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。15个候选癌症易感基因位点的候选基因变异目前正在一项大型病例对照研究中进行检查（n=1000例病例和1000例对照）。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（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。这些数据正在被分析以鉴定杂合性缺失（洛）区域，并且还与使用含有12，000个特征基因的Affytelium HG-U95 A芯片从相同样品收集的基因表达数据相关联。在22条染色体上产生了超过16个HCC的洛标记。我们发现，相对于非洛区域，洛区域中的癌基因（肿瘤基因和肿瘤抑制基因）的数量显著较高。此外，通过同源性重建研究，我们证明了这些洛标记与基因表达结果显著相关;并确定了两个洛标记，4q13.3和17q11.2，这可能是重要的，在产生肝癌洛标记。这项研究现已扩展到包括使用Affyphidogram HG-U133芯片（45，000个探针组）的表达数据和使用Affyphidogram Mapping 10 K Array（10，000个SNPS）用于细化洛区域的SNP数据。使用内部算法和AffytechnologyCCNT工具也产生了数据来研究染色体拷贝数和杂合性丢失的关系。通过表达数据的分层聚类，以及所得聚类与候选易感性位点变异的相关性，将收集的基因表达、候选位点和体细胞等位基因丢失的数据整合。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。HCV作为潜在的siRNA在肝癌发生中可能的功能作用的研究正在进行中。

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 are currently being 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 (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), and is also being correlated with gene expression data collected from the same samples using Affymetrix HG-U95A chips containing 12,000 characterized genes. More than 16 LOH signatures of HCC were generated across 22 chromosomes. We found that the number of cancer genes (tumor genes and tumor suppressor genes) was significantly higher in regions of LOH relative to regions of non-LOH. In addition, through phylogeny reconstruction studies we demonstrated that these LOH signatures correlate significantly with gene expression results; and identified two LOH signatures, 4q13.3 and 17q11.2 that may be important in generating the HCC LOH signature. This study has now been 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). Data has also been generated to investigate the relationship of chromosome copy number and loss of heterozygosity using in-house algorithms and the Affymetrix CCNT tool.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. Investigation into possible functional roles for HCV in liver carcinogensis as potential siRNAs is proceeding.