应用SELDI-TOF-MS技术建立肝癌筛选血清蛋白质指纹图谱模型

目的:建立肝癌筛选血清蛋白质指纹图谱模型．方法:用表面加强激光解析电离飞行时间质谱技术(SELDI-TOF-MS)及WCX2蛋白芯片获得新发肝癌、肝硬化患者和正常人血清的蛋白质指纹图谱,用计算机软件进行比较分析,建立肝癌的筛选模型．结果:肝癌患者与健康对照组血清蛋白质指纹图谱之间有5个标志蛋白(4477,8943,5181, 8617,13 761 Da)在肝癌患者血清中高表达,肝癌患者与肝硬化患者血清蛋白质指纹图谱之间2个标志蛋白(4477,13 761 Da)在肝癌患者血清中高表达,1个标志蛋白(4097 Da)在肝癌患者血清中低表达．SELDI-TOF-MS技术的特异性(60／60,100％);敏感度(18／20,90％)．分析系统筛选出4477,8943,13 761,4097 Da标志蛋白建立的肝癌诊断模型．结论:建立的血清蛋白质指纹图谱模型能够区分肝癌与非肝癌患者,SELDI-TOF-MS在肝癌的诊断及肿瘤特异性蛋白质生物标志分子的筛选等方面具有一定价值．     

类风湿关节炎患者血清蛋白质指纹图谱检测及其临床意义

目的初步探讨基于判别分析建立的血清蛋白质指纹图谱模型在类风湿关节炎(RA)诊断中的临床意义.方法用表面增强激光解吸电离飞行时间质谱技术及配套蛋白质芯片检测60例RA患者和31例健康人的血清蛋白质指纹图谱,并采用SPSS12.0软件判别分析处理数据和筛选标志物,以建立诊断模型.结果质荷比(m/z)为3 975 Da,6 433 Da和15 129 Da的3个蛋白质峰组合构建的诊断模型鉴别RA患者和健康人的敏感性为80.0%(48/60),特异性为80.6%(25/31).结论血清蛋白质指纹图谱在筛选RA患者血清中特异性蛋白标志物及其诊断方面具有一定价值.     

胰腺癌血清蛋白质波谱模型的构建及诊断价值研究

目的 建立胰腺癌诊断模型,寻找与胰腺癌分期相关的蛋白峰并鉴定出新型的肿瘤标志物.方法 应用表面增强激光解吸离子化飞行时间质谱(SELDI-TOF-MS)技术,用SAX2蛋白芯片,检测胰腺癌患者、胰腺良性疾病患者和正常对照者的血清蛋白指纹谱,统计建立决策树诊断模型和Logistic回归模型并评估诊断价值,蛋白芯片免疫法鉴定差异蛋白峰,ELISA法测定其在血清中的浓度.结果 胰腺癌组和正常对照组指纹谱比较.26个蛋白峰差异有统计学意义(P＜0.01),胰腺癌组和胰腺良性疾病组指纹谱比较,16个蛋白峰差异有统计学意义(P＜0.05).建立的决策树诊断模型对胰腺癌的敏感性为83.3%,特异性为100.0%,经ROC曲线评估,该模型优于糖链抗原(CA)19-9.有6个差异蛋白峰在不同分期咦腺癌中差异有统计学意义(P＜0.01),建立Logistic回归模型诊断早期胰腺癌.敏感性为81.6%,特异性为80.6%.鉴定出差异蛋白峰质荷比(M/Z)28068为C140H166,其诊断胰腺癌的敏感性＞82%,特异性＞88%.结论 应用SELDI-TOF-MS技术建立的诊断模型对胰腺癌的诊断有较大价值,明显优于CA19-9.鉴定出的差异蛋白C140rf166有望在胰腺癌诊断中发挥作用.     

成人隐匿性自身免疫性糖尿病患者血清蛋白质组学分析

目的 探讨蛋白指纹图谱分析对成人隐匿性自身免疫性糖尿病(LADA)早期诊断的意义.方法利用表面加强激光解吸电离-飞行时间质谱(SELDI-TOF-MS)技术,对40例LADA患者、33例T1DM患者、35例初诊T2DM患者以及31名正常人的血清蛋白质图谱进行检测,结合Biomarker Patterns Software(BPS)建立诊断模型. 结果 发现5个差异蛋白质峰(3476.27,5071.16,4478.48,5342.69和4097.56),在四组间比较差异有统计学意义(P<0.05);3个差异蛋白质(3476.27,5071.16,4478.48)被用来构建LADA和正常对照组诊断模型,诊断LADA的特异度为90.32%(28/31),灵敏度为95.00%(38/40);2个差异蛋白质(5342.69,4097.56)被用来构建LADA和初诊T2DM的诊断模型,诊断LADA的特异度为91.43%(32/35),灵敏度为92.50%(37/40). 结论 SELDI-TOF-MS技术可快速、准确地诊断出LADA,灵敏度和特异度较GAD-Ab高.     

