12181

1897

211

 
     

G1790A

 

7117

10442

6416

589

112

9922

494

26

 

W: wide type alleles (1772C or 1790G); M: mutant type alleles (1772 T or 1790A); HWE: Hardy-Weinberg Equilibrium; PB: population based; HB: hospital based.

Mixed: Caucasian and African-American; HNSCC: head and neck squamous cell carcinoma.

*Frequency of genotypes “AA + AG”; **Frequency of genotypes “TT + TC”.

Quantitative data synthesis

For the HIF-1α C1772T polymorphism, the overall results from the eligible studies demonstrated a significant association between the HIF-1α C1772T polymorphism and an increased cancer risk in four genetic models (TT vs. CC: OR = 1.63, 95% CI = 1.02-2.60; CT + TT vs. CC: OR = 1.15, 95% CI = 1.01-1.34; TT vs. CT + CC: OR = 2.11, 95% CI = 1.32-3.77; T vs. C: OR = 1.21, 95% CI = 1.04-1.41). In the subgroup analysis by cancer type, the HIF-1α C1772T polymorphism significantly increased the risk of breast cancer in Asians (TT vs. CC: OR = 4.42, 95% CI = 1.60-12.21; TT vs. CT + CC: OR = 4.16, 95% CI = 1.51-11.48; T vs. C: OR = 1.28, 95% CI = 1.05-1.55), other cancers (TT vs.CC: OR = 3.18, 95% CI = 1.90-5.32; TT vs. CT + CC: OR = 3.31, 95% CI = 1.98-5.53; T vs. C: OR = 1.47, 95% CI = 1.10-1.96) and lung cancer (TT vs. CT + CC: OR = 3.27, 95% CI = 1.73-6.17 ). When the data was stratified by ethnicity, the HIF-1α C1772T polymorphism was significantly correlated with an increased cancer risk in Asian population (TT vs. CC: OR = 4.10, 95% CI = 2.49-6.76; CT + TT vs. CC: OR = 1.29, 95% CI = 1.04-1.58; TT vs. CT + CC: OR = 3.67, 95% CI = 2.23-6.02; T vs. C: OR = 1.28, 95% CI = 1.04-1.57) and Caucasian population (TT vs. CT + CC: OR = 1.95, 95% CI = 1.14-3.31). In the analysis stratified by the sources of controls, a significant association was observed in the hospital-based group (CT + TT vs. CC: OR = 1.28, 95% CI = 1.01-1.62; T vs. C: OR = 1.33, 95% CI = 1.04-1.71) and the population-based group (TT vs. CT + CC: OR = 2.01, 95% CI = 1.10-3.71). Sensitivity analyses were carried out to assess the stability of the results by conducting analyses of studies with controls in HWE. The results showed significantly increased cancer risk (TT vs. CC: OR = 2.47, 95% CI = 1.81-3.36; CT + TT vs. CC: OR = 1.25, 95% CI = 1.05-1.49; TT vs. CT + CC: OR = 2.43, 95% CI = 1.41-4.19; T vs. C: OR = 1.27, 95% CI = 1.06-1.52). The other results for the HIF-1α C1772T polymorphism were similar to those when the studies with controls not in HWE were included. The main results of this pooled analysis are shown in Table 2. Figure 2 shows the forest plot of the association between cancer risk and the HIF-1α C1772T polymorphism under the allelic model.
Table 2

Meta-analysis of the HIF-1α C1772T polymorphism and cancer risk

Variables

  

TT vs.CC

CT vs.CC

CT + TT vs.CC

TT vs.CT + CC

T vs.C

Study

Case/control

I 2

Phet

OR (95% CI)

Case/control

I 2

Phet

OR (95% CI)

Case/control

I 2

Phet

OR (95% CI)

case/control

I 2

Phet

OR (95% CI)

Case/control

I 2

Phet

OR (95% CI)

