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Replication Study of ESCC Susceptibility Genetic Polymorphisms Locating in the ADH1B-ADH1C-ADH7 Cluster Identified by GWAS

  • Jiwen Wang ,

    Contributed equally to this work with: Jiwen Wang, Jinyu Wei

    Affiliations Cancer Research Institute, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China, Key Laboratory of Diagnosis and Treatment Technology on Thoracic Oncology, Zhejiang Province, China

  • Jinyu Wei ,

    Contributed equally to this work with: Jiwen Wang, Jinyu Wei

    Affiliation College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China

  • Xiaoling Xu,

    Affiliation Cancer Research Institute, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China

  • Wenting Pan,

    Affiliation College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China

  • Yunxia Ge,

    Affiliation College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China

  • Changchun Zhou,

    Affiliation Clinical Laboratory, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, Shandong Province, China

  • Chao Liu,

    Affiliation Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China

  • Jia Gao,

    Affiliation Clinical Laboratory, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Ming Yang ,

    yangm@mail.buct.edu.cn (MY); weiminmao@126.com (WM)

    Affiliation College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China

  • Weimin Mao

    yangm@mail.buct.edu.cn (MY); weiminmao@126.com (WM)

    Affiliations Cancer Research Institute, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China, Key Laboratory of Diagnosis and Treatment Technology on Thoracic Oncology, Zhejiang Province, China, Thoracic Surgery Department, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China

Replication Study of ESCC Susceptibility Genetic Polymorphisms Locating in the ADH1B-ADH1C-ADH7 Cluster Identified by GWAS

  • Jiwen Wang, 
  • Jinyu Wei, 
  • Xiaoling Xu, 
  • Wenting Pan, 
  • Yunxia Ge, 
  • Changchun Zhou, 
  • Chao Liu, 
  • Jia Gao, 
  • Ming Yang, 
  • Weimin Mao
PLOS
x

Abstract

China was one of the countries with highest esophageal squamous cell carcinoma (ESCC) incidence and mortality worldwide. Alcohol drinking has been identified as a major environmental risk-factor related to ESCC. The alcohol dehydrogenase (ADH) family are major enzymes involved in the alcohol-metabolizing pathways, including alcohol dehydrogenase 1B (ADH1B) and ADH1C. Interestingly, ADH1B and ADH1C genes locate tandemly with ADH7 in a genomic segment as a gene cluster, and are all polymorphic. Several ESCC susceptibility single nucleotide polymorphisms (SNPs) of the ADH1B-ADH1C-ADH7 cluster have been identified previously through a genome-wide association study (GWAS). In the study, we examined the association between five ADH1B-ADH1C-ADH7 cluster SNPs (rs1042026, rs17033, rs1614972, rs1789903 and rs17028973) and risk of developing ESCC. Genotypes were determined in two independent case-control sets from two regions of China. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by logistic regression. Our data demonstrated that these ADH1B-ADH1C-ADH7 cluster SNPs confer susceptibility to ESCC in these two case-control sets, which were consistent to results of the previous GWAS.

Introduction

China was one of the countries with highest incidence and mortality of esophageal squamous cell carcinoma (ESCC) worldwide [1]. Epidemiological studies show that consumption of tobacco and alcohol are major risk factors for ESCC [2], [3]. However, only a portion of individuals exposed to tobacco and alcohol develop ESCC, indicating the crucial role of host susceptibility factors in ESCC. Accumulated evidences suggested that single nucleotide polymorphisms (SNP) might explain individual differences of susceptibility to ESCC through the candidate gene approach or the genome-wide association study (GWAS) approach [4][17].

Alcohol drinking has been identified as a major environmental risk-factor related to ESCC [2], [3]. Ethanol is metabolized in vivo by alcohol dehydrogenase (ADH) family and aldehyde dehydrogenase (ALDH), which are all polymorphic in human beings [18][19]. The total activity of ADH is significantly higher in cancer tissue than in healthy mucosa [18]. The ALDH and ADH polymorphisms influence individual diversity in alcohol-oxidizing capability and drinking behavior [19]. Among the ADH family, the major enzymes involved in the alcohol-metabolizing pathways are alcohol dehydrogenase 1B (ADH1B) and ADH1C. ADH1B and ADH1C exist as several homo- and heterodimers of ADH1A subunits, exhibit high activity for ethanol oxidation and play an essential role in ethanol catabolism. ADH7 is also a member of the ADH family. Although less efficient in ethanol oxidation compared to ADH1B or ADH1C, ADH7 is the most active as a retinol dehydrogenase. Therefore, ADH7 may take part in the synthesis of retinoic acid, a hormone important for cellular differentiation. Interestingly, the aforementioned three genes locate tandemly in a genomic segment as a gene cluster. Wu et al. identified several new ESCC susceptible SNPs, including ADH1B rs1042026 and rs17033, ADH1C rs1614972 and rs1789903 as well as ADH7 rs17028973 through a GWAS based on analyses of in 2031 ESCC cases and 2044 controls with independent validation in 8092 ESCC cases and 8620 controls [4]. Considering the importance of ADH1B-ADH1C-ADH7 cluster in ESCC, we conducted this replication case-control study to validate the association between ADH1B rs1042026 and rs17033, ADH1C rs1614972 and rs1789903 as well as ADH7 rs17028973 SNPs and ESCC risk.

