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。     

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。     

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。     

表面增强激光解吸电离飞行时间质谱技术筛查肺癌血清特异性蛋白质及其临床意义

目的:探讨用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术筛查肺癌血清特异性蛋白质的临床意义。方法:应用SELDI-TOF-MS对35例正常对照组、43例治疗前肺癌病人的血清样品进行蛋白质指纹图谱测定,用BioMarker Wizard 3．01及BioMarker Parrern System 5．01分析软件对测得的数据进行处理及建立诊断模型。结果:共检测到251个蛋白质峰,筛选出差异蛋白质峰11个,以质荷比(m/z)分别为M2799_26,M3227_41,M5739_70和M8164_30的4个蛋白质峰为依据组合构建分类决策树模型,分出5个终节点。决策树模型的原始判别总准确率为91．0%(71/78),敏感性为88．4%(38/43),特异性为94．3%(33/35);交叉验证总准确率为85．9%(67/78),敏感性为88．4%(38/43),特异性为82．9%(29/35)。结论:SELDI-TOF-MS在肺癌血清特异性蛋白质的筛选及诊断模型的建立有一定的临床意义。     

早期胃癌患者外周血清差异蛋白表达谱的建立及其生物学分析

目的:分析早期胃癌患者外周血中低丰度蛋白的表达差异,以筛选诊断早期胃癌血清多肽或蛋白标志物。方法:应用高通量的AAH-BLG-1000蛋白芯片分别检测3例早期胃癌患者和3例正常对照成人的外周血清,建立早期胃癌患者外周血清差异蛋白表达谱,分析其相关生物学信息,以筛选早期胃癌的血清肿瘤标志物。结果:与正常对照组比较,早期胃癌患者外周血清中有10种蛋白表达显著上调(P0.05),52种蛋白表达显著下调(P0.05)。生物信息学分析发现差异蛋白质集中于血管生成、信号调控,免疫调节、酶联受体蛋白信号,细胞凋亡等生物进程,而差异蛋白中的VEGI、CD40L、SMAD7、PLUNC、NTN、LTβR和HEVM7个差异蛋白的特征性改变有望成为早期胃癌血清学的肿瘤标志物。结论:应用蛋白芯片技术所得的早期胃癌患者血清中的差异表达蛋白有望成为诊断早期胃癌的血清肿瘤标志物。     

SELDI-TOF-MS分析结直肠腺瘤血清蛋白质谱的变化

目的：分析结直肠腺瘤血清蛋白质谱的变化,寻找结直肠腺瘤的特异性生物标志物。方法：采用SELDI-TOF-MS技术（表面增强激光解析电离飞行时间质谱）对比分析31例结直肠腺瘤患者和11例正常人的血清蛋白质谱,用Biomarker Wizard软件对获得的蛋白质谱进行分析。结果：结直肠腺瘤组与正常对照组有24个蛋白峰有差异,其中有三个蛋白峰（8565.84D、8694.51D和5910.50D）的差异非常显著,8565.84D和8694.51D在结直肠腺瘤中高表达,在正常人中低表达,而5910.50D在两组人群中的表达相反。结论：这三个蛋白峰可能为结直肠腺瘤特异性的生物蛋白标志物。     

肺鳞癌患者与健康人血清的差异蛋白质组学研究

为筛选肺鳞癌的血清标志物,采用二维凝胶电泳(2-DE)技术分离I期肺鳞癌患者和健康人的血清蛋白质,PDquest图像分析软件识别差异蛋白质点,电喷雾串联质谱(ESI-Q-TOF MS/MS)鉴定差异蛋白,然后应用蛋白质印迹和免疫组化方法分别检测差异蛋白——结合珠蛋白-2(haptoglobin-2,HP-2)在肺鳞癌患者血清和健康人血清以及肺鳞癌组织和癌旁正常支气管上皮组织中的表达.建立了肺鳞癌患者和健康人血清的2-DE图谱,图像分析软件识别了1O个差异蛋白质点,质谱鉴定了4种差异蛋白;蛋白质印迹分析显示,HP-2在肺鳞癌血清中的表达水平显著高于健康人(P<0.05),但其表达水平与肺鳞癌的临床分期无明显相关性;免疫组化结果显示,HP-2在肺鳞癌组织中的表达水平高于癌旁正常支气管上皮组织(P<0.05).研究结果提示:HP-2是候选的肺鳞癌血清分子标志物,血清中HP-2水平对肺鳞癌诊断可能具有一定的参考价值;肺鳞癌组织中HP-2表达上调可能是患者血清中HP-2表达升高的原因之一.     