Overall

40

9301/12392

67

<0.001

1.63 (1.02-2.60)*

10562/14078

68

<0.001

1.08 (0.92-1.26)*

10958/14676

70

<0.001

1.15 (1.01-1.34)*

10540/12470

71

<0.001

2.11 (1.32-3.37)*

21738/28578

76

<0.001

1.21 (1.04-1.41)*

Overall in HWE

31

7429/9947

59

0.02

2.21 (1.27-3.83)*

8481/11109

64

<0.001

1.15 (0.98-1.36)*

8604/11556

70

<0.001

1.20 (1.02-1.41)*

8275/9350

49

0.01

2.13 (1.28-3.55)*

17208/22338

76

<0.001

1.22 (1.03-1.44)*

Cancer type

Cervical

3

664/750

66

0.09

10.11 (2.55-40.05)

739/871

60

0.08

0.98 (0.72-1.34)

749/874

80

0.01

1.32 (0.61-2.87)*

749/874

51

0.15

8.55 (2.28-32.13)

2369/1748

88

<0.001

1.41 (0.59-3.35)*

Breast

6

1859/1809

62

0.03

1.41 (0.34-5.75)*

2117/2033

37

0.16

1.01 (0.91-1.33)

2143/2046

46

0.1

1.13 (0.94-1.36)

2143/2046

60

0.04

1.38 (0.35-5.46)*

4286/4092

56

0.04

1.09 (0.80-1.48)*

Breast in HWE

5

1784/1744

55

0.08

2.30 (1.08-4.91)

2022/1963

35

0.19

1.07 (0.88-1.29)

2047/1972

56

0.06

1.12 (0.92-1.35)

2047/1972

49

0.12

2.27 (1.06-4.82)

4154/3944

65

0.02

1.09 (0.76-1.55)*

Breast in Asian

3

1605/1564

0

0.93

4.42 (1.60-12.21)

1809/1742

36

0.21

1.14 (0.92-1.41)

1832/1746

51

0.13

1.22 (0.99-1.49)

1832/1746

0

0.91

4.16 (1.51-11.48)

3664/3492

55

0.11

1.28 (1.05-1.55)

Lung

3

375/438

75

0.04

1.41 (0.07-30.44)*

471/553

75

0.02

1.13 (0.59-2.19)*

509/566

86

0.01

1.19 (0.51-2.76)*

509/566

71

0.07

3.27 (1.73-6.17)

1018/1132

89

<0.001

1.19 (0.50-2.86)*

Colorectal

4

599/2123

-

-

-

624/2175

79

0.03

0.24 (0.01-5.51)*

627/2177

71

0.02

1.12 (0.57-2.18)*

627/2177

-

-

-

1254/4354

80

0.02

0.26 (0.01-6.38)*

Prostate

6

3149/3415

70

0.01

1.34 (0.54-3.31)*

3766/4032

86

<0.001

1.34 (0.93-1.92)*

3816/4084

87

<0.001

1.36 (0.95-1.96)*

3816/4084

69

0.01

1.31 (0.54-3.20)*

7632/8168

87

<0.001

1.35 (0.96-1.89)*

Prostate in HWE

4

1814/2067

59

0.09

1.57 (0.89-2.75)

2168/2417

88

<0.001

1.42 (0.84-2.40)*

2200/2438

87

0.01

1.50 (0.89-2.40)*

2200/2438

61

0.08

1.55 (0.89-2.72)

4400/4876

85

<0.001

1.44 (0.93-2.21)*

Renal

4

1015/1035

25

0.26

0.28 (0.12-1.28)

1065/1157

74

0.01

0.62 (0.31-1.24)*

1160/1241

70

0.02

0.62 (0.33-1.18)*

1160/1241

21

0.29

1.37 (0.92-2.04)

2320/2482

44

0.15

0.91 (0.73-1.12)

Renal in HWE

2

867/847

0

0.62

0.67 (0.21-2.13)

