多种芯片检测和多元分析法在肝纤维化患者血浆蛋白表达谱分析中的初步应用
Combination of Multi Chips and Multi Data Analysis Methods in a Primary Analysis of Plasma Proteome in Liver Fibrosis Patients
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摘要:
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。 结论 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。 Abstract:Objective To analyze plasma proteome in hepatitis B virus (HBV)related liver fibrosis patients using various surface-enhanced laser desorption/ionization (SELDI) chips and multi data analysis methods. Methods Protein expressions of the plasma samples from healthy donates were compared by various kinds of SELDI chips. Three kinds of chips were chosen for further analysis of 110 samples divided into 4 groups (no fibrosis, low fibrosis, high fibrosis and cirrhosis). Besides t test analysis by Biomarker Wizard software provided by manufacture, PCA(principal component analysis), PLSR(palletized load system regression), SIMCA (soft modeling of class independent analogy)and clustering analysis were used for data analysis. Results WCX2 (weak cation exchange), SAX2 (strong anion exchange), IMAC-N(i immobilized nickel chelate affinity chromatography) chips were used for protein profiling. Twenty differential protein peaks were found by t test. However, 105, 98 and 62 differential protein peaks were found by PCA, PLSR and SIMCA, respectively. The validity of these differential peaks was also analyzed. By clustering analysis, the protein expression patterns were divided into three kinds. Conclusion The combination of multi SELDI chips and multi data analysis methods could enhance the ability of SELDI technology in disease biomarker discovery.
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