Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma

Barbara Seliger, Sven P. Dressler, Ena Wang, Roland Kellner, Christian V. Recktenwald, Friedrich Lottspeich, Francesco M. Marincola, Maja Baumgärtner, Derek Atkins, Rudolf Lichtenfels

Research output: Contribution to journalArticle

32 Citations (Scopus)

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 α-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.

Original languageEnglish
Pages (from-to)1567-1581
Number of pages15
JournalProteomics
Volume9
Issue number6
DOIs
Publication statusPublished - Mar 2009
Externally publishedYes

Fingerprint

Gene Expression Profiling
Biomarkers
Proteome
Renal Cell Carcinoma
Cells
Microarrays
Oligonucleotide Array Sequence Analysis
Complementary DNA
Microarray Analysis
Technology
Proteomics
Annexin A4
Signal transduction
Hydrolases
Tubulin
Ubiquitin
Metabolism
Cell Movement
Signal Transduction
Carrier Proteins

Keywords

  • Biomarkers
  • Proteome-based technologies
  • RCC
  • Transcriptomics

ASJC Scopus subject areas

  • Molecular Biology
  • Biochemistry

Cite this

Seliger, B., Dressler, S. P., Wang, E., Kellner, R., Recktenwald, C. V., Lottspeich, F., ... Lichtenfels, R. (2009). Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma. Proteomics, 9(6), 1567-1581. https://doi.org/10.1002/pmic.200700288

Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma. / Seliger, Barbara; Dressler, Sven P.; Wang, Ena; Kellner, Roland; Recktenwald, Christian V.; Lottspeich, Friedrich; Marincola, Francesco M.; Baumgärtner, Maja; Atkins, Derek; Lichtenfels, Rudolf.

In: Proteomics, Vol. 9, No. 6, 03.2009, p. 1567-1581.

Research output: Contribution to journalArticle

Seliger, B, Dressler, SP, Wang, E, Kellner, R, Recktenwald, CV, Lottspeich, F, Marincola, FM, Baumgärtner, M, Atkins, D & Lichtenfels, R 2009, 'Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma', Proteomics, vol. 9, no. 6, pp. 1567-1581. https://doi.org/10.1002/pmic.200700288
Seliger, Barbara ; Dressler, Sven P. ; Wang, Ena ; Kellner, Roland ; Recktenwald, Christian V. ; Lottspeich, Friedrich ; Marincola, Francesco M. ; Baumgärtner, Maja ; Atkins, Derek ; Lichtenfels, Rudolf. / Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma. In: Proteomics. 2009 ; Vol. 9, No. 6. pp. 1567-1581.
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