应用血清蛋白质指纹图谱筛选类风湿关节炎的血清标志物

目的 用表面增强激光解吸电离飞行时间质谱（SELDI—TOF—MS）和蛋白质芯片技术检测类风湿关节炎（RA）患者血清蛋白质指纹图谱，探讨基于人工神经网络的蛋白质指纹图谱模型对RA血清诊断标志物的筛选。方法 用H4蛋白芯片结合SELDI—TOF—MS测定了141例血清标本的蛋白质指纹图谱，其中RA90例，系统性红斑狼疮（SEE）20例，健康志愿者31名。将筛选出的血清蛋白质指纹图谱作为输入，建立人工神经网络预测模型，用随机抽取的93例标本（RA60例，SEE 13例，健康志愿者20名）作为训练组，进行训练与交叉验证，并用另外测试组48例（RA30例，SEE 7例，健康志愿者11名）的血清标本盲法验证该模型，同时与抗环瓜氨酸肽（抗CCP）抗体检测结果进行比较。结果 利用从训练组得出的基于人工神经网络的血清蛋白质指纹图谱模型，对测试组的48例未知血清进行预测．结果显示．对RA检测的敏感性为90％（27／30），特异性为90．9％（9／11），阳性率为90．2％（37／41），明显高于抗CCP抗体检测结果。结论血清蛋白质指纹图谱可有效筛选RA血清中特异性蛋白标志物，基于人工神经网络的血清蛋白质质谱模型较以往传统方法具有更高的敏感性和特异性。     

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技术在结直肠腺瘤血清蛋白质指纹图谱筛查模型建立中的应用

目的应用表面增强激光解吸/离子化飞行时间质谱（SELDI-TOF-MS）技术建立结直肠腺瘤（CRA）患者血清蛋白质指纹图谱筛查模型。方法随机选取CRA 42例、结直肠良性疾病（CBD）36例及正常人（HC）44例的血清标本组成建模组,应用SELDI-TOF-MS检测其蛋白质指纹谱,采用Biomarker Wizard及Biomarker Patterns软件分析建模组中各类人群血清中的差异蛋白后,建立CRA筛查最优分类树模型;另随机抽取血清标本70例（CRA及CBD各20例、HC 30例）组成测试组,盲法验证该模型对CRA的筛查效能。结果成功建立了结直肠腺瘤筛查分类树模型。测试模式下,该模型的诊断准确率87.70%、灵敏度71.43%、总特异度96.25%、阳性预测值90.91%。盲法验证该模型诊断准确率88.57%,灵敏度60.0%,总特异度100.0%,阳性预测值100.0%。结论应用SELDI-TOF-MS技术成功建立了CRA筛查模型,该模型敏感性与特异性较高。     

非小细胞肺癌患者血清蛋白质标记物的检测

目的 检测非小细胞肺癌患者(NSCLC)血清蛋白质,筛选特异的蛋白质标记物,构建用于NSCLC早期诊断的血清蛋白质指纹图谱模型.方法 应用表面增强激光解析电离飞行时间质谱(SELDI-TOF-MS)技术检测235例血清标本的蛋白质质谱,并结合生物信息学方法(支持向量机)分析数据.结果 筛选出3个质荷比(m/z)位于6628,9191和11412的蛋白质标记物,构建NSCLC早期诊断模型.联合3种潜在蛋白质标记物,经留一法交叉验证,区分NSCLC和正常健康对照的敏感性为98%,特异性为96%.盲法验证显示,该模型诊断NSCLC的敏感性为96.56%,特异性为94.79%.结论 SELDI-TOF-MS结合支持向量机建立NSCLC血清蛋白质指纹图谱模型是早期诊断NSCLC的一种敏感性高、特异性强的新方法,值得进一步研究与应用.     

