Case-control Study on p73 rs1801173 C > T Gene Polymorphism and Susceptibility to Gastric Cancer in a Chinese Han Population

Case-control Study on p73 rs1801173 C > T Gene Polymorphism and Susceptibility to Gastric Cancer in a Chinese Han Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Case-control Study on p73 rs1801173 C > T Gene Polymorphism and Susceptibility to Gastric Cancer in a Chinese Han Population Xuyu Gu, Xiaoyan Wang, Huiwen Pan, Zhenjun Gao, Guowen Ding, Yu Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-120606/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective: This study aimed to investigate the association between p73 C14T (rs1801173) polymorphism and the risk of GC in a Chinese Han population. Methods: A hospital-based case-control study was conducted. A total of 577 GC cases and 678 normal controls were recruited. Their genotypes were determined using the SnapShot method. Results: The genotype frequency distribution of the case group and the control group were consistent with the Hardy–Weinberg equilibrium. No significant difference was found in the distribution of gender, age, and drinking history between the case group and the control group. A correlation was observed between smoking and the incidence of GC ( P = 0.006). Three genotypes of CC, CT, and TT were found in the rs1801173 locus of p73 . The distribution of the dominant model/recessive model did not significantly differ ( P = 0.688; 0.937). No statistical difference was found even after adjustment was performed via logistic regression analysis ( P = 0.703; 0.990). The frequency distribution between the two groups also did not significantly differ ( P = 0.763). Conclusion: Smoking is related to the occurrence and development of GC. No association was found between p73 rs1801173 C > T SNP and the risk of GC in a Chinese Han population. However, additional larger studies and tissue-specific biological characterization are required to confirm these findings. Epigenetics & Genomics Gastric Cancer Single Nucleotide Polymorphism p73 rs1801173 Introduction Gastric cancer (GC) is one of the most common malignant tumors, ranking third in the number of cancer deaths worldwide [ 1 ] . The latest statistics from the Chinese National Cancer Center from a report in February 2018 showed that although the overall incidence of GC is declining, it still remains second in terms of incidence among all malignancies in China, just below lung cancer [ 2 ] . Despite remarkable progress, the current treatments for GC remain inefficacious, with overall 5-year survival rates < 30%. At the time of diagnosis, most patients have been diagnosed with advanced cancer and metastasis [ 3 ] . GC invasion and metastasis are complex processes involving multiple factors. Increasing evidence has proven that environmental and genetic factors contribute to the occurrence and development of GC. In addition to environmental risk factors, such as diet and HP infection, single nucleotide polymorphism (SNP) may play an important role in GC cancerization as a genetic factor. SNPs are polymorphisms in DNA sequences caused by mutations in the genome's single nucleotides. They are the most common form of human genetic variation, accounting for > 90% of all known morphologies [ 4 ] . Understanding the underlying mechanisms of GC initiation and progression may promote biomarker development for early detection of cancer. Previous epidemiological studies found increasing numbers of genetic variations that influence GC susceptibility [ 5 ] . However, the association of the polymorphisms in the genes and GC susceptibility remains largely unknown. Hua et al. found that genetic variations in LIG3 may play a weak role in modifying the risk of GC [ 6 ] . He et al. systematically analyzed the associations between nine polymorphisms in four key genes ( XPA , ERCC1 , ERCC2 , and ERCC4 ) in the nucleotide excision repair pathway and the risk of GC in a Chinese population. They concluded that the ERCC1 polymorphisms may affect the risk of GC in the Chinese Han population [ 7 ] . They also studied the genetic variations of mTORC1 and risk of GC in an Eastern Chinese population and found that the functional polymorphisms of mTOR may contribute to the risk of GC [ 8 ] . In 2004, the authors of the present study found that p73 rs1801173 C > T polymorphisms are associated with an increased risk of esophageal cancer in a Chinese population [ 9 ] . Tumor protein p73 (TP73), also known as p53 -like transcription factor, is a pivotal member of the p53 (TP53) family, which affects cell proliferation, apoptosis, and cell-cycle regulation. p73 is located at 1p36.33, mapping to a region that is often deleted in cancers [ 10 ] . p73 activates the transcription of p53 -responsive genes, which participate in cell-cycle control, DNA repair, and apoptosis and inhibits cell growth in a p53 -like manner by inducing apoptosis or G1 cell-cycle arrest [ 11 ] . Thus, p73 has tumor-suppressor functions. p73 has some significant differences from p53 . In contrast to p53 -deficient mice, those lacking p73 show no increased susceptibility to spontaneous tumorigenesis [ 12 ] . At least 19 polymorphisms have been recognized in p73 at present. Ten polymorphisms are in the exon region, eight are in the intron region, and one is in the region of gene promoter. However, the functionality of most of these polymorphisms has not been recognized yet [ 13 ] . The 1p36.3 region, where p73 is coded, is deleted in different malignancies via loss of heterogeneity. Although p73 mutation is rare and occurs in less than 2% of all cancers, the lack of heterogeneity in locus p73 is common in a variable rate in different cancers [ 14 ] . As a result, the genetic changes in p73 are critical in malignancies [ 15 ] . Two related SNPs in regions 4 (G > A) and 14 (C > T) of exon 2 in p73 resulted in the formation of a stem-loop structure that could affect the gene function via changes in its expression. Molecular epidemiology studies have indicated an association between this polymorphism and the risk of cancers in human; however, the results were inconsistent [ 16 ] . Previous research demonstrated that p73 gene polymorphisms are related to the occurrence of various cancers, including esophageal cancer [ 17 ] , rectal cancer [ 18 ] , breast cancer [ 19 ] , lung cancer [ 20 ] , and cervical cancer [ 21 ] . However, no research could be found on p73 gene polymorphism and GC susceptibility. This study aimed to investigate the relation between C14T polymorphism in p73 and the risk of GC in a Chinese Han population. Ethical approval of the study protoco This study complied with the World Medical Association Declaration of Helsinki in terms of ethical conduct of research involving human subjects and/or animals and received approval from the Review Board of Jiangsu University (Zhenjiang, China). Written informed consent was provided by all the subjects in this study. Materials And Methods Between May 2013 and June 2017, 577 subjects with GC were recruited consecutively at the Affiliated People’s Hospital of Jiangsu University (Zhenjiang, China). Their average age of the case group was 61.34 ± 11.097 years, with 394 males and 183 females. All cases were pathologically diagnosed as GC. The exclusion criteria were as follows: patients who previously had cancer and those with any metastasized cancer and undergoing radiotherapy or chemotherapy. As controls, 678 patients without cancer were matched to the cases in terms of age (± 5 years) and sex. Their average age was 62.31 ± 7.549 years, with 456 males and 222 females. The controls were recruited from the abovementioned hospital during the same time period. Both groups are Chinese Han people, and they are not related by blood. Moreover, We had access to information that could identify individual participants during or after data collection. Using a pre-tested questionnaire, trained interviewers questioned each subject personally and obtained demographic data information (e.g., age and sex) and related risk factors (such as tobacco smoking and alcohol consumption). Venous blood samples (2 mL) were collected after each interview. The definition of “smokers” was smoking one cigarette per day for > 1 year. The definition of “alcohol drinkers” was consumption of ≥ three alcoholic drinks a week for > 6 months. Blood samples were collected from patients by using vacutainers