Preliminary study on saliva proteomics of different furs in digestive system diseases

Objectives: To explore the material basis for saliva proteome of traditional Chinese medicine furs and biological markers among gastric cancer, chronic gastritis and peptic ulcer. Methods: The experiment included three pathological thin fur, thick fur, stripping fur groups and the normal fur control group. The corresponding peptide mass fingerprinting was acquired by detecting the saliva of subjects using MALDI-TOF-MS technology and Weak Cation Bead (WCX), and to established the classification prediction model. Results: 4 peaks had statistically significant differences (P < 0.05) among 187 differential protein peaks from four groups. The classification prediction model was established with identification rate of 85.31 percent and predictive capability of 39.91 percent. Conclusion: The saliva protein expression diagnosis model was established by using 4 protein peaks of 6447.39 Da, 2938.47 Da, 1472.34 Da, and 1451.77 Da as a model to distinguish normal fur from the saliva of different pathological fur.