Correlation Between CCND1 Gene Polymorphism and Genetic Susceptibility to Gastric Cancer in Chinese Population

DOI: https://doi.org/10.21203/rs.3.rs-128247/v1

Abstract

Objective: The relationship between Cyclin D1 gene (CCND1) rs9344 polymorphism and susceptibility to gastric cancer (GC) is investigated.

Methods: In a case-control study, we selected 577 cases of GC from The People's Hospital Affiliated to Jiangsu University in China along with 678 normal controls. Blood DNA was extracted and PCR amplified, gene polymorphism was determined using Snapshot method.

Results: Analysis reveals significant difference in smoking between GC and control groups (P=0.006), however, not on polymorphism (P＞0.05).

Conclusion: Smoking is associated with gastric cancer, whereas  CCND1 rs9344 polymorphism does not implicate susceptibility of gastric cancer. 

Introduction

Gastric cancer (GC) is one of the most common malignant tumors of the gastrointestinal tract and the second most common cause of cancer mortality worldwide. GC is a complex disease with multiple causes[1–2], including helicobacter pylori infection, environmental dietary factors, and genetic factors. Single Nucleotide Polymorphism (SNP)[3], as a type of genetic factors, is essential genetic information that affects susceptibility of human diseases, plays an important role in pathogenesis of GC. Cyclin D1 (CCND1) gene located in chromosome 11ql3 is the most important positive cell cycle regulator in the Cyclin family (Cyclin). A CCND1 forms a complex when binding to a cyclin-dependent kinase and stimulate cell transition from phase G1 to S[4]. CCND1 gene decrease cell cycle through gene amplification and protein over-expression, leading to independent proliferation of tumor cells[5–6].

Studies suggest that polymorphism of CCND1 gene rs9344 affects risks and clinical outcomes of malignant tumors[7–9]. Rs9344 polymorphism has been proved to be associated with breast cancer[10], rectal cancer[11], lung cancer[12], bladder cancer[13], but there is few work on its relationship with GC. This paper aims to provide evidence for early screening, diagnosis and treatment of GC.

Materials And Methods

During May 2013 and June 2017, a hospital based investigation was conducted involving 1255 random samples (577 gastric cancer patients and 678 controls). The subject group consisted of 394 male and 183 female Han Chinese aged at 61.34±11.097 years old. The control group of 678 healthy people were selected from the same survey area without blood relationship. The difference was not statistically significant in occurrence distribution of age or gender between case and control groups (P＞0.05).

This research has been approved by the ethics review committee of Jiangsu University. A standardized training was conducted before study. Subjects who participated in this study were informed of significance.

DNA extraction, PCR amplification and Snapshot typing were used .

Table 1 shows primer sequences of rs9344 locus were designed using software Primerpremier 5.0 software[14]. Extension primers were designed . The PCR reaction conditions were pre-denaturation at 95oC for 3 minutes, denaturation at 94oC for 30 seconds, annealing at 56oC for 40 s, extension at 72oC for 40 s, a total of 30 cycles, and the final 72oC extension is for 5 min. PCR amplification products were purified with ExoI and FastAP for the extension reaction, and ABI3730XL sequencer was used for sequencing and genotyping detection.

Table 1 Single nucleotide polymorphism genotypic primers required

PCR amplification primer（5’-3’）

Amplification products

Loci polymorphism

Extension of primers（5’-3’）

Extension

Extension

direction

production

F:TTCCAATCCGCCCTCCAT

249bp

[A/G]

AAGGTTAGGCGGGAGGTA

reverse

CT

R:CCCCAACCTTGTCACCCT

GGGGTTGGAACAGGGA

CT

CAGGGA


Statistical method

Data were analyzed using software SPSS 20.0 (SPSS, Chicago, Illinois, USA). Hardy-Weinberg genetic balance was used to test the genotype distribution frequency.  Comparing frequency distribution between gastric cancer and control groups by T-test and Chi-square test. Risk ratio, Odds ratio (OR) and 95% confidence interval (CI) were calculated from binary logistic regression. Both statistical tests were bilateral probability tests, with P<0.05 indicating that the difference is statistically significant.

