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.

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.

Combining proteomics, serum biomarkers and bioinformatics to discriminate between esophageal squamous cell carcinoma and pre-cancerous lesion.

OBJECTIVE: Biomarker assay is a noninvasive method for the early detection of esophageal squamous cell carcinoma (ESCC). Searching for new biomarkers with high specificity and sensitivity is very important for the early detection of ESCC. Serum surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS) is a high throughput technology for identifying cancer biomarkers using drops of sera. METHODS: In this study, 185 serum samples were taken from ESCC patients in a high incidence area and screened by SELDI. A support vector machine (SVM) algorithm was adopted to analyze the samples. RESULTS: The SVM patterns successfully distinguished ESCC from pre-cancerous lesions (PCLs). Also, types of PCL, including dysplasia (DYS) and basal cell hyperplasia (BCH), and healthy controls (HC) were distinguished with an accuracy of 95.2% (DYS), 96.6% (BCH), and 93.8% (HC), respectively. A marker of 25.1 kDa was identified in the ESCC patterns whose peak intensity was observed to increase significantly during the development of esophageal carcinogenesis, and to decrease obviously after surgery. CONCLUSIONS: We selected five ESCC biomarkers to form a diagnostic pattern which can discriminate among the different stages of esophageal carcinogenesis. This pattern can significantly improve the detection of ESCC.

Capture, enrichment, and mass spectrometric detection of low-molecular-weight biomarkers with nanoporous silicon microparticles.

Mining the disease information contained in the low-molecular-weight range of a proteomic profile is becoming of increasing interest in cancer research. This work evaluates the ability of nanoporous silicon microparticles (NPSMPs) to capture, enrich, protect, and detect low-molecular-weight peptides (LMWPs) sieved from a pool of highly abundant plasma proteins. The average pore size and porosity of NPSMPs are controlled by the electrochemical preparation conditions, and the critical parameters for admission or exclusion of protein with a definite molecular weight are determined by reflectometric-interference Fourier transform spectroscopy (RIFTS). Sodium dodecyl sulfate polyacrylamide-gel electrophoresis (SDS-PAGE) and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) analysis of the proteins captured by the NPSMPs show that the chemical nature of the NPSMPs surface and the solution pH also play vital roles in determining the affinity of NPSMPs for target analytes. It is found that carboxyl-terminated porous microparticles with a porosity of 26% (pore diameter around 9.0 nm) specifically fractionate, enrich and protect LMWPs sieved from either simulated samples or human serum samples. Moreover, NPSMPs containing captured peptides can be directly spotted onto a MALDI plate. When placed in a conventional MALDI matrix, laser irradiation of the particles results in the release of the target peptides confined in the nanopores, which are then ionized and detected in the MALDI experiment. As a proof-of-principle test case, mass spectra of NPSMPs prepared using serum from colorectal cancer patients and from control patients can be clearly distinguished by statistical analysis. The work demonstrates the utility of the method for discovery of biomarkers in the untapped LMWP fraction of human serum, which can be of significant value in the early diagnosis and management of diseases.

[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.

Identification of a new protein biomarker for colorectal cancer diagnosis.

As one of the most common cancers, colorectal cancer (CRC) is a major public health issue worldwide. Thus, the identification of novel biomarkers to aid in the early diagnosis of CRC is crucial. The aim of the present study was to identify a novel protein biomarker for CRC, and to identify its structure. In this study, a total of 99 serum samples from 73 CRC patients and 26 healthy controls were collected and analyzed by SELDI-TOF-MS. The biomarkers were separated using HPLC and detected with MALDI-TOF-MS. The qualified peaks were ranked by p-value of non-parametric tests and the top 10 peaks displaying significant differences were selected. Among the 10 protein biomarkers, the concentration of a 3.9­kDa protein in the serum of the CRC patients was much lower than that in the healthy controls. Therefore, the 3.9­kDa protein was selected as a biomarker for CRC and its separation and purification were performed. The structure of the 3.9-kDa protein biomarker was determined by LC-MS/MS, and was confirmed to be a fragment of serine/theonine kinase 4 (MST1/STK4). The 3.9­kDa protein biomarker had high sensitivity and specificity for CRC, and its potential clinical application warrants further investigation.

