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（HCC）。肿瘤遗传改变的分子研究已经确定p53是HCC中常见改变的肿瘤抑制基因。流行病学研究已经确定了慢性B肝炎病毒感染（HBV）和黄曲霉毒素B1（AFB 1）暴露作为环境危险因素的作用。然而，大多数暴露于HBV和AFB 1的个体不会发生HCC。 遗传分析被用来评估基因在确定疾病的明确途径中的作用。这种方法将基因定位和候选基因座研究结合起来，将一条通路的所有成员都作为候选基因。每个感兴趣的基因都在其内部或附近用多个多态性位点“标记”，以确定在暴露于AFB 1的人群中调节发生HCC风险的遗传因素。每个家族（GSTA 1，GSTM 1，GSTM 3，GSTP，GSTT 1，GST 12，EPHX 1，EPHX 2，GSTA 4，GSTT 2，GSTZ 1，STP，COMT，ESD，DTD，ESTZ，MGST 1）的个体成员已被标记新的或已发表的多态性，并在巢式病例对照人群中研究了它们在HCC风险中的作用。GSTM 1、GSTP、GSTT 1、EPHX 1基因座与肝癌发病风险显著相关，EPHX 2基因座与发病年龄显著相关。当结果按HBV状态分层时，GSTM 1和GSTT 1仅在HBV（+）病例中相关，而GSTP在HBV（-）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人。还将在一项大型病例对照研究（n=1000例病例和1000例对照）中检查15个候选癌症易感性位点的候选基因变异。 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。使用全基因组简单串联重复多态性（STRP）标记物、候选基因座和Affytek HuSNP芯片上存在的1，300个单核苷酸多态性（SNP）的集合检查HCC肿瘤/正常对。分析这些数据以确定杂合性缺失（洛）区域。初步分析表明，染色体3、4、8、9、16和17上的洛区域与使用含有约12，000个特征基因的Affyphidogram HG-U95 A芯片从相同样品收集的基因表达数据相关。该研究将扩展到包括使用Affyphidogram HG-U133芯片的表达数据（约45，000个探针组）和使用Affyphidogram Mapping 10 K Array（约10，000个SNP）用于细化洛区域的SNP数据。此外，上述更大规模的病例-病例对照研究将用于本扩展分析。 收集的基因表达、候选基因座和体细胞等位基因丢失数据将通过表达数据的分层聚类以及所得聚类与候选易感基因座变异的相关性进行整合。这些信息将用于开发，测试和验证癌症/正常细胞途径模型的实验室策略。

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.