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, Chin...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）展开更多
文摘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）
