摘要
研究背景
甲状腺癌是头颈部常见的恶性肿瘤之一,也是内分泌最常见恶性肿瘤。据报道,该肿瘤约占内分泌恶性肿瘤的91.1%,在所有恶性肿瘤中约占1%。美国每年新增甲状腺癌病例约为33 550,最近统计资料显示该疾病发病率有上升趋势,尤其是乳头状甲状腺癌的发病率。乳头状甲状腺癌是甲状腺癌中最常见的类型,约占甲状腺癌的80%。早期正确的诊断、及时合理的治疗对提高乳头状甲状腺癌长期生存率具有重要意义。当前临床上用于甲状腺癌诊断的方法很多,主要有超声、CT、磁共振、甲状腺放射性核素扫描、甲状腺吸碘率、细针穿刺细胞学检查。细针穿刺细胞学检查是目前临床上区分甲状腺结节良恶性最有效的手段。据统计,细针穿刺细胞学检查区分甲状腺结节良恶性的敏感性为93%,但其特异性仅为75%。上世纪90年代以来,人们开始寻找乳头状甲状腺癌相关蛋白质标记物,以期能够早期正确诊断乳头状甲状腺癌。Galectin-3、CITED-1、HBME1、cytokeratin-19及TPO等,曾被认为可作为甲状腺癌早期诊断的蛋白质标记物,但后来的研究发现,这些肿瘤标记物缺乏一定的特异性,且阳性预测值低,尚不足以用来诊断甲状腺癌。近年来,蛋白质组学的发展为肿瘤标记物的检测和肿瘤早期正确的诊断提供了新的手段,越来越多的研究希望运用蛋白质组学技术寻找可用于肿瘤早期诊断的特异性标记物。表面增强激光解析电离飞行时间质谱(SELDI-TOF-MS)是近年来开展的一项全新的蛋白质组学技术,已被证实为肿瘤潜在标记物检测的强有力的工具。同时,该项技术结合MALDI-TOF-MS. LC-MS/MS等可实现对肿瘤血清中特异性标记物的鉴定。目前这些蛋白质组学技术已经成功应用于多种恶性肿瘤生物学标记物的研究中。尽管以往曾有研究人员利用蛋白质组学技术对乳头状甲状腺癌标记物进行研究,但迄今为止尚未有该肿瘤相关的特异性蛋白质鉴定方面的报道。
本研究旨在应用SELDI-TOF-MS芯片技术对乳头状甲状腺癌患者和正常健康人血清中蛋白质组进行检测,结合生物信息学分析处理,筛选乳头状甲状腺癌血清特异性蛋白质峰,建立可用于乳头状甲状腺癌诊断的判别模型,随后联合HPLC、MALDI-TOF-MS及LC-MS/MS等一系列蛋白质组学技术对这些差异性蛋白质波峰进行分离、纯化、鉴定,最后应用抗体芯片免疫测定法对所鉴定出的目标蛋白质进行验证。
研究目的
筛选并鉴定可用于乳头状甲状腺癌血清学诊断的特异性蛋白质标记物。
材料与方法
1临床资料
224例血清标本均来自于郑州大学第一附属医院普通外科,其中包括108例乳头状甲状腺癌患者血清标本,56例良性甲状腺结节血清标本,60例健康志愿者血清标本。本实验经本院伦理委员会同意,且受试者均签署有知情同意书。乳头状甲状腺癌患者中位年龄43岁,其中男27例,女81例,血清标本均在初诊时获得。根据UICC分期标准,108例乳头状甲状腺癌患者中Ⅰ期85例,Ⅱ期12例,Ⅲ期8例,Ⅳ期3例。良性甲状腺结节患者和正常健康志愿者均与乳头状甲状腺癌患者年龄、性别相匹配。所有的乳头状甲状腺癌及良性甲状腺结节患者均得到2位以上的病理学专家证实。血清标本均在清晨空腹时抽取,室温静置1h,3000 r/min离心10 min,取上清液,然后放入—80℃液氮冰箱保存。
2主要试剂和仪器
蛋白质芯片生物系统(Ciphergen PBSⅡ+SELDI-TOF-MS)和WCX2芯片购于美国Ciphergen公司,SELDI-TOF-MS相关试剂购于美国Sigma公司。Ziptip C18购于美国Millipore公司,胰蛋白酶购于美国Promega公司,碘乙酰胺(IAM)购于德国Applichem公司,二硫苏糖醇(DTT)购于德国Bio-rad公司。基质辅助激光解析电离飞行时间质谱(MALDI-TOF-MS)购于英国Kratos Analytical公司,高效液相色谱(HPLC)购于日本Shimadzu公司,液相色谱串联质谱(LC-MS/MS)购于美国Thermo Electron公司。
3实验方法
3.1 SELDI-TOF-MS蛋白质芯片操作
冰浴中解冻血清标本,4℃离心。取96孔板置冰盒上,每孔加20μl U9缓冲液和10μl血清,4℃层析柜600 r/min振荡30 min。在震荡结束前15 min做芯片预处理,芯片装入加样器中,记下芯片号,每孔加醋酸钠200μl,层析,重复操作1次。U9处理后的96孔板置冰上,排枪加醋酸钠185μl,层析。取已处理的样本100μl加到芯片上,置层析柜4℃600 r/min结合60min,甩去残液,快速拍干。加醋酸钠200μl,振荡,甩掉,拍干,重复3次。200μl去离子水冲洗各孔2次,甩去除多余的水分。芯片风干后,每孔分两次加入50%饱和的SPA1μl,干燥后上机待测。
3.2数据收集、处理及统计学分析
随机选取60例乳头状甲状腺癌患者(Ⅰ期45例,Ⅱ期8例,Ⅲ期4例,Ⅳ期3例)和40例正常健康者作为训练组。为评估和验证所建立的分类模型的准确性,余48例乳头状甲状腺癌患者(Ⅰ期40例,Ⅱ期4例,Ⅲ期4例)和76例对照(20例正常健康人,56例良性甲状腺结节患者)作为测试组。