Materials and Methods

Study subjects

This study consisted of two case-control sets: (a) Hangzhou set: 617 patients with ESCC from Cancer Research Institute, Zhejiang Cancer Hospital (Hangzhou, Zhejiang Province, China) and sex- and age-matched (±5 years) 537 controls. Patients were recruited between January 2012 and March 2013 at Zhejiang Cancer Hospital. Control subjects were individuals who underwent a physical examination in the same hospital during the same time period as the patients were collected. (b) Jinan study: 540 patients with ESCC from Shandong Cancer Hospital, Shandong Academy of Medical Sciences (Jinan, Shandong Province, China) and sex- and age-matched (±5 years) 550 controls. Patients were recruited between June 2009 and April 2012 at Shandong Cancer Hospital. Control subjects were randomly selected from a pool of 4500 individuals from a community cancer-screening program for early detection of cancer conducted in Jinan city during the same time period as the patients were collected. The diagnosis of all patients was histologically confirmed. Individuals who smoked one cigarette per day for over 1 year were considered as smokers. Subjects were considered as alcohol drinkers, if they drank at least once per week. All subjects were ethnic Han Chinese. At recruitment, the written informed consent was obtained from each subject and each participant was then interviewed to collect detailed information on demographic characteristics, such as sex and age, and related risk factors, such as cigarette smoking, and alcohol drinking. This study was approved by the Review Board of Zhejiang Cancer Hospital and the Review Board of Shandong Cancer Hospital.

SNP selection and genotyping

A total of five ADH1B-ADH1C-ADH7 cluster SNPs (rs1042026, rs17033, rs1614972, rs1789903 and rs17028973) were included in the current study. These SNPs are ones identified by a previous ESCC GWAS in Chinese Han population [4]. All ADH1B-ADH1C-ADH7 cluster SNPs were analyzed by the MassArray system (Sequenom Inc., San Diego, California, USA). A 15% blind, random sample of study subjects was genotyped in duplicates and the reproducibility was 100%.

Statistical analyses

Pearson's χ2 test was used to examine the differences in demographic variables and genotype distributions of five ADH1B-ADH1C-ADH7 cluster SNPs between patients and controls. The associations between genotypes of these SNPs and ESCC risk were estimated by ORs and their 95% CIs computed by logistic regression models. All ORs were adjusted for age, sex, smoking or drinking status, where it was appropriate. We tested the null hypotheses of multiplicative gene-environment interaction and evaluated departures from multiplicative interaction models by including main effect variables and their product terms in the logistic regression model [4], [20][23]. A P value of less than 0.05 was used as the criterion of statistical significance, and all statistical tests were two-sided. All analyses were performed using Statistical Analysis System (version 9.0; SAS Institute) and SPSS 16.0 (SPSS Inc.).

Results

In terms of median age and sex distribution, no statistically significant differences were found between ESCC patients and healthy controls for Hangzhou set and Jinan set (all P>0.05), indicating that the frequency matching was appropriate (Table 1). However, there are more smokers and alcohol drinkers were observed among ESCC cases compared with controls in Jinan case-control sets (both P<0.05). There are no data on smoking and drinking status of controls in Hangzhou case-control set.

