Association of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms with the risk of developing breast cancer: a haplotype-based case-control study and in silico analysis

Association of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms with the risk of developing breast cancer: a haplotype-based case-control study and in silico analysis | 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 Association of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms with the risk of developing breast cancer: a haplotype-based case-control study and in silico analysis Maryam Shirvani, Arshia Yadollahi, Zahra Zamanzadeh, Morteza Abkar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3867462/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Metallothionein 1A (MT1a) is involved in many pathological conditions associated with antioxidant defense and detoxification, including cancer. The aim of this study was to investigate the possible association of MT1A rs11640851, rs8052394, and rs11076161 single nucleotide polymorphisms (SNPs) with the risk of breast cancer (BC) and clinicopathological features. The study included 100 patients with BC and 100 healthy controls. We genotyped the MT1A SNPs using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and amplification refractory mutation system PCR (ARMS-PCR) techniques. The genotypic and allelic associations of MT1A SNPs with susceptibility to BC were assessed using logistic regression analysis under co-dominant, dominant, and recessive inheritance models. The combined effect of MT1A SNPs on the BC risk was determined using the haplotype analysis. In silico analysis was performed using the Polyphen-2 and RNAsnp online servers. The rs11640851 was found to be associated with a reduced risk of BC in all three inheritance models ( P < 0.05). We also found that rs11640851 A allele has a protective effect against BC (OR: 4.812; 95% CI: 2.655–8.719; P < 0.0001). For rs8052394/rs11076161, carriers of AG/AA combined genotype had a 3.84-fold increased risk for developing BC. The haplotypes CAC and CGA were significantly associated with BC susceptibility. The rs11640851 was predicted to be deleterious using Polyphen-2 server. This study provides the first evidence that rs11640851 is significantly associated with a reduced risk of BC suggesting its protective role in the development of BC. Two haplotypes CAC and CGA were also identified as risk factors for BC. rs11640851 rs8052394 rs11076161 Polymorphism MT1A Breast cancer Figures Figure 1 Figure 2 Figure 3 Introduction Breast cancer (BC) is the most commonly diagnosed malignancy around the world, contributing 12.5% of all new cancer cases worldwide (Jaman et al., 2023 ). According to the latest data from the American Cancer Society (ACS), in 2023, an estimated 297,790 new cases of BC will be diagnosed (31% of all new cases) and 43,170 women will die from the disease (15% of all cancer-related deaths) in the United States (Siegel, Miller, Wagle, & Jemal, 2023 ). Breast cancer is a heterogeneous condition resulting from the complex interplay of multiple genetic, environmental, and lifestyle factors (Singh & kumar Sain, 2023 ; Swaminathan, Saravanamurali, & Yadav, 2023 ). Despite considerable progress towards understanding breast carcinogenesis in recent decades, there are still so many unanswered questions regarding BC etiopathogenesis (Feng et al., 2018 ; Park et al., 2022 ). It has been confirmed that antioxidant have a protective role against ROS and there is an association between polymorphisms in antioxidant coding genes and susceptibility of BC (Asadi et al., 2023 ; Yousefnia, 2014 ). Metallothioneins (MTs) are a superfamily of highly conserved, low molecular weight (6–7 kDa), sulfhydryl-rich metal-binding proteins that exist in almost all forms of life (Rodrigo et al., 2020 ). They are single-chain polypeptides, containing 61–68 amino acids, 20 of them are conserved cysteine residues distributed in two α and β metal-thiolate clusters that bind up to seven divalent metals ions (Juárez-Rebollar, Rios, Nava-Ruíz, & Méndez-Armenta, 2017 ; Vašák & Meloni, 2017 ). MTs are predominantly localized in the cytosol, and play crucial roles in maintaining homeostasis of essential metals such as copper (Cu) and zinc (Zn), detoxification of heavy metals like mercury (Hg) and cadmium (Cd), and antioxidant defense systems by scavenging reactive oxygen species (ROS) including hydroxyl free radical ( • OH), superoxide anion (O 2 •− ), and hydrogen peroxide (H 2 O 2 ) (Dai, Wang, Li, Huang, & Ye, 2021 ; Giacconi et al., 2017 ; Krezel & Maret, 2021 ). They are conventionally classified into four subfamilies in human, designated as MT1, MT2, MT3, and MT4 (Dai et al., 2021 ). MTs are encoded by a multi-gene family containing at least 18 closely related genes, 11 of which are functional genes (MT1A, MT1B, MT1E, MT1F, MT1G, MT1H, MT1M (also called MT1K), MT1X, MT2A, MT3, and MT4), and seven pseudogenes (MT1C, MT1D, MT1I, MT1J, MT1L, PT1P, and MT2B) (Raudenska et al., 2014 ). In human, these genes are clusterd on the q13 region of chromosome 16 (16q13) (Raudenska et al., 2014 ). MT1 and MT2 isoforms are expressed in almost all human tissues, while MT3 and MT4 are expressed primarily in the brain and stratified squamous epithelia, respectively (Si & Lang, 2018 ). The expression of MTs is induced by several stimuli including metals, hormones, cytokines, chemical agents, oxidative stress and inflammation (Giacconi et al., 2017 ). A growing body of evidence suggests differential expression of MT isoforms in a variety of human cancers (Datta et al., 2007 ; Gomulkiewicz et al., 2010 ; Han et al., 2013 ; Hengstler et al., 2001 ; Jayasurya, Bay, Yap, Tan, & Tan, 2000 ; Weinlich et al., 2006 ; Wülfing et al., 2007 ). Based on previous reports, MTs are overexpressed in ovarian cancer (Hengstler et al., 2001 ), bladder cancer (Wülfing et al., 2007 ), breast cancer (Gomulkiewicz et al., 2010 ), nasopharyngeal cancer (Jayasurya et al., 2000 ) and melanoma (Weinlich et al., 2006 ). On the contrary, MTs have been shown to be down-regulated in some malignancies like prostate cancer (Han et al., 2013 ) and hepatocellular carcinoma (Datta et al., 2007 ). These data support the relationship between MTs and cancer, suggesting that they may participate in the processes of carcinogenesis from tumor initiation, promotion and progression to metastasis. Furthermore, single nucleotide polymorphisms (SNPs) in the MTs gene have been shown to be associated with increased cancer risk (Forma et al., 2012 ; Krześlak et al., 2014 ; Rosa et al., 2021 ). Although a number of variants have been identified, rs11640851, rs8052394 and rs11076161 are the most common SNPs in the MT1A gene (Raudenska et al., 2014 ). The rs11640851 (647 C/A or c.80C/A) lies in exon 2 and is characterized by a threonine to asparagine substitution at codon 27 (T27N), rs8052394 (1245 A/G or c.152 A/G) is located in exon 3, and results in substitution of a lysine by an arginine residue (K51R), and rs11076161 is a variant in the first intron of MT1A gene (Raudenska et al., 2014 ). To date, few studies have focused on exploring the association of MT1A rs11640851, rs8052394 and rs11076161 SNPs with cancer susceptibility (Rosa et al., 2021 ; Wong et al., 2013 ). Accordingly, this study was designed to address the question of whether rs11640851, rs8052394 and rs11076161 SNPs and haplotypes can underlie susceptibility to BC in Iranian women for the first time. Materials and methods Subjects Over a period of 9 months, from August 2020 to April 2021, one hundred histopathologically confirmed breast cancer cases (mean age ± standard deviation (SD); 51.56 ± 11.24 years, range 29 to 80) were enrolled in the present case-control study from the Anahid Breast Cancer Clinic, Isfahan Healthcare City, Isfahan, Iran. The histopathological diagnosis of all cancer patients was confirmed by a panel of pathologist at the respective hospitals. The medical records and pathology reports were reviewed to collect demographic/clinical characteristics including age at diagnosis, tumor histological type, stage, grade, size and status of lymph node metastasis. If any of these variables were missing, the patient was excluded from study. Also, there were no women who underwent chemotherapy, hormonotherapy and/or radiotherapy in case group. Additionally, one hundred race/ethnicity-matched healthy women with no personal and/or family history of BC or any other type of cancer (mean age ± SD; 39.01 ± 10.51 years, range 17 to 76) were included in the study as controls. Demographic and clinical data were collected through interviews using a questionnaire form for all healthy subjects. All procedures were in accordance with relevant guidelines and the study protocol was approved by the Institutional Review Board /Independent Ethics Committee (IRB/IEC) of the Shahid Ashrafi Esfahani University, Isfahan, Iran. In line with the principles of the World Medical Association's Declaration of Helsinki, written informed consent was obtained from all participants after receiving an explanation of the study. DNA extraction and genotyping of the MT1A SNPs Whole venous blood (~5 mL) was taken from all participants via antecubital venipuncture and collected in vacutainer tube with K2EDTA. Genomic DNA was extracted from leukocytes using a commercial kit (ZEAN Genomic DNA Extraction Kit, Isfahan, Iran), according to the manufacturer's instructions. The quality and quantity of the extracted DNA were assessed by measuring the absorbance at 260 nm and 280 nm using NanoDrop® ND-1000 UV-Vis Spectrophotometer (Thermo Fisher Scientific™, Waltham, MA, USA). We used polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique to detect MT1A rs11640851 and rs8052394 SNPs. The final volume of the PCR mixture was 25 μl and contained 12.5 µl Taq DNA Polymerase 2X Master Mix Red (Ampliqon, Denmark), 1 μl of each primer (10 pmol) (Table 1),100 ng genomic DNA and DNase/RNase-free distilled water. All amplification reactions were carried out on an Applied Biosystems™ Thermal Cycler (Thermo Fisher Scientific, Waltham, MA, USA). The thermal cycling program was set as follows: initial denaturation at 95 °C (5 minutes), 35 cycles at 95 °C (30 seconds), 59 °C (30 s), and 72°C (40 seconds), which was followed by a final elongation step at 72°C for 5 minutes. The PCR-amplified products were loaded on a 2% agarose gel containing safe stain (CinnaGen, Co, Tehran, Iran) and then visualized with ultra-violet (UV) transilluminator. The rs11640851 and rs8052394 were detected by digestion of the PCR products with MnlI and PstI restriction endonucleases (New England Biolabs, Beverly, MA, USA) (Table 1) respectively. After 24 hours of incubation at 37°C, the digested fragments were separated by electrophoresis on a 3% agarose gel. The rs11640851 A allele remained uncut (187 bp), whereas the rs11640851 C allele was cut into two fragments of 140 and 47 bp (Table 1). The PstI restriction enzyme digests the 283 bp amplicon corresponding to rs8052394 into 165 and 118 bp fragments for allele G (Table 1). To identify MT1A rs11076161 SNP, we performed amplification refractory mutation system PCR (ARMS-PCR) assay using allele-specific primers presented in Table 1. The PCR reaction mixture and thermal cycling condition were the same as used for genotyping MT1A rs11640851 and rs8052394 SNPs. Afterwards, the PCR amplicons were separated by electrophoresis on 2% agarose gel containing safe stain and visualized under UV light. Linkage disequilibrium and haplotype analysis Pairwise linkage disequilibrium (LD) between MT1A SNPs was determined by Lewontin’s standardized coefficient (D′) and the square of the Pearson’s correlation coefficient (r 2 ) values using the online web-based SHEsis software ( http://analysis.bio-x.cn/myAnalysis.php ). To evaluate the association of haplotype with the risk of developing BC, we compared the distribution of haplotypes between cases and healthy controls using SHEsis online server. In silico analysis The sequence of MT1A mRNA in FASTA format was retrieved from National Center for Biotechnology Information (NCBI)(https://www.ncbi.nlm.nih.gov/) as an input to investigate the effect of SNPs on local RNA secondary structure. We used web-based RNAsnp server ( https://rth.dk/resources/rnasnp/ ) to predict the effect of MT1A rs11640851and rs8052394 SNPs on mRNA secondary structure. Moreover, the human metallothionein 1A protein sequence was obtained from the Ensembl database ( https://useast.ensembl.org/index.html ). For MT1A rs11640851and rs8052394 SNPs, the possible effect of amino acid substitution on the protein structure and activity was predicted using Polymorphism Phenotyping version 2 (PolyPhen-2) online server (http://genetics.bwh.harvard.edu/pph2/). Statistical analysis The IBM Statistical Package for Social Sciences (SPSS) Statistics for Windows, Version 22.0 (IBM Corp., Armonk, N.Y., USA) was used to statistical analysis of data. Any P value equal or less than 0.05 were deemed as statistically significant. Quantitative variables were expressed as means ± standard deviations (SD), and compared by Student’s t-test. Genotype distributions and allele frequencies of rs11640851, rs8052394 and rs11076161 variants were compared between groups using Fisher’s exact test. Deviation from Hardy-Weinberg Equilibrium (HWE) for MT1A SNPs was calculated using chi-squared test ( https://gene-calc.pl/hardy-weinberg-page ). Logistic regression analysis was performed to determine the possible association of MT1A SNPs with susceptibility to BC by calculating odds ratio (OR) and corresponding 95% confidence interval (95% CI), under the co-dominant, dominant and recessive inheritance models. The possible association between MT1A rs11640851, rs8052394 and rs11076161 SNPs and patientsʹ clinicopathological characteristics was estimated by ORs computed using logistic regression analysis. Results Baseline characteristics of subjects Demographics and clinical characteristics of all participants as well as pathologic features of BC patients are presented in Table 2. Student’s t-test was used to compare the mean age of two groups, which was found statistically significant (P < 0.05) (Table 2). The mean body mass index (BMI) was significantly different between the two groups, and patients with BC exhibited a higher BMI than healthy individuals (28.09 ± 4.30 kg/m 2 versus 25.64 ± 4.59 kg/m 2 ) (P 0.05) (Table 2). Furthermore 100% of BC patients were married at the time of diagnosis, and 49% of them had previously taken oral contraceptives. About 70% of women who were diagnosed with BC had no family history, and 98 % of them had unilateral tumor. With regard to histological type, 92% of BC patients were invasive ductal carcinoma (IDC), and 8% were invasive lobular carcinoma (ILC). Among BC patients, 12% were grade I, 62% were grade II, and 26% were grade III. Ninety-one percent of patients had a tumor size ≤ 2 cm and 9% had a tumor size larger than 2 cm. Among cases, 93% were negative for the malignancy while 7% had metastasis to one or more organs. Association of MT1A SNPs with BC risk under different inheritance models The genotype and allele frequencies of MT1A rs11640851, rs8052394 and rs11076161 SNPs in cases and controls are presented in Table 3. Surprisingly, the CA and AA genotypes frequencies for rs11640851 SNP were significantly higher in healthy subjects compared to BC patients (P 0.05) (Table 3). Also, the possible association of all studied SNPs with the risk of developing BC was assessed using logistic regression analysis under the co-dominant, dominant and recessive inheritance models (Table 3). Under co-dominant inheritance model, the CA and AA genotypes of rs11640851 variant were significantly associated with decreased risk of developing BC (OR: 4.039; 95% CI: 1.913 - 8.529; P = 0.0002 and OR: 10.94; 95% CI: 2.394 - 50.03; P = 0.0002), respectively (Table 3). Under the dominant and recessive inheritance models, we also observed that rs11640851 SNP was significantly associated with 5.02 and 7.97-fold reduced risk of BC, respectively (Table 3). No statistically significant difference was found in term of rs8052394 and rs11076161 SNPs between cases and controls in any inheritance models (P > 0.05) (Table 3). The allelic association revealed that the minor allele A of rs11640851 polymorphism was significantly associated with 4.81-fold reduced risk of BC (95% CI: 2.655 - 8.719; P = 0.0001) (Table 3). Risk allele frequencies were not significantly differed between the two groups for rs8052394 and rs11076161 SNPs (P > 0.05) (Table 3). Except rs11076161 (P 0.05) (Table 3). In healthy individuals, only rs11640851 was not in accordance with HWE (P < 0.05) (Table 3). Our findings revealed that the CA/AA, AA/AA and AA/AG combined genotypes of rs11640851 and rs8052394 SNPs had protective effect, reducing the risk of developing BC (P < 0.05) (Table 4). The CA/AA, CA/AC and AA/AC combined genotypes of rs11640851 and rs11076161 SNPs was also determined to be associated with significant decreased risk of BC in our population (P < 0.05) (Table 4). For rs8052394 and rs11076161 SNPs, AG/AA combined genotype could be a risk factor for developing BC (P < 0.05) (Table 4). Association of MT1A SNPs with patientsʹ clinicopathological parameters We used logistic regression analysis to identify the association between MT1A SNPs and clinicopathological characteristics of BC patients. The results showed that here was no significant association between genotype and allele frequencies of MT1A SNPs and clinicopathological features in any inheritance models (P > 0.05) (Tables 5, 6 and 7). Association between the haplotypes of MT1A SNPs and BC risk Pairwise LD between each pair of MT1A SNPs was computed to detect the association of the studied variants with susceptibility to BC (Table 8). The LD values between all pairs of SNPs were plotted using SHEsis online server based on genotype distributions of MT1A SNPs in both groups (Figure 1). Low LD coefficient (Dʹ) and r 2 values in cases and healthy subjects demonstrated that rs11640851, rs8052394 and rs11076161 SNPs were not in high linkage disequilibrium with each other (Table 8) (Figure 1). Haplotype analysis of three SNPs showed that the CAC and CGA haplotypes for MT1A rs11640851, rs8052394 and rs11076161 variants was significantly associated with 1.86 and 3.27-fold increased risk of BC, respectively (P < 0.05) (Table 9). A significantly reduced risk of BC was found in individuals carrying the AAC and AGC haplotypes (OR: 0.392; 95% CI: 0.168 - 0.914; P = 0.025 and OR: 0.009; 95% CI: 0.001 - 0.121; P = 0.002), respectively (Table 9). We also observed no significant association between patients and controls regarding the distribution of the CAA and CGC haplotypes (P > 0.05) (Table 9). In silico analysis of MT1A rs11640851 and rs8052394 SNPs The effect of rs11640851 and rs8052394 SNPs on local RNA secondary structure were analyzed by RNAsnp online server. The RNAsnp prediction revealed that rs11640851 and rs8052394 SNPs could not change the mRNA secondary structure of MT1A (Figure 2). Moreover, rs11640851 and rs8052394 SNPs were analyzed for possible deleterious effects on metallothionein-1A protein function using Polyphen-2. The rs11640851 (Thr27Asn) SNP was predicted to be damaging, whereas rs8052394 (Lys51Arg) variant was predicted to be benign by PolyPhen-2 server (Figure 3). Discussion Over the past decades, a large body of studies have provided convincing evidence that MTs play a fundamental role in tumorigenesis and progression (Cherian, Jayasurya, & Bay, 2003 ; Eckschlager, Adam, Hrabeta, Figova, & Kizek, 2009 ). As already noted, MTs help maintain the cellular homeostasis of essential metals like Zn, which serve as key structural, catalytic, and regulatory constituent of a great number of proteins (Costa, Sarmento-Ribeiro, & Gonçalves, 2023 ). This strongly suggests that they can promote cell growth through regulating Zn supply to proteins and modulating the activity of Zn-dependent enzymes and transcription factors (Krizkova et al., 2012 ). Furthermore, MTs can transfer Zn to p53, a zinc-binding transcription factor, to inhibit cell cycle progression and induce apoptosis in response to DNA damage (Krizkova et al., 2012 ). Meanwhile, it has become increasingly clear that heavy metals, as carcinogens, can cause cancer through interfering with biological processes such as cell growth, proliferation, differentiation, survival and apoptosis (Parida & Patel, 2023 ). MTs can bind to a variety of xenobiotic heavy metals to provide cell protection against heavy metal toxicity (Dai et al., 2021 ). As free radical scavengers, they have antioxidant properties and protect cells from DNA damage induced by ROS (Giacconi et al., 2017 ). Biologically, high levels of ROS promote the growth, proliferation, invasion and metastasis of cancerous cells (Prasad, Gupta, & Tyagi, 2017 ). It has already been shown that MTs reach a maximum level in late G1 phase of the cell cycle and at the G1/S transition, suggesting their involvement in cell cycle control (Nagel & Vallee, 1995 ). MTs can also induce the expression of angiogenesis-related genes, including matrix metalloproteinase-2 (MMP-2), MMP-9, and vascular endothelial growth factor (VEGF) to create new blood vessels from pre-existing ones (Rodrigo et al., 2020 ). It is well known that tumor growth, proliferation, progression, invasion, and metastatic dissemination are entirely contingent on the adequate blood supply to deliver the nutrients and oxygen (Saman, Raza, Uddin, & Rasul, 2020 ). Taken together, these data highlight the key role of MTs in the pathogenesis of cancers. The expression of MTs in human tumors have been extensively investigated, but the results still remain controversial. MTs expressions are divergent depending on the type of tumor i.e., are upregulated in some human malignancies (Gomulkiewicz et al., 2010 ; Hengstler et al., 2001 ; Jayasurya et al., 2000 ; Weinlich et al., 2006 ; Wülfing et al., 2007 ), but downregulated in other cancers (Datta et al., 2007 ; Han et al., 2013 ). Surprisingly, very few studies have explored the association between MT gene polymorphisms and the risk of developing cancer, despite its importance (Forma et al., 2012 ; Rosa et al., 2021 ; Wong et al., 2013 ). Therefore, the aim of the present study was to evaluate the genotypic and allelic association of MT1A rs11640851, rs8052394 and rs11076161 SNPs with the risk of developing BC in Iranian women under co-dominant, dominant, and recessive inheritance models. The combined effect of MT1A SNPs on the BC risk was assessed using the haplotype analysis. The impact of MT1A SNPs on RNA secondary structure and protein function was also determined using RNAsnp and Polyphen-2 servers, respectively. To the best of the author's knowledge, this is the first study to investigate the association of MT1A polymorphisms with the risk of BC. We found that rs11640851 was significantly associated with reduced risk of developing BC under co-dominant, dominant, and recessive inheritance models (Table 3 ). Moreover, rs11640851 A allele showed a protective effect against breast carcinogenesis (OR: 4.812; 95% CI: 2.655–8.719; P = 0.0001). As far as we know, no study has been conducted to determine the association of rs11640851with the risk of developing cancer. Our results show for the first time that rs11640851 has a protective effect against the development of BC in our population. However, further studies should be designed to firmly validate these findings. There were no statistically significant differences between BC patients and healthy subjects regarding genotype and allele frequencies of MT1A rs8052394 SNP ( P > 0.05) (Table 3 ). Our findings are consistent with a previous study indicating no significant associations between rs8052394 and oral squamous cell carcinoma (OSCC) risk (Rosa et al., 2021 ). In the study conducted by Rosa et al . on 28 patients with OSCC and 45 healthy subjects, they found that rs8052394 did not influence individual's susceptibility to OSCC in Brazilian population (Rosa et al., 2021 ). In 2013, Wong et al. reported that rs8052394 was not involved with pathogenesis of hepatocellular carcinoma (HCC) (Wong et al., 2013 ). They found that individuals carrying the rs8052394 A allele had a higher risk of HCC than those carrying the G allele, and rs8052394 AA genotype was also significantly associated with 4.13-fold increased risk of HCC (Wong et al., 2013 ). Conversly, another study from Taiwan demonstrated a statistically significant association between rs8052394 and increased risk of OSCC (Zavras, Yoon, Chen, Lin, & Yang, 2011 ). As regards rs11076161 SNP, no statistically significant association was detected under co-dominant, dominant and recessive inheritance models ( P > 0.05) (Table 3 ). In line with our findings, Wong et al. revealed that there is no evidence to support the association of rs11076161 with HCC risk (Wong et al., 2013 ). Similarly, Zavras et al. failed to find any association between rs11076161and predisposition to OSCC (Zavras et al., 2011 ). In contrast, Rosa et al. reported that carriers of genotype AA had a 19-fold increased risk of OSCC (Rosa et al., 2021 ). Due to conflicting results, it is difficult to reach a consensus on the association of the rs8052394 and rs11076161 variants with the risk of developing cancer. Although the reason of these discrepancies remains an important unanswered question, it can be explained in part by ethnic/racial disparities, differences in sample size and multifactorial nature of cancer. Hence, further large-scale studies in diverse populations are needed to find conclusive evidence. In conclusion, these results provide the first evidence that rs11640851 polymorphism is significantly associated with reduced risk of BC, suggesting its protective role in the development of BC. Also, two haplotypes CAC and CGA were identified as risk factors for BC. Declarations Acknowledgments Authors would like to express their sincerest appreciation to all subjects for participating in this study. Ethical approval The study protocol was approved the Institutional Review Board /Independent Ethics Committee (IRB/IEC) of the Shahid Ashrafi Esfahani University, Isfahan, Iran. Informed consent Written informed consent was obtained from all participants after receiving an explanation of the study. Conflict of interest The authors declare that they have no conflict of interest. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author (S.Y). Funding information There is no funding for this study to report. Author Contribution M.Sh: Providing the data and design, Manuscript writing A.Y.: Providing the data and design Z.Z: Conception, Providing the data and design M.A: Conception, Providing the data and design S.Y.: Conception, Providing the data and design, and the final approval of the manuscript. References Asadi, S., Abkar, M., Zamanzadeh, Z., Taghipour Kamalabad, S., Sedghi, M., & Yousefnia, S. (2023). Association of SOD2 rs2758339, rs5746136 and rs2842980 polymorphisms with increased risk of breast cancer: a haplotype-based case–control study. Genes & Genomics , 1-14. Cherian, M. G., Jayasurya, A., & Bay, B.-H. (2003). Metallothioneins in human tumors and potential roles in carcinogenesis. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 533 (1-2), 201-209. Costa, M. I., Sarmento-Ribeiro, A. B., & Gonçalves, A. C. (2023). Zinc: From Biological Functions to Therapeutic Potential. International Journal of Molecular Sciences, 24 (5), 4822. Dai, H., Wang, L., Li, L., Huang, Z., & Ye, L. (2021). Metallothionein 1: A new spotlight on inflammatory diseases. Frontiers in Immunology, 12 , 739918. Datta, J., Majumder, S., Kutay, H., Motiwala, T., Frankel, W., Costa, R., . . . Ghoshal, K. (2007). Metallothionein expression is suppressed in primary human hepatocellular carcinomas and is mediated through inactivation of CCAAT/enhancer binding protein α by phosphatidylinositol 3-kinase signaling cascade. Cancer research, 67 (6), 2736-2746. Eckschlager, T., Adam, V., Hrabeta, J., Figova, K., & Kizek, R. (2009). Metallothioneins and cancer. Current Protein and Peptide Science, 10 (4), 360-375. Feng, Y., Spezia, M., Huang, S., Yuan, C., Zeng, Z., Zhang, L., . . . Luo, W. (2018). Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes & diseases, 5 (2), 77-106. Forma, E., Krzeslak, A., Wilkosz, J., Jozwiak, P., Szymczyk, A., Rozanski, W., & Brys, M. (2012). Metallothionein 2A genetic polymorphisms and risk of prostate cancer in a Polish population. Cancer Genetics, 205 (9), 432-435. Giacconi, R., Cai, L., Costarelli, L., Cardelli, M., Malavolta, M., Piacenza, F., & Provinciali, M. (2017). Implications of impaired zinc homeostasis in diabetic cardiomyopathy and nephropathy. BioFactors, 43 (6), 770-784. Gomulkiewicz, A., Podhorska-Okolow, M., Szulc, R., Smorag, Z., Wojnar, A., Zabel, M., & Dziegiel, P. (2010). Correlation between metallothionein (MT) expression and selected prognostic factors in ductal breast cancers. Folia Histochemica et Cytobiologica, 48 (2), 242-248. Han, Y. C., Zheng, Z. L., Zuo, Z. H., Yu, Y. P., Chen, R., Tseng, G. C., . . . Luo, J. H. (2013). Metallothionein 1 h tumour suppressor activity in prostate cancer is mediated by euchromatin methyltransferase 1. The Journal of pathology, 230 (2), 184-193. Hengstler, J., Pilch, H., Schmidt, M., Dahlenburg, H., Sagemüller, J., Schiffer, I., . . . Tanner, B. (2001). Metallothionein expression in ovarian cancer in relation to histopathological parameters and molecular markers of prognosis. International journal of cancer, 95 (2), 121-127. Jaman, M. S., Al Mamun, A., Sharker, B., Maniruzzaman, M., Rana, M. R. R., Hasan, M. N., & Bhuiyan, M. R. H. (2023). Curcumin, Diallyl Sulphide, Quercetin and Gallic Acid Uses as Anticancer and Therapeutic Agents for Breast Cancer: Current Strategies and Future Perspectives. European Journal of Medical and Health Sciences, 5 (3), 32-48. Jayasurya, A., Bay, B., Yap, W., Tan, N., & Tan, B. (2000). Proliferative potential in nasopharyngeal carcinoma: correlations with metallothionein expression and tissue zinc levels. Carcinogenesis, 21 (10), 1809-1812. Juárez-Rebollar, D., Rios, C., Nava-Ruíz, C., & Méndez-Armenta, M. (2017). Metallothionein in brain disorders. Oxidative medicine and cellular longevity, 2017 . Krezel, A., & Maret, W. (2021). The bioinorganic chemistry of mammalian metallothioneins. Chemical Reviews, 121 (23), 14594-14648. Krizkova, S., Ryvolova, M., Hrabeta, J., Adam, V., Stiborova, M., Eckschlager, T., & Kizek, R. (2012). Metallothioneins and zinc in cancer diagnosis and therapy. Drug Metabolism Reviews, 44 (4), 287-301. Krześlak, A., Forma, E., Jóźwiak, P., Szymczyk, A., Smolarz, B., Romanowicz-Makowska, H., . . . Bryś, M. (2014). Metallothionein 2A genetic polymorphisms and risk of ductal breast cancer. Clinical and experimental medicine, 14 , 107-113. Nagel, W. W., & Vallee, B. L. (1995). Cell cycle regulation of metallothionein in human colonic cancer cells. Proceedings of the National Academy of Sciences, 92 (2), 579-583. Parida, L., & Patel, T. N. (2023). Systemic impact of heavy metals and their role in cancer development: a review. Environmental Monitoring and Assessment, 195 (6), 766. Park, M., Kim, D., Ko, S., Kim, A., Mo, K., & Yoon, H. (2022). Breast cancer metastasis: Mechanisms and therapeutic implications. International Journal of Molecular Sciences, 23 (12), 6806. Prasad, S., Gupta, S. C., & Tyagi, A. K. (2017). Reactive oxygen species (ROS) and cancer: Role of antioxidative nutraceuticals. Cancer letters, 387 , 95-105. Raudenska, M., Gumulec, J., Podlaha, O., Sztalmachova, M., Babula, P., Eckschlager, T., . . . Masarik, M. (2014). Metallothionein polymorphisms in pathological processes. Metallomics, 6 (1), 55-68. Rodrigo, M. A. M., Jimemez, A. M. J., Haddad, Y., Bodoor, K., Adam, P., Krizkova, S., . . . Adam, V. (2020). Metallothionein isoforms as double agents–their roles in carcinogenesis, cancer progression and chemoresistance. Drug Resistance Updates, 52 , 100691. Rosa, R. R., Garcia, M. A. J., Alves, P. T., Sousa, E. M., Pimentel, L. S., de Paula Barbosa, L., . . . Cardoso, S. V. (2021). Revisiting the metallothionein genes polymorphisms and the risk of oral squamous cell carcinoma in a Brazilian population. Medicina oral, patologia oral y cirugia bucal, 26 (3), e334. Saman, H., Raza, S. S., Uddin, S., & Rasul, K. (2020). Inducing angiogenesis, a key step in cancer vascularization, and treatment approaches. Cancers, 12 (5), 1172. Si, M., & Lang, J. (2018). The roles of metallothioneins in carcinogenesis. Journal of hematology & oncology, 11 (1), 1-20. Siegel, R. L., Miller, K. D., Wagle, N. S., & Jemal, A. (2023). Cancer statistics, 2023. Ca Cancer J Clin, 73 (1), 17-48. Singh, R., & kumar Sain, M. N. (2023). Etiology Of Breast Cancer. Journal of Pharmaceutical Negative Results , 1427-1434. Swaminathan, H., Saravanamurali, K., & Yadav, S. A. (2023). Extensive review on breast cancer its etiology, progression, prognostic markers, and treatment. Medical Oncology, 40 (8), 238. Vašák, M., & Meloni, G. (2017). Mammalian metallothionein-3: New functional and structural insights. International Journal of Molecular Sciences, 18 (6), 1117. Weinlich, G., Eisendle, K., Hassler, E., Baltaci, M., Fritsch, P., & Zelger, B. (2006). Metallothionein–overexpression as a highly significant prognostic factor in melanoma: a prospective study on 1270 patients. British journal of cancer, 94 (6), 835-841. Wong, R.