Phenotypic Characterization Analysis of Human Hepatocarcinoma by Urine Metabolomics Approach.
Phenotypic Characterization Analysis of Human Hepatocarcinoma by Urine Metabolomics Approach.
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DOI：
10.1038/srep19763
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发表时间：
2016-01-25
影响因子：
4.6
通讯作者：
Li B
中科院分区：
文献类型：
--
作者：
Liang Q;Liu H;Wang C;Li B
肝癌（HCC）是世界上最致命的癌症之一，是一个重要的疾病负担。早期发现HCC需要更好的生物标志物。将代谢组学应用于HCC患者的尿液样本，以发现无创且可靠的生物标志物，用于HCC的快速诊断。通过LC-Q-TOF-MS进行代谢谱分析，结合多变量数据分析、机器学习方法、独创性途径分析和受体工作特征曲线来选择用于HCC无创诊断的代谢物。15种差异代谢物有助于HCC患者与匹配的健康对照完全分离，涉及几个关键的代谢途径。更重要的是，5种标志物代谢物对人类HCC的诊断是有效的，分别达到96.5%的敏感性和83%的特异性，可以显著提高代谢生物标志物的诊断效能。总的来说，这些结果说明了代谢组学技术的力量，它有潜力作为一种非侵入性策略和有前途的筛查工具来评估代谢物在HCC高危患者早期诊断中的潜力，并为病理生理机制提供新的见解。
Hepatocarcinoma (HCC) is one of the deadliest cancers in the world and represents a significant disease burden. Better biomarkers are needed for early detection of HCC. Metabolomics was applied to urine samples obtained from HCC patients to discover noninvasive and reliable biomarkers for rapid diagnosis of HCC. Metabolic profiling was performed by LC-Q-TOF-MS in conjunction with multivariate data analysis, machine learning approaches, ingenuity pathway analysis and receiver-operating characteristic curves were used to select the metabolites which were used for the noninvasive diagnosis of HCC. Fifteen differential metabolites contributing to the complete separation of HCC patients from matched healthy controls were identified involving several key metabolic pathways. More importantly, five marker metabolites were effective for the diagnosis of human HCC, achieved a sensitivity of 96.5% and specificity of 83% respectively, could significantly increase the diagnostic performance of the metabolic biomarkers. Overall, these results illustrate the power of the metabolomics technology which has the potential as a non-invasive strategies and promising screening tool to evaluate the potential of the metabolites in the early diagnosis of HCC patients at high risk and provides new insight into pathophysiologic mechanisms.