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Table 1. Distribution of selected characteristics among ESCC patients and healthy controls.

https://doi.org/10.1371/journal.pone.0094096.t001

Firstly, unconditional logistic regression analysis was utilized to detect associations between five ADH1B-ADH1C-ADH7 cluster SNPs (rs1042026, rs17033, rs1614972, rs1789903 and rs17028973) and ESCC risk in Hangzhou discovery set (Table 2). All observed genotype frequencies in controls conform to Hardy–Weinberg equilibrium in Hangzhou set. Logistic regression analyses revealed that all five SNPs were significantly associated with ESCC risk (ADH1B rs1042026: allelic OR = 2.02, 95% CI = 1.66–2.47, P<0.001; ADH1B rs17033: allelic OR = 1.58, 95% CI = 1.18–2.11, P = 0.001; ADH1C rs1614972: allelic OR = 1.65, 95% CI = 1.36–2.00, P<0.001; ADH1C rs1789903: allelic OR = 1.77, 95% CI = 1.33–2.35, P<0.001; ADH7 rs17028973: allelic OR = 1.61, 95% CI = 1.35–1.92, P<0.001) (Table 2). The ADH1B rs1042026 A allele, ADH1B rs17033 G allele, ADH1C rs1614972 C allele, ADH1C rs1789903 G allele,and ADH7 rs17028973 T allele were showed to be risk alleles.

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Table 2. Associations between candidate SNPs in the ADH1B-ADH1C-ADH7 cluster and ESCC risk in Hangzhou case-control set (Discovery set).

https://doi.org/10.1371/journal.pone.0094096.t002

Associations between genotypes of five ADH1B-ADH1C-ADH7 cluster SNPs and risk of ESCC were estimated in Hangzhou discovery set (Table 3). Individuals with the ADH1B rs1042026 AG or AA genotype had an OR of 1.54(95% CI = 1.19–1.98, P = 0.001) or 5.40(95% CI = 3.19–9.11, P<0.001) for developing ESCC, respectively, compared with individuals with the GG genotype (Table 3). ADH1B rs17033 AG carriers showed a 1.67-fold increased ESCC risk compared with those carrying the rs920778 CC genotype in two validation sets (95%CI = 1.24–2.26, P = 0.001) (Table 3). A significantly increased ESCC risk associated with the ADH1C rs1614972 TC or CC genotype compared with the TT genotype was observed (OR = 1.35; 95% CI  = 1.06–1.73, P = 0.016; OR = 3.59; 95% CI  = 2.19–5.88, P<0.001). The presence of the ADH1C rs1614972 CG or GG genotype was also associated with an increased risk of ESCC (OR = 1.70; 95% CI = 1.26–2.30 or OR = 5.50; 95% CI = 1.21–25.0, respectively) compared with the absence of such a genotype. Moreover, the ADH7 rs17028973 TT genotype were significantly associated with increased risk of ESCC (OR = 3.07, 95% CI = 2.07–4.54, P<0.001). However, there was no such statistically significant association between the ADH7 rs17028973 CT genotype and ESCC risk (OR = 1.25, 95% CI = 0.98–1.61, P = 0.078).

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Table 3. Genotype frequencies of the ADH1B-ADH1C-ADH7 cluster SNPs among cases and controls and their association with ESCC risk.

https://doi.org/10.1371/journal.pone.0094096.t003

The association of ESCC risk with five ADH1B-ADH1C-ADH7 cluster SNPs was further validated in an independent case-control set. Genotyping results showed that all five SNP were significantly associated with ESCC risk in Jinan Chinese population (Table 3). Carriers of the ADH1B rs1042026 AG or AA genotype showed significantly and consistently increased risk to develop ESCC compared with GG carriers (OR = 1.47, 95% CI = 1.12–1.91, P = 0.005; OR = 4.53, 95% CI = 2.65–7.72, P<0.001) (Table 3). Similar results were found for ADH1B rs17033 AG genotype (OR = 1.58, 95% CI = 1.16–2.16, P = 0.004) (Table 3). The odds of having the ADH1C rs1614972 CC or TC genotype in patients was 1.30(95% CI = 1.01–1.68, P = 0.045) or 1.30(95% CI = 1.01–1.68, P<0.001) compared with the TT genotype (Table 3). Although ADH1C rs1789903 CG genotype was significantly associated with ESCC risk in the validation stage (OR = 1.58, 95% CI = 1.15–2.16, P = 0.005), rs1789903 GG genotype was not associated with ESCC risk (OR = 3.49, 95% CI = 0.94–13.02, P = 0.062). Additionally, ADH7 rs17028973 TT carriers showed a 2.62-fold increased ESCC risk compared with those carrying the CC genotype in the validation set (95%CI = 1.75–3.93, P<0.001) (Table 3).

