单核苷酸多态性与广州汉族人群前列腺癌的相关性研究
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摘要
研究背景：
前列腺癌是影响男性健康的最常见恶性肿瘤之一,多发生于60岁以上的老年男性,2002年全球新发病例为679000例,居于男性肿瘤的第二位。前列腺癌的发病率因地域及种族的差异而不同。前列腺癌发病率最高的地区是北美和斯堪的纳维亚半岛,而大部分亚洲国家为低发病率地区。美国黑人前列腺癌的发病率最高,达到185.7/10万,美国高加索人(白种人)107.8/10万,其它西方发达国家的发病率为50～103/10万。在欧洲国家,每年大约有190000新发病例,并且大约有80000例患者死于前列腺癌。亚洲国家中,中国和日本发病率低于其他国家,日本发病率为9/10万,中国发病率为2.3/10万(上海地区为7.7/10万)。无论是前列腺癌的高发地区还是低发地区,从1973年到1992年前列腺癌的发病人数都在逐年增加。因此研究前列腺癌发生发展的相关因素以及早期预防前列腺癌的发生是当务之急。
目前对前列腺癌的病因尚不完全清楚,年龄、种族、家族史是目前已知的危险因素。不同种族和地区间发病率的差异可能与遗传背景、环境及生活方式等多方面因素有关。有研究已证实遗传易感和环境因素都会对前列腺癌的发生造成影响,其中遗传易感的重要物质基础之一就是单核苷酸点突变所造成的单核苷酸多态性。
单核苷酸多态性(Single Nucleotide Polymorphisms, SNPs)是在基因组水平由单个核苷酸的变异所引起的DNA序列多态性。其中发生率小于1%的变异称为突变,大于1%的则称为单核苷酸多态性。SNPs在人类基因组中广泛存在,是人类可遗传的变异中最常见的一种,占所有已知多态性的90%以上。人类基因组中大概每1000个碱基就有一个SNP, SNPs总量大约为3×106个。
由于SNPs仅涉及单个碱基的变异,故可以由单个碱基的转换(transition)或颠换(transversion)所引起,也可由碱基的插入或缺失造成。通常所说的SNPs是指转换和颠换。大部分SNPs是无意义的,不影响蛋白功能或者基因表达(如非编码区SNPs)。
一般根据SNPs在基因组中的位置以及是否引起基因编码的改变,可将SNPs分为几类：约95%的SNPs位于非编码区,其中一小部分位于基因调控区,称为基因调控区SNPs (regulatory SNPs, rSNPs)；而位于基因编码区的SNPs称为编码SNPs (coding SNPs, cSNPs)。若cSNPs不改变所编码的氨基酸序列则称为同义SNPs (synonymous SNPs, sSNPs),导致氨基酸序列改变则称为非同义SNPs或错义SNPs (non-synonymous SNPs, nsSNPs)。
SNPs能通过多种途径影响基因的功能,有一部分已被证明SNPs和前列腺癌发生、发展及预后密切相关。由于SNPs在人群中分布广泛,并且这种遗传标记相对稳定,能从个体出生到死亡一直存在,使遗传分析能在发病前几十年进行开展。因此,SNPs不但能提供和证明肿瘤发生的遗传性病因,也为常见肿瘤普查提供了一种有效的检测手段。
SNPs作为第三代遗传标记,只有两种等位型,变异程度不如微卫星,但由于在基因组中总数量巨大,分布频密,具有较微卫星更稳定的遗传特性。在基因组筛查SNPs过程中一般只需阳性或阴性(+/-)分析,故易于分型检测并实现分析的自动化。相比第一、二代遗传标记更有适合对复杂疾病遗传的解析和基于群体基因识别等方面的研究,并已取代微卫星标记技术进入基因研究应用的领域。采用SNPs进行分析有助于解释不同个体之间的表型差异、不同群体和个体对疾病,特别是对复杂疾病易感性、药物耐受性以及对环境因素的反应差异,目前已广泛应用于分子诊断、临床检验、法医学、病原检测、遗传疾病和新药研发等方面。
常见的SNPs的分析方法分为三类,第一类为基于遗传流行病学的关联分析,是通过研究SNPs在疾病易感性、药物反应性及其他相关遗传表型差异的方法；第二类为细胞和生化水平的分析,通过分析酶活力、细胞信号通路等方面来阐述SNPs对基因功能方面的影响；第三类为基于SNPs影响基因表达的分子机制研究,通过对SNPs在基因转录、翻译及蛋白表达的作用进行分析,探讨SNPs影响基因功能的机制。
2008年Zheng SL等在新英格兰医学杂志上发表的文章中提到,在五个前列腺癌高风险位点中(三个位于8q24,另两个分别位于17q12和17q24.3)各选择一个危险比最高的SNP进行联合分析,这五个SNPs各自的前列腺癌患病风险比值比(OR)在1.22到1.53之间,然而当基因型同时具有四至五个SNPs时OR值将达到4.47；若将前列腺癌家族史也当做一项危险因素时(单独OR值为2.22),则具备六项危险因素中的五项甚至全部时,其OR值高达9.46(P=1.29×10-8)。该研究结果表明,多个SNPs联合分析有助于提高人群前列腺癌患病风险的预测效力。
日本冈山大学研究小组通过筛选癌症相关基因中的48个错义SNPs(非同义SNPs),发现其10个基因中共计12个SNPs对前列腺癌发病有重要影响。这10个基因包括5个肿瘤抑制基因、2个DNA修复基因、1个代谢酶基因、1个染色体分离相关基因和1个凋亡调节基因,其中9个SNPs与肿瘤的相关性是首次被发现。这12个SNPs在预测前列腺癌患病风险方面同样具有累积效应,它们确定的最高风险组与低风险组人群相比其OR值高达47.4。最高风险组在今后30年间前列腺癌患病的风险是29%,而低风险组为0.6%,其中最低风险组仅约0.2%。因此,通过整合多个肿瘤相关SNPs进行肿瘤患病风险遗传学评估,进一步提高前列腺癌高风险人群的筛查准确性和效率,并有利于肿瘤的预防和早期诊断。
由于地理位置和人种起源相近,中国人群与日本人群在遗传背景上具有很大的相似性。在日本人群大规模筛选获得的前列腺癌风险SNPs也有可能部分适用于中国人群,为了验证这一假设,并进一步将在日本人群中获得的研究成果拓展到中国及亚洲其它国家,中国、日本、韩国和新加坡四国共同发起了一项国际多中心研究,旨在检验上述前列腺癌风险SNPs在亚洲其他人群中的效力。本文中所选位点是来源于日本冈山大学研究小组的成果、同该多中心研究内容一致。
目的和意义：
1、分析前列腺癌组和对照组人群中多个特定单核苷酸多态性位点的分布情况,探讨多个特定位点与广州汉族人群前列腺癌患病风险的相关性,寻找阳性位点以用于指导前列腺癌的预防和早期诊断。
2、尝试建立多个阳性单核苷酸多态性位点联合分析广州汉族人群前列腺癌患病风险的预测模型。
方法：
自2012年6月至2013年7月,在南方医科大学珠江医院、广东省人民医院招募112例前列腺癌(Pea)患者和104例非前列腺癌及其他恶性肿瘤患者(对照组)。两家医院均位于广州,受试者均为汉族男性。病例组为经穿刺活检或术后病理确诊的前列腺癌患者,初次发病或复诊均可。对照组为非前列腺癌及其他恶性肿瘤患者,在年龄上与病例组患者保持匹配,也是在相同时期相同的医疗单位住院或门诊就诊的患者。记录患者的临床资料如发病时年龄、PSA、Gleason评分、TNM分期、治疗方式及治疗效果等,并向患者发放调查问卷,调查内容包括吸烟史、饮酒史、家族史、饮食习惯等。经患者签署知情同意书后,抽取的2m1外周静脉血采用EDTA抗凝管在-20℃(或-80℃)条件下保存。采用TIANamp Blood DNA Kit血液基因组DNA提取试剂盒进行全血基因组DNA提取。利用多重SNaPshot SNP分型技术对收集到的DNA进行基因分型,所观察的8个位点以所在基因命名分别为SMARCAD1SNP、RAD17SNP、 AXIN2SNP、CASP9SNP、PSMD8BP1SNP、DCLREIB SNP、MMP27SNP、BARD1SNP。
