Proteomic biomarker predicts therapeutical effects of oxaliplatin combining with fluoropyrimidine in metastatic gastric cancer patients by the SELDI-proteinchip platform.

BACKGROUND/AIMS: To evaluate potential protein markers for oxaliplatin-based first-line chemotherapy of metastatic gastric cancer (MGC), we have used proteomic analysis to compare the difference of serum proteomic spectra between responsive and non-responsive MGC patients. METHODOLOGY: Serum samples were collected before chemotherapy.Surface-enhanced laser desorption/ionisation time of-flight mass spectrometry (SELDI-TOF-MS) proteinchip array technology was applied to compare the differences of serous protein spectra between the twenty responsive patients and another fourteen non responsive patients. Cross validation was applied to identify the proper markers for an accurate judgment of prognosis. RESULTS: Fifty proteins were picked out for significantly increased or decreased expression(p<0.05, Student t-test). Among them, sixteen protein masses with 1081, 1267, 2941, 2985, 3148, 3165,3199, 3219, 3249, 3281, 4182, 4390, 4482, 4509,4533 and 5001 m/z were chosen as components of the best proteomic biomarker pattern for judgment of chemotherapy prognosis in MGC patients, achieving a sensitivity of 95% (19/20) and a specificity of 78.6%(11/14), respectively. CONCLUSIONS: SELDI-TOF-MS screens out the specific protein markers that help oncologists with judging the prognosis of oxaliplatin in combination with fluoropyrimidine, thus orientating first-line palliative chemotherapy for MGC patients.

Identification of the biomarkers for the prediction of efficacy in first-line chemotherapy of metastatic colorectal cancer patients using SELDI-TOF-MS and artificial neural networks.

BACKGROUND/AIMS: For patients with metastatic colorectal cancer, both FOLFOX regimen and FOLFIRI regimen are considered as first-line choices. There are no clinically useful markers that could predict the response to these regimens respectively. We aimed at identifying serum protein patterns which could predict the efficacy of chemotherapy. METHODOLOGY: Serum from 70 patients diagnosed as metastatic colorectal cancer before first-line chemotherapy were collected and analyzed for protein patterns using ANN analysis of SELDI-TOF-MS. Among the 70 cases, 44 patients received FOLFOX chemotherapy, while the other 26 patients received FOLFIRI chemotherapy. After four cycles of the treatment, RECIST criteria were used to define the responders (R) and non-responders (NR). RESULTS: A potential predicting pattern consisting of 6 biomarkers was identified in the patients receiving FOLFOX chemotherapy. Using this predicting pattern, the responders could be separated from the non-responders with a sensitivity of 92.9% and a specificity of 81.3%. Another potential predicting pattern that consisted of 7 bio-markers was identified in the patients who have received FOLFIRI chemotherapy. The sensitivity and the specificity of this predicting pattern were 92.3% and 92.3% respectively. CONCLUSIONS: Two potential pat-terns for the prediction of efficacy of FOLFOX or FOLFIRI chemotherapy were established in this preliminary study.

[Application of serum protein fingerprint in diagnosis of pancreatic cancer].

OBJECTIVE: To establish serum protein fingerprint model for early diagnosis of pancreatic cancer with surface enhanced laser desorption/ionization time of flight-mass spectrometry (SELDI-TOF-MS) and bioinformatics techniques. METHODS: A total of 73 samples were analyzed in this study, including 31 cases of pancreatic cancers, 22 cases of pancreatitis and 20 healthy individuals. Samples were first analyzed by SELDI-TOF-MS and two patterns of differentiation model were constructed with support vector machine arithmetic method. RESULTS: The pattern 1 model differentiating pancreatic cancer patients from healthy individuals had a specificity and a sensitivity of both 100.0%. The pattern 2 model differentiating pancreatic cancer from pancreatitis had a specificity of 95.5% and a sensitivity of 93.5%. CONCLUSION: SELDI-TOF-MS technique combined with bioinformatics can facilitate to identify biomarkers for pancreatic cancer.

Detection of nasopharyngeal carcinoma using surface-enhanced laser desorption and ionization mass spectrometry profiles of the serum proteome.

BACKGROUND AND OBJECTIVE: Early diagnosis of nasopharyngeal carcinoma (NPC) is difficult due to the insufficient specificity of the conventional examination method. This study was to investigate potential and consistent biomarkers for NPC, particularly for early detection of NPC. METHODS: A proteomic pattern was identified in a training set (134 NPC patients and 73 control individuals) using the surface-enhanced laser desorption and ionization-mass spectrometry (SELDI-MS), and used to screen the test set (44 NPC patients and 25 control individuals) to determine the screening accuracy. To confirm the accuracy, it was used to test another group of 52 NPC patients and 32 healthy individuals at 6 months later. RESULTS: Eight proteomic biomarkers with top-scored peak mass/charge ratios (m/z) of 8605 Da, 5320 Da, 5355 Da, 5380 Da, 5336 Da, 2791 Da, 7154 Da, and 9366 Da were selected as the potential biomarkers of NPC with a sensitivity of 90.9% (40/44) and a specificity of 92.0% (23/25). The performance was better than the current diagnostic method by using the Epstein-Barr virus (EBV) capsid antigen IgA antibodies (VCA/IgA). Similar sensitivity (88.5%) and specificity (90.6%) were achieved in another group of 84 samples. CONCLUSION: SELDI-MS profiling might be a potential tool to identify patients with NPC, particularly at early clinical stages.

[Application of serum protein markers to distinguish familial adenomatous polyposis (FAP) and sporadic colorectal adenomas].

OBJECTIVE: To screen out specifically-expressed serum protein markers in familial adenomatous polyposis (FAP) and to establish a serum protein fingerprint diagnostic model for distinguishing FAP from sporadic colorectal adenomas. METHODS: Serum samples were collected from 19 FAP cases and 16 sporadic colorectal adenomas with informed consent. Serum protein fingerprint profiles were detected by SELDI-TOF-MS with CM 10 protein chip to screen out FAP adenoma-related serum protein markers, and support vector machine (SVG) technique was used to establish the diagnostic model to distinguish FAP from sporadic colorectal adenomas. RESULTS: Six differently-expressed protein peaks (P < 0.01) were detected. Among them proteins of 5640, 3160, 4180 and 4290 m/z were highly expressed in FAP adenomas, and proteins of 3940 and 3400 m/z were highly expressed in sporadic colorectal adenomas. The accuracy of diagnostic model established with SVG to distinguish FAP adenomas and sporadic colorectal adenomas was 94.7% and 93.7%, respectively. CONCLUSION: SELDI-TOF-MS can be effectively used to screen out the differentially expressed serum protein markers in FAP adenomas and sporadic colorectal adenomas, and a diagnostic model build by SVG to distinguish them has been successfully established. Therefore, a useful breakthrough point for research on molecular mechanisms of FAP pathogenesis is provided.

[Detection and identification of specific serum biomarkers in papillary thyroid cancer].

OBJECTIVE: To detect and identify the potential specific serum biomarkers for diagnosis of papillary thyroid cancer. METHODS: Samples of 35 patients with papillary thyroid carcinoma, 40 patients with benign thyroid nodule and 34 healthy individuals were analyzed using the SELDI-TOF ProteinChip System and bioinfomation technology to find the differential peaks which were separated by HPLC and then further analyzed by LC-MS/MS. The protein sequences were analyzed by SEQUEST software and searched in Bioworks database. RESULTS: The top six mass-to-charge ratio (M/Z) peaks with the smallest P value were 6651, 6452, 7653, 7932, 15 106 and 15 848 Da, respectively. The 6651 and 6452 Da proteins were weakly expressed in papillary thyroid carcinoma but highly expressed in benign thyroid nodules and healthy individuals. The differences had statistical significance (P < 0.01). The 7653, 7932, 15 106, 15 848 Da proteins were highly expressed in papillary thyroid carcinoma but weakly expressed in benign thyroid nodules and healthy individuals. The differences were statistically significant (P < 0.01). Combination of these six proteins, using the method of leave-one-out to make crossing detection, the specificity of discriminating papillary thyroid carcinoma and non-cancer was 88.0%, and its sensitivity was 92.5%. The 6651 and 6452 Da proteins were identified as apolipoprotein C-I and apolipoprotein C-III, respectively. The 7653 and 15 106 Da proteins were identified as the same protein-alpha-globin, and the 7932 and 15,848 Da proteins were identified as the same protein-beta-globin. CONCLUSION: The detection of differentially expressed apolipoprotein C-I, apolipoprotein C-III, alpha-globin, and beta-globin may have utility for diagnosis of papillary thyroid carcinoma and are worthy of further investigation.

