Proteomic analysis of prostate cancer using surface enhanced laser desorption/ionization mass spectrometry
Pan, Y.-z.; Xiao, X.-y.; Zhao, D.; Zhang, L.; Ji, G.-y.; Li, Y.; He, D.-c.; Zhao, X.-j.; Yang, B.-x.
Zhonghua Yi Xue Za Zhi 85(45): 3172-3175
2005
ISSN/ISBN: 0376-2491 PMID: 16405834 Document Number: 586287
To identify the serum biomarkers of prostate cancer by using protein chip and bioinformatics. Eighty three prostate cancer (PCA) patients and ninety five healthy people from mass screen in Changchun were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The data of spectra were analyzed by bioinformatics tools-Biomarker Wizard and Biomarker Pattern. Compared with the spectra of healthy people, there were 18 potential markers detected in the spectra of the PCA patients, the protein expression was high in 4 of which and low in the 10 of which. The softwares Biomarkerwizard and Biomarker Pattern automatically, under given conditions, selected 8 biomarker proteins to be used to establish a five layer decision tree differentiate to diagnose PCA and differentiate PCA from healthy people with a specificity of 92.632% and a sensitivity of 96.386%. New serum biomarkers of PCA have been identified, and this SELDI mass spectrometry coupled with decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCA.