摘要
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 serum differential 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 classification 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 obtained by comparing the PCa and control groups. Conclusion： We identified new serum biomarkers of PCa. SELDI-TOF MS coupled with a decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCa. （Asian J Androl 2006 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 serum differential 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 classification 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 obtained by comparing the PCa and control groups. Conclusion： We identified new serum biomarkers of PCa. SELDI-TOF MS coupled with a decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCa. （Asian J Androl 2006 Jan; 8： 45-51）