[Screening and identification analysis of serum protein biomarkers in nephroblastoma].

OBJECTIVE: To screen and characterize the serum protein biomarkers in nephroblastoma so as to establish the proteins as the specific serum biomarkers for diagnosis and prognosis monitoring. METHODS: The differential protein peaks were located by detecting serum samples of preoperative and postoperative patients and normal children using the SELDI-TOF MS technology. After purification, the differential proteins were further analyzed by LC-MS/MS and the protein sequences searched in database. RESULTS: Two peaks with m/z of 6455.5 and 6984.4 were selected as potential biomarkers. They were weakly expressed in nephroblastoma (intensity: 1029 +/- 364, 297 +/- 126) but highly expressed in normal individuals (2108 +/- 837, 753 +/- 226); another peak with m/z of 9190.8 was weakly expressed in preoperative sera (283 +/- 154) but highly expressed in serum samples of postoperative patients and normal children (5974 +/- 657, 6231 +/- 519). The protein at 6455.5 and 9190.8 were identified as apolipoprotein C-III and haptoglobin respectively. CONCLUSION: The detection of differentially expressed apolipoprotein C-III and haptoglobin may have potential utilities for serum diagnosis, malignancy classification and prognosis monitoring of nephroblastoma and is worthy of further studies and applications.

[Application of laser capture microdissection and surface-enhanced laser desorption ionization time-of-flight mass spectrometry to screen biomarkers for early diagnosis of lung adenocarcinoma].

OBJECTIVE: To improve diagnostic methods to screen biomarkers for early diagnosis in lung adenocarcinoma by employing laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and support vector machine (SVM). METHODS: Frozen sections of thickness 8 microm were made using 6 cases of fresh lung cancer tissues and 4 cases of matched normal lung tissues. The sections were stained by improved HE solution. The homogeneous adenocarcinoma cells and normal cells were collected by LCM in each sample, and then SELDI profiles based on PBS II+SELDI-TOF-MS (IMAC protein chip) were analyzed using SVM. RESULTS: High quality cell samples were obtained by LCM quickly and precisely from normal specimens and diseased tissues without interstitium, inflammation and necrosis. Eighty four differential protein peaks were found. Top ten of them were identified as candidate biomarkers; six proteins were significantly weakly expressed in lung cancer tissue compared to normal tissues, but the other four protein were over-expressed (P < 0.05). Every candidate biomarker has undergone the blind-cross-test. Each of them can separate the lung cancer from normal samples with a sensitivity of 100% and a specificity of 100%. The 3191 m/z was considered as disease marker of lung adenocarcinoma. CONCLUSION: The method combined LCM with SELDI-TOF-MS may be able to screen potential biomarkers to distinguish lung cancer from healthy tissue with high sensitivity and specificity, which could improve early diagnosis for lung cancer.

Novel sphingomyelin biomarkers for brain glioma and associated regulation research on the PI3K/Akt signaling pathway.

Glioma is one of the most common malignant tumor types of the central nervous system. It is necessary to identify biomarkers and novel therapeutic targets for glioma. The purpose of the present study was to distinguish lipid biomarkers with differential expression patterns in glioma tissues and normal brain tissues by matrix assisted laser desorption/ionization (MALDI)-imaging and MALDI-time of flight (TOF)-mass spectrometry (MS). Additionally, identification of lipid biomarkers was performed to describe novel therapeutic targets for glioma treatment. A total of six tissues from three patients with glioma and three control patients with traumatic brain injury were analyzed using UltrafleXtreme MALDI-TOF/TOF. The expression levels of 15 lipid peaks were higher in the TBT samples compared with in the GBT samples. The expression levels of another 16 lipid peaks were higher in the GBT samples compared with in the TBT samples. 14 peaks were identified as sphingomyelins using MS/MS. Additional results were also obtained from experiments using the glioma cell line U373-MG. These results indicated that treatment with the drug desipramine (Desi) inhibited the accumulation of ceramide on the cell membranes of glioma U373-MG cells. Treatment with Desi inhibited the activation of insulin-like growth factor-1 receptor and inhibited the activation of proteins in the PI3K/Akt signaling pathway.