统计学处理：
采用Pearson's X2检验的方法对对照组每个SNP位点基因型分布情况进行Hardy-Weinberg平衡检验并通过R语言实现。计算各临床指标在病例组和对照组之间的差异或者不同基因型之间的差异时采用Pearson's X2检验。采用logistic回归分析计算特定基因型与所研究人群前列腺癌发病风险的比值比(ORs)、95%可信区间(Confidence intervals, CIs)及相应的P值,以分析前列腺癌和基因型的相关性。将年龄和肿瘤危险度对与前列腺癌患病风险相关的SNPs进行分层分析。其中,年龄方面前列腺癌组采用初次发病的年龄、对照组采用招募入组时的年龄,最终分为>72岁组和≤72岁组。在肿瘤危险度方面,分为局限型前列腺癌和进展型前列腺癌。局限型前列腺癌组须同时满足以下标准：TNM分期T1-2; NO/NX; MO/MX; Gleason评分2-7分；PSA≤50ng/ml。进展型前列腺癌组须满足如下条件之一：TNM分期T3/4; N+; M+; Gleason评分8-10分；PSA>50ng/ml。将年龄、吸烟及饮酒状况作为前列腺癌患病风险多因素回归模型的混杂因素。所用统计学软件为SPSS20.0,所有统计检验为双侧检验,计算结果P值<0.05时认为差异有统计学意义。
结果：
1、选定的8个SNPs基因型(或等位基因)分布与前列腺癌发病风险的相关性分析：
无论对年龄、吸烟、饮酒等混杂因素校正与否,SMARCAD1SNP. CASP9SNP、PSMD8BP1SNP、DCLRE1B SNP、MMP27SNP、BARD1SNP计算结果P值均大于0.05,提示病例组和对照组基因型(或等位基因)之间的差异无统计学意义。
而经初步相关性分析,提示RAD17SNP和AXIN2SNP[rs2240308]与前列腺癌存在相关性。因RAD17SNP在对照组中基因型的分布经Hardy-Weinberg平衡检验结果为P=0.0324,故认为其人群代表性有一定局限性,本文不做进一步计算,只对AXIN2SNP[rs2240308]进一步统计分析。AXIN2SNP[rs2240308]GG基因型在病例组(59.2%)中的频率明显高于对照组(39.0%),GA基因型在病例组中的频率(30.1%)低于对照组(52.0%)。G等位基因在病例组中的频率(74.3%)高于对照组(65.0%),相反,A等位基因在中对照组的频率(35.0%)高于病例组(25.7%)。GA基因型携带者与GG基因型(作为参考)携带者相比,其校正OR值为0.377(95%CI:0.206-0.688,P=0.001),有统计学意义。AA基因型携带者与GG基因型携带者相比,其校正OR值为0.830(95%CI：0.309-2.232,P=0.712),无明显统计学意义。G等位基因(作为参考)与A等位基因在病例组和对照组之间的差异仍具有统计学意义(P=0.048),其校正OR值为0.649,95%CI：0.422-0.996。
2、对阳性位点AXIN2SNP[rs2240308]按年龄分层进行分析：GA基因型携带者在≤72岁组(校正OR=0.419,95%CI:0.181-0.969,P=0.042)和>72岁组(校正OR=0.364,95%CI:0.150-0.879,P=0.025)均提示前列腺癌的患病风险相比GG基因型(作为参考)携带者下降。A等位基因携带者在≤72岁组提示患病风险下降(校正OR=0.514;95%CI:0.281-0.940；P=0.031),但在>72岁组未发现有统计学意义(校正后P=0.646)。
3、对阳性位点AXIN2SNP[rs2240308]按肿瘤危险度分层进行分析：GA基因型携带者在局限型组(校正OR=0.246,95%CI:0.100-0.607,P=0.002)和进展型组(校正OR=0.446,95%CI:0.228-0.873,P=0.018)均提示前列腺癌的患病风险相比GG基因型(作为参考)携带者下降。A等位基因携带者在局限型组提示患病风险下降(校正OR=0.408;95%CI:0.206-0.807:P=0.010),但在进展型组未发现有统计学意义(校正后P=0.271)。从另一方面看,通过分层分析,我们可以发现A等位基因在≤72岁组和局限型组中患病风险下降。
4、AXIN2SNP[rs2240308]基因型和临床指标之间的关系：各临床指标在GG基因型组和non-GG基因型组之间差异均无统计学意义。
结论：
通过病例对照研究发现,Axin2SNP [rs2240308]与广州汉族人群前列腺癌可能具有相关性。Axin2SNP [rs2240308:G/A]中的GA基因型对广州汉族人群前列腺癌患病风险中可能呈现保护性作用。据我们所知,本研究为首次发现Axin2SNP [rs2240308:G/A]和前列腺癌患病风险可能具有相关性。Axin2SNP[rs2240308]可能会成为前列腺癌的早期诊断和易感性评估的生物学标志物。
Background
Prostate cancer is the most common malignancy affecting men's health. It occurs in men over the age of60. There were679,000new cases of cancer in2002and was the second most common cancer in male. The incidence of prostate cancer is different due to the diversity of geographic and ethnic.North America and Scandinavia has the highest incidence of prostate cancer, but most of the Asian countries are low incidence areas. African Americans which has the highest incidence of prostate cancer is85.7/100,000. American Caucasians'incidence rate is107.8/100,000and other Western developed countries are50-103/100,000. In European countries, there are around190,000new cases and about80,000patients died of prostate cancer. The incidence of China and Japan are lower than in other countries Among Asian countries. Japan has the incidence rate of9/100,000and China has the incidence rate of2.3/100,000(7.7/100,000in Shanghai region). Whether in the areas of high incidence or low incidence, the incidence of prostate cancer is increasing year by year from1973to1992. Therefore, the relevant factors for prostate cancer risk and early prevention of prostate cancer is important.
