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Research Papers:

Genetic variant of miR-146a rs2910164 C>G and gastric cancer susceptibility

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Oncotarget. 2016; 7:34316-34321. https://doi.org/10.18632/oncotarget.8814

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Zu-Guang Xia, Hua-Fang Yin, Ying Long, Lei Cheng, Ling-Jun Yu, Wei-Jian Guo, Xiao-Dong Zhu, Jin Li, Ya-Nong Wang, Ya-Jun Yang, Jiu-Cun Wang, Li Jin, Li-Xin Qiu _ and Yongyue Wei

Abstract

Zu-Guang Xia1,*, Hua-Fang Yin2,*, Ying Long3,*, Lei Cheng1, Ling-Jun Yu4, Wei-Jian Guo1, Xiao-Dong Zhu1, Jin Li1, Ya-Nong Wang5, Ya-Jun Yang6,7, Jiu-Cun Wang6,7, Li Jin6,7, Li-Xin Qiu1, Yongyue Wei8

1Department of Medical Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

2Department of Medical Oncology, The Affiliated Jiangyin Hospital of Southeast University Medical College, Jiangsu, China

3Department of Medical Oncology, Wanzai City Hospital, Jiangxi, China

4Department of Informatics, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China

5Department of Gastric Cancer & Soft Tissue Sarcoma Surgery, Fudan University Shanghai Cancer Center, Shanghai, China

6Ministry of Education Key Laboratory of Contemporary Anthropology and State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China

7Fudan-Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China

8Department of Biostatistics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center For Cancer Personalized Medicine, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China

*These authors have contributed equally to this work

Correspondence to:

Li-Xin Qiu, email: mdqiulixin@hotmail.com

Yongyue Wei, email: ywei@njmu.edu.cn

Keywords: gastric cancer, genetic susceptibility, miR-146a, polymorphism

Received: January 30, 2016     Accepted: March 31, 2016     Published: April 18, 2016

ABSTRACT

The single nucleotide polymorphism (SNP) rs2910164 G>C within miR-146a has been reported that is associated with the increased risk of gastric cancer (GCa). However, the results are inconclusive, espicially among Asian populations, which probably due to small sample size in each single study. To validate this association and get a more precise estimation, we conducted a large GCa study including 1,125 cases and 1,196 controls in an eastern Chinese population. Our results showed that this SNP was not associated with GCa risk in either of the three genetic models [co-dominant model: CG vs. CC, odds ratio (OR) = 0.99, 95% confidence interval (95%CI): 0.83-1.19; GG vs. CC, OR = 1.03, 95%CI: 0.81-1.32; dominant model: (CG+GG) vs. CC, OR = 1.00, 95%CI = 0.84-1.19; recessive model: GG vs. (CG+CC), OR = 1.04, 95%CI = 0.83-1.29]. Stratified analysis by age, gender, smoking status, drinking status, or tumor location confirmed this non-significant association. In summary, these results suggest that the miR-146a SNP rs2910164 may not be a risk factor for GCa in this Chinese population. Larger and well-designed, preferably prospective studies are needed to further confirm our findings.


Genetic variant of <i>miR-146a</i> rs2910164 C>G and gastric cancer susceptibility | Xia | Oncotarget

INTRODUCTION

In China, Gastric cancer (GCa) is the second most common cancer, with an estimated 679,100 new GCa cases and 498,000 deaths in 2015, accounting for 15.8% of the cancer cases and 17.7% of cancer deaths, respectively [1]. However, the underlying mechanism of its carcinogenesis is still not fully understood. The environmental factors, such as toacco smoking, alcohol use, helicobacter pylori (HP) infection, as well as low-penetrance susceptibility genes are believed to be crucial in the etiology of GCa development [2, 3]. In addition, emerging genomic studies in recent years have identified a few genetic variants associated with GCa risk [48]; however, it is necessary to validate those previous reported genetic risk factors in the external populations.