应用蛋白芯片技术快速诊断肺结核病的研究

目的评价结核分枝杆菌蛋白芯片在检测临床标本中结核分枝杆菌抗体的应用价值。方法应用结核分枝杆菌蛋白芯片检测117例结核病患者的血清标本、103例肺部其他疾病患者和健康入的血清标本，并与痰涂片镜检法相比较。结果在菌阳肺结核、菌阴肺结核中，结核分枝杆菌抗体芯片检测的阳性率分别是100％（26／26）、49．6％（45／91），在肺部其他疾病患者和健康人中，结核分枝杆菌抗体芯片检测的阴性率是98．1％（101／103）。结论结核分枝杆菌抗体检测蛋白芯片是一种集基因工程技术、芯片技术和免疫学技术的检测一体化的新型结核病快速诊断方法，具有简便、快速、大量、敏感性高和特异性强、检测成本较低的特点，是结核病、尤其是菌阴结核病辅助诊断的有效方法。     

应用SELDI-TOF-MS技术建立直肠癌筛选血清蛋白质指纹图谱模型

目的:建立直肠癌筛选血清蛋白质指纹图谱模型并初步验证. 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蛋白芯片获得新发直肠癌、直肠息肉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,建立直肠癌的筛选模型,并对其进行了盲法验证．结果:直肠癌组与对照组共有26个蛋白质有显著性差异(P<0．05);以其中4个蛋白质生物标志物(质／荷比9 295,3 730,3 938和4 095)组建的筛选模型检测正确率为 96．8％(93／96),经盲法验证,其灵敏度为95．0％(38／40),特异性为93．4％(45／48)．结论:建立的血清蛋白质指纹图谱模型能够区分直肠癌与非直肠癌患者,SELDI-TOF-MS在直肠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．     

HER-2蛋白检测对非小细胞肺癌所致恶性胸腔积液的诊断价值

目的探讨胸腔积液HER-2蛋白检测对非小细胞肺癌（NSCLC）所致恶性胸腔积液的诊断价值。方法分别应用酶联免疫吸附法和应用免疫组织化学方法（SP法）检测20例NSCLC所致恶性胸腔积液中HER-2蛋白水平和胸液沉渣细胞HER-2蛋白的表达，同期取20例良性胸腔积液作对照。结果NSCLC所致恶性胸腔积液患者胸液中HER-2水平（6．18±2．35）ng／ml显著高于结核性胸腔积液患者（3．06±1．18）．g／ml（P〈0．01），肺癌性胸液患者血清HER-2水平（3．89±1．98）ng／ml也显著高于结核组患者（2．31±O．65）ng／ml（P〈0．05）；20例NSCLC所致恶性胸液沉渣细胞HER-2蛋白阳性率（85．O％）与20例良性胸液细胞HER02蛋白检测率（O％）具有统计学差异（P＜O．01）。结论用酶联免疫吸附法和免疫组织化学方法（SP法）检测胸腔积液HER-2蛋白，对NSCLC所致恶性胸腔积液诊断具有重要的临床价值。     

SELDI技术应用于肿瘤疗效监测的初步研究

目的应用表面增强的激光解吸电离飞行时间质谱（SELDI-TOF-MS）技术对卵巢癌及喉癌患者治疗前后血清进行疗效相关标志分子的筛选，评价此技术在肿瘤疗效监测方面的可行性。方法应用SELDI-TOF-MS技术分别对20例卵巢癌患者化疗前后及51例喉癌患者手术前后血清蛋白质指纹图谱进行分析，用Biomarker WizaM软件对结果进行统计学分析。结果卵巢癌患者化疗后血清中质荷比（m／z）4475的蛋白峰（14／20）较化疗前表达增高；喉癌患者术前血清高表达m／z13741蛋白峰，术后21例患者该蛋白表达降低，其中11例（11／21）术后m／z13741、m／z5192和m／z15100的蛋白峰表达均较术前显著下降（P〈0．01）。结论SELDI-TOF-MS技术能灵敏的检测肿瘤患者手术及化疗前后血清中的差异蛋白峰。该技术的微创、灵敏、微量等特点将可能在肿瘤疗效监测及肿瘤的个体化治疗研究中具有较好的应用前景。     

