血清蛋白质指纹图谱模型在子宫内膜异位症诊断中的应用

用表面加强激光解析电离飞行时间质谱(SELDI-TOF-MS)和蛋白质芯片检测子宫内膜异位症(endometriosis,EM)患者血清蛋白质指纹图谱,探讨诊断模型在EM诊断中的临床应用价值。用SELDI-TOF-MS技术和H4蛋白质芯片检测16例EM和16例正常女性的血清蛋白质指纹图谱,并建立诊断模型。然后,对16名健康人和16例EM患者样本进行盲法测试验证该模型。筛选出4个有明显表达差异的蛋白质,其质荷比(m/z)分别为8141、6096、5894、3269。建立的诊断模型对EM检测的灵敏度为87.5%(14/16),特异性为93.75%(15/16),总准确率为90.625%(29/32)。SELDI-TOF-MS对小样本的EM诊断具有较高的敏感性和特异性,在EM的诊断及标志物筛选等方面具有较好的诊断价值。     

一个在肺癌血清中高表达的标志分子SAA的发现及鉴定

应用表面增强基质辅助激光解析离子化-飞行时间质谱(SELDI-TOF-MS)对175例肺癌病人和43例正常人血清进行了蛋白质谱检测. 通过Biomarker Wizard™ 和Biomarker Patterns™软件分析显示11.6 kD蛋白峰在肺癌病人血清中明显高于正常对照组, 同时该蛋白峰的表达水平与肺癌病人的临床分期密切相关, 随着病情的加重而逐渐升高. 进而采用Tricine-SDS-PAGE结合质谱分析鉴定出芯片上11.6 kD的蛋白峰为血清淀粉样蛋白A(serum amyloid A protein, SAA). 使用抗SAA的特异性抗体通过免疫沉淀进一步证实了该蛋白峰为SAA. 同时还采用了ELISA方法对上述部分血清样本中SAA表达水平进行了测定, 结果显示其敏感性和特异性分别为84.1%和80%. 与蛋白质芯片使用单一差异蛋白质SAA划分结果基本一致. 上述实验结果表明, SAA能很好地划分肺癌病人和正常对照组, 很可能成为肺癌诊断和病情检测的一个标志分子.     

恶性肿瘤化疗后血糖变化的蛋白质组指纹模型

目的:应用表面增强激光解析离子飞行时间质谱(Surface-enhanced laser desorption ionization time of flight mass spectrometry,SELDI-TOF-MS)技术筛选与恶性肿瘤化疗后血糖变化情况相关的血清蛋白质组指纹并建立模型.方法:应用CM10弱阳离子芯片结合SELDI-TOF-MS技术检测197例恶性肿瘤患者化疗后血清样本的蛋白质谱,2年后随访,按血糖标准分为血糖正常组(171例)、糖耐量异常组(16例)和糖尿病组(10例),利用Biomarker Wizard软件比较各组间的血清蛋白质指纹图谱,Biomarker Pattern软件建立模型.结果:M/Z为4276和4662的两个蛋白质组成的诊断模型可将糖尿痛组与糖耐量异常组准确分组,灵敏度、特异度和准确度分别为70%、81.25%和76.92%;M/Z为2818、7535和2633的三个蛋白质组成的诊断模型可将糖尿病组与血糖正常组准确分组,灵敏度、特异度和准确度分别为80%、79.53%和82.32%;M/Z为2818、7744、3187、2564、4175、5165和3374的七个蛋白质组成的诊断模型可将糖耐量异常组与血糖正常组准确分组,灵敏度、特异度和准确度分别为87.5%、87.72%和88.77%.结论:SELDI-TOF-MS技术筛选出恶性肿瘤化疗后三组血糖情况的蛋白质指纹,M/Z为4175、4276、4086、3158、3374、3316、2044、3441、4662和4290可作为预测化疗后糖尿病的指标,M/Z为2818、3374、3352、4276、2932、8817、4070、3187、7535和15525可作为预测化疗后糖耐量异常的指标,M/Z为6021、3187、2818、2932、3273、4070、7916、8817、8057和4387可作为预测化疗后可能不会发生糖尿病的指标,这为化疗副反应的防治提供了科学依据.     