应用蛋白质组学技术筛检肝癌血清差异蛋白质并分离鉴定阿朴脂蛋白AⅡ

目的 应用蛋白质组学技术筛选并鉴定肝癌患者血清中的差异蛋白质.方法 采用表面增强激光解析及电离飞行时间质谱技术,对33例肝癌患者和33例正常对照的血清蛋白质进行了检测分析,筛选肝癌的差异蛋白;用等电点沉淀法、十二烷基硫酸钠-聚丙烯酰胺凝胶电泳及高效液相色谱串联质谱等一系列方法分离鉴定差异蛋白质.肝癌组与正常组蛋白质峰差异比较采用两样本t检验. 结果 肝癌组与正常人组血清蛋白质图谱在相对分子质量为2 000～10 000范围内,有65个蛋白质峰差异有统计学意义(P<0.05),由此组建的诊断模型灵敏度为100%,特异度为96.97%其中包括8706.5 M/Z和8579.2M/Z(t值分别为2.562和2.783,P值均<0.05),经分离鉴定其为阿朴脂蛋白AⅡ. 结论 阿朴脂蛋白AⅡ是肝癌的差异蛋白之一,可能与肝癌的发生有关.     

应用蛋白指纹图谱技术筛选原发性胆汁性肝硬化患者血清特异性标志物

目的: 探讨蛋白指纹图谱技术筛选原发性胆汁性肝硬化(PBC)患者血清中可用于诊断的特异性标志物.方法: 采用弱阳离子纳米磁性微球捕获血清中的蛋白,ProteinChip PBSII-C型蛋白质芯片阅读仪检测绘制成蛋白指纹图谱.所有蛋白指纹图谱采用Biomarker Wizard 3.1分析之后用Biomarker Pattems Sofiware 5.0识别最终可能用于PBC诊断的蛋白标志物并优化组合建立诊断模型.结果: 在PBC患者和对照组之间找到69个差异蛋白峰(P<0.05).其中质荷比(m/z)为3445,4260,8133和16 290的蛋白峰建立PBC诊断模型.该诊断模型能很好地把PBC患者从其他肝脏疾病患者和正常人群中区分出来,其敏感性为93.3%,特异性为95.1%.经双盲实验验证,该模型对PBC诊断的敏感性为92.9%,特异性为82.4%. 结论: 采用纳米磁性微球与蛋白质芯片阅读仪联用的蛋白指纹图谱技术可以检测PBC患者血清中的特异性蛋白标志物,并建立敏感性和特异性均较高的PBC诊断模型.     

早期肝纤维化外周血清标志物筛选的初步研究

目的探讨早期病毒性乙型肝炎（乙肝）肝纤维化的潜在血清标志物并建立相应的诊断模型。方法采用表面增强激光解吸/离子化时间飞行质谱（SELDI-TOF-MS）技术检测20个健康人和31例早期乙肝肝纤维化患者的血清蛋白质谱,用biomarker Wizard软件筛选出早期肝纤维化的差异表达蛋白质谱峰,用Biomarker Pattern软件对差异蛋白峰值进行线性分析并建立诊断模型。结果在分子量为2000～20000Da的区间内检测出97个峰值,15个为差异蛋白峰,其中早期肝纤维化患者有3个上调,12个下调。用质荷比为M4799和M6125的差异蛋白联合建立的诊断模型最优,其特异度为80%（16/20）,灵敏度为87%（27/31）。结论 SELDI-TOF-MS在筛选早期肝纤维化血清学标志物方面具有较高的灵敏度和特异性,作为肝纤维化早期筛选的方法尚需大宗病例证实及临床后续验证。     

