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

Aim: New technologies for the early detection of pancreatic cancer (PC) are urgently needed. The aim of thepresent study was to screen for the potential protein biomarkers in serum using proteomic fingerprint technology.Methods: Magnetic beads combined with surface-enhanced laser desorption/ionization (SELDI) TOF MS wereused to profile and compare the protein spectra of serum samples from 85 patients with pancreatic cancer, 50patients with acute-on-chronic pancreatitis and 98 healthy blood donors. Proteomic patterns associated withpancreatic cancer were identified with Biomarker Patterns Software. Results: A total of 37 differential m/zpeaks were identified that were related to PC (P < 0.01). A tree model of biomarkers was constructed with thesoftware based on the three biomarkers (7762 Da, 8560 Da, 11654 Da), this showing excellent separation betweenpancreatic cancer and non-cancer., with a sensitivity of 93.3% and a specificity of 95.6%. Blind test data showeda sensitivity of 88% and a specificity of 91.4%. Conclusions: The results suggested that serum biomarkers forpancreatic cancer can be detected using SELDI-TOF-MS combined with magnetic beads. Application of combinedbiomarkers may provide a powerful and reliable diagnostic method for pancreatic cancer with a high sensitivityand specificity.     

Serum protein fingerprint of patients with pancreatic cancer by SELDI technology

Objective： To study the serum protein fingerprint of patients with pancreatic cancer and to screen for protein molecules closely related to pancreatic cancer during the onset and progression of the disease using surface-enhanced laser desorption and ionization time of fight mass spectrometry （SELDI-TOF-MS）. Methods： Serum samples from 20 pancreatic cancers, 20 healthy volunteers and 18 patients with other pancreatic diseases. WCX magnetic beans and PBSII-C protein chips reader （Ciphergen Biosystems Ins.） were used. The protein fingerprint expression of all the Serum samples and the resulting profiles between cancer and normal were analyzed with BiomarkerWizard system. Results： Agroup ofproteomic peaks were detected. Four differently expressed potential biomarkers were identified with the relative molecular weights of 5705 Da, 4935 Da, 5318 Da and 3243 Da. Among them, two proteins with m/z5705, 5318Da down-regulated, and two proteins with m/z 4935, 3243 Da were up-regulated in pancreatic cancers. Conclusion： SELDI technology can be used to screen significant proteins of differential expression in the serum of pancreatic cancer patients. These different proteins could be specific biomarkers of the patients with pancreatic cancer in the serum and have the potential value of further investigation.     

Effect of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry on identifying biomarkers of laryngeal carcinoma

The aim is to study the serum protein fingerprint of patients with laryngeal carcinoma (LC) and to screen for protein molecules closely related to LC during the onset and progression of the disease with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Serum samples from 68 patients with LC and 117 non-cancer control samples (75 healthy volunteers and 42 Vocal fold polyps). Q10 protein chips and PBSII-C protein chips reader (Ciphergen Biosystems Inc.) were used. The protein fingerprint expression of all the Serum samples and the resulting profiles between cancer and non-cancer groups were analyzed with Biomarker Wizard system. A group of proteomic peaks were detected. Three differently expressed potential biomarkers were identified with the relative molecular weights of 5,915, 6,440 and 9,190 Da. Among the three peaks, the one with m/z 6,440 was down-regulated, and the other two peaks with m/z 5,915 and 9,190 were up-regulated in LC. This diagnostic model could distinguish LC patients from controls with a sensitivity of 92.1% and a specificity of 91.9%. Moreover, blind test data showed a sensitivity of 86.7% and a specificity of 89.1%. The data suggested that SELDI technology could be used to screen proteins with altered expression levels in the serum of LC patients. These protein peaks were considered as specific serum biomarkers of LC and have the potential value for further investigation.     