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.

Proteomic analysis of primary colon cancer-associated fibroblasts using the SELDI-ProteinChip platform.

OBJECTIVE: Cancer-associated fibroblasts (CAFs) are one of the hallmarks of the cancer microenvironment. Recent evidence has indicated that CAFs are more competent in enhancing cancer cell growth and migration than normal fibroblasts. However, the unique protein expression of CAFs has not been fully elucidated. This study aims to investigate the characterizations of colon CAFs by comparing the differential protein expression between CAFs and normal fibroblasts. METHODS: Primary fibroblasts were isolated from surgical specimen of human colon cancer and matched normal colonic tissue. Purity of the cell population was verified through immunostain analysis. Total cell lysates and conditioned media from each group of cells were extracted, and protein expression analysis was conducted using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) ProteinChip platform. RESULTS: Most primary cells showed typical fibroblast-like features after two weeks. Increased proportion of α-smooth muscle actin-positive myofibroblasts was detected within the CAFs in four of the six pairs of primary cells. Fibroblast activation protein was weakly expressed in most cells without differences. Using SELDI-TOF-MS ProteinChip platform, four protein peaks mass over charge ratio (m/z) 1142, 3011, 4035, and 4945 were detected in the total cell lysates, and two protein peaks m/z 1368 and 1389 were detected in the conditioned media. The potential candidate proteins found in the Swiss-Prot database include morphogenetic neuropeptides, FMRFamide-related peptides, insulin-like growth factor II, thymosin ß-4-like protein 3, and tight junction-associated protein 1. CONCLUSIONS: Using the SELDI-ProteinChip platform, differential protein expressions were identified in colon CAFs compared with normal colonic stromal fibroblasts. The complex proteomic alternations in colon CAFs may play important roles related to the colon cancer microenvironment.

Apoptosis of non-tumor cells contributes to increased serum cytochrome c level in a neuroblastoma xenograft model.

BACKGROUND: Neuroblastoma (NB) is one of the most common malignant solid tumors of childhood. It is still not clear whether the apoptosis of tumor cells or the non-tumor cells contributes to the increase of concentration of cytochrome c (Cyt c) in the serum of the cancer patients. The aim of this research was to identify the source of the Cyt c in the serum when the tumor grows up by subcutaneous inoculation of human NB cells into nude mice. METHODS: We subcutaneously inoculated human NB cells (KP-N-NS) into nude mice and collected the sera of tumor-bearing mice (n = 14) and control mice (n = 25) 4 weeks later in order to screen for and identify differentially expressed proteins in the serum. Differentially expressed proteins in the serum were screened by surface-enhanced laser desorption/ionization-time-of-flight (SELDI-TOF) mass spectrometry. RESULTS: The relative intensity of a protein having a mass-to-charge ratio (m/z) of 11 609 was 3338.37 ± 3410.85 in the tumor group and 59.84 ± 40.74 in the control group, indicating that the expression level of this protein in the tumor group was 55.8 times higher than that in the control group. Serum proteins were separated and purified by high-performance liquid chromatography (HPLC). Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was performed to produce peptide mass fingerprints (PMFs). Spectrum analysis and a database search revealed that the highly expressed protein (m/z = 11 605.4) from the serum of tumor-bearing mice was the mouse Cyt c. CONCLUSIONS: Increased concentration of Cyt c in the serum of tumor-bearing nude mice might be partially attributed to the secretion of this protein by non-tumor cells.

SPARCL1, Shp2, MSH2, E-cadherin, p53, ADCY-2 and MAPK are prognosis-related in colorectal cancer.

