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Journal Article
Research Support, Non-U.S. Gov't
Integrative analysis of prostate cancer aggressiveness.
Prostate 2013 September
BACKGROUND: Clinical management of prostate cancer (PC) is still highly demanding on the identification of robust biomarkers which will allow a more precise prediction of disease progression.
METHODS: We profiled both mRNA expression and DNA copy number alterations (CNAs) from laser capture microdissected cells from 31 PC patients and 17 patients with benign prostatic hyperplasia using Affymetrix GeneChip® technology. PC patients were subdivided into an aggressive (Gleason Score 8 or higher, and/or T3/T4 and/or N+/M+) and non-aggressive (all others) form of PC. Furthermore, we correlated the two datasets, as genes whose varied expression is due to a chromosomal alteration, may suggest a causal implication of these genes in the disease. All statistical analyses were performed in R version 2.15.0 and Bioconductor version 1.8.1., respectively.
RESULTS: We confirmed several common altered chromosomal regions as well as recently discovered loci such as deletions on chromosomes 3p14.1-3p13 and 13q13.3-13q14.11 supporting a possible role for RYBP, RGC32, and ELF1 in tumor suppression. Integrative analysis of expression and CN data combined with data retrieved from online databases propose PTP4A3 and ELF1 as possible factors for tumor progression.
CONCLUSIONS: Copy number data analysis revealed some significant differences between aggressive and non-aggressive tumors, while gene expression data alone could not define an aggressive group of patients. The assessment of CNA may have diagnostic and prognostic value in PC.
METHODS: We profiled both mRNA expression and DNA copy number alterations (CNAs) from laser capture microdissected cells from 31 PC patients and 17 patients with benign prostatic hyperplasia using Affymetrix GeneChip® technology. PC patients were subdivided into an aggressive (Gleason Score 8 or higher, and/or T3/T4 and/or N+/M+) and non-aggressive (all others) form of PC. Furthermore, we correlated the two datasets, as genes whose varied expression is due to a chromosomal alteration, may suggest a causal implication of these genes in the disease. All statistical analyses were performed in R version 2.15.0 and Bioconductor version 1.8.1., respectively.
RESULTS: We confirmed several common altered chromosomal regions as well as recently discovered loci such as deletions on chromosomes 3p14.1-3p13 and 13q13.3-13q14.11 supporting a possible role for RYBP, RGC32, and ELF1 in tumor suppression. Integrative analysis of expression and CN data combined with data retrieved from online databases propose PTP4A3 and ELF1 as possible factors for tumor progression.
CONCLUSIONS: Copy number data analysis revealed some significant differences between aggressive and non-aggressive tumors, while gene expression data alone could not define an aggressive group of patients. The assessment of CNA may have diagnostic and prognostic value in PC.
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