and transferred to tubes lined with ethylenediamine tetra-acetic acid. Genomic DNA was isolated from whole blood by using a QIAamp DNA Blood Mini Kit (Qiagen, Berlin, Germany). Sample DNA was amplified via PCR in accordance with the manufacturer’s recommendations. The samples were genotyped using the SnapShot method, with technical support from Shanghai Biowing Applied Biotechnology Co. as previously described. For quality control, repeated analyses were performed on 160 (12.17%) randomly selected samples with high DNA quality. Statistical analyses The distributions of demographic characteristics, selected variables, and genotypes of the p73 variant differences between the cases and controls were evaluated using the χ2 test. Logistic regression analyses was used to estimate the associations between the SNPs and the risk of GC for crude and adjusted ORs when adjusting for age, sex, smoking, and drinking status. The Bonferroni correction procedure was also applied because of the number of comparisons. The Hardy–Weinberg equilibrium (HWE) was tested using the goodness-of-fit χ 2 test to compare the observed genotype frequencies to the expected ones among the controls. All statistical analyses were performed on SPSS 20.0 (SPSS Inc., Chicago, Illinois, USA). Results Table 1 shows that the rs1801173 of p73 was located in the first chromosome. Their category was protein cod. The minor allele frequency of rs1801173 in the controls was 0.267. The result of HWE tests for the controls was 0.229 ( P > 0.05), indicating that the sample population was representative. The snapshot method was used for genotyping, and the percentage of successful tests was 98.96%. Table 1 Primary information for p73 rs1801173 gene polymorphism Genotyped SNP Gene Chr Pos (NCBI Build 38) Category MAF a for Chinese in database MAF in our controls (n = 678) P value for HWE b test in our controls Genotyping method Genotyping value (%) rs1801173 p73 1:3682346 5_prime_UTR_variant,genic_upstream_transcript_variant 0.267 0.229 0.821 snapshot 98.96 a MAF: minor allele frequency; b HWE: Hardy–Weinberg equilibrium; Table 2 shows that the cases including demographics and environmental risk factors. Smoking rate was much higher in case group as compared with the control group (34.49% vs. 27.29%, P = 0.006). The demographics (age and sex) were well matched ( P = 0.635 and P = 0.698, respectively; Table 2 ). That indicated the occurrence and development of smoking and gastric cancer. Of the alcohol consumption, no significant difference was observed between GC patients and controls ( P = 0.443, Table 2 ). Table 2 Distribution of selected demographic variables and risk factors in GC cases and controls 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 Table 3 The frequency distribution and logistic regression analysis of the p73 gene rs1801173 polymorphism in gastric cancer and control group showed that with reference to wild-type CC, the frequency distribution of TC heterozygous mutations in GC cases was 60.67% higher than 59.55 in controls group, but there was not statistically significant between the two groups ( P = 0. 657) and there was no statistical difference in gender, age, smoking, and alcohol consumption after logistic regression adjustment ( P = 0.691); the frequency distribution of TT homozygous mutants was 5.52% higher than 5.41 in controls group, but was also not statistically significant ( P = 1.000), and there was no statistical difference after logistic regression adjustment ( P = 0.979). In the dominant model, the frequency distribution of TC + TT mutations was 39.33% lower than 40.45% in controls group, but there was not statistically significant in the case-control group ( P = 0.688), and the difference was not statistically significant after regression adjustment ( P = 0.703). In the recessive model, the frequency distribution was 5.52% higher than 5.41 in controls group, but there was not statistically different ( P = 0.937). According to gender, age, smoking, and drinking, after logistic regression analysis, there was still no statistical difference between the two groups ( P = 0.990).The results of rs1801173 allele frequency distribution of p73 gene in the two groups showed that the allelic frequency distribution was not statistically significant in the case-control group ( P = 0.763). Table 3 p73 rs1801173 gene polymorphisms in GC cases and controls and logistic regression analysis Genotype GC Cases (n = 577) Controls (n = 678) Crude OR (95%CI) P Adjusted OR a (95%CI) P n % n % rs1801173 CC 341 60.67 396 59.55 1.00 1.00 TC 190 33.81 233 35.04 0.95(0.75–1.20) 0.657 0.95(0.75–1.21) 0.691 TT 31 5.52 36 5.41 1.00(0.61–1.65) 1.000 1.00(0.77–1.28) 0.979 TC + TT 221 39.33 269 40.45 0.95(0.76–1.20) 0.688 0.98(0.81-1.00) 0.703 CC + TC 531 94.48 629 94.59 1.00 1.00 TT 31 5.52 36 5.41 0.98(0.60–1.61) 0.937 1.00(0.78–1.29) 0.990 C allele 872 77.58 1025 77.07 1.00 1.00 T allele 252 22.42 305 22.93 0.97(0.80–1.17) 0.763 Stratification was conducted in accordance with four longitudinal factors, namely, sex, age, smoking, and drinking, to observe the influence of various genotypes and genetic patterns and diseases and calculate the frequency distribution of the case-control group.Table 4 shows that regardless of the gender, the distribution frequency of the four modes, namely, mutant, wild-type, recessive model, and dominant model, had no difference in the distribution of the two groups, suggesting that gender and p73 . No statistical correlation was found between the four genetic models and the occurrence of GC. Age, smoking, drinking, and temporarily also showed no statistical association. However, a notable detail that in Table 1 , a statistically significant difference was found in the association between smoking and the occurrence of GC. In the hierarchical analysis, the genetic model of the four genes did not show a statistically significant association, which is worthy of follow-up study to investigate and determine whether smoking factors are related to the rs1801173 polymorphisms of p73 . Table 4 Stratified analyses between p73 rs1801173 gene polymorphisms and risk by sex, age, smoking and drinking Variable (case/control) Adjusted OR (95% CI); P CC TC TT CC TC TT (TC + TT)VSCC TTVS(CC + TC) Sex Male 229/253 131/168 22/23 1.00 0.86(0.65–1.15); P :0.314 1.06(0.57–1.95); P :0.859 0.89(0.67–1.17); P :0.389 0.89(0.49–1.63); P :0.715 Female 112/143 59/65 9/13 1.00 1.16(0.75–1.78); P :0.502 0.88(0.37–2.14); P :0.785 1.11(0.74–1.68); P :0.607 0.84(0.35–2.02); P :0.700 Age < 62 163/182 90/117 8/16 1.00 0.86(0.61–1.22); P :0.390 0.56(0.23–1.34); P :0.186 0.82(0.59–1.15); P :0.255 1.69(0.71–4.02); P :0.228 ≥ 62 178/214 100/116 23/20 1.00 1.04(0.74–1.45); P :0.833 1.38(1.74–2.60); P :0.313 1.09(0.79–1.49); P :0.602 0.73(0.39–1.36); P :0.324 Smoking Never 228/290 119/172 21/24 1.00 0.87(0.65–1.16); P :0.331 1.11(0.60–2.05); P :0.731 0.91(0.69–1.20); P :0.498 0.86(0.47–1.57); P :0.619 Ever 113/106 71/61 10/12 1.00 1.09(0.71–1.68); P :0.691 0.78(0.32–1.89); P :0.583 1.04(0.69–1.57); P :0.849 1.32(0.56–3.14); P :0.526 Drinking Never 273/305 144/180 23/28 1.00 0.89(0.68–1.18); P :0.420 0.68(0.39–1.16); P :0.156 0.90(0.69–1.17); P :0.414 1.42(0.83–2.42); P :0.196 Ever 68/91 46/53 8/8 1.00 1.16(0.70–1.92); P :0.561 1.34(0.48–3.75); P :0.578 1.19(0.73–1.92); P :0.491 0.79(0.29–2.17); P :0.650 For p73 gene rs1801173, the genotyping was successful 99.20% in 577 cases and 678 controls. Discussion GC is a multifactorial disorder, in which genetic and environmental interactions serve an important role in the development and progression [ 23 ] . Increasing age, gender, lifestyle, dietary regime, environmental factors, and Helicobacter pylori infections are among the known risk factors for stomach cancer [ 24 ] . While dietary regime and lifestyle are the most recognized factors, enhanced identification of the genetic risk factors is expected to improve the understanding of the basic molecular events involved in tumorigenesis [ 25 ] . Genetic factors, including gene expression profile and cancer biomarkers, such as SNPs, have a crucial role in improving the early diagnosis of GC [ 26 ] . Recent studies have demonstrated that a high number of genes and various environmental factors are the causal agents of GC, and the presence of different forms of alleles in gene polymorphisms may promote the development of cancers. In this regard, p73 