数据收集前,使用All-in-one标准蛋白质芯片对SELDI-TOF-MS系统进行校正。将结合后的WCX2蛋芯片用PBS-Ⅱ+质谱阅读仪进行分析。分析参数：激光强度为170,灵敏度为6,每个样本收集点数140次。收集数据分子质量范围1000～30000 Da,优化范围是2000～20000 Da。数据分析时首先通过离散小波去除噪音,并采用单调局部极小曲线对原始质谱进行校正,减掉基线,过滤出S/N大于2的峰。以10%为最小阈值进行聚类分析,每个样本中m/z差异小于0.3%的峰归为一类,并进行标准化处理。初步筛选出的m/z峰进行Wilcoxon秩和检验,P值来衡量每个峰区分不同组别的能力。检验标准取a=0.01。然后为了区分不同组别数据差异,采用非线性支持向量机(SVM)分类器对各个样本质谱数据进行分析,建立判别模型,最后用留一法交叉验证对所建立判别模型的判别效果进行评估。
3.3差异性蛋白质峰的纯化
于—80℃冰箱中取出血清样本,置入冰水浴中解冻。取血清100μl,分别加入350μ1超纯水和700μl纯ACN。随后将混合液置入—20℃冰箱,30 min后,13000 r/min离心10 min。取上清液移入新的试管,置入SPD SpeedVac冻干20 min。冻干后的样品置入HPLC仪中,收集不同时间的纯化液。将纯化出的各时间段蛋白液分别置入SPD SpeedVac中冻干至20μl。冻干后的蛋白液各取1.5μl分别与1.5μl CHCA混合后,置入靶板上,待干。同时用细胞色素C+CHCA和胰岛素+CHCA校样。将靶板置入MALDI-TOF-MS质谱仪中进行检测,跟踪目标蛋白质峰。
3.4蛋白质标记物的鉴定
含有目标蛋白质的蛋白液加超纯水至40μl,再加入配好的浓度为0.1 mol/L的DTT溶液4μl后,置于37℃温水中水浴1h。向该混合液中加入IAM 1.6μl后,避光放置1 h,随后依次加入1.6μl的1 mol/L的DTT溶液,150μl的0.1 mol/L的NH4HCO3溶液和2μl的胰蛋白酶。然后置于37℃温水中过夜。酶解后的蛋白液填柱上样。全部上样到毛细色谱柱后,置入nano-LC-ESI-MS/MS进行质谱分析,将得到的肽质量指纹图谱导入SEQUEST检索程序于Bioworks蛋白质序列数据库查询,检索相匹配的可能蛋白质。
3.5蛋白质标记物的验证
为验证上述所鉴定出的目标蛋白质,本实验采用抗体芯片免疫测定法对所有样本进行重新分析。首先将特异性的抗体(抗人触珠蛋白α抗体；抗人载脂蛋白C-Ⅰ抗体；抗人载脂蛋白C-Ⅲ抗体)分别以共价结合方式偶联于预激活的PS20芯片上。BSA试剂阻断结合反应后,清洗未结合的抗体,上样。1.5μL的血清样本、3μL的结合缓冲液与特异性抗体所覆盖的位点室温下孵育90 min。PBS液和去离子水分别洗涤两次,干燥后,采用PBS-Ⅱ+系统的蛋白质芯片阅读器进行分析检测。
结果
1差异性蛋白质峰的检测
60例乳头状甲状腺癌患者和40例正常健康对照原始质谱数据经去噪、减掉基线及标准化处理后,聚类分析得到样本各自的峰。对所得到峰的相对强度做Wilcoxon秩和检验,P<0.01的m/z峰26个。其中7个m/z峰在乳头状甲状腺癌中高表达,19个呈低表达。从差异显著蛋白质峰的任意组合中,采用SVM筛选出预测值的Youden指数最高的组合模型,筛出m/z峰值位于9190、6631和8697的蛋白质标记物3个。与正常健康对照相比,乳头状甲状腺癌患者中,m/z峰值位于9190的蛋白质明显高表达,而峰值位于6631和8697的蛋白质表达明显降低。此外,随着肿瘤分期的增加,m/z峰值位于9190的蛋白质表达水平有升高的趋势,而峰值位于6631和8697的蛋白质表达水平则呈下降趋势。联合3种潜在蛋白质标记物,leave-1-out交叉检测,该判别模型区分乳头状甲状腺癌患者和正常健康人的敏感性是98%,特异性是97%。
2差异性蛋白质峰的验证
为验证上述所得到的判别模型的准确性和可行性,本研究用另外48例乳头状甲状腺癌患者和76例非乳头状甲状腺癌对照血清标本(20例正常健康人、56例良性甲状腺结节患者)进行盲法测试。结果显示该判别模型区分乳头状甲状腺癌患者和非癌症对照组的敏感性为95.15%,特异性为93.97%,阳性预测值为96.0%。
3蛋白质标记物的纯化与鉴定
采用HPLC技术分别对m/z峰位于9190、6631和8697的蛋白质进行分离纯化。随后对目标蛋白质进行酶解,nano-LC-MS/MS对得到的多肽混合物进行分析检测。m/z峰值位于9190的蛋白质被鉴定为触珠蛋白al,m/z峰值位于6631和8697的蛋白质分别被鉴定为载脂蛋白C-Ⅰ和载脂蛋白C-Ⅲ。
4蛋白质标记物的验证