The ESCC risk associated with the ADH1B-ADH1C-ADH7 cluster SNPs was further examined by stratifying for smoking status and alcohol drinking history due to the key role of these enzymes in metabolism of ethanol and other toxics in Jinan case-control set (Table 4 and 5). Interestingly, we found higher odds of those five polymorphisms for developing ESCC among smokers than those among non-smokers (Table 4 and 5). Similar results were also observed among alcohol drinkers except ADH1B rs17033 genetic variant (Table 4 and 5). However, no evident gene-smoking interaction or gene-drinking interaction exists in Jinan case-control set (Table 4 and 5). We also examined whether there are gene-environment interaction between five ADH1B-ADH1C-ADH7 cluster genetic variants and age and sex, but the results were negative (data not shown).

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Table 4. Risk of ESCC associated with the ADH1B rs1042026 and rs17033 SNPs by smoking status and drinking history in Jinan set.

https://doi.org/10.1371/journal.pone.0094096.t004

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Table 5. Risk of ESCC associated with the ADH1C rs1614972 and rs1789903 and ADH7 rs17028973SNPs by smoking status and drinking history in Jinan set.

https://doi.org/10.1371/journal.pone.0094096.t005

Discussion

In the current study, we examined the association between five ADH1B-ADH1C-ADH7 cluster SNPs (rs1042026, rs17033, rs1614972, rs1789903 and rs17028973) and risk of developing ESCC in a two-stage case-control design. In this replication study, we successfully validated results of a previous GWAS that these five SNPs confer susceptibility to ESCC [4]. However, no significant gene-smoking interaction or gene-drinking interaction between these ADH1B-ADH1C-ADH7 polymorphisms and ESCC was observed in this Chinese Han population.

Several molecular epidemiological studies using a candidate gene approach indicated a set of SNPs associated with ESCC susceptibility, primarily variations that are related to alcohol metabolism [24][30]. As a powerful and successful tool to identify common disease alleles, GWAS could interrogate a large amount of tagging SNPs that serve as surrogates for untested common SNPs across the genome. In published GWAS of cancers of the upper aerodigestive tract, including ESCC in individuals of European [28], [30], Japanese [9] and Chinese [4], have shown that SNPs in the ADH genes contribute to susceptibility of ESCC. Our results in this study are consistent to these reports and highlight the importance of genetic variants of the ADH genes in ESCC development.

There might be several limitations in the current case-control study. First, because it was a hospital-based study and the cases were from the hospital, inherent selection bias may exist. Thus, it is important to validate these findings in a population-based prospective study from the same geographic regions. Second, the statistical power of our study may be limited by the sample size, especially for statistical analyses of gene-covariate interaction. Third, future studies will need to address the biological function of these polymorphisms in the genesis of ESCC.

In summary, our study elucidated that the ADH1B-ADH1C-ADH7 cluster polymorphisms were associated with risk of ESCC in Chinese populations. Our data support the hypothesis that multiple ADH genes are involved in ESCC etiology and highlight the importance of genetic components in cancer development [31][41].

Acknowledgments

We thank Xiaohu Tang, Meng Li, Juan Shi, Chao Lu, Xiaojiao Zhang, and Li Liu for their technical supports and all the subjects of this study for their participation.

Author Contributions

Conceived and designed the experiments: MY WM. Performed the experiments: J. Wang J. Wei. Analyzed the data: J. Wang J. Wei XX WP YG MY. Contributed reagents/materials/analysis tools: CZ CL JG. Wrote the paper: MY J. Wang.