has been a research focus due to its role as a major tumor suppressor gene [ 27 ] . p73 has some functions similar to or independent of p53 and plays a role, particularly in compensation for loss of p53 function, in the regulation of cell cycle, DNA repair, apoptosis, and possibly cell differentiation. In the past decades, almost 146 unique variations were reported (shown in the Biomuta database) [ 28 ] , while numerous studies probed into the relationship of G4C14-A4T14 polymorphism and cancer genomics. G4A (rs2273953) and C14T (rs1801173) polymorphisms are located at positions 4 (G to A) and 14 (C to T) of exon 25′-untranslated region, which may influence the initiating AUG codon by constructing a stem loop [ 29 ] . In recent years, the G4C14-A4T14 polymorphism of p73 has been identified to be implicated in the tumorigenesis of various cancer types. However, the data from these published case-control studies were not consistent. Yang et al. [ 30 ] and Niwa et al. [ 31 ] reported that G4C14-A4T14 polymorphism is not associated with the susceptibility of cervical cancer in Uighur and Japanese population, respectively. However, Craveiro et al. [ 32 ] and Feng et al. [ 33 ] revealed that G4C14-A4T14 polymorphism leads to an increasing risk of cervical cancer. Hamajima et al. [ 34 ] demonstrated no significant differences in the genotype frequencies among the enrolled cases and controls in their study of colorectal cancer. On the contrary, Lee et al. [ 35 ] reported that GC/AT and AT/AT genotypes are significantly associated with the risk of colorectal cancer in Korean population. Arfaoui et al. [ 36 ] also uncovered no remarkable differences in genotype frequencies in cancers and controls, but they found that AT/AT genotype may cause poor prognosis of colorectal cancer. Hu et al. [ 37 ] indicated that AT/AT and GC/AT variants are associated with a remarkable decrease in the risk of lung cancer. Li et al. [ 38 ] suggested that the AT/AT and GC/AT genotypes are related, with a statistically significantly increased risk of lung cancer. However, Choi et al. [ 39 ] revealed that the G4C14-A4T14 polymorphism of p73 does not affect the susceptibility of lung cancer in Korean population. Zheng et al. [ 9 ] found that the rs1801173 C > T SNPs of p73 are associated with increased risk of ESCC. However, p73 gene polymorphism and GC susceptibility have not been reported yet. Therefore, the rs1801173 C/T polymorphism of p73 merits further functional study to elucidate the etiology of SNP and GC. In the present study, no statistically significant association was found between p73 rs1801173 C > T gene polymorphism and the risk of GC in a Chinese Han Population. Although the C mutant allele frequency was higher in patients with GC than in controls, the difference was not statistically significant. However, the frequency of smoking factors in the case group was 34.49%, higher than that in the control group by 27.29%, and the difference was statistically significant. This result showed that smoking is related to the occurrence and development of GC. Li et al. [ 40 ] studied the connection between p73 G4C14-to-A4T14 polymorphism and the risk of lung cancer. They found that the increased risk associated with the combined p73 GC/AT + AT/AT genotype in younger (≤ 50 years) subjects and light (compared with heavy) smokers suggested an early onset and lower levels of exposure characteristic of genetic susceptibility. They also observed a significantly higher risk in men than in women, particularly among smokers. However, the interaction between smoking and p73 polymorphism was only borderline significant, which warrants additional investigations with larger sample sizes. This case-control study had several limitations. First, because the patients and controls were enrolled from hospitals, inherent bias may have resulted in spurious findings. Second, the polymorphisms may not provide a comprehensive view of p73 genetic variability. Fine-mapping studies are required. Third, the statistical power was limited because of the moderate sample size and the absence of a validation cohort. In addition, considering that gene–gene interaction and gene–environment interaction play an important role in the pathogenesis of many diseases, especially chronic diseases, p73 gene polymorphism may interact with other gene polymorphisms or environmental factors, thereby affecting the incidence of GC in the population. H. pylori infections, lifestyle, and dietary regime were not investigated. Other limitations included small sample size and sampling of individuals of the same geographical region and race. Therefore, further studies considering different geographical locations and races and a larger number of participants are necessary to confirm the results of the present study. These results failed to indicate an association between p73 rs1801173 polymorphism and risk of GC. Tissue-specific biological characterization and replication studies with larger populations are also required to confirm these findings. Declarations Data Availability The data used to support the findings of this study are available from the corresponding authors upon request. Ethical Approval The research was approved by the Ethics Review Committee of Jiangsu University. Consent All patients provided written informed consent. Conflicts of Interest The authors declare that they have no conflflicts of interest. Authors’ Contributions Xuyu Gu is the co-fifirst author. Xiaoyan Wang and Xuyu Gu wrote and edited the manuscript. Yu Fan, Guowen Ding, and Zhenjun Gao provided direction and guidance throughout the preparation of this manuscript. Huiwen Pan finished the data analysis. All authors read and approved the final manuscript. Acknowledgments This study was supported in part by Jiangsu Provincial Key Research and Development Special Fund (BE2015666), Jiangsu Innovative team leading talent fund (CXTDC2016006), Jiangsu six high peak talent fund (WSW-205), Jiangsu 333 talent fund(BRA2020016) and Suqian science and technology support project fund (S201721).In addition, the authors thank all participants of this study. References Bray F. Ferlay J, Soerjomataram I. Siegel RL, Torre LA. Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [published correction appears in CA Cancer J Clin. 2020 Jul;70(4):313]. CA Cancer J Clin. 2018;68(6):394–424. doi : 10.3322/caac.21492 . Wang FH. Shen L, Li J, et al. 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Nucleic Acids Res. 2018;46(D1):D1128–36. doi: 10.1093/nar/gkx907 . Meng J. Wang S, Zhang M. Fan S, Zhang L. Liang C. TP73 G4C14-A4T14polymorphism and cancer susceptibility: evidence from 36 case-control studies. Biosci Rep. 2018;38(6):BSR20181452. Published 2018 Dec 14. doi: 10.1042/BSR20181452 . Yang Z. Nie S, Zhu H, et al Association of p53 Arg72Pro polymorphism with bladder cancer: a meta-analysis. Gene. 2013;512(2):408–13. doi: 10.1016/j.gene.2012.09.085 . Niwa Y. Hamajima N, Atsuta Y, et al Genetic polymorphisms of p73 G4C14-to-A4T14 at exon 2 and p53 Arg72Pro and the risk of cervical cancer in Japanese. Cancer Lett. 2004;205(1):55–60. doi: 10.1016/j.canlet.2003.11.014 . Craveiro R. Bravo I, Catarino R, et al The role of p73 G4C14-to-A4T14 polymorphism in the susceptibility to cervical cancer. DNA Cell Biol. 2012;31(2):224–9. doi: 10.1089/dna.2011.1294 . Feng H. Sui L, Du M. Wang Q. Meta-analysis of TP73 polymorphism and cervical cancer. Genet Mol Res. 2017;16(1):10.4238/gmr16016571. Published 2017 Jan 23. doi: 10.4238/gmr16016571 . Hamajima N. Matsuo K, Suzuki T, et al No associations of p73 G4C14-to-A4T14 at exon 2 and p53 Arg72Pro polymorphisms with the risk of digestive tract cancers in Japanese. Cancer Lett. 2002;181(1):81–5. doi: 10.1016/s0304-3835(02)00041-1 . Lee KE. Hong YS, Kim BG, et al p73 G4C14 to A4T14 polymorphism is associated with colorectal cancer risk and survival. World J Gastroenterol. 2010;16(35):4448–54. doi: 10.3748/wjg.v16.i35.4448 . Arfaoui AT. Ben Mahmoud LK, Ben Hmida A, et al Relationship between p73 polymorphism and the immunohistochemical profile of the full-length (TAp73) and NH2-truncated (∆Np73) isoforms in Tunisian patients. Appl Immunohistochem Mol Morphol. 2010;18(6):546–54. doi: 10.1097/PAI.0b013e3181e9fe58 . Hu Z. Miao X, Ma H, et al Dinucleotide polymorphism of p73 gene is associated with a reduced risk of lung cancer in a Chinese population. Int J Cancer. 