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

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...

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Bibliographic Details
Published in:Journal of traditional Chinese medicine 2012-09, Vol.32 (3), p.350-354
Main Authors: Liu, Zhizhen, Yu, Zongyang, Ouyang, Xuenong, Du, Jian, Lan, Xiaopeng, Zhao, Meng
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Biomarkers, Tumor - blood
Blood Proteins - chemistry
Carcinoma, Non-Small-Cell Lung - blood
Carcinoma, Non-Small-Cell Lung - diagnosis
Female
Humans
Male
Middle Aged
Mucus - chemistry
Proteomics
决策树模型
患者
指纹图谱
虚证
蛋白质芯片
血清蛋白质
非小细胞肺癌
预测模型
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Summary: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.
ISSN:0255-2922
DOI:10.1016/S0254-6272(13)60036-9