Open Access

SNP 1772 C > T of HIF-1α gene associates with breast cancer risk in a Taiwanese population

Cancer Cell International201414:87

https://doi.org/10.1186/s12935-014-0087-7

Received: 13 April 2014

Accepted: 25 August 2014

Published: 26 September 2014

Abstract

Background

Hypoxia inducible factor 1α (HIF-1α) is a stress-responsive transcription factor to hypoxia and its expression is correlated to tumor progression and angiogenesis. Several single nucleotide polymorphisms (SNPs) of HIF-1α gene in the oxygen-dependent degradation (ODD) domain was reportedly associated with increased HIF-1α activity.

Results

In this study, we focused on the relationship between SNP 1772 C > T (rs11549465) of HIF-1α gene and its breast cancer risk, as well as its correlation with HIF-1α expression and tumor angiogenesis. Ninety six breast cancer patients and 120 age-matched controls were enrolled. We found that 1772 T allele of HIF-1α gene was associated with increased breast cancer risk (adjusted OR = 14.51; 95% CI: 6.74-31.24). This SNP was not associated with clinicopathologic features of angiogenesis such as VEGF activity and the micro-vessel density and survival of breast cancer patients.

Conclusion

Taken together, the 1772 C > T of HIF-1α gene is a potential biomarker for breast cancer susceptibility.

Keywords

HIF-1α SNPs Breast cancer Association study Survival

Background

Single nucleotide polymorphisms (SNPs), the most common variants in human genome [1], are popular biomarkers for disease/cancer prediction and therapeutic evaluation [2]-[8]. Most SNPs have been reported to be associated with breast cancer [9]-[11], however, other SNPs are still potential to be associated with breast cancer.

Tumor hypoxia is common in tumorigenesis. Hypoxia inducible factor-1 (HIF-1) is a crucial transcription factor in cellular response to tumor hypoxia and is considered as an adverse prognostic factor in breast cancers [12]-[14]. Additionally, the HIF-1α isoform is the oxygen-regulated component that controls HIF-1 activity [15]. The degradation of HIF-1α depends on prolyl hydroxylation. Under normoxic status, oxygen-dependent prolyl hydroxylases [16],[17] may hydroxylate the HIF-1α on proline residues 402 and 564 located in the oxygen-sensitive degradation domain (ODD, encoded by codons 401-603) of HIF-1α. In contrast, degradation of HIF-1α is suppressed under hypoxic status. Therefore, the SNPs located at several proline residues of HIF-1α gene in breast cancer association are potential to modulate the HIF-1α activity.

Recent studies demonstrated that another SNP located in ODD of HIF-1α, 1772 C > T (rs11549465), may lead to an amino acid change from proline 582 to serine (P582S) and are reportedly associated with renal [18],[19], head and neck [20], prostate [21], lung [22], and pancreatic [23] cancers. Meta-analysis from 34 case-control studies also reported that SNP 1772 C > T (P582S) of HIF-1α gene is significantly associated with breast cancer risk in many countries [24]. However, the association of SNP 1772 C > T (rs11549465) of the HIF-1α gene to breast cancer remains unclear in a Taiwanese population.

The purpose of this study is to investigate the association between SNP 1772 C > T of the HIF-1α gene in breast cancer patients and healthy control subjects. Furthermore, HIF-1 has been reported to transactivate many oxygen responsive genes such as vascular endothelial growth factor (VEGF) [25]. Therefore, the relationships between genotypes of SNP 1772 C > T of HIF-1α gene and the clinicopathologic characteristics, the immunostaining expression levels of HIF-1α and VEGF, and clinical outcomes of breast cancer are also addressed in this study.

Methods and materials

Patient characteristics and control subjects

Between 1991 and 2001, a total of 96 randomly-selected female patients with breast cancer at Kaohsiung Medical University Hospital, Kaohsiung, Taiwan, were enrolled in this study. All patients underwent a standard modified radical mastectomy. Ninety-four patients (94/96, 98%) received adjuvant systemic chemotherapy with 6 cycles of 5-fluorourcil, doxorubicin and cyclophosphamide. After completion of chemotherapy, all patients received hormone therapy with tamoxifen and 92 patients (92/96, 96%) received radiation therapy. The principle of treatment was followed as described previously [26]. We collected clinical data including clinical stage, treatment outcomes and follow-up status. Controls were recruited from 120 healthy female without a history of cancer and matched to the breast cancer patients by sex and age.