Discovering differential protein expression caused by CagA-induced ERK pathway activation in AGS cells using the SELDI-ProteinChip platform.

AIM: To identify the protein expression differences related to the CagA-induced ERK pathway activation in AGS cells. METHODS: Human AGS cells transfected with cagA and blank vector were treated with specific mitogen-activated protein kinase kinase (MEK) inhibitor. Total cell proteins were combined by strong anion exchange (SAX2) and weak cation exchange (CM10) ProteinChip arrays and analyzed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) proteomics technology. Protein expression profiles were compared with those of inhibitor-untreated cagA transfectants. SwissProt/TrEMBL database searching for differentially expressed proteins was carried out using the TagIdent tool with the pI and mass information. RESULTS: When a total of 16 proteins that showed expression differences in inhibitor-untreated cagA transfectants were compared with vector transfectants, three proteins with m/z 4229, 8162 and 9084 were found to have no expression differences after treatment with MEK inhibitor, while the other 13 maintained the same expression differences after inhibitor treatment. Seven pieces of meaningful matching information for the three proteins were obtained from database searching. CONCLUSION: Biomarkers with m/z 4229, 8162 and 9084 are ERK1/2 phosphorylation dependent, and therefore are the downstream molecules of ERK1/2 in the ERK/MAPK signaling pathway. The three biomarkers may be important cancer-associated proteins according to SwissProt/TrEMBL database information.

[Preliminary study of protein expression profiling of PCOS on different state].

OBJECTIVE: To screen the serum protein expression profiles in patients having polycystic ovary syndrome (PCOS) with or without insulin resistance (IR) and search for discriminatory proteins. METHOD: Fasting serum samples of 30 PCOS patients with IR, 30 PCOS patients without IR, and 30 control individuals from Reproductive Center of Peking University Third Hospital were studied. RESULTS: There were 27 differential protein peaks between PCOS IR patients and controls, 17 between PCOS non-IR patients and controls, and 19 between PCOS IR patients and non-IR patients. Marker proteins from differentially expressed proteins were screened out using support vector machine (SVM), and were used to establish three diagnostic models for PCOS IR, PCOS non-IR, and IR, respectively. CONCLUSIONS: There were significantly different serum proteomic patterns in different types of PCOS. Using Protein Chip combined with SVM, computer diagnostic models for PCOS with and without IR were set up quickly and efficiently. These discriminatory proteins may help us understand the proteomic changes in serum and find out potential biomarkers of PCOS and IR.

[Use of laser capture microdissection and surface-enhanced laser desorption ionization time-of-flight mass spectrometry to screen differential proteins in lung adenocarcinoma and lung squamous carcinoma].

OBJECTIVE: To screen biomarkers for classification in lung adenocarcinoma and lung squamous carcinoma by using laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and support vector machine (SVM). METHODS: Six specimens of lung adenocarcinoma tissues and seven specimens of lung squamous carcinoma tissues obtained during operation were made into frozen sections and stained by improved H-E solution. About 1.2 x 10(5) of homogeneous adenocarcinoma cells and 1.4 x 10(5) of homogeneous lung squamous carcinoma cells were collected using LCM. Then SELDI profiles based on PBS II(+)SELDI-TOF-MS (IMAC protein chip) and the data were analyzed by support vector machine (SVM). RESULTS: Eighty seven differential protein peaks were found and top ten of them were identified as candidate biomarkers. The expression levels of 6 proteins among them with the molecular weights of 3333, 3592, 3848, 5036, 5191, and 5211 respectively in the lung squamous cancer tissues were weaker than those in the adenocarcinoma tissues, and the expression levels of 4 proteins with the molecular weights of 2505, 4004, 4847, and 11 412 in the lung squamous carcinoma tissues were stronger than those in the adenocarcinoma tissues. The expression of the protein with the molecular weight of 4847 in the squamous cancer was significantly stronger than that in the adenocarcinoma (p + 0.032). A discriminatory pattern consisting of 3 proteins with the molecular weights of 4847, 11 412, and 3592 was established with a sensitivity of 100% and a specificity of 100% respectively in separating adenocarcinoma from squamous carcinoma. CONCLUSION: There is a difference in protein component between adenocarcinoma and squamous carcinoma. LCM combined with SELDI-TOF-MS help screen a biomarker pattern to distinguish lung adenocarcinoma from lung squamous carcinoma with high sensitivity and specificity.