A Serum Biomarker Model to Diagnose Pancreatic Cancer Using Proteomic Fingerprint Technology

OBJECTIVE To establish a serum protein pattern model for screening pancreatic cancer. METHODS Twenty-nine serum samples from patients with pancreatic cancer were collected before surgery,and an additional 57 serum samples from age and sex-matched individuals without cancer were used as controls.WCX magnetic beans and a PBS Ⅱ-C protein chip reader (Ciphergen Biosystems Inc) were employed to detect the protein fingerprint expression of all serum samples. The resulting profiles comparing serum from cancer and normal patients were analyzed with the Biomarker Wizard system,to establish a model using the Biomarker Pattern system software. A double-blind test was used to determine the sensitivity and specificity of the model. RESULTS A group of 4 biomarkers(relative molecular weights were 5,705 Da,4,935 Da,5,318 Da,3,243 Da) were selected to set up a decision tree to produce the classification model to effectively screen pancreatic cancer patients.The results yielded a sensitivity of 100%(20/20),specificity of 97.4% (37/38).The ROC curve was 99.7%.A double-blind test used to challenge the model resulted in a sensitivity of 88.9% and a specificity of 89.5%. CONCLUSION New serum biomarkers of pancreatic cancer have been identified.The pa ern of combined markers provides a powerful and reliable diagnostic method for pancreatic cancer with high sensitivity and specificity.     

乳腺Paget 病患者血清标志物的研究*

目的:目前,蛋白质芯片技术已成为肿瘤蛋白质组学研究的重要工具之一。本研究应用表面增强激光解析电离飞行时间质谱技术（surfaced enhanced laser desorption/ionization time of flight mass spectrometry ,SELDI-TOF-MS ）寻找乳腺Paget病患者血清特异性蛋白,找出最佳的标志蛋白组合模式作为临床诊断指标,以期用于乳腺Paget病患者的早期诊断。方法:用弱阳离子蛋白芯片（Weak cation exchanger protein chip,WCX2）及SELDI-TOF-MS 技术检测10例健康者、10例乳房湿疹和15例乳腺Paget病患者血清中蛋白的相对含量。使用PBS Ⅱ-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Biomarkerwizard 和Biomarker Patterns System 软件分析。结果:Paget病患者与健康人相比质荷比为3 868Da和8 876Da的2 个蛋白峰差异有统计学意义（P<0.01）;与慢性湿疹相比质荷比为2 911Da、3 868Da、5 097Da的3 个蛋白质峰差异有统计学意义（P<0.05）。 分析系统筛选出3 868Da及8 876Da标志蛋白建立起一个乳腺Paget病的诊断模型,对乳腺Paget病的诊断特异性为100% ,敏感度为73.3% 。结论:SELDI-TOS-MS 技术对乳腺Paget病早期诊断和鉴别慢性湿疹具有诊断价值,在特异性肿瘤标记物的筛选等方面有一定应用前景,无创安全,值得推广。      

应用SELDI蛋白质芯片技术筛选肺腺癌血清标志物的研究

目的 通过SELDI蛋白质芯片技术筛选肺腺癌特异血清标志物。方法 采用表面增强激光解析离子化一飞行时间质谱技术（SELDI-TOF-MS），选用弱阳离子加疏水膜芯片对15例肺腺癌患者，30例健康人血清进行检测，筛选在肺腺癌患者血清中差异表达的蛋白质。结果 在质荷比0—20000范围内，共检测到180个蛋白峰，建立了由8个差异表达蛋白质组成的肺腺癌诊断模型。这8个蛋白质中有6个在肺腺癌中表达上调，2个表达下调。软件分析结果显示在预测组中诊断肺腺癌的敏感性为93．33％（14／15）、特异性为100．00％（30／30）；对检测组进行双盲检测，敏感性为73．33％（11／15）、特异性为86．67％（26／30）。结论 由8个差异表达蛋白及其特定组合构成的诊断模型可以区分肺腺癌和健康人。SELDI蛋白质芯片技术能直接筛选出肺腺癌患者血清中相对特异的潜在标志物，具有较好的临床应用价值。     