947/929

13

0.28

0.92 (0.67-1.26)

952/936

29

0.24

0.90 (0.67-1.22)

952/936

0

0.64

0.69 (0.22-2.17)

1904/1872

37

0.21

0.89 (0.67-1.19)

Oral

4

549/547

0

0.46

2.01 (0.75-5.41)

542/668

50

0.14

0.90 (0.55-1.47)

589/679

16

0.3

1.04 (0.66-1.64)

589/679

93

<0.001

22.82 (0.28-1887.72)*

1178/1358

88

<0.001

2.52 (0.71-8.98)*

Oral in HWE

2

446/423

-

-

-

478/443

0

0.5

1.28 (0.69-2.38)

479/443

0

0.4

1.35 (0.73-2.49)

479/443

-

-

-

958/886

0

0.32

1.41 (0.78-2.56)

Other

12

1033/2151

30

0.2

3.18 (1.90-5.32)

1190/2445

67

<0.001

1.18 (0.79-1.78)*

1276/2622

60

<0.001

1.34 (0.95-1.87)*

1276/2622

0

0.52

3.31 (1.98-5.53)

2434/4940

58

0.01

1.47 (1.10-1.96)*

Other in HWE

9

880/1032

56

0.08

5.10 (1.72-15.07)

1032/1758

60

0.01

1.47 (0.97-2.21)*

1041/1763

64

0.01

1.52 (0.99-2.34)*

1041/1763

24

0.27

4.47 (1.53-13.00)

2082/3526

67

0.01

1.52 (1.02-2.28)*

Ethnicity

Asian

20

5124/5781

0

0.96

4.10 (2.49-6.76)

5678/6335

50

0.01

1.20 (0.99-1.46)*

5787/6400

75

<0.001

1.29 (1.04-1.58)*

5787/6400

0

0.98

3.67 (2.23-6.02)

11574/12800

61

<0.001

1.28 (1.04-1.57)

Caucasian

16

1791/4247

74

<0.001

1.54 (0.72-3.27)*

2220/4781

76

<0.001

0.93 (0.65-1.33)*

2385/4921

59

0.01

1.07 (0.80-1.43)*

2385/4921

58

0.003

1.95 (1.14-3.31)*

4770/9842

78

<0.001

1.20 (0.91-1.57)

Caucasian in HWE

9

1473/3153

76

<0.001

2.28 (0.62-8.35)*

1738/3152

79

<0.001

1.20 (0.99-1.46)*

1776/3535

82

<0.001

1.28 (0.88-1.86)*

1776/3535

69

0.002

2.08 (0.68-6.37)*

3552/7070

86

<0.001

1.34 (0.86-2.07)

Source of control

HB

17

4608/5249

77

<0.001

3.28 (1.29-8.30)*

5259/6029

60

<0.001

1.18 (0.96-1.45)*

5348/6086

72

<0.001

1.28 (1.01-1.62)*

5348/6086

71

<0.001

2.85 (1.24-6.54)*

10696/12172

80

<0.001

1.33 (1.04-1.71)*

HB in HWE

15

3340/3962

35

0.13

4.88 (2.96-8.04)

3748/4467

56

0.01

1.24 (0.99-1.57)*

3810/4488

67

<0.001

1.33 (1.02-1.74)*

3810/4488

4

0.4

4.23 (2.58-6.93)

7620/8976

74

<0.001

1.38 (1.06-1.80)*

PB

23

4693/5303

54

0.01

1.33 (0.76-2.31)*

5303/7143

74

<0.001

0.99 (0.77-1.29)*

5521/8203

70

<0.001

1.10 (0.89-1.36)*

5521/8203

72

<0.001

2.02 (1.10-3.71)*

11042/16406

74

<0.001

1.18 (0.95-1.45)*

PB in HWE

15

4089/5985

49

0.04

1.51 (0.74-3.11)*

4733/6642

72

<0.001

1.10 (0.85-1.43)*

4794/6681

72

<0.001

1.17 (0.93-1.48)*

4794/6681

46

0.63

1.51 (1.01-2.27)

9588/13362

75

<0.001

1.14 (0.89-1.45)*

HWE: Hardy-Weinberg Equilibrium; PB: population based; HB: hospital based; Phet: P value for heterogeneity. *Random-effects model was used when P value for heterogeneity test <0.05; otherwise, fixed-effects model was used.

