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质谱在肝脏良恶性肿瘤鉴别中的应用价值

Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors.

作者信息

Li Bo, Li Boan, Guo Tongsheng, Sun Zhiqiang, Li Xiaohan, Li Xiaoxi, Wang Han, Chen Weijiao, Chen Peng, Qiao Mengran, Xia Lifang, Mao Yuanli

机构信息

Center for Clinical Laboratory, 302 Hospital of PLA, Beijing, China (mainland).

Graduate Student Team, Medical University of PLA, Beijing, China (mainland).

出版信息

Med Sci Monit. 2017 Apr 4;23:1636-1644. doi: 10.12659/msm.901064.

Abstract

BACKGROUND Differentiation of malignant from benign liver tumors remains a challenging problem. In recent years, mass spectrometry (MS) technique has emerged as a promising strategy to diagnose a wide range of malignant tumors. The purpose of this study was to establish classification models to distinguish benign and malignant liver tumors and identify the liver cancer-specific peptides by mass spectrometry. MATERIAL AND METHODS In our study, serum samples from 43 patients with malignant liver tumors and 52 patients with benign liver tumors were treated with weak cation-exchange chromatography Magnetic Beads (MB-WCX) kits and analyzed by the Matrix-Assisted Laser Desorption Time of Flight Mass Spectrometry (MALDI-TOF-MS). Then we established genetic algorithm (GA), supervised neural networks (SNN), and quick classifier (QC) models to distinguish malignant from benign liver tumors. To confirm the clinical applicability of the established models, the blinded validation test was performed in 50 clinical serum samples. Discriminatory peaks associated with malignant liver tumors were subsequently identified by a qTOF Synapt G2-S system. RESULTS A total of 27 discriminant peaks (p<0.05) in mass spectra of serum samples were found by ClinPro Tools software. Recognition capabilities of the established models were 100% (GA), 89.38% (SNN), and 80.84% (QC); cross-validation rates were 81.67% (GA), 81.11% (SNN), and 86.11% (QC). The accuracy rates of the blinded validation test were 78% (GA), 84% (SNN), and 84% (QC). From the 27 discriminatory peptide peaks analyzed, 3 peaks of m/z 2860.34, 2881.54, and 3155.67 were identified as a fragment of fibrinogen alpha chain, fibrinogen beta chain, and inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), respectively. CONCLUSIONS Our results demonstrated that MS technique can be helpful in differentiation of benign and malignant liver tumors. Fibrinogen and ITIH4 might be used as biomarkers for the diagnosis of malignant liver tumors.

摘要

背景 鉴别肝脏恶性肿瘤与良性肿瘤仍然是一个具有挑战性的问题。近年来,质谱(MS)技术已成为诊断多种恶性肿瘤的一种有前景的策略。本研究的目的是建立分类模型以区分肝脏良性和恶性肿瘤,并通过质谱鉴定肝癌特异性肽段。

材料与方法 在我们的研究中,43例肝脏恶性肿瘤患者和52例肝脏良性肿瘤患者的血清样本用弱阳离子交换色谱磁珠(MB-WCX)试剂盒处理,并通过基质辅助激光解吸飞行时间质谱(MALDI-TOF-MS)进行分析。然后我们建立了遗传算法(GA)、监督神经网络(SNN)和快速分类器(QC)模型来区分肝脏恶性肿瘤与良性肿瘤。为了确认所建立模型的临床适用性,在50份临床血清样本中进行了盲法验证试验。随后通过qTOF Synapt G2-S系统鉴定与肝脏恶性肿瘤相关的鉴别峰。

结果 ClinPro Tools软件在血清样本质谱图中总共发现了27个判别峰(p<0.05)。所建立模型的识别能力分别为100%(GA)、89.38%(SNN)和80.84%(QC);交叉验证率分别为81.67%(GA)、81.11%(SNN)和86.11%(QC)。盲法验证试验的准确率分别为78%(GA)、84%(SNN)和84%(QC)。在所分析的27个鉴别肽峰中,m/z为2860.34、2881.54和3155.67的3个峰分别被鉴定为纤维蛋白原α链、纤维蛋白原β链和α-胰蛋白酶抑制剂重链H4(ITIH4)的片段。

结论 我们的结果表明,质谱技术有助于鉴别肝脏良性和恶性肿瘤。纤维蛋白原和ITIH4可能用作诊断肝脏恶性肿瘤的生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8451/5388305/3c1c2ad6113f/medscimonit-23-1636-g001.jpg

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