Related Experiment Video
Updated: Mar 9, 2026
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Prognostic value of cross-omics screening for kidney clear cell renal cancer survival
Slavica Dimitrieva1, Ralph Schlapbach2, Hubert Rehrauer2
1Functional Genomics Center Zurich, ETH Zurich and University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland. slavica.dimitrieva@fgcz.ethz.ch.
Background:
Kidney renal clear cell carcinoma (KIRC) is a type of cancer that is resistant to chemotherapy and radiotherapy and has limited treatment possibilities. Large-scale molecular profiling of KIRC tumors offers a great potential to uncover the genetic and epigenetic changes underlying this disease and to improve the clinical management of KIRC patients. However, in practice the clinicians and researchers typically focus on single-platform molecular data or on a small set of genes. Using molecular and clinical data of over 500 patients, we have systematically studied which type of molecular data is the most informative in predicting the clinical outcome of KIRC patients, as a standalone platform and integrated with clinical data.
Results:
We applied different computational approaches to preselect on survival-predictive genomic markers and evaluated the usability of mRNA/miRNA/protein expression data, copy number variation (CNV) data and DNA methylation data in predicting survival of KIRC patients. Our analyses show that expression and methylation data have statistically significant predictive powers compared to a random guess, but do not perform better than predictions on clinical data alone. However, the integration of molecular data with clinical variables resulted in improved predictions. We present a set of survival associated genomic loci that could potentially be employed as clinically useful biomarkers.
Conclusions:
Our study evaluates the survival prediction of different large-scale molecular data of KIRC patients and describes the prognostic relevance of such data over clinical-variable-only models. It also demonstrates the survival prognostic importance of methylation alterations in KIRC tumors and points to the potential of epigenetic modulators in KIRC treatment.
Reviewers:
An extended abstract of this research paper was selected for the CAMDA Satellite Meeting to ISMB 2015 by the CAMDA Programme Committee. The full research paper then underwent one round of Open Peer Review under a responsible CAMDA Programme Committee member, Djork-Arné Clevert, PhD (Bayer AG, Germany). Open Peer Review was provided by Martin Otava, PhD (Janssen Pharmaceutica, Belgium) and Hendrik Luuk, PhD (The Centre for Disease Models and Biomedical Imaging, University of Tartu, Estonia). The Reviewer comments section shows the full reviews and author responses.
Insights
This study investigated molecular data for kidney renal clear cell carcinoma (KIRC) patients. Integrating molecular data with clinical information improved survival predictions, highlighting methylation alterations as potential therapeutic targets.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Kidney renal clear cell carcinoma (KIRC) presents limited treatment options due to resistance to conventional therapies.
- Large-scale molecular profiling of KIRC tumors is crucial for uncovering genetic and epigenetic drivers.
- Current clinical and research practices often focus on single molecular platforms or limited gene sets.
Purpose of the Study:
- To systematically assess the predictive power of different molecular data types for KIRC patient survival.
- To determine the most informative molecular data for standalone and integrated prediction models.
- To identify potential clinically useful biomarkers for KIRC prognosis.
Main Methods:
- Applied computational approaches for preselecting survival-predictive genomic markers.
- Evaluated mRNA, miRNA, protein expression, copy number variation (CNV), and DNA methylation data.
- Integrated molecular data with clinical variables for enhanced survival prediction.
Main Results:
- Expression and methylation data showed statistically significant predictive power for KIRC survival.
- Neither expression nor methylation data alone outperformed predictions based on clinical data.
- Integration of molecular data with clinical variables significantly improved survival prediction accuracy.
Conclusions:
- Molecular data, particularly methylation, holds prognostic relevance in KIRC.
- Combined molecular and clinical data offer superior survival prediction compared to clinical data alone.
- Epigenetic modulators represent a promising avenue for KIRC treatment strategies.
More Related Videos
06:38A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
05:36Comparing Metastatic Clear Cell Renal Cell Carcinoma Model Established in Mouse Kidney and on Chicken Chorioallantoic Membrane
Published on: February 8, 2020
