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. 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 analyzes 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 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.