DNA extraction and PCR-RFLP

Genomic DNA was isolated from paraffin-embedded tumor tissues of surgical specimens and peripheral blood of 120 normal controls as described [27],[28]. The sequence of primers for HIF-1α is as follows: forward 5′-AGGACACAGATTTAGACTTGG-3′ and reverse 5′-GGAATACTGTAACTGTGCTTTG-3′. PCR reaction mixture (10 μl) contained 1 μl of 10× PCR buffer, 0.3 μl of 50 mM MgCl2, 0.2 μl of 10 mM dNTP each, 0.6 μl DMSO, 0.14 μl of Taq enzyme, 0.12 μl of 350 μg/ml primers mix (1:1), 2 μl DNA extracts and 5.64 μl distilled water. PCR was performed with the following protocol: 94°C (1 min); 4°Cycles of 94°C (15 s), 64°C (15 s), 70°C (8 s); 4°Cycles of 94°C (15 s), 61°C (15 s), 70°C (8 s); 4°Cycles of 94°C (15 s), 58°C (15 s), 70°C (8 s); 60°Cycles of 94°C for (15 s), 55°C (15 s), 70°C (8 s); 94°C (1 min) and 60°C (5 min). The available restriction enzyme for HIF-1α 1772 C > T (rs11549465) was retrieved from the SNP-RFLP freeware [29]-[31]. PCR products were digested with the Hph I restriction enzyme (NEB) at 37°C for overnight and then they were subjected to 3% agarose electrophoresis and stained with SYBR Safe™ DNA gel stain (Invitrogen) for visualization of the PCR-restriction fragment length polymorphism (PCR-RFLP) patterns.

Sequencing

Typical patterns of genotyping by PCR-RFLP have confirmed by sequencing. DNA amplicon from PCR reaction was purified using a MiniElute PCR purification kit (Qiagen) [28] for commercial sequencing.

Immunohistochemical analyses of HIF-1α and VEGF proteins

Streptoavidin-biotin based immunohistochemical staining (IHC) was performed to detect HIF-1α and VEGF protein levels as previously described [32]. Immunoreactivity of HIF-1α was located in both nuclei and cytoplasm. Using a semiquantitative scale described previously [33], the HIF-1α expression were classified as follows: 1+, nuclear staining in less than 1% of cells; 2+, nuclear staining in 1-10% of cells and/or with weak cytoplasmic staining; 3+, nuclear staining in 10-50% of cells and/or with distinct cytoplasmic staining; 4+, nuclear staining in more than 50% of cells and/or with strong cytoplasmic staining. For further analysis, we defined two groups of low and high HIF-1α expression: 1+ or 2+ staining pattern regarded as low expression, and 3+ or 4+ staining pattern as high expression. VEGF expression was assessed according to the intensity of cytoplasmic staining as described previously [32]. VEGF expression was detected tumor cells in a distinct and strongly cytoplasmic staining. VEGF staining was defined as four grades as follows: no staining, weak, distinct and strong cytoplasmic staining. Distinct and strong cytoplasmic staining was defined as high VEGF and negative or weak cytoplasmic staining was defined as low VEGF expression.

Immunohistochemical analysis for microvessel detection

Microvessel density (MVD) represents tumor angiogenesis by using immunostaining of endothelial cells with monocloncal antibody, recognizing the CD31 endothelial glycoprotein. Each slide was scanned at low magnification (× 100) to identify the four areas of high density of microvessels (hotspots). The number of stained vessels per in each hotspot was counted at high power fields (× 400). Any stained endothelia cell was considered as a countable single microvessel. Large vessels with thick muscular walls were excluded. MVD was classified as either low (35.0) or high (>35.0/high power field (HPF)); 35.0 was the median value.

Statistical analysis

Statistical significance was evaluated by the chi-square test and Fisher exact test. Overall survival curves were analyzed by the Kaplan-Meier method, and differences between the curves were analyzed by log-rank test. The p values smaller than 0.05 are regarded as significance.

Results

In Table 1, the mean age of the breast cancer patients was 46.5 years (range 19-73 years), and this was 44.6 years for controls (range 21-77 years). There was no significant difference between breast cancer patients and controls in age (p = 0. 22).
Table 1

HIF-1α 1772 C > T genotype and allele frequencies in breast cancer patients and control subjects

Parameters

Breast cancer patients

Control subjects

pvaluea

CrudeOR

AdjustedORb

pvalue

95% CI

Age

46.5 ± 9.9 (19-73)

44.6 ± 11.5 (21-77)

0.224

    

CC (%)

53 (55%)

116 (97%)

 

1.00

1.00

  

CT (%)

21 (22%)

0 (0%)

     

TT (%)

22 (23%)

4 (3%)

<0.001

12.04

11.33

<0.001

3.70-34.72

CT/CC (%)

74 (77%)

116 (97%)

 

1.00

1.00

  

TT (%)

22 (23%)

4 (3%)

<0.001

8.62

8.31

<0.001

2.74-25.25

CC (%)

53 (55%)

116 (97%)

 

1.00

1.00

  

CT/TT (%)

43 (45%)

4 (3%)

<0.001

23.53

23.23

<0.001

7.92-68.09

C genotype (%)

127 (66%)

232 (97%)

 

1.00

1.00

  

T genotype (%)

65 (34%)

8 (3%)

<0.001

14.84

14.51

<0.001

6.74-31.24

aComparisons were performed by Chi-Square test.

bAdjusted by age by conditional logistic regression analysis.