非小细胞肺癌患者呼出气冷凝液中CEA检测的临床意义

目的研究非小细胞肺癌（NSCLC）患者呼出气冷凝液（EBC）中癌胚抗原（CEA）检测的临床意义。方法采用德国JAEGER公司的EBC收集器，收集NSCLC组和正常对照组的EBC，所有对象同时抽血留血清待测。采用非平衡放射免疫分析法（R1A）测定EBC和血清中CEA。结果①肺癌组CEA检测值，EBC为4．52±2．44mg／L，血清为7．73±3．19mg／L；正常对照组CEA检测值，EBC为1．62±1．30mg／L，血清为2．28±1．30mg／L，两组比较有显著统计学意义（P〈0．01）；②腺癌组EBC和血清标本中CEA水平分别为（4．91±2．56mg／L）和（9．17±3．75mg／L），高于正常对照组（P〈0．05）；③Ⅰ期、Ⅱ期、Ⅲ期和Ⅳ期NSCLC患者EBC中CEA水平分别为3．32±1.58mg／L、4．11±2．45mg／L、4．75±2．50mg／L、5．48±2．41mg／L，TNM分期越高，EBC中CEA的水平越高，F=3．12，P〈0．05；④肺癌组EBC和血清CEA检测值呈线性正相关，回归方程=1．61x-1．51，相关系数（r）=0．8615，P〈0．05；⑤肺癌组EBC中CEA检测的敏感性和特异性为51．5％和93．3％，血清中为60．6％和91．1％。肺癌组同时进行EBC和血清中CEA检测，联合敏感性为80．9％，联合特异性85．0％。结论在EBC中检测CEA水平有助于肺癌的早期诊断和临床分期的判断。     

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．     

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.     

遗传性非息肉病性结直肠癌血清的蛋白质组学

目的:研究遗传性非息肉病性结直肠癌(HNPCC)与散发性结直肠癌患者术前血清蛋白质的表达差异,以期发现可用于HNPCC诊断的生物学指标.方法:应用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术结合蛋白质芯片分别检测20例HNPCC和25例散发性结直肠癌患者术前血清蛋白质组分.将获得的蛋白质谱采用美国C...     

表面增强激光解析电离飞行时间质谱法筛选肺癌患者血清和肺泡灌洗液中标志蛋白的研究

目的 应用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和BALF中的差异性表达蛋白,探讨是否可作为诊断肺癌的肿瘤标志物.方法 应用SELDI-TOF-MS技术通过弱阳离子交换蛋白芯片(WCX-2芯片)分别检测35例肺癌和18例肺部良性病变患者血清和BALF中的蛋白质质谱图,用Biomarker Pattern软件分析肺癌的差异蛋白并初步建立诊断模型,通过盲筛进一步验证诊断模型.结果 在肺癌患者血清中发现5个高表达的蛋白质波峰,选用其中质荷比为5639的差异蛋白波峰建立分类树模型,其诊断的敏感度为80%(28/35),特异度为78%(14/18).盲法验证的敏感度为85%(17/20),特异度为90%(9/10),粗符合率为87%(26/30),Youden指数为0.7.在肺癌患者BALF中发现8个高表达蛋白质波峰,选用其中质荷比为7976和11 809的差异蛋白波峰建立分类树模型,其诊断的敏感度为86%(30/35),特异度为72%(13/18).盲法验证的敏感度为90%(18/20),特异度为90%(9/1O),粗符合率为90%(27/30),Youden指数为0.8.平行试验结果显示两者联合应用时诊断肺癌的敏感度、准确率及特异度均为100%,具有互补作用.结论SELDI-TOF-MS技术可筛选出肺癌患者血清和BALF中差异性表达蛋白,作为一种肿瘤标志物,其诊断敏感度高,特异度好,尤其是BALF中差异性表达蛋白的测定可能具有较好的临床应用前景.

