用激光捕获显微切割联合蛋白质芯片筛选肺鳞癌和腺癌组织差异蛋白

目的 应用激光捕获显微切割(LCM)联合表面增强激光解吸离子化飞行时间质谱(SELDI-TOF-MS)蛋白质芯片及支持向量机(SVM)方法筛选肺鳞癌和腺癌差异表达蛋白质,探讨二者在蛋白水平的差异,为筛选肺癌分型标志物提供依据.方法 将6例新鲜肺鳞癌及7例腺癌组织标本用LCM选择性获取1.4×105个同质鳞癌细胞和1.2×105个同质腺癌细胞.经PBS Ⅱ+型SELDI-TOF-MS分析仪(IMAC芯片)分析鳞癌及腺癌细胞蛋白质表达谱,比对差异峰;应用SVM筛选并验证候选标志蛋白的判别效能.结果 比较鳞癌和腺癌细胞的SELDI谱图,共筛选出87个蛋白峰.将差异最明显的10个蛋白峰作为候选标志蛋白.与腺癌相比,4种蛋白(相对分子质量分别为2505、4004、4847及11 412)在鳞癌中呈高表达;与鳞癌相比,6种蛋白(相对分子质量分别为3333、3592、3848、5036、5191及5211)在腺癌中呈高表达.其中相对分子质量为4847的蛋白在鳞癌和腺癌中表达差异有统计学意义.用SVM建立分类预测模型并评价各模型效能,筛选出一个由3种蛋白质(相对分子质量分别为4847、11 412和3592)组成的分型标志蛋白组合模式,其敏感度和特异度均为100%.结论 肺鳞癌和腺癌在蛋白水平存在差异;LCM联合SELDI蛋白质芯片技术有可能筛选出敏感性高、特异性强的肺癌分型标志蛋白组合模式.     

类风湿关节炎合并间质性肺疾病差异蛋白的初步筛选

目的筛选类风湿关节炎合并间质性肺疾病(RA-ILD)血清差异蛋白,初步建立血清差异蛋白指纹图谱分类树。方法应用表面加强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术获得15例RA-ILD患者(RA-ILD组)和13例健康成人(对照组)的血清蛋白质指纹图谱,采用SPSS 13.0、Biomarker Patterns软件、Bio-Marker Wizard软件进行相关统计学分析并初步建立诊断分类树。结果 RA-ILD组患者血清共检测出143个蛋白峰,其中4个蛋白峰强度与对照组比较,差异有统计学意义(P<0.05),质荷比分别为2 876.75、3 382.59、11 525.30和11 886.00。以质荷比11 525.30、2 876.75建立诊断分类树,灵敏度为93.33%(14/15),特异度为92.31%(12/13)。结论 SELDI-TOF-MS技术用于RA-ILD血清蛋白质谱分析可筛选出有意义的差异表达蛋白,有进一步研究价值。     

宫颈癌患者与宫颈息肉蛋白质谱的差异分析

目的 探索宫颈癌患者与宫颈息肉的血清蛋白质谱差异,初步探讨其用于宫颈癌诊断的临床价值.方法 收集73例宫颈癌患者和39例宫颈息肉的血清标本,随机分为训练组(56例宫颈癌和20例健康人)与测试组(17例宫颈癌和19例健康对照).采用表面增强激光解析离子化飞行时间质谱(SELDI-TOF MS)技术检测所有血清标本的蛋白质谱.用Biomarker Wizard统计软件比较训练组宫颈癌与宫颈息肉对照间的蛋白质谱差异,再用Biomarker Pattern软件筛选出一组差异蛋白构建决策分类树模型以鉴别宫颈癌病例和宫颈息肉病例,最后用测试组对分类模型进行验证.结果 宫颈癌患者和对照组血清蛋白指纹图谱共有148个明显表达差异的蛋白峰,筛选质荷比(m/z)为2957、2974、2982、3016、3282、5928、5948、6647、6661的9个蛋白质峰作为标志蛋白(P<0.01)建立人工神经网络诊断模型,利用该模型对宫颈癌癌进行盲法预测,结果表明该诊断模型检测宫颈癌的灵敏度为94.2%,特异度为91.3%.结论 宫颈癌与宫颈息肉人群的血清蛋白质谱存在差异,SELDI-TOF -MS技术筛选出的血清差异蛋白有助于宫颈癌的早期鉴别诊断.     