用SELDI技术预测吉非替尼治疗肺癌疗效的前瞻性研究报告

目的:介绍SELDL-TOF-MS技术预测吉非替尼治疗肺癌疗效观察,探索新的用药指征.方法:应用CM10弱阳离子芯片结合表面增强飞行时间质谱(SELDL-TOF-MS)技术检测9例晚期肺癌患者口服吉非替尼前血清样本的蛋白质谱,口服吉非替尼1个月后,根据实体瘤近期疗效标准分为服药有效组(CR PR)(4例)和无效组(SD PD)(5例),利用BiomarkerWizard软件比较各组间的血清蛋白质指纹图谱.结果:有效组与无效组相比有4个蛋白质峰有显著差异性,M/Z分别为4889,1576,1762和8693,与无效组相比.有效组上调的峰为2个,其M/Z为1576和1762,下调的峰为2个,其M/Z为4889和8693.结论:SELDI-TOF-MS技术可筛选出预测吉非替尼治疗疗效的相关蛋白质组指纹,此方法捕获的蛋白质组指纹,可作为一种选择用药的新指标.     

基于MALDI-TOF MS技术子宫内膜癌血清学诊断模型的建立及验证

目的:通过基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)技术建立子宫内膜癌(EC)血清学诊断模型。方法:收集53例EC初诊患者和37例子宫内膜良性病变患者血清,按照2∶1随机分为训练组60例(EC患者35例,良性病变患者25例)和验证组30例(EC患者18例,良性病变患者12例);通过弱阳离子交换磁珠(MB-WCX)提取血清蛋白,经MALDI-TOF MS筛选差异蛋白。根据训练组中差异蛋白建立诊断模型,用验证组验证诊断模型的敏感性、特异性及诊断效率。结果:筛选出47个差异蛋白峰(P0.05),联合ROC曲线分析筛出AUC0.80的差异峰14个,其中在EC组表达上调的蛋白有7个;m/z分别为1779.63和1866.76 Da的2个蛋白峰差异性最显著,AUC分别为0.935和0.969,且EC组的平均峰值强度和峰下面积明显低于良性病变组。以上述2个蛋白建立诊断模型,经验证其敏感性为88.89%,特异性为91.67%,诊断效率为90%。结论:利用MALDI-TOF MS技术建立的EC血清学诊断模型有较高的敏感性和特异性,有望用于EC的早期筛查和辅助诊断。提示蛋白峰m/z 1779.63和1866.76 Da可能成为EC的潜在肿瘤标志物。     

血清SELDI蛋白质指纹图谱在乳腺癌术前分级中的应用

应用SELDI技术和生物信息学方法从血清中筛选能反映乳腺癌术前分期的蛋白质峰并构建检测模型。采用CM10芯片,对34例Ⅰ～Ⅱ期乳腺癌患者和31例Ⅲ～Ⅳ期乳腺癌的血清进行了检测,发现11个蛋白质峰在两组患者之间表达量有显著性差异（P<0.05）,M/Z为 M2042.87、M2459.83、M3881.37、M4804.47、M6683.24和 M6706.06的6个蛋白质峰被选为分类变量构成决策树分类模型,该模型的交叉验证（测试组）总准确率为80.0%,Ⅰ～Ⅱ期乳腺癌检出率为82.4%,Ⅲ～Ⅳ期检出率为77.4%。SELDI技术在乳腺癌患者术前分级的判断方面具有一定的应用价值。     

SELDI-TOF-MS筛选慢性阻塞性肺疾病血清标志蛋白的初步研究

目的:利用表面增强激光解吸电离飞行时间质谱技术(SELDI-TOF-MS)筛选慢性阻塞性肺疾病(COPD)血清特异标志物。方法:应用SELDI-TOF-MS技术检测30例COPD稳定期患者和30例健康对照者血清蛋白指纹图谱,采用Biomarker pattern软件进行分析,建立COPD的诊断模型。结果:COPD患者血清蛋白图谱与对照组相比,在相对分子质量2000-15 000范围内共检测到75个蛋白峰,发现19个有统计学差异的蛋白峰(P0.05)。通过对COPD组与对照组间的数据作进一步分析,经BPS软件分析,建立质荷比(M/Z)3 167、4 645的差异蛋白组成的诊断模型,其诊断敏感度为96.67%,特异度为96.67%。结论:SELDI-TOF-MS技术是一种快速、简单易行、用量少和高通量的分析方法。能直接筛选出COPD血清中特异表达标志物,用特异表达标志物建立的诊断模型能有效区分COPD患者与健康对照者,有望成为COPD诊断的辅助指标。     

