影视片中的听力理解障碍：分析与策略.docx

青岛大学
硕士学位论文
应用SELDI--TOF--MS技术筛选早期胃癌血清生物标志物研究
姓名:李春伟
申请学位级别:硕士
专业:普通外科
指导教师:张佃良
20110517
应用SELDI-TOF-MS技术筛选早期胃癌血清分子标志物研究
摘要
目的:应用表面加强解吸电离一飞行时间质谱(surface-erthancod laser desorption/ionization -flight mass spectrometry,)技术对胃癌
患者血清蛋白指纹图谱进行分析,筛选候选肿瘤标志物以建立诊断模型,寻找更可
靠的评价标准用于早期胃癌诊断和临床分型以指导个体化治疗,旨在进一步提高对肿瘤分期和预后的判断,以更好地监测治疗效果。
方法:,运用Biomarker Wizard TM和 Biomarker PatternsTM软件分析处理数据筛选具有明确临床价值的分子标记物或其组合,以进行早期分子诊断并建立胃癌诊断分类树模型。
结论:利用SELDI-0F-MS技术在人血清中可以筛选到早期胃癌快速诊断的特异性标志物,建立早期胃癌诊断的蛋白质模型;该方法在胃癌诊断尤其早期胃癌诊断方面具有重要的价值,值得进一步研究。
硕士研究生:李春伟普通外科指导教师:张佃良教授主任医师
关键词:胃癌 分子标志物表面加强解吸电离一飞行时间质谱 蛋白质组模式.
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Analysis of serum biomarkers screening for diagnosis of stage I gastric
cancer using SELDI-TOF-MS
Abstract
Objective:To make a proteomic analysis using surface-enhanced laser desorption/ionization time—of=-flight mass spectrometry(SELDI-TOF—MS)on the serum of stage I gastric cancer patients and establish a early diagnostic model for identifying stage I gastric cancer preliminarily.
Methods:Serum samples from 1 69 gastric cancer patients and 83 age and gender-matched healthy individuals were analyzed by SELDI·-TOF·-MS Protein Chip array resulting SELDI··TOF——MS spectral data were analyzed using the Biomarker Wizard TM and Biomarker PatternsTM software to f'md differential proteins and develop a classification tree for gastric carlcer.
Results:A total of 34 mass peaks were peaks at mass:charge ratios(nVz)
of2873,3 163,4526,5762,6121 and 7778 were used to construct the diagnostic model I.
The model I could effectively distinguish gastric cancel-samples from control samples,
achieving a sensitivity and specifici够of %and %, addition, we identified three of the six protein peaks at 2873,6121and 7778m/z,which could construct the diagnostic model model II could distinguish between stage I gastric cancer and stage II/III/N gastr