2005;114(3):455–60. doi: 10.1002/ijc.20746 . Li G. Sturgis EM, Wang LE, et al Association of a p73 exon 2 G4C14-to-A4T14 polymorphism with risk of squamous cell carcinoma of the head and neck. Carcinogenesis. 2004;25(10):1911–6. doi: 10.1093/carcin/bgh197 . Choi JE. Kang HG, Chae MH, et al No association between p73 G4C14-to-A4T14 polymorphism and the risk of lung cancer in a Korean population. Biochem Genet. 2006;44(11–12):543–50. doi: 10.1007/s10528-006-9056-8 . Li G. Wang LE, Chamberlain RM. Amos CI, Spitz MR. Wei Q. p73 G4C14-to-A4T14 polymorphism and risk of lung cancer. Cancer Res. 2004;64(19):6863–6. doi: 10.1158/0008-5472.CAN-04-1804 . Cite Share Download PDF Status: Under Review Version 1 posted Review # 1 received at journal 09 May, 2021 Reviewer # 1 agreed at journal 26 Mar, 2021 Editor assigned by journal 07 Dec, 2020 Reviewers invited by journal 07 Dec, 2020 Submission checks completed at journal 02 Dec, 2020 Editor invited by journal 01 Dec, 2020 First submitted to journal 18 Nov, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-120606","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":5744440,"identity":"abe1b67c-658c-4735-91e3-bda3e74e844a","order_by":0,"name":"Xuyu Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYFACxsYHCRU2PPzsDURrYW42+HAmTUay5wDRWtjbJGe2HLYxuOFApAb5iMRmY96G8zwMNxgYP3zMIUKL4Y3Exse8O27zMM5uYJacuY0YLTNAtpy5zcMsc4CNmZdILW3SvG3neNgkEojUIi+RCPR+2wEeHqK1GPA8BAVyMo8Ez8Fm4vwi357+EBiVdvb2x5sPfvhIlC0H4EzGBiLUg2whUt0oGAWjYBSMZAAASxM4t6hcwnUAAAAASUVORK5CYII=","orcid":"","institution":"Southeast University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xuyu","middleName":"","lastName":"Gu","suffix":""},{"id":5744441,"identity":"09c0d9b7-2f38-420e-9fe7-85e5406386b5","order_by":1,"name":"Xiaoyan Wang","email":"","orcid":"","institution":"Suqian First People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Wang","suffix":""},{"id":5744442,"identity":"188ad7d2-d113-4d33-bfa0-20788fe895d3","order_by":2,"name":"Huiwen Pan","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huiwen","middleName":"","lastName":"Pan","suffix":""},{"id":5744443,"identity":"d3c9ac18-eeed-4c8b-b981-6de712b58c74","order_by":3,"name":"Zhenjun Gao","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenjun","middleName":"","lastName":"Gao","suffix":""},{"id":5744444,"identity":"cd735526-32e8-4dc2-89f2-6c1926ebd106","order_by":4,"name":"Guowen Ding","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guowen","middleName":"","lastName":"Ding","suffix":""},{"id":5744445,"identity":"f5308dab-da76-41c1-b9ca-415ca9e77591","order_by":5,"name":"Yu Fan","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2020-12-02 21:03:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-120606/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-120606/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13629943,"identity":"387cc9af-0992-447a-9829-031f3f993ab8","added_by":"auto","created_at":"2021-09-17 08:10:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":332229,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-120606/v1/61531b90-3e13-4586-ae9e-78fc9d567d1d.pdf"}],"financialInterests":"","formattedTitle":"Case-control Study on p73 rs1801173 C \u003e T Gene Polymorphism and Susceptibility to Gastric Cancer in a Chinese Han Population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is one of the most common malignant tumors, ranking third in the number of cancer deaths worldwide\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The latest statistics from the Chinese National Cancer Center from a report in February 2018 showed that although the overall incidence of GC is declining, it still remains second in terms of incidence among all malignancies in China, just below lung cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Despite remarkable progress, the current treatments for GC remain inefficacious, with overall 5-year survival rates\u0026thinsp;\u0026lt;\u0026thinsp;30%. At the time of diagnosis, most patients have been diagnosed with advanced cancer and metastasis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. GC invasion and metastasis are complex processes involving multiple factors. Increasing evidence has proven that environmental and genetic factors contribute to the occurrence and development of GC. In addition to environmental risk factors, such as diet and HP infection, single nucleotide polymorphism (SNP) may play an important role in GC cancerization as a genetic factor. SNPs are polymorphisms in DNA sequences caused by mutations in the genome's single nucleotides. They are the most common form of human genetic variation, accounting for \u0026gt;\u0026thinsp;90% of all known morphologies\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Understanding the underlying mechanisms of GC initiation and progression may promote biomarker development for early detection of cancer. Previous epidemiological studies found increasing numbers of genetic variations that influence GC susceptibility\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, the association of the polymorphisms in the genes and GC susceptibility remains largely unknown. Hua et al. found that genetic variations in \u003cem\u003eLIG3\u003c/em\u003e may play a weak role in modifying the risk of GC\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. He et al. systematically analyzed the associations between nine polymorphisms in four key genes (\u003cem\u003eXPA\u003c/em\u003e, \u003cem\u003eERCC1\u003c/em\u003e, \u003cem\u003eERCC2\u003c/em\u003e, and \u003cem\u003eERCC4\u003c/em\u003e) in the nucleotide excision repair pathway and the risk of GC in a Chinese population. They concluded that the \u003cem\u003eERCC1\u003c/em\u003e polymorphisms may affect the risk of GC in the Chinese Han population\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. They also studied the genetic variations of \u003cem\u003emTORC1\u003c/em\u003e and risk of GC in an Eastern Chinese population and found that the functional polymorphisms of \u003cem\u003emTOR\u003c/em\u003e may contribute to the risk of GC\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. In 2004, the authors of the present study found that \u003cem\u003ep73\u003c/em\u003e rs1801173 C\u0026thinsp;\u0026gt;\u0026thinsp;T polymorphisms are associated with an increased risk of esophageal cancer in a Chinese population\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTumor protein p73 (TP73), also known as \u003cem\u003ep53\u003c/em\u003e-like transcription factor, is a pivotal member of the \u003cem\u003ep53\u003c/em\u003e (TP53) family, which affects cell proliferation, apoptosis, and cell-cycle regulation. \u003cem\u003ep73\u003c/em\u003e is located at 1p36.33, mapping to a region that is often deleted in cancers\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003ep73\u003c/em\u003e activates the transcription of \u003cem\u003ep53\u003c/em\u003e-responsive genes, which participate in cell-cycle control, DNA repair, and apoptosis and inhibits cell growth in a \u003cem\u003ep53\u003c/em\u003e-like manner by inducing apoptosis or G1 cell-cycle arrest\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Thus, \u003cem\u003ep73\u003c/em\u003e has tumor-suppressor functions. \u003cem\u003ep73\u003c/em\u003e has some significant differences from \u003cem\u003ep53\u003c/em\u003e. In contrast to \u003cem\u003ep53\u003c/em\u003e-deficient mice, those lacking \u003cem\u003ep73\u003c/em\u003e show no increased susceptibility to spontaneous tumorigenesis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAt least 19 polymorphisms have been recognized in \u003cem\u003ep73\u003c/em\u003e at present. Ten polymorphisms are in the exon region, eight are in the intron region, and one is in the region of gene promoter. However, the functionality of most of these polymorphisms has not been recognized yet\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The 1p36.3 region, where p73 is coded, is deleted in different malignancies via loss of heterogeneity. Although \u003cem\u003ep73\u003c/em\u003e mutation is rare and occurs in less than 2% of all cancers, the lack of heterogeneity in locus p73 is common in a variable rate in different cancers\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. As a result, the genetic changes in \u003cem\u003ep73\u003c/em\u003e are critical in malignancies\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Two related SNPs in regions 4 (G\u0026thinsp;\u0026gt;\u0026thinsp;A) and 14 (C\u0026thinsp;\u0026gt;\u0026thinsp;T) of exon 2 in \u003cem\u003ep73\u003c/em\u003e resulted in the formation of a stem-loop structure that could affect the gene function via changes in its expression. Molecular epidemiology studies have indicated an association between this polymorphism and the risk of cancers in human; however, the results were inconsistent\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePrevious research demonstrated that \u003cem\u003ep73\u003c/em\u003e gene polymorphisms are related to the occurrence of various cancers, including esophageal cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, rectal cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, breast cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, lung cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, and cervical cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, no research could be found on \u003cem\u003ep73\u003c/em\u003e gene polymorphism and GC susceptibility. This study aimed to investigate the relation between C14T polymorphism in \u003cem\u003ep73\u003c/em\u003e and the risk of GC in a Chinese Han population.