[Protein profiles of serum in gastric cancer at a high-risk area by SELDI-TOF mass spectrometry].

OBJECTIVE: To explore the specific and sensitive biomarkers for gastric cancer detection, a surface-enhanced laser desorption and ionization protein chip mass spectrometry (SELDI-TOF-MS) was used to generate protein profiles of serum in gastric cancer at a high-risk area. METHODS: A total of 36 gastric cancer cases and 46 subjects with superficial gastritis were selected from Linqu county, Shandong province, a high-risk area of gastric cancer. Serum samples were collected and Q10 protein chips were used to detect the serum proteomic patterns, and the sensitivity and specificity were assessed. RESULTS: For the comparison of the gastric cancer group (26 out of 36 gastric cancer cases) versus superficial gastritis group (37 out of 46 subjects), the 6 most discriminating peaks (m/z 8587, 6945, 8243, 3899, 7035, and 9943) were identified by the ProteinChip Data Analysis System (ZUCIPDAS). The sensitivity and specificity of this pattern were 88.5% and 97.3%, respectively. A total of 19 subjects (10 gastric cancer cases and 9 superficial gastritis subjects) was selected to test the accuracy of this pattern by using blind method, and the sensitivity and specificity were 80.0% and 88.9% ,respectively. CONCLUSION: Our findings suggest that SELDI profiling of serum might be a potential for gastric cancer detection and screening in high-risk population.

[Establishment of a diagnostic model of serum protein fingerprint pattern for esophageal cancer screening in high incidence area and its clinical value].

OBJECTIVE: To analyze the alterations of serum proteomic pattern in esophageal squamous cell carcinoma (ESCC) by SELDI-TOF-MS, to establish a diagnostic model of ESCC screening in high incidence area and investigate its clinical value. METHODS: SELDI-TOF-MS and CM10 proteinChip were used to detect the serum proteomic patterns of 36 cases of ESCC and 38 healthy control subjects in high incidence area. The data were analyzed and a diagnostic model was established by using support vector machine (SVM). The diagnostic model was evaluated by leave-one-out cross validation. RESULTS: At the molecular weight range of 2000 to 20,000, 31 protein peaks were significantly different between ESCC and controls (P < 0.01). A diagnostic model consisting of 4 protein peaks could do the best in diagnosis of ESCC and controls. The accuracy was 85.1%, sensitivity was 86.1%, specificity was 84.2%, and positive value was 83.8%. CONCLUSION: The diagnostic model formed by 4 protein peaks, established in this study, can well distinguish ESCC from healthy subjects. It provides a new approach for ESCC screening in high incidence area.

[Preoperative molecular staging of colorectal cancers by CM10 ProteinChip and SELDI-TOF-MS analysis].

OBJECTIVE: To detect the serum proteomic patterns by using SELDI-TOF-MS and CM10 ProteinChip techniques in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in colorectal cancer staging. METHODS: A total of 76 serum samples were obtained from CRC patients at different clinical stages, including Dukes A (n = 10), Dukes B (n = 19), Dukes C (n = 16) and Dukes D (n = 31). Different stage models were developed and validated by bioinformatics methods of support vector machines, discriminant analysis and time-sequence analysis. RESULTS: The model I formed by six proteins of peaks at m/z 2759.6, 2964.7, 2048.0, 4795.9, 4139.8 and 37 761.6 could do the best as potential biomarkers to distinguish local CRC patients (Dukes A and Dukes B) from regional CRC patients (Dukes C ) with an accuracy of 86.7%. The model II formed by 3 proteins of peaks at m/z 6885.3, 2058.3 and 8567.8 could do the best to distinguish locoregional CRC patients (Dukes A, B and C) from systematic CRC patients (Dukes D) with an accuracy of 75.0%. The mode III could distinguish Dukes A from Dukes B with an accuracy of 86.2% (25/29). The model IV could distinguish Dukes A from Dukes C with an accuracy of 84.6% (22/26). The model V could distinguish Dukes B from Dukes C with an accuracy of 85.7% (30/35). The model VI could distinguish Dukes B from Dukes D with an accuracy of 80.0% (40/50). The model VII could distinguish Dukes C from Dukes D with an accuracy of 78.7% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously. CONCLUSION: Our findings indicate that this method can well be used in preoperative staging of colorectal cancers and the screened tumor markers may serve for guidance of integrating treatment of colorectal cancers.

Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients.

OBJECTIVES: To detect the serum proteomic patterns by using SELDI-TOF-MS (surface enhanced laser desorption/ ionization-time of flight-mass spectrometry) technology and CM10 ProteinChip in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in the tumour staging of colorectal cancer. METHODS: SELDI-TOF-MS and CM10 ProteinChip were used to detect the serum proteomic patterns of 76 patients with colorectal cancer, among them, 10 Stage I, 19 Stage II, 16 Stage III and 31 Stage IV samples. Different stage models were developed and validated by support vector machines, discriminant analysis and time-sequence analysis. RESULTS: The Model I formed by 6 protein peaks (m/z: 2759.58, 2964.66, 2048.01, 4795.90, 4139.77 and 37761.60) could be used to distinguish local CRC patients (Stage I and Stage II) from regional CRC patients (Stage III) with an accuracy of 86.67% (39/45). The Model II formed by 3 protein peaks (m/z: 6885.30, 2058.32 and 8567.75) could be used to distinguish locoregional CRC patients (Stage I, Stage II and Stage III) from systematic CRC patients (Stage IV) with an accuracy of 75.00% (57/76). The Model III could distinguish Stage I from Stage II with an accuracy of 86.21% (25/29). The Model IV could distinguish Stage I from Stage III with accuracy of 84.62% (22/26). The Model V could distinguish Stage II from Stage III with accuracy of 85.71% (30/35). The Model VI could distinguish Stage II from Stage IV with accuracy of 80.00% (40/50). The Model VII could distinguish Stage III from Stage IV with accuracy of 78.72% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously. CONCLUSION: This method showed great success in preoperatively determining the colorectal cancer stage of patients.

[Detecting biomarkers from serum in nephroblastoma patients with support vector machine].

OBJECTIVE: To find new biomarkers and to establish serum protein fingerprint models for early detection and diagnosis of nephroblastoma by SELDI-TOF-MS and bioinformatics tools. METHODS: Seventy five serum samples from 35 nephroblastoma patients, 30 children's abdominal solid tumor patients, and 20 healthy children were bound to WCX2 protein chip and tested by surface enhanced laser desorption/ionization time of flight-mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by support vector machine(SVM). RESULTS: Four peaks with m/z of 6984.5, 6455.5, 6914.0, 3256.7 were selected as potential biomarkers. The detective model combined with 2 biomarkers could separate nephroblastoma from the healthy group with a sensitivity of 100%, and a specificity of 100%. The diagnostic model combined with 2 biomarkers could separate nephroblastoma from other child's abdominal solid tumors with a sensitivity of 93.3%, and a specificity of 100%. CONCLUSION: High sensitivity and specificity achieved by this method show great potential for early diagnosis of nephroblastoma, and screening for new tumor biomarkers.

[Application of serum protein fingerprint model and support vector machine in diagnosis of thyroid cancer].

OBJECTIVE: To detect new biomarkers and to establish a serum protein fingerprint model for early detection and diagnosis of thyroid cancer. METHODS: The serum samples of 40 thyroid cancer patients, 9 thyroid adenoma patient, and 32 healthy individuals were randomly divided into 2 sets: training set (n = 66, including 32 thyroid cancer patients, 9 thyroid adenoma patients, and 25 healthy individuals) and test set (n = 15). The serum protein were bound to WCX2 chip and tested by surface enhanced laser desorption/ionization time of flight-mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by support vector machine (SVM) to establish a diagnostic model. RESULTS: The detective model combined with 3 biomarkers could differentiate the serum of thyroid cancer from that of healthy individual with a specificity of 86% and a sensitivity of 100%. The diagnostic model combined with 3 biomarkers could differentiate thyroid cancer from thyroid adenoma with a specificity of 88.9% and a sensitivity of 96.9%. The positive predictive value to differentiate papillary thyroid carcinoma from the thyroid cancer of other types was 97%, and the positive predictive value of thyroid carcinoma of other pathological types was 71%. CONCLUSION: The combination of SELDI with bioinformatics tools is a novel, effective, and highly specific and sensitive method for thyroid cancer detection and diagnosis.

Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population.