SELDI技术筛选肺癌患者血清标志蛋白的临床价值

目的探讨表面增强激光解析电离飞行时间质谱（SELDI-TOF-MS）技术筛选肺癌患者血清标志蛋白的临床价值。方法用SELDI-TOF-MS技术、弱阳离子交换蛋白芯片,检测肺癌和肺良性病变患者的血清蛋白质质谱图;用Biomarker Pattern软件分析肺癌差异蛋白并初建其诊断模型,通过盲筛验证诊断模型。结果发现有统计学差异的蛋白峰20个,其中肺癌患者血清高表达蛋白质波峰14个,低表达蛋白质波峰6个;用质荷比2 090.77、2 503.31 Da的差异蛋白峰建立分类树模型,其诊断肺癌的灵敏度88%,特异度95%;盲筛验证灵敏度90%,特异度100%,粗符合率93.33%,Youden指数0.9。结论SELDI-TOF-MS技术筛选的肺癌血清差异性蛋白及分类树模型,诊断肺癌的灵敏度高、特异性好。     

SELDI-TOF-MS在筛选肝癌高发家系与无癌家系血清蛋白差异中的应用

目的：比较肝癌高发家系患者和无癌家系患者血清蛋白质谱图,探寻两者间的血清蛋白差异。方法用表面增强激光解吸电离-飞行时间质谱技术（ SELDI-TOF-MS）及CM10蛋白芯片对25例肝癌高发家系患者和25例无癌家系患者血清样本进行分析,将获得的血清蛋白质指纹图谱,用计算机软件进行比较分析,建立高发家系患者和无癌家系患者的比较筛选模型。结果肝癌高发家系组与无癌家系组共有15个蛋白质差异有统计学意义。筛选出4个蛋白峰建立诊断模型的灵敏度为96.0％（24/25）,特异度为88.0％（22/25）,准确率为92.0％（46/50）。结论肝癌高发家系患者组和无癌家系患者组的血清存在一定的差异蛋白, SELDI技术在特异性蛋白生物标志分子的筛选等方面具有较好的临床应用前景。     

胃癌与慢性胃炎唾液蛋白质组鉴别诊断模型

目的:探讨用蛋白质组学质谱技术筛选慢性胃炎患者与胃癌患者唾液蛋白质表达谱,寻找可用于胃癌与慢性胃炎鉴别诊断的特异性生物标志物.方法:采用蛋白质组学基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)技术,并运用弱阳离子磁珠(WCX)检测慢性胃炎患者和胃癌患者的唾液,得到相应的肽质量指纹图谱,建立鉴别诊断模型.结果:慢性胃炎患者与胃癌患者两组共得到蛋白质峰为77个,其中1个有统计差异显著的蛋白峰(P<0.05),质荷比为:6021.72 Da,通过分析差异蛋白峰表达谱,建立了分类预测模型,识别率为83.54%,预测能力60.23%.以此模型进行临床回代检验结果14例慢性胃炎,其中10例被准确检出,23例胃癌,22例被准确检出,灵敏度71.43%(10/14),特异度95.65%(22/23).结论:初步得到了慢性胃炎与胃癌唾液差异蛋白质表达谱,并初步建立了以6021.72 Da蛋白质区分慢性胃炎与胃癌的唾液蛋白鉴别诊断模型.     

Application of Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Coupled With an Artificial Neural Network Model for the Diagnosis of Hepatocellular Carcinoma

Background/Aims: There are no satisfactory biomarkers for hepatocellular carcinoma (HCC). The surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technique has been used to identify biomarkers for cancer. Methodology: Four hundred thirty five serum samples were tested by SELDI-TOF-MS matching on a gold chip. Samples were assigned to a training set and a testing set according to collection order. The training set was used to identify statistically significant peaks and to develop the artificial neural network (ANN) model for diagnosing HCC. The testing set was used in a blind test to validate the diagnostic efficiency of the ANN model. Results: A total of 75 proteins that differed between patients and controls were identified (p<0.05). Seven of these proteins (p<0.01; m/z at 4207Da, 6604Da, 7734Da, 8106Da, 8545Da, 8599Da, 8894Da) were chosen to develop the ANN model. The model was subjected to a blind test using the testing set for HCC diagnosis. Sensitivity and specificity were 84.00% and 81.25%, respectively, and the accuracy was 81.90%. Conclusions: These results suggest that patients with HCC may have serum proteins that differ from healthy controls. The ANN is a new method for diagnosing and identifying HCC.     

