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Updated: Nov 10, 2025

Comparing Metastatic Clear Cell Renal Cell Carcinoma Model Established in Mouse Kidney and on Chicken Chorioallantoic Membrane
Published on: February 8, 2020
Molecular and Metabolic Subtypes in Sporadic and Inherited Clear Cell Renal Cell Carcinoma
Maria F Czyzyk-Krzeska1,2,3, Julio A Landero Figueroa3,4, Shuchi Gulati5
1Department of Cancer Biology, University of Cincinnati, Cincinnati, OH 45267, USA.
Abstract:
The promise of personalized medicine is a therapeutic advance where tumor signatures obtained from different omics platforms, such as genomics, transcriptomics, proteomics, and metabolomics, in addition to environmental factors including metals and metalloids, are used to guide the treatments. Clear cell renal carcinoma (ccRCC), the most common type of kidney cancer, can be sporadic (frequently) or genetic (rare), both characterized by loss of the von Hippel-Lindau (VHL) gene that controls hypoxia inducible factors. Recently, several genomic subtypes were identified with different prognoses. Transcriptomics, proteomics, metabolomics and metallomic data converge on altered metabolism as the principal feature of the disease. However, in view of multiple biochemical alterations and high level of tumor heterogeneity, identification of clearly defined subtypes is necessary for further improvement of treatments. In the future, single-cell combined multi-omics approaches will be the next generation of analyses gaining deeper insights into ccRCC progression and allowing for design of specific signatures, with better prognostic/predictive clinical applications.
Insights
Personalized medicine uses multi-omics data to tailor treatments for clear cell renal carcinoma (ccRCC). Further research into tumor heterogeneity and subtypes will improve prognostic and predictive clinical applications.
Area of Science:
- Oncology
- Genetics
- Biochemistry
Background:
- Clear cell renal carcinoma (ccRCC) is the most common kidney cancer, often linked to von Hippel-Lindau (VHL) gene loss.
- Tumor heterogeneity and multiple biochemical alterations complicate treatment strategies.
Purpose of the Study:
- To explore the potential of multi-omics data in understanding ccRCC.
- To identify distinct ccRCC subtypes for improved therapeutic guidance.
Main Methods:
- Analysis of genomics, transcriptomics, proteomics, metabolomics, and metallomics data.
- Integration of environmental factors and tumor signatures.
Main Results:
- Altered metabolism is a key feature across transcriptomic, proteomic, metabolomic, and metallomic profiles.
- Identification of genomic subtypes with varying prognoses.
Conclusions:
- Multi-omics approaches are crucial for deciphering ccRCC complexity.
- Future single-cell multi-omics analyses will enhance prognostic/predictive applications and personalized treatment strategies.
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