为进一步验证鉴定结果的准确性,本实验采用分别结合有抗人触珠蛋白α抗体、抗人载脂蛋白C-Ⅰ抗体和抗人载脂蛋白C-Ⅲ抗体的蛋白质芯片进行免疫检测。观察到m/z峰值位于9190的蛋白质可被偶联有抗人触珠蛋白α抗体的芯片捕获,而m/z峰值位于6631和8697的蛋白质可分别被偶联有抗人载脂蛋白C-Ⅰ抗体和抗人载脂蛋白C-Ⅲ抗体的芯片所结合。
结论
m/z峰位于9190、6631和8697的蛋白质分别被鉴定为触珠蛋白α1、载脂蛋白C-Ⅰ和载脂蛋白C-Ⅲ。联合此三种蛋白质,可有效的区分乳头状甲状腺癌患者和非癌者,在乳头状甲状腺癌的诊断中具有一定的价值和广泛的应用前景。本实验是基于有限样本的研究,仍需扩大样本量,使其具有更高的准确率和推广性。此外,SELDI-TOF-MS芯片分析、HPLC纯化、MALDI-TOF-MS跟踪监测及LC-MS/MS蛋白质鉴定等一些列蛋白质组学技术的联合应用,为肿瘤蛋白质标记物的检测鉴定提供了有效的技术支持。
Background
Thyroid carcinoma is the most common endocrine malignancy, and a common cancer among the malignancies of head and neck. It comprises 91.5% of all endocrine malignancies and 1% of all malignant diseases. An estimated 33 550 new cases are diagnosed annually in the United States and recent statistics shows the incidence of thyroid carcinoma has increased, especially in papillary thyroid carcinomas (PTC). PTC is the most common type, which accounts for 80% of all thyroid cancers. Early accurate diagnosis and timely treatment are critical for improving long-term survival of PTC patients. Many diagnostic tools have been used for thyroid carcinoma, such as sonography, computed tomography, magnetic resonance imaging, cytological examination and fine-needle aspiration. Currently, although ultrasound-guided fine-needle aspiration biopsy is considered as the most effective test for distinguishing malignant from benign thyroid nodules, its sensitivity is approximately 93% and its specificity is 75%. At the same time, researchers have been seeking valuable biomarkers for thyroid carcinoma diagnosis, such as galectin-3, CITED-1, HBME1, cytokeratin-19 and TPO, and so on. What is disappointing is that all these biomarkers either are lacking specificity to some degree, or have a poor positive predictive value. To distinguish a malignant thyroid nodule from a benign lesion more accurately, the diagnostic test, however, still needs to be improved. Recent advances in the proteomics study have introduced novel techniques for the screening of cancer biomarkers and improved early and accurate diagnosis of cancer diseases to a new horizon. Surfaced enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS), which