血清蛋白质谱模型对胃腺癌诊断的应用性研究

[目的]探讨用蛋白质芯片技术筛选胃腺癌患者血清蛋白质表达谱，寻找血清中的标志性蛋白。[方法]采用蛋白质生物芯片表面增强激光解析电离飞行时间质谱（SELDI）技术，运用SAX2（Strong Anionic Exchanger）蛋白质芯片检测胃腺癌患者，胃炎患者和健康者血清，建立诊断模型，然后进行单盲模型验证。[结果]发现5910Da,5084Da和8691Da的三个蛋白质荷比峰（M/Z）在胃腺癌和健康组比较中具有显著性差异。5910Da,6440Da的两个蛋白质荷比峰（M/Z）在胃腺癌和胃炎组中比较具有显著性差异。[结论]建立了胃腺癌的血清蛋白指纹质谱，为以后的胃癌蛋白质组学研究奠定了一定的基础，建立了以5910Da,5084Da,8691Da和6440Da四个蛋白质峰为模型区分胃腺癌与非胃腺癌的血清蛋白表达质谱诊断模型，为胃腺癌的临床诊断提供了一条崭新的途径和方法。     

应用液体蛋白芯片-飞行时间质谱技术从血清中筛选胃癌肿瘤标志蛋白

目的:应用液体蛋白芯片-飞行时间质谱技术从胃癌患者血清中筛选潜在的标志蛋白.方法:采用液体蛋白芯片技术和基质辅助激光解吸电离飞行时间质谱技术(MALDI-TOF MS)对20例胃癌患者和20例正常人的血清蛋白谱进行检测,采用FlexAnalysis3.0和ClinProTools2.1软件进行图谱分析和统计学处理.结果:胃癌患者与正常人血清蛋白质谱比较,胃癌患者血清中有7个差异蛋白高表达,6个差异蛋白低表达,其中,差异最显著的两个蛋白的质荷比(m/z)分别是2863.71Da和4965.08Da.结论:利用液体蛋白芯片-飞行时间质谱技术可从血清中筛选出胃癌潜在的标志蛋白,此技术对于发现和筛选血清中的胃癌标志蛋白是一种很有前途的方法.     

应用液体蛋白心芯片-飞脚质谱技术从血清中筛选胃癌肿瘤标志蛋白

目的：应用液体蛋白芯片-飞行时间质谱技术从胃癌患者血清中筛选潜在的标志蛋白。方法：采用液体蛋白芯片技术和基质辅助激光解吸电离飞行时间质谱技术（MALDI—TOFMS）对20例胃癌患者和20例正常人的血清蛋白谱进行检测,采用FlexAnalysis3．0和ClinProTools2．1软件进行图谱分析和统计学处理。结果：胃癌患者与正常人血清蛋白质谱比较,胃癌患者血清中有7个差异蛋白高表达,6个差异蛋白低表达,其中,差异最显著的两个蛋白的质荷比（m／z）分别是2863．71Da和4965．08Da。结论：利用液体蛋白芯片-飞行时间质谱技术可从血清中筛选出胃癌潜在的标志蛋白,此技术对于发现和筛选血清中的胃癌标志蛋白是一种很有前途的方法。     

Serum protein profiles to identify head and neck cancer.

PURPOSE: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. The purpose of this study was to determine whether a new proteomic technology could correctly identify protein expression profiles for cancer in patient serum samples. EXPERIMENTAL DESIGN: Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry ProteinChip system was used to screen for differentially expressed proteins in serum from 99 patients with HNSCC and 102 normal controls. Protein peak clustering and classification analyses of the surface-enhanced laser desorption/ionization spectral data were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Fremont, CA). RESULTS: Several proteins, with masses ranging from 2778 to 20800 Da, were differentially expressed between HNSCC and the healthy controls. The serum protein expression profiles were used to develop and train a classification and regression tree algorithm, which reliably achieved a sensitivity of 83.3% and a specificity of 100% in discriminating HNSCC from normal controls. CONCLUSIONS: We propose that this technique has potential for the development of a screening test for the detection of HNSCC.     