OR = odds ratio; CI = Confidence interval.

In Figure 1A, RFLP results demonstrated that CC genotype yielded one band (76 base pairs), CT genotype yielded two bands (76 bp, C-allele; 153 bp, T allele) and TT genotype yielded one band (153 bp). The corresponding genotypes of homozygous and heterozygous patterns from PCR-RFLP had confirmed by DNA sequence analysis (Figure 1B).
Figure 1

PCR-RFLP genotyping and sequencing of SNP 1772 C > T of HIF-1α gene. (A) PCR-RFLP genotyping of SNP 1772 C > T of HIF-1α gene (76 bp, C-allele; 153 bp, T-allele) in formalin-fixed, paraffin-embedded breast cancer tissues. (B) Sequence chromatograms of PCR-RFLP product contained SNP 1772 C > T of HIF-1α gene. Arrow indicated location of 1772 C > T.

Based on PCR-RFLP analysis, the genotype distribution of control group was 116 CC (97%), 0 CT (0%) and 4 TT (3%). In contrast, the genotype distributions of breast cancer patients were 53 CC (55%), 21 CT (22%), and 22 TT (23%). The genotype distribution in breast cancer patients differed significantly from that of controls (p < 0.001). The allele frequencies in controls and cancer patients were 232 C (97%)/8 T (3%) and 127 C (66%)/65 T (34%), respectively. The T-allele distribution in breast cancer patients differed significantly from that of controls (p < 0.001, adjusted OR = 14.51).

Immunoreactivity of HIF-1α was distributed in both nuclei and cytoplasm (Figure 2A). VEGF expression was measured by its cytoplasmic staining (Figure 2B). Microvessel density (MVD) representing tumor angiogenesis was measured by immunostaining of CD31 endothelial glycoprotein (Figure 2C).
Figure 2

Expression of (A) HIF-1α , (B) VEGF and (C) CD34 for microvessel density (200×) of a 34 year-old female patient with T2N1M0 breast cancer.

When connecting the results of these stainings with HIF-1α genotypes with clinicopathological analysis (Table 2), there were no significant correlation between 1772 C > T genotypes (CC, CT and TT) of HIF-1α gene and age (p = 0.117), T-stage (p = 0.303), N-stage (p = 0.936), local recurrence (p = 0.817), distant metastasis (p = 0.572), HIF-1α expression (p = 0.311), VEGF expression (p = 0.375) and microvessel density (p = 0.211).
Table 2

Clinicopathologic characteristics, clinical outcomes in breast cancer patients with different HIF-1α 1772 C > T genotypes

Genotype

CC (%)

CT (%)

TT (%)

pvalue

Case number

53 (55%)

21 (22%)

22 (23%)

 

Age

   

0.117a

 Mean ± SD (years)

46.5 ± 9.7

43.1 ± 10.3

49.4 ± 9.6

 

 Range (years)

27 ~ 68

19 ~ 62

31 ~ 73

 

Laterality

   

0.463b

 Left

26 (52%)

10 (20%)

14 (28%)

 

 Right

27 (58%)

11 (24%)

8 (17%)

 

T-stage

   

0.303b

 T1 or T2

35 (53%)

13 (20%)

18 (27%)

 

 T3 or T4

18 (60%)

8 (27%)

4 (13%)

 

N-stage

   

0.936b

 Node negative

14 (58%)

5 (21%)

5 (21%)

 

 Node positive

39 (54%)

16 (22%)

17 (24%)

 

HIF-1α expression

   

0.311b

 Low

34 (51%)

15 (22%)

18 (27%)

 

 High

19 (66%)

6 (21%)

4 (14%)

 

VEGF expression

   

0.375b

 Low

18 (62%)

7 (24%)

4 (14%)

 

 High

35 (52%)

14 (21%)

18 (27%)

 

Microvessel density

   

0.211b

 Low

32 (63%)

8 (16%)

11 (22%)

 

 High

21 (47%)

13 (29%)

11 (24%)

 

aby ANOVA test.

bby Chi-Square test.

In Table 3, the multi-variable analyses in the determination of risk factors of disease-free survival and overall survival indicated that T-stage (Exp. (B) = 4.7270, p < 0.001) and microvessel density (Exp. (B) = 2.6082, p < 0.05) were the most influential factors (Table 3). However, the SNP 1772 C > T genotypes of HIF-1α gene were not correlated with the disease-free survival (p = 0.35, Cox regression) and overall survival (p = 0.59, Cox regression) by multi-variable analyses. Similarly, Kaplan-Meier analysis (Figures 3A and 3B) also showed a nonsignificant impact of 1772 C > T genotypes of HIF-1α gene on disease-free survival (p = 0.820, Log-Rank test) and overall survival curves (p = 0.963, Log-Rank test), respectively.
Table 3

Multivariate analysis of the risk factors on disease-free and overall survival in the 96 breast cancer patients