-H., Huang, C.-H., Yeh, C.-B., Lee, H.-S., Chien, M.-H., & Yang, S.-F. (2013). Effects of metallothionein-1 genetic polymorphism and cigarette smoking on the development of hepatocellular carcinoma. Annals of surgical oncology, 20 , 2088-2095. Wülfing, C., van Ahlen, H., Eltze, E., Piechota, H., Hertle, L., & Schmid, K.-W. (2007). Metallothionein in bladder cancer: correlation of overexpression with poor outcome after chemotherapy. World journal of urology, 25 , 199-205. Yousefnia, S. (2014). Association between genetic polymorphism of catalase (CAT) C-262T, Cu/Zn superoxide dismutase (SOD1) A251G and risk of age-related macular degeneration. Molecular and Biochemical Diagnosis Journal, 1 (2), 77-88. Zavras, A., Yoon, A., Chen, M., Lin, C., & Yang, S. (2011). Metallothionein-1 genotypes in the risk of oral squamous cell carcinoma. Annals of surgical oncology, 18 , 1478-1483. Tables Table 1. List of primers and restriction enzymes used in PCR-RFLP and ARMS-PCR assays for genotyping of the MT1A rs11640851, rs8052394 and rs11076161 polymorphisms SNP Primer sequence Annealing temperature (°C) Amplicon size (bp) Restriction enzyme Fragment length (bp) rs11640851 F 5ʹ-ACTTGGCTCAGCCCCAGATT-3ʹ 59 187 MnlI C allele: 140, 47 R 5ʹ-CACTCAGCTGGCAGCATTTG-3ʹ A allele: 187 rs8052394 F 5ʹ-ACTAAGTGTCCTCTGGGGCTG-3ʹ 59 283 PstI A allele: 283 R 5ʹ-AATGGGTCACGGTTGTATGG-3ʹ G allele: 165, 118 rs11076161 F 5ʹ-CTGCTGTTATCTTCTGTATAAAA-3ʹ 58 173 - A allele: 173 F 5ʹ-CTGCTGTTATCTTCTGTATAAAG-3ʹ C allele: 173 R 5ʹ-GTTTCCTGCCTCTGACTTG-3ʹ Abbreviation: PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; ARMS-PCR, Amplification Refractory Mutation System PCR; MT1A, metallothionein 1A; SNP, single nucleotide polymorphism. Table 2: Demographic and clinicopathological characteristics of breast cancer patients and healthy controls Variables Cases (n=100) % Controls (n=100) % P value Age, years (mean ± SD) 51.56 ± 11.24 39.01 ± 10.51 0.0001 Range 29 - 80 17 - 76 - ≤ 40 16 54 0.0001 > 40 84 46 0.0001 Weight, kg (mean ± SD) 72.71 ± 11.01 67.71 ± 11.45 0.002 Height, cm (mean ± SD) 161.13 ± 6.47 162.71 ± 5.70 0.691 BMI, kg/m 2 (mean ± SD) 28.09 ± 4.30 25.64 ± 4.59 0.0001 Underweight ( < 18.5) 1 4 0.369 Normal (18.5 - 24.9) 21 43 0.001 Overweight (25 - 29.9) 46 37 0.251 Obese ( ≥ 30) 32 16 0.013 Smoking status 0.767 Non-smoker 95 93 Smoker 5 7 Marital status 0.0001 Single 0 17 Married 100 83 History of taking the oral contraceptive pills 0.031 Yes 49 33 No 51 67 Family history of breast cancer Yes 30 No 70 Laterality Unilateral 98 Bilateral 2 Histological type IDC 92 ILC 8 Histological grade I 12 II 62 III 26 Tumor size, cm ≤ 2 91 > 2 9 Other organ metastasis Yes 7 No 93 Abbreviation: SD, standard deviation; BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma. Values are presented as mean ± SD for age, weight, height and BMI, and as % for other variables. P values were calculated using t-test for continuous variables and Chi-square test for categorical variables. Bold values indicate statistically significant differences ( P < 0.05). Table 3: Genotype and allele distribution of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms in breast cancer patients and healthy controls Genotypes Cases (n=100) % Controls (n=100) % OR (95% CI) P value rs11640851 0.0001 CC 86 55 1.000 - CA 12 31 4.039 (1.913 - 8.529) 0.0002 AA 2 14 10.94 (2.394 - 50.03) 0.0002 Dominant (CC vs. CA+AA) - - 5.026 (2.524 - 10.01) 0.0001 Recessive (CC+CA vs. AA) - - 7.976 (1.762 - 36.09) 0.0029 A allele 8.00 29.50 4.812 (2.655 - 8.719) 0.0001 HWE P value 0.064 0.010 - - rs8052394 0.870 AA 74 76 1.000 - AG 26 24 0.898 (0.473 - 1.705) 0.870 GG 0 0 ND ND Dominant (AA vs. AG+GG) - - 0.898 (0.473 - 1.705) 0.870 Recessive (AA+AG vs. GG) - - ND ND G allele 13.00 12.00 0.918 (0.408 - 2.064) 1.000 HWE P value 0.135 0.172 - - rs11076161 0.229 AA 51 47 1.000 - AC 47 46 1.062 (0.602 - 1.873) 0.885 CC 2 7 3.797 (0.751 - 19.20) 0.161 Dominant (AA vs. AC+CC) - - 1.173 (0.673 - 2.044) 0.671 Recessive (AA+AC vs. CC) - - 3.688 (0.746 - 18.21) 0.169 C allele 25.50 30.00 1.252 (0.807 - 1.941) 0.371 HWE P value 0.017 0.340 - - Abbreviation: OR, odds ratio; CI, confidence interval; HWE, Hardy-Weinberg equilibrium. ND, not determined. P value were calculated by Chi-square test. Bold values indicate statistically significant differences ( P < 0.05). Table 4: Distribution of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms combined genotypes in breast cancer patients and healthy controls Combined genotypes Cases (n=100) % Controls (n=100) % OR (95% CI) P value rs11640851/rs8052394 CC/AA 64 44 1.000 - CC/AG 22 11 0.727 (0.320 - 1.650) 0.542 CC/GG 0 0 ND ND CA/AA 8 22 4.000 (1.633 - 9.795) 0.001 CA/AG 4 9 3.272 (0.948 - 11.29) 0.074 CA/GG 0 0 ND ND AA/AA 2 10 7.272 (1.519 - 34.81) 0.005 AA/AG 0 4 ND 0.031 AA/GG 0 0 ND ND rs11640851/rs11076161 CC/AA 47 34 1.000 - CC/AC 37 16 0.597 (0.286 - 1.245) 0.202 CC/CC 2 5 3.455 (0.632 - 18.88) 0.233 CA/AA 4 10 3.455 (0.999 - 11.95) 0.048 CA/AC 8 21 3.628 (1.437 - 9.162) 0.008 CA/CC 0 0 ND ND AA/AA 0 3 ND 0.081 AA/AC 2 9 6.220 (1.262 - 30.64) 0.021 AA/CC 0 2 ND 0.185 rs8052394/rs11076161 AA/AA 35 42 1.000 - AA/AC 38 29 0.636 (0.328 - 1.229) 0.186 AA/CC 1 5 4.166 (0.464 - 37.35) 0.226 AG/AA 16 5 0.260 (0.086 - 0.782) 0.014 AG/AC 9 17 1.574 (0.624 - 3.966) 0.368 AG/CC 1 2 1.666 (0.145 - 19.16) 1.000 GG/AA 0 0 ND ND GG/AC 0 0 ND ND GG/CC 0 0 ND ND Abbreviation: OR, odds ratio; CI, confidence interval; ND, not determined. P value calculated by Chi-square test. Bold values indicate statistically significant differences ( P < 0.05). Table 5: Association of MT1A rs11640851polymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients Variables Genotypes n (%) OR (95% CI) P value Age, years ≤ 40 > 40 CC 13 (81.25) 73 (86.90) 1.000 - CA 3 (18.75) 9 (10.72) 0.534 (0.127 - 2.240) 0.408 AA 0 (0.00) 2 (2.38) ND ND Dominant (CC vs. CA+AA) - - 0.653 (0.160 - 2.664) 0.693 Recessive (CC+CA vs. AA) - - ND ND A allele 3 (9.37) 13 (7.73) 0.810 (0.217 - 3.024) 0.488 BMI, kg/m 2 ≤ 25 > 25 CC 18 (81.81) 68 (87.17) 1.000 - CA 4 (18.19) 8 (10.26) 0.529 (0.143 - 1.957) 0.458 AA 0 (0.00) 2 (2.57) ND ND Dominant (CC vs. CA+AA) - - 0.661 (0.185 - 2.357) 0.728 Recessive (CC+CA vs. AA) - - ND ND A allele 4 (9.9) 12 (7.69) 0.833 (0.254 - 2.724) 0.484 Family history of breast cancer Yes No CC 28 (93.34) 58 (82.85) 1.000 - CA 1 (3.33) 11 (15.72) 5.310 (0.652 - 43.20) 0.102 AA 1 (3.33) 1 (1.43) 0.482 (0.029 - 8.005) 1.000 Dominant (CC vs. CA+AA) - - 2.896 (0.606 - 13.83) 0.218 Recessive (CC+CA vs. AA) - - 0.420 (0.025 - 6.850) 1.000 A allele 3 (5.00) 13 (9.28) 1.944 (0.533 - 7.091) 0.401 Histological type IDC ILC CC 80 (86.96) 6 (75.00) 1.000 - CA 10 (10.86) 2 (25.00) 2.666 (0.472 - 15.04) 0.253 AA 2 (2.18) 0 (0.00) ND ND Dominant (CC vs. CA+AA) - - 2.222 (0.401 - 12.30) 0.595 Recessive (CC+CA vs. AA) - - ND ND A allele 14 (7.60) 2 (12.50) 1.734 (0.357 - 8.410) 0.622 Histological grade Grade I and II Grade III CC 66 (89.19) 20 (76.92) 1.000 - CA 7 (9.46) 5 (19.23) 2.357 (0.674 - 8.243) 0.286 AA 1 (1.35) 1 (3.85) 3.300 (0.197 - 55.17) 0.422 Dominant (CC vs. CA+AA) - - 2.475 (0.767 - 7.980) 0.185 Recessive (CC+CA vs. AA) - - 2.920 (0.176 - 48.44) 0.999 A allele 9 (6.08) 7 (13.46) 2.402 (0.846 - 6.820) 0.133 Tumor size, cm ≤ 2 > 2 CC 77 (84.61) 9 (100.0) 1.000 - CA 12 (13.04) 0 (0.00) ND ND AA 2 (2.17) 0 (0.00) ND ND Dominant (CC vs. CA+AA) - - ND ND Recessive (CC+CA vs. AA) - - ND ND A allele 16 (8.79) 0 (0.00) ND ND Other organ metastasis Yes No CC 6 (85.71) 80 (86.02) 1.000 - CA 1 (14.29) 11 (11.82) 0.825 (0.090 - 7.512) 1.000 AA 0 (0.00) 2 (2.15) ND ND Dominant (CC vs. CA+AA) - - 0.975 (0.108 - 8.770) 1.000 Recessive (CC+CA vs. AA) - - ND ND A allele 1 (7.14) 15 (8.06) 1.140 (0.139 - 9.324) 1.000 Abbreviation: BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; ND, not determined. Values are presented as number (%). P values were calculated using Chi-square test. Table 6: Association of MT1A rs8052394 polymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients Variables Genotypes n (%) OR (95% CI) P value Age, years ≤ 40 > 40 AA 14 (87.50) 60 (71.42) 1.000 - AG 2 (12.50) 24 (28.58) 2.800 (0.591 - 13.26) 0.226 GG 0 (0.00) 0 (0.00) ND ND Dominant (AA vs. AG+GG) - - 2.800 (0.591 - 13.26) 0.226 Recessive (AA+AG vs. GG) - - ND ND G allele 2 (6.25) 24 (14.28) 2.500 (0.560 - 11.15) 0.265 BMI, kg/m 2 ≤ 25 > 25 AA 18 (81.81) 56 (71.79) 1.000 - AG 4 (18.19) 22 (28.21) 1.767 (0.537 - 5.813) 0.419 GG 0 (0.00) 0 (0.00) ND ND Dominant (AA vs. AG+GG) - - 1.767 (0.537 - 5.813) 0.419 Recessive (AA+AG vs. GG) - - ND ND G allele 4 (9.09) 22 (14.10) 1.641 (0.534 - 5.043) 0.457 Family history of breast cancer Yes No AA 21 (70.00) 53 (75.71) 1.000 - AG 9 (30.00) 17 (24.29) 0.748 (0.288 - 1.948) 0.621 GG 0 (0.00) 0 (0.00) ND ND Dominant (AA vs. AG+GG) - - 0.748 (0.288 - 1.948) 0.621 Recessive (AA+AG vs. GG) - - ND ND G allele 9 (15.00) 17 (12.14) 0.783 (0.327 - 1.872) 0.647 Histological type IDC ILC AA 68 (73.91) 6 (75.00) 1.000 - AG 24 (26.09) 2 (25.00) 0.944 (0.178 - 5.001) 1.000 GG 0 (0.00) 0 (0.00) ND ND Dominant (AA vs. AG+GG) - - 0.944 (0.178 - 5.001) 1.000 Recessive (AA+AG vs. GG) - - ND ND G allele 24 (13.04) 2 (12.50) 0.952 (0.203 - 4.453) 1.000 Histological grade Grade I and II Grade III AA 56 (75.67) 18 (69.23) 1.000 - AG 18 (24.33) 8 (30.77) 1.382 (0.514 - 3.712) 0.604 GG 0 (0.00) 0 (0.00) ND ND Dominant (AA vs. AG+GG) - - 1.382 (0.514 - 3.712) 0.604 Recessive (AA+AG vs. GG) - - ND ND G allele 18 (12.16) 8 (15.38) 1.313 (0.533 - 3.230) 0.632 Tumor size, cm ≤ 2 > 2 AA 68 (74.72) 6 (66.66) 1.000 - AG 23 (25.28) 3 (33.34) 1.478 (0.341 - 6.393) 0.692 GG 0 (0.00) 0 (0.00) ND ND Dominant (AA vs. AG+GG) - - 1.478 (0.341 - 6.393) 0.692 Recessive (AA+AG vs. GG) - - ND ND G allele 23 (12.64) 3 (16.66) 1.382 (0.371 - 5.147) 0.710 Other organ metastasis Yes No AA 6 (85.71) 68 (73.11) 1.000 - AG 1 (14.29) 25 (26.89) 2.205 (0.252 - 19.24) 0.672 GG 0 (0.00) 0 (0.00) ND ND Dominant (AA vs. AG+GG) - - 2.205 (0.252 - 19.24) 0.672 Recessive (AA+AG vs. GG) - - ND ND G allele 1 (12.14) 25 (13.44) 2.018 (0.252 - 16.11) 0.699 Abbreviation: BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; ND, not determined. Values are presented as number (%). P values were calculated using Chi-square test. Table 7: Association of MT1A rs11076161 polymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients Variables Genotypes n (%) OR (95% CI) P value Age, years ≤ 40 > 40 AA 8 (50.00) 43 (51.20) 1.000 - AC 8 (50.00) 39 (46.42) 0.907 (0.310 - 2.648) 1.000 CC 0 (0.00) 2 (2.38) ND ND Dominant (AA vs. AC+CC) - - 0.953 (0.327 - 2.777) 1.000 Recessive (AA+AC vs. CC) - - ND ND C allele 8 (25.00) 43 (25.59) 1.032 (0.431 - 2.468) 1.000 BMI, kg/m 2 ≤ 25 > 25 AA 9 (40.91) 42 (53.84) 1.000 - AC 11 (50.00) 36 (46.16) 0.701 (0.261 - 1.881) 0.616 CC 2 (9.09) 0 (0.00) ND ND Dominant (AA vs. AC+CC) - - 0.593 (0.227 - 1.548) 0.338 Recessive (AA+AC vs. CC) - - ND ND C allele 15 (34.09) 36 (23.07) 0.580 (0.280 - 1.198) 0.170 Family history of breast cancer Yes No AA 16 (53.33) 35 (50.00) 1.000 - AC 13 (43.34) 34 (48.57) 1.195 (0.500 - 2.856) 0.825 CC 1 (3.33) 1 (1.43) 0.457 (0.026 - 7.779) 1.000 Dominant (AA vs. AC+CC) - - 1.142 (0.485 - 2.692) 0.828 Recessive (AA+AC vs. CC) - - 0.420 (0.025 - 6.950) 1.000 C allele 15 (25.00) 36 (25.71) 1.038 (0.517 - 2.083) 1.000 Histological type IDC ILC AA 48 (52.17) 3 (37.50) 1.000 - AC 42 (45.66) 5 (62.50) 1.904 (0.429 - 8.452) 0.474 CC 2 (2.17) 0 (0.00) ND ND Dominant (AA vs. AC+CC) - - 1.818 (0.410 - 8.056) 0.482 Recessive (AA+AC vs. CC) - - ND ND C allele 46 (25.00) 5 (31.25) 1.363 (0.450 - 4.131) 0.765 Histological grade Grade I and II Grade III AA 39 (52.70) 12 (46.15) 1.000 - AC 33 (44.60) 14 (53.85) 1.378 (0.560 - 3.390) 0.502 CC 2 (2.70) 0 (0.00) ND ND Dominant (AA vs. AC+CC) - - 1.300 (0.530 - 3.184) 0.650 Recessive (AA+AC vs. CC) - - ND ND C allele 37 (25.00) 14 (26.92) 1.105 (0.539 - 2.263) 0.853 Tumor size, cm ≤ 2 > 2 AA 47 (51.65) 4 (44.45) 1.000 - AC 42 (46.15) 5 (55.55) 1.398 (0.352 - 5.555) 0.733 CC 2 (2.20) 0 (0.00) ND ND Dominant (AA vs. AC+CC) - - 1.335 (0.336 - 5.294) 0.738 Recessive (AA+AC vs. CC) - - ND ND C allele 46 (25.27) 5 (27.77) 1.137 (0.384 - 3.362) 1.000 Other organ metastasis Yes No AA 4 (57.14) 47 (50.54) 1.000 - AC 2 (28.58) 45 (48.38) 1.914 (0.334 - 10.97) 0.378 CC 1 (14.28) 1 (1.08) 0.085 (0.004 - 1.632) 0.181 Dominant (AA vs. AC+CC) - - 1.305 (0.276 - 6.155) 1.000 Recessive (AA+AC vs. CC) - - 0.065 (0.003 - 1.176) 0.135 C allele 4 (28.57) 47 (25.26) 0.843 (0.253 - 2.822) 0.499 Abbreviation: BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; ND, not determined. Values are presented as number (%). P values were calculated using Chi-square test. Table 8: Pairwise LD of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms in breast cancer patients and healthy controls Cases Controls Locus pair D′ r 2 D′ r 2 rs11640851 / rs8052394 0.096 0.000 0.157 0.008 rs11640851 / rs11076161 0.347 0.031 0.284 0.079 rs8052394 / rs11076161 0.409 0.009 0.583 0.108 Abbreviation: LD, linkage disequilibrium. r 2 reflects statistical power to detect LD. Table 9: Haplotype distribution of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms in breast cancer patients and healthy controls haplotype Frequency OR (95% CI) P value rs1164085 rs8052394 rs11076161 Cases Controls A A C 0.040 0.097 0.392 (0.168 - 0.914) 0.025 A G C 0.000 0.050 0.009 (0.001 - 0.121) 0.002 C A A 0.603 0.518 1.445 (0.970 - 2.151) 0.069 C A C 0.196 0.117 1.865 (1.070 - 3.249) 0.026 C G A 0.102 0.034 3.278 (1.344 - 7.993) 0.006 C G C 0.018 0.036 0.503 (0.140 - 1.803) 0.282 Abbreviation: SNPs, single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval. P value were calculated using Pearson's chi-square test. Bold values indicate statistically significant differences ( P < 0.05). Additional Declarations No competing interests reported. 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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-3867462","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267301644,"identity":"e5b99d61-7d63-436b-817d-0a4b9a3808b1","order_by":0,"name":"Maryam Shirvani","email":"","orcid":"","institution":"Shahid Ashrafi Esfahani University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Shirvani","suffix":""},{"id":267301645,"identity":"88603505-c227-4611-8fdf-aed2cd5e9542","order_by":1,"name":"Arshia Yadollahi","email":"","orcid":"","institution":"Shahid Ashrafi Esfahani University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Arshia","middleName":"","lastName":"Yadollahi","suffix":""},{"id":267301646,"identity":"215764ba-990e-40ca-bb98-dcb9227654a0","order_by":2,"name":"Zahra Zamanzadeh","email":"","orcid":"","institution":"Shahid Ashrafi Esfahani University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Zamanzadeh","suffix":""},{"id":267301647,"identity":"323ea868-63ee-4c3b-bdb7-cc16956572cd","order_by":3,"name":"Morteza Abkar","email":"","orcid":"","institution":"Shahid Ashrafi Esfahani University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Morteza","middleName":"","lastName":"Abkar","suffix":""},{"id":267301648,"identity":"ef963a99-9684-49ab-93f5-21f77ed4ef03","order_by":4,"name":"Saghar Yousefnia","email":"data:image/png;base64,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","orcid":"","institution":"Semnan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Saghar","middleName":"","lastName":"Yousefnia","suffix":""}],"badges":[],"createdAt":"2024-01-15 19:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3867462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3867462/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49764733,"identity":"0301d61d-d391-40e9-9d0b-6f534a673750","added_by":"auto","created_at":"2024-01-17 16:31:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12527,"visible":true,"origin":"","legend":"\u003cp\u003eLinkage disequilibrium (LD) plot of \u003cem\u003eMT1A \u003c/em\u003ers11640851, rs8052394 and rs11076161\u003cem\u003e \u003c/em\u003epolymorphisms\u003cem\u003e \u003c/em\u003ein A) breast cancer patients and B) healthy controls\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3867462/v1/6006b3aede2e4492a2d37284.png"},{"id":49764009,"identity":"ed9a1be4-9ac3-482d-9af4-fcf074ba2236","added_by":"auto","created_at":"2024-01-17 16:23:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120624,"visible":true,"origin":"","legend":"\u003cp\u003eRNAsnp analysis of MT1A rs11640851 and rs8052394 SNPs. Secondary RNA structure of c.80C\u0026gt;A SNP in a1) wild-type and a2) mutant, Secondary RNA structure of c.152A/G SNP in b1) wild-type and b2) mutant.