论文部分内容阅读
[目的]运用多种表面增强激光解吸-离子化飞行时间质谱(surface-enhanced laser desorption/ionization time of flight mass spectrometry,SELDI-TOF-MS)蛋白质芯片及多元分析方法寻找由乙型肝炎病毒(hepatitis B virus,HBV)感染引起的肝纤维化病程相关的血浆生物标志物。[方法]选用多种SELDI化学表面芯片,比较分析肝纤维化病人和正常血浆样本,筛选和确定3种最佳芯片类型。用这3种芯片分析无肝纤维化、轻度肝纤维化、重度肝纤维化和肝硬化4组共110例患者的血浆样本。运用主成分分析(PCA)、偏最小二乘回归(PLSR)、软独立建模分类法(SIMCA)等数据分析技术寻找差异蛋白。用聚类分析法研究差异蛋白的表达相似性。[结果]3种最适芯片类型分别是弱阳离子交换芯片(WCX2)、强阴离子交换芯片(SAX2)、固定化镍金属螯合亲和层析芯片(IMAC-Ni)。这3种芯片吸附的蛋白质种类互不相同,所发现的差异蛋白质峰也不同。经t检验分析,3种芯片共发现了20个差异蛋白峰。运用PCA、PLSR、SIMCA等数据分析技术,分别发现了105、98、62个差异峰,并对差异峰的重要性的可信度进行衡量。运用聚类分析技术,将差异蛋白的表达模式分组。[结论]联用多种SELDI芯片检测,结合多元分析方法,使SELDI技术成为筛选疾病相关的生物学标志物的有力工具。
[Objective] The aim of the present study was to find out the molecular markers of Hepatitis B virus (HBV) infection caused by hepatitis B virus (hepatitis B virus) using multiple surface enhanced laser desorption / ionization time of flight mass spectrometry (SELDI-TOF- B virus, HBV) -based plasma biomarkers related to the course of liver fibrosis. [Methods] A variety of SELDI chemical surface chips were selected for comparative analysis of liver fibrosis patients and normal plasma samples to screen and determine the three best chip types. The three kinds of chips were used to analyze plasma samples of 110 patients in 4 groups without liver fibrosis, mild liver fibrosis, severe liver fibrosis and cirrhosis. Principal component analysis (PCA), partial least-squares regression (PLSR), soft independent modeling (SIMCA) and other data analysis techniques were used to find the differential proteins. Clustering analysis was used to study the expression similarity of the differentially expressed proteins. [Result] The three optimal chip types were weak cation exchange chip (WCX2), strong anion exchange chip (SAX2) and immobilized nickel metal chelate affinity chromatography chip (IMAC-Ni). The three kinds of chips adsorbed different kinds of proteins, and different protein peaks were found to be different. T test analysis, three kinds of chips found a total of 20 differential protein peaks. Using PCA, PLSR, SIMCA and other data analysis techniques, 105,98,62 differential peaks were found separately, and the credibility of the importance of differential peaks was measured. Clustering analysis was used to classify the expression patterns of the differentially expressed proteins. [Conclusion] The combination of multiple SELDI chip detection combined with multivariate analysis method makes SELDI technology a powerful tool for screening disease-related biomarkers.