PURPOSE: The low specificity and sensitivity of the carcinoembryonic antigen test makes it not an ideal biomarker for the detection of colorectal cancer. We developed and evaluated a proteomic approach for the simultaneous detection and analysis of multiple proteins for distinguishing individuals with colorectal cancer from healthy individuals. EXPERIMENTAL DESIGN: We subjected serum samples (including 55 colorectal cancer patients and 92 age- and sex-matched healthy individuals) from 147 individuals, for analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Peaks were detected with Ciphergen SELDI software version 3.0. Using a multilayer artificial neural network with a back propagation algorithm, we developed a classifier for separating the colorectal cancer groups from the healthy groups. RESULTS: The artificial neural network classifier separated the colorectal cancer from the healthy samples, with a sensitivity of 91% and specificity of 93%. Four top-scored peaks, at m/z of 5,911, 8,930, 8,817, and 4,476, were finally selected as the potential "fingerprints" for detection of colorectal cancer. CONCLUSIONS: The combination of SELDI-TOF mass spectrometry with the artificial neural networks in the analysis of serum protein yields significantly higher sensitivity and specificity values for the detection and diagnosis of colorectal cancer.

Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis. Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and specificity for hepatocellular carcinoma at an early stage. In this study, the serum proteins in patients with hepatocellular carcinoma or liver cirrhosis and in normal controls were analysed. Surface enhanced laser desorption/ionization time-of-flight mass (SELDI-TOF-MS) spectrometry was used to fingerprint serum protein using the protein chip technique and explore the value of the fingerprint, coupled with artificial neural network, to diagnose hepatocellular carcinoma. METHODS: Of the 106 serum samples obtained, 52 were from patients with hepatocellular carcinoma, 22 from patients with liver cirrhosis and 32 from healthy volunteers. The samples were randomly assigned into a training group (n = 70, 35 patients with hepatocellular carcinoma, 14 with liver cirrhosis, and 21 normal controls) and a testing group (n = 36, 17 patients with hepatocellular carcinoma, 8 with liver cirrhosis, and 11 normal controls). An artificial neural network was trained on data from 70 individuals in the training group to develop an artificial neural network diagnostic model and this model was tested. The 36 sera in the testing group were analysed with blind prediction by using the same flowchart and procedure of data collection. The 36 serum protein spectra were clustered with the preset clustering method and the same mass/charge (M/Z) peak values as those in the training group. Matrix transfer was performed after data were output. Then the data were input into the previously built artificial neural network model to get the prediction value. The M/Z peaks of the samples with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3.1 for noise filtering. The first threshold for noise filtering was set at 5, and the second was set at 2. The 10% was the minimum threshold for clustering. The statistical analysis of the data of serum protein mass spectrum was performed in the groups (normal vs. hepatocellular carcinoma, and liver cirrhosis vs. hepatocellular carcinoma) with the t test. RESULTS: Comparison between the groups of hepatocellular carcinoma and normal control: The mass spectra from 56 samples (hepatocellular carcinoma and normal controls) in the training group were analysed and 241 peaks were obtained. In addition, 21 peaks from them were used for comparison between the groups of hepatocellular carcinoma and normal controls (P < 0.01). Only 2 peaks at 3015 M/Z and 5900 M/Z were selected with significant difference (P < 10 (-9)). A model was developed based on these two proteins with different M/Z. It was confirmed that this artificial neural network model can be used for comparison between the groups of hepatocellular carcinoma and normal controls. The sensitivity was 100% (17/17), and the specificity was 100% (11/11). Comparison between the groups of hepatocellular carcinoma and liver cirrhosis: The mass spectra from 49 samples in the training group (including patients with hepatocellular carcinoma and liver cirrhosis) were analysed and 208 peaks were obtained. In addition, 21 peaks from them were used for comparison between the groups of hepatocellular carcinoma and liver cirrhosis (P < 0.01). Only 2 peaks at 7759 M/Z, 13134 M/Z were selected with significant difference (P < 10 (-9)). A model was developed based on these two proteins with different M/Z. It was confirmed that this artificial neural network model can be used for comparison between the groups of hepatocellular carcinoma and liver cirrhosis. The sensitivity was 88.2% (15/17), and the specificity was 100% (8/8). CONCLUSIONS: The specific biomarkers selected with the SELDI technology could be used for early diagnosis of hepatocellular carcinoma.