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3867462/v1/ab4ad196e2262995095135d3.png"},{"id":49764011,"identity":"3f0e6d9f-dfc5-4993-b6e7-aec75cb77bc4","added_by":"auto","created_at":"2024-01-17 16:23:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92095,"visible":true,"origin":"","legend":"\u003cp\u003ePolyPhen-2 analysis of \u003cem\u003eMT1A\u003c/em\u003e rs11640851 and rs8052394 SNPs. A) Polyphen-2 predicted T27N substitution in metallothionein-1A to be damaging, B) Polyphen-2 predicted K51R substitution in metallothionein-1A to be benign.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3867462/v1/3e532dd582c864345aecefec.png"},{"id":49813162,"identity":"d1fe9973-6a0a-42b2-88eb-db83ee4e31f1","added_by":"auto","created_at":"2024-01-18 12:42:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":677268,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3867462/v1/888a2ae6-37ce-4ee6-93a4-3148a42b8577.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms with the risk of developing breast cancer: a haplotype-based case-control study and in silico analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BC) is the most commonly diagnosed malignancy around the world, contributing 12.5% of all new cancer cases worldwide (Jaman et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to the latest data from the American Cancer Society (ACS), in 2023, an estimated 297,790 new cases of BC will be diagnosed (31% of all new cases) and 43,170 women will die from the disease (15% of all cancer-related deaths) in the United States (Siegel, Miller, Wagle, \u0026amp; Jemal, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Breast cancer is a heterogeneous condition resulting from the complex interplay of multiple genetic, environmental, and lifestyle factors (Singh \u0026amp; kumar Sain, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Swaminathan, Saravanamurali, \u0026amp; Yadav, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite considerable progress towards understanding breast carcinogenesis in recent decades, there are still so many unanswered questions regarding BC etiopathogenesis (Feng et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has been confirmed that antioxidant have a protective role against ROS and there is an association between polymorphisms in antioxidant coding genes and susceptibility of BC (Asadi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yousefnia, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetallothioneins (MTs) are a superfamily of highly conserved, low molecular weight (6\u0026ndash;7 kDa), sulfhydryl-rich metal-binding proteins that exist in almost all forms of life (Rodrigo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They are single-chain polypeptides, containing 61\u0026ndash;68 amino acids, 20 of them are conserved cysteine residues distributed in two α and β metal-thiolate clusters that bind up to seven divalent metals ions (Ju\u0026aacute;rez-Rebollar, Rios, Nava-Ru\u0026iacute;z, \u0026amp; M\u0026eacute;ndez-Armenta, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vaš\u0026aacute;k \u0026amp; Meloni, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). MTs are predominantly localized in the cytosol, and play crucial roles in maintaining homeostasis of essential metals such as copper (Cu) and zinc (Zn), detoxification of heavy metals like mercury (Hg) and cadmium (Cd), and antioxidant defense systems by scavenging reactive oxygen species (ROS) including hydroxyl free radical (\u003csup\u003e\u0026bull;\u003c/sup\u003eOH), superoxide anion (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026bull;\u0026minus;\u003c/sup\u003e), and hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) (Dai, Wang, Li, Huang, \u0026amp; Ye, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Giacconi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Krezel \u0026amp; Maret, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They are conventionally classified into four subfamilies in human, designated as MT1, MT2, MT3, and MT4 (Dai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). MTs are encoded by a multi-gene family containing at least 18 closely related genes, 11 of which are functional genes (MT1A, MT1B, MT1E, MT1F, MT1G, MT1H, MT1M (also called MT1K), MT1X, MT2A, MT3, and MT4), and seven pseudogenes (MT1C, MT1D, MT1I, MT1J, MT1L, PT1P, and MT2B) (Raudenska et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In human, these genes are clusterd on the q13 region of chromosome 16 (16q13) (Raudenska et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). MT1 and MT2 isoforms are expressed in almost all human tissues, while MT3 and MT4 are expressed primarily in the brain and stratified squamous epithelia, respectively (Si \u0026amp; Lang, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The expression of MTs is induced by several stimuli including metals, hormones, cytokines, chemical agents, oxidative stress and inflammation (Giacconi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A growing body of evidence suggests differential expression of MT isoforms in a variety of human cancers (Datta et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gomulkiewicz et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hengstler et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jayasurya, Bay, Yap, Tan, \u0026amp; Tan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Weinlich et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; W\u0026uuml;lfing et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Based on previous reports, MTs are overexpressed in ovarian cancer (Hengstler et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), bladder cancer (W\u0026uuml;lfing et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), breast cancer (Gomulkiewicz et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), nasopharyngeal cancer (Jayasurya et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and melanoma (Weinlich et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). On the contrary, MTs have been shown to be down-regulated in some malignancies like prostate cancer (Han et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and hepatocellular carcinoma (Datta et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These data support the relationship between MTs and cancer, suggesting that they may participate in the processes of carcinogenesis from tumor initiation, promotion and progression to metastasis. Furthermore, single nucleotide polymorphisms (SNPs) in the MTs gene have been shown to be associated with increased cancer risk (Forma et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Krześlak et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rosa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough a number of variants have been identified, rs11640851, rs8052394 and rs11076161 are the most common SNPs in the \u003cem\u003eMT1A\u003c/em\u003e gene (Raudenska et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The rs11640851 (647 C/A or c.80C/A) lies in exon 2 and is characterized by a threonine to asparagine substitution at codon 27 (T27N), rs8052394 (1245 A/G or c.152 A/G) is located in exon 3, and results in substitution of a lysine by an arginine residue (K51R), and rs11076161 is a variant in the first intron of \u003cem\u003eMT1A\u003c/em\u003e gene (Raudenska et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). To date, few studies have focused on exploring the association of \u003cem\u003eMT1A\u003c/em\u003e rs11640851, rs8052394 and rs11076161 SNPs with cancer susceptibility (Rosa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Accordingly, this study was designed to address the question of whether rs11640851, rs8052394 and rs11076161 SNPs and haplotypes can underlie susceptibility to BC in Iranian women for the first time.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eSubjects\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver a period of 9 months, from August 2020 to April 2021, one hundred histopathologically confirmed breast cancer cases (mean age \u0026plusmn; standard deviation (SD); 51.56 \u0026plusmn; 11.24 years, range 29 to 80) were enrolled in the present case-control study from the Anahid Breast Cancer Clinic, Isfahan Healthcare City, Isfahan, Iran. The histopathological diagnosis of all cancer patients was confirmed by a panel of pathologist\u0026nbsp;at the respective hospitals. The medical records and pathology reports were reviewed to collect demographic/clinical characteristics including age at diagnosis, tumor histological type, stage, grade, size and status of lymph node metastasis. If any of these variables were missing, the patient was excluded from study. Also, there were no women who underwent chemotherapy, hormonotherapy and/or radiotherapy in case group.\u0026nbsp;Additionally, one hundred race/ethnicity-matched healthy women with no personal and/or family history of BC or any other type of cancer (mean age \u0026plusmn; SD; 39.01 \u0026plusmn; 10.51 years, range 17 to 76) were included in the study as controls. Demographic and clinical data were collected through interviews using a questionnaire form for all healthy subjects. All procedures were in accordance with relevant guidelines and the study protocol was approved by the Institutional Review Board /Independent Ethics Committee (IRB/IEC) of the Shahid Ashrafi Esfahani University, Isfahan, Iran. \u0026nbsp;In line with the principles of the World Medical Association\u0026apos;s Declaration of Helsinki, written informed consent was obtained\u0026nbsp;from all participants after receiving an explanation of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and genotyping of the MT1A SNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole venous blood (~5 mL) was taken from all participants via antecubital venipuncture and collected in vacutainer tube with K2EDTA. Genomic DNA was extracted from leukocytes using a commercial kit (ZEAN Genomic DNA Extraction Kit,\u0026nbsp;Isfahan, Iran), according to the manufacturer\u0026apos;s instructions. The quality and quantity of the extracted DNA were assessed by measuring the absorbance at 260 nm and 280 nm using NanoDrop\u0026reg; ND-1000 UV-Vis Spectrophotometer (Thermo Fisher Scientific\u0026trade;, Waltham, MA, USA). We used polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique to detect\u0026nbsp;MT1A rs11640851 and rs8052394 SNPs.\u0026nbsp;The final volume of the PCR mixture was 25 \u0026mu;l and contained 12.5 \u0026micro;l Taq DNA Polymerase 2X Master Mix Red (Ampliqon, Denmark), 1 \u0026mu;l of each primer (10 pmol) (Table 1),100 ng genomic DNA and DNase/RNase-free distilled water.\u0026nbsp;All amplification reactions were carried out on an Applied Biosystems\u0026trade; Thermal Cycler (Thermo Fisher Scientific, Waltham, MA, USA). The thermal cycling program was set as follows:\u0026nbsp;initial denaturation at 95 \u0026deg;C (5 minutes), 35 cycles\u0026nbsp;at 95 \u0026deg;C (30 seconds), 59 \u0026deg;C (30 s), and 72\u0026deg;C (40 seconds), which was followed by a final elongation\u0026nbsp;step at 72\u0026deg;C for 5 minutes. The PCR-amplified products were loaded on a 2%\u0026nbsp;agarose gel\u0026nbsp;containing safe stain (CinnaGen, Co, Tehran, Iran) and then visualized with ultra-violet (UV) transilluminator. The rs11640851 and rs8052394 were detected by digestion of the PCR products with\u0026nbsp;MnlI\u0026nbsp;and\u0026nbsp;PstI restriction endonucleases (New England Biolabs, Beverly, MA, USA) (Table 1) respectively. After 24 hours of incubation at 37\u0026deg;C, the digested fragments were separated by electrophoresis\u0026nbsp;on a 3% agarose gel. The rs11640851 A allele remained uncut (187 bp), whereas the rs11640851 C allele was cut into two fragments of 140 and 47 bp (Table 1). The PstI restriction enzyme digests the 283 bp amplicon corresponding to rs8052394 into 165 and 118 bp fragments for allele G (Table 1). To identify MT1A rs11076161 SNP, we performed amplification refractory mutation system PCR (ARMS-PCR)\u0026nbsp;assay using allele-specific primers presented in Table 1. The PCR reaction mixture and thermal cycling condition\u0026nbsp;were the same as used for genotyping MT1A\u0026nbsp;rs11640851 and rs8052394 SNPs. Afterwards, the PCR amplicons were separated by electrophoresis on 2% agarose gel containing safe stain and visualized under UV light.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3582274/\"\u003eLinkage disequilibrium and haplotype analysis\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePairwise linkage disequilibrium (LD) between MT1A SNPs\u0026nbsp;was determined by\u0026nbsp;Lewontin\u0026rsquo;s standardized coefficient (D\u0026prime;) and the square of the Pearson\u0026rsquo;s correlation coefficient (r\u003csup\u003e2\u003c/sup\u003e) values using the online web-based SHEsis software (\u003ca href=\"http://analysis.bio-x.cn/myAnalysis.php\"\u003ehttp://analysis.bio-x.cn/myAnalysis.php\u003c/a\u003e). To evaluate the association of haplotype with the risk of developing BC, we compared the distribution of haplotypes between cases and healthy controls using SHEsis online server.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn silico analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequence of MT1A mRNA in FASTA format was retrieved from National Center for Biotechnology Information (NCBI)(https://www.ncbi.nlm.nih.gov/)\u0026nbsp;as an input to investigate the effect of SNPs on local RNA secondary structure. We used web-based RNAsnp server (\u003ca href=\"https://rth.dk/resources/rnasnp/\"\u003ehttps://rth.dk/resources/rnasnp/\u003c/a\u003e) to predict the effect of MT1A rs11640851and rs8052394 SNPs on mRNA secondary structure. Moreover, the human metallothionein 1A protein sequence was obtained from the Ensembl database (\u003ca href=\"https://useast.ensembl.org/index.html\"\u003ehttps://useast.ensembl.org/index.html\u003c/a\u003e). For\u0026nbsp;MT1A rs11640851and rs8052394 SNPs, the possible effect of amino acid substitution on the protein structure and activity was predicted using Polymorphism Phenotyping version 2 (PolyPhen-2) online server (http://genetics.bwh.harvard.edu/pph2/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe IBM Statistical Package for Social Sciences (SPSS) Statistics for Windows, Version 22.0 (IBM Corp., Armonk, N.Y., USA) was used to statistical analysis of data. Any\u0026nbsp;P value equal or less than\u0026nbsp;0.05 were deemed as statistically significant. Quantitative variables were expressed as means \u0026plusmn; standard deviations (SD), and compared by Student\u0026rsquo;s t-test.