At present the cause of prostate cancer is not fully understood. The known risk factors are age, race, family history. Difference in incidence between ethnic and region may due to the genetic background, environment, lifestyle and other factors. Studies have confirmed that genetic susceptibility and environmental factors will have an impact on the incidence of prostate cancer. Single nucleotide polymorphism is one of the important fields in genetic susceptibility.
Single Nucleotide Polymorphisms (SNPs) are DNA sequence polymorphisms at the genomic level which were caused by a single nucleotide mutation. Wherein the rate of variation of less than1%is referred to the mutant and the rate greater than1%is called single nucleotide polymorphism. SNPs which is widespread human genetic variation in the human genome may be the most common type, accounting for over90%of all known polymorphisms. Each of about1000bp in the human genome is SNP and total number of SNPs is about3×106.
Because SNPs involves only a single base mutation, it can be converted by transition or transversion caused, may also be caused by insertion or deletion of single nucleotides. SNPs are commonly caused by transitions and transversions. Most SNPs is meaningless and does not affect protein function or gene expression (such as non-coding region SNPs).
According to the location of SNPs in the genome and the change of gene encoding, the SNPs can be divided into several categories. Approximately95%of the SNPs in the non-coding region, which is located in a small portion of the gene regulatory region, were referred to as gene control region SNPs (rSNPs). which is located in the coding region of the gene coding SNPs called SNPs (cSNPs). Furthermore, if cSNPs does not change the amino acid sequence encoded, it could be called synonymous SNPs (sSNPs). But if it change the amino acid sequence, it could be called non-synonymous SNPs or missense SNPs (nsSNPs). SNPs can affect the function of genes through a variety of ways. Some SNPs have been shown related to prostate cancer development and prognosis.
Due to SNPs are widely distributed in the population and this genetic marker is relatively stable existing from birth to death, so that genetic analysis can be carried out before the onset of decades.Therefore, SNPs and only able to provide proof of the genetic etiology of tumorigenesis, but also provides an effective means of common cancer screening.Not only SNPs can be able to provide proof of the genetic etiology of tumorigenesis, but also provides an effective means of common cancer screening.
As the third generation of SNP genetic markers, only have two allelic type which is less than microsatellite variability. Because of the huge number of frequency distribution in total genome, it is more stable than microsatellite genetic markers. SNPs in the genomic screening process generally only need to analysis positive or negative (+/-), so it is easy to conduct genotyping analysis automatically. Compared to the first and second generation of genetic markers are more suitable for exploring population-based genetic condition and other aspects of the study in complex genetic diseases. It has been replaced microsatellite marker technology in the field of genetic research. SNPs were used to explain the phenotypic differences between individuals, different complex diseases susceptibility of different groups or individuals, drug and environmental factors response. It has been widely used in clinical diagnosis, forensics, pathogen detection, genetic diseases, new drug development and other aspects.
Common methods of analysis of SNPs can be divided into three categories. The first category is association analysis based genetic epidemiology method, including the research of SNPs in disease susceptibility, drug reactions and other phenotypic differences.The second category is about the analysis in cellular and biochemical levels including enzyme activity and other aspects in cell signaling pathways to illustrate the impact of SNPs on gene function. The third category focus on SNPs impacting on molecular mechanisms of gene expression. Through the role of SNPs in the gene transcription, translation and protein expression to explore the mechanism of SNPs affecting gene function.
Zheng SL et al. have published an article in the New England Journal of Medicine. They found high-risk SNPs in five loci (three in8q24,17q12and the other two SNPs located in17q24.3). They choose a highest hazard ratio SNPs in each loci to analysis totally. Each of these five SNPs has the prostate cancer risk odds ratio (OR) between1.22to1.53. However, with four to five SNPs occured at the same time, the OR value will reach to4.47. If also defined family history of prostate cancer as a risk factor, OR value was2.22. Then combined five or even more risk factors, the OR values could be up to9.46(P=1.29×10-8). The study results showed that the joint analysis of multiple SNPs can improve the effectiveness of prediction in the risk of prostate cancer.
The research group in Okayama University had screened48missense SNPs (non-synonymous SNPs) in cancer-related genes. They found12SNPs in10genes and these SNPs have a significant impact on the incidence of prostate cancer. This10genes include five tumor suppressor genes, two DNA repair genes, a metabolic enzyme gene, a chromosome segregation gene and an apoptosis-related gene. Nine SNPs which are correlated with prostate cancer were first discovered. This12SNPs in prediction of prostate cancer risk also has a cumulative effect. The highest risk group's OR value can be up to47.4comparing with the low-risk group. The highest risk group the incidence of prostate cancer in the next30years was29%, while the low-risk group was0.6%and the lowest risk group was only about0.2%. Thus, through the integration of multiple tumor-associated SNPs risk to conduct tumor genetics evaluation, can further improve the accuracy and efficiency of prostate cancer screening in high-risk populations. It also can improve the prevention or early diagnosis of prostate cancer.
Because of similar geographic and ethnic origin, Chinese population and Japanese population have great similarity in genetic background. These prostate cancer risk SNPs obtained by large-scale screening of the Japanese population may also apply to the Chinese population. In order to test this hypothesis and further expand this research results from Japan into China and other Asian countries, China, Japan, South Korea and Singapore jointly launched an international multi-center study. This study was designed to test the effectiveness of these prostate cancer risk SNPs in other Asian populations. The selected SNPs in this article is derived from the achievement of Okayama University research group and is consistent with multi-center study.
