运用蛋白指纹图谱技术筛选类风湿关节炎患者血清特异性标志物

目的: 用纳米磁珠与基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)技术检测类风湿关节炎(RA)患者血清蛋白质组,筛选候选蛋白标志物并建立诊断模型,初步探讨所建立的诊断模型在RA早期诊断中的临床意义。方法: 采用弱阳离子(WCX)纳米磁性微球捕获60例RA患者、35例骨性关节炎(OA)患者和36名健康体检者血清中的蛋白质。所获蛋白经PBSⅡ-C蛋白质芯片阅读仪绘制蛋白指纹图谱。这些蛋白指纹图谱经Ciphergen ProteinChip和Biomarker Wizard软件分析之后,再用Biomarker Patterns 软件进行识别,最终筛选出RA的特异性蛋白标志物,并优化组合建立诊断模型。结果: 在RA患者和对照组之间共找到33个差异蛋白峰(P<0.05)。其中质荷比(m/z)为15 715.5D、7 771.4D、8 959.4D、8 469.8D和8 710.8D的蛋白峰用于建立RA诊断模型。经双盲实验验证,该模型对RA诊断的敏感性为86.7%,特异性为90.0%。结论: 采用WCX纳米磁性微球联用MALDI-TOF-MS技术可检出RA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RA诊断模型。     

基于WCX纳米磁珠的血清蛋白指纹图谱模型在大肠癌诊断中的应用

目的: 探讨蛋白指纹图谱技术筛选大肠癌(CRC)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 PBS II-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 Patterns 软件识别最终用于诊断的蛋白标志物,并优化组合建立诊断模型。结果: 在大肠癌患者和对照组之间找到68个差异蛋白峰(P<0.01),其中质荷比(m/z)为2 870.7、3 084.0、9 180.5和13 748.8的蛋白峰可用于建立大肠癌的诊断模型。该诊断模型能很好地把大肠癌患者从正常人群中区分出来,其敏感性为92.85%(91/98),特异性为91.25%(73/80)。经双盲实验验证,该模型对大肠癌诊断的敏感性为86.95%(40/46),特异性为85.00%(34/40)。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大肠癌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大肠癌诊断模型。     

构建腰椎间盘突出症血瘀证的血清蛋白指纹图谱模型

背景：腰椎间盘突出症血瘀证与非血瘀证的相互关系尚不明确。目的：构建腰椎间盘突出症血瘀证的血清蛋白指纹图谱模型。方法：按照病例对照研究方法,遵循组间民族、性别及年龄等相匹配原则筛选180例研究对象,其中120例分为腰椎间盘突出症血瘀证组及腰椎间盘突出症非血瘀证组,每组60例;健康对照组60例。抽取受试者外周血的血清样本,应用表面增强激光解吸/电离飞行时间质谱及蛋白质芯片技术检测并绘制蛋白质质谱图,随后用Biomarker Wizard软件识别蛋白峰信息,建立腰椎间盘突出症血瘀证的血清诊断模型,并通过双盲验证法对模型进行验证及通过ExPASy数据库对相关差异蛋白进行蛋白检索。结果与结论：在腰椎间盘突出症血瘀证、腰椎间盘突出症非血瘀证及健康者各组之间找到11个蛋白质峰有统计学意义(P < 0.05),其中2个蛋白质呈高表达、9个蛋白质呈低表达;用Biomarker Patterns Software构建腰椎间盘突出症血瘀证血清学诊断模型并验证,其敏感性为86.667%,特异性为94.167%,阳性预测值为88.136%。表明腰椎间盘突出症血瘀证患者的血清中存在多种异常表达的蛋白质;由11个差异蛋白组成的血清蛋白质指纹图谱模型可作为腰椎间盘突出症血瘀证的血清标志物诊断模型。 中国组织工程研究杂志出版内容重点：肾移植;肝移植;移植;心脏移植;组织移植;皮肤移植;皮瓣移植;血管移植;器官移植;组织工程全文链接：     