References

  1. 1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, et al. (2009) Cancer statistics, 2009. CA Cancer J Clin 59: 225–249.
  2. 2. Gao YT, McLaughlin JK, Blot WJ, Ji BT, Benichou J, et al. (1994) Risk factors for esophageal cancer in Shanghai, China. I. Role of cigarette smoking and alcohol drinking. Int J Cancer 58: 192–196.
  3. 3. Hu J, Nyrén O, Wolk A, Bergström R, Yuen J, et al. (1994) Risk factors for oesophageal cancer in northeast China. Int J Cancer 57: 38–46.
  4. 4. Wu C, Kraft P, Zhai K, Chang J, Wang Z, et al. (2012) Genome-wide association analyses of esophageal squamous cell carcinoma in Chinese identify multiple susceptibility loci and gene-environment interactions. Nat Genet 44: 1090–1097.
  5. 5. Abnet CC, Wang Z, Song X, Hu N, Zhou FY, et al. (2012) Genotypic variants at 2q33 and risk of esophageal squamous cell carcinoma in China: a meta-analysis of genome-wide association studies. Hum Mol Genet 21: 2132–2141.
  6. 6. Wu C, Hu Z, He Z, Jia W, Wang F, et al. (2011) Genome-wide association study identifies three new susceptibility loci for esophageal squamous-cell carcinoma in Chinese populations. Nat Genet 43: 679–684.
  7. 7. Wang LD, Zhou FY, Li XM, Sun LD, Song X, et al. (2010) Genome-wide association study of esophageal squamous cell carcinoma in Chinese subjects identifies susceptibility loci at PLCE1 and C20orf54. Nat Genet 42: 759–763.
  8. 8. Abnet CC, Freedman ND, Hu N, Wang Z, Yu K, et al. (2010) A shared susceptibility locus in PLCE1 at 10q23 for gastric adenocarcinoma and esophageal squamous cell carcinoma. Nat Gene 42: 764–767.
  9. 9. Cui R, Kamatani Y, Takahashi A, Usami M, Hosono N, et al. (2009) Functional variants in ADH1B and ALDH2 coupled with alcohol and smoking synergistically enhance esophageal cancer risk. Gastroenterology 137: 1768–1775.
  10. 10. Bass AJ, Meyerson M (2009) Genome-wide association study in esophageal squamous cell carcinoma. Gastroenterology 137: 1573–1576.
  11. 11. Zhang X, Wei J, Zhou L, Zhou C, Shi J, et al. (2013) A functional BRCA1 coding sequence genetic variant contributes to risk of esophageal squamous cell carcinoma. Carcinogenesis 34: 2309–2313.
  12. 12. Zhou L, Zhang X, Li Z, Zhou C, Li M, et al. (2013) Association of a genetic variation in a miR-191 binding site in MDM4 with risk of esophageal squamous cell carcinoma. PLoS One 8: e64331.
  13. 13. Shi J, Sun F, Peng L, Li B, Liu L, et al. (2013) Leukocyte telomere length-related genetic variants in 1p34.2 and 14q21 loci contribute to the risk of esophageal squamous cell carcinoma. Int J Cancer 132: 2799–2807.
  14. 14. Zhou L, Yuan Q, Yang M (2012) A functional germline variant in the P53 polyadenylation signal and risk of esophageal squamous cell carcinoma. Gene 506: 295–297.
  15. 15. Liu L, Zhou C, Zhou L, Peng L, Li D, et al. (2012) Functional FEN1 genetic variants contribute to risk of hepatocellular carcinoma, esophageal cancer, gastric cancer and colorectal cancer. Carcinogenesis 33: 119–123.
  16. 16. Wu H, Zheng J, Deng J, Hu M, You Y, et al. (2013) A genetic polymorphism in lincRNA-uc003opf.1 is associated with susceptibility to esophageal squamous cell carcinoma in Chinese populations. Carcinogenesis 34: 2908–2917.
  17. 17. Oze I, Matsuo K, Suzuki T, Kawase T, Watanabe M, et al. (2009) Impact of multiple alcohol dehydrogenase gene polymorphisms on risk of upper aerodigestive tract cancers in a Japanese population. Cancer Epidemiol Biomarkers Prev 18: 3097–3102.
  18. 18. Agrawal A, Bierut LJ (2012) Identifying genetic variation for alcohol dependence. Alcohol Res 34: 274–281.
  19. 19. Yang CX, Matsuo K, Ito H, Hirose K, Wakai K, et al. (2005) Esophageal cancer risk by ALDH2 and ADH2 polymorphisms and alcohol consumption: exploration of gene-environment and gene-gene interactions. Asian Pac J Cancer Prev 6: 256–262.
  20. 20. Yang M, Sun T, Wang L, Yu D, Zhang X, et al. (2008) Functional variants in cell death pathway genes and risk of pancreatic cancer. Clin Cancer Res 14: 3230–3236.
  21. 21. Yang M, Guo H, Wu C, He Y, Yu D, et al. (2009) Functional FEN1 polymorphisms are associated with DNA damage levels and lung cancer risk. Hum Mutat 30: 1320–328.
