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Objective: To screen out the differentially expressed proteins in saliva from breast cancer patients with white tongue coating and yellow tongue coating and subjects from healthy control group in order to find a biomarker for the early diagnosis of breast cancer and explore the mechanism and principle behind the formation of the tongue coating in traditional Chinese medicine (TCM). Methods: Breast cancer patients with thick white tongue coating and thick yellow tongue coating were enrolled, 10 patients for each group, and 10 normal individuals with thin white tongue coating were also enrolled. Proteins were extracted from the serum of the subjects, underwent denaturation, reduction and enzymolysis, labeled by isobaric tags for relative and absolute quantitation (iTRAQ) and identified by ESI-QTOF-Q II mass spectrometer. Peak spectrum were obtained, and analyzed by Data Analysis, MASCOTT 2.2 Searching and Scaffold. Then the results of differential expression were obtained. Results: There were 464 proteins identified, and 125 proteins met the strict quantitative criteria. Compared with heath control, 11 differentially expressed proteins were selected for breast cancer patients, with white tongue coating and yellow coating, while 26 differentially expressed proteins were selected between breast cancer with white tongue coating and yellow tongue coating. Conclusions: iTRAQ combined with liquid chromatography linked to tandem mass spectrometry (LC-MS/MS) analysis could swiftly and effectively perform proteomics study on differentially expressed proteins in saliva. And certain proteins closely connected with the occurrence of breast cancer and mechanism behind formation of tongue coating of TCM were also discovered.

Objective: To establish a diagnostic model for serum protein finger-print of thyroid cancer patients. Methods: serum samples of 40 patient with thyroid cancer and 30 healthy subjects were tested by surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Protein finger-print was obtained, and 2 specific differentially expressed proteins were selected. A diagnostic model was established with the help of bioinformatics. Results: By comparing the data of differentially expressed proteins from 2 groups, and 121 protein peaks were obtained within the range 2000Da - 20000Da. Eighteen protein peaks showed significant difference in 2 groups ( P < 0.01), 5 protein peaks with mass to charge (M/Z) value 2050.69, 2940.50, 3942.92, 5345.54 and 6918.33 were selected by the Biomarker Pattern Software to construct decision-tree classification diagnostic model for serum differentially expressed proteins. In the test group of this model, the accuracy was 81.4% (57/70), sensitivity 80.0 (32/40), and specificity 83.3% (25/30). Conclusions: the model for serum protein finger-print is able to achieve the optimal effects distinguishing the thyroid cancer patient and healthy individuals. SELDI technology is a quick and effective tool in the diagnosis of thyroid cancer and screening of specific tumor marker.

Objective: Using Surface enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) analysis of the salivary proteins of breast cancer patients (liver qi stagnation syndrome and deficiency of both liver and kidney yin syndrome), and to determine specific protein marker and establish a salivary protein fingerprint for the differentiation of liver qi stagnation syndrome and deficiency of both liver and kidney yin syndrome in patients with breast cancer. Methods: Fourty-seven saliva samples from breast cancer patients (26 patients were of liver qi stagnation syndrome, 21 patients of deficiency of both live and kidney yin syndrome) were tested using SELDI-TOF-MS to establish diagnostic model of traditional Chinese medicine (TCM) for breast cancer. Results: In these 2 groups, 243 proteins peaks were identified and 33 protein peaks showed significantly statistical difference. A diagnostic model consisting of 2 proteins (mass to charge ratio (M/Z) value was 8087.575 Da and 3378.142 Da respectively) was established and could be used to distinguish liver qi stagnation syndrome and deficiency of both liver and kidney yin syndrome. In 47 breast cancer patients, 25 of liver qi stagnation syndrome were correctly diagnosed, and 21 of deficiency of both liver and kidney yin syndrome were correctly ruled out. The sensitivity is 96.15% (25/26), and specificity 80.95% (17/21). Conclusions: Using SELDI-TOF-MS technique to establish salivary protein fingerprint model for TCM syndrome differentiation of breast cancer can provide a novel method of high specificity and sensitivity for the differentiation of TCM syndromes, and further investigation and application should be performed.

Objective: Using surface-enhanced laser desorption/ionization (SELDI) mass spectrometry to test the salivary protein fingerprint from the patients with thyroid cancer, benign nodular goiter and healthy subjects, to screen out the differentially expressed proteins between each group, and establish a diagnostic model with the help of bioinformatics to provide a novel approach for the early diagnosis of thyroid cancer. Methods: Saliva protein fingerprints of 49 patients with thyroid cancer, 34 patients with nodular goiter and 43 healthy subjects were tested using SEDLI technique. Specific differentially expressed proteins in the saliva of thyroid cancer patient were screened out and a diagnostic model was established with the help of bioinformatics. Results: The total accuracy of the cross validation (test group) for the diagnostic model of thyroid cancer and health control was 81.8% (72/88), sensitivity 88.9% (40/45), specificity 74.4% (32/43); The total accuracy of the cross validation (test group) for the diagnostic model of thyroid cancer and nodular goiter was 89.1% (74/83), sensitivity 87.7%(43/49), specificity 91.1% (31/34). Conclusions: By using SELDI technique, a preliminary diagnostic model for thyroid cancer was established, which may provide a novel approach of high sensitivity and strong specificity for the early diagnosis and pre-operation diagnosis of thyroid cancer. It deserves further investigation and application.