应用蛋白质谱建立活动性肺结核病的血清诊断模型

目的 应用蛋白质谱技术研究活动性肺结核病患者的血清诊断标志物,建立诊断模型早期诊断活动性肺结核.方法 应用表面加强激光解吸-电离飞行时间质谱技术(SELDI-TOF-MS)和蛋白芯片技术检测264例活动性肺结核患者、其他呼吸疾病患者和正常人血清;应用Ciphergen蛋白芯片3.1.1软件比较、分析血清蛋白峰,找出差异蛋白;应用Biomarker Pattern 5.0软件建立活动性肺结核的诊断模型.结果 129例活动性肺结核血清和135例对照血清蛋白指纹图谱比较,有50个差异蛋白峰(P<0.01).以69例其他呼吸疾病血清和66例健康人血清为对照,选择5个蛋白峰(4360、3311、8160、5723、15173 m/z)建立诊断模型1,该模型诊断活动性肺结核的灵敏度为82.95%,特异性为89.63%,准确率为86.36%.以69例其他呼吸疾病血清为对照,选择3个蛋白峰(5643、4486、4360 m/z)建立诊断模型2,该模型诊断活动性肺结核的灵敏度为96.9%,特异性为97.8%,准确率为97.3%.结论 应用蛋白质谱技术分析肺结核病患者血清可能建立一种新的结核病早期诊断方法 .     

WCX纳米磁珠在宫颈鳞癌血清蛋白质组中的应用及其临床意义

目的　研究宫颈鳞癌患者血清蛋白，筛选差异蛋白并建立诊断模型，并探讨其临床意义。 方法　用WCX纳米磁珠联合基质辅助激光解析离子化飞行时间质谱检测77例宫颈鳞癌，13例宫颈上皮内瘤变Ⅲ级患者和52名健康人的血清。用Biomarker Wizard 软件分析并找出差异蛋白，再用Biomarker Patterns软件建立诊断模型。同时与SCC-Ag作比较。 结果 建立了由3974、3398和13732等差异蛋白峰组成的宫颈癌诊断模型，其敏感性为100%（32/32），特异性为93.8%（30/32）。扩大样本验证，其敏感性为77.8%（35/45），特异性为75%（15/20）。另外13例CINⅢ级患者有11例被检出，34例SCC-Ag阴性的患者中有30例被检出。结论 由3974、3398和13732三个差异蛋白组成的诊断模型有助于区分宫颈鳞癌，CINⅢ级患者与健康人。同时也能检出SCC-Ag阴性的宫颈鳞癌患者。     

采用SELDI-TOF质谱技术分析胃癌患者血清蛋白质谱的变化

目的：利用蛋白质芯片和生物信息学方法从胃癌患者血清中筛选标志蛋白质。方法：采用表面增强激光解吸电离飞行时间质谱技术（SELDI-TOF-MS）和Q10蛋白芯片对30例胃癌患者、30例胃炎患者、20例术后胃癌患者和40例健康者血清的蛋白质指纹图谱进行了检测,使用PBSⅡ-C型蛋白质芯片阅读机读取数据,获得的结果采用CIPHERGEN公司的Biomarker wizard和Biomarker Patterns System软件分析。结果：胃癌患者与健康者血清蛋白质指纹图谱相比有18个显著差异蛋白质,其中8个蛋白质在患者血清中高表达,10个蛋白质在患者血清中低表达。三个蛋白质（5907Da、5080Da、8691Da）组合构建诊断模型可鉴别胃癌与健康者。胃癌患者与胃炎患者血清蛋白质指纹图谱相比有13个显著差异蛋白质,其中7个蛋白质在胃癌患者血清中高表达,6个蛋白质在胃癌患者血清中低表达。二个蛋白质（5907Da、6436Da）组合构建诊断模型可鉴别胃癌与胃炎患者。结论：实验证明利用蛋白质组学和生物信息学方法可以从血清中筛选出胃癌相关的标志蛋白质,可见蛋白质芯片技术对于发现和筛选血清中的胃癌标志蛋白质及疗效判断是一种有效、快速的工具。     

