Applied research on serum protein fingerprints for prediction of Qi deficiency syndrome and phlegm and blood stasis in patients with non-small cell lung cancer

Abstract

ObjectiveThis 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. MethodsSerum 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. ResultsA 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%. ConclusionSELDI-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.