单核苷酸多态性分析技术及其在肿瘤研究中的应用

单核苷酸多态性(SNP)是第3代遗传标记，由于其在基因组中分布的高密度性，被认为在多基因疾病的诊断及治疗方面有着广阔的应用前景。现综述近年来SNP分析技术的进展及SNP在肿瘤研究中的应用。     

单核苷酸多态性在卵巢癌临床应用中的研究进展

卵巢癌死亡率自90年代以来始终高居妇科恶性肿瘤之首,对化疗药物耐药是导致大多数患者复发的根本原因。卵巢癌易感性及化疗敏感性等在不同患者间具有个体差异,广泛构成了人类基因组所有变异的单核苷酸多态性（single nucleotide polymorphism,SNP）可能是解释此种差异的原因之一。在近年的报道中,从SNP角度进行卵巢癌分子机制的研究越来越多,为肿瘤的遗传性、发生发展和治疗提供了新的思路,并且有潜力作为重要的基因工具应用于生物医学各个领域中。本文就近5年来PubMed上收录发表的卵巢癌易感性、化学治疗以及预后相关的SNP研究作一综述。     

食管癌治疗敏感基因单核苷酸多态性

研究显示食管癌治疗过程中表现的个体差异与遗传多态性有密切关系.已证实顺铂及5氟尿嘧啶(5-FU)代谢相关基因、凋亡及血管生成相关基因单核苷酸多态性与食管癌治疗敏感性密切相关.     

DNA修复基因单核苷酸多态性在肿瘤化疗敏感性预测的应用进展

恶性肿瘤严重威胁着人类的健康,化学药物治疗在肿瘤的三大治疗方法中占有重要的地位。同一药物在不同个体产生的效果不完全相同,这种不同是由人类基因组中的遗传多态性即遗传差异决定的。人类基因组中一类普遍的遗传多态性是基因组中散在的单个碱基的不同,即单核苷酸多态性（single nucleotide polymorphims,SNPs）,是继第一代限制性片断长度多态性（restriction fragment length polymorphisms,RFLP）和第二代微卫星多态性（micwsmellite polymorphisms）之后的新一代遗传标记。     

单核苷酸多态性与肿瘤遗传易感性的研究进展

目的:总结国内外关于单核苷酸多态性(SNP)与肿瘤的相关研究进展。方法:应用Medline和CNKI期刊全文数据库检索系统,以"SNP"和"肿瘤"为关键词,检索1999-01-2010-11相关肿瘤易感性分析的文献。纳入标准:1)SNP的定义和应用;2)肿瘤易感基因的分类;3)SNP的作用机制;4)SNP与环境因子在肿瘤发展过程中的协同作用。根据纳入标准纳入分析43篇文献。结果:肿瘤易感相关基因包括癌基因和抑癌基因、DNA修复基因、代谢酶基因以及免疫相关基因等几大类。这些基因上含有多个SNP位点,其中有部分SNP可导致不同个体间肿瘤易感性的差异。结论:研究SNP在恶性肿瘤发生、发展过程中的致肿瘤性作用,有助于进一步理解肿瘤的发生机制,有望在肿瘤靶向治疗上取得新的突破。     

相关基因的单核苷酸多态性与肿瘤的遗传易感

提到肿瘤的遗传 ,人们自然会想到单基因改变所致的家族性遗传性肿瘤综合征。然而 ,这种遗传方式在整个肿瘤的发病中只占很少的比例 (<1% ) ,绝大多数肿瘤的发生是环境因素与肿瘤易感因素共同作用的结果。而基因的单核苷酸点突变所造成的基因多态性 (ｓｉｎｇｌｅｎｕｃｌｅｏｔｉｄｅｐｏｌｙｍｏｒｐｈｉｓｍｓ ,ＳＮＰｓ)是遗传易感的重要物质基础之一。一、环境因素与遗传因素交互作用的方式及在肿瘤发生中的意义众所周知 ,致癌的环境因素包括物理 (如辐射 )、化学 (如苯丙芘、烟草 )及生物 (如病毒、细菌、毒素 )因素 ,但无论何种因素 ,…     

食管鳞癌易感基因的单核苷酸多态性

我国人群个体食管鳞状细胞癌(ESCC)易感因素主要为易感基因某些位点的变异.其中某些基因位点参与了DNA损伤、修复过程,某些与抑癌基因、代谢酶、微量元素、吸烟等因素相关.这些易感基因位点上的单核苷酸多态性与ESCC的发生密切相关.     

