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Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy.

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 overtreatment. In this study, we have performed gene expression profiling of tumor tissue specimens from 36 patients with prostate cancer to identify transcripts that delineate aggressive and indolent cancer. Key genes were validated using previously published data and by tissue microarray analysis. Two molecular subgroups were identified with a significant overrepresentation of tumors from patients with biochemical recurrence in one of the groups. We successfully validated key transcripts association with recurrence using two publically available datasets totaling 669 patients. Twelve genes were found to be independent predictors of recurrence in multivariate logistical regression analysis. SFRP4 gene expression was consistently up regulated in patients with recurrence in all three datasets. Using an independent cohort of 536 prostate cancer patients we showed SFRP4 expression to be an independent predictor of recurrence after prostatectomy (HR?=?1.35; p?=?0.009). We identified SFRP4 to be associated with disease recurrence. Prospective studies are needed in order to assess the clinical usefulness of the identified key markers in this study.

SUBMITTER: Mortensen MM

PROVIDER: S-EPMC4629186 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy.

Mortensen Martin Mørck MM Høyer Søren S Lynnerup Anne-Sophie AS Ørntoft Torben Falck TF Sørensen Karina Dalsgaard KD Borre Michael M Dyrskjøt Lars L

Scientific reports 20151102

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 overtreatment. In this study, we have performed gene expression profiling of tumor tissue specimens from 36 patients with prostate cancer to identify transcripts that delineate aggressive and indolent cancer. Key genes were validated using previously published data and by tissue microarray analysis. Two mole ...[more]

PMID: 26522007

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Project description:BackgroundProstate cancer (PCa) is a leading reason of death in men and the most diagnosed malignancies in the western countries at the present time. After radical prostatectomy (RP), nearly 30% of men develop clinical recurrence with high serum prostate-specific antigen levels. An important challenge in PCa research is to identify effective predictors of tumor recurrence. The molecular alterations in microRNAs are associated with PCa initiation and progression. Several miRNA microarray studies have been conducted in recurrence PCa, but the results vary among different studies.MethodsWe conducted a meta-analysis of 6 available miRNA expression datasets to identify a panel of co-deregulated miRNA genes and overlapping biological processes. The meta-analysis was performed using the 'MetaDE' package, based on combined P-value approaches (adaptive weight and Fisher's methods), in R version 3.3.1.ResultsMeta-analysis of six miRNA datasets revealed miR-125A, miR-199A-3P, miR-28-5P, miR-301B, miR-324-5P, miR-361-5P, miR-363*, miR-449A, miR-484, miR-498, miR-579, miR-637, miR-720, miR-874 and miR-98 are commonly upregulated miRNA genes, while miR-1, miR-133A, miR-133B, miR-137, miR-221, miR-340, miR-370, miR-449B, miR-489, miR-492, miR-496, miR-541, miR-572, miR-583, miR-606, miR-624, miR-636, miR-639, miR-661, miR-760, miR-890, and miR-939 are commonly downregulated miRNA genes in recurrent PCa samples in comparison to non-recurrent PCa samples. The network-based analysis showed that some of these miRNAs have an established prognostic significance in other cancers and can be actively involved in tumor growth. Gene ontology enrichment revealed many target genes of co-deregulated miRNAs are involved in "regulation of epithelial cell proliferation" and "tissue morphogenesis". Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that these miRNAs regulate cancer pathways. The PPI hub proteins analysis identified CTNNB1 as the most highly ranked hub protein. Besides, common pathway analysis showed that TCF3, MAX, MYC, CYP26A1, and SREBF1 significantly interact with those DE miRNA genes. The identified genes have been known as tumor suppressors and biomarkers which are closely related to several cancer types, such as colorectal cancer, breast cancer, PCa, gastric, and hepatocellular carcinomas. Additionally, it was shown that the combination of DE miRNAs can assist in the more specific detection of the PCa and prediction of biochemical recurrence (BCR).ConclusionWe found that the identified miRNAs through meta-analysis are candidate predictive markers for recurrent PCa after radical prostatectomy.

Project description:Background and objectiveAfter radical prostatectomy (RP), prostate cancer (PCa) patients may experience biochemical recurrence (BCR) and clinical recurrence, which remains a dominant issue in PCa treatment. The purpose of this study was to identify a protein-coding gene classifier associated with microRNA (miRNA)-mediated regulation to provide a comprehensive prognostic index to predict PCa recurrence after RP.MethodsCandidate classifiers were constructed using two machine-learning algorithms (a least absolute shrinkage and selector operation [LASSO]-based classifier and a decision tree-based classifier) based on a discovery cohort (n = 156) from The Cancer Genome Atlas (TCGA) database. After selecting the LASSO-based classifier based on the prediction accuracy, both an internal validation cohort (n = 333) and an external validation cohort (n = 100) were used to examined the classifier using survival analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox proportional hazards regression analyses. Functional enrichment analysis of co-expressed genes was carried out to explore the underlying moleculer mechanisms of the genes included in the classifier.ResultsWe constructed a three-gene classifier that included FAM72B, GNE, and TRIM46, and we identified four upstream prognostic miRNAs (hsa-miR-133a-3p, hsa-miR-222-3p, hsa-miR-1301-3p, and hsa-miR-30c-2-3p). The classifier exhibited a remarkable ability (area under the curve [AUC] = 0.927) to distinguish PCa patients with high and low Gleason scores in the discovery cohort. Furthermore, it was significantly associated with clinical recurrence (p < 0.0001, log rank statistic = 20.7, AUC = 0.733) and could serve as an independent prognostic factor of recurrence-free survival (hazard ratio: 1.708, 95% CI: 1.180-2.472, p < 0.001). Additionally, it was a predictor of BCR according to BCR-free survival analysis (p = 0.0338, log rank statistic = 4.51).ConclusionsThe three-gene classifier associated with miRNA-mediated regulation may serve as a novel prognostic biomarker for PCa patients after RP.