miRNA, a small non-coding RNA molecule consisting of ~22 nucleotides, has crucial biological functions in post-transcriptional regulation of genes, as well as cell differentiation, proliferation, and apoptosis [912]. All these functions have critical roles in the development of cancer, including gastric cancer [13, 14]. Notebly, single nucleotide polymorphisms (SNPs) in miRNA genes could repress the efficiency of miRNA transcript processing [15]. The SNP rs2910164 within microRNA-146a (miR-146a) is placed in the passenger strand, of which the C allele probably causes hairpins mispaired [16] and thus, affect miR-146a maturing process [17]. This miRNA SNP has been extensively explored to cancer risk in scientific community [1720]. The SNP rs2910164 G>C has been reported which is associated with the increased risk of GCa [21, 22]. However, the results are inconclusive, espicially among Asian populations [21, 2325], which probably due to the moderate effect size or small sample size in each single study. Recently, an updated meta-analysis in this field suggested a correlation between this SNP and increased GCa risk [26]. However, due to insufficient sample size of each recruited study and potential heterogeneity among these study cohorts, the results of meta-analysis should be interpreted with caution. Therefore, we conducted a large GCa study of 1,125 cases and 1,196 controls in a well-established gastric cancer study cohort to validate this association in an eastern Chinese population.

RESULTS

Table 1 described the population characteristics of this hospital-based case-control study, as reported previously [27]. Briefly, 1,125 GCa cases were averagely aged at 58.60±11.36 years, 71.1% male; 1,196 age and gender matched cancer-free controls were aged at 58.62 ± 11.75 years, and 69.1% were male. Age, gender, smoking status, as well as drinking status were further adjusted for the following multivariate analysis.

Table 1: Distributions of selected variables in gastric cancer cases and cancer-free controls

Variables

Cases N(%)

Controls N(%)

Pa

All subjects

1,125(100)

1,196 (100)

 

Age, yr

 

 

0.557

 Range

21-86

22-86

 

 Mean b

58.60 ± 11.36

58.62 ± 11.75

 

 ≤ 50

234 (20.8)

271 (22.7)

 

 51-60

383 (34.0)

384 (32.1)

 

 61-70

339 (30.1)

372 (31.1)

 

 >70

169 (15.0)

169 (14.1)

 

Sex

 

 

0.282

 Males

800 (71.1)

826 (69.1)

 

 Females

325 (28.9)

370 (30.9)

 

Smoking status

 

 

<0.0001

 Never

686 (61.0)

610 (51.0)

 

 Former

17 (1.5)

120 (10.0)

 

 Current

422 (37.5)

466 (39.0)

 

Drinking status

 

 

0.008

 Yes

270 (24.0)

345 (28.8)

 

 No

855 (76.0)

851 (71.2)

 

Pack-years

 

 

<0.0001

 0

686 (61.0)

610 (51.0)

 

 ≤ 25 (mean)

227 (20.2)

355 (29.7)

 

 > 25 (mean)

212 (18.8)

231 (19.3)

 

Tumor site

 

 

 

 GCA

305 (27.1)

 

 NGCA

820 (72.9)

 

Abbreviations: GCA, gastric cardia adenocarcinoma; NGCA, non-gastric cardia adenocarcinoma;

a Two-sided χ2 test for distributions between cases and controls.

b Age was described as mean ± SD.

Allele frequencies of the rs2910164 A>G SNP were listed by cases and controls in Table 2, as well as the association between this SNP and GCa risk. SNP rs2910164 showed a non-significant association with GCa risk in our study population [co-dominant genetic model: heterozygotes (CG) vs. wild-type (CC), odds ratio (OR) = 0.99, 95% confidence interval (95%CI): 0.83-1.19; homozygotes (GG) vs. CC, OR = 1.03, 95%CI: 0.81-1.32; dominant genetic model: (CG+GG) vs. CC, OR = 1.00, 95%CI = 0.84-1.19; recessive genetic model: GG vs. (CG+CC), OR = 1.04, 95%CI = 0.83-1.29]. Stratification analyses according to age, gender, smoking, drinking, and tumor location indicated consistent results (Table 3).

