Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Molecular Stratification of Clear Cell Renal Cell Carcinoma Reveals Distinct Subtypes and Survival Patterns

ABSTRACT: Renal cell carcinoma (RCC) tumors express varying gene profiles, dependent on varying genetic events. Here we analyzed a series of ccRCC tumors to show that two subtypes of ccRCC exist based on their molecular signature. These subtypes (ccA and ccB) are identifiable by a robust set of genes representing distinct biological pathways and are correlated with patient survival post-nephrectomy. Keywords: Patient sample study

ORGANISM(S): Homo sapiens

PROVIDER: GSE16449 | GEO | 2010/02/17

SECONDARY ACCESSION(S): PRJNA116017

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Meta-analysis of Clear Cell Renal Cell Carcinoma Gene Expression Defines a Variant Subgroup and Identifies Gender Influences on Tumor Biology

Project description:Clear cell renal cell carcinoma comprises two dominant subtypes, ccA and ccB, with gender disparity providing additional disease information. A third minor subgroup has distinct expression profiles consistent with von Hippel-Lindau wild type status and displays variant histology features. 44 new tumor samples and six large, publicly available, ccRCC gene expression databases were identified that cumulatively provided data for 480 tumors for metaanalysis via meta-array compilation.

2011-11-05 | E-GEOD-33093 | biostudies-arrayexpress

Meta-analysis of Clear Cell Renal Cell Carcinoma Gene Expression Defines a Variant Subgroup and Identifies Gender Influences on Tumor Biology

2011-11-05 | GSE33093 | GEO

microRNAseq of clear-cell renal cell carcinoma on surgical resection specimens of untreated primary tumors in humans

Project description:We aimed to assess differences in miRNA expression profiles in transcriptomic molecular subtypes of ccRCC (Beuselinck et al, Clinical Cancer Research 2015) and to correlate miRNAs with overal survival since diagnosis of ccRCC. Sequencing was done for 129 primary ccRCC (formalin-fixed paraffin-embedded surgical resection specimens, previously untreated) and 16 normal kidney specimens. Normal kidney was obtained from the nephrectomy specimens at time of ccRCC removal, in tissue blocks containing only normal kidney (so nót the normal kidney immediately adjacent to ccRCC). The samples were sequenced in 2 major batches (2014 and 2017).

2020-07-22 | E-MTAB-9380 | biostudies-arrayexpress

Proteomic analysis of serum EVs derived from PSC, CCA and PSC-CCA patients

Project description:Cholangiocarcinomas (CCAs) are heterogeneous biliary cancers with dismal prognosis. Their etiologies remain mostly unclear, although primary sclerosing cholangitis (PSC) is a well-known risk factor. There is an urgent need of accurate non-invasive biomarkers for early CCA diagnosis and to predict patient´s prognosis, which may improve their clinical management and outcome. Here, by high-throughput proteomic analysis of serum extracellular vesicles (EVs), we identified diagnostic and/or prognostic biomarkers specific for CCAs in patients with PSC, and others common to all CCA subtypes. Bulk RNA tissue transcriptomic data revealed that these biomarkers are predominantly expressed in hepatobiliary tissues, being expressed in different liver cell types. Furthermore, single-cell RNAseq (scRNA-seq) analysis revealed that the expression of these biomarkers was preferentially found in malignant cholangiocytes within CCA tumors. In summary, these results highlight the potential of serum EVs to reflect cancer presence, especially for difficult-to-diagnose malignancies, including CCA."

2023-07-20 | PXD026197 | Pride

Immunomodulatory response to neoadjuvant nivolumab in non-metastatic clear cell renal cell carcinoma

Project description:Novel perioperative strategies are needed to reduce recurrence rates in patients undergoing nephrectomy for high-risk, non-metastatic clear cell renal cell carcinoma (ccRCC). We conducted a prospective, phase I trial of neoadjuvant nivolumab prior to nephrectomy in 15 evaluable patients with non-metastatic ccRCC. We leveraged tissue from that cohort to elucidate the effects of PD-1 inhibition on immune cell populations in ccRCC and correlate the evolving immune milieu with anti-PD-1 response. We found that nivolumab durably induces a pro-inflammatory state within the primary tumor, and baseline immune infiltration within the primary tumor correlates with nivolumab responsiveness. Nivolumab increases CTLA-4 expression in the primary tumor, and subsequent nephrectomy increases circulating concentrations of sPD-L1, sPD-L3 (sB7-H3), and s4-1BB. These findings form the basis to consider neoadjuvant immune checkpoint inhibition (ICI) for high-risk ccRCC while the tumor remains in situ and provide the rationale for perioperative strategies of novel ICI combinations.

