Discovery of laryngeal carcinoma by serum proteomic pattern analysis

Laryngeal carcinoma is the most common malignancy among head and neck tumors. The purpose of this study is to find biomarkers for laryngeal carcinoma in patient blood serum using the Surface Enhanced Laser Desorption/Ionization (SELDI) technique. Serum samples from 33 laryngeal carcinoma (12 cases of glottis, 18 of supraglottis and 3 of subglottis) patients and 31 age- and sex-matched healthy people were analyzed by SELDI-TOF on a ProteinChip reader, PBSII-C. Protein profiles were generated using WCX2 protein chips. Protein peak clustering and classification analyses were performed utilizing the Biomarker Wizard and Biomarker Pattern software packages, respectively. The results showed that sixteen peaks had significant difference between laryngeal cancer patients and healthy group, eight of which were up-regulated in the patient samples, and the others were down-regulated. Two protein peaks 8153 Da and 2035 Da were automatically chosen for the system training and development of a classification tree. The analysis yielded a correct percentage of 96.9% for patients and 96.7% for control. The results suggest that serum is a useful resource for the detection of specific biomarkers for laryngeal carcinoma. Proteinchip Array System was a useful tool for a high throughput screening of large-sized serum samples to discover potential biomarkers for carcinoma.     

Diagnostic application of serum proteomic patterns in gastric cancer patients by ProteinChip surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one of thecurrently used techniques to identify biomarkers for cancers. This study was planned to establish a system to accurately distinguish gastric cancer patients by using SELDI-TOF-MS. A total of 100 serum samples obtained from 60 individuals with gastric cancer and 40 healthy individuals were screened. Protein expression profiles were expressed on CM10 ProteinChip arrays and analyzed. Peak intensities were analyzed with the Biomarker Wizard software to identify peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern software 5.0. Seventeen protein peaks showed significant differences between the two groups. The decision tree which gave the highest discrimination included four peaks at mass 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and specificity for classification of the decision tree were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested on a blinded test set, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). These results suggest that serum protein profiling by the SELDI system may distinguish gastric cancer patients from healthy controls with relatively high sensitivity and specificity.     

SELDI-TOF-MS筛选慢性阻塞性肺疾病血清标志蛋白的初步研究

目的:利用表面增强激光解吸电离飞行时间质谱技术(SELDI-TOF-MS)筛选慢性阻塞性肺疾病(COPD)血清特异标志物。方法:应用SELDI-TOF-MS技术检测30例COPD稳定期患者和30例健康对照者血清蛋白指纹图谱,采用Biomarker pattern软件进行分析,建立COPD的诊断模型。结果:COPD患者血清蛋白图谱与对照组相比,在相对分子质量2000-15 000范围内共检测到75个蛋白峰,发现19个有统计学差异的蛋白峰(P0.05)。通过对COPD组与对照组间的数据作进一步分析,经BPS软件分析,建立质荷比(M/Z)3 167、4 645的差异蛋白组成的诊断模型,其诊断敏感度为96.67%,特异度为96.67%。结论:SELDI-TOF-MS技术是一种快速、简单易行、用量少和高通量的分析方法。能直接筛选出COPD血清中特异表达标志物,用特异表达标志物建立的诊断模型能有效区分COPD患者与健康对照者,有望成为COPD诊断的辅助指标。     

Proteomic approaches to biomarker discovery in lung cancers by SELDI technology

Lung cancer is one of the most common tumors all over the world and one of those with higher mortality in clinic. For instance, 169500 new cases of lung cancer were estimated in the United States for 2001[1]. In recent years, both morbidity and mortality of lung cancer were reported gradually increasing in our country. Therefore, it has become an urgent task to search and discover specific biomarkers for lung cancer. In tumor genesis, certain cellular proteins must have changed their express…     