Figure 2

Forest plot of the association between cancer risk and the HIF-1α C1772T polymorphism using the allelicmodel (T vs. C).

For HIF-1α G1790A polymorphism, as shown in Table 3, the association between the HIF-1α G1790A polymorphism and increased cancer risk was significant for the pooled ORs under all of the genetic models (AA vs. GG: OR = 5.11, 95% CI = 2.08-12.56; GA vs. GG: OR = 1.45, 95% CI = 1.05-1.99; AA + AG vs. GG: OR = 1.63, 95% CI = 1.16-2.30; AA vs. GA + GG: OR = 4.41, 95% CI = 1.80-10.84; A vs. G: OR = 1.77, 95% CI = 1.23-2.25). In the subgroup analysis by cancer type, a significant association was observed in lung cancer (AA vs. GG: OR = 5.42, 95% CI = 2.74-10.70; GA vs. GG: OR = 1.72, 95% CI = 1.22-2.41; AA + AG vs. GG: OR = 2.14, 95% CI = 1.56-2.94; AA vs. GA + GG: OR = 4.52, 95% CI = 2.31-8.83; A vs. G: OR = 2.27, 95% CI = 1.74-2.95), pancreatic cancer (AA + AG vs. GG: OR = 3.14, 95% CI = 1.99-2.97; A vs. G: OR = 3.08, 95% CI = 1.98-4.78) and renal cancer (AA vs. GA + GG: OR = 3.09, 95% CI = 1.38-6.92). When the data were stratified by ethnicity, significantly increased cancer risk was observed in Asian population and Caucasian population. When the studies were stratified by the source of controls, a significant association was observed for population-based controls under the homozygote model, the dominant comparison model and the allelic model. Sensitivity analyses were conducted after the removal of the studies with controls not in HWE, the results for the HIF-1α G1790A polymorphism were similar to those when the studies with controls not in HWE were included. Table 3 shows the main results of this pooled analysis for the HIF-1α G1790A polymorphism. Figure 3 shows the forest plot of the association between cancer risk and the HIF-1α G1790A polymorphism under the dominant model.
Table 3

Meta-analysis of the HIF-1α G1790A polymorphism and cancer risk

Variables

  

AA vs.GG

GA vs. GG

AA + AG vs.GG

AA vs.GA + GG

A vs.G

Study

Case/control

I 2

Phet

OR (95% CI)

Case/control

I 2

Phet

OR (95% CI)

Case/control

I 2

Phet

OR (95% CI)

Case/control

I 2

Phet

OR (95% CI)

Case/control

I 2

Phet

OR (95% CI)

Overall

30

6538/9948

57

0.01

5.11 (2.08-12.56)*

7005/10442

77

<0.001

1.45 (1.05-1.99)*

7117/10442

83

<0.001

1.63 (1.16-2.30)*

7117/10442

58

0.01

4.41 (1.80-10.84)*

14234/20884

86

<0.001

1.77 (1.23-2.25)*

Overall in HWE

29

6449/9699

61

0.003

5.14 (1.67-15.86)*

6873/10138

69

<0.001

1.35 (1.01-1.81)*

6971/10154

79

<0.001

1.53 (1.10-2.12)*

6971/10154

60

0.004

4.80 (1.58-14.55)*

13942/20308

85

<0.001

1.70 (1.17-2.46)*

Cancer type

Breast

4

623/501

0

0.34

1.44 (0.38-5.44)

692/550

53

0.12

1.03 (0.70-1.52)