基于支持向量机的大肠黏液腺癌术前血清蛋白质标志物检测及分析

应用表面加强激光解吸电离-飞行时间质谱(SELDI-TOF-MS)技术和CM10蛋白质芯片从大肠黏液腺癌和非黏液腺癌患者中成功地筛选出了大肠黏液腺癌患者血清特异性相关蛋白.应用美国CipherGen公司CM10蛋白质芯片和PBSⅡ型蛋白质芯片阅读仪检测53例大肠癌患者(黏液腺癌12例,非黏液腺癌41例)患者血清蛋白质指纹图谱.采用ZUCI-Protein Chip Data Analyze System分析软件包进行分析,离散小波去噪音,结合支持向量机筛选肿瘤标志物,建立大肠黏液腺癌的术前诊断模型.12例大肠黏液腺癌患者与41例大肠非黏液腺癌患者的血清蛋白质有12个蛋白质峰强度有显著差异.其中质荷比为24 297和23 434 m/z处的蛋白质峰强度统计学P值分别为0.0067和0.0092,差异有极显著统计学意义.支持向量机筛选出24 297、3 322、3 822和4 353 m/z蛋白质峰作为生物标志物进行检测和预测准确率,其中12例大肠黏液腺癌患者中有10例患者被正确识别,41例大肠癌非黏液腺癌患者中有39例被正确识别,准确率为92.45%(49/53).该方法可以较好地应用于区别大肠黏液腺癌和非黏液腺癌,进行术前病理鉴别,指导进行大肠黏液腺癌的手术和综合治疗.     

利用免疫组织化学及蛋白质组学对树莓提取物抑制大鼠肝癌机制的研究

目的：探讨树莓预防大鼠原发性肝癌增殖抑制和凋亡诱导作用,寻找树莓预防大鼠原发性肝癌的特异性蛋白质靶点。方法：利用二乙基亚硝胺（DEN）建立大鼠原发性肝癌动物模型;通过免疫组织化学方法研究树莓提取物对于大鼠原发性肝癌的预防效果和形态学变化,利用蛋白质组学研究树莓预防大鼠原发性肝癌的特异性蛋白质靶点。结果：树莓提取物能抑制PCNAJVEGF的表达,抑制细胞增殖,并诱导细胞凋亡。蛋白质组学差异分析表明：成瘤组大鼠血清在蛋白质峰2597．93M／Z,4513．88M／Z上与高剂量树莓干预组大鼠血清具有显著性差异（P〈0．05）。结论：树莓提取物可以抑制肝癌细胞PCNA的表达,从而抑制肝癌细胞的增殖;还可以诱导肝癌细胞凋亡;蛋白质峰2597．93M／Z及4513．88M／Z所表达蛋白为其特异性作用靶点。     

血清蛋白质指纹图谱诊断早期胃癌临床意义

目的：应用SELDI蛋白质芯片检测胃癌患者血清蛋白质指纹图谱,筛选候选肿瘤标志物以建立诊断模型。方法：表面加强激光解吸电离-飞行时间质谱（SELDI-TOF-MS）技术及其配套蛋白质芯片检测34例胃癌患者（Ⅰ／Ⅱ期12例与Ⅲ／Ⅳ期22例）和30例健康人的血清蛋白质组图谱,运用判别分析处理数据筛选标志物并建立诊断模型。结果：2046m／z、1179m／z、1817m／z、1752m／z和1588m／z等5个蛋白质峰组合所构建的诊断模型能达到鉴别胃癌患者和健康人的最佳诊断效果,特异度94．1％（32／34）,灵敏度93．3％（28／30）。单个4665m／z蛋白质峰诊断模型可达到鉴别Ⅰ／Ⅱ期与Ⅲ／Ⅳ期胃癌效果,其特异度91．6％（11／12）,灵敏度95．4％（21／22）。结论：该方法在胃癌的诊断尤其是早期诊断方面具有一定价值,值得进一步研究。     

Diagnostic application of serum proteomic patterns in gastric cancer patients by ProteinChip surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one of thecurrently used techniques to identify biomarkers for cancers. This study was planned to establish a system to accurately distinguish gastric cancer patients by using SELDI-TOF-MS. A total of 100 serum samples obtained from 60 individuals with gastric cancer and 40 healthy individuals were screened. Protein expression profiles were expressed on CM10 ProteinChip arrays and analyzed. Peak intensities were analyzed with the Biomarker Wizard software to identify peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern software 5.0. Seventeen protein peaks showed significant differences between the two groups. The decision tree which gave the highest discrimination included four peaks at mass 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and specificity for classification of the decision tree were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested on a blinded test set, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). These results suggest that serum protein profiling by the SELDI system may distinguish gastric cancer patients from healthy controls with relatively high sensitivity and specificity.     

