Discrimination analysis of mass spectrometry proteomics for cervical cancer detection

To study the serum protein fingerprint of patients with cervical cancer and to screen for protein molecules closely related to cervical cancer during the onset and progression of the disease using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Serum samples from 85 patients with cervical cancer and 80 healthy volunteers. 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 resulting profiles between cancer and normal 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 3974?Da, 4175?Da, 5906?Da. This diagnostic model can distinguish cervical cancer from healthy controls with a sensitivity of 93.3% and a specificity of 95%. Blind test data indicated a sensitivity of 87.5% and a specificity of 90%. MALDI technology can be used to screen significant proteins of differential expression in the serum of cervical cancer patients. These different proteins could be specific biomarkers of the patients with cervical 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.     

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.     

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.     

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.     

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。     

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.     

应用液体蛋白芯片飞行时间质谱系统研究胃癌的血清蛋白差异表达谱*

目的:应用液体蛋白芯片飞行时间质谱系统分析胃癌患者血清蛋白质表达谱,寻找具有潜在诊断意义的血清标志物。方法:收集血清样本62 例,其中正常对照组（N 组）16 例,胃癌组（T 组）28 例,验证组18 例。经WCX磁珠纯化、MALDI-TOF-MS 及ClinproTools生物信息学方法研究其血清蛋白表达谱,并筛选出差异蛋白质峰,运用数据挖掘算法,构建胃癌的血清蛋白诊断模型,并在验证组中验证其准确性。结果:1）通过对比胃癌组和正常组的血清蛋白质谱图,分析得到 25 个具有显著差异的蛋白质峰,其中差异最显著的前两位质核比分别为 5 248.49 m/z和5 754.25 m/z,其灵敏度分别为 84 .61 %和73 .07 %,特异性分别为 100%和93 .75 %,能很好地区分胃癌组和正常组。2）通过 ANN 的数据挖掘的方法,在具有显著差别的 25 个蛋白质峰中,筛选了组合能力最强的6 个蛋白峰（分别为4 268.05 m/z、5 636.53 m/z、5 248.49 m/z、2 933.15 m/z、1 450.13 m/z和1 349.4m/z）,建立了胃恶性肿瘤的诊断模型,其识别率为100%,预测能力为 90 .59 %,准确性为 100%。将已知信息的验证组 18 例分别代入已建立的模型,特异性和灵敏性分别为75 %和100%。结论:液体蛋白芯片飞行时间质谱系统作为研究蛋白表达谱的工具,能够用于筛选潜在的胃恶性肿瘤的血清标志物,利用其优点并结合统计学的方法,建立血清学胃癌的诊断模型,能为胃恶性肿瘤的筛查提供帮助。      

Biomarkers Screening Between Preoperative and Postoperative Patients in Pancreatic Cancer

Objective: To investigate discriminating protein patterns and potential biomarkers in serum samples betweenpre/postoperative pancreatic cancer patients and healthy controls. Methods: 23 serum samples from PC patients(12 preoperative and 11 postoperative) and 76 from healthy controls were analyzed using matrix-assisted laserdesorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) technique combined with magneticbeads-based weak cation-exchange chromatography (MB-WCX). ClinProTools software selected several markersthat made a distinction between pancreatic cancer patients and healthy controls. Results: 49 m/z distinctive peakswere found among the three groups, of which 33 significant peaks with a P < 0.001 were detected. Two proteinscould distinguish the preoperative pancreatic cancer patients from the healthy controls. About 15 proteins maybe potential biomarkers in assessment of pancreatic cancer resection. Conclusion: MB-MALDI-TOF-MS methodcould generate serum peptidome profiles of pancreatic cancer and provide a new approach to identify potentialbiomarkers for diagnosis and prognosis of this malignancy.     

构建血清蛋白质质谱模型判别家族性腺瘤性息肉病与散发性肠腺瘤

目的 筛选家族性腺瘤性息肉病(FAP)的特异表达蛋白,构建判别FAP与散发性肠腺瘤的血清蛋白指纹图谱诊断模型.方法 采集19例FAP和16例散发性肠腺瘤患者的血清,以表面增强激光解吸电离飞行时间质谱仪(SELDI-MS-TOF)和阴离子CM01蛋白质芯片检测并筛选两组对象间的血清差异表达蛋白质峰,以支持向量机方法构建判别模型.结果 FAP与散发性肠腺瘤相比,P<0.01的蛋白质峰有6个,其中质荷比为5640、3160、4180和4290的蛋白质峰在FAP中高表达,质荷比为3940和3400的蛋白质峰在散发性肠腺瘤患者中高表达.以质倚比分别为5640、3160和4290的蛋白质峰为基础,联合质荷比为3940、13 750和4300的蛋白质峰所建立的模型判别效果最佳,对FAP与散发性肠腺瘤的判别准确率分别为94.7%和93.7%.结论 SELDI-TOF-MS能有效筛选FAP与散发性肠腺瘤的差异表达蛋白,支持向量机方法所建立的质谱模型判别效果较好,为进一步研究FAP的分子发病机制提供了切入点.     