Variablea

SE

pvalueb

Exp. (B)

95% CI of Exp. (B)

Disease-free survival time

     

 Age

0.0253

0.1280

0.9622

0.9156

~1.0112

 1772 C > T genotype

0.2715

0.3527

1.2871

0.7559

~2.1913

 T-stage

0.4033

0.0001

4.7270

2.1445

~10.4196

 N-stage

0.5291

0.9816

1.0122

0.3588

~2.8554

 Microvessel density

0.4877

0.0493

2.6082

1.0028

~6.7837

 VEGF expression

0.5858

0.2901

1.8584

0.5895

~5.8587

 HIF-1α expression

0.4346

0.0732

2.1784

0.9294

~5.1059

Overall survival time

     

 Age

0.022

0.4883

0.9846

0.9422

~1.0288

 1772 C > T genotype

0.300

0.5908

0.8508

0.4722

~1.5330

 T-stage

0.446

0.0017

4.0350

1.6850

~9.6624

 N-stage

0.589

0.8594

1.1099

0.3502

~3.5181

 Microvessel density

0.646

0.0052

6.0924

1.7175

~21.6115

 VEGF expression

0.610

0.9791

0.9841

0.2979

~3.2517

 HIF-1α expression

0.481

0.3225

1.6094

0.6269

~4.1315

aby Cox regression. SE, standard error; Exp. (B), exponent (B); CI, confidence interval.

bBold numbers indicate significance.

Figure 3

Kaplan-Meier disease-free and overall survival curves in breast cancer patients with different genotypes (1772 C > T) of HIF-1α gene. (A) Disease-free survival. (B) overall survival curves.

Discussion

The SNP 1772 C > T of HIF-1α gene chosen in current study are located within ODD of the HIF-1α. We found that T allele of the SNP 1772 C > T (P582S) of HIF-1α gene was significantly higher in 96 breast cancer patients than in 120 controls. In contrast, the association results of SNP 1772 C > T of HIF-1α gene with different kinds of cancers were not consistent in literature review. For example, the SNP 1772 C > T of HIF-1α gene was detected in several cancers [18]-[21],[23] but it was absent for colorectal [34], and cervical [35] cancers.

Within ODD of the HIF-1α, proline residues 402 and 564 were reported to independently determine tightly binding to the von Hippel-Lindau (VHL) protein for HIF-1α ubiquitination and degradation under nonhypoxia condition [17],[36]-[39]. In current study, however, the proline residue 582 located within ODD of the HIF-1α, i.e., the SNP 1772 C > T, was unable to interfere the binding of HIF-1α with VHL and to impair HIF-1α prolyl hydroxylation [40]. Similarly, the genotypes of SNP 1772 C > T of HIF-1α gene did not show significant difference between low and high HIF-1α levels in terms of immunostaining (Table 2). Other study [41] found that the HIF-1α overexpressed in immunostaining measurement for invasive breast cancer in the absence of 1772 C > T transition of HIF-1α gene. Accordingly, the role of SNP 1772 C > T of HIF-1α gene in its protein expression level is not clear. In future, the examination of more expression patterns of HIF-1α protein in these patients may clearly investigate this relationship.

Furthermore, the genotypes of SNP 1772 C > T of HIF-1α gene are not significantly associated with clinicopathologic characteristics and clinical outcome of breast cancer (Table 2) although SNP 1772 C > T of HIF-1α gene confers significant association with breast cancer (Table 1). Similar results were reported in prostate cancer study [21]. Therefore, the SNP 1772 C > T of HIF-1α gene is a good predictor for breast cancer risk but may be a poor clinicopathologic-associated factor.

The relationship between expression levels of HIF-1α and survival of breast cancer patients has been investigated. For example, high levels of HIF-1α were reportedly associated with decreased overall survival (p = 0.059) and disease-free survival (p = 0.110) [42]. Similarly, we found that HIF-1α expression shows the association with disease-free survival (p = 0.0732) but weak association with overall survival (p = 0.3225) (Table 3). These results suggest that expression levels of HIF-1α may be the potential risk factor for survival prediction of breast cancer.

The phenomena mentioned above may be partly explained by the multigene theory for carcinogenesis [43]. Furthermore, many SNPs may be associated with breast cancer. Although only single SNP was examined in our study, the SNP-SNP interaction [9],[44]-[48] tumor may play a joint effect to associate with cancer and it is warranted for further investigation for multiple SNPs in breast cancer association.

Conclusion

Taken together, SNP 1772 C > T (P582S) of HIF-1α gene confers significant association with breast cancer risk but it show no association with the clinicopathologic features and survival of breast cancer patients.

Authors' contributions

C-JH and H-WC managed for genotyping studies. C-JH and S-LL drafted the manuscript. M-FH and C-YC were responsible for the sample collection and pathology experiments. Y-HY performed statistics analyses. S-FL and H-WC were involved in discussion and editing the manuscript. All authors read and approved the final manuscript.