应用液体蛋白芯片飞行时间质谱系统研究胃癌、肝癌血清差异蛋白表达

目的：研究胃癌、肝癌和正常人的血清差异蛋白表达,寻找具有潜在诊断意义的血清标志物,以利于胃癌、肝癌的早期诊断.方法：收集胃癌患者20例和肝癌患者10例以及正常人20例血清样本,经ClinProt磁珠纯化、基质辅助激光解析电离飞行时间质谱（MALDI-TOF-MS）分析及ClinPro-Tools生物信息学方法研究其血清蛋白表达谱.结果：质荷比（m/z）为2863u和4965u的蛋白峰能较好地鉴别胃癌和非癌组.m/z为1618u和4965u的蛋白峰能较好地鉴别肝癌和非癌组.m/z为4965u的蛋白峰在胃癌、肝癌中较正常人均异常高表达.结论：液体蛋白芯片飞行时间质谱系统作为研究蛋白表达谱的工具,具有较好的重复性与精度,筛选得到的m/z为2863,1618,4965u的蛋白可能为潜在的胃癌、肝癌肿瘤标志物.     

应用MALDI-TOF-MS分析大肠癌血浆相关蛋白差异表达

目的 通过对大肠癌患者血浆蛋白质组学表达差异的研究,以期寻找大肠癌早期诊断及新相关特异的性标志物.方法 采用弱阳离子(MB-WCX)磁珠结合基质辅助激光解吸电离飞行时间质谱(MAL-DI-TOF-MS)技术检测样品血浆蛋白质质谱,运用Ciphergen ProtemChip Software 3.2.1软件及Biomark Wizard 5.0软件进行数据分析筛选,并对差异表达显著的质谱峰于Swiss-Prot及TrEMBL蛋白数据库进行检索.结果 通过对比研究大肠癌组、大肠腺瘤组及正常对照组血浆差异表达质谱峰,共发现23个差异表达蛋白质谱峰,其中在大肠癌组中4个显著差异高表达(P＜0.05),荷质比(M/Z)分别为6 683、8 700、9 187、9 342;虽在大肠腺瘤组与正常组蛋白质谱峰表达无显著性差异(P＞0.05),但三组间荷质比为6 683、8 700蛋白表达呈一定递进性趋势;检索Swiss Prot蛋白质数据库结果:M/Z 6 683是成纤维细胞生长因子受体-l(FGFRl),其余均为未知蛋白,有待进一步鉴定.本研究通过表达差异蛋白建立诊断模型对大肠癌预测的准确率、敏感性、特异性分别为89.19％、83.33％、84％.结论 运用MALDI-TOF-MS技术分析大肠癌血浆蛋白质差异表达对大肠癌早期诊断以及发现或寻找新的肿瘤标志物可能具有一定的临床价值.     

MALDI—TOF质谱技术筛选Ⅰ期肺鳞癌血清肿瘤标记物

目的：MALDI-TOF—MS技术筛选肺鳞癌特异性肿瘤标记物,以提高肺鳞癌早期诊断水平．方法：采集19例Ⅰ期肺鳞癌患者和19例年龄、性别、吸烟量等与之匹配的健康对照者血清,应用MALDI-TOF—MS技术结合MB-WCX蛋白芯片和AutoflexⅡ—C质谱分析仪检测各血清标本,经计算机软件数据处理以筛选肺鳞癌血清差异蛋白质波峰．结果：与正常人比较发现肺鳞癌显著异常表达的潜在标记物3个,Mr分别为3261．69,3192．07和2556．92（P〈0．01）,并据此建立了肺鳞癌分子诊断模型．另采集22例肺鳞癌和19例健康者血清盲检验证该诊断模型具有95．5％的敏感性和94．7％的特异性．结论：MALDI-TOF质谱技术是一种快速、简便易行且高通量的分析方法,不仅能直接筛选出肺鳞癌患者血清中相对特异的潜在肿瘤标记物,而且可能具有较好的临床应用前景,同时为临床寻找新的肺鳞癌血清肿瘤标记物提供理论依据．     

