【摘要】 目的筛选肝细胞癌门静脉癌栓形成相关的血清蛋白质分子标记物并建立预测模型。方法收集135例肝细胞癌患者血清,95例(其中33例为肝细胞癌伴门静脉癌栓,62例为肝细胞癌不伴门静脉癌栓)为建模型组,40例(其中18例为肝细胞癌伴门静脉癌栓,22例为肝细胞癌不伴门静脉癌栓)为盲法验证组,采用弱阳离子交换蛋白质芯片(WCX2)为检测介质,经表面加强激光解吸电离飞行时间质谱(SELDITOFMS)测定得到蛋白质谱,通过BioMarkerWizardSoftware比较两组患者血清蛋白质谱,利用BiomarkerPatternsSoftware建立决策树模型并进行盲法验证。结果在m/z1100～30000范围内,检测出的100个蛋白峰中16个蛋白峰有显著差异(P<0.01),m/z为3478、1314、1744、1725、2022和3380的蛋白峰在肝细胞癌伴门静脉癌栓组中上调,而m/z为8901、9353、9415、8773、2766、2745、8697、7773、8569和1373的蛋白峰在肝细胞癌伴门静脉癌栓组中下调;选择m/z为3478、2022、8901、9415、8773、2766、和2745的7个蛋白峰建立的决策树模型其敏感性为75.8%(25/33),特异性为82.3%(51/62);盲法验证该模型准确率87.5%(35/40),灵敏性100%(18/18),特异性77.3%(17/22),阳性预测值78.3%(18/23),阴性预测值100%(17/17)。结论筛选出的16个蛋白分子标记物可能与肝细胞癌门静脉癌栓形成有关;决策树模型可能对预测肝细胞癌门静脉癌栓的发生有重要的临床意义。更多还原

【Abstract】 Objective 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. Methods 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. Results 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. Conclusion 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.更多还原