Objective To explore protein expression profile in chronic gastritis saliva by proteomics mass spectrum techniques, and seek for the specific biomarkers of diagnosis for chronic gastritis. Method Peptide mass fingerprint of saliva were collected from chronic gastritis patients and healthy subjects by using MALDI-TOF-MS technique after saliva sample had been treated with WCX magnetic beads. The diagnostic cast was developed based on the peptide mass fingerprint which were obtained from the saliva of chronic gastritis patients and healthy subjects. Result Totally 74 protein peaks were identified in the chronic gastritis group and the control group. 5 specific protein peaks (P < 0.05) were found with statistically significant differential expression level. The 3 specific protein peaks with a mass-to-charge ratio (m/z) of 5502.36Da, 1441.75Da and 3442.47Da were used to build a predictive model for the diagnosis of chronic gastritis. The predictive model has identification rate of 91.7% and predictive ability of 73.33%. Clinical back substitution analysis indicated that this diagnostic model can discriminate chronic gastritis from controls with a precision rate of 90.63% (29/32), a sensitivity of 100% (14/14), a specificity of 83.33% and a positive predictive value of 82.35% (14/17). Conclusion Saliva protein fingerprint mass spectrum was initially obtained, which established a certain base for future ulterior proteome studies in chronic gastritis. Furthermore, the diagnostic cast on 5502.36Da, 1441.75Da and 3442.47Da protein peaks of protein expression mass spectrum from chronic gastritis saliva protein was developed to discriminate chronic gastritis clinically. A completely new method and idea of chronic gastritis proteome investigation was provided, which has a really important significance on screening the specific biomarker and needs to be further studied.

Objective: To investigate the difference of salivary protein mass spectrum between different traditional Chinese medicine (TCM) syndromes of thyroid cancer and establish a diagnostic model to provide an objective base for TCM syndrome of thyroid cancer. Methods: Salivary protein fingerprints of 49 thyroid cancer patients (23 cases of phlegm and blood stasis syndrome, and 26 cases of liver qi stagnation) were detected by SELDI-TOF-MS (Surface enhanced laser desorption/ionization time-of-flight mass spectrometry). And a diagnostic model was established with the help of bioinformatics. Results: Through the comparison of salivary protein fingerprints from the two groups, 29 protein peaks showed significant difference (P

Objective The salivary proteins of breast cancer patients of liver-qi stagnation syndrome and of liver-kidney yin deficiency syndrome were examined using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), and specific protein markers were screened out to establish a salivary protein fingerprint model for distinguishing breast cancer liver-qi stagnation and liver-kidney yin deficiency. Methods The protein patterns of 47 salivary specimens (26 cases of liver-qi stagnation of breast cancer, 21 cases of liver-kidney yin deficiency of breast cancer) were examined with SELDI-TOF-MS to establish a TCM syndrome diagnosis model for breast cancer. Results 243 protein peaks were detected in the specimens, 33 of which showed significant difference between the two groups. The liver-qi stagnation syndrome and the liver-kidney yin deficiency syndrome of breast cancer could be correctly distinguished by the diagnostic model comprised of two proteins with M/Z of 8087.575 and 3378.142 Da, respectively; 25 out of the 26 cases of liver-qi stagnation syndrome was correctly diagnosed, and all the 21 cases of liver-kidney yin deficiency were correctly excluded; the sensitivity reached 96.15% (25/26) and the specificity was 80.95%(17/21). Conclusion The salivary protein fingerprint model for TCM syndrome differentiation established with SELDI-TOF-MS provided a highly specific, sensitive new method that is worth further study and application.

To explore protein expression profile in peptic ulcer saliva by proteomics mass spectrum techniques, seek for specific biomarkers of peptic ulcer diagnosis. Method Peptide mass fingerprint of saliva collected from peptic ulcer patients and healthy subjects was investigated by using MALDI-TOF-MS technique after saliva sample had been treated with WCX magnetic beads. The diagnostic cast was developed based on the peptide mass fingerprint obtained from the saliva of chronic gastritis patients and healthy subjects. Result Totally 74 protein peaks were identified from the saliva of chronic gastritis patients and healthy subjects as being associated with peptic ulcer, among which 5 specific protein peaks (P