Molecular Genetic Epidemiology of Primary Hepatocellular

大多数癌症表现为复杂的表型，并通过基因-基因，和/或基因-环境相互作用表现出来。研究人类复杂癌症表型的理想范例是原发性肝细胞癌。对肿瘤遗传改变的分子研究已经证实，P53是一种肿瘤抑制基因，在肝细胞癌中通常会改变。流行病学研究已经确定慢性乙肝病毒感染(乙肝病毒)和黄曲霉毒素B1(AFB1)暴露是环境风险因素。然而，大多数暴露在乙肝病毒和黄曲霉毒素B受体1下的人不会患上肝癌。 基因分析正被用来评估基因在确定疾病的过程中所起的作用。这种方法融合了基因作图和候选基因座研究，将一条路径的所有成员都作为候选。每个感兴趣的基因都被标记了多个多态位点，在其内部或附近，以确定在暴露于AFB1的人群中调节发生肝癌风险的遗传因素。在嵌套病例对照人群中，每个家族的个体成员(GSTA1、GSTM1、GSTM3、GSTP、GSTT1、GST12、EPHX1、EPHX2、GSTA4、GSTT2、GSTZ1、STP、COMT、ESD、DTD、CYP、MGST1)都被标记了新的或已公布的多态，并研究了它们在肝癌风险中的作用。GSTM1、GSTP、GSTT1、EPHX1基因座与肝癌风险显著相关，EPHX2基因座与发病年龄相关。当结果按患者的乙肝病毒状态分层时，GSTM1和GSTT1仅在乙肝病毒()病例中相关，而GSTP在乙肝病毒(-)病例中相关。这些结果表明，这些基因是更详细的功能和遗传分析的候选基因。15个候选癌症易感基因位点的候选基因变异也将在一项大型病例对照研究中进行检查(n=1000例病例和1000名对照)。 在复杂性状分析中重要的遗传信息可以从癌症的遗传变异和体细胞组织(肿瘤)变异的联合研究中获得。使用一系列全基因组的简单串联重复序列多态(STRP)标记、候选基因座和Affymetrix HuSNP芯片上存在的1300个单核苷酸多态(SNPs)来检测肝癌肿瘤/正常配对。正在对这些数据进行分析，以确定杂合性缺失(LOH)的区域。初步分析表明，染色体3、4、8、9、16和17上的LOH区域与使用Affymetrix HG-U95A芯片收集的基因表达数据相关，这些芯片包含约12,000个特征基因。这项研究将扩大到包括使用Affymetrix HG-U133芯片(约45,000个探针组)的表达数据和使用Affymetrix映射10K阵列(约10,000个SNP)精炼LOH区域的SNP数据。此外，上述较大规模的病例-病例对照研究将用于这一扩展分析。 收集的关于基因表达、候选基因座和体细胞等位基因丢失的数据将通过表达数据的分层聚类以及所产生的聚类与候选易感基因座的变异的关联来整合。这些信息将被用于开发、测试和验证癌症/正常细胞通路模型的实验室策略。

The majority of cancer presents as a complex phenotype and is manifest through gene-gene, and/or gene-environment interactions. An ideal paradigm for the investigation of complex cancer phenotypes in humans is primary hepatocellular carcinoma (HCC). Molecular studies of genetic alterations in tumors have identified p53 as a tumor suppressor gene commonly altered in HCC. Epidemiologic studies have firmly established the role of chronic hepatitis B virus infection (HBV) and aflatoxin B1 (AFB1) exposure as environmental risk factors. However, the majority of individuals exposed to HBV and AFB1 do not develop HCC. Genetic analysis is being used to assess the role of genes in well-described pathways in determining disease. This approach merges gene mapping and candidate locus studies by including as candidates all the members of a pathway. Each gene of interest is "tagged" with multiple polymorphic sites, in or near it, to identify genetic factors modulating the risk of developing HCC among populations exposed to AFB1. The individual members of each family (GSTA1, GSTM1, GSTM3, GSTP, GSTT1, GST12, EPHX1, EPHX2, GSTA4, GSTT2, GSTZ1, STP, COMT, ESD, DTD, CYP, MGST1) have been tagged with new or published polymorphisms, and their role in HCC risk examined, in a nested case-control population. The loci GSTM1, GSTP, GSTT1, EPHX1 showed significant association with HCC risk while the EPHX2 locus was associated with age of onset. When results were stratified by the HBV status of the case, GSTM1 and GSTT1 were associated only in the HBV(+) cases, while GSTP was associated in the HBV(-) cases. These results indicate that these genes are candidates for more detailed functional and genetic analysis. Candidate gene variation at the 15 candidate cancer susceptibility loci will also be examined in a large case-control study (n=1000 cases and 1000 controls). Genetic information important in complex trait analysis may be accessible from the joint study of heritable variation and somatic tissue (tumor) variation in cancer. HCC tumor/normal pairs were examined using a collection of genome-wide simple tandem repeat polymorphism (STRP) markers, candidate loci, and the 1,300 single nucleotide polymorphisms (SNPs) present on the Affymetrix HuSNP chip. This data is being analyzed to identify regions of loss of heterozygosity (LOH). A preliminary analysis indicating regions of LOH on chromosomes 3,4,8,9,16 and 17 is being correlated with gene expression data collected from the same samples using Affymetrix HG-U95A chips containing ~12,000 characterized genes. The study will be expanded to include expression data using the Affymetrix HG-U133 chips (~ 45,000 probe sets) and SNP data for refining the regions of LOH using the Affymetrix Mapping 10K Array (~10,000 SNPS). In addition, the larger case-case control study mentioned above will be utilized in this expanded analysis. Data collected on gene expression, candidate loci, and somatic allele loss will be integrated via hierarchical clustering of expression data, and correlation of the resulting clusters with variation at candidate susceptibility loci. This information will be used to develop, test, and validate laboratory strategies for pathway models of the cancer/normal cell.