generates the protein fingerprint by MS, has been proved a powerful tool for potential biomarker discovery. Recently, the SELDI-TOF-MS analysis has been successfully used to identify specific biomarkers for various cancers. When combining with other peoteomics technologies, such as MALDI-TOF-MS, LC-MS/MS and so on, the candidate protein biomarkers can be identified. In search of biomarkers for diagnosing PTC, a few pilot studies based on proteomics were conducted, in which SELDI-TOF-MS has been utilized. However, to our knowledge, no specific protein biomarkers have been identified and validated in these reports.
In this study, we first used SELDI-TOF-MS technology to screen potential protein patterns specific for PTC and then purified the candidate protein biomarker peaks by HPLC, MALDI-TOF-MS, and identified by LC-MS/MS. Finally, we confirmed these biomarkers by ProteinChip Immunoassays.
Objective
The aim of this study was to discover and identify potential protein biomarkers for PTC specifically.
Materials and methods
1 Clinical Materials
Serum samples were obtained from 224 individuals with informed consent in the Department of General Surgery, the First Affiliated Hospital of Zhengzhou University. These 224 individuals included 108 patients with PTC,56 patients with benign thyroid node, and 60 healthy individuals. Patients with PTC had a median age of 43 years (ranging from 23 to 75 years,27 men and 81 women), and the sera was obtained at the time of diagnosis. All 108 patients were distributed in 4 stages according to UICC. In stageⅠthere were 85 patients, stageⅡ,Ⅲ&Ⅳconsisted of 12, 8 & 3 patients respectively. The benign thyroid node group and the healthy individuals group were age-and gender-matched with the PTC group. Pathological diagnosis of all the PTC and benign thyroid nodes were confirmed independently by two pathologists. All serum samples were collected preoperatively in the morning before breakfast. The sera were left at room temperature for 1 h, centrifuged at 3000 r/min for 10 min, and then stored at-80℃.