\u0026nbsp;Genotype distributions and allele frequencies of rs11640851, rs8052394 and rs11076161 variants were compared between groups\u0026nbsp;using\u0026nbsp;Fisher\u0026rsquo;s exact test. Deviation from Hardy-Weinberg Equilibrium (HWE) for MT1A SNPs was calculated using chi-squared test (\u003ca href=\"https://gene-calc.pl/hardy-weinberg-page\"\u003ehttps://gene-calc.pl/hardy-weinberg-page\u003c/a\u003e). Logistic regression analysis was performed to determine the possible association of MT1A SNPs with susceptibility to BC by calculating odds ratio (OR) and corresponding 95% confidence interval (95% CI), under the co-dominant, dominant and recessive inheritance models. The possible association between MT1A rs11640851, rs8052394 and rs11076161 SNPs and patientsʹ clinicopathological characteristics was estimated by ORs computed using logistic regression analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographics and clinical characteristics of all participants as well as pathologic features of BC patients are presented in Table 2. Student\u0026rsquo;s t-test was used to compare the mean age of two groups, which was found statistically significant (P \u0026lt; 0.05) (Table 2). The mean body mass index (BMI) was significantly different between the two groups, and patients with BC exhibited a higher BMI than healthy individuals (28.09 \u0026plusmn; 4.30 kg/m\u003csup\u003e2\u003c/sup\u003e versus 25.64 \u0026plusmn; 4.59 kg/m\u003csup\u003e2\u003c/sup\u003e) (P \u0026lt; 0.05) (Table 2). No significant difference was found between BC patients and healthy controls in terms of smoking status (P \u0026gt; 0.05) (Table 2). Furthermore 100% of BC patients were married at the time of diagnosis, and 49% of them had previously taken oral contraceptives. About 70% of women who were diagnosed with BC had no family history, and 98 % of them had unilateral tumor. With regard to histological type, 92% of BC patients were invasive ductal carcinoma (IDC),\u0026nbsp;and 8% were invasive lobular carcinoma (ILC). Among BC patients, 12% were grade I, 62% were grade II, and 26% were grade III. Ninety-one percent of patients\u0026nbsp;had a tumor size \u0026le; 2 cm\u0026nbsp;and 9% had a tumor size larger than 2 cm. Among cases, 93% were negative for the malignancy\u0026nbsp;while\u0026nbsp;7% had metastasis to one or more organs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of MT1A SNPs with BC risk under different inheritance models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genotype and allele frequencies of MT1A rs11640851, rs8052394 and rs11076161 SNPs\u0026nbsp;in cases and controls are presented in Table 3. Surprisingly, the CA and AA genotypes frequencies for rs11640851 SNP were significantly higher in healthy subjects compared to BC patients (P \u0026lt; 0.05) (Table 3). There was no statistically significant difference between two groups with regard to genotype distributions of rs8052394 and rs11076161 SNPs (P\u0026nbsp;\u0026gt; 0.05) (Table 3). Also, the possible association of all studied SNPs with the risk of developing BC was assessed using logistic regression analysis under the co-dominant, dominant and recessive inheritance models\u0026nbsp;(Table 3). \u0026nbsp;Under co-dominant inheritance model, the CA and AA genotypes of rs11640851 variant were significantly associated with decreased risk of developing BC (OR: 4.039; 95% CI:\u0026nbsp;1.913 - 8.529; P = 0.0002 and\u0026nbsp;OR: 10.94; 95% CI: 2.394 - 50.03; P\u0026nbsp;= 0.0002), respectively (Table 3). Under the dominant and recessive inheritance models, we also observed that rs11640851 SNP was significantly associated with 5.02 and\u0026nbsp;7.97-fold\u0026nbsp;reduced risk of BC, respectively (Table 3). No statistically significant difference was found in term of rs8052394 and rs11076161 SNPs between cases and controls in any inheritance models (P \u0026gt; 0.05) (Table 3). The allelic association revealed that the minor allele A of rs11640851 polymorphism was significantly associated with 4.81-fold reduced risk of BC (95% CI: 2.655 - 8.719; P = 0.0001) (Table 3). Risk allele frequencies were not significantly differed between the two groups for rs8052394 and rs11076161 SNPs (P \u0026gt; 0.05) (Table 3). Except rs11076161 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the genotype distributions of two other studied SNPs were in agreement with HWE in BC patients (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table 3). In healthy individuals, only rs11640851 was not in accordance with HWE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table 3).\u0026nbsp;Our findings revealed that the CA/AA, AA/AA and AA/AG combined genotypes of rs11640851 and rs8052394 SNPs had protective effect, reducing the risk of developing BC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table 4). The CA/AA, CA/AC and AA/AC combined genotypes of rs11640851 and rs11076161 SNPs was also determined to be associated with significant decreased risk of BC in our population (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table 4). For rs8052394 and rs11076161 SNPs, AG/AA combined genotype could be a risk factor for developing BC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of MT1A SNPs with patientsʹ clinicopathological parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used logistic regression analysis to identify the association between MT1A SNPs and clinicopathological characteristics of BC patients. The results showed that\u0026nbsp;here was no significant association between\u0026nbsp;genotype and allele frequencies of MT1A SNPs and clinicopathological features\u0026nbsp;in any inheritance models\u0026nbsp;(P \u0026gt; 0.05) (Tables 5, 6 and 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between the haplotypes of MT1A SNPs and BC risk\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePairwise LD between each pair of MT1A SNPs was computed to detect the association of the studied variants with susceptibility\u0026nbsp;to BC (Table 8). The LD values between all pairs of SNPs were plotted using SHEsis online server based on genotype distributions of MT1A SNPs in both groups (Figure 1). Low LD coefficient (Dʹ) and r\u003csup\u003e2\u003c/sup\u003e values in cases and healthy subjects demonstrated that rs11640851, rs8052394 and rs11076161 SNPs\u0026nbsp;were not in high linkage disequilibrium with each other (Table 8)\u0026nbsp;(Figure 1). Haplotype analysis of three SNPs showed that the CAC and CGA haplotypes for MT1A rs11640851, rs8052394 and rs11076161 variants\u0026nbsp;was significantly associated with 1.86 and 3.27-fold increased risk of BC, respectively\u0026nbsp;(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table 9). A significantly reduced risk of BC was found in individuals carrying the AAC and AGC haplotypes (OR: 0.392; 95% CI: 0.168 - 0.914; P\u0026nbsp;= 0.025 and OR: 0.009; 95% CI: 0.001 - 0.121; P\u0026nbsp;= 0.002), respectively (Table 9). We also observed no significant association\u0026nbsp;between patients and controls regarding the distribution of the CAA and CGC haplotypes (P \u0026gt; 0.05) (Table 9).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn silico analysis of MT1A rs11640851 and rs8052394 SNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effect of rs11640851 and rs8052394 SNPs on local RNA secondary structure were analyzed by RNAsnp online server. The RNAsnp prediction revealed that rs11640851 and rs8052394 SNPs could not change the mRNA secondary structure of MT1A (Figure 2). Moreover, rs11640851 and rs8052394 SNPs were analyzed for possible deleterious effects on metallothionein-1A protein function using Polyphen-2. The rs11640851 (Thr27Asn) SNP was predicted to be damaging, whereas rs8052394 (Lys51Arg) variant was predicted to be benign by PolyPhen-2 server (Figure 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the past decades, a large body of studies have provided convincing evidence that MTs play a fundamental role in tumorigenesis and progression (Cherian, Jayasurya, \u0026amp; Bay, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Eckschlager, Adam, Hrabeta, Figova, \u0026amp; Kizek, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). As already noted, MTs help maintain the cellular homeostasis of essential metals like Zn, which serve as key structural, catalytic, and regulatory constituent of a great number of proteins (Costa, Sarmento-Ribeiro, \u0026amp; Gon\u0026ccedil;alves, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This strongly suggests that they can promote cell growth through regulating Zn supply to proteins and modulating the activity of Zn-dependent enzymes and transcription factors (Krizkova et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, MTs can transfer Zn to p53, a zinc-binding transcription factor, to inhibit cell cycle progression and induce apoptosis in response to DNA damage (Krizkova et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Meanwhile, it has become increasingly clear that heavy metals, as carcinogens, can cause cancer through interfering with biological processes such as cell growth, proliferation, differentiation, survival and apoptosis (Parida \u0026amp; Patel, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). MTs can bind to a variety of xenobiotic heavy metals to provide cell protection against heavy metal toxicity (Dai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As free radical scavengers, they have antioxidant properties and protect cells from DNA damage induced by ROS (Giacconi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Biologically, high levels of ROS promote the growth, proliferation, invasion and metastasis of cancerous cells (Prasad, Gupta, \u0026amp; Tyagi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It has already been shown that MTs reach a maximum level in late G1 phase of the cell cycle and at the G1/S transition, suggesting their involvement in cell cycle control (Nagel \u0026amp; Vallee, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). MTs can also induce the expression of angiogenesis-related genes, including matrix metalloproteinase-2 (MMP-2), MMP-9, and vascular endothelial growth factor (VEGF) to create new blood vessels from pre-existing ones (Rodrigo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is well known that tumor growth, proliferation, progression, invasion, and metastatic dissemination are entirely contingent on the adequate blood supply to deliver the nutrients and oxygen (Saman, Raza, Uddin, \u0026amp; Rasul, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Taken together, these data highlight the key role of MTs in the pathogenesis of cancers.\u003c/p\u003e \u003cp\u003eThe expression of MTs in human tumors have been extensively investigated, but the results still remain controversial. MTs expressions are divergent depending on the type of tumor i.e., are upregulated in some human malignancies (Gomulkiewicz et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hengstler et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jayasurya et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Weinlich et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; W\u0026uuml;lfing et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), but downregulated in other cancers (Datta et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Surprisingly, very few studies have explored the association between MT gene polymorphisms and the risk of developing cancer, despite its importance (Forma et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rosa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, the aim of the present study was to evaluate the genotypic and allelic association of \u003cem\u003eMT1A\u003c/em\u003e rs11640851, rs8052394 and rs11076161 SNPs with the risk of developing BC in Iranian women under co-dominant, dominant, and recessive inheritance models. The combined effect of \u003cem\u003eMT1A\u003c/em\u003e SNPs on the BC risk was assessed using the haplotype analysis. The impact of \u003cem\u003eMT1A\u003c/em\u003e SNPs on RNA secondary structure and protein function was also determined using RNAsnp and Polyphen-2 servers, respectively. To the best of the author's knowledge, this is the first study to investigate the association of MT1A polymorphisms with the risk of BC. We found that rs11640851 was significantly associated with reduced risk of developing BC under co-dominant, dominant, and recessive inheritance models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, rs11640851 A allele showed a protective effect against breast carcinogenesis (OR: 4.812; 95% CI: 2.655\u0026ndash;8.719; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001). As far as we know, no study has been conducted to determine the association of rs11640851with the risk of developing cancer. Our results show for the first time that rs11640851 has a protective effect against the development of BC in our population. However, further studies should be designed to firmly validate these findings.\u003c/p\u003e \u003cp\u003eThere were no statistically significant differences between BC patients and healthy subjects regarding genotype and allele frequencies of \u003cem\u003eMT1A\u003c/em\u003e rs8052394 SNP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Our findings are consistent with a previous study indicating no significant associations between rs8052394 and oral squamous cell carcinoma (OSCC) risk (Rosa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the study conducted by Rosa \u003cem\u003eet al\u003c/em\u003e. on 28 patients with OSCC and 45 healthy subjects, they found that rs8052394 did not influence individual's susceptibility to OSCC in Brazilian population (Rosa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In 2013, Wong \u003cem\u003eet al.\u003c/em\u003e reported that rs8052394 was not involved with pathogenesis of hepatocellular carcinoma (HCC) (Wong et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). They found that individuals carrying the rs8052394 A allele had a higher risk of HCC than those carrying the G allele, and rs8052394 AA genotype was also significantly associated with 4.13-fold increased risk of HCC (Wong et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Conversly, another study from Taiwan demonstrated a statistically significant association between rs8052394 and increased risk of OSCC (Zavras, Yoon, Chen, Lin, \u0026amp; Yang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As regards rs11076161 SNP, no statistically significant association was detected under co-dominant, dominant and recessive inheritance models (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In line with our findings, Wong \u003cem\u003eet al.\u003c/em\u003e revealed that there is no evidence to support the association of rs11076161 with HCC risk (Wong et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, Zavras \u003cem\u003eet al.\u003c/em\u003e failed to find any association between rs11076161and predisposition to OSCC (Zavras et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast, Rosa \u003cem\u003eet al.\u003c/em\u003e reported that carriers of genotype AA had a 19-fold increased risk of OSCC (Rosa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to conflicting results, it is difficult to reach a consensus on the association of the rs8052394 and rs11076161 variants with the risk of developing cancer. Although the reason of these discrepancies remains an important unanswered question, it can be explained in part by ethnic/racial disparities, differences in sample size and multifactorial nature of cancer. Hence, further large-scale studies in diverse populations are needed to find conclusive evidence.\u003c/p\u003e \u003cp\u003eIn conclusion, these results provide the first evidence that \u003cem\u003ers11640851\u003c/em\u003epolymorphism is significantly associated with reduced risk of BC, suggesting its protective role in the development of BC. Also, two haplotypes CAC and CGA were identified as risk factors for BC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors would like to express their sincerest appreciation to all subjects for participating in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved the Institutional Review Board /Independent Ethics Committee (IRB/IEC) of the Shahid Ashrafi Esfahani University, Isfahan, Iran.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants after receiving an explanation of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author (S.Y).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There is no funding for this study to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM.Sh:\u0026nbsp;\u003c/strong\u003eProviding the data and design, Manuscript writing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.Y.:\u0026nbsp;\u003c/strong\u003eProviding the data and design\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZ.Z:\u0026nbsp;\u003c/strong\u003eConception, Providing the data and design\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM.A:\u0026nbsp;\u003c/strong\u003eConception, Providing the data and design\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS.Y.:\u0026nbsp;\u003c/strong\u003eConception, Providing the data and design, and the final approval of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAsadi, S., Abkar, M., Zamanzadeh, Z., Taghipour Kamalabad, S., Sedghi, M., \u0026amp; Yousefnia, S. (2023). Association of SOD2 rs2758339, rs5746136 and rs2842980 polymorphisms with increased risk of breast cancer: a haplotype-based case\u0026ndash;control study. \u003cem\u003eGenes \u0026amp; Genomics\u003c/em\u003e, 1-14.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCherian, M. G., Jayasurya, A., \u0026amp; Bay, B.-H. (2003). Metallothioneins in human tumors and potential roles in carcinogenesis. \u003cem\u003eMutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 533\u003c/em\u003e(1-2), 201-209.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCosta, M. I., Sarmento-Ribeiro, A. B., \u0026amp; Gon\u0026ccedil;alves, A. C. (2023). Zinc: From Biological Functions to Therapeutic Potential. \u003cem\u003eInternational Journal of Molecular Sciences, 24\u003c/em\u003e(5), 4822.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDai, H., Wang, L., Li, L., Huang, Z., \u0026amp; Ye, L. (2021). Metallothionein 1: A new spotlight on inflammatory diseases. \u003cem\u003eFrontiers in Immunology, 12\u003c/em\u003e, 739918.