Cao Meiqun

Preliminary studies on diagnostic cast of peptic ulcer based on saliva proteome and bioinformatics

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<0.05) were found with statistically significant differential expression level. The 3 specific protein peaks with a mass-to-charge ratio (m/z) of 2934.36Da, 5502.38Da and 3472.94Da were used to build a predictive model for diagnosis of peptic ulcer. This predictive model has an identification rate of 88.4% and predictive ability of 80.35%. Clinical back substitution analysis indicated that this diagnostic model can discriminate chronic gastritis from controls with a precision of 88.57% (31/35), a sensitivity of 82.35% (14/17)and a specificity of 94.44% (17/18). Conclusion Saliva protein fingerprint mass spectrum from peptic ulcer was preliminarily obtained; the diagnostic cast on 2934.36Da, 5502.38Da and 3472.94Da protein peaks of protein expression mass spectrum from peptic ulcer saliva protein was developed to discriminate peptic ulcer clinically.

Application of iTRAQ quantitative proteomics in identification of serum biomarkers in breast cancer

Aim: This study aims to explore the presence of informative protein biomarkers in the human serum proteome. Patients and Methods; Serum samples collected from 20 breast cancer patients which were divided into two groups of breast cancer, and 10 health controls and 10 mammary fibroma patients were profiled using iTRAQ technology coupled with LC-ESI-QTOF-MS, and Mascot searching. Western-blotting were used for validation of the candidate biomarkers on a new group of breast cancer and healthy subjects as well as mammary fibroma patients. Results: 335 proteins were identified, and 11 proteins were associated with breast cancer, includind 1 upregulated protein and 10 downregulated proteins. Three biomarker candidates generated from iTRAQ experiments were successfully verified using Western-blotting. Conclusion: This study provided a global view of potential mechanisms and potentional biomarkers of breast cancer, and demonstrated that iTRAQ combined with LC-ESI-QTOF-MS quantitative proteomics is a powerful tool for biomarker discovery.