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。     

应用蛋白质指纹图谱技术对胰腺癌标志物的筛选分析研究

目的：应用表面增强激光解吸离子化飞行时间质谱（surfaceenhancedlaserdesorption／ionization—timeofflight—massspectrometry，SELDI—TOF—MS）筛选胰腺癌患者血清蛋白标志物。方法：应用强阴离子交换芯片（stronganionicexchangechromatography，SAX），选择最佳结合、洗脱缓冲液对18例胰腺癌患者及18例正常人血清标本进行蛋白质质谱分析。结果：应用SAX，经SELDI—TOF—MS分析，在胰腺癌患者血清筛选出与正常人血清有统计学意义差异蛋白8个（P〈0.05），其中3个为高表达，相对分子量为4136．60、4465．92、4359．53Da，5个为低表达，相对分子量分别为15856．8、7564．52、15116．1、2042．78、2016．44Da。在18例胰腺癌患者中包括11例导管腺癌和7例其他类型癌，两者之间有统计学意义的差异蛋白1个，相对分子量4136．60Da。应用ROC曲线分析，确定6个差异蛋白对胰腺癌有中等诊断价值，敏感度为83％～94％，特异度为56％～67％。结论：经SELDI—TOF—MS技术在胰腺癌患者血清中筛选出灵敏度和特异度较好的差异表达蛋白，有助于胰腺癌的诊断。     

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。     

表面增强激光解析电离飞行时间质谱技术在前列腺癌蛋白质组研究中的应用

目的应用表面增强激光解析电离飞行时间质谱（SELDI—TOF—MS）和蛋白质芯片技术分析前列腺癌（PCa）差异蛋白表达及其与病理分级和临床分期的关系,初步探讨雄激素难治性前列腺癌（HRPC）的发病机制。方法以病理确诊的PCa和良性前列腺增生（BPH）各45例,另以HRPC与激素依赖性前列腺癌（ADPC）各21例患者血清为研究对象,采用SELDI—TOF-MS固相金属亲和芯片技术检测血清的蛋白表达图谱,用BiomarkerWizard软件分析差异蛋白。结果在PCa和BPH患者血清中检测到9个差异蛋白（P〈0．01）,各差异蛋白在不同病理分级间表达差异有统计学意义。HRPC与ADPC有6个血清蛋白表达,差异有统计学意义（P〈0．05）。结论通过SELDI—TOF—MS蛋白质芯片技术检测PCa的标志蛋白可以提高PCa诊断的敏感度和特异度;差异蛋白在PCa不同病理分级间表达差别可能与PCa分化、肿瘤侵袭生长有关。HRPC与ADPC患者血清的差异蛋白可能在ADPC向HRPC转变过程中发挥着重要作用,检测差异蛋白可以明确诊断,评估预后,指导治疗。     

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.     

Fibrinogen gamma overexpression in pancreatic cancer identified by large-scale proteomic analysis of serum samples

Detection of serum markers for pancreatic cancer has been elusive. Although CA 19-9 is most commonly used, its sensitivity and specificity are modest. We used large-scale proteomics to identify potential serum markers for pancreatic cancer. Samples were analyzed using high-resolution two-dimensional gel electrophoresis to identify differentially expressed proteins in 32 normal and 30 pancreatic cancer patients. Up to 1,744 protein spots were resolved for each serum sample. Candidate proteins were identified using mass spectrometry. ANOVA was used to identify proteins that could discriminate cancer from normal sera. Serum fibrinogen level was also measured using enzymatic assay. Immunohistochemistry was used to detect fibrinogen in resected pancreatic cancers. One hundred fifty-four proteins were commonly overexpressed in all pancreatic cancers. Nine protein spots (four with identifications by mass spectrometry) could effectively separate cancer from normal controls using cross-validation. These proteins successfully discriminated all pancreatic cancer samples (30 of 30) and 94% of normal (30 of 32) samples. Prominent among these candidates was fibrinogen gamma, which was subsequently confirmed to be overexpressed in pancreatic cancer sera by enzymatic analysis (54.1 +/- 64.1 versus 0.0 +/- 0.0 mg/dL, P < 0.05) and tissue by immunohistochemistry (67% versus 29%, P < 0.05) relative to normal pancreas. Proteomic analysis combining two-dimensional gel electrophoresis and mass spectrometry successfully identified 154 potential serum markers for pancreatic cancer. Of these, fibrinogen gamma, a protein associated with the hypercoagulable state of pancreatic cancer, discriminated cancer from normal sera. Fibrinogen is a potential tumor marker in pancreatic cancer.     

