胰腺癌相关糖尿病的血清蛋白质组学分析

目的 检测胰腺癌相关糖尿病的血清蛋白标志物,并建立诊断模型.方法 应用表面增强激光解析电离飞行时间质谱( SELDI-TOF-MS)技术检测17例胰腺癌相关糖尿病与17例新发2型糖尿病、17例健康对照者血清的差异表达蛋白,用Biomarker Patterns Software 5.0软件建立胰腺癌相关糖尿病诊断模型并验证.结果 在胰腺癌相关糖尿病、新发2型糖尿病,健康者各10例的蛋白指纹图谱中筛选出12个差异表达蛋白峰,其中质荷比为6116、6695、8936 Da的蛋白峰被选为建立胰腺癌相关糖尿病诊断模型的蛋白峰.该诊断模型的诊断正确率为90％.盲法验证各组另7例样本,正确诊断胰腺癌相关糖尿病患者达100％,新发2型糖尿病患者为71％,健康人群为86％.经检索蛋白质数据库,与以上3种差异表达蛋白分子质量最为接近的蛋白分别为金属硫蛋白、胰腺干细胞增殖分化因子和成纤维细胞生长因子 1.结论 通过SELDI方法筛选出3种胰腺癌相关糖尿病的血清蛋白标志物,建立了可靠的胰腺癌相关糖尿病的诊断模型.     

高发区食管癌及癌前病变的血清蛋白质谱分析

目的 检测高发区食管癌及癌前病变患者血清蛋白质谱,建立蛋白指纹图谱模型并探究其筛查价值.方法 收集38名健康对照者、63例食管鳞状上皮不典型增生患者(I级26例,Ⅱ级26例,Ⅲ级11例)和36例进展期食管癌患者的内镜活检和血清标本,用CM10蛋白芯片及表面增强激光解析电离飞行时间质谱(SELDI-TOF-MS)技术检测标本的蛋白表达谱.支持向量机算法分别建立食管癌及癌前病变诊断模型,并经留一法交叉验证.结果 ①区分进展期食管癌和健康对照的诊断模型特异性为89.47%,敏感性为83.33%.②区分进展期食管癌和Ⅰ、Ⅱ、Ⅲ级不典型增生的诊断模型的特异性分别为92.31%、80.77%、90.91%,敏感性分别为80.56%、83.33%、94.44%.③在上述诊断模型中,质荷比(m/z)峰值在相对分子质量4291、5644、5664、8775处重复出现.结论 SELDI-TOF-MS技术和支持向量机算法的应用,为高发区高危人群中食管癌及癌前病变的早期筛查和诊断提供了一条新途径.4291、5644、5664、8775 4个质荷比峰对食管各级病变有相似的分类作用,可能是与食管癌变过程中相关的生物学标志物.     

应用蛋白指纹图谱技术筛选原发性胆汁性肝硬化患者血清特异性标志物

目的: 探讨蛋白指纹图谱技术筛选原发性胆汁性肝硬化(PBC)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 PBSII-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 Pattems Sofiware 5.0识别最终可能用于PBC诊断的蛋白标志物并优化组合建立诊断模型.结果: 在PBC患者和对照组之间找到69个差异蛋白峰(P<0.05).其中质荷比(m/z)为3445,4260,8133和16 290的蛋白峰建立PBC诊断模型.该诊断模型能很好地把PBC患者从其他肝脏疾病患者和正常人群中区分出来,其敏感性为93.3%,特异性为95.1%.经双盲实验验证,该模型对PBC诊断的敏感性为92.9%,特异性为82.4%. 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PBC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PBC诊断模型.     

SELDI技术筛选肺癌患者血清标志蛋白的临床价值

目的探讨表面增强激光解析电离飞行时间质谱（SELDI-TOF-MS）技术筛选肺癌患者血清标志蛋白的临床价值。方法用SELDI-TOF-MS技术、弱阳离子交换蛋白芯片,检测肺癌和肺良性病变患者的血清蛋白质质谱图;用Biomarker Pattern软件分析肺癌差异蛋白并初建其诊断模型,通过盲筛验证诊断模型。结果发现有统计学差异的蛋白峰20个,其中肺癌患者血清高表达蛋白质波峰14个,低表达蛋白质波峰6个;用质荷比2 090.77、2 503.31 Da的差异蛋白峰建立分类树模型,其诊断肺癌的灵敏度88%,特异度95%;盲筛验证灵敏度90%,特异度100%,粗符合率93.33%,Youden指数0.9。结论SELDI-TOF-MS技术筛选的肺癌血清差异性蛋白及分类树模型,诊断肺癌的灵敏度高、特异性好。     