Purpose
1. To investigate the association between SNPs and the risk of prostate cancer in Guangzhou Han population, positive sites to guidance for prostate cancer prevention and early diagnosis. Using positive SNP for prostate cancer prevention and early diagnosis.
2. Try to establish a risk model with number of positive single-nucleotide polymorphisms in predicting the risk of prostate cancer among Guangzhou Han population.
Method
From June2012to July2013, recruited112cases of prostate cancer (Pca) patients and104cases of non-prostate cancer patients and healthy volunteers (control group) from Southern Medical University Zhujiang Hospital and Guangdong Province People's Hospital.Case group included biopsy or pathologic diagnosis of prostate cancer patients, the initial onset or referral. Control group included patients with prostate cancer and other malignancies with age matched patients maintained. The could be in the same patient hospitalization or outpatient medical units during the same period. Recorded clinical data such as age at onset, PSA, Gleason score, TNM stage, treatment, and treatment effects and distributed questionnaires to patients. The questionnaires included smoking history, drinking history, family history, diet habit and so on.
A2mL blood sample was obtained from each participant, and it remained at room temperature for no more than six hours. Genomic DNA was extracted using a TIANamp Blood DNA Kit (TIANGEN Biotech, Beijing, China) according to the manufacturer's instructions, and was stored at-20℃. We analyzed the Axin2SNP (rs2240308) and seven SNPs of the other genes (not shown in this paper) using these samples. The genetic analyses were performed using the ABI SNaPshot multiplex system (Life Technologies Corporation, Carlsbad, CA, USA).
Statistical analysis
We compared the proportion (percentage) of the each genotype and allele of the SNP [rs2240308:G/A] and other seven SNPs in the controls and prostate cancer cases. The association between the SNP and incidence of prostate cancer was analyzed using a logistic regression model. The odds ratio (OR),95%confidence interval (CI) and corresponding p values for the association between the prostate cancer risk and the genotypes or alleles were calculated. The data for each genotype or allele was compared with that of the common homozygote or allele as the reference group. We also stratified our analyses by the age of the patient at diagnosis (≤72or≥72years) and by the aggressiveness of the disease (localized or advanced prostate cancer). Localized prostate cancer inclusion criteria are T1-2, N0, M0, Gleason score2-7, and PSA levels≤50ng/mL. Advanced prostate cancer inclusion criteria are T3/4or N+or M+or Gleason score8-10or PSA levels>50ng/mL.
In these analyses, the data were adjusted for the age, smoking status and drinking status. The data are shown as the means±standard deviation (SD). The Chi-square test was used to compare the distribution of the control males and prostate cancer patients or of the clinical characteristics. The Mann-Whitney U test was also performed to analyze the statistical significance of differences in the age and PSA level at diagnosis. All statistical analyses were conducted using the SPSS software program, version20.0. The differences were considered to be significant for values of p<0.05.
Results
1. Eight SNPs genotypes (or allele) distribution and prostate cancer risk association analysis:
Whether or not adjusted for age, smoking history, drinking history and other confounding factors, the p value of these six SNPs (including SMARCAD1SNP, CASP9SNP, PSMD8BP1SNP, DCLRE1B SNP, MMP27SNP and BARD1SNP) was greater than0.05individually. And this suggested that the case group and the control group genotype (or equivalent differences in allele) has not statistically significant association in these SNPs except RAD17SNP and AXIN2SNP.
RAD17SNP and AXIN2SNP [rs2240308] were associated with prostate cancer risk. Due to the result of Hardy-Weinberg equilibrium test with RAD17SNP genotypes in the control group is p=0.0324, it might affect its population representativeness. So this study only focused on AXIN2SNP [rs2240308] with further statistical analysis. AXIN2SNP [rs2240308] GG genotype frequencies in case group (59.2%) was significantly higher than control group (39.0%) and GA genotype frequencies in the case group (30.1%) was higher than the control group (52.0%).
G allele frequency (74.3%) is higher than control group (65.0%) in the case group, on the other hand, A allele frequency in the control group (35.0%) was higher than the case group (25.7%). GA genotype compared with the GG genotype (as a reference) carriers was statistically significant and adjusted OR was0.377(95%CI:0.206-0.688, p=0.001). AA genotype compared with the GG genotype was no statistically significant and adjusted OR was0.830(95%CI:0.309-2.232, p=0.712). G allele (as a reference) and the A allele differences between cases and controls remained statistically significant (p=0.048) and adjusted OR was0.649,95%CI:0.422-0.996.
2. Further stratified analysis AXIN2SNP [rs2240308] by age:
Compared to the GG genotype (as a reference), GA genotype carriers in≤72years group (adjusted OR=0.419,95%CI:0.181-0.969, p=0.042). and>72years group (adjusted OR=0.364,95%CI:0.150-0.879, p=0.025) both showed the lower risk of prostate cancer. A allele in≤72years group showed the lower risk of prostate cancer (adjusted OR=0.514,95%CI:0.281-0.940; p=0.031), but in the age>72group did not find statistically significant (p=0.646).
3. Further stratified analysis AXIN2SNP [rs2240308] by tumor progression:
Compared to the GG genotype (as a reference) GA genotype carriers in localized group (adjusted OR=0.246,95%CI:0.100-0.607, p=0.002) and progressive group (adjusted OR=0.446,95%CI:0.228-0.873, p=0.018) both showed lower risk of prostate cancer. A allele carriers in localized group showed lower risk of prostate cancer (adjusted OR=0.408;95%CI:0.206-0.807, p=0.010), but not found statistically significant in advanced group (p=0.271). On the other hand, the allele [A] was associated with a reduced incidence of prostate cancer in younger patients and in the patients with localized cancer.
4. Association between AXIN2SNP [rs2240308] genotype and clinical characteristics (cancer aggressiveness, Gleason score, PSA level, age at diagnosis, smoking and drinking status, hypertension):
Among clinical indicators, we found no significant difference between the GG genotype and non-GG genotype.
Conclusion
In conclusion, our study demonstrates that there is a significant association between the SNP [rs2240308:G/A] of the Axin2gene and prostate cancer risk. This is, to our knowledge, the first study showing the possible involvement of the Axin2polymorphism in prostate cancer development. Although additional studies with larger and more diverse populations and a functional analysis of the polymorphism are necessary to confirm and extend our findings, we believe that the Axin2SNP [rs2240308] could be a useful biomarker for the predisposition to prostate and for the early diagnosis of the disease.