2 Reagents and instruments
ProteinChip Biosystems (Ciphergen PBSⅡ+SELDI-TOFMS) and WCX2 chip were purchased from Ciphergen Biosystems (USA). All other SELDI-TOF-MS related reagents were acquired from Sigma (USA). Ziptip C18 was purchased from Millipore (USA). Trypsase was purchased from Promega (USA). IAM was purchased from AppliChem(GER). DTT was purchased from Bio-rad (GER). MALDI-TOF-MS was purchased from Kratos Analytical Co (UK) and HPLC was purchased from Shimadzu (JPN). LC-MS/MS was purchased from Thermo Electron Corporation (USA).
3 Methods
3.1 SELDI-TOF-MS analysis of serum protein profiles
Frozen serum samples were defrosted on ice and spun at 4℃. Each serum sample(10μL) was denatured by addition of 20μL of U9 buffer and vortexed at 4℃for 30 min. WCX2 proteinchip arrays was pretreatmented before 15 min of finishing the vibration. The proteinchip was placed in the bioprocessor, and wrote down the chip number. Each hole was added with 200μl sodium acetate and then was vortexed. Repeat this operation once. The 96-hole plate with prepared by U9 was placed on ice, and added with 185μl sodium acetate, then vortexed. The diluted serum sample was allowed to react with the surface of the WCX2 chip for 60 min at room temperature. After removing the remaining liquid and drying the array surface rapidly, adding 200μl sodium acetate, and then vortexed. Repeat the operation three times. Each spot was then washed two times with 200μl deionized water, and removed the remaining water. After drying the array surface in the air,1μl 50% saturated SPA was applied and allowed to dry, and placed them on the device for testing.
3.2 Purification of candidate protein markers using HPLC
Frozen serum samples were defrosted on ice. Each serum sample (100μl) was mixed with 350μl ultrapure water and 700μl pure ACN, and then incubated for for 30 min at-20℃. After that, the mixture was centrifuged at 13000 r/min for 10 min. The supernatant was removed into new tubes and then placed in SPD SpeedVac for 20 min. The freeze-dried samples were then loaded into HPLC. Each peak fraction was collected and concentrated using SpeedVac. The mixture with 1.5μl CHCA and 1.5μl concentrated fraction was spotted to the MALDI plate. At the same time, the instrument was calibrated by cytochrome C+CHCA and Insulin+CHCA. And then, the taget plate was placed into the AXIMA-CFRTM+MALDI-TOF mass spectrometer to trace the candidate protein biomarkers.
3.3 Identification of candidate protein biomarkers by LC-MS/MS
Each fraction which contains the candidate protein biomarker was added ultrapure water to 40μl and mixed with 4μl 0.1 mol/L DTT for 1 h in 37℃water. Then the mixture was alkylated by 1.6μl iodoacetamide in the dark for 1 h. After that, the candidate proteins were proteolysed with 2μl trypsin in 150 ul NH4HCO3 overnight at 37℃. Protein digests obtained above were loaded onto a home-made C18 column and followed with nano-LC-ESI-MS/MS analysis. All MS/MS data were searched against a human protein database downloaded from Bioworks using the SEQUEST program.
3.4 Confirmation of candidate protein biomarkers using ProteinChip Immunoassays
To confirm the identity of the candidate protein biomarkers, all samples from the initial experiments were reanalyzed by using ProteinChip immunoassays. Specific antibody arrays were prepared by covalently coupling the appropriate antibodies to preactivated ProteinChip arrays. Antibodies (anti-human haptoglobinα-chain; anti-human apolipoprotein C-I; anti-human apolipoprotein C-III) were covalently coupled to PS20 arrays, respectively. After blocking with BSA and washing to remove uncoupled antibodies, antibody-coated spots were incubated with 1.5μL of serum samples and 3μL of binding buffer for 90 min. Spots were then washed with PBST, PBS and deionized water twice respectively before drying. SELDI-TOF-MS analysis was performed on a PBS-II ProteinChip reader with CHCA as matrix.