Results And General Features Of Samples

Results in Table 2 showed that there was no statistically significant difference  between two groups' age (P=0.065). There was no statistically significant difference in gender frequency between the case and control groups (P=0.635). Smoking ratio in case group was 34.49% higher than control group 27.29%. Alcohol  ratio was 21.49% in case lower than 23.30% in control group, alcohol consumption was not statistically significant difference between two groups (P=0.443).

Table 2  Distribution of selected demographic variables and risk factors in cases and controls

Variable

Overall Cases (n=577)

Overall Controls (n=678)

P

 

n (%)

n (%)

 

Age (years)

61.34±11.097

62.31±7.549

0.065

Age (years)

 

 

 

< 62

268 (46.45)

324 (47.79)

 

≥62

309(53.55)

354(52.21)

0.635

Sex

 

 

 

Male

394 (68.28)

456(67.26)

 

Female

183(31.72)

222(32.74)

0.698

Smoking status

 

 

 

Never

378 (65.51)

493(72.71)

 

Ever

199(34.49)

185 (27.29)

0.006

Alcohol use

 

 

 

Never

453 (78.51)

520(76.70)

 

Ever

124 (21.49)

158(23.30)

0.443

 

Primary information for gene CCND1 rs9344 Polymorphisms

Table 3 shows that rs9344 is located in 11 chromosome, category is protein coding, minor allele frequency(MAF) for Chinese in genecard database is 0.442 and in our controls is 0.451. Hardy-Weinberg equilibrium test in our controls is 0.295(P>0.05), which means that our sample population is representative. We use Snapshot method as genotyping and the percentage of successful tests is 99.20%.

Table 3  Primary information for gene CCND1 rs9344 Polymorphisms

GenotyPed SNPs

Gene

Chr Pos (NCBI Build 38)

Category

MAFa for Chinese in database

MAF in our controls (n = 678)

P value for HWEb test in our controls

Genotyping method

Genotyping value (%)

rs9344

CCND1

11：69648142

Protein Coding

0.442

0.451

0.295

Snapshot

99.20

a MAF: minor allele frequency;

b HWE: Hardy–Weinberg equilibrium;

GeneCards Datebase: https://www.genecards.org/

 

Table 4 Hardy-Weinberg equilibrium test : There was no statistically significant difference between the case and control groups (P>0.05). Genotype frequency conforms to the law of genetic balance, the selected samples are representative.

Table 4  Hardy–Weinberg equilibrium analysis for rs9344

Group                   rs9344                                            

 

 

AA

AG

GG

2

P

Case

Observed

168

302

107

 

 

 

Expected

176.4

285.3

115.4

1.983

0.159

Control

Observed

182

343

143

 

 

 

Expected

187.1

332.9

148.1

0.620

0.431

P value＜0.05 means statistically significant difference；αtakes two sides check；df=1.

 

Table 5 shows: The A allele frequency in case group is higher than control group (47.43%>45.07%); The G allele distribution in case group is lower than control group (52.57%<54.93%), yet this difference is no statistically significant between the two groups (P=0.237).

Table 5  Analysis of rs9344 A allele and G allele between cases and controls

Variable

Case

Control

P

OR(95% CI)

A allele

638(47.43)

516(45.07)

 

 

G allele

707(52.57)

629(54.93)

0.237

0.91(0.78-1.07)


The results of Table 6 showed that occurrence of AG was slightly higher in case than in control (52.34% >51.35%) with no statistical significance (P=0.722). There was still no statistically significant difference(P=0.787) after being logistics regression test.

Occurrence of GG in case group was lower than in control group (18.54%<21.40%), but the difference was not statistically significant (P=0.208). GG were still not statistically different (P=0.189) after being logistics regression test.

In dominant model, mutation（AG+GG）frequency was lower in case group than control group (70.88% <72.75%), but difference was not statistically significant (P=0.464). There was still no statistical difference between the two groups (P=0.496) after logistic regression analysis. For recessive model dominated, GG/(AA+AG) with no statistical difference (P=0.209).