Table 2: Logistic regression analysis of associations between the genotypes of miR146A and gastric cancer risk

Variants

Genotypes

Cases (N=1,125)

Controls (N=1,196)

Pa

Crude OR (95% CI)

P

Adjusted OR (95% CI) b

Pb

Rs2910164

 

CC

397 (35.3)

420 (35.1)

0.946c

1.00

 

1.00

 

 

CG

536 (47.6)

577 (48.2)

 

0.98 (0.82-1.18)

0.850

0.99 (0.83-1.19)

0.922

 

GG

192 (17.1)

199 (16.6)

 

1.02 (0.80-1.30)

0.868

1.03 (0.81-1.32)

0.801

 

CG/GG

728 (64.7)

776 (64.9)

0.931d

0.99 (0.84-1.18)

0.931

1.00 (0.84-1.19)

0.988

Additive genetic model

1.01 (0.90-1.13)

0.930

1.01 (0.90-1.14)

0.853

 

CC/CG

933 (82.9)

997 (83.4)

 

1.00

 

1.00

 

 

GG

192 (17.1)

199 (16.6)

0.783e

1.03 (0.83-1.28)

0.783

1.04 (0.83-1.29)

0.744

a Chi square test for genotype distributions between cases and controls

b Adjusted for age, sex, smoking and drinking status in logistic regression models

c for additive genetic models; d for dominant genetic models; e for recessive genetic models.

Table 3: Stratification analysis for associations between miR146A variant genotypes and gastric cancer risk

Variables

Rs2910164 (Cases/Controls)

Crude OR (95% CI)

P

Adjusted ORa (95% CI)

Pa

CC

CG/GG

N

%

N

%

Age, yr

 ≤59 (median)

202/217

34.9/35.6

376/393

65.1/64.4

1.03 (0.81-1.30)

0.822

1.04 (0.82-1.32)

0.769

 >59 (median)

195/203

35.6/34.6

352/383

64.4/65.4

0.96 (0.75-1.22)

0.723

0.95 (0.74-1.22)

0.677

Gender

 Males

291/284

36.4/34.4

509/542

63.6/65.6

0.92 (0.75-1.12)

0.401

0.93 (0.76-1.14)

0.496

 Females

106/136

32.6/36.8

219/234

67.4/63.2

1.20 (0.88-1.64)

0.253

1.19 (0.86-1.63)

0.291

Smoking status

 Never

249/214

36.3/35.1

437/396

63.7/64.9

0.95 (0.76-1.19)

0.649

0.97 (0.77-1.22)

0.764

 Former

6/39

35.3/32.5

11/81

64.7/67.5

0.88 (0.30-2.56)

0.819

1.10 (0.36-3.36)

0.872

 Current

142/167

33.6/35.8

280/299

66.4/64.2

1.10 (0.84-1.45)

0.495

1.09 (0.82-1.44)

0.560

Drinking status

 Never

309/300

36.1/35.3

546/551

63.9/64.7

0.96 (0.79-1.17)

0.702

0.97 (0.80-1.19)

0.771

 Ever

88/120

32.6/34.8

182/225

67.4/65.2

1.10 (0.79-1.55)

0.569

1.10 (0.79-1.55)

0.572

Pack-years

 0

249/214

36.3/35.1

437/396

63.7/64.9

0.95 (0.76-1.19)

0.649

0.96 (0.76-1.21)

0.726

 ≤25 (mean)

72/130

31.7/36.6

155/225

68.3/63.4

1.24 (0.87-1.77)

0.226

1.20 (0.84-1.73)

0.322

 >25 (mean)

76/76

35.8/32.9

136/155

64.2/67.1

0.88 (0.59-1.30)

0.514

0.97 (0.64-1.46)

0.877

Tumor site

 GCA

110/420

36.1/35.1

195/776

63.9/64.9

0.96 (0.74-1.25)

0.756

0.96 (0.74-1.25)

0.752

 NGCA

287/420

35.0/35.1

533/776

65.0/64.9

1.01 (0.83-1.21)

0.957

1.01 (0.84-1.22)

0.901

Abbreviations: GCA, gasric cardia adenocarcinoma; NGCA, non-gastric cardia adenocarcinoma;

a Obtained in logistic regression models with adjustment for age, sex, smoking and drinking status

DISCUSSION

Genetic susceptibility has been a research focus in cancer studies. Recently, miR-146a has drawn an increasing attention for its potential connection to several types of cancers, including gastric cancer. Several studies have indicated the common SNP rs2910164 in miR-146a as a moderate risk allele for gastric cancer [26, 28]. However, the results should be intepreted with causion, largely due to inadequate sample size in each independent study. Our study was performed with a relatively large sample size in a well-established gastric cancer study cohort among eastern Chinese population. This study showed an insignificant association, as well as among a series of subgroup analyses. The results suggest a considerably heterogeneous effect of this SNP among various cancer types. On the other hand, the observed association may be due to chance.