应用血清蛋白质指纹图谱筛选上皮性卵巢癌及预测肿瘤耐药性的研究

目的 建立卵巢癌蛋白质组诊断模型,并经过盲法验证.在此基础之上,比较分析多药耐药基因糖蛋白MDR1和多药耐药相关蛋白MRP阳性和阴性组的血清蛋白质指纹.方法 应用蛋白质芯片SELDI-TOF MS技术和生物信息学方法 ,比较36例上皮性卵巢癌病人和30例正常人血清蛋白质谱,用30例包括良恶性卵巢瘤病人和正常人的血清作盲筛验证.同时利用免疫组化方法 检验MDR1和MRP在上皮性卵巢癌组织的表达,进而分析耐药蛋白阳性和阴性组的血清蛋白质谱.结果 卵巢癌和正常血清比较,值P<0.01的差异峰有29个,15个峰上调,14个峰下调.用其中的3个标志蛋白建立诊断模型(相对分子质量为5486、6463及8575),该诊断模型的敏感性100%,特异性93.33%.盲筛验证表明阳性预测值90%.卵巢癌组织MDR1阳性率69.4%.阳性与阴性组血清蛋白质谱比较,P<0.01的差异蛋白质峰有20个.MRP阳性率63.8%.P<0.01的差异蛋白质峰有1个.结论 应用SELDI方法 建立卵巢癌血清蛋白质谱诊断模型是实现卵巢癌筛查的理想途径;并且能够明确的将多药耐药的病例筛选出来,MDR1免疫组化结果 可以做为筛选卵巢癌耐药病例的建模依据.     

重型肝炎预后相关蛋白质质谱分析及预测模型构建

【摘要】 目的 比较不同临床结局乙肝病毒（HBV）相关重型肝炎和正常人血浆中的蛋白质指纹图谱，筛选出与重型肝炎预后相关的差异蛋白，为重型肝炎预后判断提供依据。方法 采用CM10联合表面增强激光解析 飞行时间 质谱（SELDI TOF MS）技术，检测35例HBV相关重型肝炎和15例正常人血浆蛋白质谱获得血浆蛋白指纹图谱。结果 HBV相关重型肝炎和正常人血浆中一共检测出57个蛋白峰，最终筛选出M/Z为M3492 79、M5649 11、M3936 56、M7568 86、M8601 12的5个差异蛋白峰建立分类树模型，该模型对重型肝炎预后判断的敏感性为7200%，特异性为10000%，准确性为80%。结论 SELDI TOF MS技术可以用于重型肝炎患者预后相关血浆蛋白的筛选，利用这些差异表达的蛋白建立的分类树模型对重型肝炎患者预后判断具有重要的临床价值。     

WCX纳米磁珠在子宫内膜癌血清蛋白质组中的应用

目的利用弱阳离子交换型(WCX)纳米磁珠联合表面增强激光解析离子化飞行时间质谱(SELDI-TOF-MS)分析子宫内膜癌患者血清蛋白质谱,筛选潜在的特异性肿瘤标志物,探讨其临床意义。方法应用WCX纳米磁珠联合SELDI-TOF-MS对24例子宫内膜癌患者及28例正常人血清标本进行蛋白质质谱分析,用BiomarkWizard软件分析差异蛋白峰,并在Swiss蛋白数据库中搜索鉴定差异蛋白,受试者工作特征(ROC)曲线评价该技术的应用价值。结果在质荷比(m/z)2 000~20 000范围内,子宫内膜癌患者血清中筛选出与正常人血清有统计学意义的差异蛋白11个(P<0.05),其中6个高表达:m/z分别为4 175、5 315、7 970、8 047、15 930、16 074;5个低表达:m/z分别为4 592、4 675、7 019、9 188、9 358。m/z为7 970的差异蛋白与子宫珠蛋白(UG)相符。应用ROC曲线分析,确定11个差异蛋白对子宫内膜癌有中等诊断价值,灵敏度为70.5%~96.4%,特异度为89.3%~100%。结论 WCX纳米磁珠联合SELDI-TOF-MS能在子宫内膜癌患者血清中筛选出灵敏度和特异度较好的差异表达蛋白,其有助于子宫内膜癌的筛查、早期诊断及预后监测。     

