Ontology highlight
ABSTRACT:
PROVIDER: PRJNA121269 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We performed a mass spectrometry-based proteomic analysis of normal and malignant prostate tissues from 22 men who underwent surgery for prostate cancer. Prostate cancer samples included Grade Groups (3 to 5), with 8 patients experiencing recurrence and 14 without evidence of recurrence with a mean of 6.8 years of follow-up.
2022-05-04 | PXD028118 | Pride
Project description:This SuperSeries is composed of the following subset Series: GSE26022: [Gene Expression Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26242: [Gene Expression Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26245: [miRNA Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26247: [miRNA Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy Refer to individual Series
2011-06-01 | E-GEOD-26367 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the following subset Series: GSE18916: Expression data from 42 prostate cancer samples - 16 recurrent and 26 recurrence-free GSE18917: Expression data from 22 prostate cancer samples - 6 recurrent and 16 recurrence-free from the validation dataset Refer to individual Series
2011-06-15 | E-GEOD-18918 | biostudies-arrayexpress
Project description:Optimizing molecular signatures for prostate cancer recurrence
| PRJNA134591 | ENA
Project description: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.
2015-07-01 | E-GEOD-46602 | biostudies-arrayexpress
Project description:Prostate tumors with the gene fusion TMPRSS2:ERG have been reported to have a significantly higher risk of recurrence compared with tumors lacking the fusion. Tumors from 139 patients who underwent radical prostatectomy were analyzed for the expression of 502 cancer-related genes to identify genes differentially regulated in TMPRSS2:ERG fusion tumors as well as identify biomarkers of biochemical recurrence. 139 prostate fresh-frozen tumors from radical prostatectomy surgery where profiled on the Illumina Human Cancer DASL Panel. 69 tumors were positive for the gene fusion TMPRSS2:ERG while 70 where not. 33 of the 139 patients experienced biochemical recurrence. Data was analyzed for differential genes in TMPRSS2:ERG fusion positive tumors as well as clinical and molecular biomarkers of recurrence.
2009-10-22 | E-GEOD-18655 | biostudies-arrayexpress
Project description:Prostate cancer is the most common malignancy in men. Yet, the modest benefit of treatment highlights the unmet need for prognostic biomarkers in prostate cancer (1). Few large prostate oncogenome resources currently exist that combine the molecular and clinical outcome data necessary for prognostic discovery. To determine the extent to which genomic aberrations reflect the risk of prostate cancer-specific outcomes, we profiled more than 100 primary prostate cancers with long-term follow-up for genome-wide copy number alterations (CNA). We also updated the long-term clinical outcome (median 8 years) of an additional independent cohort of 181 primary prostate cancers that we previously profiled for CNA and expression changes (2). Together, we found that CNA burden across the genome, defined as the percent of the tumor genome affected by CNA, is prognostic for recurrence and metastasis in these two cohorts. This prognostic significance of CNA is independent of Gleason grade, a major existing histopathological prognostic variable in prostate cancer. Moreover, in intermediate-risk Gleason 7 prostate cancers that show a wide range of outcomes, CNA burden is also prognostic for biochemical recurrence, independent of prostate-specific antigen or nomogram score. CNA burden therefore has the potential to stratify patients by their risk of recurrence in an otherwise intermediate risk subpopulation. We further demonstrate that CNA burden can be established in diagnostic FFPE needle biopsies using low-input whole genome sequencing. Together, this work highlights the potential of oncogenomics to identify useful and clinically amenable prognostic factors that may inform prostate cancer outcome and treatment. Human prostate samples were profiled on Agilent 1M aCGH arrays per manufacturer's instructions. A pooled reference normal DNA was used as the reference.
2014-06-30 | E-GEOD-54691 | biostudies-arrayexpress
Project description:Prostate cancer has a broad spectrum of clinical behavior, hence biomarkers are urgently needed for risk stratification. We previously described the protective effect of signal transducer and activator of transcription 3 (STAT3) in a prostate cancer mouse model. We now show the importance of STAT3-regulated metabolic functions and explain their influence on aggressive prostate cancer. By utilizing a gene co-expression network in addition to laser microdissected proteomics from human and murine FFPE samples, we established a workflow that facilitates the discovery of new biomarkers. We thereby identified the protective effect of pyruvate dehydrogenase kinase 4 (PDK4) in prostate cancer. PDK4 is a key regulator of the citrate cycle and low PDK4 is significantly associated with disease recurrence.
2020-04-22 | PXD014251 | Pride
Project description:Prostate cancer is the second leading cause of cancer death in the United States and Europe. Diagnosis and risk estimation of cancer recurrence is often critical with the common clinicopathologic parameters of prostate-specific antigen, tumor stage and grade. Therefore it is mandatory to develop new diagnostic and prognostic markers for prostate cancer. miRNAs have been shown to be novel markers in a series of other cancer types. We show for the first time, that good overall classification of normal and malignant prostate tissue was possible with combination of just two miRNAs (hsa-miR-205, hsa-miR-183). Further, hsa-miR-96 is shown to be associated with the recurrence-free interval after radical prostatectomy.
2009-09-15 | GSE14857 | GEO
Project description:Prostate Cancer Stem Cells (CSCs) are considered one of the main reasons the tumor recurrence after chemotherapy. Here we employed a chemoresistant xenograft prostate cancer model in NOD/SCID mice and found CD54 is a reliable new marker for prostate CSCs. Gene expression profile were utilized to analyze stemness-related genes in cancer stem cells.
2016-02-23 | GSE78196 | GEO