We acknowledge some limitations of the present study. First, although age, sex, smoking and drinking status, and tumor site were taken into consideration for subgroup analysis, other important risk factors, such as diet, microbial virulence, and HP infection, were missing in the study, which might also contribute to the etiology of GCa [29, 30]. Second, new classification of GCa tumor types, which was not available for the patients diagnosed years ago, is also important and may have an interaction effect with genetic variants on gastric cancer risk [31]. Third, the number of cases was largely reduced in the stratified analysis, which may lead to an insufficient statistical power.

In summary, these results suggest that the SNP rs2910164 of miR-146a may not be associated with the risk of GCa in this Chinese population. However, analysis of this SNP incorporating diet, HP infection status or Lauren classification probably provide an updated result.

MATERIALS AND METHODS

Study subjects

This study included patients who were recruited from our ongoing molecular epidemiology study of GCa, and the cases and controls were described previously [27, 32, 33]. Briefly, 1,125 unrelated ethnic Han Chinese patients with newly diagnosed and histopathologically confirmed primary gastric cardia adenocarcinoma and non-gastric cardia adenocarcinoma (NGCA) were recruited from Fudan University Shanghai Cancer Center (FUSCC) in Eastern China between January 2009 and March 2011. Patients other than histopathologically confirmed primary GCa were excluded. In addition, 1,196 age and sex-matched cancer-free ethnic Han Chinese controls were recruited from the Taizhou Longitudinal (TZL) study conducted at the same time period in Eastern China as described previously [34]. Blood samples from both GCa patients and cancer-free controls were provided by the tissue bank of FUSCC and the TZL study, respectively. All participants had signed a written informed consent for donating their biological samples to the tissue bank for scientific research. Demographic data and environmental exposure history of each participants were collected. The overall response rate was approximately 91% for cases and 90% for controls. This research protocol was approved by the FUSCC Institutional Ethics Review Board.

SNP genotyping

According to a relevant protocol, we extracted genomic DNA from peripheral blood. The rs2910164 SNP was genotyped by the TaqMan assay with ABI7900HT real-time PCR system (Applied Biosystems) as reported previously [27]. Participants’ status was unrevealed in the genotyping process. As recommend by the company, four negative controls (without DNA templates) and two duplicated samples were included in each 384-plate for the quality control. The assays were repeated for 5% of the samples, and the results were 100% concordant.

Statistical methods

The χ2 test was used to assess differences in the distributions of demographic characteristics between cases and controls. The association between SNP and GCa risk was assessed by odds ratio (OR) and 95% confidence intervals (CIs) in heterozygous (CG vs. CC), homozygous (GG vs. CC), dominant (CG+GG vs. CC), recessive (GG vs. CG+CC), and additive (G vs. C) genetic models, respectively. OR values were calculated by both univariate and multivariate logistic regression models. Moreover, logistic regression tests for each genetic model were adjusted for age, sex, drinking and smoking status. Furthermore, the association between the miR-146a rs2910164 SNP and GCa risk was also stratified by age, sex, smoking or drinking status, and primary tumor site. All statistical process above was achieved by SAS Version 9.1 software (SAS Institute, Cary, NC, USA).

ACKNOWLEDGMENTS

This study was supported by the grant from the National Natural Science Foundation of China (81101808, 81302039, 81402764), the Natural Science Foundation of Jiangsu, China (BK20140907), the Natural Science Foundation of Shanghai, China (13ZR1407800), the grant from the Ministry of Health (201002007), and the Outstanding Young Teachers Training Program of Nanjing Medical University. This work was also partially supported by A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

CONFLICTs OF INTEREST

The authors have declared that no competing interests exist.