Declarations

Acknowledgement

This work was supported by grants from the National Science Council (NSC92-2314-B-037-051, NSC102-2622-B-037-003-CC2, and MOST 103-2320-B-037-008), the KMU Cancer Research Foundation (QC094002), the National Sun Yat-Sen University-KMU Joint Research Project (#NSYSU-KMU 103-p014), and Health and welfare surcharge of tobacco products, the Ministry of Health and Welfare, Taiwan, Republic of China (MOHW103-TD-B-111-05). We thank Sung-Wei Li, MD, for clinical data collection; De-Leung Gu in technical assistance of RFLP; Wen-Tsue Chen in immunohistochemistry stain; and Susan L. Olmstead, Ph.D., Johns Hopkins University for English revision.

Authors’ Affiliations

(1)
Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University Faculty of medicine, Kaohsiung Medical University
(2)
Department of Radiation Oncology, Kaohsiung Medical University Hospital
(3)
Institute of Clinical Medicine, Kaohsiung Medical University
(4)
Kaohsiung Municipal Ta-Tung Hospital
(5)
Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University
(6)
Department of Pathology, Kaohsiung Medical University Hospital, Kaohsiung Medical University
(7)
Institute of Biomedical Sciences, National Sun Yat-Sen University
(8)
Department of Pathology, Kaohsiung Medical University Hospital, Kaohsiung Medical University
(9)
School of Pharmacy, Kaohsiung Medical University
(10)
Department of Medical Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University
(11)
Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University
(12)
Institute of Medical Science and Technology, National Sun Yat-sen University
(13)
Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University