质谱技术在诊断急淋中枢神经系统白血病中的应用研究

目的研究质谱技术在诊断急淋中枢神经系统白血病中的应用价值。方法选取我院2015年1月~12月收治的急性淋巴白细胞(ALL)患者20例作为研究对象,及健康体检者10名为研究对象,应用SELDI-TOF-MS质谱技术检测其脑脊液建立蛋白质指纹模型图,并分析差异蛋白。结果与对照组比较,单纯ALL组发现有8个差异显著的蛋白峰,差异有统计学意义(P0.05);与单纯ALL组比较,ALL合并CNSL组发现有11个差异显著的蛋白峰,差异有统计学意义(P0.05)。对差异蛋白建立决策树模型,用此模型诊断ALL合并CNSL,敏感性85.71%(6/7),特异性为91.30%(21/23)。结论应用SELDI-TOF-MS质谱技术建立急淋中枢神经系统白血病患者脑脊液蛋白质指纹模型图,发现差异蛋白,有助于疾病诊断。     

基于Au蛋白芯片技术的狼疮肾炎患者尿蛋白标志物研究

目的 寻找狼疮肾炎(LN)患者尿蛋白标志物,探讨基于Au蛋白芯片技术预测LN的应用价值.方法 采用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术及Au蛋白芯片检测166例LN患者和对照者尿蛋白指纹图,通过t检验评价蛋白表达差异性,筛选LN患者尿标志蛋白并结合人工神经网络(ANN)技术建立诊断模型,评价其预测LN的价值.对部分筛选的差异蛋白通过比对标准蛋白质谱数据进行初步鉴定.结果 LN患者与对照者尿中差异蛋白质峰有24个(t值为-6.44～10.14,P<0.05),通过BPS软件筛选4863、9744、8762、33 832、67 403和80 806质荷比(m/z)蛋白质建立的ANN模型预测LN的灵敏度为100%(26/26),特异度为95%(38/40).其中5个11 700、22 509、33 832、67 403和80 806 m/z蛋向与标准蛋白质谱比对显示可能为β_2-微球蛋白、视黄醇结合蛋白、α_1-微球蛋白、白蛋白和转铁蛋白.结论 基于Au蛋白芯片技术的尿蛋白指纹图谱检测在LN的早期警示、判断蛋白尿类型及指导免疫抑制剂的使用中具有潜在应用价值.     

SELDI-TOF-MS技术在肝癌诊断和介入治疗评价中的价值

目的:建立肝癌血清学诊断模型,探讨评估SELDI-TOF-MS技术在肝癌诊断和介入治疗评价中的价值.方法:用弱阳离子交换芯片(CM10芯片)和表面增强激光解吸电离飞行时间质谱仪(surface-enhanced laser desorption ionization time-of-flight mass spectrometry,SELDI-TOF-MS)技术,测定60例肝癌患者和60例正常对照者的血清蛋白质指纹图谱,应用BiomarkerWizard统计软件比较肝癌组和正常对照组血清蛋白质表达的差异性,采用Biomarker Pattern软件分析数据建立肝癌诊断模型,比较介入治疗前后血清蛋白质指纹图谱的差异性.结果:在质荷比(M/Z)为2000-10000范围内,和正常血清比较,肝癌的差异峰有3个(M/Z为4182Da、5710Da、6992Da;P<0.01),4182Da和5710Da下调,6992Da上调.用这3个差异蛋白峰建立肝癌诊断模型,诊断肝癌的灵敏度为93.3%(28/30),特异度为90.0%(27/30),正确率为91.7%(55/60),约登指数为0.833.差异蛋白峰(M/Z4182Da)在介入术后1mo明显上调(P<0.05).结论:应用SELDI-TOF-MS技术进行肝癌血清蛋白质指纹图谱分析,建立肝癌诊断树模型,对肝癌的诊断有一定的价值;筛选出的差异蛋白峰对肝癌的介入治疗评估有一定的应用价值.     