Proteomic approaches to biomarker discovery in lung cancers by SELDI technology

The purpose of the present work is to identify protein profiles that could be used to discover specific biomarkers in serum and discriminate lung cancer. Thirty serum samples from patients with lung cancer (15 cases of primary brochogenic carcinoma, 9 cases of metastasis lung cancer and 6 cases of lung cancer after chemotherapy) and twelve from healthy individuals were analyzed by SELDI (Surfaced Enhanced Laser Desorption/Ionization) technology. Anion-exchange columns were used to fractionate the sera with 6 designated pH washing solutions. Two types of protein chip arrays, IMAC-Cu and WCX2, were employed. Protein chips were examined in PBSII ProteinChip Reader (Ciphergen Biosystems Inc.) and the resulting profiles between cancer and normal were analyzed with Biomarker Wizard System. In total, 15 potential lung cancer biomarkers, of which 6 were up-regulated and 9 were down-regulated, were discovered in the serum samples from patients with lung cancer. 5 of 15 these biomarkers were able to be detected on both WCX2 and IMAC-Cu protein chips. The sensitivities provided by the individual markers range from 44.8% to 93.1% and the specificities were 85.0%–94.4%. Our results suggest that serum is a capable resource for detection of lung cancer with specific biomarkers. Moreover, protein chip array system was shown to be a useful tool for identification, as well as detection of disease biomarkers in sera.     

Evaluation of proteomics-identified CCL18 and CXCL1 as circulating tumor markers for differential diagnosis between ovarian carcinomas and benign pelvic masses

A lack of sensitive and specific tumor markers for early diagnosis and treatment is a major cause for the high mortality rate of ovarian cancer. The purpose of this study was to identify potential proteomics-based biomarkers useful for the differential diagnosis between ovarian cancer and benign pelvic masses. Serum samples from 41 patients with ovarian cancer, 32 patients with benign pelvic masses, and 41 healthy female blood donors were examined, and proteomic profiling of the samples was assessed by surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy (MS). A confirmatory study was also conducted with serum specimens from 58 patients with ovarian carcinoma, 37 patients with benign pelvic masses, and 48 healthy women. A classification tree was established using Biomarker Pattern Software. Six differentially expressed proteins (APP, CA 125, CCL18, CXCL1, IL-8, and ITIH4) were separated by high-performance liquid chromatography and identified by matrix-assisted laser desorption/ionization (MALDI)-MS/MS and database searches. Two of the proteins overexpressed in ovarian cancer patients, chemokine CC2 motif ligand 18 (CCL18) and chemokine CXC motif ligand 1 (CXCL1), were automatically selected in a multivariate predictive model. These two protein biomarkers were then validated and evaluated by enzyme-linked immunosorbent assay (ELISA) in 535 serum specimens (130 ovarian cancer, 64 benign ovarian masses, 36 lung cancer, 60 gastric cancer, 55 nasopharyngeal carcinoma, 48 hepatocellular carcinoma, and 142 healthy women). The combined use of CCL18 and CXCL1 as biomarkers for ovarian cancer had a sensitivity of 92% and a specificity of 97%. The multivariate ELISA analysis of the two putative markers in combination with CA 125 resulted in a sensitivity of 99% for healthy women and 94% for benign pelvic masses, and a specificity of 92% for both groups; these values were significantly higher than those obtained with CA 125 alone (p and lt;0.05). We conclude that serum CCL18 and CXCL1 are potentially useful as novel circulating tumor markers for the differential diagnosis between ovarian cancer and benign ovarian masses.     

Identification of complement C3a as a candidate biomarker in human chronic hepatitis C and HCV-related hepatocellular carcinoma using a proteomics approach

Although the significant risk factors for hepatocellular carcinoma (HCC) are well known from epidemiological studies, diagnosis of this disease at an early stage is difficult, and HCC remains one of the leading causes of cancer death worldwide. Thus, to identify any useful HCC-related biomarkers is still a need. We performed SELDI-TOF MS to identify differentially expressed proteins in HCC serum using weak cation exchange protein chips. Protein characterization was performed by 2-DE separation and nano flow LC-MS/MS. A total of 55 sera were collected from HCC patients and compared with those from 48 patients with chronic hepatitis and 9 healthy individuals. A candidate marker of about 8900 Da was detected as differentially expressed in patients with chronic hepatitis C and hepatitis C virus (HCV)-related HCC. We identified this differentially expressed protein as complement C3a. The expression of C3a in HCC sera was further validated by PS20 chip immunoassay and Western blotting. Complement C3a was found to be elevated in patients with chronic hepatitis C and HCV-related HCC. The combination of SELDI-TOF MS and 2-DE provides a solution to identify disease-associated serum biomarkers.     