散发性大肠癌血清多肽肿瘤标志物的高通量筛选

目的:以常规体检者和明确诊断的大肠癌患者为研究对象,对其血清进行多肽谱分析,统计分析获得大肠癌特异血清多肽峰,为大肠癌的分子诊断提供理论依据,提高大肠癌的早期诊断水平。方法:1)收集研究对象外周非抗凝血并记录其人口学特征,将非抗凝血进行离心分离血清并保存;2)用Dynabeads RPC18磁珠分离提取血清蛋白质,Bruker UltraFlex TOF/TOF采集信号并用分析软件Clinprot tools 2.2(Bruker)分析筛选出大肠癌血清显著差异峰;3)用SPSS13.0分析大肠癌患者和健康人多肽峰的差异,进行Logistic回归分析差异多肽对形成大肠癌的影响。结果:本次研究共获得111名健康人和94名大肠癌患者的血清多肽峰信息,其中109名健康人和91名大肠癌患者同时具有性别、年龄等人口学信息。筛选出差异多肽峰105个,其中76个多肽在大肠癌患者和健康人间的分布差异有统计学意义(P0.05)。运用Logistic回归分析,进入回归方程(P0.05)的有:年龄,质荷比(m/z)分别为1061.10、1213.09、1607.32、1867.02、1897.95、2011.67和5078.81的七种多肽。结论:液体蛋白芯片飞行时间质谱系统可高效、精确地筛查血清多肽。大肠癌患者与健康人的血清多肽存在差异,筛选得到的质荷比(m/z)分别为1061.10、1213.09、1607.32、1867.02、1897.95、2011.67和5078.81的七种多肽可能作为早期诊断大肠癌的潜在肿瘤标志物。     

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.     

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.     

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.     

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.     

Differential expression of proteomics models of colorectal cancer, colorectal benign disease and healthy controls

Background

Colorectal cancer (CRC) is often diagnosed at a late stage with concomitant poor prognosis. The hypersensitive analytical technique of proteomics can detect molecular changes before the tumor is palpable. The surface-enhanced laser desorption/ionization-time of flight-mass spectra (SELDI-TOF-MS) is a newly-developed technique of evaluating protein separation in recent years. The protein chips have established the expression of tumor protein in the serum specimens and become the newly discovered markers for tumor diagnosis. The objective of this study was to find new markers of the diagnosis among groups of CRC, colorectal benign diseases (CBD) and healthy controls. The assay of SELDI-TOF-MS with analytical technique of protein-chip bioinformatics was used to detect the expression of protein mass peaks in the sera of patients or controls. One hundred serum samples, including 52 cases of colorectal cancer, 27 cases of colorectal benign disease, and 21 cases of healthy controls, were examined by SELDI-TOF-MS with WCX2 protein-chips.

Results

The diagnostic models (I, II and III) were setup by analyzed the data and sieved markers using Ciphergen - Protein-Chip-Software 5.1. These models were combined with 3 protein mass peaks to discriminate CRC, CBD, and healthy controls. The accuracy, the sensitivity and the particularity of cross verification of these models are all highly over 80%.

Conclusions

The SELDI-TOF-MS is a useful tool to help diagnose colorectal cancer, especially during the early stage. However, identification of the significantly differentiated proteins needs further study.     

Biomarkers of HIV-1 associated dementia: proteomic investigation of sera

Background

Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.

Methods

Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.

Results

A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).

Conclusion

Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.     

Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is characterized by degeneration of motor neurons. We tested the hypothesis that proteomic analysis will identify protein biomarkers that provide insight into disease pathogenesis and are diagnostically useful. To identify ALS specific biomarkers, we compared the proteomic profile of cerebrospinal fluid (CSF) from ALS and control subjects using surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS). We identified 30 mass ion peaks with statistically significant (p < 0.01) differences between control and ALS subjects. Initial analysis with a rule-learning algorithm yielded biomarker panels with diagnostic predictive value as subsequently assessed using an independent set of coded test subjects. Three biomarkers were identified that are either decreased (transthyretin, cystatin C) or increased (carboxy-terminal fragment of neuroendocrine protein 7B2) in ALS CSF. We validated the SELDI-TOF-MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS.     