REFERENCES

1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015; 65:87-108.

2. Ye W, Held M, Lagergren J, Engstrand L, Blot WJ, McLaughlin JK, Nyren O. Helicobacter pylori infection and gastric atrophy: risk of adenocarcinoma and squamous-cell carcinoma of the esophagus and adenocarcinoma of the gastric cardia. J Natl Cancer Inst. 2004; 96:388-396.

3. Ishaq S, Nunn L. Helicobacter pylori and gastric cancer: a state of the art review. Gastroenterol Hepatol Bed Bench. 2015; 8:S6-S14.

4. Wang Z, Dai J, Hu N, Miao X, Abnet CC, Yang M, Freedman ND, Chen J, Burdette L, Zhu X, Chung CC, Ren C, Dawsey SM, Wang M, Ding T, Du J, et al. Identification of new susceptibility loci for gastric non-cardia adenocarcinoma: pooled results from two Chinese genome-wide association studies. Gut. 2015.

5. Qiu LX, Cheng L, He J, Zhou ZR, Wang MY, Zhou F, Guo WJ, Li J, Sun MH, Zhou XY, Wang YN, Yang YJ, Wang JC, Jin L, Zhu XD, Wei QY. PSCA polymorphisms and gastric cancer susceptibility in an eastern Chinese population. Oncotarget. 2016; 7:9420-8. doi: 10.18632/oncotarget.7137.

6. Mocellin S, Verdi D, Pooley KA, Nitti D. Genetic variation and gastric cancer risk: a field synopsis and meta-analysis. Gut. 2015; 64:1209-1219.

7. Qiu LX, He J, Cheng L, Zhou F, Wang MY, Sun MH, Zhou XY, Li J, Guo WJ, Wang YN, Yang YJ, Wang JC, Jin L, Zhu XD, Wei QY. Genetic variant of PRKAA1 and gastric cancer risk in an eastern Chinese population. Oncotarget. 2015; 6:42661-42666. doi: 10.18632/oncotarget.6124.

8. Qiu LX, Hua RX, Cheng L, He J, Wang MY, Zhou F, Zhu XD, Sun MH, Zhou XY, Li J, Wang YN, Yang YJ, Wang JC, Jin L, Guo WJ, Wei QY. Genetic variant rs4072037 of MUC1 and gastric cancer risk in an eastern chinese population. Oncotarget. 2016; doi: 10.18632/oncotarget.7527.

9. Ambros V. The functions of animal microRNAs. Nature. 2004; 431:350-355.

10. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004; 116:281-297.

11. Qiu LX, He J, Wang MY, Zhang RX, Shi TY, Zhu ML, Mao C, Sun S, Lv FF, Zheng CL, Zhu XD. The association between common genetic variant of microRNA-146a and cancer susceptibility. Cytokine. 2011; 56:695-698.

12. Chen JF, Mandel EM, Thomson JM, Wu Q, Callis TE, Hammond SM, Conlon FL, Wang DZ. The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat Genet. 2006; 38:228-233.

13. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000; 100:57-70.

14. Sherr CJ. Principles of tumor suppression. Cell. 2004; 116:235-246.

15. Slezak-Prochazka I, Durmus S, Kroesen BJ, van den Berg A. MicroRNAs, macrocontrol: regulation of miRNA processing. RNA. 2010; 16:1087-1095.

16. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA. 2004; 10:1507-1517.

17. Jazdzewski K, Murray EL, Franssila K, Jarzab B, Schoenberg DR, de la Chapelle A. Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A. 2008; 105:7269-7274.

18. Mittal RD, Gangwar R, George GP, Mittal T, Kapoor R. Investigative role of pre-microRNAs in bladder cancer patients: a case-control study in North India. DNA Cell Biol. 2011; 30:401-406.

19. Hu Z, Liang J, Wang Z, Tian T, Zhou X, Chen J, Miao R, Wang Y, Wang X, Shen H. Common genetic variants in pre-microRNAs were associated with increased risk of breast cancer in Chinese women. Hum Mutat. 2009; 30:79-84.

