Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Expression data from prostate cancer and benign prostate glands

ABSTRACT: Prostate cancer is a leading cause of cancer death amongst males. The main clinical dilemma in treating prostate cancer is the high number of indolent cases that confer a significant risk of over diagnosis and over treatment. In this study we have performed a genome expression profiling of tumor tissue specimens from 36 patients with prostate cancer to identify transcripts that delineate aggressive and indolent cancer. We included normal prostate biopsies from 14 patients in our analysis. Unsupervised hierarchical cluster analysis separated the cancer samples into two groups with a significant overrepresentation of tumors from patients with biochemical recurrence in one of the groups (Chi2, p=0.02). The samples were separated by basically three clusters of genes that showed differential expression between the two sample clusters - totaling 371 transcripts. Ingenuity Pathway Analysis revealed that one cluster contained genes associated with invasive properties of the tumor, another genes associated with the cell cycle, and the last cluster genes involved in several biological functions. We successfully validated the transcripts association with recurrence using two publicly available patient datasets totaling 669 patients. Twelve genes were found to be independent predictors of recurrence in multivariate logistical regression analysis. In this study we have performed a genome expression profiling of tumor tissue specimens from 36 patients with prostate cancer to identify transcripts that delineate aggressive and indolent cancer. We included normal prostate biopsies from 14 patients in our analysis.

ORGANISM(S): Homo sapiens

SUBMITTER: Martin Mortensen

PROVIDER: E-GEOD-46602 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Detection of DNA hypermethylation has emerged as a novel molecular biomarker for the evaluation of prostate cancer diagnosis and prognosis, but the use of single gene hypermethylation could produce unspecific results. Defining the specific gene hypermethylation profile for prostate cancer could provide groups of genes that specifically discriminate the patients with indolent and aggressive tumors. To compare the methylation profile of normal and malignant prostate we performed Genome-wide methylation analysis on 83 tumor samples, that included all clinical stages of the disease, 10 normal prostate samples and LNCaP, DU145 and PC3 prostate cancer cell lines using the GoldenGate Methylation Cancer Panel I (Illumina, Inc.) that interrogate for 1505 CpGs. We found 41 genes significantly hypermethylated in more than 20% of the tumors analyzed (p<0.01). Of these we identified GSTM2 and PENK as novel hypermethylated genes in prostate cancer that were simultaneously methylated in 40.9% of the tumors analyzed. We also identified panels of genes more frequently methylated in tumor samples with clinico-pathological indicators of poor prognosis: high Gleason score, elevated Ki-67 or advanced disease. Of these, we found that simultaneous hypermethylation of CFTR and HTR1B is common in patients with high Gleason score and high ki-67 levels and might signal the population at higher risk of therapeutic failure. DNA hypermethylation profile was associated with cancer-specific mortality (log-rank, p=0.007) and biochemical recurrence-free survival (log-rank, p=0.0008). In conclusion, our data strongly indicate that epigenetic silencing of GSTM2 and PENK is a common event in prostate cancer that could be used as a molecular marker for prostate cancer diagnosis. In addition, simultaneous HTR1B and CFTR hypermethylation could help discriminate aggressive from indolent prostate tumors.A DNA hypermethylation profile associated with cancer-specific mortality and biochemical recurrence in patients receiving radical prostatectomy is presented. GST2 and PENK are new hipermethylated genes identified. Simultaneous hypermethylation of CFTR and HTR1B could help discriminate aggressive disease.

Project description:Current protocols for the screening of prostate cancer cannot accurately discriminate clinically indolent tumors from more aggressive ones. One reliable indicator of outcome has been the determination of organ-confined versus nonorgan-confined disease but even this determination is often only made following prostatectomy. This underscores the need to explore alternate avenues to enhance outcome prediction of prostate cancer patients. Fluids that are proximal to the prostate, such as expressed prostatic secretions (EPS), are attractive sources of potential prostate cancer biomarkers as these fluids likely bathe the tumor. Direct-EPS samples from 16 individuals with extracapsular (n = 8) or organ-confined (n = 8) prostate cancer were used as a discovery cohort, and were analyzed in duplicate by a nine-step MudPIT on a LTQ-Orbitrap XL mass spectrometer. A total of 624 unique proteins were identified by at least two unique peptides with a 0.2% false discovery rate. A semiquantitative spectral counting algorithm identified 133 significantly differentially expressed proteins in the discovery cohort. Integrative data mining prioritized 14 candidates, including two known prostate cancer biomarkers: prostate-specific antigen and prostatic acid phosphatase, which were significantly elevated in the direct-EPS from the organ-confined cancer group. These and five other candidates (SFN, MME, PARK7, TIMP1, and TGM4) were verified by Western blotting in an independent set of direct-EPS from patients with biochemically recurrent disease (n = 5) versus patients with no evidence of recurrence upon follow-up (n = 10). Lastly, we performed proof-of-concept SRM-MS-based relative quantification of the five candidates using unpurified heavy isotope-labeled synthetic peptides spiked into pools of EPS-urines from men with extracapsular and organ-confined prostate tumors. This study represents the first efforts to define the direct-EPS proteome from two major subclasses of prostate cancer using shotgun proteomics and verification in EPS-urine by SRM-MS.

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Expression data from prostate cancer and benign prostate glands

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