胰腺癌患者血浆k-ras基因与肿瘤标志物联合检测及其意义

目的 :了解胰腺癌患者血浆中肿瘤标志物水平和k ras基因突变情况 ,评价基因突变与肿瘤标志物联合检测对胰腺癌患者的诊断价值。方法 :收集经手术或病理确诊为胰腺恶性疾病患者 2 1例 ,ELISA检测血浆CA19 9、CA2 42、CA5 0、CEA水平 ,PCR RFLP检测k ras基因突变 ,并与 11例胰腺良性疾病患者对照。结果 :胰腺癌患者血浆中k ras基因突变率 73.7%,胰腺良性疾病k ras基因无突变。k ras基因突变检测的敏感性与特异性分别为 6 1.9%和10 0 %,血浆k ras、CA19 9、CA2 42联合检测的敏感性和特异性分别为 85 .7%和 71.9%。结论 :联合检测血浆中k ras基因与肿瘤标志物可提高胰腺癌诊断的敏感性 ,对胰腺癌筛查、诊断与鉴别诊断有一定的临床意义。     

应用SELDI-TOF-MS技术建立肝癌筛选血清蛋白质指纹图谱模型

目的:建立肝癌筛选血清蛋白质指纹图谱模型．方法:用表面加强激光解析电离飞行时间质谱技术(SELDI-TOF-MS)及WCX2蛋白芯片获得新发肝癌、肝硬化患者和正常人血清的蛋白质指纹图谱,用计算机软件进行比较分析,建立肝癌的筛选模型．结果:肝癌患者与健康对照组血清蛋白质指纹图谱之间有5个标志蛋白(4477,8943,5181, 8617,13 761 Da)在肝癌患者血清中高表达,肝癌患者与肝硬化患者血清蛋白质指纹图谱之间2个标志蛋白(4477,13 761 Da)在肝癌患者血清中高表达,1个标志蛋白(4097 Da)在肝癌患者血清中低表达．SELDI-TOF-MS技术的特异性(60／60,100％);敏感度(18／20,90％)．分析系统筛选出4477,8943,13 761,4097 Da标志蛋白建立的肝癌诊断模型．结论:建立的血清蛋白质指纹图谱模型能够区分肝癌与非肝癌患者,SELDI-TOF-MS在肝癌的诊断及肿瘤特异性蛋白质生物标志分子的筛选等方面具有一定价值．     

应用蛋白质指纹图谱技术快速鉴别诊断菌阴肺结核

目的 探索应用蛋白质指纹图谱技术于菌阴肺结核与肺炎的鉴别诊断。方法 从本院临床病例中,选择菌阴肺结核和肺炎患者及健康者各60例,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血清蛋白,并应用Ciphergen蛋白芯片3.1.1软件进行比较,分析其相关蛋白峰值并进行统计学处理。结果 对180例菌阴肺结核、肺炎患者、健康者的血清蛋白指纹图谱数据进行比较,发现有5个蛋白峰(1 028.49、4 796.56、7 564.77、8 048.02、11 526.75 m/z)存在显著的差异,有统计学意义(P<0.01)。由这5个蛋白峰组成的诊断模型鉴别诊断菌阴肺结核与肺炎的总有效率84.2%(101/120),敏感性与特异性分别为82.5%(52/63),85.9%(49/57);阳性预测值86.7%(52/60),阴性预测值为81.7%(49/60)。诊断模型在判别肺炎、菌阴肺结核患者与健康者之间,总有效率达89.4%(161/180),特异性为100%(60/60),灵敏度为84.2%(101/120),阳性预测值100%(101/101),阴性预测值75.9%(60/79)。结论 蛋白质指纹图谱技术具有方法简便、检测快速,标本用量少的优点,是筛选结核病特异性标志物的有效手段,通过蛋白质指纹图谱技术检测,发现了具有良好鉴别诊断的“诊断模型”。     

表面增强激光解析电离飞行时间质谱法筛选肺癌患者血清和肺泡灌洗液中标志蛋白的研究

目的 应用表面增强激光解析电离飞行时间质谱(SELDI-TOF-MS)技术筛选肺癌患者血清和BALF中的差异性表达蛋白,探讨是否可作为诊断肺癌的肿瘤标志物.方法 应用SELDI-TOF-MS技术通过弱阳离子交换蛋白芯片(WCX-2芯片)分别检测35例肺癌和18例肺部良性病变患者血清和BALF中的蛋白质质谱图,用Biomarker Pattern软件分析肺癌的差异蛋白并初步建立诊断模型,通过盲筛进一步验证诊断模型.结果 在肺癌患者血清中发现5个高表达的蛋白质波峰,选用其中质荷比为5639的差异蛋白波峰建立分类树模型,其诊断的敏感度为80%(28/35),特异度为78%(14/18).盲法验证的敏感度为85%(17/20),特异度为90%(9/10),粗符合率为87%(26/30),Youden指数为0.7.在肺癌患者BALF中发现8个高表达蛋白质波峰,选用其中质荷比为7976和11 809的差异蛋白波峰建立分类树模型,其诊断的敏感度为86%(30/35),特异度为72%(13/18).盲法验证的敏感度为90%(18/20),特异度为90%(9/1O),粗符合率为90%(27/30),Youden指数为0.8.平行试验结果显示两者联合应用时诊断肺癌的敏感度、准确率及特异度均为100%,具有互补作用.结论SELDI-TOF-MS技术可筛选出肺癌患者血清和BALF中差异性表达蛋白,作为一种肿瘤标志物,其诊断敏感度高,特异度好,尤其是BALF中差异性表达蛋白的测定可能具有较好的临床应用前景.