\u003c/p\u003e\n\u003ch2\u003eEthical approval of the study protoco\u003c/h2\u003e\n\u003cp\u003eThis study complied with the World Medical Association Declaration of Helsinki in terms of ethical conduct of research involving human subjects and/or animals and received approval from the Review Board of Jiangsu University (Zhenjiang, China). Written informed consent was provided by all the subjects in this study.\u003c/p\u003e"},{"header":"Materials And Methods","content":" \u003cp\u003eBetween May 2013 and June 2017, 577 subjects with GC were recruited consecutively at the Affiliated People\u0026rsquo;s Hospital of Jiangsu University (Zhenjiang, China). Their average age of the case group was 61.34\u0026thinsp;\u0026plusmn;\u0026thinsp;11.097 years, with 394 males and 183 females. All cases were pathologically diagnosed as GC. The exclusion criteria were as follows: patients who previously had cancer and those with any metastasized cancer and undergoing radiotherapy or chemotherapy. As controls, 678 patients without cancer were matched to the cases in terms of age (\u0026plusmn;\u0026thinsp;5\u0026nbsp;years) and sex. Their average age was 62.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.549 years, with 456 males and 222 females. The controls were recruited from the abovementioned hospital during the same time period. Both groups are Chinese Han people, and they are not related by blood. Moreover, We had access to information that could identify individual participants during or after data collection.\u003c/p\u003e \u003cp\u003eUsing a pre-tested questionnaire, trained interviewers questioned each subject personally and obtained demographic data information (e.g., age and sex) and related risk factors (such as tobacco smoking and alcohol consumption). Venous blood samples (2\u0026nbsp;mL) were collected after each interview. The definition of \u0026ldquo;smokers\u0026rdquo; was smoking one cigarette per day for \u0026gt;\u0026thinsp;1\u0026nbsp;year. The definition of \u0026ldquo;alcohol drinkers\u0026rdquo; was consumption of \u0026ge;\u0026thinsp;three alcoholic drinks a week for \u0026gt;\u0026thinsp;6\u0026nbsp;months.\u003c/p\u003e \u003cp\u003eBlood samples were collected from patients by using vacutainers and transferred to tubes lined with ethylenediamine tetra-acetic acid. Genomic DNA was isolated from whole blood by using a QIAamp DNA Blood Mini Kit (Qiagen, Berlin, Germany). Sample DNA was amplified via PCR in accordance with the manufacturer\u0026rsquo;s recommendations. The samples were genotyped using the SnapShot method, with technical support from Shanghai Biowing Applied Biotechnology Co. as previously described. For quality control, repeated analyses were performed on 160 (12.17%) randomly selected samples with high DNA quality.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e Statistical analyses\u003c/h2\u003e \u003cp\u003eThe distributions of demographic characteristics, selected variables, and genotypes of the \u003cem\u003ep73\u003c/em\u003e variant differences between the cases and controls were evaluated using the χ2 test. Logistic regression analyses was used to estimate the associations between the SNPs and the risk of GC for crude and adjusted ORs when adjusting for age, sex, smoking, and drinking status. The Bonferroni correction procedure was also applied because of the number of comparisons. The Hardy\u0026ndash;Weinberg equilibrium (HWE) was tested using the goodness-of-fit χ\u003csup\u003e2\u003c/sup\u003e test to compare the observed genotype frequencies to the expected ones among the controls. All statistical analyses were performed on SPSS 20.0 (SPSS Inc., Chicago, Illinois, USA).\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the rs1801173 of \u003cem\u003ep73\u003c/em\u003e was located in the first chromosome. Their category was protein cod. The minor allele frequency of rs1801173 in the controls was 0.267. The result of HWE tests for the controls was 0.229 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the sample population was representative. The snapshot method was used for genotyping, and the percentage of successful tests was 98.96%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePrimary information for \u003cem\u003ep73\u003c/em\u003e rs1801173 gene polymorphism\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyped SNP\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eChr Pos (NCBI Build 38)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMAF\u003csup\u003ea\u003c/sup\u003e for Chinese in database\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMAF in our controls (n\u0026thinsp;=\u0026thinsp;678)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eP value for HWE\u003csup\u003eb\u003c/sup\u003e test in our controls\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping method\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping value (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1801173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep73\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1:3682346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5_prime_UTR_variant,genic_upstream_transcript_variant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.821\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esnapshot\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e MAF: minor allele frequency;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e HWE: Hardy\u0026ndash;Weinberg equilibrium;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the cases including demographics and environmental risk factors. Smoking rate was much higher in case group as compared with the control group (34.49% vs. 27.29%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). The demographics (age and sex) were well matched (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.635 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.698, respectively; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). That indicated the occurrence and development of smoking and gastric cancer. Of the alcohol consumption, no significant difference was observed between GC patients and controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.443, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistribution of selected demographic variables and risk factors in GC cases and controls\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35.679px;\"\u003e\n\u003cth style=\"height: 35.679px;\" align=\"left\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/th\u003e\n\u003cth style=\"height: 35.679px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOverall Cases (n\u0026thinsp;=\u0026thinsp;577)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35.679px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOverall Controls (n\u0026thinsp;=\u0026thinsp;678)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35.679px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e61.34\u0026thinsp;\u0026plusmn;\u0026thinsp;11.097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e62.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e268 (46.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e324 (47.