Molecular Genetic Epidemiology of Primary Hepatocellular Carcinoma

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的候选基因变异目前正在进行一项大型病例对照研究(n=1000个病例和1000个对照)。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。这些数据正在进行分析，以确定杂合性缺失(LOH)区域，并与使用包含12,000个特征基因的Affymetrix HG-U95A芯片从相同样本中收集的基因表达数据进行关联。在22条染色体上产生了16个以上的肝细胞癌杂合性缺失签名。我们发现杂合性缺失区域的癌基因(肿瘤基因和抑癌基因)的数量明显高于非杂合性缺失区域。此外，通过系统发育重建研究，我们证明了这些LOH标记与基因表达结果显著相关；并确定了两个LOH标记，4q13.3和17q11.2，它们可能在产生肝细胞癌LOH标记中起重要作用。这项研究现在已经扩展到包括使用Affymetrix HG-U133芯片(45,000个探针组)的表达数据和使用Affymetrix Mapping10K阵列(10,000个SNP)精炼LOH区域的SNP数据。还使用内部算法和Affymetrix CCNT工具生成了数据，以研究染色体拷贝数和杂合性丢失的关系。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。作为潜在的siRNA，丙型肝炎病毒在肝癌发生中的可能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