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDatta, J., Majumder, S., Kutay, H., Motiwala, T., Frankel, W., Costa, R., . . . Ghoshal, K. (2007). Metallothionein expression is suppressed in primary human hepatocellular carcinomas and is mediated through inactivation of CCAAT/enhancer binding protein \u0026alpha; by phosphatidylinositol 3-kinase signaling cascade. \u003cem\u003eCancer research, 67\u003c/em\u003e(6), 2736-2746.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEckschlager, T., Adam, V., Hrabeta, J., Figova, K., \u0026amp; Kizek, R. (2009). Metallothioneins and cancer. \u003cem\u003eCurrent Protein and Peptide Science, 10\u003c/em\u003e(4), 360-375.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFeng, Y., Spezia, M., Huang, S., Yuan, C., Zeng, Z., Zhang, L., . . . Luo, W. (2018). Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. \u003cem\u003eGenes \u0026amp; diseases, 5\u003c/em\u003e(2), 77-106.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eForma, E., Krzeslak, A., Wilkosz, J., Jozwiak, P., Szymczyk, A., Rozanski, W., \u0026amp; Brys, M. (2012). Metallothionein 2A genetic polymorphisms and risk of prostate cancer in a Polish population. \u003cem\u003eCancer Genetics, 205\u003c/em\u003e(9), 432-435.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGiacconi, R., Cai, L., Costarelli, L., Cardelli, M., Malavolta, M., Piacenza, F., \u0026amp; Provinciali, M. (2017). Implications of impaired zinc homeostasis in diabetic cardiomyopathy and nephropathy. \u003cem\u003eBioFactors, 43\u003c/em\u003e(6), 770-784.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGomulkiewicz, A., Podhorska-Okolow, M., Szulc, R., Smorag, Z., Wojnar, A., Zabel, M., \u0026amp; Dziegiel, P. (2010). Correlation between metallothionein (MT) expression and selected prognostic factors in ductal breast cancers. \u003cem\u003eFolia Histochemica et Cytobiologica, 48\u003c/em\u003e(2), 242-248.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHan, Y. C., Zheng, Z. L., Zuo, Z. H., Yu, Y. P., Chen, R., Tseng, G. C., . . . Luo, J. H. (2013). Metallothionein 1 h tumour suppressor activity in prostate cancer is mediated by euchromatin methyltransferase 1. \u003cem\u003eThe Journal of pathology, 230\u003c/em\u003e(2), 184-193.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHengstler, J., Pilch, H., Schmidt, M., Dahlenburg, H., Sagem\u0026uuml;ller, J., Schiffer, I., . . . Tanner, B. (2001). Metallothionein expression in ovarian cancer in relation to histopathological parameters and molecular markers of prognosis. \u003cem\u003eInternational journal of cancer, 95\u003c/em\u003e(2), 121-127.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJaman, M. S., Al Mamun, A., Sharker, B., Maniruzzaman, M., Rana, M. R. R., Hasan, M. N., \u0026amp; Bhuiyan, M. R. H. (2023). Curcumin, Diallyl Sulphide, Quercetin and Gallic Acid Uses as Anticancer and Therapeutic Agents for Breast Cancer: Current Strategies and Future Perspectives. \u003cem\u003eEuropean Journal of Medical and Health Sciences, 5\u003c/em\u003e(3), 32-48.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJayasurya, A., Bay, B., Yap, W., Tan, N., \u0026amp; Tan, B. (2000). Proliferative potential in nasopharyngeal carcinoma: correlations with metallothionein expression and tissue zinc levels. \u003cem\u003eCarcinogenesis, 21\u003c/em\u003e(10), 1809-1812.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJu\u0026aacute;rez-Rebollar, D., Rios, C., Nava-Ru\u0026iacute;z, C., \u0026amp; M\u0026eacute;ndez-Armenta, M. (2017). Metallothionein in brain disorders. \u003cem\u003eOxidative medicine and cellular longevity, 2017\u003c/em\u003e.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKrezel, A., \u0026amp; Maret, W. (2021). The bioinorganic chemistry of mammalian metallothioneins. \u003cem\u003eChemical Reviews, 121\u003c/em\u003e(23), 14594-14648.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKrizkova, S., Ryvolova, M., Hrabeta, J., Adam, V., Stiborova, M., Eckschlager, T., \u0026amp; Kizek, R. (2012). Metallothioneins and zinc in cancer diagnosis and therapy. \u003cem\u003eDrug Metabolism Reviews, 44\u003c/em\u003e(4), 287-301.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKrześlak, A., Forma, E., J\u0026oacute;źwiak, P., Szymczyk, A., Smolarz, B., Romanowicz-Makowska, H., . . . Bryś, M. (2014). Metallothionein 2A genetic polymorphisms and risk of ductal breast cancer. \u003cem\u003eClinical and experimental medicine, 14\u003c/em\u003e, 107-113.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNagel, W. W., \u0026amp; Vallee, B. L. (1995). Cell cycle regulation of metallothionein in human colonic cancer cells. \u003cem\u003eProceedings of the National Academy of Sciences, 92\u003c/em\u003e(2), 579-583.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eParida, L., \u0026amp; Patel, T. N. (2023). Systemic impact of heavy metals and their role in cancer development: a review. \u003cem\u003eEnvironmental Monitoring and Assessment, 195\u003c/em\u003e(6), 766.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePark, M., Kim, D., Ko, S., Kim, A., Mo, K., \u0026amp; Yoon, H. (2022). Breast cancer metastasis: Mechanisms and therapeutic implications. \u003cem\u003eInternational Journal of Molecular Sciences, 23\u003c/em\u003e(12), 6806.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePrasad, S., Gupta, S. C., \u0026amp; Tyagi, A. K. (2017). Reactive oxygen species (ROS) and cancer: Role of antioxidative nutraceuticals. \u003cem\u003eCancer letters, 387\u003c/em\u003e, 95-105.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRaudenska, M., Gumulec, J., Podlaha, O., Sztalmachova, M., Babula, P., Eckschlager, T., . . . Masarik, M. (2014). Metallothionein polymorphisms in pathological processes. \u003cem\u003eMetallomics, 6\u003c/em\u003e(1), 55-68.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRodrigo, M. A. M., Jimemez, A. M. J., Haddad, Y., Bodoor, K., Adam, P., Krizkova, S., . . . Adam, V. (2020). Metallothionein isoforms as double agents\u0026ndash;their roles in carcinogenesis, cancer progression and chemoresistance. \u003cem\u003eDrug Resistance Updates, 52\u003c/em\u003e, 100691.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRosa, R. R., Garcia, M. A. J., Alves, P. T., Sousa, E. M., Pimentel, L. S., de Paula Barbosa, L., . . . Cardoso, S. V. (2021). Revisiting the metallothionein genes polymorphisms and the risk of oral squamous cell carcinoma in a Brazilian population. \u003cem\u003eMedicina oral, patologia oral y cirugia bucal, 26\u003c/em\u003e(3), e334.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSaman, H., Raza, S. S., Uddin, S., \u0026amp; Rasul, K. (2020). Inducing angiogenesis, a key step in cancer vascularization, and treatment approaches. \u003cem\u003eCancers, 12\u003c/em\u003e(5), 1172.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSi, M., \u0026amp; Lang, J. (2018). The roles of metallothioneins in carcinogenesis. \u003cem\u003eJournal of hematology \u0026amp; oncology, 11\u003c/em\u003e(1), 1-20.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSiegel, R. L., Miller, K. D., Wagle, N. S., \u0026amp; Jemal, A. (2023). Cancer statistics, 2023. \u003cem\u003eCa Cancer J Clin, 73\u003c/em\u003e(1), 17-48.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSingh, R., \u0026amp; kumar Sain, M. N. (2023). Etiology Of Breast Cancer. \u003cem\u003eJournal of Pharmaceutical Negative Results\u003c/em\u003e, 1427-1434.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSwaminathan, H., Saravanamurali, K., \u0026amp; Yadav, S. A. (2023). Extensive review on breast cancer its etiology, progression, prognostic markers, and treatment. \u003cem\u003eMedical Oncology, 40\u003c/em\u003e(8), 238.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVa\u0026scaron;\u0026aacute;k, M., \u0026amp; Meloni, G. (2017). Mammalian metallothionein-3: New functional and structural insights. \u003cem\u003eInternational Journal of Molecular Sciences, 18\u003c/em\u003e(6), 1117.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWeinlich, G., Eisendle, K., Hassler, E., Baltaci, M., Fritsch, P., \u0026amp; Zelger, B. (2006). Metallothionein\u0026ndash;overexpression as a highly significant prognostic factor in melanoma: a prospective study on 1270 patients. \u003cem\u003eBritish journal of cancer, 94\u003c/em\u003e(6), 835-841.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWong, R.-H., Huang, C.-H., Yeh, C.-B., Lee, H.-S., Chien, M.-H., \u0026amp; Yang, S.-F. (2013). Effects of metallothionein-1 genetic polymorphism and cigarette smoking on the development of hepatocellular carcinoma. \u003cem\u003eAnnals of surgical oncology, 20\u003c/em\u003e, 2088-2095.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eW\u0026uuml;lfing, C., van Ahlen, H., Eltze, E., Piechota, H., Hertle, L., \u0026amp; Schmid, K.-W. (2007). Metallothionein in bladder cancer: correlation of overexpression with poor outcome after chemotherapy. \u003cem\u003eWorld journal of urology, 25\u003c/em\u003e, 199-205.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYousefnia, S. (2014). Association between genetic polymorphism of catalase (CAT) C-262T, Cu/Zn superoxide dismutase (SOD1) A251G and risk of age-related macular degeneration. \u003cem\u003eMolecular and Biochemical Diagnosis Journal, 1\u003c/em\u003e(2), 77-88.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZavras, A., Yoon, A., Chen, M., Lin, C., \u0026amp; Yang, S. (2011). Metallothionein-1 genotypes in the risk of oral squamous cell carcinoma. \u003cem\u003eAnnals of surgical oncology, 18\u003c/em\u003e, 1478-1483. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u0026nbsp; \u0026nbsp;List of primers and restriction enzymes used in PCR-RFLP and ARMS-PCR assays for genotyping of the\u0026nbsp;\u003cem\u003eMT1A\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ers11640851, rs8052394 and rs11076161\u003cem\u003e\u0026nbsp;\u003c/em\u003epolymorphisms\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"863\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.268518518518519%\" valign=\"top\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.83796296296296%\" valign=\"top\"\u003e\n \u003cp\u003ePrimer sequence\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.310185185185185%\" valign=\"top\"\u003e\n \u003cp\u003eAnnealing temperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eAmplicon size (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eRestriction enzyme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eFragment length (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.268518518518519%\" valign=\"top\"\u003e\n \u003cp\u003ers11640851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.83796296296296%\" valign=\"top\"\u003e\n \u003cp\u003eF 5ʹ-ACTTGGCTCAGCCCCAGATT-3ʹ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.310185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMnlI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eC allele: 140, 47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.268518518518519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.83796296296296%\" valign=\"top\"\u003e\n \u003cp\u003eR 5ʹ-CACTCAGCTGGCAGCATTTG-3ʹ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.310185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eA allele: 187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.268518518518519%\" valign=\"top\"\u003e\n \u003cp\u003ers8052394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.83796296296296%\" valign=\"top\"\u003e\n \u003cp\u003eF 5ʹ-ACTAAGTGTCCTCTGGGGCTG-3ʹ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.310185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePstI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eA allele: 283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.268518518518519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.83796296296296%\" valign=\"top\"\u003e\n \u003cp\u003eR 5ʹ-AATGGGTCACGGTTGTATGG-3ʹ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.310185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eG allele: 165, 118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.268518518518519%\" valign=\"top\"\u003e\n \u003cp\u003ers11076161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.83796296296296%\" valign=\"top\"\u003e\n \u003cp\u003eF 5ʹ-CTGCTGTTATCTTCTGTATAAAA-3ʹ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.310185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eA allele: 173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.268518518518519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.83796296296296%\" valign=\"top\"\u003e\n \u003cp\u003eF 5ʹ-CTGCTGTTATCTTCTGTATAAAG-3ʹ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.310185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eC allele: 173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.268518518518519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.83796296296296%\" valign=\"top\"\u003e\n \u003cp\u003eR 5ʹ-GTTTCCTGCCTCTGACTTG-3ʹ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.310185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; ARMS-PCR, Amplification Refractory Mutation System PCR; MT1A, metallothionein 1A; SNP, single nucleotide polymorphism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Demographic and clinicopathological characteristics of breast cancer patients and healthy controls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eCases (n=100)\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eControls (n=100)\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years \u0026nbsp;(mean \u0026plusmn; SD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e51.56 \u0026plusmn; 11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e39.01 \u0026plusmn; 10.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e29 - 80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e17 - 76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026le; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eWeight, kg (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e72.71 \u0026plusmn; 11.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e67.71 \u0026plusmn; 11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eHeight, cm (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e161.13 \u0026plusmn; 6.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e162.71 \u0026plusmn; 5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e28.09 \u0026plusmn; 4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e25.64 \u0026plusmn; 4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Underweight ( \u0026lt; 18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Normal (18.5 - 24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Overweight (25 - 29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Obese ( \u0026ge; 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eHistory of taking the oral contraceptive pills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history of breast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eLaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Unilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Bilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;IDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;ILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eHistological grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eTumor size, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026le; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eOther organ metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e SD, standard deviation; BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma.\u003c/p\u003e\n\u003cp\u003eValues are presented as mean \u0026plusmn; SD for age, weight, height and BMI, and as % for other variables.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values were calculated using t-test for continuous variables and Chi-square test for categorical variables.\u003c/p\u003e\n\u003cp\u003eBold values indicate statistically significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Genotype and allele distribution of \u003cem\u003eMT1A\u0026nbsp;\u003c/em\u003ers11640851, rs8052394 and rs11076161\u003cem\u003e\u0026nbsp;\u003c/em\u003epolymorphisms in breast cancer patients and healthy controls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eGenotypes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003eCases (n=100)\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003eControls (n=100)\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003ers11640851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e4.039 (1.913 - 8.529)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e10.94 (2.394 - 50.