Discovery And Identification Of Serum Biomarkers Of Papillary Thyroid Carcinoma

BackgroundThyroid 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.ObjectiveThe aim of this study was to discover and identify potential protein biomarkers for PTC specifically.Materials and methods1 Clinical MaterialsSerum 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 instrumentsProteinChip 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 Methods3.1 SELDI-TOF-MS analysis of serum protein profilesFrozen 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 HPLCFrozen 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/MSEach 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 ImmunoassaysTo 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 processingThe 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 analysisAfter 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.Results1 Serum protein profiles and data processingSerum 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 validationThe 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 biomarkersSerum 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 biomarkersA 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).ConclusionsIn 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.