698/553

60

0.08

1.05 (0.72-1.53)

698/553

0

0.36

1.41 (0.37-5.40)

1396/1466

65

0.56

1.07 (0.76-1.52)

Cervical

3

708/819

0

0.99

0.35 (0.04-3.39)

740/871

57

0.13

0.62 (0.40-0.98)

740/837

51

0.15

0.60 (0.38-0.94)

740/837

0

0.99

0.36 (0.04-3.450

1480/1746

42

0.19

0.59 (0.38-0.91)

Oral

4

517/633

75

0.02

72.11 (2.08-2502.44)*

542/670

70

0.02

3.17 (1.26-7.92)*

583/670

92

<0.001

7.92 (1.58-39.64)*

583/670

75

0.02

58.05 (1.70-1985.77)*

1166/1340

96

0.01

9.66 (1.31-71.15)*

Prostate

3

1866/2230

-

-

-

1927/2280

1

0.37

1.42 (0.97-2.07)

1928/2280

7

0.34

1.44 (0.98-2.10)

1928/2280

-

-

-

3856/4560

10

0.33

1.45 (0.99-2.11)

Renal

4

1016/1267

0

0.95

5.10 (2.21-11.73)

1123/1354

92

<0.001

1.51 (0.45-5.05)*

1139/1364

92

<0.001

1.58 (0.49-5.04)*

1139/1364

0

0.76

3.09 (1.38-6.92)

2278/2728

89

<0.001

1.53 (0.60-3.92)*

Renal in HWE

3

937/1018

-

-

-

991/1076

0

0.42

1.00 (0.69-1.47)

993/1076

0

0.6

1.04 (0.71-1.52)

993/1076

-

-

-

1986/2152

0

0.78

1.07 (0.74-1.55)

Lung

3

405/481

0

0.87

5.42 (2.74-10.70)

466/555

0

0.57

1.72 (1.22-2.41)

509/566

0

0.46

2.14 (1.56-2.94)

509/566

0

0.79

4.52 (2.31-8.83)

1018/1132

0

0.48

2.27 (1.74-2.95)

Colorectal

2

545/2327

-

-

-

554/2336

-

-

-

554/2336

0

0.45

0.97 (0.57-1.63)

554/2336

-

-

-

1108/4672

-

-

-

Pancreatic

2

255/391

-

-

-

319/423

82

0.02

1.61 (0.24-10.76)*

322/423

63

0.1

3.14 (1.99-4.97)

322/423

-

-

-

644/846

0

0.42

3.08 (1.98-4.78)

Other

7

593/1377

-

-

-

642/1377

74

<0.001

1.53 (0.65-3.59)*

644/1377

72

<0.001

1.57 (0.70-3.53)*

644/1377

-

-

-

1288/2754

69

0.01

1.57 (0.75-3.30)*

Ethnicity

Asian

15

3607/4263

13

0.33

3.50 (2.05-5.98)

4010/4614

74

<0.001

1.44 (1.04-1.99)*

4063/4630

76

<0.001

1.49 (1.07-2.08)*

4063/4630

0

0.45

3.12 (1.83-5.32)

8126/9260

77

<0.001

1.49 (1.08-2.05)*

Caucasian

13

1829/4357

0

0.69

6.63 (3.11-14.12)

1926/4450

81

<0.001

1.36 (0.58-3.19)*

1948/4460

82

<0.001

1.45 (0.69-3.04)*

1948/4460

0

0.49

4.21 (2.04-8.71)

3896/8920

75

<0.001

1.65 (0.84-3.24)*

Caucasian in HWE

12

1750/4108

0

0.74

12.40 (2.19-70.22)

1794/4172

68

0.01

1.10 (0.48-2.49)*

1802/4172

67

0.01

1.22 (0.62-2.37)*

1802/4172

0

0.79

11.37 (2.02-63.93)