References

  1. Chuang LY, Yang CH, Tsui KH, Cheng YH, Chang PL, Wen CH, Chang HW: Restriction enzyme mining for SNPs in genomes. Anticancer Res. 2008, 28 (4A): 2001-2007.PubMedGoogle Scholar
  2. Cantor CR: The use of genetic SNPs as new diagnostic markers in preventive medicine. Ann N Y Acad Sci. 2005, 1055: 48-57. 10.1196/annals.1323.009.View ArticlePubMedGoogle Scholar
  3. Chang HW, Chuang LY, Tsai MT, Yang CH: The importance of integrating SNP and cheminformatics resources to pharmacogenomics. Curr Drug Metab. 2012, 13 (7): 991-999. 10.2174/138920012802138679.View ArticlePubMedGoogle Scholar
  4. Liu SG, Gao C, Zhang RD, Jiao Y, Cui L, Li WJ, Chen ZP, Wu MY, Zheng HY, Zhao XX, Yue ZX, Li ZG: FPGS rs1544105 polymorphism is associated with treatment outcome in pediatric B-cell precursor acute lymphoblastic leukemia. Cancer Cell Int. 2013, 13 (1): 107-10.1186/1475-2867-13-107.View ArticlePubMed CentralPubMedGoogle Scholar
  5. Ding H, Jing X, Ding N, Fu Z, Song Y, Zhu J: Single nucleotide polymorphisms of CD20 gene and their relationship with clinical efficacy of R-CHOP in patients with diffuse large B cell lymphoma. Cancer Cell Int. 2013, 13 (1): 58-10.1186/1475-2867-13-58.View ArticlePubMed CentralPubMedGoogle Scholar
  6. Lin HJ, Kung YJ, Lin YJ, Sheu JJ, Chen BH, Lan YC, Lai CH, Hsu YA, Wan L, Tsai FJ: Association of the lumican gene functional 3′-UTR polymorphism with high myopia. Invest Ophthalmol Vis Sci. 2010, 51 (1): 96-102. 10.1167/iovs.09-3612.View ArticlePubMedGoogle Scholar
  7. Kuo HC, Yang KD, Juo SH, Liang CD, Chen WC, Wang YS, Lee CH, Hsi E, Yu HR, Woon PY, Lin IC, Huang CF, Hwang DY, Lee CP, Lin LY, Chang WP, Chang WC: ITPKC single nucleotide polymorphism associated with the Kawasaki disease in a Taiwanese population. PLoS One. 2011, 6 (4): e17370-10.1371/journal.pone.0017370.View ArticlePubMed CentralPubMedGoogle Scholar
  8. Hwang CW, Lu CH, Sun SF, Sung TY, Chung HY, Huang SY, Hung HC, Chen CH, Sun YM, Lin YY, Liu WS, Wen ZH: Comprehensive association analysis of 10 single nucleotide polymorphisms associated with osteoporosis among a Taiwanese population. Int J Hum Genet. 2011, 11 (4): 249-257.Google Scholar
  9. Lin GT, Tseng HF, Yang CH, Hou MF, Chuang LY, Tai HT, Tai MH, Cheng YH, Wen CH, Liu CS, Huang CJ, Wang CL, Chang HW: Combinational polymorphisms of seven CXCL12-related genes are protective against breast cancer in Taiwan. OMICS. 2009, 13 (2): 165-172. 10.1089/omi.2008.0050.View ArticlePubMedGoogle Scholar
  10. Chen FM, Ou-Yang F, Yang SF, Tsai EM, Hou MF: P53 codon 72 polymorphism in Taiwanese breast cancer patients. Kaohsiung J Med Sci. 2013, 29 (5): 259-264. 10.1016/j.kjms.2012.09.004.View ArticlePubMedGoogle Scholar
  11. Chang WC, Woon PY, Hsu YW, Yang S, Chiu YC, Hou MF: The association between single-nucleotide polymorphisms of ORAI1 gene and breast cancer in a Taiwanese population. ScientificWorldJournal. 2012, 2012: 916587-PubMed CentralPubMedGoogle Scholar
  12. Semenza GL: Targeting HIF-1 for cancer therapy. Nat Rev Cancer. 2003, 3 (10): 721-732. 10.1038/nrc1187.View ArticlePubMedGoogle Scholar
  13. Bos R, Zhong H, Hanrahan CF, Mommers EC, Semenza GL, Pinedo HM, Abeloff MD, Simons JW, van Diest PJ, van der Wall E: Levels of hypoxia-inducible factor-1 alpha during breast carcinogenesis. J Natl Cancer Inst. 2001, 93 (4): 309-314. 10.1093/jnci/93.4.309.View ArticlePubMedGoogle Scholar
  14. Gruber G, Greiner RH, Hlushchuk R, Aebersold DM, Altermatt HJ, Berclaz G, Djonov V: Hypoxia-inducible factor 1 alpha in high-risk breast cancer: an independent prognostic parameter?. Breast Cancer Res. 2004, 6 (3): R191-R198. 10.1186/bcr775.View ArticlePubMed CentralPubMedGoogle Scholar
  15. Semenza GL: HIF-1: mediator of physiological and pathophysiological responses to hypoxia. J Appl Physiol. 2000, 88 (4): 1474-1480.PubMedGoogle Scholar
  16. Epstein AC, Gleadle JM, McNeill LA, Hewitson KS, O'Rourke J, Mole DR, Mukherji M, Metzen E, Wilson MI, Dhanda A, Tian YM, Masson N, Hamilton DL, Jaakkola P, Barstead R, Hodgkin J, Maxwell PH, Pugh CW, Schofield CJ, Ratcliffe PJ: C. elegans EGL-9 and mammalian homologs define a family of dioxygenases that regulate HIF by prolyl hydroxylation. Cell. 2001, 107 (1): 43-54. 10.1016/S0092-8674(01)00507-4.View ArticlePubMedGoogle Scholar
  17. Jaakkola P, Mole DR, Tian YM, Wilson MI, Gielbert J, Gaskell SJ, Kriegsheim A, Hebestreit HF, Mukherji M, Schofield CJ, Maxwell PH, Pugh CW, Ratcliffe PJ: Targeting of HIF-alpha to the von Hippel-Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation. Science. 2001, 292 (5516): 468-472. 10.1126/science.1059796.View ArticlePubMedGoogle Scholar