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dominant (CC vs. CA+AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e5.026 (2.524 - 10.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Recessive (CC+CA vs. AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e7.976 (1.762 - 36.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; A allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e29.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e4.812 (2.655 - 8.719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; HWE \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003ers8052394\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e0.898 (0.473 - 1.705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dominant (AA vs. AG+GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e0.898 (0.473 - 1.705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Recessive (AA+AG vs. GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; G allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e0.918 (0.408 - 2.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; HWE \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003ers11076161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e1.062 (0.602 - 1.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e3.797 (0.751 - 19.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dominant (AA vs. AC+CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e1.173 (0.673 - 2.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Recessive (AA+AC vs. CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e3.688 (0.746 - 18.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; C allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e25.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e1.252 (0.807 - 1.941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; HWE \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e OR, odds ratio; CI, confidence interval; HWE, Hardy-Weinberg equilibrium. ND, not determined.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value were calculated by Chi-square test.\u003c/p\u003e\n\u003cp\u003eBold values indicate statistically significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Distribution of \u003cem\u003eMT1A\u0026nbsp;\u003c/em\u003ers11640851, rs8052394 and rs11076161\u003cem\u003e\u0026nbsp;\u003c/em\u003epolymorphisms combined genotypes in breast cancer patients and healthy controls\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eCombined genotypes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eCases (n=100)\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eControls (n=100)\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003ers11640851/rs8052394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CC/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CC/AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.727 (0.320 - 1.650)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CC/GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e4.000 (1.633 - 9.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CA/AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e3.272 (0.948 - 11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CA/GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e7.272 (1.519 - 34.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003ers11640851/rs11076161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CC/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CC/AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.597 (0.286 - 1.245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CC/CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e3.455 (0.632 - 18.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e3.455 (0.999 - 11.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CA/AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e3.628 (1.437 - 9.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CA/CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e6.220 (1.262 - 30.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003ers8052394/rs11076161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.636 (0.328 - 1.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AA/CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e4.166 (0.464 - 37.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AG/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.260 (0.086 - 0.782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AG/AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.574 (0.624 - 3.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; AG/CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.666 (0.145 - 19.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; GG/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; GG/AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; GG/CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation:\u0026nbsp;OR, odds ratio; CI, confidence interval; ND, not determined.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value calculated by Chi-square test.\u003c/p\u003e\n\u003cp\u003eBold values indicate statistically significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e Association of \u003cem\u003eMT1A\u0026nbsp;\u003c/em\u003ers11640851polymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"858\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;\u0026nbsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e13 (81.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e73 (86.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e3 (18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e9 (10.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.534 (0.127 - 2.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (CC vs. CA+AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.653 (0.160 - 2.664)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (CC+CA vs. AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eA allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e3 (9.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e13 (7.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.810 (0.217 - 3.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;\u0026nbsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e18 (81.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e68 (87.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e4 (18.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e8 (10.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.529 (0.143 - 1.957)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (CC vs. CA+AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.661 (0.185 - 2.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (CC+CA vs. AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eA allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e4 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e12 (7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.833 (0.254 - 2.724)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history of breast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e28 (93.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e58 (82.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e1 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e11 (15.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e5.310 (0.652 - 43.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e1 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.482 (0.029 - 8.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (CC vs. CA+AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.896 (0.606 - 13.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (CC+CA vs. AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.420 (0.025 - 6.850)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eA allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e3 (5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;13 (9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.944 (0.533 - 7.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eIDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e80 (86.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e6 (75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e10 (10.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e2 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.666 (0.472 - 15.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (CC vs. CA+AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.222 (0.401 - 12.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (CC+CA vs. AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eA allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e14 (7.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e2 (12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.734 (0.357 - 8.410)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eHistological grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eGrade I and II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eGrade III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e66 (89.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e20 (76.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e7 (9.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e5 (19.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.357 (0.674 - 8.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e1 (3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e3.300 (0.197 - 55.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (CC vs. CA+AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.475 (0.767 - 7.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (CC+CA vs. AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.920 (0.176 - 48.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eA allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e9 (6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e7 (13.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.402 (0.846 - 6.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eTumor size, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e77 (84.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e9 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e12 (13.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (CC vs. CA+AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (CC+CA vs. AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eA allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;16 (8.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eOther organ metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e6 (85.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e80 (86.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e1 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e11 (11.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.825 (0.090 - 7.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (CC vs. CA+AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.975 (0.108 - 8.770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (CC+CA vs. AA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eA allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1 (7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e15 (8.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.140 (0.139 - 9.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; ND, not determined.\u003c/p\u003e\n\u003cp\u003eValues are presented as number (%).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values were calculated using Chi-square test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u003c/strong\u003e Association of \u003cem\u003eMT1A\u0026nbsp;\u003c/em\u003ers8052394 polymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"858\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;\u0026nbsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e14 (87.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e60 (71.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e24 (28.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.800 (0.591 - 13.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AG+GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.800 (0.591 - 13.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AG vs. GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eG allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e24 (14.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.500 (0.560 - 11.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;\u0026nbsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e18 (81.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e56 (71.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e4 (18.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e22 (28.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.767 (0.537 - 5.813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AG+GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.767 (0.537 - 5.813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AG vs. GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eG allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e4 (9.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e22 (14.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.641 (0.534 - 5.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history of breast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e21 (70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e53 (75.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e9 (30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e17 (24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.748 (0.288 - 1.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AG+GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.748 (0.288 - 1.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AG vs. GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eG allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e9 (15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e17 (12.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.783 (0.327 - 1.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eIDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e68 (73.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e6 (75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e24 (26.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e2 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.944 (0.178 - 5.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AG+GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.944 (0.178 - 5.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AG vs. GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eG allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e24 (13.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e2 (12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.952 (0.203 - 4.453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eHistological grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eGrade I and II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eGrade III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e56 (75.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e18 (69.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e18 (24.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e8 (30.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.382 (0.514 - 3.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AG+GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.382 (0.514 - 3.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AG vs. GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eG allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e18 (12.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e8 (15.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.313 (0.533 - 3.230)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eTumor size, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e68 (74.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e6 (66.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e23 (25.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e3 (33.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.478 (0.341 - 6.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AG+GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.478 (0.341 - 6.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AG vs. GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eG allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e23 (12.