Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrated multi-omics characterization reveals metabolic reprogramming associated with impaired mitochondrial function in clear cell renal cell carcinoma.

ABSTRACT: Integrated multi-omics characterization reveals metabolic reprogramming associated with impaired mitochondrial function in clear cell renal cell carcinoma.

PROVIDER: PRJNA483492 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Integrated multi-omics characterization reveals metabolic reprogramming associated with impaired mitochondrial function in clear cell renal cell carcinoma.

Project description:An altered metabolism is involved in the development of clear cell - renal cell carcinoma (ccRCC), and in this tumor many altered genes play a fundamental role in controlling cell metabolic activities. In this study we delineated a large-scale metabolomic profile of human ccRCC, and integrated it with transcriptomic data to connect the variations in cancer metabolism with gene expression changes. Moreover, to better analyze the specific contribution of metabolic gene alterations potentially associated with tumorigenesis and tumor progression, we evaluated the transcription profile of primary renal tumor cells. Untargeted metabolomic analysis revealed a signature of an increased glucose uptake and utilization in ccRCC. In addition, metabolites related to pentose phosphate pathway were also altered in the tumor samples in association with changes in Krebs cycle intermediates and related metabolites. Using an integrated multi-omics approach we identified NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 4-like 2 (NDUFA4L2) as the most highly expressed gene in renal cancer cells and evaluated its role in sustaining angiogenesis, chemoresistance, and mitochondrial dysfunction. Finally we showed that silencing of NDUFA4L2 affects cell viability, increases mitochondrial mass, and induces ROS generation in hypoxia. These findings delineate a ccRCC metabolic signature characterized by an anaerobic switch that favors rerouting of the sugar metabolism toward the PPP, and impairs the mitochondrial activity though the overexpression of NDUFA4L2. Thus, this protein could serve as a marker of ccRCC aggressiveness, as well as a potential novel therapeutic target.

2019-01-17 | GSE117890 | GEO

Understanding the biological processes of clear cell renal carcinoma carcinogenesis: an integrative multi-omics approach.

Project description:Multi-Omics analysis to gain novel insights into clear cell renal carcinoma aetiology and progression. The DNA methylation data of 121 clear clear renal carcinoma (ccRCC) were integrated with WGS and transcriptomic data using Multi-Omics Factor Analysis (MOFA) to detect the inter-patient variations related to aetiological and disease progression related factors.

2024-06-07 | GSE269180 | GEO

Genomic characterization of Chinese clear cell renal cell carcinoma

Project description:Large-scale initiatives like The Cancer Genome Atlas (TCGA) performed omics studies on hundreds of kidney cancer patients, but predominantly on Caucasians. We now investigated genomics of Chinese clear cell renal cell carcinoma (ccRCC) patients.

2020-02-22 | GSE126964 | GEO

An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma

Project description:Changes in cellular metabolism contribute to the development and progression of tumors, and can render tumors vulnerable to interventions. However, studies of human cancer metabolism remain limited due to technical challenges of detecting and quantifying small molecules, the highly interconnected nature of metabolic pathways, and the lack of designated tools to analyze and integrate metabolomics with other âomics data. Our study generates the largest comprehensive metabolomics dataset on a single cancer type, and provides a significant advance in integration of metabolomics with sequencing data. Our results highlight the massive re-organization of cellular metabolism as tumors progress and acquire more aggressive features. The results of our work are made available through an interactive public data portal for cancer research community. 10 RNA samples from human ccRCC tumors analyzed from the high glutathione cluster

2016-03-08 | E-GEOD-74734 | biostudies-arrayexpress

Clear-cell renal cell carcinomas tumors

Project description:Whole genome expression of 39 clear-cell renal cell carcinomas tumors 39 frozen tissue from recent nephrectomies for clear-cell renal cell carcinoma; association with clinical characteristics

2011-05-28 | E-GEOD-29609 | biostudies-arrayexpress

Understanding the biological processes of clear cell renal carcinoma carcinogenesis: an integrative multi-omics approach.

Project description:Understanding the biological processes of clear cell renal carcinoma carcinogenesis: an integrative multi-omics approach.

| PRJNA1120468 | ENA

Clear-cell renal cell carcinomas tumors

Project description:Whole genome expression of 39 clear-cell renal cell carcinomas tumors

2011-05-28 | GSE29609 | GEO

CTSP Kidney Renal Clear Cell Carcinoma E2805T6

Project description:CTSP Kidney Renal Clear Cell Carcinoma E2805T6

| PRJNA562745 | ENA

An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma

Project description:Changes in cellular metabolism contribute to the development and progression of tumors, and can render tumors vulnerable to interventions. However, studies of human cancer metabolism remain limited due to technical challenges of detecting and quantifying small molecules, the highly interconnected nature of metabolic pathways, and the lack of designated tools to analyze and integrate metabolomics with other –omics data. Our study generates the largest comprehensive metabolomics dataset on a single cancer type, and provides a significant advance in integration of metabolomics with sequencing data. Our results highlight the massive re-organization of cellular metabolism as tumors progress and acquire more aggressive features. The results of our work are made available through an interactive public data portal for cancer research community.

2016-03-08 | GSE74734 | GEO

Deep visual multi-omics profiling reveals mechanisms that underly cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

Project description:Clear cell renal cell carcinoma (ccRCC) exhibits significant intra-tumoral heterogeneity (ITH) at both morphological and genetic levels, complicating treatment and contributing to disease progression. Among these, rhabdoid ccRCCs stand out as highly aggressive tumors distinguished by cells with unique morphological features and poor clinical outcomes3. However, the relationship between distinct cellular morphologies, specific molecular alterations, and their impact on tumor behavior remains poorly understood. Unraveling the molecular underpinnings of cells with clinically relevant histological features will be critical for developing more effective treatments targeting key cellular subsets responsible for tumor progression. In this study, we integrated advanced AI-based image analysis with single-cell isolation and multi-omics profiling to dissect the link between clinically relevant morphological and molecular features of ccRCC cells. Using a novel digital pathology workflow, we precisely quantified low-grade, high-grade, and rhabdoid morphologies in ccRCC diagnostic images. By isolating two sets of 1,000 morphologically distinct cells for detailed mRNA and protein expression analyses we uncovered increasing dysregulation of cancer-relevant molecular pathways associating with higher histopathological grades. Rhabdoid ccRCC cells (grade 4) exhibited a distinct molecular profile, including upregulated FOXM1-driven proliferation, disrupted cell-matrix interactions, and enhanced immune evasion pathways. Despite high T-cell infiltration in rhabdoid areas, we identified a rhabdoid cell-specific immunosuppressive network driven by cytokines, IFN-beta, and integrin signaling, likely contributing to T-cell exhaustion. These findings provide a basis for novel combination treatment strategies aimed at enhancing immune checkpoint inhibitor (ICI) efficacy in patients with aggressive rhabdoid ccRCC.

2025-02-21 | PXD061011 | Pride

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