  18. Ollerenshaw M, Page T, Hammonds J, Demaine A: Polymorphisms in the hypoxia inducible factor-1alpha gene (HIF1A) are associated with the renal cell carcinoma phenotype. Cancer Genet Cytogenet. 2004, 153 (2): 122-126. 10.1016/j.cancergencyto.2004.01.014.View ArticlePubMedGoogle Scholar
  19. Clifford SC, Astuti D, Hooper L, Maxwell PH, Ratcliffe PJ, Maher ER: The pVHL-associated SCF ubiquitin ligase complex: molecular genetic analysis of elongin B and C, Rbx1 and HIF-1alpha in renal cell carcinoma. Oncogene. 2001, 20 (36): 5067-5074. 10.1038/sj.onc.1204602.View ArticlePubMedGoogle Scholar
  20. Tanimoto K, Yoshiga K, Eguchi H, Kaneyasu M, Ukon K, Kumazaki T, Oue N, Yasui W, Imai K, Nakachi K, Poellinger L, Nishiyama M: Hypoxia-inducible factor-1alpha polymorphisms associated with enhanced transactivation capacity, implying clinical significance. Carcinogenesis. 2003, 24 (11): 1779-1783. 10.1093/carcin/bgg132.View ArticlePubMedGoogle Scholar
  21. Orr-Urtreger A, Bar-Shira A, Matzkin H, Mabjeesh NJ: The homozygous P582S mutation in the oxygen-dependent degradation domain of HIF-1 alpha is associated with increased risk for prostate cancer. Prostate. 2007, 67 (1): 8-13. 10.1002/pros.20433.View ArticlePubMedGoogle Scholar
  22. Kuo WH, Shih CM, Lin CW, Cheng WE, Chen SC, Chen W, Lee YL: Association of hypoxia inducible factor-1alpha polymorphisms with susceptibility to non-small-cell lung cancer. Transl Res. 2012, 159 (1): 42-50. 10.1016/j.trsl.2011.09.003.View ArticlePubMedGoogle Scholar
  23. Ruiz-Tovar J, Fernandez-Contreras ME, Martin-Perez E, Gamallo C: Association of thymidylate synthase and hypoxia inducible factor-1alpha DNA polymorphisms with pancreatic cancer. Tumori. 2012, 98 (3): 364-369.PubMedGoogle Scholar
  24. Yang X, Zhu HC, Zhang C, Qin Q, Liu J, Xu LP, Zhao LJ, Zhang Q, Cai J, Ma JX, Cheng HY, Sun XC: HIF-1alpha 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. 2013, 8 (11): e80396-10.1371/journal.pone.0080396.View ArticlePubMed CentralPubMedGoogle Scholar
  25. Prchal JT: Delivery on demand-a new era of gene therapy?. N Engl J Med. 2003, 348 (13): 1282-1283. 10.1056/NEJMcibr035011.View ArticlePubMedGoogle Scholar
  26. Huang CJ, Hou MF, Lin SD, Chuang HY, Huang MY, Fu OY, Lian SL: Comparison of local recurrence and distant metastases between breast cancer patients after postmastectomy radiotherapy with and without immediate TRAM flap reconstruction. Plast Reconstr Surg. 2006, 118 (5): 1079-1086. 10.1097/01.prs.0000220527.35442.44. discussion 1087-1078View ArticlePubMedGoogle Scholar
  27. Singer G, Kurman RJ, Chang HW, Cho SK, Shih Ie M: Diverse tumorigenic pathways in ovarian serous carcinoma. Am J Pathol. 2002, 160 (4): 1223-1228. 10.1016/S0002-9440(10)62549-7.View ArticlePubMed CentralPubMedGoogle Scholar
  28. Chang HW, Cheng CA, Gu DL, Chang CC, Su SH, Wen CH, Chou YC, Chou TC, Yao CT, Tsai CL, Cheng CC: High-throughput avian molecular sexing by SYBR green-based real-time PCR combined with melting curve analysis. BMC Biotechnol. 2008, 8: 12-10.1186/1472-6750-8-12.View ArticlePubMed CentralPubMedGoogle Scholar
  29. Chang HW, Cheng YH, Chuang LY, Yang CH: SNP-RFLPing 2: an updated and integrated PCR-RFLP tool for SNP genotyping. BMC Bioinformatics. 2010, 11: 173-10.1186/1471-2105-11-173.View ArticlePubMed CentralPubMedGoogle Scholar
  30. Chang HW, Yang CH, Chang PL, Cheng YH, Chuang LY: SNP-RFLPing: restriction enzyme mining for SNPs in genomes. BMC Genomics. 2006, 7 (1): 30-10.1186/1471-2164-7-30.View ArticlePubMed CentralPubMedGoogle Scholar
  31. Yang CH, Cheng YH, Chuang LY, Chang HW: Drug-SNPing: an integrated drug-based, protein interaction-based tagSNP-based pharmacogenomics platform for SNP genotyping. Bioinformatics. 2013, 29 (6): 758-764. 10.1093/bioinformatics/btt037.View ArticlePubMedGoogle Scholar
  32. Chen WT, Huang CJ, Wu MT, Yang SF, Su YC, Chai CY: Hypoxia-inducible factor-1alpha is associated with risk of aggressive behavior and tumor angiogenesis in gastrointestinal stromal tumor. Jpn J Clin Oncol. 2005, 35 (4): 207-213. 10.1093/jjco/hyi067.View ArticlePubMedGoogle Scholar
  33. Zhong H, De Marzo AM, Laughner E, Lim M, Hilton DA, Zagzag D, Buechler P, Isaacs WB, Semenza GL, Simons JW: Overexpression of hypoxia-inducible factor 1alpha in common human cancers and their metastases. Cancer Res. 1999, 59 (22): 5830-5835.PubMedGoogle Scholar