 

Table 6 Logistic regression analysis of CCND1 gene rs9344 Polymorphism between cases and controls 

Genotype

GC Cases

(n=577)

 

 

Controls

(n=678)

Crude OR

(95%CI)

P 

Adjusted OR a

(95%CI)

P 

n

%

 

n

%

 

 

 

 

rs9344

 

 

 

 

 

 

 

 

 

AA

168

29.12

 

182

27.25

1.00

 

 

 

AG

302

52.34

 

343

51.35

0.95(0.74-1.24)

0.722

0.96(0.74-1.25)

0.787

GG

107

18.54

 

143

21.40

0.81(0.59-1.12)

0.208

0.90(0.76-1.06)

0.189

AG+GG

409

70.88

 

486

72.75

0.91(0.71-1.17)

0.464

0.99(0.85-1.09)

0.496

GG

107

18.54

 

143

21.40

0.84(0.63-1.11)

0.209

0.91(0.79-1.04)

0.173

AA+AG

470

81.46

 

525

78.60

1.00

 

 

 

 

Table 7 shows that stratified analysis, we analyzed stratification according to gender, age, smoking and drinking factors. The results showed that differences were not statistically significant in heterozygous mutants, homozygous mutants, dominant models or recessive models.

Table 7  Stratified analyses between rs9344 Polymorphism and risk by sex, age, BMI, smoking status and alcohol consumption

Variable

 (case/control)

Adjusted OR  (95% CI); P

AA

AG

GG

AA

AG

GG

(AG+GG)VSAA

GGVS(AA+AG)

gender

 

 

 

 

 

 

 

 

Male

119/119

200/233

75/96

1.00

0.86(0.63-1.18);

P:0.344

0.78(0.53-1.16);

P:0.220

0.84(0.62-1.13);

P:0.242

0.84(0.60-1.18);

P:0.304

Female

49/63

102/110

32/47

1.00

1.19(0.75-1.89);

P:0.454

0.88(0.49-1.57);

P:0.655

1.10(0.71-1.70);

P:0.678

0.78(0.43-1.29);

P:0.329

Age

 

 

 

 

 

 

 

 

<62

68/91

151/160

49/66

1.00

1.26(0.86-1.86);

P:0.234

0.99(0.61-1.61);

P:0.979

1.18(0.82-1.71);

P:0.367

0.85(0.56-1.28);

P:0.442

≥62

100/91

151/183

58/77

1.00

0.75(0.53-1.07);

P:0.115

0.69(0.44-1.07);

P:0.095

0.73(0.52-1.03);

P:0.069

0.82(0.56-1.20);

P:0.314

Smoking

 

 

 

 

 

 

 

 

Never

106/136

205/246

67/102

1.00

1.07(0.78-1.46);

P:0.077

0.08(0.57-1.26);

P:0.401

1.00(0.74-1.35);

P:0.985

0.81(1.14-0.57);

P:0.219

Ever

62/46

97/97

40/41

1.00

0.74(0.46-1.19);

P:0.217

0.72(0.41-1.29);

P:0.273

0.74(0.47-1.15);

P:0.181

0.88(0.54-1.43);

P:0.601

Alcohol

 

 

 

 

 

 

 

 

Never

128/145

241/257

84/109

1.00

1.06(0.79-1.43);

P:0.689

0.87(0.60-1.27);

P:0.473

1.01(0.76-1.33);

P:0.967

0.84(0.61-1.15);

P:0.280

Ever

40/37

61/86

23/34

1.00

0.66(0.38-1.14);

P:0.135

0.63(0.31-1.25);

P:0.184

0.65(0.38-1.10);

P:0.105

0.82(0.46-1.49);

P:0.520

For CCND1 gene rs9344 locus, genotype was detected successfully 99.20% in 1255 samples.

Discussion

The early symptoms of GC are not obvious. Patients have often reached middle or late stages when diagnosed clearly, with a low 5 years survival rate of 30%[15,16]. Current treatments for GC includes surgery, chemotherapy, drug targeted therapy, etc. Finding specific biomarkers is beneficial for early screening diagnosis, and clinical targeted drug therapy.