20. Ma XP, Zhang T, Peng B, Yu L, Jiang de K. Association between microRNA polymorphisms and cancer risk based on the findings of 66 case-control studies. PLoS One. 2013; 8:e79584.

21. Zeng Y, Sun QM, Liu NN, Dong GH, Chen J, Yang L, Wang B. Correlation between pre-miR-146a C/G polymorphism and gastric cancer risk in Chinese population. World J Gastroenterol. 2010; 16:3578-3583.

22. Okubo M, Tahara T, Shibata T, Yamashita H, Nakamura M, Yoshioka D, Yonemura J, Ishizuka T, Arisawa T, Hirata I. Association between common genetic variants in pre-microRNAs and gastric cancer risk in Japanese population. Helicobacter. 2010; 15:524-531.

23. Hishida A, Matsuo K, Goto Y, Naito M, Wakai K, Tajima K, Hamajima N. Combined effect of miR-146a rs2910164 G/C polymorphism and Toll-like receptor 4 +3725 G/C polymorphism on the risk of severe gastric atrophy in Japanese. Dig Dis Sci. 2011; 56:1131-1137.

24. Pu JY, Dong W, Zhang L, Liang WB, Yang Y, Lv ML. No association between single nucleotide polymorphisms in pre-mirnas and the risk of gastric cancer in Chinese population. Iran J Basic Med Sci. 2014; 17:128-133.

25. Peng S, Kuang Z, Sheng C, Zhang Y, Xu H, Cheng Q. Association of microRNA-196a-2 gene polymorphism with gastric cancer risk in a Chinese population. Dig Dis Sci. 2010; 55:2288-2293.

26. Chen ZF, Ma LL, Xue HB. Common polymorphisms of the microRNA genes (miR-146a and miR-196a-2) and gastric cancer risk: an updated meta-analysis. Genet Mol Res. 2015; 14:8589-8601.

27. He J, Qiu LX, Wang MY, Hua RX, Zhang RX, Yu HP, Wang YN, Sun MH, Zhou XY, Yang YJ, Wang JC, Jin L, Wei QY, Li J. Polymorphisms in the XPG gene and risk of gastric cancer in Chinese populations. Hum Genet. 2012; 131:1235-1244.

28. Wei Y, Li L, Gao J. The association between two common polymorphisms (miR-146a rs2910164 and miR-196a2 rs11614913) and susceptibility to gastric cancer: A meta-analysis. Cancer Biomark. 2015; 15:235-248.

29. Jeong M, Park JM, Han YM, Park KY, Lee DH, Yoo JH, Cho JY, Hahm KB. Dietary prevention of Helicobacter pylori-associated gastric cancer with kimchi. Oncotarget. 2015; 6:29513-29526. doi: 10.18632/oncotarget.4897.

30. Cover TL, Peek RM, Jr. Diet, microbial virulence, and Helicobacter pylori-induced gastric cancer. Gut Microbes. 2013; 4:482-493.

31. Qiu M, Zhou Y, Zhang X, Wang Z, Wang F, Shao J, Lu J, Jin Y, Wei X, Zhang D, Wang F, Li Y, Yang D, Xu R. Lauren classification combined with HER2 status is a better prognostic factor in Chinese gastric cancer patients. BMC Cancer. 2014; 14:823.

32. He J, Xu Y, Qiu LX, Li J, Zhou XY, Sun MH, Wang JC, Yang YJ, Jin L, Wei QY, Wang Y. Polymorphisms in ERCC1 and XPF genes and risk of gastric cancer in an eastern Chinese population. PLoS One. 2012; 7:e49308.

33. He J, Wang MY, Qiu LX, Zhu ML, Shi TY, Zhou XY, Sun MH, Yang YJ, Wang JC, Jin L, Wang YN, Li J, Yu HP, Wei QY. Genetic variations of mTORC1 genes and risk of gastric cancer in an Eastern Chinese population. Mol Carcinog. 2013; 52:E70-79.

34. Wang X, Lu M, Qian J, Yang Y, Li S, Lu D, Yu S, Meng W, Ye W, Jin L. Rationales, design and recruitment of the Taizhou Longitudinal Study. BMC Public Health. 2009; 9:223.


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