3604/8344

68

0.01

1.65 (1.17-2.32)*

Source of control

HB

13

3197/3945

45

0.12

1.54 (0.35-6.70)

3510/4234

77

<0.001

1.37 (0.92-2.05)*

3554/4248

79

<0.001

1.40 (0.93-2.11)*

3554/4248

35

0.19

3.13 (1.74-5.62)

7108/8496

79

<0.001

1.38 (0.93-2.05)*

PB

17

3133/5705

66

0.01

11.55 (6.62-20.12)*

3295/5882

78

<0.001

1.51 (0.88-2.58)*

3563/6194

85

<0.001

1.90 (1.06-3.39)*

3563/6194

69

0.002

10.27 (2.42-43.63)*

6726/11788

89

<0.001

2.25 (1.18-4.29)*

PB in HWE

16

3054/5456

67

0.006

15.51 (2.53-94.94)*

3163/5604

60

0.01

1.34 (0.85-2.11)*

3417/5906

81

<0.001

1.71 (0.97-3.03)*

3417/5906

66

0.007

14.20 (2.38-84.61)*

6434/11212

89

<0.001

2.33 (1.91-2.84)*

HWE: Hardy-Weinberg Equilibrium; PB: population based; HB: hospital based; Phet: P value for heterogeneity. *Random-effects model was used when P value for heterogeneity test <0.05; otherwise, fixed-effects model was used.

Figure 3

Overall association between the HIF-1α G1790A polymorphism and cancer risk for all subjects using the dominant model (GA + AA vs. GG).

Test of heterogeneity

There was significant heterogeneity observed in the allelic comparison model, the dominant comparison model and the heterozygote comparison model (Tables 2 and 3), and the heterogeneity was effectively decreased or removed in the subgroups stratified by ethnicity, cancer types and source of controls (Tables 2 and 3).

Sensitivity analysis

We performed sensitivity analysis by removing each individual study (including the restudies with controls not in HWE) sequentially for both the HIF-1α C1772T and the HIF-1α G1790A polymorphism (Figure 4 and Additional file 1). The results indicated that the overall significance of the pooled ORs was not altered by any single study in the genetic models for the HIF-1α C1772T/G1790A polymorphisms and cancer susceptibility, suggesting stability and reliability in our overall results.
Figure 4

The influence of individual studies on the summary odds ratio (OR) for the HIF-1α G1790A polymorphism.

Bias diagnostics

A Begg’s funnel plot and Egger’s test were used to assess the publication bias in this meta-analysis. As shown in Figure 5, for the HIF-1α C1772T polymorphism, the funnel plots for the comparison of the five models appear to be basically symmetric. The Egger’s linear regression test did not show any evidence of significant publication bias in five models (TT vs. CC: t = 0.50, P = 0.62; TC vs. CC: t = -0.19, P = 0.85; TT vs. CT + CC: t = 1.11, P = 0.28; T vs. C: t = 1.39, P = 0.17; CT + TT vs. CC: t = 0.59, P = 0.56). For the HIF-1α G1790A polymorphism, no visual publication bias was detected in the funnel plot (Figure 6) and the result showed no significant evidence of a publication bias in five models(AA vs. GG: t = 0.03, P = 0.98; GA vs. GG: t = -0.86, P = 0.40; AA vs. GA + GG: t = 0.33, P = 0.75; AA + AG vs.GG: t = -0.40, P = 0.69; A vs. G: t = -0.41, P = 0.68).
Figure 5

Begg’s funnel plot for evaluating the publication bias of the meta-analysis for the HIF-1α C1772T polymorphism.

Figure 6

Begg’s funnel plot for evaluating the publication bias of the meta-analysis for the HIF-1α G1790A polymorphism.

