Analysis of Racial Disparities in HCC by Systems Metabolomics

 描述(由申请人提供)：在美国，肝细胞癌是一个严重的健康问题。与欧洲裔美国人(EA)相比，非裔美国人(AA)的肝癌发病率较高，且与确诊时肿瘤分期较高和生存率较低有关。有报道称，甲胎蛋白对感染丙型肝炎病毒的非裔美国人诊断肝癌的敏感性低于所有其他种族患者的总和。我们先前通过使用LC-MS(LC-MS)和GC-MS(GC-MS)对肝细胞癌患者和肝硬变患者的血清进行代谢组学分析，初步调查了种族差异。通过对LC/GC-MS数据的分层分析，我们确定了在AA和EA中区分肝细胞癌病例和肝硬变对照病例的不同候选者。这项应用建立在这些有希望的初步结果的基础上，以寻找和验证种族特异性代谢物作为肝细胞癌的生物标记物。这将通过有针对性地分析肝组织和血清中代表AA和EA的肝组织和血清中的代谢物来实现。以特定种族的方式区分肝细胞癌病例和肝硬变对照的代谢物将通过统计和基于网络的方法进行选择。然后，一种面向系统的方法将这些代谢物与其他候选生物标记物(基因、多糖和蛋白质)相结合，用于选择肝细胞癌中受干扰的关键信号通路。在通过来自肝癌病例、肝硬变患者和健康受试者的独立样本验证候选之后，我们将通过体外和体内实验来阐明它们的功能作用。成功完成这项工作 研究将使我们能够识别特定于种族的肝癌生物标志物和受干扰的通路，以及那些由AA和EA共享的生物标志物和干扰通路。这些发现不仅有助于更好地了解肝癌的种族差异，而且有助于通过种族特异性生物标志物提高肝癌的诊断水平。

 DESCRIPTION (provided by applicant): Hepatocellular carcinoma (HCC) is a significant health problem in the United States. Compared to European Americans (EA), the incidence of HCC is higher in African Americans (AA) and is associated with more advanced tumor stage at diagnosis and lower survival rates. It has been reported that the sensitivity of α- fetoprotein (AFP) for the diagnosis of HCC in African Americans with hepatitis C virus (HCV) infection is lower than that of patients of all other racial groups combined. We previously performed preliminary investigation into racial disparities through metabolomics profiling of sera from HCC cases and patients with liver cirrhosis by using both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Through stratified analysis of the LC/GC-MS data, we identified different candidates that distinguish HCC cases from the cirrhotic controls among AA and EA. This application builds on these promising preliminary results to find and validate race-specific metabolites as biomarkers for HCC. This will be accomplished by targeted analysis of metabolites in liver tissues and sera from HCC cases and patients with liver cirrhosis representing AA and EA. Metabolites that differentiate HCC cases from cirrhotic controls in a race-specific manner will be selected by statistical and network-based methods. Then, a systems-oriented approach that combines these metabolites with other candidate biomarkers (genes, glycans, and proteins) will be utilized for the selection of key signaling pathways perturbed in HCC. Following validation of the candidates via independent samples from HCC cases, patients with liver cirrhosis, and healthy subjects, we will elucidate their functional roles through both in vitro and in vivo experiments. Successful completion of this research will enable us to identify HCC biomarkers and perturbed pathways that are race-specific as well as those that are shared by AA and EA. These findings will contribute not only to a greater understanding of racial disparities in HCC but also to improving diagnosis of HCC through race-specific biomarkers.