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e309(53.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e354(52.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.635\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e394 (68.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e456(67.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e183(31.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e222(32.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.698\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSmoking status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e378 (65.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e493(72.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eEver\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e199(34.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e185 (27.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAlcohol use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e453 (78.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e520(76.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eEver\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e124 (21.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e158(23.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.443\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e The frequency distribution and logistic regression analysis of the \u003cem\u003ep73\u003c/em\u003e gene rs1801173 polymorphism in gastric cancer and control group showed that with reference to wild-type CC, the frequency distribution of TC heterozygous mutations in GC cases was 60.67% higher than 59.55 in controls group, but there was not statistically significant between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0. 657) and there was no statistical difference in gender, age, smoking, and alcohol consumption after logistic regression adjustment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.691); the frequency distribution of TT homozygous mutants was 5.52% higher than 5.41 in controls group, but was also not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000), and there was no statistical difference after logistic regression adjustment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.979). In the dominant model, the frequency distribution of TC\u0026thinsp;+\u0026thinsp;TT mutations was 39.33% lower than 40.45% in controls group, but there was not statistically significant in the case-control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.688), and the difference was not statistically significant after regression adjustment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.703). In the recessive model, the frequency distribution was 5.52% higher than 5.41 in controls group, but there was not statistically different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.937). According to gender, age, smoking, and drinking, after logistic regression analysis, there was still no statistical difference between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.990).The results of rs1801173 allele frequency distribution of \u003cem\u003ep73\u003c/em\u003e gene in the two groups showed that the allelic frequency distribution was not statistically significant in the case-control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.763).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003ep73\u003c/em\u003e rs1801173 gene polymorphisms in GC cases and controls and logistic regression analysis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGC Cases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;577)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;678)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCrude OR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAdjusted OR \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ers1801173\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e341\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e60.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e59.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e233\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95(0.75\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.657\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95(0.75\u0026ndash;1.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.691\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00(0.61\u0026ndash;1.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00(0.77\u0026ndash;1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.979\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTC\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95(0.76\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.688\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98(0.81-1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u0026thinsp;+\u0026thinsp;TC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e531\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e629\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98(0.60\u0026ndash;1.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.937\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00(0.78\u0026ndash;1.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.990\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC allele\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e77.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e77.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT allele\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.97(0.80\u0026ndash;1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eStratification was conducted in accordance with four longitudinal factors, namely, sex, age, smoking, and drinking, to observe the influence of various genotypes and genetic patterns and diseases and calculate the frequency distribution of the case-control group.Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that regardless of the gender, the distribution frequency of the four modes, namely, mutant, wild-type, recessive model, and dominant model, had no difference in the distribution of the two groups, suggesting that gender and \u003cem\u003ep73\u003c/em\u003e. No statistical correlation was found between the four genetic models and the occurrence of GC. Age, smoking, drinking, and temporarily also showed no statistical association. However, a notable detail that in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, a statistically significant difference was found in the association between smoking and the occurrence of GC. In the hierarchical analysis, the genetic model of the four genes did not show a statistically significant association, which is worthy of follow-up study to investigate and determine whether smoking factors are related to the rs1801173 polymorphisms of \u003cem\u003ep73\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStratified analyses between \u003cem\u003ep73\u003c/em\u003e rs1801173 gene polymorphisms and risk by sex, age, smoking and drinking\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(case/control)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAdjusted OR (95% CI); \u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTT\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTT\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(TC\u0026thinsp;+\u0026thinsp;TT)VSCC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTTVS(CC\u0026thinsp;+\u0026thinsp;TC)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229/253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131/168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22/23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86(0.65\u0026ndash;1.15);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06(0.57\u0026ndash;1.95);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89(0.67\u0026ndash;1.17);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89(0.49\u0026ndash;1.63);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.715\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112/143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59/65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16(0.75\u0026ndash;1.78);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.502\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88(0.37\u0026ndash;2.14);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11(0.74\u0026ndash;1.68);