This project is based on the relationship between polymorphism in CCND1 gene and GC, with few similar studies currently reported. In 2010, Kuo et al [17] studied 358 GC patients and 358 healthy people and found that rs9344 locus polymorphism was associated with gastric cancer risk, and that subjects carrying variants AG and GG genotypes had lower risk than carrying AA genotypes. Rs9344 genotype maybe a useful biomarker for detection of early GC. Kuo et alalso investigated the interaction between rs9344 genotype and individual smoking status on GC risk and found that there was a synergistic effect between gene and smoking, which may increase the develop risk of GC. Another Yokoyama et al [18] suggested that there was no correlation between CCND1 rs9344 gene polymorphism and risk of GC in Japanese population in 2001. Our study suggest that there is no significant association between CCND1 rs9344 polymorphism and gastric cancer besides that smoking is a risk factor.

Alcohol drinking in this study was not related to gastric cancer, which is consistent with the results from Yokoyama et al, but differs from the research by Inoue M[19]. According to Inoue M research, drinking alcohol may be a potential risk factor for gastric cancer: the reason why drinking alcohol as risk factor may be that occurrence of gastric cancer is a slow process under joint action of multiple factors. Long-term heavy drinking can cause damage to gastric mucosa itself. On the other hand, alcohol intake in wine can stimulate gastric mucosa to reduce barrier function and increase gastric mucosa permeability, resulting in increased absorption of carcinogens. Another study by Tahara et al. [20-21] showed that CCND1 rs9344 polymorphism was associated with high methylation risk of CpG island promoter in gastric cancer, which was the first report to show a potential association between CCND1 hypermethylation status in gastric cancer. A meta analysis[22] showed that analysis shows the relationship between site polymorphism and gastric cancer is different between Asian and Caucasian populations. Rs9344 polymorphism increases risk of GC in Caucasians but not in Asian.

The occurrence and development of gastric cancer is affected by various factors, in addition to genetic background, other factors that should be taken into consideration, may include environment, lifestyle, race, ethnicity, eating habits, genetic heterogeneity, sample size, etc. Therefore, the relationship between CCND1 gene and gastric cancer susceptibility needs further verification, larger samples, and elaborate design in the further .

Abbreviations

GC: gastric cancer; CCND1: Cyclin D1

Declarations

Acknowledgements

The authors thank to all members of the study team.In addition, the authors thank all participants of this study.

Authors’ Contributions

Xuyu Gu is the co-fifirst author. Huiwen Pan and Xuyu Gu wrote and edited the manuscript. Yu Fan, Keping Chen provided direction and guidance throughout the preparation of this manuscript. Huiwen Pan finished the data analysis. Chen Zou revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

Funding

Funded by the Jiangsu Provincial Key Research and Development Special Fund (BE2015666), Jiangsu Innovation Found For Team Leading Talents(CXTDC2016006), Jiangsu Natural Science Foundation(BK20171304), Jiangsu Six Peak Talent Fund (WSW-205) and Jiangsu 333 Talent Fund (BRA2016140).

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Ethics approval and consent to participate

The study was approved by the Institutional theAffiliated People's Hospital of Jiangsu University. The patients consented to participate.

Consent for publication

Written informed consent for research and publication from the patients was obtained.

Competing interests

The authors declare that they have no competing interests

Author details

1Jiangsu University School of Life Science, Zhenjiang, 212000, P.R. China;2School of Medicine, Southeast University, Nanjing, 210009, P.R. China;3Cancer Institute,Affiliated people's hospital of Jiangsu university, Zhenjiang, 212000, P.R. China. # Co-First Author: Xuyu Gu. * Corresponding author: Yu Fan, E-mail: [email protected]; Keping Chen:[email protected].

