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近日，中国科学院大连化学物理研究所高分辨分离分析及代谢组学研究组，在肝癌诊断型代谢标志物研究中取得新进展，相关结果发表在Hepatology杂志上。
肝细胞癌（Hepatocellular carcinoma，HCC）是世界范围内发病率位居第六
、致死率居第三的恶性肿瘤，乙肝病毒感染是肝癌的主要风险因素。我国是肝癌的高发区，降低肝癌发病率、死亡率是国家科技重大专项的目标之一。由于肝癌早期无明显临床症状，发展快且易转移，临床发现时大多已是中晚期，临床治疗效果不佳。为解决这一难题，急需发展新型、稳定可靠的方法实现肝癌的早期筛查。针对此问题，研究团队联合吉林大学附属第一医院、第二军医大学东方肝胆外科医院、华中科技大学同济医学院、厦门大学中山医院、北京大学人民医院、山东省立医院等6家临床相关研究机构，纳入1448例受试者，受试者中包括健康对照、慢性乙肝肝炎、肝硬化和肝癌等。研究采用大连化物所自主开发的基于LC-MS的大规模代谢组分析技术，鉴定和验证了一组新型的肝癌组合代谢标志物：甘氨酸胆酸、苯丙酰色氨酸。大规模临床验证结果显示，该组合标志物能够有效的在高风险的肝硬化患者人群中发现肝癌患者，其受试者操作特征曲线下面积（AUC）达到0.807-0.930，优于传统的肝癌临床标志物甲胎蛋白（AFP）的0.65-0.725，同时与AFP具有较好的互补性。二者联合应用可有效避免AFP阴性肝癌患者的漏诊，联合应用的诊断正确率可达80.6%-100%。
同时，该组合
标志物能够从肝硬化患者中灵敏的发现小肝癌（单一肿瘤直径<2cm）患者（AUC：0.753-0.866）。对肝细胞癌的诊断具有较好的特异性，不受胃癌、胆管细胞癌等疾病的干扰。
基于巢式人群队列研究的数据表明，
该组合标志物可在肝癌发生前1年对高危人群提供风险预测（AUC：0.79），联合AFP可进一步提高预测准确率（AUC：0.88），这一标志物组合有望用作肝癌临床前预测的指标。
研究工作得到国家重点研发计划、国家科技重大专项、国家基金委重点项目和面上项目等的资助。（来源：
中国科学院大连化学物理研究所）
论文链接
大连化物所肝癌诊断型代谢标志物研究取得进展
Abstract
Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1448 subjects, mainly including normal controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC were recruited from multi-center in China. Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl-tryptophan and glycocholate, was defined. This panel had a higher diagnostic performance than did α-fetoprotein (AFP) in differentiating HCC from a high-risk population of cirrhosis, such as area under the receiver-operating characteristic curve (AUC) of 0.930, 0.892, 0.807 for panel versus 0.657, 0.725, 0.650 for AFP in the discovery, test and cohort 1 of the validation set, respectively. In the nested case-control study, this panel had high sensitivity (rang 80.0-70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger AUC than did AFP (0.866 versus 0.682) to distinguish small-HCC, and 80.6% of the AFP false-negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from other two cancers and HCC tissue specimens, respectively.
CONCLUSIONS:
The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at-risk populations. This article is protected by copyright. All rights reserved.