蛋白质指纹技术与MATLAB软件联合应用预测FOLFOX4方案治疗大肠癌耐药的前瞻性研究

目的:借助于蛋白质指纹技术及MATLAB软件探索预测FOLFOX4方案治疗大肠癌耐药的可能性。方法:选择12例曾手术并接受FOLFOX4方案化疗并有确切疗效的大肠癌患者,应用CM10弱阳离子芯片结合表面增强飞行时间质谱(SELDI-TOF-MS)技术于化疗前检测患者血清样本的蛋白质谱,动态观察该方案化疗后2周至半年内,根据实体瘤近期疗效标准分为用药稳定组(SD)(7例)和无效组(PD)(5例),应用Biomarker Wizard软件得出两组间有统计学意义的差异指纹。用MATLAB软件进行多项式曲线拟合得出每个差异指纹的拟合曲线及曲线方程。结果:术后稳定组与无效组相比有3个蛋白质峰有显著差异性,M/Z分别为1204、2868、4176,其中与稳定组相比,无效组上调的峰M/Z为2868,下调的峰M/Z为1204和4176。用MATLAB软件进行多项式曲线拟合得出每个差异指纹的曲线且曲线方程均呈线性的函数关系。结论:MATLAB软件根据实测值获得的曲线方程及曲线呈有意义的线性函数关系,因此借助于蛋白质指纹技术预测FOLFOX4方案治疗大肠癌的耐药是可行的。以此为平台扩大病例数进一步验证即可获得能应用于临床的预测指...     

Using the SELDI ProteinChip System to Detect Changes in Protein Expression in Vero Cells after Infection

Human herpes simplex virus 1 (HSV-l) causes facial, ocular, and encephalitic disease and is associated with latent infection and cancer. Here, we developed a means of studying the pathogenesis of HSV-l infection at the protein level by using the SELDI Protein Chip to detect changes of protein expression in Vero cells cultured in vitro. After infection with HSV-l and culture for 12, 24 or 48 h, cells were harvested and lysed. IMAC3 arrays were applied to SELDI-TOF-MS to detect proteomic differences before and after infection. The chip detected a series of differentially expressed protein peaks. Interestingly, both peaks at 16 912 Da and 17 581 Da corresponded precisely with the molecular mass of ISG 15, which may participate in antiviral activity during the process of infection. Thus, the results we obtained can serve as a basis to study the pathogenesis of HSV-1 and the interaction between the virus and its host. In addition, they can help in the discovery of new therapeutic targets for treatment of HSV-l infection.     

Using the SELDI ProteinChip system to detect changes in protein expression in Vero cells after infection

Human herpes simplex virus 1 (HSV-1) causes facial, ocular, and encephalitic disease and is associated with latent infection and cancer. Here, we developed a means of studying the pathogenesis of HSV-1 infection at the protein level by using the SELDI Protein Chip to detect changes of protein expression in Vero cells cultured in vitro. After infection with HSV-1 and culture for 12, 24 or 48 h, cells were harvested and lysed. IMAC3 arrays were applied to SELDI-TOF-MS to detect proteomic differences before and after infection. The chip detected a series of differentially expressed protein peaks. Inte-restingly, both peaks at 16 912 Da and 17 581 Da corresponded precisely with the molecular mass of ISG15, which may participate in antiviral activity during the process of infection. Thus, the results we obtained can serve as a basis to study the pathogenesis of HSV-1 and the interaction between the virus and its host. In addition, they can help in the discovery of new therapeutic targets for treatment of HSV-1 infection.     