前列腺癌是影响男性健康的最常见恶性肿瘤之一,多发生于60岁以上的老年男性,2002年全球新发病例为679000例,居于男性肿瘤的第二位。前列腺癌的发病率因地域及种族的差异而不同。前列腺癌发病率最高的地区是北美和斯堪的纳维亚半岛,而大部分亚洲国家为低发病率地区。美国黑人前列腺癌的发病率最高,达到185.7/10万,美国高加索人(白种人)107.8/10万,其它西方发达国家的发病率为50～103/10万。在欧洲国家,每年大约有190000新发病例,并且大约有80000例患者死于前列腺癌。亚洲国家中,中国和日本发病率低于其他国家,日本发病率为9/10万,中国发病率为2.3/10万(上海地区为7.7/10万)。无论是前列腺癌的高发地区还是低发地区,从1973年到1992年前列腺癌的发病人数都在逐年增加。因此研究前列腺癌发生发展的相关因素以及早期预防前列腺癌的发生是当务之急。
目前对前列腺癌的病因尚不完全清楚,年龄、种族、家族史是目前已知的危险因素。不同种族和地区间发病率的差异可能与遗传背景、环境及生活方式等多方面因素有关。有研究已证实遗传易感和环境因素都会对前列腺癌的发生造成影响,其中遗传易感的重要物质基础之一就是单核苷酸点突变所造成的单核苷酸多态性。
单核苷酸多态性(Single Nucleotide Polymorphisms, SNPs)是在基因组水平由单个核苷酸的变异所引起的DNA序列多态性。其中发生率小于1%的变异称为突变,大于1%的则称为单核苷酸多态性。SNPs在人类基因组中广泛存在,是人类可遗传的变异中最常见的一种,占所有已知多态性的90%以上。人类基因组中大概每1000个碱基就有一个SNP, SNPs总量大约为3×106个。
由于SNPs仅涉及单个碱基的变异,故可以由单个碱基的转换(transition)或颠换(transversion)所引起,也可由碱基的插入或缺失造成。通常所说的SNPs是指转换和颠换。大部分SNPs是无意义的,不影响蛋白功能或者基因表达(如非编码区SNPs)。
一般根据SNPs在基因组中的位置以及是否引起基因编码的改变,可将SNPs分为几类：约95%的SNPs位于非编码区,其中一小部分位于基因调控区,称为基因调控区SNPs (regulatory SNPs, rSNPs)；而位于基因编码区的SNPs称为编码SNPs (coding SNPs, cSNPs)。若cSNPs不改变所编码的氨基酸序列则称为同义SNPs (synonymous SNPs, sSNPs),导致氨基酸序列改变则称为非同义SNPs或错义SNPs (non-synonymous SNPs, nsSNPs)。
SNPs能通过多种途径影响基因的功能,有一部分已被证明SNPs和前列腺癌发生、发展及预后密切相关。由于SNPs在人群中分布广泛,并且这种遗传标记相对稳定,能从个体出生到死亡一直存在,使遗传分析能在发病前几十年进行开展。因此,SNPs不但能提供和证明肿瘤发生的遗传性病因,也为常见肿瘤普查提供了一种有效的检测手段。
SNPs作为第三代遗传标记,只有两种等位型,变异程度不如微卫星,但由于在基因组中总数量巨大,分布频密,具有较微卫星更稳定的遗传特性。在基因组筛查SNPs过程中一般只需阳性或阴性(+/-)分析,故易于分型检测并实现分析的自动化。相比第一、二代遗传标记更有适合对复杂疾病遗传的解析和基于群体基因识别等方面的研究,并已取代微卫星标记技术进入基因研究应用的领域。采用SNPs进行分析有助于解释不同个体之间的表型差异、不同群体和个体对疾病,特别是对复杂疾病易感性、药物耐受性以及对环境因素的反应差异,目前已广泛应用于分子诊断、临床检验、法医学、病原检测、遗传疾病和新药研发等方面。
常见的SNPs的分析方法分为三类,第一类为基于遗传流行病学的关联分析,是通过研究SNPs在疾病易感性、药物反应性及其他相关遗传表型差异的方法；第二类为细胞和生化水平的分析,通过分析酶活力、细胞信号通路等方面来阐述SNPs对基因功能方面的影响；第三类为基于SNPs影响基因表达的分子机制研究,通过对SNPs在基因转录、翻译及蛋白表达的作用进行分析,探讨SNPs影响基因功能的机制。
2008年Zheng SL等在新英格兰医学杂志上发表的文章中提到,在五个前列腺癌高风险位点中(三个位于8q24,另两个分别位于17q12和17q24.3)各选择一个危险比最高的SNP进行联合分析,这五个SNPs各自的前列腺癌患病风险比值比(OR)在1.22到1.53之间,然而当基因型同时具有四至五个SNPs时OR值将达到4.47；若将前列腺癌家族史也当做一项危险因素时(单独OR值为2.22),则具备六项危险因素中的五项甚至全部时,其OR值高达9.46(P=1.29×10-8)。该研究结果表明,多个SNPs联合分析有助于提高人群前列腺癌患病风险的预测效力。
日本冈山大学研究小组通过筛选癌症相关基因中的48个错义SNPs(非同义SNPs),发现其10个基因中共计12个SNPs对前列腺癌发病有重要影响。这10个基因包括5个肿瘤抑制基因、2个DNA修复基因、1个代谢酶基因、1个染色体分离相关基因和1个凋亡调节基因,其中9个SNPs与肿瘤的相关性是首次被发现。这12个SNPs在预测前列腺癌患病风险方面同样具有累积效应,它们确定的最高风险组与低风险组人群相比其OR值高达47.4。最高风险组在今后30年间前列腺癌患病的风险是29%,而低风险组为0.6%,其中最低风险组仅约0.2%。因此,通过整合多个肿瘤相关SNPs进行肿瘤患病风险遗传学评估,进一步提高前列腺癌高风险人群的筛查准确性和效率,并有利于肿瘤的预防和早期诊断。
由于地理位置和人种起源相近,中国人群与日本人群在遗传背景上具有很大的相似性。在日本人群大规模筛选获得的前列腺癌风险SNPs也有可能部分适用于中国人群,为了验证这一假设,并进一步将在日本人群中获得的研究成果拓展到中国及亚洲其它国家,中国、日本、韩国和新加坡四国共同发起了一项国际多中心研究,旨在检验上述前列腺癌风险SNPs在亚洲其他人群中的效力。本文中所选位点是来源于日本冈山大学研究小组的成果、同该多中心研究内容一致。
目的和意义：
1、分析前列腺癌组和对照组人群中多个特定单核苷酸多态性位点的分布情况,探讨多个特定位点与广州汉族人群前列腺癌患病风险的相关性,寻找阳性位点以用于指导前列腺癌的预防和早期诊断。
2、尝试建立多个阳性单核苷酸多态性位点联合分析广州汉族人群前列腺癌患病风险的预测模型。
方法：
自2012年6月至2013年7月,在南方医科大学珠江医院、广东省人民医院招募112例前列腺癌(Pea)患者和104例非前列腺癌及其他恶性肿瘤患者(对照组)。两家医院均位于广州,受试者均为汉族男性。病例组为经穿刺活检或术后病理确诊的前列腺癌患者,初次发病或复诊均可。对照组为非前列腺癌及其他恶性肿瘤患者,在年龄上与病例组患者保持匹配,也是在相同时期相同的医疗单位住院或门诊就诊的患者。记录患者的临床资料如发病时年龄、PSA、Gleason评分、TNM分期、治疗方式及治疗效果等,并向患者发放调查问卷,调查内容包括吸烟史、饮酒史、家族史、饮食习惯等。经患者签署知情同意书后,抽取的2m1外周静脉血采用EDTA抗凝管在-20℃(或-80℃)条件下保存。采用TIANamp Blood DNA Kit血液基因组DNA提取试剂盒进行全血基因组DNA提取。利用多重SNaPshot SNP分型技术对收集到的DNA进行基因分型,所观察的8个位点以所在基因命名分别为SMARCAD1SNP、RAD17SNP、 AXIN2SNP、CASP9SNP、PSMD8BP1SNP、DCLREIB SNP、MMP27SNP、BARD1SNP。
统计学处理：