Surface enhanced laser desorption/ionization profiling: New diagnostic method of HBV-related hepatocellular carcinoma

Background and Aim:  To screen for serum biomarkers of HBV-related hepatocellular carcinoma (HCC) and HBV-related liver cirrhosis (LC) in an attempt to seek a new method for differential diagnosis of HCC and LC using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) techniques.
Methods:  Using SELDI-TOF-MS, serum proteins/peptide profiles on the immobilized metal ion affinity capture (IMAC) protein chips were obtained from 29 HCC patients and 30 LC patients. Discriminant analysis was carried out to establish new diagnostic methods using protein/peptide peaks with or without α-fetoprotein (AFP).
Results:  Forty-five protein/peptide peaks changed much more in the HCC group than they did in the LC group. Discriminant analysis using the Wilcoxon rank-sum test showed high sensitivity and specificity in distinguishing HCC from LC. The most significantly differentiating peak, 3892, offered 69.0% sensitivity, 83.3% specificity and 80% positive predictive value in distinguishing HCC and LC. Interestingly, six HCC patients with negative serum AFP were confirmed by peak 3892. The combination of multi-protein peaks (m/z = 9297, 29 941) with AFP offered an 82.8% sensitivity, 93.3% specificity and 92.3% positive predictive value, which was much better than AFP alone ( P  = 0.013).
Conclusions:  Special proteins/peptides of serum may differentiate HBV-related HCC and HBV-related LC, indicating that SELDI-TOF-MS may be useful to distinguish HCC from LC with the proper discriminant analytical method. SELDI peak 3892 may be a complementary diagnostic marker to positive AFP for HCC and a potential marker for the diagnosis of AFP-negative HCC as well.     

表面增强的激光解析电离飞行时间质谱技术在肝细胞癌血清学诊断中的应用

目的:检测HBV感染携带者与肝细胞癌(HCC)患者血清蛋白质的差异性表达,以发现HCC的肿瘤诊断标志物.方法:用表面增强的激光解析电离飞行时间质谱(SELDI-TOF-MS)技术检测27例HCC患者,27例HBV感染携带者,25例健康对照血清中的蛋白质谱,并用Biomarker Patterns System 5．0软件分析,建立诊断模型．结果:检测HCC患者与HBV感染携带者。正常对照与HCC患者,正常对照与HBV感染携带者的差异性蛋白分子,据此构建分类模型,得到的灵敏度和特异度分别为93％,85％; 96％,96％;84％,89％．其中相对分子质量为8141 Da的蛋白峰在HCC组明显高于HBV感染组(p<10-5);相对分子质量为3448 Da的蛋白峰在HCC及HBV感染携带组表达均较正常组显著增高(P<10-5),而在HCC-HBV组无明显差异(P>0．05),提示其可能为HBV感染的一种标志蛋白;相对分子质量为777) Da的蛋白峰在3组中均有差异性表达．结论:SELDI蛋白芯片技术检测血清蛋白质谱法诊断HCC具有较高的灵敏度和特异度,操作简单快捷,临床应用前景广阔,为HCC诊断提供了新的血清学方法．     

New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples

~~New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples@Thomas Gbel$Klinik für Gastroenterologie,Hepatologie und Infektiologie,Heinrich-Heine-UniversitatDusseldorf,Moorenstr.e,D…     

Screening serum hepatocellular carcinoma-associated proteins by SELDI-based protein spectrum analysis

AIM： To find out potential serum hepatocellular carcinoma （HCC）-associated proteins with low molecular weight and low abundance 13y SELDI-based serum protein spectra analysis, that will have much application in the diagnosis or differentiated diagnosis of HCC, as well as giving a better understanding of the mechanism of hepato-card nogenesis.
METHODS： Total serum samples were collected with informed consent from 81 HCC patients with HBV（＋）/ cirrhosis（＋）, 36 cirrhosis patients and 43 chronic hepatitis B patients. Serum protein fingerprint profiles were first generated by selected WCX2 protein chip capture integrating with SELDI-TOF-MS, then normalized and aligned by Ciphergen SELDI Software 3.1.1 with Biomarker Wizard..Comparative analysis of the intensity of corresponding protein fingerprint peaks in normalized protein spectra, some protein peaks with significant difference between H.CC and cirrhosis or chronic hepatitis B were found.
RESULTS： One hundred and twenty-eight serum protein peaks betweeri.2000 and 30000Da were identified under the condition of signal-to-noise 〉 5 and minimum threshold for cluster 〉 20%. Eighty-seven of these proteins were showed significant differences in intensity between HCC and cirrhosis （P 〈 0.05）. Of the above differential proteins, 45 proteins had changes greater than two-fold, including 15 upregulated proteins and 30 downregulated proteins i.n HCC serum. Between HCC and chronic hepatitis B, 9 of 52 differential proteins （P 〈 0.05） had intensities of more than two-fold, including 2 upregulated proteins and 7 downregulated proteins in HCC serum. Between cirrhosis and chronic hepatitis B, 28 of 79 significant differential proteins （P 〈 0.05） changes greater than two-fold in intensity, including 17 upregulated proteins and 11 downregulated proteins in cirrhosis serum. For the analysis of these leading differential proteins in subtraction difference mode among three diseases, the five common downregulated proteins in HCC serum （M/Z 287     