AIM: To investigate the expression of markers that are correlated with the prognosis of colorectal cancer (CRC) patients. METHODS: One hundred and fifty-six CRC patients were followed up for more than 3 years after radical surgery. Immunohistochemical (IHC) analysis was performed to detect the expression of 14 pathway-related markers (p53, APC, p21ras, E-cadherin, endothelin-B receptor, Shp2, ADCY-2, SPARCL1, neuroligin1, hsp27, mmp-9, MAPK, MSH2 and rho) in specimens from these patients. Bioinformatics analysis involving a Support Vector Machine (SVM) was used to determine the best prognostic model from combinations of these markers. RESULTS: Seven markers (SPARCL1, Shp2, MSH2, E-cadherin, p53, ADCY-2 and MAPK) were significantly related to the prognosis and clinical pathological features of the CRC patients (P < 0.05). Prognostic models were established through SVM from combinations of these 7 markers and proved able to differentiate patients with dissimilar survival, especially in stage II/III patients. According to the best prognostic model, the p53/SPARCL1 model, patients having high p53 and low SPARCL1 expression had about 50% lower 3-year survival than others (P < 0.001). CONCLUSION: SPARCL1, Shp2, MSH2, E-cadherin, p53, ADCY-2 and MAPK are potential prognostic markers in CRC. A p53/SPARCL1 bioinformatics model may be used as a supplement to tumor-nodes-metastasis staging.

Analysis of urinary proteomic patterns for type 2 diabetic nephropathy by ProteinChip.

OBJECTIVE: To detect urinary proteomic profiling of patients with type 2 diabetes by using ProteinChip array technology, for searching new potential biomarkers in early diagnosis of type 2 diabetic nephropathy (T2DN). METHODS: A total of 95 urine samples from type 2 diabetic patients with normoalbuminuria (DM, n=30), microalbuminuria (DNl, n=25) and macroalbuminuria (DN2, n=20), and healthy controls (n=20) were analyzed by SELDI-TOF-MS (the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry) technology combined with bioinformatics tools. RESULTS: Over 300 proteins or peptides from 1 to 80 kDa were obtained using ProteinChip. About 40 of them with the m/z values from 2008.78 to 79176.55 Da were significantly differentiated between type 2 diabetic patients and control subjects. Four proteins of mass 2797.03, 4545.77, 4984.03 and 9083.71 Da were selected as the potential biomarkers for T2DN with sensitivity of 88% and specificity of 96.7%. CONCLUSION: ProteinChip technology can help to discover new biomarkers and provide a novel non-invasive tool to early diagnosis of T2DN.

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.

Identification biomarkers of eutopic endometrium in endometriosis using artificial neural networks and protein fingerprinting.

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein chip array technology was used to detect biomarkers of eutopic endometrium in endometriosis patients. Five potential biomarkers (6,898 m/z, 5,891 m/z, 5,385 m/z, 6,448 m/z, and 5,425 m/z) were found.

Mining novel biomarkers for prognosis of gastric cancer with serum proteomics.

BACKGROUND: Although gastric cancer (GC) remains the second cause of cancer-related death, useful biomarkers for prognosis are still unavailable. We present here the attempt of mining novel biomarkers for GC prognosis by using serum proteomics. METHODS: Sera from 43 GC patients and 41 controls with gastritis as Group 1 and 11 GC patients as Group 2 was successively detected by Surface Enhanced Laser Desorption/ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) with Q10 chip. Peaks were acquired by Ciphergen ProteinChip Software 3.2.0 and analyzed by Zhejiang University-ProteinChip Data Analysis System (ZJU-PDAS). CEA level were evaluated by chemiluminescence immunoassay. RESULTS: After median follow-up periods of 33 months, Group 1 with 4 GC patients lost was divided into 20 good-prognosis GC patients (overall survival more than 24 months) and 19 poor-prognosis GC patients (no more than 24 months). The established prognosis pattern consisted of 5 novel prognosis biomarkers with 84.2% sensitivity and 85.0% specificity, which were significantly higher than those of carcinoembryonic antigen (CEA) and TNM stage. We also tested prognosis pattern blindly in Group 2 with 66.7% sensitivity and 80.0% specificity. Moreover, we found that 4474-Da peak elevated significantly in GC and was associated with advanced stage (III+IV) and short survival (p < 0.03). CONCLUSION: We have identified a number of novel biomarkers for prognosis prediction of GC by using SELDI-TOF-MS combined with sophisticated bioinformatics. Particularly, elevated expression of 4474-Da peak showed very promising to be developed into a novel biomarker associated with biologically aggressive features of GC.

[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.

[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.