Discussion

HIF-1 is a heterodimeric transcription factor and a key regulator of the cellular response to hypoxia [5]. It is composed of HIF-1α and HIF-1β subunits, which are members of the bHLH-PAS transcription factor family. HIF-1α is a unique O2-regulated subunit that determines the function of HIF-1. HIF-1α upregulates the expression of genes whose protein products function to increase O2 availability or to allow metabolic adaptation to O 2 deprivation, including VEGF, Epo, NOS2 and others. Most of these aforementioned proteins have been implicated in tumor development and progression [35, 64, 65]. Recent studies have reported that the overexpression of HIF-1α is significantly associated with cell proliferation, tumor susceptibility, tumor size, lymph node metastasis and prognosis [12, 35, 66]. The HIF-1α gene is located on chromosome 14q21-24 and contains a total of 35 common SNPs, according to the dbSNP database (http://www.ncbi.nlm.nih.gov/SNP). Two polymorphisms, C1772T (rs11549465) and G1790A (rs11549467), result in an amino acid substitution of proline to serine and alanine to threonine, respectively, and the present studies show that C1772T (rs11549465) is not in substantial linkage disequilibrium (LD) with G1790A (rs11549467) (R2 = 0.002). Under normoxic condition, the hydroxylation of proline 402 and proline 564 occurs within the oxygen-dependent degradation (ODD) domain of HIF-1α, and HIF-1α is rapidly degraded. The two SNPs examined here are located within the ODD/pVHL binding domain in exon 12 of the HIF-1α gene and may enhance the transcription activity of the HIF-1α gene by causing structural changes, increasing the stability of HIF-1α protein and affecting the expression of downstream target genes [8, 14, 17]. Over the last few years, a great number of studies have been performed to investigate the association between these HIF-1α polymorphisms and cancer risk in different populations. However, the results of these studies remain inconclusive. In a meta-analysis conducted by Zhao et al. in 2009 [67], the HIF-1 C1772T polymorphism was reported to be associated with increased cancer risk, while no significant association was found between the HIF-1α G1790A polymorphism and cancer risk. Additionally, Li et al. reported that the HIF-1α C1772T polymorphism correlates with urinary cancer risk in Caucasian population, and the G1790A polymorphism may increase the risk of prostate cancer [68]. Due to the important role of HIF-1α polymorphisms in the development of cancer and due to the limited statistical power of the previous studies, we conducted a comprehensive literature search and performed a meta-analysis on all of the available case-control studies to systematically evaluate the exact relationship between the C1772T/G1790A polymorphisms in HIF-1α and cancer susceptibility.

Regarding the HIF-1α C1772T polymorphism, our results suggested a significant association in four genetic comparison models, providing convincing evidence that the HIF-1α C1772T polymorphism may be a risk factor in cancer development. When sensitivity analyses were performed, the results were similar to those when the studies with controls not in HWE were included, suggesting that our results were very robust. Moreover, when the data were stratified by cancer type, a significant association was observed between the C1772T polymorphism and breast cancer in Asians. This may be due to the specific genetic variant induced over-expression of HIF-1 under hypoxic condition in breast cancer cells, and the different life style, ethnicity and body composition between Asians and Caucasians, which could contribute to the results. A significant association was also observed in lung cancer. When subgroup analyses were performed according to ethnicity and source of controls, a significant association was found in Asian population, Caucasian population and in hospital-based studies. Zhao et al.[67] reported that the genotype TT was significantly associated with an increased cancer risk in Asians, but the CI was very wide due to the lack of mutant homozygotes in Asians. In our meta-analysis, we also found that the C1772T polymorphism was a risk factor in Asians (Dominant model: OR = 1.29, 95% CI = 1.04-1.58; Allelic model: OR = 1.47, 95% CI = 1.04-1.57). Beyond that, we had not found any significant associations in prostate cancer, renal cancer or oral cancer.