Abstract：

Objective To detect the protein markers in serum and bronchoalveolar lavage fluid (BALF) of the patients with lung cancer by surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) technology, and to explore if they can be used as markers for the diagnosis of lung cancer.Methods SELDI-TOF-MS technology and protein chips weak cation exchange (WCX-2 chip) were used to detect the protein mass spectrum in serum and BALF of 35 patients with lung cancer and 18 cases of benign pulmonary diseases.The different protein markers were analyzed by Biomarker Pattern Software and the initial diagnosis models were set up.The diagnosis models were verified further by blind screen to confirm the efficacy of diagnosis.Results Five protein peaks in the sera of the patients with lung cancer were significantly higher (P ＜ 0.05 ).The protein peak with a mass/charge ratio (M/Z)of 5639 was selected to establish the classification tree model.The sensitivity of diagnosis was 80% (28/35) and the specificity was 78% (14/18).The results verified by blind screen showed a sensitivity of 85% (17/20),a specificity of 90% (9/10), a crude accuracy (CA) of 87% ( 26/30 ) and Youden' s index (γ) of 0.7.Eight protein peaks in the BALF of the patients with lung cancer were significantly higher ( P ＜ 0.05).The different protein peaks with M/Z of 7976 and 11 809 respectively were selected to establish the classification tree model.The sensitivity of diagnosis was 86% (30/35) and the specificity was 72% (13/18).The results verified by blind screen showed a sensitivity of 90% (18/20), a specificity of 90% (9/10), a CA of 90% (27/30) and γof 0.8.There was a complementary role in combination of differential proteins in serum and BALF and the sensitivity, specificity and accuracy of diagnosis for lung cancer were 100% by parallel test.Conclusions The SELDI-TOF-MS technology can screen out the differential protein markers in serum and BALF of the patients with lung cancer, which show high sensitivity and specificity as tumor markers.The differential proteins in the BALF may be more promising for clinical application.     

蛋白质指纹图谱技术在肺结核、肺癌鉴别诊断中的应用研究

目的 探索应用表面增强激光解析离子化飞行时间质谱（SELDI-TOF-MS）技术于肺结核、肺癌的鉴别诊断。 方法 肺结核、肺癌患者及正常人各65例,收集其血清标本,采用WCX2芯片技术对血清蛋白进行捕获,用蛋白芯片阅读器PBSⅡ对芯片进行扫描、分析。 结果65例活动性肺结核与65例肺癌患者血清蛋白质谱数据的比较,4个蛋白峰(5 335 m/z、8 048 m/z、11 700 m/z、11 683 m/z)倾向于肺结核,以此鉴别肺癌差异有统计学意义(P<0.01)。该诊断模型判别的总准确率为74.6%（97/130）,灵敏度80.0%(52/65),特异度69.2%(45/65)。 结论 此方法简便、快速,标本用量少,为肺结核、肺癌患者提供了1种新的无创诊断和鉴别诊断方法 。     

凋亡抑制蛋白Livin在非小细胞肺癌患者血清中的表达

目的 通过检测凋亡抑制蛋白Livin在非小细胞肺癌患者、肺炎患者及健康者血清中的含量,探讨Livin对于诊断非小细胞肺癌的临床意义.方法 采用酶联免疫吸附试验对85例非小细胞肺癌患者、20例肺炎患者、20名正常健康人血清Livin水平进行检测.结果 非小细胞肺癌组血清Livin水平表达最高,肺炎组次之,健康对照组最低.三组间具有显著差异(P＜0.001).血清Livin浓度随着肿瘤分期的升高而升高.Ⅲ～Ⅳ期非小细胞肺癌患者血清Livin水平高于Ⅰ～Ⅱ期患者(P＜0.05).结论 Livin作为一种凋亡抑制蛋白对非小细胞肺癌的诊断及判断患者预后具有一定的临床价值.     

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.     

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.