Predicting Early Intrahepatic Recurrence of Hepatocellular Carcinoma after Microwave Ablation Using SELDI-TOF Proteomic Signature

Background/Aims

Despite great progress in the treatment of hepatocellular carcinoma (HCC) over the last-decade, intrahepatic recurrence is still the most frequent serious adverse event after all the treatments including microwave ablation. This study aimed to predict early recurrence of HCC after microwave ablation using serum proteomic signature.

Methods

After curative microwave ablation of HCC, 86 patients were followed-up for 1 year. Serum samples were collected before microwave ablation. The mass spectra of proteins were generated using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Serum samples from 50 patients were randomly selected as a training set and for biomarkers discovery and model development. The remaining serum samples were categorized for validation of the algorithm.

Results

According to preablation serum protein profiling obtained from the 50 HCC samples in the training set, nine significant differentially-expressed proteins were detected in the serum samples between recurrent and non-recurrent patients. Decision classification tree combined with three candidate proteins with m/z values of 7787, 6858 and 6646 was produced using Biomarker Patterns Software with sensitivity of 85.7% and specificity of 88.9% in the training set. When the SELDI marker pattern was tested with the blinded testing set, it yielded a sensitivity of 80.0%, a specificity of 88.5% and a positive predictive value of 86.1%.

Conclusions

Differentially-expressed protein peaks in preablation serum screened by SELDI are associated with prognosis of HCC. The decision classification tree is a potential tool in predicting early intrahepatic recurrence in HCC patients after microwave ablation.     

Diagnostic model of saliva peptide finger print analysis of oral squamous cell carcinoma patients using weak cation exchange magnetic beads

Saliva diagnostics utilizing nanotechnology and molecular technologies to detect oral squamous cell carcinoma (OSCC) has become an attractive field of study. However, no specific methods have been established. To refine the diagnostic power of saliva peptide fingerprints for the early detection of OSCC, we screened the expression spectrum of salivary peptides in 40 T1 stage OSCC patients (and healthy controls) using MALDI-TOF-MS combined with magnetic beads. Fifty proteins showed significantly different expression levels in the OSCC samples (P<0.05). Potential biomarkers were also predicted. The novel diagnostic proteomic model with m/z peaks of 1285.6 Da and 1432.2 Da are of certain value for early diagnosis of OSCC.     

Diagnostic model of saliva peptide finger print analysis of primary Sj?gren's syndrome patients by using weak cation exchange magnetic beads

Saliva diagnostics has become an attractive field utilizing nanotechnology and molecular technologies for pSS (primary Sjögren''s syndrome). However, no specific methods have been established. To refine the diagnostic power of the saliva peptide finger print for the early detection of pSS, we screened the expression spectrum of salivary peptides in pSS patients by using mass spectrometry MALDI-TOF-MS (matrix-assisted laser-desorption ionization-time-of-flight MS) combined with magnetic bead. The present study was comprised 12 pSS patients and 13 healthy controls and broken down to two different phases. In the initial ‘exploratory phase’, we enrolled seven pSS patients with eight age- and sex-matched healthy volunteers. Proteomics analysis of the unstimulated salivary samples was conducted to generate proportional peptide mass fingerprints. A diagnostic model was established. The testing cohort of the second ‘validation phase’ was represented by five pSS patients and five age- and sex-matched healthy controls. The diagnostic power of this diagnostic panel was then validated. The results showed seven m/z (mass-to-charge) ratio peaks with significant differences. Five peptides were up-regulated and two down-regulated in the pSS patients compared with matched healthy subjects. In the validation phase, four out of five pSS patients were diagnosed as pSS, and four of the five healthy controls were diagnosed as healthy controls, respectively. Potential biomarkers were also primarily predicted. The novel diagnostic proteomic model with m/z peaks 1068.1 Da, 1196.2 Da, 1738.4 Da, 3375.3 Da, 3429.3 Da, 3449.7 Da and 3490.6 Da is of certain value for early diagnosis of pSS.     

Enriched sera protein profiling for detection of non-small cell lung cancer biomarkers

Background

Non Small Cell Lung Cancer (NSCLC) is the major cause of cancer related-death. Many patients receive diagnosis at advanced stage leading to a poor prognosis. At present, no satisfactory screening tests are available in clinical practice and the discovery and validation of new biomarkers is mandatory. Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-ToF-MS) is a recent high-throughput technique used to detect new tumour markers. In this study we performed SELDI-ToF-MS analysis on serum samples treated with the ProteoMiner? kit, a combinatorial library of hexapeptide ligands coupled to beads, to reduce the wide dynamic range of protein concentration in the sample. Serum from 44 NSCLC patients and 19 healthy controls were analyzed with IMAC30-Cu and H50 ProteinChip Arrays.