For the HIF-1α G1790A polymorphism, the pooled results from all of the eligible studies suggested that the G1790A polymorphism in HIF-1α is significantly associated with an increased cancer risk in all of the genetic models. We also conducted subgroup analyses based on the cancer type, ethnicities and source of controls. In the subgroup analysis according to cancer type, the results suggested that the HIF-1α G1790A polymorphism significantly increased the risk of lung cancer, renal cancer, oral cancer and pancreatic cancer, but the CI for the oral cancer subgroup was very wide. This may be due to the lack of mutant homozygotes detected, and the association could have been caused by chance. More studies based on large populations should be prusued. The study reported by Putra et al. indicated that even though they did not found any significant differences in genotype for G1790A between lung cancer patients and healthy controls, however, the G1790A variant allele was significantly higher in lung cancer patients, and TP53 LOH and 1p34 LOH were more frequently observed in individuals with the HIF-1α G1790A polymorphism, suggesting that this polymorphism may induce mutations in some tumor suppressor genes involved in lung cancer development [46]. Here, we found a significant association between the G1790A polymorphism and lung cancer risk. When the data were stratified according to ethnicity classification and source of controls, similar to the C1772T polymorphism, significantly increased risks were also found in Asian populations, Caucasian populations and population-based studies. After sensitivity analyses were performed, our results did not vary substantially, which strongly suggests an association between the HIF-1α G1790A polymorphism and increased cancer risk. One important factor that could influence the results is heterogeneity. In our study, significant heterogeneity existed in the analysis of the heterozygote model, the dominant model and the allelic model for the HIF-1α C1772T/G1790A polymorphism. When we performed a subgroup analysis according to cancer type, ethnicity or source of controls, the heterogeneity was reduced significantly or disappeared. The significant heterogeneity may due to the differences in ethnicity or cancer types or even in the selection of the controls. Furthermore, publication bias was not observed in our meta-analysis of the HIF-1α G1790A/C1772T polymorphisms. We also performed a sensitivity analysis to evaluate the sources of heterogeneity. The pooled ORs did not vary substantially, indicating that the results of our meta-analysis are robust and reliable.

To a certain extent, our meta-analysis still includes several limitations that should be interpreted and taken into consideration. First, in the era of GWAS, researchers can obtain the GWAS data for these two SNPs from all cancer studies and conduct a meta-analysis with the GWAS data instead of relying on published data, which may be biased toward positive findings. Second, the lack of observations concerning gene-gene and gene-environment interactions could influence our results. Third, although the total number of studies was not small, there were still not sufficient eligible studies for us to analyze different types of cancers, such as colorectal carcinoma, renal cell carcinoma or glioma; more studies are needed to explore the potential relationship between HIF-1αC1772T/G1790A polymorphisms and cancer susceptibility. Forth, the lack of detailed original data, such as the age and sex of the populations, smoking status, or alcohol consumption in the eligible studies may influence our extended analyses. However, our meta-analysis also has many advantages. First, we searched all possible publications, and the total number of eligible studies was much larger than other previously published meta-analyses; therefore, our results are more convincing. Second, no publication bias was detected in our meta-analysis. Finally, all of the data were extracted from well-selected studies, providing stronger statistical power for our study.

Conclusions

In conclusion, this meta-analysis provides powerful evidence that both the C1772T and G1790A polymorphisms in the HIF-1α gene may contribute to individual susceptibility to cancers. It will be necessary to perform additional research to evaluate the relationship between HIF-1α C1772T/G1790A polymorphisms and cancer risk. Moreover, large sample case-control studies assessing gene-to-gene and gene-to-environment interactions are required to verify these findings.

Authors’ information

Qing Yan, Pin Chen and Songtao Wang are joint first authors.

Notes

Declarations

Acknowledgements

This work is supported by the National Natural Science Foundation of China (grant 30901534, 81172694 and 81473013); the Grant for the 135 Key Medical Project of Jiangsu Province (No. XK201117); and the National high technology research and development program 863 (No. 2012AA02A508).

Authors’ Affiliations

(1)
Department of Neurosurgery, The First Affiliated Hospital, Nanjing Medical University

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