SELDI-TOF-MS蛋白质芯片技术建立胃癌血清特异性标志物诊断分类树研究

目的:观察胃癌手术前后血清蛋白质谱的变化,筛选能够快速诊断胃癌的特异性标志物。方法:采用IMAC#3蛋白质芯片和表面增强激光解吸离子化飞行时间质谱(SELDI-TOF-MS)蛋白质芯片技术,对46例胃癌患者和40例正常人的血清蛋白质谱进行分析。结果:胃癌术前血清与正常人血清蛋白质谱有14个蛋白质表达量有明显差异。以热休克蛋白27、葡萄糖调节蛋白、抑制素、蛋白质二硫化物异构酶A 3这4个蛋白质所组成的模板,可将胃癌与正常人正确分组,利用该模板建立胃癌诊断的分类树模型用于诊断胃癌的灵敏度95.7%,特异性92.5%,术后血清蛋白质谱中,原表达上调的蛋白质明显下调。结论:SELDI-TOF-MS蛋白质芯片技术为建立蛋白质模板用以诊断胃癌提供了可靠的技术平台。     

帕金森病患者血清中M/Z2260肽的表达与鉴定

目的探讨帕金森病(Parkinson’s disease,PD)患者血清蛋白质生物标志物。方法选择原发性PD患者和正常人血清为样本,用弱阳离子交换(weak cationic exchange,WCX)磁珠捕获血清蛋白质组分,用基质辅助的激光解析离子化飞行时间质谱仪(matrix assisted laser desorption/ionization time of flight masss pectrometer,MALDI-TOF-MS)检测各捕获组分的蛋白质质谱,用统计学方法分析原发性PD患者和健康人的蛋白质谱,筛选差异分子,用LC-MALDI-TOF/TOF鉴定差异分子,用酶联免疫吸附法(ELISA)测定差异表达分子含量。结果在相对分子质量1000～50000范围内共检测到107个蛋白峰,其中9个蛋白质的表达发生显著变化(P<0.01)。一个相对分子质量为2260.1的蛋白质在PD血清中显著升高,是血清富组氨酸糖蛋白(histidine-rich glycoprotein,HRG)的C端降解产物。ELISA方法测定结果显示PD患者血清HRG含量低于正常人(P<0.05)。结论 PD患者血清低相对分子质量蛋白质的表达与正常人差异有统计学意义。HRG在PD患者血清中的含量低于正常人,而其C端降解片段在PD患者血清中的表达显著高于正常人。     

Detection of lung adenocarcinoma using magnetic beads based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry serum protein profiling

Background Recently, due to the rapid development of proteomic techniques, great advance has been made in many scientific fields. We aimed to use magnetic beads （liquid chip） based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry （MALDI-TOF-MS） technology to screen distinctive biomarkers for lung adenocarcinoma （adCA）, and to establish the diagnostic protein profiles. Methods Using weak cation exchange magnetic beads （MB-WCX） to isolate and purify low molecular weight proteins from sera of 35 lung adCA, 46 benign lung diseases （BLDs） and 44 healthy individuals. The resulting spectra gained by anchor chip-MALDI-TOF-MS were analyzed by ClinProTools and a pattern recognition genetic algorithm （GA）. Results In the working mass range of 800-10 000 Da, 99 distinctive peaks were resolved in lung adCA versus BLDs, while 101 peaks were resolved in lung adCA versus healthy persons. The profile gained by GA that could distinguish adCA from BLDs was comprised of 4053.88, 4209.57 and 3883.33 Da with sensitivity of 80%, specificity of 93%, while that could separate adCA from healthy control was comprised of 2951.83 Da and 4209.73 Da with sensitivity of 94%, specificity of 95%. The sensitivity provided by carcinoembryonic antigen （CEA） in this experiment was significantly lower than our discriminatory profiles （P 〈0.005）. We further identified a eukaryotic peptide chain release factor GTP-binding subunit （eRF3b） （4209 Da） and a complement C3f （1865 Da） that may serve as candidate biomarkers for lung adCA. Conclusion Magnetic beads based MALDI-TOF-MS technology can rapidly and effectively screen distinctive proteins/polypeptides from sera of lung adCA patients and controls, which has potential value for establishing a new diagnostic method for lung adCA.     