肝癌、肝硬化患者前期处理后腹水中差异蛋白质组学分析及意义

目的 分析肝癌、肝硬化患者腹水前期处理后的蛋白质组学变化,并探讨其意义.方法 采用表面加强激光解析电离飞行时间质谱技术（SELDI-TOF-MS技术）对27例肝癌（肝癌组）、46例肝硬化患者（肝硬化组）腹水前期处理后蛋白质进行检测,对蛋白质波谱进行分析.结果 肝癌组在7 852 Da、11 467 Da蛋白质表达量高于肝硬化组,在4 475 Da蛋白质表达量低于肝硬化组（P均＜0.05）.SELDI-TOF-MS技术检测7 852 Da、11 467 Da、4 475 Da区分肝癌和肝硬化灵敏度为81.48％ （22/27）,特异性为86.96％ （40/46）.结论 腹水经前期处理后,肝癌、肝硬化患者在7 852 Da、11 467 Da、4 475 Da蛋白质峰有显著差异,通过这三个蛋白质峰检测可以区分肝癌与肝硬化.     

弓形虫急性感染小鼠血清蛋白质质谱分析的初步研究

目的 分析弓形虫急性感染小鼠血清蛋白质组学变化。方法 16只同窝配对的C57BL/6J小鼠, 随机分成弓形虫急性感染组和对照组, 每组8只。弓形虫急性感染组每鼠腹腔感染经CF?11纤维素纯化的RH株弓形虫速殖子 （1×104 /ml） 0.2 ml, 对照组每鼠腹腔接种0.2 ml 灭菌生理盐水。感染后4 d, 处死全部小鼠, 应用弱阳离子 （WCX） 纳米磁性微球捕获小鼠血清蛋白, 采用基质辅助激光解析电离飞行时间质谱 （MALDI?TOF?MS） 技术检测两组小鼠血清蛋白质变化, 并采用Au? toflex analysis 与Clin ProTools 软件进行分析与比较。结果 在分子量为1 000~16 000 Da区段, 与对照组小鼠血清蛋白指纹图谱比较, 弓形虫急性感染小鼠血清蛋白质谱有13种蛋白表达差异有统计学意义, 其中9种蛋白质表达上调, 4种蛋白质表达下调。表达上调的多肽质荷比 （m/z值）分别为1 932.76、 1 976.85、 2 090.53、 5 004.5、 5 776.01、 5 803.05、 5 847.99、 5 877.51和7 501.58, 表达下调的多肽m/z值分别为1 866.40、 4 063.71、 8 120.31和8 203.83。结论 弓形虫急性感染小鼠血清蛋白质组发生显著改变, 采用WCX纳米磁性微球联用MALDI?TOF?MS技术可检出弓形虫急性感染小鼠血清中的特异性蛋白标志物。     

Preliminary study on proteomics of gastric carcinoma and its clinical significance

AIM: To explore the preliminary identification of serum protein pattern models that may be novel potential biomarkers in the detection of gastric cancer. METHODS: A total of 130 serum samples, including 70 from patients with gastric cancer and 60 from healthy adults, were detected by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by Biomarker Patterns Software (BPS). Thirty serum samples of gastric cancer patients and 30 serum samples of healthy adults were grouped into the training group to build models, and the other 70 samples were used to test and evaluate the models. The samples of the test group were judged only with their peaks' height and were separated into cancer group or healthy control group by BPS automatically and the judgments were checked with the histopathologic diagnosis of the samples. RESULTS: Sixteen mass peaks were found to be potential biomarkers with a significant level of P<0.01. Among them, nine mass peaks showed increased expression in patients with gastric cancer. Analyzed by BPS, two peaks were chosen to build the model for gastric cancer detection. The sensitivity, specificity, and accuracy of the model were 90%, 36/40, 86.7%, 26/30, and 88.6%, 62/70, respectively, which were greatly higher than those of clinically used serum biomarkers CEA (carcinoembryonic antigen), CA19-9 and CA72-4. Stage I/II gastric cancer samples of the test group were all judged correctly. CONCLUSION: The novel biomarkers in serum and the established model could be potentially used in the detection of gastric cancer. However, large-scale studies should be carried on to further explore the clinical impact on the model.