运用MALDI-TOF MS方法建立食管癌患者血清蛋白指纹图谱诊断模型

 目的 本研究利用基质辅助激光解析离子化飞行时间质谱（matrix-assisted laser desorption/ionization time-of-flight mass spectrometry,MALDI-TOF MS）技术检测食管癌患者血清蛋白指纹图谱,建立食管癌诊断模型,探讨其临床应用价值。方法采用弱阳离子蛋白芯片（WCX磁珠）对血清进行分析前处理,运用MALDI-TOF MS技术检测119例标本（75例食管癌和44例健康对照）血清蛋白质谱图,通过蛋白芯片数据分析系统进行数据处理,以遗传算法结合支持向量机运算建立食管癌与健康对照组、早期食管癌与中晚期食管癌组诊断模型,随机抽取79例建模标本（50例食管癌和29例健康对照）进行训练与交叉验证,并选择新病例（30例食管癌和23例健康对照）血清标本进行测试。结果采集食管癌患者和健康对照者的血清蛋白质纹图谱,经数据分析找到75个有显著性差异的质荷比峰（P<0.05）和71个有非常显著性差异的质荷比峰（P<0.01）;软件包运算后,建立两个诊断模型：模型1：区分食管癌与健康对照组,由11个蛋白质峰（2 087,2 210,3 258,3 973,4 283,4 645,4 092,4 210,1 985,2 818和2 046 Da）组成,该诊断模型检测食管癌的敏感度为92.4%,特异性为87.4%;模型2：区分早期食管癌与中晚期食管癌组,由8个蛋白质峰（4 195,4 074,4 268,2 106,4 905,5 965,2 863 和 3 953 Da）组成,该诊断模型检测食管癌的敏感度为87.5%,特异性为89.7%。结论运用MALDI-TOF MS技术结合磁珠分选的方法可检测食管癌血清质谱图,建立具有较高的敏感度和特异性食管癌诊断模型。     

应用表面加强激光解吸电离-飞行时间-质谱技术检测结直肠癌肝转移

目的 寻找与结直肠癌肝转移相关的蛋白质,建立结直肠癌肝转移的血清蛋白质指纹图谱诊断预测模型.方法 应用表面加强激光解吸电离-飞行时间-质谱(SELDI-TOF-MS)技术,对36例结直肠癌无肝转移患者和36例结直肠癌伴肝转移患者的术前空腹外周静脉血标本,进行蛋白质指纹图谱测定,运用Biomarker Wizard软件,建立结直肠癌肝转移的诊断预测模型.用44例结直肠癌患者和44例结直肠癌伴肝转移患者,对所建立的诊断预测模型进行盲法验证.结果 比较36例结直肠癌无肝转移患者和36例结直肠癌伴肝转移患者的血清蛋白质,得到10个差异蛋白峰(P<0.05),质荷比分别为2398、2814、4084、4289、4465、6422、6619、11 482、11 649和13 714.若以P<0.01为标准,则有3个蛋白质峰差异有统计学意义,质荷比分别为2398、2814和13714.建立终末节点数为9的诊断预测模型,其敏感性为91.7%,特异性为97.2%.验证结果显示,敏感性为75.0%,特异性为81.8%.结论 运用SELDI-TOF-MS技术所建立的血清蛋白指纹图谱模型,在预测结直肠癌肝转移中具有非常高的敏感性与特异性,可望成为预测诊断工具.     

A serum proteomic pattern for the detection of colorectal adenocarcinoma using surface enhanced laser desorption and ionization mass spectrometry