Abstract：

Objective To detect the protein markers in serum and bronchoalveolar lavage fluid (BALF) of the patients with lung cancer by surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) technology, and to explore if they can be used as markers for the diagnosis of lung cancer.Methods SELDI-TOF-MS technology and protein chips weak cation exchange (WCX-2 chip) were used to detect the protein mass spectrum in serum and BALF of 35 patients with lung cancer and 18 cases of benign pulmonary diseases.The different protein markers were analyzed by Biomarker Pattern Software and the initial diagnosis models were set up.The diagnosis models were verified further by blind screen to confirm the efficacy of diagnosis.Results Five protein peaks in the sera of the patients with lung cancer were significantly higher (P ＜ 0.05 ).The protein peak with a mass/charge ratio (M/Z)of 5639 was selected to establish the classification tree model.The sensitivity of diagnosis was 80% (28/35) and the specificity was 78% (14/18).The results verified by blind screen showed a sensitivity of 85% (17/20),a specificity of 90% (9/10), a crude accuracy (CA) of 87% ( 26/30 ) and Youden' s index (γ) of 0.7.Eight protein peaks in the BALF of the patients with lung cancer were significantly higher ( P ＜ 0.05).The different protein peaks with M/Z of 7976 and 11 809 respectively were selected to establish the classification tree model.The sensitivity of diagnosis was 86% (30/35) and the specificity was 72% (13/18).The results verified by blind screen showed a sensitivity of 90% (18/20), a specificity of 90% (9/10), a CA of 90% (27/30) and γof 0.8.There was a complementary role in combination of differential proteins in serum and BALF and the sensitivity, specificity and accuracy of diagnosis for lung cancer were 100% by parallel test.Conclusions The SELDI-TOF-MS technology can screen out the differential protein markers in serum and BALF of the patients with lung cancer, which show high sensitivity and specificity as tumor markers.The differential proteins in the BALF may be more promising for clinical application.     

Preliminary study on proteomics of gastric carcinoma and its clinical significance

AIM: To explore the preliminary identification of serum protein pattern models that may be novel potential biomarkers in the detection of gastric cancer.METHODS: A total of 130 serum samples, including 70 from patients with gastric cancer and 60 from healthy adults, were detected by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by Biomarker Patterns Software (BPS). Thirty serum samples of gastric cancer patients and 30 serum samples of healthy adults were grouped into the training group to build models, and the other 70 samples were used to test and evaluate the models. The samples of the test group were judged only with their peaks'height and were separated into cancer group or healthy control group by BPS automatically and the judgments were checked with the histopathologic diagnosis of the samples.RESULTS: Sixteen mass peaks were found to be potential biomarkers with a significant level of P＜0.01.Among them, nine mass peaks showed increased expression in patients with gastric cancer. Analyzed by BPS, two peaks were chosen to build the model for gastric cancer detection. The sensitivity, specificity, and accuracy of the model were 90%, 36/40, 86.7%, 26/30,and 88.6%, 62/70, respectively, which were greatly higher than those of clinically used serum biomarkers CEA (carcinoembryonic antigen), CA19-9 and CA72-4.Stage Ⅰ/Ⅱ gastric cancer samples of the test group were all judged correctly.CONCLUSION: The novel biomarkers in serum and the established model could be potentially used in the detection of gastric cancer. However, large-scale studies should be carried on to further explore the clinical impact on the model.     