类风湿关节炎并发间质性肺疾病患者血清蛋白质指纹图谱的临床价值分析

目的:应用表面加强激光解吸电离飞行时间质谱筛选出类风湿关节炎并发间质性肺疾病(RA-ILD)患者血清中的差异蛋白,并对其临床价值进行初步分析。方法:用WCX2(弱阳离子交换芯片)蛋白芯片结合表面增强激光解吸电离飞行时间质谱(Surface enhanced laser desorption/ionization time of flight mass spectrum,SELDI-TOF-MS)检测19例RA-ILD患者,15例单纯RA、未合并间质性肺疾病患者,13例相对健康的对照组患者的血清样本,应用Biomarker Wizard软件、Biomarker Pattern软件以及SPSS13.0对测得的数据进行处理,初步构建分类树模型并随机选取部分病例进行验证。结果:患者血清中共检测出142个蛋白峰,经方差分析,发现了4个蛋白质峰差异有统计学意义,质荷比分别为3382.59、3453.39、11886.0、5825.48,Biomarker Pattern软件对所检测的所有数据分析后,发现以质荷比11689.4、2266.49、1020.22、4392.28、5074.38、2764.69为最适合建立分类树模型,灵敏度为92.31%(12/13),特异度为91.18%(31/34)。随机选取RA-ILD、RA、相对健康的对照组患者的血清样本各10例进行模型验证,灵敏度为90%(9/10),特异度为90%(18/20)。结论:SELDI-TOF-MS技术可筛选出RA-ILD患者血清中差异性表达蛋白,作为生物标志物,其诊断敏感度高,特异度好,对其测定具有较好的临床应用前景。     

应用SELDI-TOF-MS技术分析VKC泪液的差异表达蛋白

目的:探讨用蛋白质芯片技术筛选春季卡他性结膜炎(vernal keratoconjunctivis,VKC)患者泪液中蛋白质表达谱,寻找泪液中的标志性蛋白。方法:采用表面增强激光解离飞行时间质谱技术(surface-enhanced la-ser desorption/ionization time of flight mass spectrometry,SELDI-TOF-MS),运用CM10蛋白质芯片检测66例VKC患者和62例正常对照组泪液中蛋白质谱,获得的蛋白质谱采用Biomarker Wizard软件分析,初步筛选蛋白质峰,结合生物信息学的支持向量机(support vector machines,SVM)方法建立并测试VKC患者泪液中的蛋白质指纹图谱模型。结果:在芯片上捕获到145种蛋白质,用质谱仪筛选出VKC患者与正常对照组相比的23种差异蛋白,从中再次筛选出3种蛋白质组成VKC的蛋白质谱最优化模型,VKC患者泪液中质荷比(m/z)分别为2024.3,6630.2和8598.9的3种蛋白质表达上调。模型经三倍交叉验证后用盲法测定,其敏感性和特异性分别为90.91%和93.55%,阳性预测值为93.75%。结论:蛋白质芯片技术可快速、有效地筛选出VKC患者泪液差异蛋白,结合SVM可建立一个由3种蛋白质组成的蛋白质指纹图谱模型,可对VKC做很好的诊断预测,对这3种蛋白质尤其是m/z为2024.8的蛋白质进行研究,有助于VKC病因学进展及诊断标记物的发现。     

利用SELDI-TOF质谱技术分析大肠癌患者血清蛋白质谱的变化

目的：研究大肠癌血清蛋白质谱的变化，从而筛选特异性蛋白标志物。 方法： 利用IMAC3蛋白质芯片和SELDI-TOF质谱技术，对64例大肠癌病人和40名正常人的血清蛋白质谱进行分析。获得的蛋白质谱采用Ciphergen公司的Biomarker Wizard和Biomarker Pattern软件分析。 结果： 通过对大肠癌术前血清与正常人血清蛋白质谱分析发现共有19个蛋白质表达量有明显差异。并获得分子量为5 972.67 D、5 927.21 D、6 113.48 D、5 908.55 D和4 292.51 D这5个蛋白质组成的模板，可将大肠癌与正常人正确分组，其正确分组率分别为97.5％（56/64）和80％（32/40）。术后血清蛋白质谱中，原高表达的蛋白质明显下调。 结论： 结果表明通过大肠癌手术前后及正常对照血清中蛋白质谱的比较，筛选得到用以诊断大肠癌的特异性蛋白标志物并用以预后的判断。SELDI-TOF蛋白质芯片技术为建立蛋白质模板从而早期诊断大肠癌提供了可靠的技术平台。     

SELDI-TOF-MS技术检测心绞痛患者血浆蛋白指纹图谱的研究

目的： 采用表面增强激光解析/电离飞行时间质谱（SELDI-TOF-MS）技术检测心绞痛患者血浆蛋白质指纹图谱,从中筛选出特异的分子标志物。方法: 应用SELDI-TOF-MS技术及金属离子螯合（IMAC-3）蛋白质芯片对20例心绞痛患者和29例正常对照者血浆样本进行检测,借助生物信息学工具（非线性的支持向量机,SVM）提出心绞痛的诊断模型,并运用留一交叉验证法来评估该模型的判别效能。结果: 用SELDI-TOF-MS技术筛选出由3个有显著差异的蛋白质峰［质荷比（m/z）分别为2 667.3、5 914.0和6 890.5］组合构建的诊断模型,可将20例心绞痛患者和29例正常人全部正确分组,诊断特异性和灵敏度均为100%。结论: SELDI-TOF-MS技术在心绞痛的诊断中具有较高灵敏度和特异性,发现的蛋白质峰可能在心绞痛的发病中起一定作用,血浆中分子标志物的发现有助于心绞痛的早期诊断。     

Discovery of serum protein biomarkers in rheumatoid arthritis using MALDI-TOF-MS combined with magnetic beads

The aim of this study was to discover potential biomarkers for rheumatoid arthritis (RA) using Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with magnetic beads. Proteomic fingerprint technology combining magnetic beads with MALDI-TOF-MS was used to profile and compare the proteomes in serum samples from 60 patients with RA, 35 patients with osteoarthritis and 36 healthy controls. The proteomic pattern associated with RA was identified by Biomarker Patterns Software. Model of biomarkers was constructed and evaluated through the Biomarker Patterns Software. A total of 33 discriminative peaks were identified to be related with RA, in which the 5 peaks with the mass-charge ratio (m/z) peaks at 15,715.5, 7,771.4, 8,959.4, 8,469.8 and 8,710.8 Da were used to construct a model for the diagnosis of RA by pattern recognition software. The blind testing data indicated a sensitivity of 86.7% and a specificity of 90.0% in RA diagnosis. These results demonstrated that potential protein biomarkers for RA could be discovered in serum by MALDI-TOF-MS combined with WCX magnetic beads. The diagnosis mode tree based on the five candidate biomarkers could provide a powerful and reliable diagnostic method for RA with high sensitivity and specificity.     

Development of proteomic patterns for detecting lung cancer

Lung cancer is at present the number one cause of cancer death and no biomarker is available to detect early lung cancer in serum samples so far. The objective of this study is to find specific biomarkers for detection of lung cancer using Surface Enhanced Laser Desorption/Ionization (SELDI) technology. In this study, serum samples from 30 lung cancer patients and 51 age-and sex-matched healthy were analyzed by SELDI based ProteinChip reader, PBSII-C. The spectra were generated on WCX2 chips and protein peaks clustering and classification analyses were performed utilizing Biomarker Wizard and Biomarker Patterns software packages, respectively. Three protein peaks were automatically chosen for the system training and the development of a decision classification tree. The constructed model was then used to test an independent set of masked serum samples from 15 lung cancer patients and 31 healthy individuals. The analysis yielded a sensitivity of 93.3%, and a specificity of 96.7%. These results suggest that the serum is a capable resource for detection of specific lung cancer biomarkers. SELDI technique combined with an artificial intelligence classification algorithm can both facilitate the discovery of better biomarkers for lung cancer and provide a useful tool for molecular diagnosis in future.     

MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of colorectal cancer

The aim of present study is to study the serum protein fingerprint of patients with colorectal cancer (CRC) and to screen protein molecules that are closely related to colorectal cancer during the onset and progression of the disease with Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Serum samples from 144 patients with CRC and 120 healthy volunteers were adopted in present study. Weak cation exchange (WCX) magnetic beads and PBSII-C protein chips reader (Ciphergen Biosystems Ins.) were used. The protein fingerprint expression of all the Serum samples and the resulted profiles between cancer and normal groups were analyzed with Biomarker Wizard system. Several proteomic peaks were detected and four potential biomarkers with different expression profiles were identified with their relative molecular weights of 2870.7 Da, 3084 Da, 9180.5 Da, and 13748.8 Da, respectively. Among the four proteins, two proteins with m/z 2870.7 and 3084 were down-regulated, and the other two with m/z 9180.5 and 13748.8 were up-regulated in serum samples from CRC patients. The present diagnostic model could distinguish CRC from healthy controls with the sensitivity of 92.85% and the specificity of 91.25%. Blind test data indicated a sensitivity of 86.95% and a specificity of 85%. The result suggested that MALDI technology could be used to screen critical proteins with differential expression in the serum of CRC patients. These differentially regulated proteins were considered as potential biomarkers for the patients with CRC in the serum and of the potential value for further investigation.     

