Poon Terence C W, Yip Tai-Tung, Chan Anthony T C, Yip Christine, Yip Victor, Mok Tony S K, Lee Conrad C Y, Leung Thomas W T, Ho Stephen K W, Johnson Philip J
Department of Clinical Oncology, The Chinese University of Hong Kong, Shatin, Hong Kong, the People's Republic of China.
Clin Chem. 2003 May;49(5):752-60. doi: 10.1373/49.5.752.
Detection of hepatocellular carcinoma (HCC) in patients with chronic liver disease (CLD) is difficult. We investigated the use of comprehensive proteomic profiling of sera to differentiate HCC from CLD.
Proteomes in sera from 20 CLD patients with alpha-fetoprotein (AFP) <500 microg/L (control group) and 38 HCC patients (disease group) were profiled by anion-exchange fractionation (first dimension), two types (IMAC3 copper and WCX2) of ProteinChip Arrays (second dimension), and time-of-flight mass spectrometry (third dimension). Bioinformatic tests were used to identify tumor-specific proteomic features and to estimate the values of the tumor-specific proteomic features in the diagnosis of HCC. Cross-validation was performed, and we also validated the models with pooled sera from the control and disease groups, serum from a CLD patient with AFP >500 microg/L, and postoperative sera from two HCC patients.
Among 2384 common serum proteomic features, 250 were significantly different between the HCC and CLD cases. Two-way hierarchical clustering differentiated HCC and CLD cases. Most HCC cases with advanced disease were clustered together and formed two subgroups that contained significantly more cases with lymph node invasion or distant metastasis. For differentiation of HCC and CLD by an artificial network (ANN), the area under the ROC curve was 0.91 (95% confidence interval, 0.82-1.01; P <0.0005) for all cases and 0.954 (95% confidence interval, 0.881-1.027; P <0.0005) for cases with nondiagnostic serum AFP (<500 microg/L). At a specificity of 90%, the sensitivity was 92%. Both cluster analysis and ANN correctly classified the pooled serum samples, the CLD serum sample with increased AFP, and the HCC patient in complete remission.
Tumor-specific proteomic signatures may be useful for detection and classification of hepatocellular cancers.
在慢性肝病（CLD）患者中检测肝细胞癌（HCC）具有一定难度。我们研究了利用血清的综合蛋白质组分析来区分HCC与CLD。
通过阴离子交换分级分离（第一维）、两种类型（IMAC3铜离子和WCX2）的蛋白质芯片阵列（第二维）以及飞行时间质谱（第三维）对20例甲胎蛋白（AFP）<500μg/L的CLD患者（对照组）和38例HCC患者（疾病组）的血清蛋白质组进行分析。采用生物信息学测试来识别肿瘤特异性蛋白质组特征，并评估这些肿瘤特异性蛋白质组特征在HCC诊断中的价值。进行了交叉验证，我们还使用对照组和疾病组的混合血清、1例AFP>500μg/L的CLD患者的血清以及2例HCC患者的术后血清对模型进行了验证。
在2384个常见血清蛋白质组特征中，HCC和CLD病例之间有250个存在显著差异。双向层次聚类区分了HCC和CLD病例。大多数晚期HCC病例聚集在一起，形成了两个亚组，其中淋巴结侵犯或远处转移的病例明显更多。对于通过人工神经网络（ANN）区分HCC和CLD，所有病例的ROC曲线下面积为0.91（95%置信区间，0.82 - 1.01；P<0.0005），血清AFP非诊断性（<500μg/L）的病例为0.954（95%置信区间，0.881 - 1.027；P<0.0005）。在特异性为90%时，敏感性为92%。聚类分析和ANN均正确分类了混合血清样本、AFP升高的CLD血清样本以及完全缓解的HCC患者。
肿瘤特异性蛋白质组特征可能有助于肝细胞癌的检测和分类。