单核苷酸多态性在恶性肿瘤精准医学研究中的意义

摘 要：精准医学是应用现代医学前沿技术结合患者生活环境和临床数据,实现疾病的精准分类及诊断,制定个性化的疾病预防和治疗方案的新型医学概念和医疗模式。单核苷酸多态性（SNP）是导致人类基因组变化的最根本原因。SNP研究是人类基因组计划应用的重要步骤,用于高危群体的发现、疾病相关基因的鉴定、药物的设计和测试以及生物学的基础研究等。本文就SNP在恶性肿瘤精准医学中的意义作简要综述。     

肿瘤患者二氢嘧啶脱氢酶基因单核苷酸多态性分析

[目的]检测中国大陆肿瘤患者二氢嘧啶脱氢酶（DPD）基因Exon2、Exon13、Exon14和Exon18的单核苷酸多态性（SNP）类型及其发生频率，观察此SNP是否存在种族差异。[方法]2004年10月～2005年8月随机收集142例患者血标本，其中鼻咽癌患者60例，结直肠癌患者82例。采用PCR技术扩增DPYD基因的Exon2、Exon13、Exon14和Exon18外显子，从正反两个方向对扩增片段进行DNA测序和分析。[结果]在DPYD基因Exon2、Exon13、Exon14、Exon18中发现4种SNP,即T85C（7．04％）、A1627G（20．77％）、T1896C（11．62％）和G2194A（0．70％）。C61T、G62A、A74G、G1601A、T1679G、C1714G和GIVS14＋1ASNP在本研究中未发现。[结论]中国大陆肿瘤患者DPD基因Exon2、Exon13、Exon14、Exon18中存在4种SNP，分别为T185C、A1627G、T1896C和G2194A。     

PTEN基因单核苷酸多态性与喉癌的关联分析

目的:探讨中国汉族人群PTEN基因与喉癌的关联性。方法:通过生物信息学方法在公共SNP数据库中查找该基因2个SNP位点,用PCR-RFLP法对汉族人群散发喉癌患者(91例)和对照组(104例)进行分型、病例-对照关联分析及单倍型分析。结果:基因型频率分析表明,2个SNP均符合Hardy-Weinberg平衡,P均>0.05。病例对照分析显示,rs2735343与喉癌相关,χ2=6.447,P=0.014;rs701848与喉癌不相关,χ2=0.12,P=0.720。单倍体型分析表明,rs2735343-rs701848单倍体型与喉癌密切相关,χ2=12.92,P=0.0048。结论:PTEN本身或其邻近位点与中国汉族人群喉癌发生密切相关。     

MDM2 SNP309 and cancer risk: a combined analysis

A paper by Bond et al. reported that a single-nucleotide polymorphism (SNP) in the intronic promoter region of the mouse double minute 2 (MDM2) gene (called SNP309) can significantly change the expression of MDM2 and thereby suppress the p53 pathway. Furthermore, it was shown that SNP309 accelerates tumor formation in Li-Fraumeni patients. This initial report aroused the attention of many researchers, which investigated the role of SNP309 for the risk and the onset of cancer in different tissues. To provide a more robust estimate of the effect of this polymorphism on cancer risk, we combined the available genotype data for breast, colorectal and lung cancers. For breast cancer, we combined the data from 11 studies including 5737 cases and 6703 controls. For colorectal cancer, we combined the data from five studies with 1620 cases and 886 controls. For lung cancer, we performed a fixed-effect meta-analysis from seven studies including 4276 cases and 5318 controls. Our results suggest that the SNP309 variant does not have an impact on the risk of breast [odds ratio (OR) = 0.97, 95% confidence interval (CI) = 0.87-1.08] or colorectal cancers (OR = 0.97, 95% CI = 0.76-1.25). However, the combined estimate of the ORs for lung cancer revealed an increased risk for GG versus TT (OR = 1.27, 95% CI = 1.12-1.44). The data show that SNP309 alone has little or no effect on the risk of common cancers, but it might modify the time of tumor onset and prognosis.     