2023-12-18 | GSE245372 | GEO

Genomic Heterogeneity and the Small Renal Mass

Project description:Tumor heterogeneity is believed to represent a barrier to pre-operative genomic characterization in kidney cancer. Previous studies of heterogeneity in clear cell renal cell carcinoma (ccRCC) evaluated only large and metastatic tumors. In small renal tumors in which multiple biopsies are not feasible, the extent of heterogeneity remains unknown. In this study, we evaluated how the extent of genomic heterogeneity in small and large ccRCC. A total of 23 small (<4cm ) and 24 large (>7cm ) ccRCC had 3 regions sampled for evaluation of copy number, clear-cell A/clear-cell B (ccA/ccB) classification, and cell cycle progression (CCP) score. Small tumors have less genomic complexity and significantly fewer subclonal events. Pre-treatment genomic characterization based on a single biopsy in small ccRCC can provide insight into biologic potential to make clinical decisions.

| EGAS00001002919 | EGA

Computational analysis of cholangiocarcinoma phosphoproteomes identifies patient-specific drug targets

Project description:We used mass-spectrometry based phosphoproteomics and computational methods to identify patient-specific drug targets in primary CCA and CCA-derived cell lines. We analyzed 13 primary CCA with matched background tissue and 8 different cell lines leading to the identification and quantification of >13,000 phosphorylation sites. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm identified inhibitors of HDAC and PI3K pathway members as highly ranking in primary CCA relative to background. The accuracy of drug rankings based on predicted responses was confirmed using cell line models of CCA. Together, our study uncovers frequently overactive biochemical pathways in primary CCA and provides a proof-of-concept of the use of machine learning for ranking drugs based on efficacy within individual patient tumors.

2023-03-11 | PXD027329 | Pride

Renal cell carcinoma: a proteomic approach

Project description:The proteome of clinical tissue samples diagnosed with clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) were evaluated analyzed along with the dataset identifier PXD022018 to establish a potential discriminative biomarker panel of proteins for these tumors subtypes.

2021-12-15 | PXD023296 | Pride

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma

Project description:We report a kidney cancer tissue-based prognostic biomarker encompassing 15 genes (15G score) to classify patients into low versus high risk for recurrence after curative nephrectomy. The 15G score was independently associated with disease free survival adjusting for clinicopathologic variables as well as existing clinical risk calculators or nomograms. By improving risk stratification of patients with ccRCC, the 15G score has the capacity to facilitate selection of biopsy confirmed small renal cancers (T1a) for treatment versus surveillance; inform intensity and duration of surveillance after curative nephrectomy; and to potentially facilitate patient selection for adjuvant systemic therapy. We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched based on grade/stage to patients without recurrence. Capture whole transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNAseq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease specific survival (DSS) and overall survival (OS) in two validation datasets (combined n=761).

2024-04-01 | GSE254566 | GEO

Robust Stratification of Breast Cancer Subtypes

Project description:Breast cancer, the second leading cause of cancer death of women worldwide, is a heterogenous disease with multiple different subtypes. These subtypes carry important implications for prognosis and therapy. Interestingly, it is known that these different subtypes not only have different biological behaviors, but also have distinct gene expression profiles. However, it has not been rigorously explored whether particular transcriptional isoforms are also differentially expressed among breast cancer subtypes, or whether transcript isoforms from the same sets of genes can be used to differentiate subtypes. To address these questions, we analyzed the patterns of transcript isoform expression using a small set of RNA-sequencing data for eleven Estrogen Receptor positive (ER+) subtype and fifteen triple negative (TN) subtype tumors. We identified specific sets of isoforms that distinguish these tumor subtypes with higher fidelity than standard mRNA expression profiles. We found that alternate promoter usage, alternative splicing, and alternate 3’UTR usage are differentially regulated in breast cancer subtypes. Profiling of isoform expression in a second, independent cohort of 68 tumors confirmed that expression of splice isoforms differentiates breast cancer subtypes. Furthermore, analysis of RNAseq data from 594 cases from the TCGA cohort confirmed the ability of isoform usage to distinguish breast cancer subtypes. Also using our expression data, we identified several RNA processing factors that were differentially expressed between tumor subtypes and/or regulated by Estrogen Receptor, including YBX1, YBX2, MAGOH, MAGOHB, and PCBP2. RNAi knock-down of these RNA processing factors in MCF7 cells altered isoform expression. These results indicate that global dysregulation of splicing in breast cancer occurs in a subtype-specific and reproducible manner and is driven by specific differentially expressed RNA processing factors.

2017-02-15 | GSE94899 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data