Results

Comparing SELDI-ToF-MS protein profiles of NSCLC patients and healthy controls, 28 protein peaks were found significantly different (p < 0.05), and were used as predictors to build decision classification trees. This statistical analysis selected 10 protein peaks in the low-mass range (2-24 kDa) and 6 in the high-mass range (40-80 kDa). The classification models for the low-mass range had a sensitivity and specificity of 70.45% (31/44) and 68.42% (13/19) for IMAC30-Cu, and 72.73% (32/44) and 73.68% (14/19) for H50 ProteinChip Arrays.

Conclusions

These preliminary results suggest that SELDI-ToF-MS protein profiling of serum samples pretreated with ProteoMiner? can improve the discovery of protein peaks differentially expressed between NSCLC patients and healthy subjects, useful to build classification algorithms with high sensitivity and specificity. However, identification of the significantly different protein peaks needs further study in order to provide a better understanding of the biological nature of these potential biomarkers and their role in the underlying disease process.     

Identification of potential serum biomarkers for Wilms tumor after excluding confounding effects of common systemic inflammatory factors

Wilms tumor is the most common pediatric tumor of the kidney. Previous studies have identified several serum biomarkers for Wilms tumor; however, they lack sufficient specificity and may not adequately distinguish Wilms tumor from confounding conditions. To date, no specific protein biomarker has been confirmed for this pediatric tumor. To identify novel serum biomarkers for Wilms tumor, we used proteomic technologies to perform protein profiling of serum samples from pre-surgery and post-surgery patients with Wilms tumor and healthy controls. Some common systemic inflammatory factors were included to control for systemic inflammation. By comparing protein peaks among the three groups of sera, we identified two peaks (11,526 and 4,756 Da) showing significant differential expression not only between pre-surgery and control sera but also between pre-surgery and post-surgery sera. These two peaks were identified as serum amyloid A1 (SAA1) and apolipoprotein C-III (APO C-III). Western blot analysis confirmed that both proteins were expressed at higher levels in pre-surgery sera than in post-surgery and control sera. Using the method of leave-1-out for cross detection, we demonstrate that detection of these two candidate biomarkers had high sensitivity and specificity in discriminating pre-surgery sera from post-surgery and normal control sera. Taken together, these findings suggest that SAA1 and APO C-III are two potential biomarkers for Wilms tumor.     

Proteomic technologies for identification of serum potential biomarkers of autoimmune demyelinating polyneuropathies

Time-of-flight MALDI mass spectrometry (MALDI-TOF-MS) profiling of blood serum of patients with Guillain-Barré syndrome (GBS, 36 samples), chronic inflammatory demyelinating polyneuropathy (CIDP, 24 samples) and practically healthy donors (HD) (35 samples) was carried out in order to identify potential biomarkers of autoimmune demyelinating polyneuropathies (ADP). To simplify the peptide-protein mixture of serum prior to MALDI-TOF-MS analysis samples were pre-fractionated on magnetic microparticles with a weak cation-exchange (MB-WCX) surface. Comparative analysis of mass spectrometric data using the classification algorithms (genetic and neural network-controlled) revealed a characteristic set of peaks, agreed change area with a high specificity and sensitivity of the differentiated mass spectrometry profiles of the blood serum of patients with DPNP and healthy donors (for GBS values of these characteristics reached 100 and 100, and for CIDP 94.1 and 100% respectively). Comparative analysis of mass spectrometric profiles of serum samples obtained from patients with GBS and CIDP, allowed to build a classification model to differentiate these diseases from each other, with a specificity of 88.9 and a sensitivity of 80%.     

Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons

Despite having a relatively low incidence, renal cell carcinoma (RCC) is one of the most lethal urologic cancers. For successful treatment including surgery, early detection is essential. Currently there is no screening method such as biomarker assays for early diagnosis of RCC. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF) is a recent technical advance that can be used to identify biomarkers for cancers. In this study, we investigated whether SELDI protein profiling and artificial intelligence analysis of serum could distinguish RCC from healthy persons and other urologic diseases (nonRCC). The SELDI-TOF data was acquired from a total of 36 serum samples with weak cation exchange-2 protein chip arrays and filtered using ProteinChip software. We used a decision tree algorithm c4.5 to classify the three groups of sera. Five proteins were identified with masses of 3900, 4107, 4153, 5352, and 5987 Da. These biomarkers can correctly separate RCC from healthy and nonRCC samples.     