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.84(0.35\u0026ndash;2.02);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.700\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163/182\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90/117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8/16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86(0.61\u0026ndash;1.22);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.390\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56(0.23\u0026ndash;1.34);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.186\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82(0.59\u0026ndash;1.15);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.255\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.69(0.71\u0026ndash;4.02);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.228\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178/214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100/116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23/20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04(0.74\u0026ndash;1.45);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.38(1.74\u0026ndash;2.60);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.313\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09(0.79\u0026ndash;1.49);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.602\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73(0.39\u0026ndash;1.36);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.324\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e228/290\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119/172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21/24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87(0.65\u0026ndash;1.16);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11(0.60\u0026ndash;2.05);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.731\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91(0.69\u0026ndash;1.20);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.498\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86(0.47\u0026ndash;1.57);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.619\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEver\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113/106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71/61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10/12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09(0.71\u0026ndash;1.68);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.691\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78(0.32\u0026ndash;1.89);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04(0.69\u0026ndash;1.57);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.849\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.32(0.56\u0026ndash;3.14);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.526\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e273/305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144/180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23/28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89(0.68\u0026ndash;1.18);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.68(0.39\u0026ndash;1.16);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90(0.69\u0026ndash;1.17);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.42(0.83\u0026ndash;2.42);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.196\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEver\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68/91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46/53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8/8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16(0.70\u0026ndash;1.92);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.561\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34(0.48\u0026ndash;3.75);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.578\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19(0.73\u0026ndash;1.92);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79(0.29\u0026ndash;2.17);\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e:0.650\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003e\n\u003cp\u003eFor \u003cem\u003ep73\u003c/em\u003e gene rs1801173, the genotyping was successful 99.20% in 577 cases and 678 controls.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eGC is a multifactorial disorder, in which genetic and environmental interactions serve an important role in the development and progression\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Increasing age, gender, lifestyle, dietary regime, environmental factors, and Helicobacter pylori infections are among the known risk factors for stomach cancer\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. While dietary regime and lifestyle are the most recognized factors, enhanced identification of the genetic risk factors is expected to improve the understanding of the basic molecular events involved in tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Genetic factors, including gene expression profile and cancer biomarkers, such as SNPs, have a crucial role in improving the early diagnosis of GC\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies have demonstrated that a high number of genes and various environmental factors are the causal agents of GC, and the presence of different forms of alleles in gene polymorphisms may promote the development of cancers. In this regard, \u003cem\u003ep73\u003c/em\u003e has been a research focus due to its role as a major tumor suppressor gene\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003ep73\u003c/em\u003e has some functions similar to or independent of \u003cem\u003ep53\u003c/em\u003e and plays a role, particularly in compensation for loss of \u003cem\u003ep53\u003c/em\u003e function, in the regulation of cell cycle, DNA repair, apoptosis, and possibly cell differentiation. In the past decades, almost 146 unique variations were reported (shown in the Biomuta database)\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, while numerous studies probed into the relationship of G4C14-A4T14 polymorphism and cancer genomics. G4A (rs2273953) and C14T (rs1801173) polymorphisms are located at positions 4 (G to A) and 14 (C to T) of exon 25\u0026prime;-untranslated region, which may influence the initiating AUG codon by constructing a stem loop\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In recent years, the G4C14-A4T14 polymorphism of \u003cem\u003ep73\u003c/em\u003e has been identified to be implicated in the tumorigenesis of various cancer types. However, the data from these published case-control studies were not consistent.\u003c/p\u003e \u003cp\u003eYang et al.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e and Niwa et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e reported that G4C14-A4T14 polymorphism is not associated with the susceptibility of cervical cancer in Uighur and Japanese population, respectively. However, Craveiro et al.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e and Feng et al.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e revealed that G4C14-A4T14 polymorphism leads to an increasing risk of cervical cancer. Hamajima et al. \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e demonstrated no significant differences in the genotype frequencies among the enrolled cases and controls in their study of colorectal cancer. On the contrary, Lee et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e reported that GC/AT and AT/AT genotypes are significantly associated with the risk of colorectal cancer in Korean population. Arfaoui et al.\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e also uncovered no remarkable differences in genotype frequencies in cancers and controls, but they found that AT/AT genotype may cause poor prognosis of colorectal cancer. Hu et al.\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e indicated that AT/AT and GC/AT variants are associated with a remarkable decrease in the risk of lung cancer. Li et al.\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e suggested that the AT/AT and GC/AT genotypes are related, with a statistically significantly increased risk of lung cancer. However, Choi et al.\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e revealed that the G4C14-A4T14 polymorphism of \u003cem\u003ep73\u003c/em\u003e does not affect the susceptibility of lung cancer in Korean population. Zheng et al.