Huang Cheng, Fan Jia, Zhou Jian, Liu Yin-kun, Cui Jie-feng, Kang Xiao-nan, Yang Peng-yuan, Tang Zhao-you
Liver Cancer Institute & Department of Liver Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Zhonghua Yi Xue Za Zhi. 2005 Mar 23;85(11):781-5.
To screen serum proteome biomarkers and establish predictive model with relation to the formation of portal vein tumor thrombi (PVTT) in hepatocellular carcinoma (HCC) patients.
Serum samples were collected from 135 HCC patients, which were divided, into training set (including 33 HCC patients with PVTT and 62 HCC patients without PVTT) and blind testing set (including 18 HCC patients with PVTT and 22 HCC patients without PVTT). Special serum protein or peptide pattern was determined by SELDI-TOF-MS measurement after treating the sample onto WCX2 protein chip for each case. The obtained data were analyzed by BioMarker Wizard software to screen serum proteome biomarkers with relation to the formation of PVTT, while decision tree classification algorithm and blind validation were determined by Biomarker Patterns Software.
Ranging from 1100 to 30 000 at the m/z value, 100 protein features were detected in the serum protein pattern stably. Among them, 6 protein peaks with the m/z value of 3478, 1314, 1744, 1725, 2022 and 3380 were upregulated, 10 proteins peaks with the m/z value of 8901, 9353, 9415, 8773, 2766, 2745, 8697, 7773, 8569 and 1373 were downregulated respectively in the group of HCC with PVTT. The 7 candidate protein peaks with the m/z value of 3478, 2022, 8901, 9415, 8773, 2766 and 2745 were selected to establish predictive model by BPS with a sensitivity of 75.8% (25/33) and specificity of 82.3% (51/62). An accuracy of 87.5% (35/40), sensitivity of 100% (18/18), specificity of 77.3% (17/22), positive predictive value of 78.3% (18/23), and negative predictive value of 100% (17/17) were validated in blind testing set.
Sixteen candidate proteome biomarkers may be related with the formation of PVTT in HCC patients. Decision tree classification algorithm may have great clinical significance in predicting the formation of PVTT.
筛选血清蛋白质组生物标志物，并建立与肝细胞癌（HCC）患者门静脉癌栓（PVTT）形成相关的预测模型。
收集135例HCC患者的血清样本，分为训练集（包括33例伴有PVTT的HCC患者和62例不伴有PVTT的HCC患者）和盲法测试集（包括18例伴有PVTT的HCC患者和22例不伴有PVTT的HCC患者）。对每个样本在WCX2蛋白芯片上处理后，通过表面增强激光解吸电离飞行时间质谱（SELDI-TOF-MS）测定特殊的血清蛋白质或肽图谱。所得数据用BioMarker Wizard软件进行分析，以筛选与PVTT形成相关的血清蛋白质组生物标志物，同时用Biomarker Patterns软件确定决策树分类算法并进行盲法验证。
在质荷比（m/z）值为1100至30000范围内，在血清蛋白质图谱中稳定检测到100个蛋白质特征峰。其中，质荷比为3478、1314、1744、1725、2022和3380的6个蛋白峰在伴有PVTT 的HCC组中上调，质荷比为8901、9353、9415、8773、2766、2745、8,697、7773、8569和1373的10个蛋白峰分别下调。选择质荷比为3478、2022、8901、9415、8773、2766和2745的7个候选蛋白峰，用Biomarker Patterns Software（BPS）建立预测模型，灵敏度为75.8%（25/33），特异度为82.3%（51/62）。在盲法测试集中验证的准确率为87.5%（35/40），灵敏度为100%（18/18），特异度为77.3%（17/22），阳性预测值为78.3%（18/23），阴性预测值为100%（17/17）。
16个候选蛋白质组生物标志物可能与HCC患者PVTT的形成有关。决策树分类算法在预测PVTT的形成方面可能具有重要的临床意义。
