Molecular changes during progression from early stage to advanced urothelial carcinoma
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ABSTRACT:
PROVIDER: PRJNA529496 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:It is unclear how urothelial carcinoma of the urinary bladder change, genetically and phenotypically, as patients progress from recurrent non muscle-invasive (NMI) to muscle-invasive (MI) disease or disease that require radical cystectomy. This data set contains gene expression data from 200 samples in a longitudinal study of such patients. Overlap with previously GEO-published cohorts from our lab is described in the metadata.
2020-01-20 | GSE128959 | GEO
Project description:Micro-RNA expression profiles in upper tract urothelial carcinoma differentiate stage and tumor progression
| PRJNA490638 | ENA
Project description:The expression profile and sequence variants of 476 early stage urothelial carcinoma were studied using whole transcriptome sequencing. RNA-Seq libraries were prepared by Ribo-Zero treatment of total-RNA (to reduce the rRNA content) followed by library preparation using ScriptSeq. RNA-Seq libraries were paired-end sequenced (2x 101 bp) on Illumina HiSeq 2000 and the resulting fastq files were processed using tools from the Genome Analysis Toolkit (GATK and from the Tuxedo suite. Access to the sequence data (bam and vcf files), containing person identifying information, needs signature on a controlled access form, and can be accessed at The European Genome-phenome Archive (EGA) using the study ID EGAS00001001236 following request. An expression matrix of FPKM values are available without restriction at ArrayExpress.
2016-06-17 | E-MTAB-4321 | biostudies-arrayexpress
Project description:Background: A gel-free proteomic approach was utilized to perform in-depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long-term goal of this study is to generate a large-scale, label-free proteomic data set from histologically well-classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers. Methods and Results: Cases of early-stage (I-II) and advanced-stage (III-IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung adenocarcinomas were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). From the analysis of ADC and normal specimens, 5,933 protein groups were identified. To examine the protein expression profile of ADC, a peak area-based quantitation method was used. In early- and advanced-stage ADC, 33 and 39 proteins were differentially-expressed respectively between normal and tumor tissue (adjusted p-value < 0.01, fold change ≥ 4). For early- and advanced stage ADC tumors compared to normal patient-matched tissue, 11 and 22 proteins and 23 and 16 proteins were identified as down- and up-regulated, respectively. In silico functional analysis of the up-regulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most over-represented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes. Conclusions: Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage-specific drug targets for rational intervention.
2021-09-09 | PXD019259 | Pride
Project description:Micro-RNA expression profiles in upper tract urothelial carcinoma differentiate stage and tumor progression: screening data
| PRJNA489690 | ENA
Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.
2010-03-22 | GSE17260 | GEO
Project description:Micro-RNA expression profiles in upper tract urothelial carcinoma differentiate stage and tumor progression: secondary data set
| PRJNA490640 | ENA
Project description:The long-term survival of advanced urothelial carcinoma (UCa) patients is limited due to an innate resistance to treatment. We identified elevated expression of the histone methyltransferase EZH2 as a hallmark of aggressive UCa, and therefore, hypothesized that EPZ-011989, a small molecule catalytic inhibitor of EZH2, might have anti-tumor effects in UCa. Here we show that in a carcinogen-induced mouse bladder cancer model, EZH2 inhibition results in reduced tumor progression, elevated tumor infiltrating T cells, increased pro-inflammatory cytokine secretion and MHC class II expression on UCa cells. Treatment of mice with EPZ011989 causes increased MHC class II expression on the urothelium, and was able to activate CD4+ T cells, which is completely abrogated with genetic ablation of the acquired immune system. These findings support a unique role for EZH2 catalytic activity in mediating UCa immune evasion and, for the first time, prove the efficacy of EZH2 catalytic inhibitors in a UCa model, a finding that can potentially be expanded to humans with UCa.
2022-08-15 | GSE209851 | GEO
Project description:The long-term survival of advanced urothelial carcinoma (UCa) patients is limited due to an innate resistance to treatment. We identified elevated expression of the histone methyltransferase EZH2 as a hallmark of aggressive UCa, and therefore, hypothesized that EPZ-011989, a small molecule catalytic inhibitor of EZH2, might have anti-tumor effects in UCa. Here we show that in a carcinogen-induced mouse bladder cancer model, EZH2 inhibition results in reduced tumor progression, elevated tumor infiltrating T cells, increased pro-inflammatory cytokine secretion and MHC class II expression on UCa cells. Treatment of mice with EPZ011989 causes increased MHC class II expression on the urothelium, and was able to activate CD4+ T cells, which is completely abrogated with genetic ablation of the acquired immune system. These findings support a unique role for EZH2 catalytic activity in mediating UCa immune evasion and, for the first time, prove the efficacy of EZH2 catalytic inhibitors in a UCa model, a finding that can potentially be expanded to humans with UCa.
2022-08-15 | GSE209852 | GEO
Project description:BACKGROUND: Because of the distinct clinical presentation of early and advanced stage ovarian cancer, we aim to clarify whether these disease entities are solely separated by time of diagnosis or whether they arise from distinct molecular events. METHODS: Sixteen early and sixteen advanced stage ovarian carcinomas, matched for histological subtype and differentiation grade, were included. Genomic aberrations were compared for each early and advanced stage ovarian cancer by array comparative genomic hybridization. To study how the aberrations correlate to the clinical characteristics of the tumors we clustered tumors based on the genomic aberrations. RESULTS: The genomic aberration patterns in advanced stage cancer equalled those in early stage, but were more frequent in advanced stage (p?=?0.012). Unsupervised clustering based on genomic aberrations yielded two clusters that significantly discriminated early from advanced stage (p?=?0.001), and that did differ significantly in survival (p?=?0.002). These clusters however did give a more accurate prognosis than histological subtype or differentiation grade. CONCLUSION: This study indicates that advanced stage ovarian cancer either progresses from early stage or from a common precursor lesion but that they do not arise from distinct carcinogenic molecular events. Furthermore, we show that array comparative genomic hybridization has the potential to identify clinically distinct patients. Sixteen early and sixteen advanced stage ovarian carcinomas
2012-08-22 | E-GEOD-24418 | biostudies-arrayexpress