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e found that the rs1801173 C\u0026thinsp;\u0026gt;\u0026thinsp;T SNPs of \u003cem\u003ep73\u003c/em\u003e are associated with increased risk of ESCC. However, \u003cem\u003ep73\u003c/em\u003e gene polymorphism and GC susceptibility have not been reported yet. Therefore, the rs1801173 C/T polymorphism of \u003cem\u003ep73\u003c/em\u003e merits further functional study to elucidate the etiology of SNP and GC.\u003c/p\u003e \u003cp\u003eIn the present study, no statistically significant association was found between \u003cem\u003ep73\u003c/em\u003e rs1801173 C\u0026thinsp;\u0026gt;\u0026thinsp;T gene polymorphism and the risk of GC in a Chinese Han Population. Although the C mutant allele frequency was higher in patients with GC than in controls, the difference was not statistically significant. However, the frequency of smoking factors in the case group was 34.49%, higher than that in the control group by 27.29%, and the difference was statistically significant. This result showed that smoking is related to the occurrence and development of GC. Li et al.\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e studied the connection between \u003cem\u003ep73\u003c/em\u003e G4C14-to-A4T14 polymorphism and the risk of lung cancer. They found that the increased risk associated with the combined \u003cem\u003ep73\u003c/em\u003e GC/AT\u0026thinsp;+\u0026thinsp;AT/AT genotype in younger (\u0026le;\u0026thinsp;50\u0026nbsp;years) subjects and light (compared with heavy) smokers suggested an early onset and lower levels of exposure characteristic of genetic susceptibility. They also observed a significantly higher risk in men than in women, particularly among smokers. However, the interaction between smoking and \u003cem\u003ep73\u003c/em\u003e polymorphism was only borderline significant, which warrants additional investigations with larger sample sizes.\u003c/p\u003e \u003cp\u003eThis case-control study had several limitations. First, because the patients and controls were enrolled from hospitals, inherent bias may have resulted in spurious findings. Second, the polymorphisms may not provide a comprehensive view of \u003cem\u003ep73\u003c/em\u003e genetic variability. Fine-mapping studies are required. Third, the statistical power was limited because of the moderate sample size and the absence of a validation cohort. In addition, considering that gene\u0026ndash;gene interaction and gene\u0026ndash;environment interaction play an important role in the pathogenesis of many diseases, especially chronic diseases, \u003cem\u003ep73\u003c/em\u003e gene polymorphism may interact with other gene polymorphisms or environmental factors, thereby affecting the incidence of GC in the population. H. pylori infections, lifestyle, and dietary regime were not investigated. Other limitations included small sample size and sampling of individuals of the same geographical region and race. Therefore, further studies considering different geographical locations and races and a larger number of participants are necessary to confirm the results of the present study. These results failed to indicate an association between \u003cem\u003ep73\u003c/em\u003e rs1801173 polymorphism and risk of GC. Tissue-specific biological characterization and replication studies with larger populations are also required to confirm these findings.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data used to support the findings of this study are available from the corresponding authors upon request.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThe research was approved by the Ethics Review Committee of Jiangsu University.\u003c/p\u003e\n\u003ch2\u003eConsent\u003c/h2\u003e\n\u003cp\u003eAll patients provided written informed consent.\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflflicts of interest.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; Contributions\u003c/h2\u003e\n\u003cp\u003eXuyu Gu is the co-fifirst author. Xiaoyan Wang and Xuyu Gu wrote and edited the manuscript. Yu Fan, Guowen Ding, and Zhenjun Gao provided direction and guidance throughout the preparation of this manuscript. Huiwen Pan finished the data analysis. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis study was supported in part by Jiangsu Provincial Key Research and Development Special Fund (BE2015666), Jiangsu Innovative team leading talent fund (CXTDC2016006), Jiangsu six high peak talent fund (WSW-205), Jiangsu 333 talent fund(BRA2020016) and Suqian science and technology support project fund (S201721).In addition, the authors thank all participants of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F. Ferlay J, Soerjomataram I. Siegel RL, Torre LA. 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Wei Q. p73 G4C14-to-A4T14 polymorphism and risk of lung cancer. Cancer Res. 2004;64(19):6863\u0026ndash;6. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/0008-5472.CAN-04-1804\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gastric Cancer, Single Nucleotide Polymorphism, p73, rs1801173","lastPublishedDoi":"10.21203/rs.3.rs-120606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-120606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to investigate the association between \u003cem\u003ep73\u003c/em\u003e C14T (rs1801173) polymorphism and the risk of GC in a Chinese Han population. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA hospital-based case-control study was conducted. A total of 577 GC cases and 678 normal controls were recruited. Their genotypes were determined using the SnapShot method. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe genotype frequency distribution of the case group and the control group were consistent with the Hardy–Weinberg equilibrium. No significant difference was found in the distribution of gender, age, and drinking history between the case group and the control group. A correlation was observed between smoking and the incidence of GC (\u003cem\u003eP \u003c/em\u003e= 0.006). Three genotypes of CC, CT, and TT were found in the rs1801173 locus of \u003cem\u003ep73\u003c/em\u003e. The distribution of the dominant model/recessive model did not significantly differ (\u003cem\u003eP \u003c/em\u003e= 0.688; 0.937). No statistical difference was found even after adjustment was performed via logistic regression analysis (\u003cem\u003eP \u003c/em\u003e= 0.703; 0.990). The frequency distribution between the two groups also did not significantly differ (\u003cem\u003eP \u003c/em\u003e= 0.763). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eSmoking is related to the occurrence and development of GC. No association was found between \u003cem\u003ep73\u003c/em\u003e rs1801173 C \u0026gt; T SNP and the risk of GC in a Chinese Han population.\u003cem\u003e \u003c/em\u003eHowever, additional larger studies and tissue-specific biological characterization are required to confirm these findings.\u003c/p\u003e","manuscriptTitle":"Case-control Study on p73 rs1801173 C \u0026gt; T Gene Polymorphism and Susceptibility to Gastric Cancer in a Chinese Han Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-08 15:41:22","doi":"10.21203/rs.3.rs-120606/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-05-10T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable pending editorial decision\n"},{"type":"reviewerAgreed","content":"","date":"2021-03-27T00:00:00+00:00","index":1,"fulltext":""},{"type":"editorAssigned","content":"","date":"2020-12-08T00:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-12-08T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-12-02T21:03:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-12-02T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-11-19T00:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dfc1c300-6171-4e91-b73d-428f2a2e42a9","owner":[],"postedDate":"December 8th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":1375004,"name":"Epigenetics \u0026 Genomics"}],"tags":[],"updatedAt":"2020-12-08T15:41:22+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-08 15:41:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-120606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-120606","identity":"rs-120606","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}