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Aim:To identify the serum biomarkers of prostate cancer(PCa)by protein chip and bioinformatics.Methods:Serum samples from 83 PCa patients and 95 healthy men were taken from a mass screening in Changchun,China.Protein profiling was carried out using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry(SELDI-TOF MS).The data of spectra were analyzed using two bioinformatics tools.Results:Eighteen serumdifferential proteins were identified in the PCa group compared with the control group(P<0.01).There were fourproteins at the higher serum level and 14 proteins at the lower serum level in the PCa group.A decision tree classifi-cation algorithm that used an eight-protein mass pattern was developed to correctly classify the samples.A sensitivityof 92.0% and a specificity of 96.7% for the study group were obtained by comparing the PCa and control groups.Conclusion:We identified new serum biomarkers of PCa.SELDI-TOF MS coupled with a decision tree classifica-tion algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCa.(Asian J Androl2006 Jan;8:45-51)
Aim: To identify the serum biomarkers of prostate cancer (PCa) by protein chip and bioinformatics. Methods: Serum samples from 83 PCa patients and 95 healthy men were taken from a mass screening in Changchun, China. Protein profiling was carried out using surface- enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF MS) .The data of spectra were analyzed using two bioinformatics tools.Results: Eighteen serumdifferential proteins were identified in the PCa group compared with the control group (P <0.01 ). There were four proteins at the higher serum level and 14 proteins at the lower serum level in the PCa group. A decision tree classifi-cation algorithm that used an eight-protein mass pattern was developed to correctly classify the samples. A sensitivity of 92.0% and a specificity of 96.7% for the study group were compared by comparing the PCa and control groups. Confc: We identified new serum biomarkers of PCa. SELDI-TOF MS coupled with a decision tree classifica- tion algor ithm will provide a highly accurate and innovative approach for the early diagnosis of PCa. (Asian J Androl 2006 Jan; 8: 45-51)