评估SELDI-TOF-MS技术检测肾移植术后病人尿液的能力

目的:研究表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术检测肾移植术后病人尿液中蛋白质的能力。方法:根据尿液标本中蛋白质浓度进行稀释处理,采用SELDI-TOF-MS技术,用3种不同芯片(NP20、CM10、IMAC30)与蛋白质浓度的不同组合分别检测肾移植术后病人的尿液。结果:采用NP20/蛋白质未稀释、CM10/蛋白质浓度约0.1g/L、IMAC30/蛋白质浓度约2g/L时,质谱图中蛋白质峰的个数和丰度达到最佳;单一芯片中NP20捕获质荷比为5000～20000的蛋白质能力最佳,而CM10和IMAC30在质荷比为2000～5000时有较大的捕获能力;3种芯片中CM10具有最大的捕获蛋白质的能力。结论:3种芯片检测尿液中蛋白质的能力不同;为了更好地发现疾病特异性标志物,最好多种芯片同时检测,且在各种芯片得到最佳质谱图时的浓度进行检测。     

A critical assessment of SELDI-TOF-MS for biomarker discovery in serum and tissue of patients with an ovarian mass

ABSTRACT: BACKGROUND: Less than 25% of patients with a pelvic mass who are presented to a gynecologist will eventually be diagnosed with epithelial ovarian cancer. Since there is no reliable test to differentiate between different ovarian tumors, accurate classification could facilitate adequate referral to a gynecological oncologist, improving survival. The goal of our study was to assess the potential value of a SELDI-TOF-MS based classifier for discriminating between patients with a pelvic mass. METHODS: Our study design included a well-defined patient population, stringent protocols and an independent validation cohort. We compared serum samples of 53 ovarian cancer patients, 18 patients with tumors of low malignant potential, and 57 patients with a benign ovarian tumor on different ProteinChip arrays. In addition, from a subset of 84 patients, tumor tissues were collected and microdissection was used to isolate a pure and homogenous cell population. RESULTS: Diagonal Linear Discriminant Analysis (DLDA) and Support Vector Machine (SVM) classification on serum samples comparing cancer versus benign tumors, yielded models with a classification accuracy of 71-81% (cross-validation), and 73-81% on the independent validation set. Cancer and benign tissues could be classified with 95-99% accuracy using cross-validation. Tumors of low malignant potential showed protein expression patterns different from both benign and cancer tissues. Remarkably, none of the peaks differentially expressed in serum samples were found to be differentially expressed in the tissue lysates of those same groups. CONCLUSION: Although SELDI-TOF-MS can produce reliable classification results in serum samples of ovarian cancer patients, it will not be applicable in routine patient care. On the other hand, protein profiling of microdissected tumor tissue may lead to a better understanding of oncogenesis and could still be a source of new serum biomarkers leading to novel methods for differentiating between different histological subtypes.     

Strategy for purification and mass spectrometry identification of SELDI peaks corresponding to low-abundance plasma and serum proteins

Analysis by SELDI-TOF-MS of low abundance proteins makes it possible to select peaks as candidate biomarkers. Our aim was to define a purification strategy to optimise identification by MS of peaks detected by SELDI-TOF-MS from plasma or serum, regardless of any treatment by a combinatorial peptide ligand library (CPLL). We describe 2 principal steps in purification. First, choosing the appropriate sample containing the selected peak requires setting up a databank that records all the m/z peaks detected from samples in different conditions. Second, the specific purification process must be chosen: separation was achieved with either chromatographic columns or liquid-phase isoelectric focusing, both combined when appropriate with reverse-phase chromatography. After purification, peaks were separated by gel electrophoresis and the candidate proteins were analyzed by nano-liquid-chromatography-MS/MS. We chose 4m/z peaks (9400, 13,571, 13,800 and 15,557) selected for their differential expression between two conditions, as examples to explain the different strategies of purification, and we successfully identified 3 of them. Despite some limitations, our strategy to purify and identify peaks selected from SELDI-TOF-MS analysis was effective.