胃癌患者血清蛋白质指纹图谱的初步探讨

目的:探讨胃癌发生、发展过程中患者血清蛋白质指纹图谱,筛选与胃癌病理分期密切相关的蛋白质分子。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(SELDI-TOF-MS)技术检测30例胃癌患者和26例健康者、3例胃良性病患者血清样本的蛋白质谱,同时采用相关计算机软件筛选不同组别间差异蛋白。结果:初步筛选出对早期胃癌有代表性的4个差异蛋白,质荷比(M/Z)分别为6193、6363、16073和16317;2个与胃癌脏器转移相关的差异蛋白(32463、34906M/Z)。结论:SELDI-TOF-MS蛋白质芯片用于胃癌患者血清蛋白质谱分析可筛选出有意义的差异表达蛋白,有进一步研究的价值。     

血清蛋白质质谱辅助孤立性肺结节的诊断

目的采用表面增强激光解析离子-飞行时间质谱（SELDI-TOF-MS）技术建立肺腺癌患者血清蛋白质质谱,筛选出相对特异标记物,探讨其用于辅助诊断孤立性肺结节的意义。方法肺腺癌患者71例,均经手术病理证实;正常志愿者71名,按照性别、年龄、吸烟史与71例肺腺癌患者配对。用WCX2芯片检测各血清标本,筛选肺腺癌相对特异性蛋白质峰。孤立性肺结节患者53例,其中28例接受手术治疗,根据术后病理诊断验证筛选出的肺腺癌相对特异性蛋白用于辅助诊断孤立性肺结节的意义。结果与正常志愿者比较,肺腺癌患者有5个显著高表达的相对特异性蛋白质,其相对分子质量分别为4047．79、4203．99、4959．81、5329．30和7760．12。28例接受手术治疗的孤立性肺结节患者中,有24例术后病理为肺腺癌,证实筛选出的5种肺腺癌相对特异性蛋白质具有较高的诊断价值。结论SELDI-TOF-MS技术是一种快速、简便易行且高通量的分析方法,能直接筛选出肺腺癌患者血清中相对特异的潜在标记物,将其用于辅助诊断孤立性肺结节可能具有较好的临床应用前景。     

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.     

Identification of serum biomarkers for pancreatic adenocarcinoma by proteomic analysis

Diagnosis of pancreatic adenocarcinoma (PaCa) at an early stage is important for successful treatment and improving the prognosis of patients. Serum samples were applied to strong anionic exchange chromatography (SAX) protein chips for protein profiling by surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to distinguish PaCa from noncancer. The Wilcoxon rank-sum test, decision tree algorithm, and logistic regression were used to statistically analyze the multiple protein peaks. Sixty-one protein peaks between 2000 and 30 000  m/z ratios were detected to establish multiple decision classification trees for differentiating the known disease states. A sensitivity of 0.833 and a specificity of 1.000 were obtained in distinguishing PaCa from healthy controls and benign pancreatic diseases. Six protein biomarkers related to different PaCa TNM stages were detected ( P  < 0.01). One protein biomarker ( m/z 4016) rich in PaCa had a down-regulated trend when preoperative and postoperative samples ( P  < 0.05) were compared. Three protein biomarkers ( m/z 4155, 4791, and 28 068) were detected in the differential diagnosis of the three test groups ( P  < 0.05). A peak m/z 28 068 was identified as C14orf16 using ProteinChip immunoassay. C14orf166 levels were significantly higher in the serum of patients with PaCa compared with the control group using a sandwich immunoenzymatic system. Immunolabeling of tissue sections revealed that the C14orf166 protein was strongly expressed in tumor cells. The results suggest that SELDI-TOF-MS serum profiling is helpful for the diagnostic, prognostic or therapeutic effects of PaCa, which is superior to CA 19-9. The identified protein biomarker C14orf166 is a potential biomarker of PaCa. ( Cancer Sci 2009; 100: 2292–2301)