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  • Title: [Serum proteomic spectra of esophageal squamous cell carcinoma patients analyzed with IMAC3 protein chip].
    Author: Liu CZ, Zhu PY, Shi MX, Liu JB, Liao P, Xiang CQ, Zhang YX, Wang WJ.
    Journal: Ai Zheng; 2008 Mar; 27(3):272-8. PubMed ID: 18334116.
    Abstract:
    BACKGROUND & OBJECTIVE: Serum protein fingerprinting technology can help to identify the molecular changes related to esophageal carcinogenesis. This study was to screen serum markers and establish the predictive models that may be of help to serologic diagnosis of esophageal squamous cell carcinoma (ESCC). METHODS: Serum samples were collected from 68 ESCC patients and 44 age-and sex-matched healthy subjects, and randomized into a training set (55 ESCC patients and 35 healthy subjects) and a blind testing set (13 ESCC patients and 9 healthy subjects). Serum samples were applied to immobilized metal affinity capture (IMAC3) proteinchip surfaces and tested by surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS). The data were analyzed by Biomarker Wizard software to screen serum proteomic biomarkers. Decision classification tree models were established by bioinformatics. Double-blind test was used to determine the sensitivity and specificity of the classification tree models. RESULTS: A total of 78 effective protein peaks were detected at the molecular range of 1.5 to 20 ku, among which 25 were significantly different between ESCC patients and healthy subjects (P<0.001). All the peptide pattern data were sampled randomly for 1,000 times using 3-cross validation approach, and 1,000 decision tree models were obtained. Twenty decision trees with the highest cross validation rate were chosen to construct the classification models which can differentiate ESCC patients from healthy subjects. With these decision trees, 18 samples were correctly forecasted from 22 blind testing samples, with a sensitivity of 92.31% and a specificity of 66.67%. CONCLUSION: SELDI-TOF-MS technique combined with decision tree model can help to identify serum proteomic biomarkers related to ESCC and the predictive models can discriminate ESCC patients from healthy people effectively.
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