ArticleProteomics2009
Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma.
Article in Proteomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- Integrative proteomics and pharmacogenomics analysis of methylphenidate treatment response.Translational psychiatry · 2019Article
- The past, present, and future of coral heat stress studies.Ecology and evolution · 2019Review
- Article
- Identification of genes associated with renal cell carcinoma using gene expression profiling analysis.Oncology letters · 2016Article
- Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma.PloS one · 2016Article
- A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration.BMC bioinformatics · 2013Article
- MicroRNAs in liver disease.Gastroenterology · 2012Review
- DIPSBC--data integration platform for systems biology collaborations.BMC bioinformatics · 2012Article
- Identification of a Novel Biomarker for Biliary Tract Cancer Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry.International journal of proteomics · 2012Article
- Identification and characterization of human leukocyte antigen class I ligands in renal cell carcinoma cells.Proteomics · 2011Article
- GAP promoter library for fine-tuning of gene expression in Pichia pastoris.Applied and environmental microbiology · 2011Article
- Linkage of microRNA and proteome-based profiling data sets: a perspective for the priorization of candidate biomarkers in renal cell carcinoma?Journal of proteome research · 2011Article
- Metabolic profiling reveals key metabolic features of renal cell carcinoma.Journal of cellular and molecular medicine · 2011Article
- The application of a three-step proteome analysis for identification of new biomarkers of pancreatic cancer.International journal of proteomics · 2011Article
- Stage-related alterations in renal cell carcinoma--comprehensive quantitative analysis by 2D-DIGE and protein network analysis.PloS one · 2011Article
- Breaking through a plateau in renal cell carcinoma therapeutics: development and incorporation of biomarkers.Molecular cancer therapeutics · 2010Review
- Primary cell cultures from human renal cortex and renal-cell carcinoma evidence a differential expression of two spliced isoforms of Annexin A3.The American journal of pathology · 2010Article
- Systematic comparative protein expression profiling of clear cell renal cell carcinoma: a pilot study based on the separation of tissue specimens by two-dimensional gel electrophoresis.Molecular & cellular proteomics : MCP · 2009Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Results obtained from expression profilings of renal cell carcinoma using different "ome"-based approaches and comprehensive data analysis demonstrated that proteome-based technologies and cDNA microarray analyses complement each other during the discovery phase for disease-related candidate biomarkers. The integration of the respective data revealed the uniqueness and complementarities of the different technologies. While comparative cDNA microarray analyses though restricted to up-regulated targets largely revealed genes involved in controlling gene/protein expression (19%) and signal transduction processes (13%), proteomics/PROTEOMEX-defined candidate biomarkers include enzymes of the cellular metabolism (36%), transport proteins (12%), and cell motility/structural molecules (10%). Candidate biomarkers defined by proteomics and PROTEOMEX are frequently shared, whereas the sharing rate between cDNA microarray and proteome-based profilings is limited. Putative candidate biomarkers provide insights into their cellular (dys)function and their diagnostic/prognostic value but still warrant further validation in larger patient numbers. Based on the fact that merely three candidate biomarkers were shared by all applied technologies, namely annexin A4, tubulin alpha-1A chain, and ubiquitin carboxyl-terminal hydrolase L1, the analysis at a single hierarchical level of biological regulation seems to provide only limited results thus emphasizing the importance and benefit of performing rather combinatorial screenings which can complement the standard clinical predictors.
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