Diagnosis of Pancreatic Adenocarcinoma Using Protein Chip Technology

Background: To develop a serum-specific protein fingerprint which is capable of differentiating samples from patients with pancreatic cancer and those with other pancreatic conditions. Methods: We used SELDI-TOF-MS coupled with CM10 chips and bioinformatics tools to analyze a total of 118 serum samples in this study; 78 serum samples were analyzed to establish the diagnostic models and the other 40 samples were analyzed on the second day as an independent test set. Results: The analysis of this independent test set yielded a specificity of 91.6% and a sensitivity of 91.6% for pattern 1, which distinguished pancreatic adenocarcinoma (PC) from healthy individuals and a specificity of 80.0% and a sensitivity of 90.9% for pattern 2, which distinguished PC from chronic pancreatitis. Conclusion: This study indicated that the SELDI-TOF-MS technique can facilitate the discovery of better serum tumor biomarkers and a combination of specific models is more accurate than a single model in diagnosis Of PC.     

Preliminary study on proteomics of gastric carcinoma and its clinical significance

AIM: To explore the preliminary identification of serum protein pattern models that may be novel potential biomarkers in the detection of gastric cancer. METHODS: A total of 130 serum samples, including 70 from patients with gastric cancer and 60 from healthy adults, were detected by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by Biomarker Patterns Software (BPS). Thirty serum samples of gastric cancer patients and 30 serum samples of healthy adults were grouped into the training group to build models, and the other 70 samples were used to test and evaluate the models. The samples of the test group were judged only with their peaks' height and were separated into cancer group or healthy control group by BPS automatically and the judgments were checked with the histopathologic diagnosis of the samples. RESULTS: Sixteen mass peaks were found to be potential biomarkers with a significant level of P<0.01. Among them, nine mass peaks showed increased expression in patients with gastric cancer. Analyzed by BPS, two peaks were chosen to build the model for gastric cancer detection. The sensitivity, specificity, and accuracy of the model were 90%, 36/40, 86.7%, 26/30, and 88.6%, 62/70, respectively, which were greatly higher than those of clinically used serum biomarkers CEA (carcinoembryonic antigen), CA19-9 and CA72-4. Stage I/II gastric cancer samples of the test group were all judged correctly. CONCLUSION: The novel biomarkers in serum and the established model could be potentially used in the detection of gastric cancer. However, large-scale studies should be carried on to further explore the clinical impact on the model.     

胰腺癌患者血清 Dickkopf-1水平检测及其临床意义

目的：探讨Dickkopf-1（ DKK-1）在胰腺癌辅助诊断中的价值。方法采用ELISA法检测50例胰腺癌患者（胰腺癌组）和50例健康查体者（对照组）血清DKK-1水平,采用电化学发光法检查检测CA19-9水平;比较两组DKK-1表达情况及DKK-1、CA19-9诊断胰腺癌的敏感度、特异度和准确性。结果胰腺癌组血清DKK-1水平及阳性率均明显高于对照组（P均＜0．05）;DKK-1与CA19-9诊断胰腺癌的敏感度分别为52％、78％,P＜0．05;特异度分别为92％、84％,P＞0．05;准确性分别为72％、81％,P＞0．05;若两者联合检测则敏感度和准确性分别提高至90％和84％。结论胰腺癌患者血清DKK-1水平明显增高,可作为临床中胰腺癌的辅助诊断指标;血清DKK-1与CA 19-9联合检测有助于提高胰腺癌的诊断水平。     

Detection of pancreatic cancer with normal carbohydrate antigen 19-9 using protein chip technology

AIM: To develop a method to differentiate pancreatic cancer patients from healthy or benign individuals when carbohydrate antigen (CA) 19-9 is normal.METHODS: Forty-one serum samples from patients with pancreatic lesions and blood samples from 20 healthy individuals were collected at the first stage of the experiment according to the enrolment criteria. General characteristics and some clinical features were carefully compared to ensure that the results were reasonable. All the blood samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) combined with CM10 chips and a related bioinformatics analysis program to generate diagnostic models with different proteins. Forty-seven consecutive samples were tested at the next stage to verify the veracity and efficiency of the models.RESULTS: The sex, age, and serum CA19-9 levels among the three groups (malignant, benign, and healthy) were statistically matched (P values were 0.957, 0.145, and 0.382, respectively). Two patterns were generated. Pattern 1 with four proteins theoretically had a specificity and sensitivity of 100% in distinguishing pancreatic cancer from healthy individuals, while it was 86.7% and 86.4%, respectively, in the subsequent practical verification. The positive predictive value (PPV) of the model was 86.4%. One of the four proteins was expressed highly in pancreatic cancer while the other three were expressed weakly. Pattern 2 consisted of six proteins that showed a specificity of 70.0% and sensitivity of 77.3% for differentiating malignancy from benign tumors. Its PPV reached 85.0%. Only one of these six proteins showed high expression in the malignant group.CONCLUSION: SELDI-TOF-MS may facilitate diagnosis or differential diagnosis of pancreatic cancer when CA19-9 is normal. Pattern 1 may serve as a useful screening tool.     

