Proteiomic patterns for endometrial cancer using SELDI-TOF-MS

Serum samples from endometrial cancer (EC) patients and healthy females were analyzed using surface-enhanced laser desorption-ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to discover the potential diagnostic biomarker for detection of EC. A preliminary training set of spectra derived from 40 EC patients and 30 healthy women were used to develop a proteomic model that effectively discriminated cancer patients from healthy women. The training set had a specificity of 100% and sensitivity of 92.5% in the EC detection. A blind test set, including 20 new cancer cases and 10 healthy women, was used to validate the sensitivity and specificity of this multivariate model, which had a corresponding results of 60% in specificity and 75% in sensitivity, respectively. The combination of SELDI-TOF-MS with bioinformatics tools could help find new biomarkers and establish the detection of EC with high sensitivity and specificity.

Proteomic and metabolic prediction of response to therapy in gastric cancer

Several new treatment options for gastric cancer have been introduced but the prognosis of patients diagnosed with gastric cancer is still poor. Disease prognosis could be improved for high-risk individuals by implementing earlier screenings. Because many patients are asymptomatic during the early stages of gastric cancer,the diagnosis is often delayed and patients present with unresectable locally advanced or metastatic disease. Cytotoxic treatment has been shown to prolong survival in general,but not all patients are responders. The application of targeted therapies and multimodal treatment has improved prognosis for those with advanced disease.However,these new therapeutic strategies do not uniformly benefit all patients.Predicting whether patients will respond to specific therapies would be of particular value and would allow for stratifying patients for personalized treatment strategies.Metabolic imaging by positron emission tomography was the first technique with the potential to predict the response of esophagogastric cancer to neoadjuvant therapy.Exploring and validating tissue-based biomarkers are ongoing processes.In this review,we discuss the status of several targeted therapies for gastric cancer,as well as proteomic and metabolic methods for investigating biomarkers for therapy response prediction in gastric cancer.

Identification of differentially expressed proteins in human stage Ⅰ lung adenocarcinoma and tumor-adjacent tissues with two-dimension gel electrophoresis profiling

Objective： The aim of this study was to establish reproducible two-dimensional electrophoretic assay used for profiling and identification of differentially expressed proteins in human stage I lung adenocarcinoma and paired normal tumor-adjacent tissue. Methods： The proteins from 12 human stage I lung adenocarcinoma tissues and normal tumor-adjacent tissues were separated using isoelectric focusing electrophoresis （the first dimension） and the subsequent homogeneous SDS-polyacrylamide gel electrophoresis （SDS-PAGE） （the second dimension）. The differentially expressed proteins were determined with PDQuest image analysis software, and identified using matrix-assisted laser desorption/ionization time of flight mass spectrometry （MALDI-TOF-MS） and database searching. Results： The well-reproducible 2-DE gel patterns of human stage I lung adenocarcinoma and normal tumor-adjacent tissues were profiled and 26 differentially expressed proteins uncovered. Nine of these 26 protein spots were cut out from the preparation gels and determined with MALDI-TOF-MS. Searching against the protein database, four candidate proteins were identified. They were 60S acidic ribosomal protein P2, Cathepsin B1, Apolipoprotein A-I precursor, and La 4.1 protein. Conclusion： In this study, high reproducible 2-DE gel protein images of human stage I lung adenocarcinoma and paired normal tumor-adjacent tissues were achieved successfully, and 4 differentially expressed proteins were revealed. These data will be helpful for screen of early biomarker and study of molecular mechanisms of human lung adenocarcinoma.

Valproic acid alters differential protein expression in SH-SY5Y cells

This study sought to identify differentially expressed proteins in SH-SY5Y cells treated with valproic acid, using two-dimensional difference gel electrophoresis analysis. Three proteins were unambi-guously identified： the eukaryotic translation initiation factor 4A isoform 1 and ATP6V1B2 protein were downregulated, while the heterogeneous nuclear ribonucleoprotein K was upregulated. Moreover, all three proteins are associated with altered expression due to oxidative stress. Ma-trix-assisted laser desorption/ionization-time of flight mass spectrometry and protein immunoblotting assay confirmed the differential expression of eukaryotic translation initiation factor 4A isoform 1. The results indicate that valproic acid exerts an antioxidation effect by regulating the expression of eukaryotic translation initiation factor 4A isoform 1.

Applied research on serum protein fingerprints for prediction of Qi deficiency syndrome and phlegm and blood stasis in patients with non-small cell lung cancer

OBJECTIVE:This study screened serum tumor biomarkers by surface enhanced laser desorption/ionization time-of-flight mass spectrometry(SELDI-TOF-MS) to establish a subset which could be used for the prediction of Qi deficiency syndrome and phlegm and blood stasis in patients with non-small cell lung cancer;and as diagnostic model of Chinese medicine.METHODS:Serum samples from 63 lung cancer patients with Qi deficiency syndrome and phlegm and blood stasis,and 28 lung cancer patients with non-Qi deficiency syndrome and phlegm and blood stasis were analyzed using SELDI-TOF-MS with a PBS II-C protein chip reader.Protein profiles were generated using immobilized metal affinity capture(IMAC3) protein chips.Differentially-expressed proteins were screened.Protein peak clustering and classification analyses were performed using Biomarker Wizard and Biomarker Pattern software packages,respectively.RESULTS:A total of 268 effective protein peaks were detected in the 1,000-10,000 Da molecular range for the 15 serum proteins screened(P<0.05).The decision tree model was M 2284.97,with a sensitivity of 96.2% and a specificity of 66.7%.CONCLUSION:SELDI-TOF-MS techniques,combined with a decision tree model,can help identify serum proteomic biomarkers related to Qi deficiency syndrome and phlegm and blood stasis in lung cancer patients;and the predictive model can be used to discriminate between Chinese medicine diagnostic models of disease.