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Prediction of prostate cancer clinical outcome remains a major challenge after diagnosis. Several high throughput approaches were applied to analyze the genome abnormalities of prostate cancer. To evaluate whether copy number variation (CNV) of genomes in prostate cancer (T), benign prostate tissues adjacent to tumor (AT) and blood of prostate cancer patients predicts biochemical (PSA) relapse and the kinetics of relapse, 241 samples were analyzed through Affymetrix SNP 6.0 chips. Using gene specific CNV from T, the genome model correctly predicted 73% cases for relapse and 75% for short PSADT. The gene
specific CNV model from AT correctly predicted 67% cases for relapse and 77% for short PSADT. Using median size of CNV from blood, the genome model correctly predicted 81% for relapse and 69% for short PSADT. To analyzeepigenome abnormalities of prostate cancer, genome wide methylation analyses were performed using both array and whole genome
methylation sequencing approaches for 91 human prostate specimens. A gene methylation prediction model was shown to predict prostate cancer relapse with sensitivity of 80.0% and specificity of 85.0%. Through whole genome methylation sequencing, we found both intragene and promoter CpG islands contributed to the suppression of RNA transcription. Most of the differential methylation between T and AT occurred in regions outside the CpG islands. Our analysis indicates that genome or epigenome abnormalities of benign or malignant tissues are predictive of clinical outcome of a malignancy.(JianhuaLuo, Genome and epigenome markers of prostate cancer)
Last date updated on July, 2014