New potential biomarkers in the diagnosis of esophageal squamous cell carcinoma

Objective: To analyse the alterations of serum proteins in cases of esophageal squamous cell carcinoma (ESCC) in order to screen and validate serum marker patterns for the diagnosis of ESCC in the high-risk populations of Xinjiang, China. Methods: The serum proteomic patterns of 188 cases, including 139 patients with ESCC (54 Uygur, 45 Kazakh and 40 Han subjects) and 49 sex- and age-matched healthy controls, were detected using the SELDI-TOF-MS (surface-enhanced laser desorption/ionization–time of flight–mass spectrometry) technology with the CM10 ProteinChip. Differences in protein peaks between patients with ESCC and controls were analysed using the Biomarker Pattern Software, and a primary diagnosis model of ESCC was developed and validated with SVM (support vector machines). This model was further evaluated by a large-scale blind test. Results: Two hundred and eighty-three protein peaks were detected within the molecular range of 0–20?kDa, among which, 140 peaks were significantly different between ESCC cases and controls (p?m/z 5667, 5709, 5876, 5979, 6043 and 6102) was established with a sensitivity of 97.12% and a specificity of 83.87%. The large-scale blind test generated a sensitivity of 91.43% and a specificity of 88.89%. Conclusions: The differential protein peaks analysed by SELDI-TOF-MS may contain promising serum biomarkers for screening ESCC. The diagnostic model which combined only six protein peaks had a satisfactory discriminatory power. The model should be further evaluated in other populations of ESCC patients and tested against controls. The nature and function of the discriminating proteins have yet to be elucidated.     

Predicting Early Intrahepatic Recurrence of Hepatocellular Carcinoma after Microwave Ablation Using SELDI-TOF Proteomic Signature

Background/Aims

Despite great progress in the treatment of hepatocellular carcinoma (HCC) over the last-decade, intrahepatic recurrence is still the most frequent serious adverse event after all the treatments including microwave ablation. This study aimed to predict early recurrence of HCC after microwave ablation using serum proteomic signature.

Methods

After curative microwave ablation of HCC, 86 patients were followed-up for 1 year. Serum samples were collected before microwave ablation. The mass spectra of proteins were generated using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Serum samples from 50 patients were randomly selected as a training set and for biomarkers discovery and model development. The remaining serum samples were categorized for validation of the algorithm.

Results

According to preablation serum protein profiling obtained from the 50 HCC samples in the training set, nine significant differentially-expressed proteins were detected in the serum samples between recurrent and non-recurrent patients. Decision classification tree combined with three candidate proteins with m/z values of 7787, 6858 and 6646 was produced using Biomarker Patterns Software with sensitivity of 85.7% and specificity of 88.9% in the training set. When the SELDI marker pattern was tested with the blinded testing set, it yielded a sensitivity of 80.0%, a specificity of 88.5% and a positive predictive value of 86.1%.

Conclusions

Differentially-expressed protein peaks in preablation serum screened by SELDI are associated with prognosis of HCC. The decision classification tree is a potential tool in predicting early intrahepatic recurrence in HCC patients after microwave ablation.     

Discovery and identification of Serum Amyloid A protein elevated in lung cancer serum

Two hundred and eighteen serum samples from 175 lung cancer patients and 43 healthy individuals were analyzed by using Surface Enhaced Laser Desorption/Ionization Time of Flight Mass Spectrome-try (SELDI-TOF-MS). The data analyzed by both Biomarker Wizard™ and Biomarker Patterns™ software showed that a protein peak with the molecular weight of 11.6 kDa significantly increased in lung cancer. Meanwhile, the level of this biomarker was progressively increased with the clinical stages of lung cancer. The candidate biomarker was then obtained from tricine one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis by matching the molecular weight with peaks on WCX2 chips and was identified as Serum Amyloid A protein (SAA) by MALDI/MS-MS and database searching. It was further validated in the same serum samples by immunoprecipitation with commercial SAA antibody. To confirm the SAA differential expression in lung cancer patients, the same set of serum samples was measured by ELISA assay. The result showed that at the cutoff point 0.446 (OD value) on the Receiver Operating Characteristic (ROC) curve, SAA could better discriminate lung cancer from healthy individuals with sensitivity of 84.1% and specificity of 80%. These findings demonstrated that SAA could be characterized as a biomarker related to pathological stages of lung cancer.     

Proteomic approaches to biomarker discovery in lung cancers by SELDI technology

Lung cancer is one of the most common tumors all over the world and one of those with higher mortality in clinic. For instance, 169500 new cases of lung cancer were estimated in the United States for 2001[1]. In recent years, both morbidity and mortality of lung cancer were reported gradually increasing in our country. Therefore, it has become an urgent task to search and discover specific biomarkers for lung cancer. In tumor genesis, certain cellular proteins must have changed their express…