4 Data collection and processing
The SELDI-TOF-MS instrument was calibrated by the All-in-one peptide molecular mass standard before the collection of data. MS analysis was performed on a PBS-II ProteinChip reader. The mass spectra of the proteins were generated using an average of 140 laser shots at a laser intensity of 170 arbitrary units and detector sensitivity was set at 6. The scope of data collection is 1000 to 30000 daltons, and the optimize detection mass range was set from 2000 to 20000 daltons for all study sample profiles. The first step of data analysis was to use the undecimated discrete wavelet transform method to denoise the signals. Second, the spectra were subjected to baseline correction by aliging with a monotone local minimum curve and mass calibration. Third, the peaks were filtered to maintain a S/N of more than two. Finally, to match peaks across spectra, we pooled the detected peaks if the relative difference in their mass sizes was not more then 0.3%. The minimal percentage of each peak, appearing in all the spectra, is specified to ten. The matched peak across spectra is defined as a peak cluster. To distinguish between data of different groups, we used a nonlinear SVM classifier. The leave-one-out crossing validation approach was applied to estimate the accuracy of this classifier.
5 Statistical analysis
After the data of MS were filtered out the noice and with clustering analysis, the capability of each peak in distinguishing data of different groups was estimated by the P value of Wilcoxon test. The testing standard is set atα=0.01.
Results
1 Serum protein profiles and data processing
Serum samples from the training set were analyzed and compared by SELDI-TOF-MS with WCX2 chip. All MS data were baseline subtracted and normalized using total ion current, and the peak clusters were generated by Biomarker Wizard software. After carrying out Wilcoxon rank sum tests to determine relative signal strength,26 peaks with P value<0.01 were obtained. Seven protein peaks were found up-regulated and 19 peaks were found down-regulated in PTC group. From the random combination of protein peaks with remarkable variation, SVM screened out the combined model with maximum Youden index of the predicted value, identifying 3 markers positioned at 9190,6631 and 8697 respectively. In the PTC group, the 9190 Da protein was remarkably elevated while 6631 & 8697 Da proteins were significantly decreased. In addition, the level of 9190 Da protein progressively increased with the clinical stage I, II, III and IV, and the expression of 6631,8697 Da proteins gradually decreased in higher stages. Combining 3 potential markers, using the method of leave-1-out for cross detection, the sensitivity of discriminating 60 PTC and 40 normal subjects was 98%, and its specificity was 97%.
2 Protein peak validation
The remaining 48 PTC and 76 control serum samples (20 healthy controls and 56 patients with benign thyroid node) as a blind testing set, were analyzed to validate the accuracy and validity of the classification model derived from the training set. The classification model distinguished the PTC samples from controls with a sensitivity of 95.15%, specificity of 93.97%, and positive predictive value of 96.0%, respectively.
3 Purification and identification of candidate protein biomarkers
Serum samples from PTC patients were used for the purification of the up-regulated candidate protein biomarker (9190 Da), and serum samples from healthy controls were used for the purification of the two down-regulate proteins (6631,8697 Da) using WCX SPE and C18 HPLC.
After digestion with modified trypsin, the peptide mixture was analyzed by nano-LC-MS/MS. The candidate biomarker with m/z 9190 was identified as haptoglobin al chain, while another two biomarkers were identified as apolipoprotein C-I (6631 Da) and apolipoprotein C-Ⅲ(8697 Da). The whole sequence of the three candidate protein markers is given by combination of high sequence coverage and accurate molecular weight (MW) measurement using MALDI-TOF-MS.
4 Validation of three candidate protein biomarkers
A ProteinChip-array-based immunoassay was used to specifically capture haptoglobin al chain, apolipoprotein C-Ⅰand apolipoprotein C-Ⅲfrom crude serum samples and confirm the significance of each marker. The anti-haptoglobin a-chain antibody specifically captured the previously identified 9190 Da protein. The anti-apolipoprotein C-Ⅰarray was developed to capture apolipoprotein C-Ⅰ(6631 Da) and the apolipoprotein C-III antibody against specifically captured apolipoprotein C-Ⅲ(8697 Da).
Conclusions
In summary, we have identified a set of protein peaks that could discriminate PTC from non-cancer controls. From the protein peaks specific for PTC disease, we identified haptoglobinα1 chain, apolipoprotein C-I and apolipoprotein C-Ⅲas potential proteomic biomarkers of PTC. Further studies with larger sample sizes will be needed to verify the specific protein markers. An efficient strategy, composed of SELDI-TOF-MS analysis, HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification has been proved very successful.
引文
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