Purpose: New serum biomarkers are needed to improve the early detection of colorectal adenocarcinoma. We performed surface enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to screen for differentially expressed proteins in serum and build a proteomic diagnostic pattern for the detection of colorectal adenocarcinoma to improve the prognosis of patients with this disease. Experimental Design: In an attempt to improve current approaches to the serologic diagnosis of colorectal cancer, we analyzed serum samples from subjects with or without colorectal cancer using SELDI-MS. Using a case-control study design, SELDI-MS profile of serum samples from 74 colorectal adenocarcinoma patients were compared with 48 age-and sex-matched healthy subjects using a ProteinChip reader, PBSII-C. Proteomic MS spectra were generated using IMAC3 chips, and protein peaks clustering and classification analyses were performed to build a proteomic pattern that could differentiate patients with colorectal adenocarcinoma from healthy subjects utilizing Biomarker Wizard and Biomarker Patterns software packages, respectively. The constructed pattern was then used to test an independent set of masked serum samples from 60 colorectal cancer patients and 39 healthy subjects.Results: Among the differentially expressed protein peaks identified by SELDI-MS profiling that had the ability to distinguish between patients and healthy subjects, we determined a minimum set of two protein peaks for system training and for developing a decision classification pattern. Masked analysis of an independent set of serum samples showed the diagnostic pattern could differentiate patients with different stages of colorectal cancer from healthy subjects with a sensitivity of 95.00 percent and specificity of 94.87 percent. Conclusion: SELDI-TOF-MS profiling of serum proteins combined with bioinformatics tools can be applied to accurately differentiate patients with colorectal cancer from healthy subjects. The high sensitivity and specificity achieved by the constructed clustering analysis algorithm show great potential for the early detection of colorectal cancer.     

宫颈癌患者血清蛋白指纹图谱的检测及其意义

背景与目的:目前针对宫颈癌没有特异性的肿瘤标志物。表面增强激光解吸离子化飞行时间质谱(surface-enhanced laser desorption/ionization time-of-flight mass spectrometry,SELDI-TOF-MS)是最新应用的一项检测肿瘤标志物的技术。本研究应用SELDI-TOF-MS检测宫颈癌患者血清蛋白指纹图谱,筛选候选肿瘤标志物并建立诊断模型,初步探讨其在宫颈癌早期诊断中的价值。方法:取91例早期宫颈鳞癌患者和15例宫颈上皮内瘤变Ⅲ级(cervical intraepithelialneoplasia,CINⅢ)患者的血清标本进行实验,同时用55名健康人血清作为对照。用弱阳离子交换芯片(weak cation exchange,WCX2)检测各血清标本获得血清蛋白指纹图谱。用Biomarker Patterns软件分析宫颈癌差异蛋白并建立诊断模型。通过盲法分析进一步验证诊断模型的可靠性,并对结果进行统计学分析。结果:在分子量1.5～20ku范围内,共检测到122个蛋白峰,其中19个差异峰有统计学意义(P<0.001)。建立了由分子量为3977和5807的两个差异蛋白组成的宫颈癌诊断模型,其敏感性为97.29%(36/37),特异性为83.78%(31/37)。扩大样本盲法验证结果,其敏感性为94.44%(51/54),特异性为94.44%(17/18)。结论:由3977和5807两个差异蛋白组成的宫颈癌诊断模型有助于区分宫颈癌和健康人群。     

乳腺癌患者与健康人血清蛋白质谱的差异分析

目的探索乳腺癌患者与健康人群的血清蛋白质谱差异,寻找能够帮助鉴别诊断乳腺癌的候选血清蛋白标志物。方法收集117例乳腺癌患者和56例健康人的血清标本,随机分为训练组（74例乳腺癌和36例健康人）与测试组（43例乳腺癌和20例健康对照）。采用表面增强激光解析离子化飞行时间质谱（SELDI—TOFMS）技术检测所有血清标本的蛋白质谱。用Biomarker Wizard统计软件比较训练组乳腺癌与健康对照间的蛋白质谱差异,再用Biomarker Pattern软件筛选出一组差异蛋白构建决策分类树模型以鉴别乳腺癌病例和健康人群,最后用测试组对分类模型进行验证。结果乳腺癌组和健康对照组的血清蛋白质谱存在14个差异显著的蛋白峰,以质荷比分别为3958、4288、4974、5902、8518、8930、9282和11360的8个差异蛋白构建决策树分类模型,鉴别乳腺癌与健康对照组的敏感性为82．43％（61／74）,特异性为83．33％（30／36）,准确性为82．73％（91／110）,用测试组进行验证的敏感性为86．05％（37／43）,特异性为65．00％（13／20）,准确性为79．37％（50／63）。结论乳腺癌与健康人群的血清蛋白质谱存在差异,SELDI—TOFMS技术筛选出的血清差异蛋白有助于乳腺癌的鉴别诊断。     

Haptoglobin-alpha subunit as potential serum biomarker in ovarian cancer: identification and characterization using proteomic profiling and mass spectrometry.