采用Pearson's X2检验的方法对对照组每个SNP位点基因型分布情况进行Hardy-Weinberg平衡检验并通过R语言实现。计算各临床指标在病例组和对照组之间的差异或者不同基因型之间的差异时采用Pearson's X2检验。采用logistic回归分析计算特定基因型与所研究人群前列腺癌发病风险的比值比(ORs)、95%可信区间(Confidence intervals, CIs)及相应的P值,以分析前列腺癌和基因型的相关性。将年龄和肿瘤危险度对与前列腺癌患病风险相关的SNPs进行分层分析。其中,年龄方面前列腺癌组采用初次发病的年龄、对照组采用招募入组时的年龄,最终分为>72岁组和≤72岁组。在肿瘤危险度方面,分为局限型前列腺癌和进展型前列腺癌。局限型前列腺癌组须同时满足以下标准：TNM分期T1-2; NO/NX; MO/MX; Gleason评分2-7分；PSA≤50ng/ml。进展型前列腺癌组须满足如下条件之一：TNM分期T3/4; N+; M+; Gleason评分8-10分；PSA>50ng/ml。将年龄、吸烟及饮酒状况作为前列腺癌患病风险多因素回归模型的混杂因素。所用统计学软件为SPSS20.0,所有统计检验为双侧检验,计算结果P值<0.05时认为差异有统计学意义。
结果：
1、选定的8个SNPs基因型(或等位基因)分布与前列腺癌发病风险的相关性分析：
无论对年龄、吸烟、饮酒等混杂因素校正与否,SMARCAD1SNP. CASP9SNP、PSMD8BP1SNP、DCLRE1B SNP、MMP27SNP、BARD1SNP计算结果P值均大于0.05,提示病例组和对照组基因型(或等位基因)之间的差异无统计学意义。
而经初步相关性分析,提示RAD17SNP和AXIN2SNP[rs2240308]与前列腺癌存在相关性。因RAD17SNP在对照组中基因型的分布经Hardy-Weinberg平衡检验结果为P=0.0324,故认为其人群代表性有一定局限性,本文不做进一步计算,只对AXIN2SNP[rs2240308]进一步统计分析。AXIN2SNP[rs2240308]GG基因型在病例组(59.2%)中的频率明显高于对照组(39.0%),GA基因型在病例组中的频率(30.1%)低于对照组(52.0%)。G等位基因在病例组中的频率(74.3%)高于对照组(65.0%),相反,A等位基因在中对照组的频率(35.0%)高于病例组(25.7%)。GA基因型携带者与GG基因型(作为参考)携带者相比,其校正OR值为0.377(95%CI:0.206-0.688,P=0.001),有统计学意义。AA基因型携带者与GG基因型携带者相比,其校正OR值为0.830(95%CI：0.309-2.232,P=0.712),无明显统计学意义。G等位基因(作为参考)与A等位基因在病例组和对照组之间的差异仍具有统计学意义(P=0.048),其校正OR值为0.649,95%CI：0.422-0.996。
2、对阳性位点AXIN2SNP[rs2240308]按年龄分层进行分析：GA基因型携带者在≤72岁组(校正OR=0.419,95%CI:0.181-0.969,P=0.042)和>72岁组(校正OR=0.364,95%CI:0.150-0.879,P=0.025)均提示前列腺癌的患病风险相比GG基因型(作为参考)携带者下降。A等位基因携带者在≤72岁组提示患病风险下降(校正OR=0.514;95%CI:0.281-0.940；P=0.031),但在>72岁组未发现有统计学意义(校正后P=0.646)。
3、对阳性位点AXIN2SNP[rs2240308]按肿瘤危险度分层进行分析：GA基因型携带者在局限型组(校正OR=0.246,95%CI:0.100-0.607,P=0.002)和进展型组(校正OR=0.446,95%CI:0.228-0.873,P=0.018)均提示前列腺癌的患病风险相比GG基因型(作为参考)携带者下降。A等位基因携带者在局限型组提示患病风险下降(校正OR=0.408;95%CI:0.206-0.807:P=0.010),但在进展型组未发现有统计学意义(校正后P=0.271)。从另一方面看,通过分层分析,我们可以发现A等位基因在≤72岁组和局限型组中患病风险下降。
4、AXIN2SNP[rs2240308]基因型和临床指标之间的关系：各临床指标在GG基因型组和non-GG基因型组之间差异均无统计学意义。
结论：
通过病例对照研究发现,Axin2SNP [rs2240308]与广州汉族人群前列腺癌可能具有相关性。Axin2SNP [rs2240308:G/A]中的GA基因型对广州汉族人群前列腺癌患病风险中可能呈现保护性作用。据我们所知,本研究为首次发现Axin2SNP [rs2240308:G/A]和前列腺癌患病风险可能具有相关性。Axin2SNP[rs2240308]可能会成为前列腺癌的早期诊断和易感性评估的生物学标志物。
Background
Prostate cancer is the most common malignancy affecting men's health. It occurs in men over the age of60. There were679,000new cases of cancer in2002and was the second most common cancer in male. The incidence of prostate cancer is different due to the diversity of geographic and ethnic.North America and Scandinavia has the highest incidence of prostate cancer, but most of the Asian countries are low incidence areas. African Americans which has the highest incidence of prostate cancer is85.7/100,000. American Caucasians'incidence rate is107.8/100,000and other Western developed countries are50-103/100,000. In European countries, there are around190,000new cases and about80,000patients died of prostate cancer. The incidence of China and Japan are lower than in other countries Among Asian countries. Japan has the incidence rate of9/100,000and China has the incidence rate of2.3/100,000(7.7/100,000in Shanghai region). Whether in the areas of high incidence or low incidence, the incidence of prostate cancer is increasing year by year from1973to1992. Therefore, the relevant factors for prostate cancer risk and early prevention of prostate cancer is important.
