A preliminary non-invasive salivary proteomics diagnostic model for TCM syndrome differentiation in thyroid cancer based on SELDI and bioinformatics techniques

Abstract

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<0.01). M/Z 3809.85, 3110.04, 4970.80 were selected to construct the optimal decision-tree model. In the test group, accuracy was 85.7% (42/49), sensitivity 84.6% (22/46), and specificity 86.9% (20/23). Conclusion: A preliminary model for TCM syndrome differentiation of thyroid cancer - salivary protein fingerprint - was established, which may provide a novel method and approach to the investigation of the essence of TCM syndromes.