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作 者:李长煜[1] 王小林[1] 龚高全[1] 程洁敏[1] 陈颐[1] 刘凌晓[1] 李国平[1] 王成刚[1] 施东华[1]
机构地区:[1]复旦大学附属中山医院放射科,上海200032
出 处:《复旦学报(医学版)》2010年第4期459-463,共5页Fudan University Journal of Medical Sciences
摘 要:目的应用液体蛋白质芯片飞行时间质谱技术研究肝癌门静脉癌栓(portal vein tumor thrombus,PVTT)形成相关标志物,并建立预测模型。方法采取非PVTT和PVTT原发性肝癌(hepatocellularcarcinoma,HCC)患者血清各20例,采用磁珠分离系统纯化、基质辅助激光解析电离飞行时间质谱(matrix-assisted laser desorption/ionization ti me of flight mass spectrometry,MALDI-TOF-MS)分析及ClinProTools生物信息学方法研究其血清蛋白表达谱,通过神经网络算法(statistical neural networks,SNN)建立图谱诊断模型并进行验证。结果经数据对比分析找到39个差异有统计学意义的质荷比(m/z)峰(P<0.05),从中筛选出最具划分能力的8个质荷比峰(m/z=4 420.43、4 055.19、2 988.80、4 091.46、4 072.24、6 935.04、4 109.53和5 321.17),并以此建立HCC的PVTT诊断模型,模型识别率达到97.5%,而预测率达到92.73%。非PVTT组和PVTT组的准确性分别为100%和95%。结论筛选得到的蛋白可能成为原发性肝癌PVTT的相关标志物,诊断模型可能对预测PVTT的形成具有重要临床意义。Objective To screen potential protein biomarkers associated with the formation of portal vein tumor thrombus(PVTT) in hepatocellular carcinoma(HCC) patients,and to build a predictive model. Methods The serum samples from 40 HCC patients(20 with PVTT and 20 free of PVTT) were enriched by the magnetic bead and analyze by matrix-assisted laser desorption/ionization time of flight mass spectrometry(MALDI-TOF-MS) and ClinProTools,respectively.Diagnostic model was established and identified by statistical neural networks(SNN). Results Thirty-nine peaks with significant statistical differences in mass charge ratio(m/z) were found(P0.05).Eight protein peaks(m/z=4 420.43,4 055.19,2 988.80,4 091.46,4 072.24,6 935.04,4 109.53 and 5 321.17) among them were selected to establish the predictive model of PVTT.Specificity and accuracy of this model was 97.5% and 92.73%,repectively.Predictive values in patients with and without PVTT were 100% and 95%. Conclusions These 8 protein peaks may be potential biomarkers of PVTT in HCC patients,and the predictive model may have great clinical significance in predicting the formation of PVTT.
关 键 词:原发性肝癌 门静脉癌栓 液体蛋白芯片飞行时间质谱 蛋白质组
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