At present the cause of prostate cancer is not fully understood. The known risk factors are age, race, family history. Difference in incidence between ethnic and region may due to the genetic background, environment, lifestyle and other factors. Studies have confirmed that genetic susceptibility and environmental factors will have an impact on the incidence of prostate cancer. Single nucleotide polymorphism is one of the important fields in genetic susceptibility.
Single Nucleotide Polymorphisms (SNPs) are DNA sequence polymorphisms at the genomic level which were caused by a single nucleotide mutation. Wherein the rate of variation of less than1%is referred to the mutant and the rate greater than1%is called single nucleotide polymorphism. SNPs which is widespread human genetic variation in the human genome may be the most common type, accounting for over90%of all known polymorphisms. Each of about1000bp in the human genome is SNP and total number of SNPs is about3×106.
Because SNPs involves only a single base mutation, it can be converted by transition or transversion caused, may also be caused by insertion or deletion of single nucleotides. SNPs are commonly caused by transitions and transversions. Most SNPs is meaningless and does not affect protein function or gene expression (such as non-coding region SNPs).
According to the location of SNPs in the genome and the change of gene encoding, the SNPs can be divided into several categories. Approximately95%of the SNPs in the non-coding region, which is located in a small portion of the gene regulatory region, were referred to as gene control region SNPs (rSNPs). which is located in the coding region of the gene coding SNPs called SNPs (cSNPs). Furthermore, if cSNPs does not change the amino acid sequence encoded, it could be called synonymous SNPs (sSNPs). But if it change the amino acid sequence, it could be called non-synonymous SNPs or missense SNPs (nsSNPs). SNPs can affect the function of genes through a variety of ways. Some SNPs have been shown related to prostate cancer development and prognosis.
Due to SNPs are widely distributed in the population and this genetic marker is relatively stable existing from birth to death, so that genetic analysis can be carried out before the onset of decades.Therefore, SNPs and only able to provide proof of the genetic etiology of tumorigenesis, but also provides an effective means of common cancer screening.Not only SNPs can be able to provide proof of the genetic etiology of tumorigenesis, but also provides an effective means of common cancer screening.
As the third generation of SNP genetic markers, only have two allelic type which is less than microsatellite variability. Because of the huge number of frequency distribution in total genome, it is more stable than microsatellite genetic markers. SNPs in the genomic screening process generally only need to analysis positive or negative (+/-), so it is easy to conduct genotyping analysis automatically. Compared to the first and second generation of genetic markers are more suitable for exploring population-based genetic condition and other aspects of the study in complex genetic diseases. It has been replaced microsatellite marker technology in the field of genetic research. SNPs were used to explain the phenotypic differences between individuals, different complex diseases susceptibility of different groups or individuals, drug and environmental factors response. It has been widely used in clinical diagnosis, forensics, pathogen detection, genetic diseases, new drug development and other aspects.
Common methods of analysis of SNPs can be divided into three categories. The first category is association analysis based genetic epidemiology method, including the research of SNPs in disease susceptibility, drug reactions and other phenotypic differences.The second category is about the analysis in cellular and biochemical levels including enzyme activity and other aspects in cell signaling pathways to illustrate the impact of SNPs on gene function. The third category focus on SNPs impacting on molecular mechanisms of gene expression. Through the role of SNPs in the gene transcription, translation and protein expression to explore the mechanism of SNPs affecting gene function.
Zheng SL et al. have published an article in the New England Journal of Medicine. They found high-risk SNPs in five loci (three in8q24,17q12and the other two SNPs located in17q24.3). They choose a highest hazard ratio SNPs in each loci to analysis totally. Each of these five SNPs has the prostate cancer risk odds ratio (OR) between1.22to1.53. However, with four to five SNPs occured at the same time, the OR value will reach to4.47. If also defined family history of prostate cancer as a risk factor, OR value was2.22. Then combined five or even more risk factors, the OR values could be up to9.46(P=1.29×10-8). The study results showed that the joint analysis of multiple SNPs can improve the effectiveness of prediction in the risk of prostate cancer.
The research group in Okayama University had screened48missense SNPs (non-synonymous SNPs) in cancer-related genes. They found12SNPs in10genes and these SNPs have a significant impact on the incidence of prostate cancer. This10genes include five tumor suppressor genes, two DNA repair genes, a metabolic enzyme gene, a chromosome segregation gene and an apoptosis-related gene. Nine SNPs which are correlated with prostate cancer were first discovered. This12SNPs in prediction of prostate cancer risk also has a cumulative effect. The highest risk group's OR value can be up to47.4comparing with the low-risk group. The highest risk group the incidence of prostate cancer in the next30years was29%, while the low-risk group was0.6%and the lowest risk group was only about0.2%. Thus, through the integration of multiple tumor-associated SNPs risk to conduct tumor genetics evaluation, can further improve the accuracy and efficiency of prostate cancer screening in high-risk populations. It also can improve the prevention or early diagnosis of prostate cancer.
Because of similar geographic and ethnic origin, Chinese population and Japanese population have great similarity in genetic background. These prostate cancer risk SNPs obtained by large-scale screening of the Japanese population may also apply to the Chinese population. In order to test this hypothesis and further expand this research results from Japan into China and other Asian countries, China, Japan, South Korea and Singapore jointly launched an international multi-center study. This study was designed to test the effectiveness of these prostate cancer risk SNPs in other Asian populations. The selected SNPs in this article is derived from the achievement of Okayama University research group and is consistent with multi-center study.