  34. Kuwai T, Kitadai Y, Tanaka S, Kuroda T, Ochiumi T, Matsumura S, Oue N, Yasui W, Kaneyasu M, Tanimoto K, Nishiyama M, Chayama K: Single nucleotide polymorphism in the hypoxia-inducible factor-1alpha gene in colorectal carcinoma. Oncol Rep. 2004, 12 (5): 1033-1037.PubMedGoogle Scholar
  35. Fu SL, Miao J, Ding B, Wang XL, Cheng WJ, Dai HH, Han SP: A polymorphism in the 3′ untranslated region of Hypoxia-Inducible Factor-1 alpha confers an increased risk of cervical cancer in a Chinese population. Neoplasma. 2014, 61 (1): 63-69. 10.4149/neo_2014_002.View ArticlePubMedGoogle Scholar
  36. Salceda S, Caro J: Hypoxia-inducible factor 1alpha (HIF-1alpha) protein is rapidly degraded by the ubiquitin-proteasome system under normoxic conditions. Its stabilization by hypoxia depends on redox-induced changes. J Biol Chem. 1997, 272 (36): 22642-22647. 10.1074/jbc.272.36.22642.View ArticlePubMedGoogle Scholar
  37. Maxwell PH, Wiesener MS, Chang GW, Clifford SC, Vaux EC, Cockman ME, Wykoff CC, Pugh CW, Maher ER, Ratcliffe PJ: The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis. Nature. 1999, 399 (6733): 271-275. 10.1038/20459.View ArticlePubMedGoogle Scholar
  38. Masson N, Willam C, Maxwell PH, Pugh CW, Ratcliffe PJ: Independent function of two destruction domains in hypoxia-inducible factor-alpha chains activated by prolyl hydroxylation. Embo J. 2001, 20 (18): 5197-5206. 10.1093/emboj/20.18.5197.View ArticlePubMed CentralPubMedGoogle Scholar
  39. Min JH, Yang H, Ivan M, Gertler F, Kaelin WG, Pavletich NP: Structure of an HIF-1alpha -pVHL complex: hydroxyproline recognition in signaling. Science. 2002, 296 (5574): 1886-1889. 10.1126/science.1073440.View ArticlePubMedGoogle Scholar
  40. Percy MJ, Mooney SM, McMullin MF, Flores A, Lappin TR, Lee FS: A common polymorphism in the oxygen-dependent degradation (ODD) domain of hypoxia inducible factor-1alpha (HIF-1alpha) does not impair Pro-564 hydroxylation. Mol Cancer. 2003, 2: 31-10.1186/1476-4598-2-31.View ArticlePubMed CentralPubMedGoogle Scholar
  41. Vleugel MM, Greijer AE, van der Wall E, van Diest PJ: Mutation analysis of the HIF-1alpha oxygen-dependent degradation domain in invasive breast cancer. Cancer Genet Cytogenet. 2005, 163 (2): 168-172. 10.1016/j.cancergencyto.2005.05.008.View ArticlePubMedGoogle Scholar
  42. Bos R, van der Groep P, Greijer AE, Shvarts A, Meijer S, Pinedo HM, Semenza GL, van Diest PJ, van der Wall E: Levels of hypoxia-inducible factor-1alpha independently predict prognosis in patients with lymph node negative breast carcinoma. Cancer. 2003, 97 (6): 1573-1581. 10.1002/cncr.11246.View ArticlePubMedGoogle Scholar
  43. Goncalves R, Bose R: Using multigene tests to select treatment for early-stage breast cancer. J Natl Compr Canc Netw. 2013, 11 (2): 174-182. quiz 182PubMedGoogle Scholar
  44. Yang CH, Chuang LY, Cheng YH, Lin YD, Wang CL, Wen CH, Chang HW: Single nucleotide polymorphism barcoding to evaluate oral cancer risk using odds ratio-based genetic algorithms. Kaohsiung J Med Sci. 2012, 28 (7): 362-368. 10.1016/j.kjms.2012.02.002.View ArticlePubMedGoogle Scholar
  45. Yen CY, Liu SY, Chen CH, Tseng HF, Chuang LY, Yang CH, Lin YC, Wen CH, Chiang WF, Ho CH, Chen HC, Wang ST, Lin CW, Chang HW: Combinational polymorphisms of four DNA repair genes XRCC1, XRCC2, XRCC3, and XRCC4 and their association with oral cancer in Taiwan. J Oral Pathol Med. 2008, 37 (5): 271-277. 10.1111/j.1600-0714.2007.00608.x.View ArticlePubMedGoogle Scholar
  46. Zheng SL, Sun J, Wiklund F, Smith S, Stattin P, Li G, Adami HO, Hsu FC, Zhu Y, Balter K, Kader AK, Turner AR, Liu W, Bleecker ER, Meyers DA, Duggan D, Carpten JD, Chang BL, Isaacs WB, Xu J, Gronberg H: Cumulative association of five genetic variants with prostate cancer. N Engl J Med. 2008, 358 (9): 910-919. 10.1056/NEJMoa075819.View ArticlePubMedGoogle Scholar
  47. Vogelsang M, Wang Y, Veber N, Mwapagha LM, Parker MI: The cumulative effects of polymorphisms in the DNA mismatch repair genes and tobacco smoking in oesophageal cancer risk. PLoS One. 2012, 7 (5): e36962-10.1371/journal.pone.0036962.View ArticlePubMed CentralPubMedGoogle Scholar
  48. Chang WC, Fang YY, Chang HW, Chuang LY, Lin YD, Hou MF, Yang CH: Identifying association model for single-nucleotide polymorphisms of ORAI1 gene for breast cancer. Cancer Cell Int. 2014, 14 (1): 29-10.1186/1475-2867-14-29.View ArticlePubMed CentralPubMedGoogle Scholar

Copyright

© Huang et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.