Molecular Genetic Epidemiology of Primary Hepatocellular

原发性肝细胞的分子遗传学流行病学

• 批准号: 7288880
• 负责人: Kenneth H Buetow
• 金额: --
• 依托单位: DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7288880
• 关键词: Molecular; Genetic; Epidemiology; Primary; Hepatocellular

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.

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。用于研究人类复杂癌症表型的理想范式是原发性肝细胞癌（HCC）。肿瘤中遗传改变的分子研究已将p53鉴定为通常在HCC中改变的肿瘤抑制基因。流行病学研究已经牢固确定了慢性丙型肝炎病毒感染（HBV）和黄曲霉毒素B1（AFB1）的作用，作为环境风险因素。但是，暴露于HBV和AFB1的大多数人不会发展HCC。遗传分析用于评估基因在确定疾病中描述的途径中的作用。这种方法通过将途径的所有成员包括在内，将基因映射和候选基因座研究融合在一起。每个感兴趣的基因在其附近或附近都被“标记”，以确定调节暴露于AFB1的种群中HCC的风险的遗传因素。 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 人口。基因座GSTM1，GSTP，GSTT1，EPHX1与HCC风险显示显着相关，而EPHX2基因座与发病年龄有关。当结果按病例的HBV状态进行分层时，GSTM1和GSTT1仅在HBV（+）情况下关联，而GSTP在HBV（ - ）情况下是相关的。这些结果表明，这些基因是用于更详细的功能和遗传分析的候选者。目前正在一项大型病例对照研究（n = 1000例和1000个对照组）中检查15个候选癌症易感性基因座的候选基因变异。在复杂性状分析中重要的遗传信息可以从癌症中可遗传性变异和体细胞（肿瘤）变异的联合研究中获取。使用全基因组简单串联重复多态性（STRP）标记，候选基因座和1,300个单核苷酸多态性（SNP）的集合来检查HCC肿瘤/正常对。正在分析该数据以识别杂合性丧失（LOH）的区域，并且也与使用含有12,000个特征基因的Affymetrix HG-U95A芯片相关的基因表达数据相关。在22个染色体上产生了超过16个HCC的LOH特征。我们发现，相对于非LOH区域，LOH区域中癌症基因的数量（肿瘤基因和肿瘤抑制基因）的数量明显更高。此外，通过系统发育重建研究，我们证明了这些LOH特征与基因表达结果显着相关。并确定了两个LOH签名，即4Q13.3和17Q11.2，这对于生成HCC LOH签名可能很重要。现已扩展了这项研究，以使用Affymetrix HG-U133芯片（45,000个探针集）和SNP数据包括表达数据，并使用Affymetrix映射10K阵列（10,000 SNP）来完善LOH的区域。还生成了数据来研究使用内部算法的染色体拷贝数和杂合性丧失的关系，以及在基因表达，候选基因座和体细胞等位基因损失上收集的affymetrix ccnt工具。该信息将用于开发，测试和验证癌症/正常细胞途径模型的实验室策略。随着潜在的siRNA的进行，对HCV在肝癌中可能的功能作用的研究正在进行中。