Purpose
1. To investigate the association between SNPs and the risk of prostate cancer in Guangzhou Han population, positive sites to guidance for prostate cancer prevention and early diagnosis. Using positive SNP for prostate cancer prevention and early diagnosis.
2. Try to establish a risk model with number of positive single-nucleotide polymorphisms in predicting the risk of prostate cancer among Guangzhou Han population.
Method
From June2012to July2013, recruited112cases of prostate cancer (Pca) patients and104cases of non-prostate cancer patients and healthy volunteers (control group) from Southern Medical University Zhujiang Hospital and Guangdong Province People's Hospital.Case group included biopsy or pathologic diagnosis of prostate cancer patients, the initial onset or referral. Control group included patients with prostate cancer and other malignancies with age matched patients maintained. The could be in the same patient hospitalization or outpatient medical units during the same period. Recorded clinical data such as age at onset, PSA, Gleason score, TNM stage, treatment, and treatment effects and distributed questionnaires to patients. The questionnaires included smoking history, drinking history, family history, diet habit and so on.
A2mL blood sample was obtained from each participant, and it remained at room temperature for no more than six hours. Genomic DNA was extracted using a TIANamp Blood DNA Kit (TIANGEN Biotech, Beijing, China) according to the manufacturer's instructions, and was stored at-20℃. We analyzed the Axin2SNP (rs2240308) and seven SNPs of the other genes (not shown in this paper) using these samples. The genetic analyses were performed using the ABI SNaPshot multiplex system (Life Technologies Corporation, Carlsbad, CA, USA).
Statistical analysis
We compared the proportion (percentage) of the each genotype and allele of the SNP [rs2240308:G/A] and other seven SNPs in the controls and prostate cancer cases. The association between the SNP and incidence of prostate cancer was analyzed using a logistic regression model. The odds ratio (OR),95%confidence interval (CI) and corresponding p values for the association between the prostate cancer risk and the genotypes or alleles were calculated. The data for each genotype or allele was compared with that of the common homozygote or allele as the reference group. We also stratified our analyses by the age of the patient at diagnosis (≤72or≥72years) and by the aggressiveness of the disease (localized or advanced prostate cancer). Localized prostate cancer inclusion criteria are T1-2, N0, M0, Gleason score2-7, and PSA levels≤50ng/mL. Advanced prostate cancer inclusion criteria are T3/4or N+or M+or Gleason score8-10or PSA levels>50ng/mL.
In these analyses, the data were adjusted for the age, smoking status and drinking status. The data are shown as the means±standard deviation (SD). The Chi-square test was used to compare the distribution of the control males and prostate cancer patients or of the clinical characteristics. The Mann-Whitney U test was also performed to analyze the statistical significance of differences in the age and PSA level at diagnosis. All statistical analyses were conducted using the SPSS software program, version20.0. The differences were considered to be significant for values of p<0.05.
Results
1. Eight SNPs genotypes (or allele) distribution and prostate cancer risk association analysis:
Whether or not adjusted for age, smoking history, drinking history and other confounding factors, the p value of these six SNPs (including SMARCAD1SNP, CASP9SNP, PSMD8BP1SNP, DCLRE1B SNP, MMP27SNP and BARD1SNP) was greater than0.05individually. And this suggested that the case group and the control group genotype (or equivalent differences in allele) has not statistically significant association in these SNPs except RAD17SNP and AXIN2SNP.
RAD17SNP and AXIN2SNP [rs2240308] were associated with prostate cancer risk. Due to the result of Hardy-Weinberg equilibrium test with RAD17SNP genotypes in the control group is p=0.0324, it might affect its population representativeness. So this study only focused on AXIN2SNP [rs2240308] with further statistical analysis. AXIN2SNP [rs2240308] GG genotype frequencies in case group (59.2%) was significantly higher than control group (39.0%) and GA genotype frequencies in the case group (30.1%) was higher than the control group (52.0%).
G allele frequency (74.3%) is higher than control group (65.0%) in the case group, on the other hand, A allele frequency in the control group (35.0%) was higher than the case group (25.7%). GA genotype compared with the GG genotype (as a reference) carriers was statistically significant and adjusted OR was0.377(95%CI:0.206-0.688, p=0.001). AA genotype compared with the GG genotype was no statistically significant and adjusted OR was0.830(95%CI:0.309-2.232, p=0.712). G allele (as a reference) and the A allele differences between cases and controls remained statistically significant (p=0.048) and adjusted OR was0.649,95%CI:0.422-0.996.
2. Further stratified analysis AXIN2SNP [rs2240308] by age:
Compared to the GG genotype (as a reference), GA genotype carriers in≤72years group (adjusted OR=0.419,95%CI:0.181-0.969, p=0.042). and>72years group (adjusted OR=0.364,95%CI:0.150-0.879, p=0.025) both showed the lower risk of prostate cancer. A allele in≤72years group showed the lower risk of prostate cancer (adjusted OR=0.514,95%CI:0.281-0.940; p=0.031), but in the age>72group did not find statistically significant (p=0.646).
3. Further stratified analysis AXIN2SNP [rs2240308] by tumor progression:
Compared to the GG genotype (as a reference) GA genotype carriers in localized group (adjusted OR=0.246,95%CI:0.100-0.607, p=0.002) and progressive group (adjusted OR=0.446,95%CI:0.228-0.873, p=0.018) both showed lower risk of prostate cancer. A allele carriers in localized group showed lower risk of prostate cancer (adjusted OR=0.408;95%CI:0.206-0.807, p=0.010), but not found statistically significant in advanced group (p=0.271). On the other hand, the allele [A] was associated with a reduced incidence of prostate cancer in younger patients and in the patients with localized cancer.
4. Association between AXIN2SNP [rs2240308] genotype and clinical characteristics (cancer aggressiveness, Gleason score, PSA level, age at diagnosis, smoking and drinking status, hypertension):
Among clinical indicators, we found no significant difference between the GG genotype and non-GG genotype.
Conclusion
In conclusion, our study demonstrates that there is a significant association between the SNP [rs2240308:G/A] of the Axin2gene and prostate cancer risk. This is, to our knowledge, the first study showing the possible involvement of the Axin2polymorphism in prostate cancer development. Although additional studies with larger and more diverse populations and a functional analysis of the polymorphism are necessary to confirm and extend our findings, we believe that the Axin2SNP [rs2240308] could be a useful biomarker for the predisposition to prostate and for the early diagnosis of the disease.
引文
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