PURPOSE: The objective of this study was to identify and characterize new serum biomarkers in ovarian cancer patients using mass spectrometric protein profiling and specific immunological assays. Experimental Design: Serum samples from 80 cancer patients and 91 healthy women were analyzed by surface enhanced laser desorption and ionization-mass spectrometry (MS) profiling. A candidate biomarker was purified by affinity chromatography, and its sequence was determined by liquid chromatography-tandem MS. An antibody was generated from the synthesized peptide for quantitative validation in the cases and controls. CA125 was determined and compared with the same set of specimens. RESULTS: Using surface enhanced laser desorption and ionization, we found a serum biomarker at approximately 11700 Da, which had peak intensity significantly higher in cases (1.366) compared with controls (0.208, P = 0.002), and subsequently identified this as the alpha chain of haptoglobin. ELISA indicated that Hp-alpha was 相似文献   

Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification

OBJECTIVE: In order to improve the prognosis of pancreatic cancer patients, it is crucial to explore novel tools for its early diagnosis. Here, we attempted to screen serum biomarkers to distinguish pancreatic cancer from non-cancer individuals. METHODS: 47 serum samples from pancreatic cancer patients, 39 of whom had small surgically resectable cancers, were collected before surgery, and an additional 53 serum samples from age- and sex-matched individuals without cancer were used as controls. The surface-enhanced laser desorption/ionization (SELDI) ProteinChip was applied to analyze serum protein profiling. 54 samples (27 with pancreatic cancer and 27 controls) were analyzed in the training set by a decision tree algorithm to be able to separate pancreatic cancer from controls. A double-blind test was used to determine the sensitivity and specificity of the classification model. RESULTS: A panel of six biomarkers was selected to set up a decision tree as the classification model. The model separated effectively pancreatic cancer from control samples, achieving a sensitivity of 88.9% and a specificity of 74.1%. The double-blind test challenged the model with a sensitivity of 80% and a specificity of 84.6%. CONCLUSION: The SELDI ProteinChip combined with an artificial intelligence classification algorithm shows great potential for the diagnosis of pancreatic cancer.     

表面增强激光解吸技术预测胃癌术后转移复发的临床研究

 目的　应用表面增强激光解吸（SELDI）技术筛选胃癌术后转移相关的血清蛋白质组指纹并建立预测模型。方法　应用CM10弱阳离子芯片结合表面增强飞行时间质谱（SELDI-TOF-MS）技术检测60例胃癌根治术后患者和26名健康对照血清样本的蛋白质谱，经过2年随访分为转移组（22例）和无转移组（38例），利用Biomarker Wizard软件比较各组间的血清蛋白质指纹图谱，Biomarker Pattern软件建立预测模型。结果　术后转移患者和健康对照相比有 74个蛋白质峰差异有统计学意义，术后无转移患者和健康对照相比有69个蛋白质峰差异有统计学意义，术后转移患者和无转移患者相比有14个蛋白质峰有显著性差异，质荷比（m／z）为4768和8841的两个蛋白质组成的诊断模型可将无转移胃癌患者与转移胃癌患者准确的分组，在学习模式下，灵敏度和特异度分别为95.46 %（21／22），86.84 %（33／38），准确度为90 %（54／60）。在测试模式下，灵敏度和特异度分别为81.82 %（18／22）和84.21 %（32／38），准确度为83.33 %（50／60）。结论　SELDI-TOF-MS技术可筛选出胃癌转移复发相关的蛋白质组指纹，在m／z为4768和8841的两个蛋白质峰建立的决策树模型可用于对术后胃癌患者转移、复发的早期预测。     