Comparative analysis of serum proteomes: discovery of proteins associated with osteonecrosis of the femoral head

No means exist to evaluate the activity status, turnover, and prognosis of idiopathic osteonecrosis of the femoral head (IONFH) except for X-ray evidence of segmental collapse as a very good marker for prognosis. Moreover, the only current method for diagnosis of this disease is through physical examination and diagnostic imaging results, and no serum biochemical markers exist. A comparative analysis of serum proteomes was performed to discover proteins associated with osteonecrosis of the femoral head. Two-dimensional electrophoresis (2-DE) patterns of human sera from 10 patients with IONFH and 10 normal subjects were analyzed. The differentially expressed spots were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), and 7 proteins were found. The expression levels of tissue-type plasminogen activator (t-PA), bone-carboxyglutamate protein (BGP), c-sis, and an unknown protein were downregulated in the sera of patients with IONFH, whereas the other 3 proteins, including plasminogen activator inhibitor type 1 (PAI-1), crosslaps, and anti-p53 antibody, were upregulated. To examine their applicability as diagnostic markers, levels of the 6 identified proteins in serum were validated from patients with IONFH, osteoarthritis (OA), rheumatoid arthritis (RA), and fracture using the enzyme-linked immunosorbent assay (ELISA) method. It was found that only serum levels of t-PA, PAI-1, crosslaps, and anti-p53 antibody in patients with IONFH were always significantly different from those in patients with OA, RA, and fracture. These results suggest that serum levels of t-PA, PAI-1, crosslaps, and anti-p53 antibody could be used as noninvasive diagnostic biomarkers for IONFH.     

Correlation of serum protein biomarkers with disease activity in psoriatic arthritis

ABSTRACT

Objective: To assess the correlation of serum protein biomarkers with disease activity across different domains of psoriatic arthritis (PsA).

Material and methods: A cross-sectional cohort of 45 adult patients with PsA fulfilling the classification for psoriatic arthritis (CASPAR) criteria was recruited from University of California San Diego (UCSD) Arthritis Clinics. Clinical data and serum samples were collected and serum was analyzed for protein biomarkers hypothesized to be relevant to disease activity in PsA. Correlations were evaluated for clinical disease activity measures across disease domains.

Results: Biomarkers with the highest correlation to the composite indices and disease domains were SAA, IL-6, YKL-40, and ICAM-1. In addition, several biomarkers were moderately correlated with individual composite indices and/or disease domains. Low or no correlation was observed with some biomarkers, e.g. MMP-3, MMP-1, EGF, VEGF, and IL-6R. In contrast, the correlation of all biomarkers with certain disease domains was low; specifically, pain, percent body surface area of psoriasis, and patient global assessment. The multi-biomarker disease activity score (MBDA) developed for rheumatoid arthritis (RA) showed high correlations with most composite indices and some disease domains in PsA.

Conclusions: These data suggest biomarker analysis can reflect disease activity across disease domains in PsA. Certain domains would likely benefit from the evaluation of additional biomarkers.     

Comparison of the Diagnostic Accuracy of the MSLN Gene Products,Mesothelin and Megakaryocyte Potentiating Factor,as Biomarkers for Mesothelioma in Pleural Effusions and Serum

The MSLN gene products, soluble mesothelin and megakaryocyte potentiating factor (MPF), are being investigated as biomarkers for the asbestos-related cancer malignant mesothelioma (MM). Pleural fluid biomarkers of MM can be elevated when serum levels remain normal. The aim of this study was to determine if this was true for MPF and to compare levels of mesothelin. Biomarker concentrations were compared in 66 MM patients, 39 patients with other malignancies, 37 with benign disease, 18 asbestos-exposed healthy individuals, and 53 patients with chronic kidney disease. In pleural effusions, MPF and soluble mesothelin concentrations were both significantly elevated in MM patients relative to controls. No significant difference between the area under the receiver operator curve (AUC) for MPF (0.945 ± 0.02) and mesothelin (0.928 ± 0.03) when distinguishing MM from all other causes of effusion was observed. MPF and mesothelin serum concentrations were highly correlated and of equivalent diagnostic accuracy with AUCs of 0.813 ± 0.04 and 0.829 ± 0.03, respectively. Serum levels of both markers increased with decreasing kidney function. In conclusion, MPF is elevated in the pleural effusions of MM patients similar to that of mesothelin. Mesothelin and MPF convey equivalent diagnostic information for distinguishing MM from other diseases in pleural effusions as well as serum.