Assessing the utility of SELDI-TOF and model averaging for serum proteomic biomarker discovery

The SELDI-TOF technique was used to profile serum proteins from Type 1 diabetes (T1D) patients and healthy autoantibody-negative (AbN) controls. Univariate and multivariate analyses were performed to identify putative biomarkers for T1D and to assess the reproducibility of the SELDI technique. We found 146 protein/peptide peaks (581 total peaks discovered) in human serum showing statistical differences in expression levels between T1D patients and controls, with 84% of these peaks showing technical replication. Because individual proteins did not offer great power for disease prediction, we used our model averaging approach that combines the information from multiple multivariate models to accurately classify T1D and control subjects (88.9% specificity and 90.0% sensitivity). Analyses of a test subset of the data showed less accuracy (82.8% specificity and 76.2% sensitivity), although the results are still positive. Unfortunately, no multivariate model could be replicated using the same samples. This first attempt of high throughput analyses of the human serum proteome in T1D patients suggests that model averaging is a viable method for developing biomarkers; however, the reproducibility of SELDI-TOF is currently not sufficient to be used for classification of complex diseases like T1D.     

New potential biomarkers in the diagnosis of esophageal squamous cell carcinoma

Objective: To analyse the alterations of serum proteins in cases of esophageal squamous cell carcinoma (ESCC) in order to screen and validate serum marker patterns for the diagnosis of ESCC in the high-risk populations of Xinjiang, China. Methods: The serum proteomic patterns of 188 cases, including 139 patients with ESCC (54 Uygur, 45 Kazakh and 40 Han subjects) and 49 sex- and age-matched healthy controls, were detected using the SELDI-TOF-MS (surface-enhanced laser desorption/ionization–time of flight–mass spectrometry) technology with the CM10 ProteinChip. Differences in protein peaks between patients with ESCC and controls were analysed using the Biomarker Pattern Software, and a primary diagnosis model of ESCC was developed and validated with SVM (support vector machines). This model was further evaluated by a large-scale blind test. Results: Two hundred and eighty-three protein peaks were detected within the molecular range of 0–20?kDa, among which, 140 peaks were significantly different between ESCC cases and controls (p?m/z 5667, 5709, 5876, 5979, 6043 and 6102) was established with a sensitivity of 97.12% and a specificity of 83.87%. The large-scale blind test generated a sensitivity of 91.43% and a specificity of 88.89%. Conclusions: The differential protein peaks analysed by SELDI-TOF-MS may contain promising serum biomarkers for screening ESCC. The diagnostic model which combined only six protein peaks had a satisfactory discriminatory power. The model should be further evaluated in other populations of ESCC patients and tested against controls. The nature and function of the discriminating proteins have yet to be elucidated.     

Toward the proteomic identification of biomarkers for the prediction of HBV related hepatocellular carcinoma

Early detection is a key step for effective intervention of hepatocellular carcinoma (HCC), the lack of sensitive and specific biomarkers is a major reason for the high rate of HCC-related mortality. This report described an integrated strategy by combining SELDI-ProteinChip, sophisticated algorithm analysis, acetonitrile (ACN) pre-treatment and two-dimensional electrophoresis (2DE)-peptide mass fingerprinting (PMF) techniques to identify serological markers for the prediction of HBV-related HCC. Proteomic profiling of three groups of serum specimens from HBV-related HCC (50 cases), HBV infection (45 cases), and normal subjects (30 cases) was conducted by using SELDI-ProteinChip system and the resulting different protein peaks were subjected to stepwise statistical analyses. Three most discriminatory peaks at 5890, 11615, and 11724 Da, respectively, were screened out from the statistical algorithm and a predictive model based on the three peaks was constructed and tested using the newly enrolled serum samples. 2DE was applied to separate and compare the serum samples that were pre-treated by ACN precipitation. The protein spots obviously intensified in HCC sera in the 2DE region of 12 kDa were identified by PMF to be serum SAA, which was validated by SELDI-TOF spectra of HCC sera after immunoprecipitation using anti-SAA antibody and by Western blot experiments. Given the fact that SAA is not a specific biomarker, further attempt is being made to identify the other two most discriminatory peaks to realize the possibility of using the predictive model for HCC surveillance and prediction.     