References

  1. Jemal A, Bray F, Center MM, et al.Global cancer statistics[J]. CA Cancer J Clin, 2011, 61(2): 69-90.
  2. Chen WQ, Zheng RS, Baade PD, et al. Cancer statistics in China, 2015 [J]. CA Cancer J Clin, 2016, 66(2): 115-132.
  3. Xue JX, Qin ZQ, Li X, et al. Genetic polymorphisms in cyclin D1 are associated with risk of renal cell cancer in the Chinese population[J]. Oncotarget, 2017, 8(46):80889-80899.
  4. Lin JT, Li HY, Chang NS, et al. WWOX suppresses prostate cancer cell progression through cyclin D1-mediated cell cycle arrest in the G1 phase[J]. Cell Cycl, 2015, 14(3): 408-416.
  5. Chen XM, Zhao TS, Li L, et al. CCND1 G870A Polymorphism with altered Cyclin D1 transcripts expression is associated with the risk of glioma in a Chinese population[J]. DNA Cell Biol, 2012, 31(6): 1107-1113.
  6. Teixeira Mendes LS, Peters N, Attygalle AD, et al. Cyclin D1 overexpression in proliferation centres of small lymphocytic lymphoma /chronic lymphocytic leukaemia[J]. J Clin Pathol, 2017, 70(10): 899-902.
  7. Wang L, Habuchi T, Mitsumori K, et al. Increased risk of prostate cancer associated with AA genotype of cyclin D1 gene A870G polymorphism[J]. International Journal of Cancer, 2003, 103(1): 116-120.
  8. Bala S, Peltomaki P. Cyclin D1 as a genetic modifier in hereditary nonpolyposis colorectal cancer[J] . Cancer Research, 2001, 61(16): 6042-6045.
  9. Qiuling S, Yuxin Z, Suhua Z, et al. Cyclin D1 gene polymorphism and susceptibility to lung cancer in a Chinese population[J]. Carcinogenesis, 2003, 24(9): 1499-1503.
  10. Liu LC, Su CH, Wang HC, et al. Contribution of personalized Cyclin D1 genotype to triple negative breast cancer risk[J] . BioMedicine, 2014, 4(1): 3-7.
  11. Xu Ming, Ni Bing, Yang Li, et al. CCND1 G870A polymorphism and colorectal cancer risk: An updated meta-analysis[J] . Molecular and clinical oncology, 2016, 4(6): 1078-1084.
  12. Zhou Changxi, An Huaijie, Hu Mingdong, et al. The cyclin D1 (CCND1) G870A polymorphism and lung cancer susceptibility: a meta-analysis[J]. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine, 2013, 34(6): 3831-3837.
  13. Li Jing, Luo Fei, Zhang Hongtuan, et al. The CCND1 G870A polymorphism and susceptibility to bladder cancer[J]. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine, 2014, 35 (1): 171-177．
  14. Lalitha S. Primer premier 5[J]. Biotech Software Internet Report, 2000, 1(6): 270-272.
  15. Herszényi L, Tulassay Z. Epidemiology of gastrointestinal and liver tumors [J]. Eur Rev Med Pharmacol Sci, 2010, 14(4): 249-258.
  16. Stamatakos M, Palla V, Karaisko I, et al. Cell cyclins:triggering elements of cancer or not[J]. World J Surg Oncol, 2010 ,8(5): 111-114.
  17. Kuo WH, Huang CY, Fu CK, et al. The significant association of CCND1 genotypes with gastric cancer in Taiwan[J]. Anticancer Res, 2015, 34(9): 4963-4968.
  18. Yokoyama A, Muramatsu T, Omori T, et al. Alcohol and aldehyde dehydrogenase gene polymorphisms and oropharyngolaryngeal, esophageal and stomach cancers in Japanese alcoholics[J]. Carcinogenesis, 2001, 22(3): 433-435.
  19. Inoue M, Tajima K, Hirose K, et al. Life-style and subsite of gastric cancer joint effect of smoking and drinking habits[J]. International Journal of Cancer, 2010, 56(4): 494-499.
  20. Kahraman A, Erkasap N, Köken T, et al. The antioxidative and antihistaminic properties of quercetin in ethanol-induced gastric lesions[J]. Toxicology, 2003, 183(1-3): 133-142.
  21. Tahara, Shibata TNakamura Met al. Association between cyclin D1 polymorphism with CpG island promoter methylation status of tumor suppressor genes in gastric cancer[J]. Dig Dis Sci, 2010, 55 (12): 3449-3457.
  22. Zhang Y, Zeng X, Lu H, et al. Association between cyclin D1 (CCND1) G870A polymorphism and gastric cancer risk: a meta analysis[J]. Oncotarget, 2016, 7(40): 66109-66118.