CA19-9在胰腺癌中的应用价值及局限性

胰腺癌的诊治是世界性难题,糖类抗原CA19-9对于胰腺癌的诊断、疗效观察及预后判断等方面具有一定的临床价值,与其他肿瘤标志物比较,具有较好的敏感性及特异性,是最常用的胰腺癌的标志物。但其也存在诸多的局限性,尤其是具有较高的假阳性率及在Lewis a阴性基因型患者中的假阴性,极大限制了其作为胰腺癌诊断标准的应用。因此,血清CA19-9水平的升高需结合影像学及组织学证据,才能做出正确判断。     

Proteomic profiling of cholangiocarcinoma: diagnostic potential of SELDI-TOF MS in malignant bile duct stricture

Proteomic techniques promise to improve the diagnosis of cholangiocarcinoma (CC) in both tissue and serum as histological diagnosis and existing serum markers exhibit poor sensitivities. We explored the use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to identify potential protein biomarkers of CC. Twenty-two resected CC samples were compared with adjacent noninvolved bile duct tissue. Serum from patients with CC (n=20) was compared with patients with benign disease (n=20), and healthy volunteers (n=25). Samples were analyzed on hydrophobic protein chips via SELDI-TOF MS, and classification models were developed using logistic regression and cross-validation analysis. Univariate analysis revealed 14 individual peaks differentially expressed between CC and bile duct tissue, 4 peaks between CC and benign disease, and 12 peaks between CC and sera of healthy volunteers. The 4,462 mass-to-charge serum peak had superior discriminatory ability to carbohydrate antigen 19.9 (CA19.9) and carcinoembryonic antigen (CEA) (P=.004; receiver operating characteristic [ROC] area under the curve [AUC]=0.76, 0.73, and 0.70, respectively). The training models developed panels of peaks that distinguished CC from bile duct tissue (92.5% sensitivity, 92.3% specificity; ROC AUC=0.96), CC from benign serum (65.0% sensitivity, 70.0% specificity; ROC AUC=0.83), and CC from sera of healthy volunteers (75.0% sensitivity, 100% specificity; ROC AUC=0.92). Serum results were further improved with the inclusion of CA19.9 and CEA (ROC AUC=0.86 and 0.99 for CC vs benign and healthy volunteer serum, respectively). In conclusion, biomarker panels are capable of distinguishing CC from nonmalignant tissue; serum markers have important diagnostic implications for unknown bile duct stricture.     

Potential biomarkers for diagnosis of sarcoidosis using proteomics in serum

BACKGROUND: Sarcoidosis is a multi-systemic inflammatory disorder, which affects the lungs in 90% of the cases. The main pathologic feature is chronic inflammation resulting in non-caseating granuloma formation. Until now there is no satisfying biomarker for diagnosis or prognosis of sarcoidosis. This study is focused on the detection of potential biomarkers in serum for the diagnosis of sarcoidosis using surface-enhanced laser desorption ionization-time of flight-mass spectrometry (SELDI-TOF-MS). METHODS: For detection of potential biomarkers, protein profiles of anion exchange fractionated serum of 35 sarcoidosis patients and 35 healthy controls were compared using SELDI-TOF-MS. Sensitivities and specificities of the potential biomarkers obtained with SELDI-TOF-MS, generated with decision tree algorithm, were compared to the conventional markers angiotensin converting enzyme (ACE) and soluble interleukin-2 receptor (sIL-2R). RESULTS: Optimal classification was achieved with metal affinity binding arrays. A single marker with a mass-to-charge (m/z) value of 11,955 resulted in a sensitivity and specificity of 86% and 63%, respectively. A multimarker approach of two peaks, m/z values of 11,734 and 17,377, resulted in a sensitivity and specificity of 74% and 71%, respectively. These sensitivities and specificities were higher compared to measurements of ACE and sIL-2R. Identification of the peak at m/z 17,377 resulted in the alpha-2chain of haptoglobin. CONCLUSIONS: This study acts as a proof-of-principle for the use of SELDI-TOF-MS in the detection of new biomarkers for sarcoidosis. The peak of the multimarker at m/z 17,377 was identified as the alpha-2chain of haptoglobin.