  22. 22. Yang M, Zhang L, Bi N, Ji W, Tan W, et al. (2011) Association of P53 and ATM polymorphisms with risk of radiation-induced pneumonitis in lung cancer patients treated with radiotherapy. Int J Radiat Oncol Biol Phys 79: 1402–1407.
  23. 23. Yang M, Sun T, Zhou Y, Wang L, Liu L, et al. (2012) The functional cytotoxic T lymphocyte-associated Protein 4 49G-to-A genetic variant and risk of pancreatic cancer. Cancer 118: 4681–4686.
  24. 24. Lewis SJ, Smith GD (2005) Alcohol, ALDH2, and esophageal cancer: a meta-analysis which illustrates the potentials and limitations of a Mendelian randomization approach. Cancer Epidemiol Biomarkers Prev 14: 1967–1971.
  25. 25. Hiyama T, Yoshihara M, Tanaka S, Chayama K (2007) Genetic polymorphisms and esophageal cancer risk. Int J Cancer 121: 1643–1658.
  26. 26. Ng D, Hu N, Hu Y, Wang C, Giffen C, et al. (2008) Replication of a genome-wide case-control study of esophageal squamous cell carcinoma. Int J Cancer 123: 1610–1615.
  27. 27. Hashibe M, McKay JD, Curado MP, Oliveira JC, Koifman S, et al. (2008) Multiple ADH genes are associated with upper aerodigestive cancers. Nat Genet 40: 707–709.
  28. 28. Akbari MR, Malekzadeh R, Shakeri R, Nasrollahzadeh D, Foumani M, et al. (2009) Candidate gene association study of esophageal squamous cell carcinoma in a high-risk region in Iran. Cancer Res 69: 7994–8000.
  29. 29. Wu M, Chang SC, Kampman E, Yang J, Wang XS, et al. (2013) Single nucleotide polymorphisms of ADH1B, ADH1C and ALDH2 genes and esophageal cancer: a population-based case-control study in China. Int J Cancer 132: 1868–1877.
  30. 30. McKay JD, Truong T, Gaborieau V, Chabrier A, Chuang SC, et al. (2011) A genome-wide association study of upper aerodigestive tract cancers conducted within the INHANCE consortium. PLoS Genet 7: e1001333.
  31. 31. Zheng J, Deng J, Xiao M, Yang L, Zhang L, et al. (2013) A sequence polymorphism in miR-608 predicts recurrence after radiotherapy for nasopharyngeal carcinoma. Cancer Res 73: 5151–5162.
  32. 32. Zheng J, Liu B, Zhang L, Jiang L, Huang B, et al. (2012) The protective role of polymorphism MKK4-1304 T>G in nasopharyngeal carcinoma is modulated by Epstein-Barr virus' infection status. Int J Cancer 130: 1981–1990.
  33. 33. Liu L, Wu C, Wang Y, Zhong R, Wang F, et al. (2011) Association of candidate genetic variations with gastric cardia adenocarcinoma in Chinese population: a multiple interaction analysis. Carcinogenesis 32: 336–342.
  34. 34. Zhong R, Liu L, Zou L, Sheng W, Zhu B, et al. (2013) Genetic variations in the TGFβ signaling pathway, smoking and risk of colorectal cancer in a Chinese population. Carcinogenesis 34(4): 936–942.
  35. 35. Chen W, Song H, Zhong R, Zhu B, Guo H, et al. (2013) Risk of GWAS-identified genetic variants for breast cancer in a Chinese population: a multiple interaction analysis. Breast Cancer Res Treat 142: 637–644.
  36. 36. Zhong R, Liu L, Tian Y, Wang Y, Tian J, et al. (2014) Genetic variant in SWI/SNF complexes influences hepatocellular carcinoma risk: a new clue for the contribution of chromatin remodeling in carcinogenesis. Sci Rep 4: 4147.
  37. 37. Yao J, Liu L, Yang M (2014) Interleukin-23 receptor genetic variants contribute to susceptibility of multiple cancers. Gene 533: 21–25.
  38. 38. Lv Z, Liu W, Li D, Liu L, Wei J, et al. (2014) Association of functional FEN1 genetic variants and haplotypes and breast cancer risk. Gene 538: 42–45.
  39. 39. Liu J, Tang X, Li M, Lu C, Shi J, et al. (2013) Functional MDM4 rs4245739 genetic variant, alone and in combination with P53 Arg72Pro polymorphism, contributes to breast cancer susceptibility. Breast Cancer Res Treat 140: 151–157.
  40. 40. Liu L, Wu G, Xue F, Li Y, Shi J, et al. (2013) Functional CYP1A1 genetic variants, alone and in combination with smoking, contribute to development of head and neck cancers. Eur J Cancer 49: 2143–2151.
  41. 41. Chen YD, Zhang X, Qiu XG, Li J, Yuan Q, et al. (2013) Functional FEN1 genetic variants and haplotypes are associated with glioma risk. J Neurooncol 111: 145–151.