肾母细胞瘤患儿血清蛋白质标记物的筛选及鉴定

目的 筛选肾母细胞瘤患儿特异性血清蛋白质标记物并对其进行鉴定,以确定其作为肾母细胞瘤血清学诊断和预后监测的特异标志物.方法 应用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术检测肾母细胞瘤患儿手术前后及正常小儿的血清蛋白质组,筛选差异蛋白质峰,并对目标蛋白质进行分离纯化、酶解,采用液质联用串联质谱(LC-MS/MS)分析,用SEQUST检索程序查询美国Bioworks公司提供的蛋白质序列数据库.结果 经SELDI-TOF-MS技术筛选出m/z位于6455.5和6984.4的蛋白质标志物在肾母细胞瘤组低表达(表达强度为1029±364、297±126),正常小儿组高表达(2108 ±837、753±226),差异均有统计学意义(均P＜0.01);m/z位于9190.8的蛋白质标志物在肾母细胞瘤术前组低表达(283±154),术后组和正常小儿组高表达(5974 ±657、6231±519)差异均有统计学意义(均P＜0.01).对m/z位于6455.5和9190.8的标志物进行鉴定,结果分别为载脂蛋白CⅢ和触珠蛋白.结论 检测血清中载脂蛋白CⅢ和触珠蛋白含量可能成为肾母细胞瘤的血清学诊断、恶性度分级和预后监测指标,值得进一步研究与应用.     

应用液体蛋白芯片-飞行时间质谱系统研究乳腺癌血清差异蛋白表达

目的：分析乳腺癌与健康人血清蛋白质谱差异,筛选特异性蛋白标志物,建立乳腺癌诊断预测模型,评价其诊断价值。方法将58例乳腺癌和57例健康女性的血清,随机分为建模组和验证组,应用弱阳离子纳米磁珠（MB-WCX）结合基质辅助激光解吸离子飞行质谱（MALDI-TOF-MS）技术建立血清蛋白质谱,选择遗传算法建立乳腺癌诊断预测模型,利用诊断模型对验证组进行盲筛验证。结果通过蛋白质谱,筛选出29个显著差异蛋白质峰（P＜0．05）。其中乳腺癌中表达上调2个,表达下调27个,利用其中8个差异峰（质荷比分别为698．3、797.37、1449.56、1466．65、1520．74、1584．55、2379．26、2739．69）建立诊断模型,获得了96．55％（28/29）的敏感性和96.42％（27/28）的特异性,经独立样本双盲验证,得到灵敏度为93．10％（27/29）,特异度为96．55％（28/29）。结论应用弱阳离子纳米磁珠联合MALDI-TOF-MS技术可筛选出血清蛋白质谱中的差异蛋白质,据此建立的诊断模型具有较高的敏感性和特异性,对于乳腺癌的辅助诊断有一定的临床意义。     

蛋白质指纹图谱在胃癌新辅助化疗疗效评价中的应用

[目的]探讨蛋白质指纹图谱与胃癌新辅助化疗疗效的关系.[方法]采用SELDI-TOF蛋白质芯片技术,检测胃癌化疗前血清蛋白指纹图谱,通过比较分析发现差异蛋白质,进一步筛选与新辅助化疗疗效相关的血清蛋白质指纹图谱差异.[结果]17 162、22 331、29 901M/Z蛋白质在无效组中均高表达(P＜0.01),有效组和健康组比较无统计学意义.[结论]17 162、22 331、29901M/Z3种蛋白质有可能成为筛选胃癌新辅助化疗疗效的蛋白分子.血清蛋白质指纹图谱对筛选胃癌新辅助化疗疗效预测分子有潜在应用前景.     

Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma

Background Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis. Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and specificity for hepatocellular carcinoma at an early stage. In this study, the serum proteins in patients with hepatocellular carcinoma or liver cirrhosis and in normal controls were analysed. Surface enhanced laser desorption/ionization time-of-flight mass (SELDI-TOF-MS) spectrometry was used to fingerprint serum protein using the protein chip technique and explore the value of the fingerprint, coupled with artificial neural network, to diagnose hepatocellular carcinoma.Methods Of the 106 serum samples obtained, 52 were from patients with hepatocellular carcinoma, 22 from patients with liver cirrhosis and 32 from healthy volunteers. The samples were randomly assigned into a training group (n=70, 35 patients with hepatocellular carcinoma, 14 with liver cirrhosis, and 21 normal controls) and a testing group (n=36, 17 patients with hepatocellular carcinoma, 8 with liver cirrhosis, and 11 normal controls). An artificial neural network was trained on data from 70 individuals in the training group to develop an artificial neural network diagnostic model and this model was tested. The 36 sera in the testing group were analysed with blind prediction by using the same flowchart and procedure of data collection. The 36 serum protein spectra were clustered with the preset clustering method and the same mass/charge (M/Z) peak values as those in the training group.　 Matrix transfer was performed after data were output. Then the data were input into the previously built artificial neural network model to get the prediction value. The M/Z peaks of the samples with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3.1 for noise filtering. The first threshold for noise filtering was set at 5, and the second was set at 2. The 10% was the minimum threshold for clustering. The statistical analysis of the data of serum protein mass spectrum was performed in the groups (normal vs. hepatocellular carcinoma, and liver cirrhosis vs. hepatocellular carcinoma) with the t test. Results Comparison between the groups of hepatocellular carcinoma and normal control: The mass spectra from 56 samples (hepatocellular carcinoma and normal controls) in the training group were analysed and 241 peaks were obtained. In addition, 21 peaks from them were used for comparison between the groups of hepatocellular carcinoma and normal controls (P&lt;0.01). Only 2 peaks at 3015 M/Z and 5900 M/Z were selected with significant difference ［P&lt;10（-9）］. A model was developed based on these two proteins with different M/Z. It was confirmed that this artificial neural network model can be used for comparison between the groups of hepatocellular carcinoma and normal controls. The sensitivity was 100% (17/17), and the specificity was 100% (11/11). Comparison between the groups of hepatocellular carcinoma and liver cirrhosis: The mass spectra from 49 samples in the training group (including patients with hepatocellular carcinoma and liver cirrhosis) were analysed and 208 peaks were obtained. In addition, 21 peaks from them were used for comparison between the groups of hepatocellular carcinoma and liver cirrhosis (P&lt;0.01). Only 2 peaks at 7759 M/Z, 13134 M/Z were selected with significant difference ［P&lt;10（-9）］. A model was developed based on these two proteins with dfferent M/Z. It was confirmed that this artificial neural network model can be used for comparison between the groups of hepatocellular carcinoma and liver cirrhosis. The sensitivity was 88.2% (15/17), and the specificity was 100% (8/8).Conclusions The specific biomarkers selected with the SELDI technology could be used for early diagnosis of hepatocellular carcinoma.     

Study of differential proteins in lung adenocarcinoma using laser capture microdissection combined with liquid chip-mass spectrometry technology