Genome-wide analysis of allelic imbalance in prostate cancer using the Affymetrix 50K SNP mapping array

Prostate cancer (PCa) is the most commonly diagnosed non-cutaneous cancer in male subjects in Western countries. The widespread use of prostate-specific antigen (PSA) has increased the detection of this cancer form in earlier stages. Moreover, it has increased the need for new diagnostic procedures to be developed for patient stratification based on risk of progression. We analysed laser-microdissected prostate tumour tissue from 43 patients with histologically verified PCa, using the new high-resolution Affymetrix Mapping 50K single-nucleotide polymorphism array. The results showed six major loss of heterozygosity regions at chromosomes 6q14-16, 8p23-11, 10q23, 13q13-21 and 16q21-24 and a novel region at chromosome 21q22.2, all of which reveal concomitant copy number loss. Tumour development was further characterised by numerous novel genomic regions almost exclusively showing copy number loss. However, tumour progression towards a metastatic stage, as well as poor differentiation, was identified by specific patterns of copy number gains of genomic regions located at chromosomes 8q, 1q, 3q and 7q. Androgen ablation therapy was further characterised by copy gain at chromosomes 2p and 10q. In conclusion, patterns of allelic imbalance were discovered in PCa, consisting allelic loss as an early event in tumour development, and distinct patterns of allelic amplification related to tumour progression and poor differentiation.     

An analysis of cancer research funding in the UK

Until recently, a lack of comparable and reliable data on ongoing research activity has been a significant limiting factor in strategic planning for cancer research. This article describes a new initiative in the UK, which is aimed at facilitating the coordination and strategic planning of cancer research at the national level.     

Using high-throughput SNP technologies to study cancer

Identifying genes involved in the development of cancer is crucial to fully understanding cancer biology, for developing novel therapeutics for cancer treatment and for providing methods for cancer prevention and early diagnosis. The use of polymorphic markers, in particular single nucleotide polymorphisms (SNPs), promises to provide a comprehensive tool for analysing the human genome and identifying those genes and genomic regions contributing to the cancer phenotype. This review summarizes the various analytical methodologies in which SNPs are used and presents examples of how each of these methodologies have been used to locate genes and genomic regions of interest for various cancer types. Additionally many of the current SNP-analysing technologies will be reviewed with particular attention paid to the advantages and disadvantages of each and how each technology can be applied to the analysis of the genome for identifying cancer-related genes.     

Tools for protein-protein interaction network analysis in cancer research

As cancer is a complex disease, the representation of a malignant cell as a protein-protein interaction network (PPIN) and its subsequent analysis can provide insight into the behaviour of cancer cells and lead to the discovery of new biomarkers. The aim of this review is to help life-science researchers without previous computer programming skills to extract meaningful biological information from such networks, taking advantage of easyto-use, public bioinformatics tools. It is structured in four parts: the first section describes the pipeline of consecutive steps from network construction to biological hypothesis generation. The second part provides a repository of public, user-friendly tools for network construction, visualisation and analysis. Two different and complementary approaches of network analysis are presented: the topological approach studies the network as a whole by means of structural graph theory, whereas the global approach divides the PPIN into sub-graphs, or modules. In section three, some concepts and tools regarding heterogeneous molecular data integration through a PPIN are described. Finally, the fourth part is an example of how to extract meaningful biological information from a colorectal cancer PPIN using some of the described tools.     

Statistical analysis of DNA microarray data in cancer research.

Microarray techniques have been widely used to monitor gene expression in many areas of biomedical research. They have been widely used for tumor diagnosis and classification, prediction of prognoses and treatment, and understanding of molecular mechanisms, biochemical pathways, and gene networks. Statistical methods are vital for these scientific endeavors. This article reviews recent developments of statistical methods for analyzing data from microarray experiments. Emphasis has been given to normalization of expression from multiple arrays, selecting significantly differentially expressed genes, tumor classifications, and gene expression pathways and networks.     

An analysis of research activity in major UK cancer centres

The organisation of cancer research is critical to its overall creativity and productivity. Cancer centres are a major organisational structure for this research, however, little is known about their effect on research or how national policy-making intersects with this complex policy nexus. This study of the evolution of United Kingdom cancer centres (UKCC), part of a wider European and United States programme, uses a bibliometric analysis of research activity prior to the creation of the NCRI and after its formation (1995-2004/5). In terms of critical research mass UKCC are very heterogeneous with a fourfold difference between the top and bottom quintiles. UK centres published just over one eighth of the total UKCC in 1995 but almost a quarter by 2004. This centrification occurred in the absence of any national strategy. Overall these centres conduct more fundamental (laboratory-based) research than that being conducted in the wider network but this hides major heterogeneity. UKCC collaborate with European investigators in 5-28% of all their outputs and with USA the range is between 6% and 21%. We have also derived new measures of research impact on clinical management and the general public as well as the impact of national policy on research assessment for certain types of cancer research.