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e3 (16.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.382 (0.371 - 5.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eOther organ metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e6 (85.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e68 (73.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e1 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e25 (26.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.205 (0.252 - 19.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AG+GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.205 (0.252 - 19.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AG vs. GG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eG allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e1 (12.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e25 (13.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e2.018 (0.252 - 16.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; ND, not determined.\u003c/p\u003e\n\u003cp\u003eValues are presented as number (%).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values were calculated using Chi-square test. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7:\u003c/strong\u003e Association of \u003cem\u003eMT1A\u0026nbsp;\u003c/em\u003ers11076161\u003cem\u003e\u0026nbsp;\u003c/em\u003epolymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"858\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;\u0026nbsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e8 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e43 (51.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e8 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e39 (46.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.907 (0.310 - 2.648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AC+CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.953 (0.327 - 2.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AC vs. CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eC allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;8 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e43 (25.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.032 (0.431 - 2.468)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;\u0026nbsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e9 (40.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e42 (53.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e11 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e36 (46.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.701 (0.261 - 1.881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (9.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AC+CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.593 (0.227 - 1.548)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AC vs. CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eC allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e15 (34.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e36 (23.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.580 (0.280 - 1.198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history of breast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e16 (53.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e35 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e13 (43.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e34 (48.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.195 (0.500 - 2.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e1 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.457 (0.026 - 7.779)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AC+CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.142 (0.485 - 2.692)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AC vs. CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.420 (0.025 - 6.950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eC allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e15 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e36 (25.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.038 (0.517 - 2.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eIDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e48 (52.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e3 (37.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e42 (45.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e5 (62.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.904 (0.429 - 8.452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AC+CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.818 (0.410 - 8.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AC vs. CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eC allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e46 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e5 (31.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.363 (0.450 - 4.131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eHistological grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eGrade I and II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eGrade III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e39 (52.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e12 (46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e33 (44.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e14 (53.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.378 (0.560 - 3.390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AC+CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.300 (0.530 - 3.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AC vs. CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eC allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e37 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e14 (26.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.105 (0.539 - 2.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eTumor size, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e47 (51.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e4 (44.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e42 (46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e5 (55.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.398 (0.352 - 5.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AC+CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.335 (0.336 - 5.294)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AC vs. CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eC allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e46 (25.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e5 (27.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.137 (0.384 - 3.362)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eOther organ metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e4 (57.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e47 (50.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e2 (28.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e45 (48.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.914 (0.334 - 10.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e1 (14.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.085 (0.004 - 1.632)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eDominant (AA vs. AC+CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e1.305 (0.276 - 6.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eRecessive (AA+AC vs. CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.065 (0.003 - 1.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" valign=\"top\"\u003e\n \u003cp\u003eC allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e4 (28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e47 (25.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.097902097902098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\"\u003e\n \u003cp\u003e0.843 (0.253 - 2.822)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\" valign=\"top\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; ND, not determined.\u003c/p\u003e\n\u003cp\u003eValues are presented as number (%).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values were calculated using Chi-square test. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8:\u003c/strong\u003e Pairwise LD of \u003cem\u003eMT1A\u0026nbsp;\u003c/em\u003ers11640851, rs8052394 and rs11076161\u003cem\u003e\u0026nbsp;\u003c/em\u003epolymorphisms\u003cem\u003e\u0026nbsp;\u003c/em\u003ein breast cancer patients and healthy controls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.61538461538461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.769230769230769%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.884615384615383%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.61538461538461%\" valign=\"top\"\u003e\n \u003cp\u003eLocus pair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eD\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003er \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.769230769230769%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eD\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003er \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.61538461538461%\" valign=\"top\"\u003e\n \u003cp\u003ers11640851\u003cstrong\u003e/\u003c/strong\u003ers8052394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.769230769230769%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.61538461538461%\" valign=\"top\"\u003e\n \u003cp\u003ers11640851\u003cstrong\u003e/\u003c/strong\u003ers11076161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.769230769230769%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.61538461538461%\" valign=\"top\"\u003e\n \u003cp\u003ers8052394\u003cstrong\u003e/\u003c/strong\u003ers11076161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.769230769230769%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e LD, linkage disequilibrium.\u003c/p\u003e\n\u003cp\u003er\u003csup\u003e2\u003c/sup\u003e reflects statistical power to detect LD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9:\u003c/strong\u003e Haplotype distribution of \u003cem\u003eMT1A\u0026nbsp;\u003c/em\u003ers11640851, rs8052394 and rs11076161\u003cem\u003e\u0026nbsp;\u003c/em\u003epolymorphisms in breast cancer patients and healthy controls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.26573426573427%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ehaplotype\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.293706293706293%\" rowspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.377622377622377%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFrequency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.993006993006993%\" rowspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.27972027972028%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.79020979020979%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.575268817204302%\" valign=\"top\"\u003e\n \u003cp\u003ers1164085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.03763440860215%\" valign=\"top\"\u003e\n \u003cp\u003ers8052394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"top\"\u003e\n \u003cp\u003ers11076161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.096774193548388%\" valign=\"top\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.575268817204302%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.03763440860215%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.096774193548388%\" valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e0.392 (0.168 - 0.914)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.575268817204302%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.03763440860215%\" valign=\"top\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.096774193548388%\" valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e0.009 (0.001 - 0.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.575268817204302%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.03763440860215%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.096774193548388%\" valign=\"top\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e1.445 (0.970 - 2.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.575268817204302%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.03763440860215%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.096774193548388%\" valign=\"top\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e1.865 (1.070 - 3.249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.575268817204302%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.03763440860215%\" valign=\"top\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.096774193548388%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e3.278 (1.344 - 7.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.771561771561771%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305361305361306%\" valign=\"top\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.188811188811188%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.293706293706293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.888111888111888%\" valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.48951048951049%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.993006993006993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.27972027972028%\" valign=\"top\"\u003e\n \u003cp\u003e0.503 (0.140 - 1.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.79020979020979%\" valign=\"top\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e SNPs, single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value were calculated using Pearson\u0026apos;s chi-square test.\u003c/p\u003e\n\u003cp\u003eBold values indicate statistically significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rs11640851, rs8052394, rs11076161, Polymorphism, MT1A, Breast cancer","lastPublishedDoi":"10.21203/rs.3.rs-3867462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3867462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetallothionein 1A (MT1a) is involved in many pathological conditions associated with antioxidant defense and detoxification, including cancer. The aim of this study was to investigate the possible association of \u003cem\u003eMT1A\u003c/em\u003e rs11640851, rs8052394, and rs11076161 single nucleotide polymorphisms (SNPs) with the risk of breast cancer (BC) and clinicopathological features.\u003c/p\u003e \u003cp\u003eThe study included 100 patients with BC and 100 healthy controls. We genotyped the \u003cem\u003eMT1A\u003c/em\u003e SNPs using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and amplification refractory mutation system PCR (ARMS-PCR) techniques. The genotypic and allelic associations of \u003cem\u003eMT1A\u003c/em\u003e SNPs with susceptibility to BC were assessed using logistic regression analysis under co-dominant, dominant, and recessive inheritance models. The combined effect of \u003cem\u003eMT1A\u003c/em\u003e SNPs on the BC risk was determined using the haplotype analysis. In silico analysis was performed using the Polyphen-2 and RNAsnp online servers.\u003c/p\u003e \u003cp\u003eThe rs11640851 was found to be associated with a reduced risk of BC in all three inheritance models (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We also found that rs11640851 A allele has a protective effect against BC (OR: 4.812; 95% CI: 2.655\u0026ndash;8.719; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). For rs8052394/rs11076161, carriers of AG/AA combined genotype had a 3.84-fold increased risk for developing BC. The haplotypes CAC and CGA were significantly associated with BC susceptibility. The rs11640851 was predicted to be deleterious using Polyphen-2 server.\u003c/p\u003e \u003cp\u003eThis study provides the first evidence that rs11640851 is significantly associated with a reduced risk of BC suggesting its protective role in the development of BC. Two haplotypes CAC and CGA were also identified as risk factors for BC.\u003c/p\u003e","manuscriptTitle":"Association of MT1A rs11640851, rs8052394 and rs11076161 polymorphisms with the risk of developing breast cancer: a haplotype-based case-control study and in silico analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-17 16:23:27","doi":"10.21203/rs.3.rs-3867462/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f3c7df6-51da-4097-a9bc-6c0478fd96c2","owner":[],"postedDate":"January 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-18T12:34:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-17 16:23:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3867462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3867462","identity":"rs-3867462","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}