Project description:BackgroundCurrent clinical tools have limited accuracy in differentiating patients with localized prostate cancer who are at risk of recurrence from patients with indolent disease. We aimed to identify a gene expression signature that jointly with clinical variables could improve upon the prediction of clinical recurrence after RP for patients with stage T2 PCa.MethodsThe study population includes consented patients who underwent a radical retropubic prostatectomy (RP) and bilateral pelvic lymph node dissection at the University of Southern California in the PSA-era (1988-2008). We used a nested case-control study of 187 organ-confined patients (pT2N0M0): 154 with no recurrence ("controls") and 33 with clinical recurrence ("cases"). RNA was obtained from laser capture microdissected malignant glands representative of the overall Gleason score of each patient. Whole genome gene expression profiles (29,000 transcripts) were obtained using the Whole Genome DASL HT platform (Illumina, Inc). A gene expression signature of PCa clinical recurrence was identified using stability selection with elastic net regularized logistic regression. Three existing datasets generated with the Affymetrix Human Exon 1.0ST array were used for validation: Mayo Clinic (MC, n = 545), Memorial Sloan Kettering Cancer Center (SKCC, n = 150), and Erasmus Medical Center (EMC, n = 48). The areas under the ROC curve (AUCs) were obtained using repeated fivefold cross-validation.ResultsA 28-gene expression signature was identified that jointly with key clinical variables (age, Gleason score, pre-operative PSA level, and operation year) was predictive of clinical recurrence (AUC of clinical variables only was 0.67, AUC of clinical variables, and 28-gene signature was 0.99). The AUC of this gene signature fitted in each of the external datasets jointly with clinical variables was 0.75 (0.72-0.77) (MC), 0.90 (0.86-0.94) (MSKCC), and 0.82 (0.74-0.91) (EMC), whereas the AUC for clinical variables only in each dataset was 0.72 (0.70-0.74), 0.86 (0.82-0.91), and 0.76 (0.67-0.85), respectively.ConclusionsWe report a novel gene-expression based classifier identified using agnostic approaches from whole genome expression profiles that can improve upon the accuracy of clinical indicators to stratify early stage localized patients at risk of clinical recurrence after RP. Prostate 76:1239-1256, 2016. © 2016 Wiley Periodicals, Inc.

Project description:BackgroundThis study was undertaken to investigate the expression of guanyl nucleotide-releasing protein for Ras 3 (RasGRP3) in the cell lines and tissues in BPH and prostate cancer (PCa), as well as its associations with cancer invasion and prognosis in prostate carcinomas.MethodsExpression analysis of RasGRP3 was accomplished using immunohistochemical staining of PCa and BPH tissues. Pearson's χ2 test was used to analyze the association between RasGRP3 expression and specific clinical parameters. Survival and PSA relapse curves were evaluated using the Kaplan-Meier curves and log-rank tests, and the differences were assessed using the Cox regression methods. In addition, human PCa cell lines PC-3, DU145, LNCaP, PC3M-1E8, PC3M-2B4 and BPH-1 were examined for expression of RasGRP3 using western blot and quantitative polymerase chain reaction (Q-PCR) analysis. After PC-3 cells were transfected by small interfering RNA targeting RasGRP3, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and migratory assays were employed to determine the vitality and aggressive capability of tumor cell in vitro.ResultsExpression of RasGRP3 was significantly correlated (P=0.038 and P=0.021) with Gleason score (< or =6 versus > or =7) and T stage (T1-T2 versus T3-T4), respectively. PCa with RasGRP3-positive expression may increase the risk of PSA recurrence and decrease cancer-specific survival (P=0.0291 and P=0.0044). The expression of RasGRP3 was also associated with PSA recurrence and cancer-specific survival in univariate (P<0.001 and P<0.001) and multivariate analyses (P<0.001 and P=0.003). RasGRP3 mRNA and proteins were found to be positively expressed in PCa cell lines. There was higher expression of RasGRP3 in PC-3, DU145 and PC3M-1E8 than in LNCaP, PC3M-2B4 and BPH-1. Knockdown of RasGRP3 inhibited the proliferation, migration and invasion capabilities of PC-3 cells.ConclusionsThese data suggested that elevated RasGRP3 expression may play a key role in the malignant progression of PCa, especially in invasion and metastasis, and may be a potential marker of biochemical recurrence.

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Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy.

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Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy.

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