Proteomic technologies for identification of serum biomarkers of potential autoimmune demyelinating polyneuropathies

Time-of-flight MALDI mass spectrometry (MALDI-TOF MS) profiling of blood serum of patients with Guillain-Barré syndrome (GBS, 36 samples), chronic inflammatory demyelinating polyneuropathy (CIDP, 24 samples), and practically healthy donors (HD, 35 samples) was carried out in order to identify potential biomarkers of autoimmune demyelinating polyneuropathies (ADP). To simplify the peptide-protein mixture of serum prior to MALDI-TOF-MS analysis, samples were prefractionated on magnetic beads with a weak cation-exchange (MB-WCX) surface. Comparative analysis of mass spectrometric data using the classification algorithms (genetic and neural network-controlled) revealed a characteristic set of peaks and a correlating change area with a high specificity and sensitivity of the differentiated mass spectrometry profiles of the blood serum of patients with DPNP and healthy donors (for GBS, values of these characteristics reached 100 and 100%, and for CIDP, 94.1 and 100% respectively). Comparative analysis of mass spectrometric profiles of serum samples obtained from patients with GBS and CIDP allowed us to build a classification model to differentiate these diseases from each other, with a specificity of 88.9 and a sensitivity of 80%.     

SELDI-TOF-MS Proteomic Profiling of Serum,Urine, and Amniotic Fluid in Neural Tube Defects

Neural tube defects (NTDs) are common birth defects, whose specific biomarkers are needed. The purpose of this pilot study is to determine whether protein profiling in NTD-mothers differ from normal controls using SELDI-TOF-MS. ProteinChip Biomarker System was used to evaluate 82 maternal serum samples, 78 urine samples and 76 amniotic fluid samples. The validity of classification tree was then challenged with a blind test set including another 20 NTD-mothers and 18 controls in serum samples, and another 19 NTD-mothers and 17 controls in urine samples, and another 20 NTD-mothers and 17 controls in amniotic fluid samples. Eight proteins detected in serum samples were up-regulated and four proteins were down-regulated in the NTD group. Four proteins detected in urine samples were up-regulated and one protein was down-regulated in the NTD group. Six proteins detected in amniotic fluid samples were up-regulated and one protein was down-regulated in the NTD group. The classification tree for serum samples separated NTDs from healthy individuals, achieving a sensitivity of 91% and a specificity of 97% in the training set, and achieving a sensitivity of 90% and a specificity of 97% and a positive predictive value of 95% in the test set. The classification tree for urine samples separated NTDs from controls, achieving a sensitivity of 95% and a specificity of 94% in the training set, and achieving a sensitivity of 89% and a specificity of 82% and a positive predictive value of 85% in the test set. The classification tree for amniotic fluid samples separated NTDs from controls, achieving a sensitivity of 93% and a specificity of 89% in the training set, and achieving a sensitivity of 90% and a specificity of 88% and a positive predictive value of 90% in the test set. These suggest that SELDI-TOF-MS is an additional method for NTDs pregnancies detection.     

Discovery and identification of Serum Amyloid A protein elevated in lung cancer serum

Two hundred and eighteen serum samples from 175 lung cancer patients and 43 healthy individuals were analyzed by using Surface Enhaced Laser Desorption/Ionization Time of Flight Mass Spectrome-try (SELDI-TOF-MS). The data analyzed by both Biomarker Wizard™ and Biomarker Patterns™ software showed that a protein peak with the molecular weight of 11.6 kDa significantly increased in lung cancer. Meanwhile, the level of this biomarker was progressively increased with the clinical stages of lung cancer. The candidate biomarker was then obtained from tricine one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis by matching the molecular weight with peaks on WCX2 chips and was identified as Serum Amyloid A protein (SAA) by MALDI/MS-MS and database searching. It was further validated in the same serum samples by immunoprecipitation with commercial SAA antibody. To confirm the SAA differential expression in lung cancer patients, the same set of serum samples was measured by ELISA assay. The result showed that at the cutoff point 0.446 (OD value) on the Receiver Operating Characteristic (ROC) curve, SAA could better discriminate lung cancer from healthy individuals with sensitivity of 84.1% and specificity of 80%. These findings demonstrated that SAA could be characterized as a biomarker related to pathological stages of lung cancer.