利用基质辅助激光解析电离飞行时间质谱联合磁珠技术寻找乳腺癌血清蛋白标志物

目的探索乳腺癌与乳腺良性疾病和健康人血清蛋白质谱表达差异,寻找具有鉴别诊断意义的血清蛋白标志物。方法实验分为两大组：（1）决策树模型组共293例标本,包括3个亚组,分别为乳腺癌组110例标本、乳腺良性疾病组113例和健康组70例,建立决策树（乳腺癌诊断）模型;（2）盲法验证组共34例标本,包括3个亚组分别为乳腺癌组7例标本、乳腺良性疾病组13例及健康组14例,进行盲筛验证决策树模型。采用弱阳离子磁珠捕获乳腺癌患者血清中的蛋白,使用基质辅助激光解析电离飞行时间质谱（MALDI—TOF—MS）仪检测绘制蛋白峰。应用Biomarker Wizard TM3．1软件和Biomarker Patterns TM5．0软件分析数据。统计分析采用方差分析法和秩和检验法。计算决策树模型诊断的准确率以及盲法验证模型诊断乳腺癌的敏感性和特异性。结果在决策树模型组中检测到了47个差异有统计学意义的蛋白峰（P〈0．050）。应用BPS5．0软件,以相对损失最小的原则从这47个蛋白峰中选取了4个蛋白峰,分别为相对分子质量（Mr,本文中相当于质荷比m／z）9292．5、Mr11707．2、Mr15504．5和Mr16107．9,用其建立决策树模型（乳腺癌诊断模型）。该模型判断乳腺癌、乳腺良性疾病及健康人的准确率分别为99．09％、95．58％、92．86％。盲法验证该模型诊断乳腺癌的敏感性为71．43％,特异性为88．89％。结论应用MALDI—TOF—MS联合磁珠技术可以检测乳腺癌血清中差异蛋白峰并可以建立决策树（乳腺癌诊断）模型。选择的4个差异蛋白蜂建立的决策树模型诊断乳腺癌具有好的准确性和较好的敏感性及特异性。决策树模型能将乳腺癌与乳腺良性疾病及健康人相鉴别。寻找到的Mr9292．54、Mr11707．2、Mr15504．5以及Mr16107．9的蛋白峰有望成为鉴别乳腺癌与乳腺良性疾病和健康人的有效的肿瘤血清蛋白标记物。     

蛋白质组学法检测膀胱移行细胞癌患者尿液中特异性肿瘤标志物

 目的:检测膀胱移行细胞癌患者尿液中差异表达蛋白质,筛选新的肿瘤标志物,进而探讨其与TCC发病机制的相关性,以及蛋白质组学法在膀胱移行细胞癌早期无创诊断方面的研究应用价值。 方法:选取青岛大学医学院附属医院泌尿外科TCC住院病人尿液标本24例,另外选取12例TCC术后病人、12例健康志愿者的尿液标本作为对照。采用蛋白质芯片技术结合表面增强激光解析/离子化-飞行时间-质谱技术检测各组尿液标本的差异表达蛋白质,然后到蛋白数据库中鉴定筛选肿瘤标志物。 结果:TCC组与对照组尿液标本的蛋白质存在差异表达,IMAC-Cu-3蛋白质芯片共发现6个蛋白及2个蛋白簇表达水平发生变化。联合检测3438Da及8002Da蛋白可使诊断TCC的敏感度达83.3%,特异性达75.0%。3438Da蛋白已被鉴定为α-defensin蛋白;搜索SWISS-PRO蛋白数据库,4310Da~5150Da蛋白簇为gp40蛋白。 结论:SELDI-TOF-MS蛋白质芯片技术是一种快速、简便易行、用量少和高通量分析方法,能直接筛选出膀胱移行细胞癌患者尿液中特异的肿瘤标志物,为蛋白组学方法在膀胱移行细胞癌早期无创诊断、判断预后及探讨发病机制方面的研究应用开拓了广阔前景。