Background In recent years the proportion of lung adenocarcinoma （adCA） which occurs in lung cancer patients has increased. Using laser capture microdissection （LCM） combined with liquid chip-mass spectrometry technology, we aimed to screen lung cancer biomarkers by studying the proteins in the tissues of adCA. Methods We used LCM and magnetic bead based weak cation exchange （MB-WCX） to separate and purify the homogeneous adCA cells and normal cells from six cases of fresh adCA and matched normal lung tissues. The proteins were analyzed and identified by matrix assisted laser desorption/ionization time-of-fight mass spectrometry （MALDI-OF-MS）. We screened for the best pattern using a radial basic function neural network algorithm. Results About 2.895x10s and 1.584x10s cells were satisfactorily obtained by LCM from six cases of fresh lung adCA and matched normal lung tissues, respectively. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. Comparing the differentially expressed proteins between the lung adCA and the matched normal lung group, 221 and 239 protein peaks, respectively, were found in the mass-to-charge ration （M/Z） between 800 Da and 10 000 Da. According to ttest, the expression of two protein peaks at 7521.5 M/Zand 5079.3 M/Z had the largest difference between tissues. They were more weakly expressed in the lung adCA compared to the matched normal group. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart. A discriminatory pattern which can separate the lung adCA from the matched normal lung tissue consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by a radial basic function neural network algorithm with a sensitivity of 100% and a specificity of 100%. Conclusions Differential proteins in lung adCA were screened using LCM combined with liquid chip-mass spectrometry technology, and a biomarker model was established. It is possible that this technology is going to become a powerful tool in screening and early diagnosis of lung adCA.     

帕金森病患者血清低分子量蛋白质差异表达分析

目的探讨帕金森病(Parkinson’s disease,PD)患者区别于正常人的血清蛋白质差异表达。方法选择原发性PD患者35例和正常人35例,用弱阳离子交换(weak cationic exchange,WCX)磁珠捕获血清蛋白质组分,用MALDI-TOF-MS(matrix assisted laser desorption/ionization time of flight mass spectrometer)检测各样品的蛋白质质谱,统计学筛选差异表达分子,监督神经网络算法建立区分模型,盲法验证。结果在PD组和对照组之间筛查到8个差异分子(非参数检验Z值范围为-4.458～-3.059,P<0.05)。以监督神经网络算法建立区分模型,其判断正确率为81.4%。对25例新样本的盲法验证结果显示,模型的正确率为76.0%。结论PD患者血清蛋白质的表达谱有别于正常人。蛋白质组学数据结合生物信息学方法可能有助于PD的诊断。     

多囊卵巢综合征患者在不同疾病状态下蛋白质谱的变化

目的了解多囊卵巢综合征（PCOS）患者不同疾病状态下血清蛋白质谱变化。方法采用表面增强激光解离飞行时间质谱（SELDI—TOF MS）弱阳离子交换蛋白芯片对PCOS患者胰岛素抵抗（IR）、非胰岛素抵抗（non-IR）患者和正常对照者（每组各30例）的血清蛋白质谱进行检测。结果筛查出PCOS IR组与正常组相比有27个差异蛋白质峰,PCOS non—IR组与正常组相比有17个差异蛋白质峰,PCOS IR和non—IR组相比有19个差异蛋白质峰。进一步运用支持向量机（SVM）在差异蛋白质中筛选标志性蛋白质建立了PCOS IR、PCOS non-IR和IR诊断模型,3组模型的敏感性、特异性、阳性及阴性预测值均达80％以上。结论PCOS患者在IR和non-IR两种疾病状态下血清蛋白质表达谱发生显著的变化;运用SELDI—TOF MS技术结合SVM可以快速而有效地建立PCOS诊断模型。     

应用SELDI—TOF—MS技术分析多发性骨髓瘤患者血清差异表达蛋白

目的研究多发性骨髓瘤患者血清蛋白质谱的变化,从而筛选出特异性蛋白标志物。方法利用CM10蛋白芯片和SELDI-TOF—MS技术对30例初诊为多发性骨髓瘤的患者和33例健康人的血清蛋白进行分析。获得的蛋白质谱图采用Ciphergen公司的BiomarkerWizard软件分析。结果通过对多发性骨髓瘤患者血清与健康人血清蛋白质谱图分析发现有30个蛋白峰表达量有明显差异（P〈0．05）,与健康对照组相比,10个蛋白峰表达上调,20个蛋白峰表达下调,质荷比为3472．79、7778．39、4093．01、4971．15、5343．98的蛋白更具意义。结论结果表明通过多发性骨髓瘤患者与健康对照血清蛋白质谱的比较,有助于筛选得到多发性骨髓瘤的特异性标志物。