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

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.

Figure 1

Classification of unique proteins identified by classical proteomics. The pie charts display the classification of differentially expressed proteins identified by classical proteomics of 21 RCC lesions and corresponding normal kidney epithelium according to (A) functional protein classes and (B) cellular compartments. Each segment in the pie charts represents either a functional gene family (A) or a cellular compartment (B) as indicated by the respective labels. In addition, the relative distribution is indicated as % of the total target number analyzed. Functional protein classes and distribution into cellular compartments < 5% were combined and categorized as “other”.

Figure 2

Classification of unique proteins identified by PROTEOMEX. The pie charts display the classification of differentially expressed protein spots defined by PROTEOMEX using three RCC cell lines and one normal renal epithelium representing cell line and based on immunostainings obtained with serum samples from 7 healthy individuals and 8 RCC patients, respectively according to (A) functional protein classes and (B) the subcellular distribution of the identified targets. The layout is in analogy to Fig. 1

Figure 3

Classification of differentially expressed genes identified by transcriptome analyses. The pie charts display the data sets obtained by transcriptomics of 13 RCC lesions of the clear cell type, 1 chromophobic and 2 chromophilic RCC lesion along with corresponding normal kidney epithelium and 1 leiomyosarcoma according to their function (A) and cellular localization (B). The layout is in analogy to Fig. 1.

Figure 4

Combined target sharing between the different “ome”-based approaches. Each of the distinct “ome”-based technologies is represented by an oval. The total number of targets identified via the given approach is listed in brackets. The number of shared proteins between the different approaches is stated in the overlapping segments, whereas the three candidate biomarkers defined by all three approaches are stated in the core segment according to their gene names.

Figure 5

Relative distribution pattern of the distinct gene/protein families and compartments using the three ”ome”-based technologies. The bar plots display the relative distribution pattern of genes/proteins which are differentially expressed/immunoreactive applying the various techniques. Black bars represent the number of genes identified by transcriptomics, white bars the number of proteins identified by classical proteomics and grey bars represent the PROTEOMEX targets. (A) illustrates the relative distribution of the gene/protein families, (B) the subcellular distribution of the proteins. The total number of genes/proteins in each bar segment is listed under n.

Figure 6

Immunohistochemical analysis for alpha-enolase expression by tissue micro arrays technology. RCC tissue micro arrays were stained with the alpha-enolase specific antibody as described in Material and methods section. Panel A shows the staining pattern of RCC of clear cell subtype, panel B that of chromophilic subtype, panel C that of chromophobic subtype and panel D renal cell adenomas of oncocytic type. Light grey segments indicate strong positive, dark grey segments intermediate, black segments weak staining and blank segments no staining. The relative staining frequencies are indicated by providing the respective percentage for each segment.

Figure 7

Immunohistochemical analysis for annexin A3 expression by tissue micro arrays technology. RCC tissue micro arrays were stained with the annexin A3 specific antibody as described in Material and methods section. Panel A shows the staining pattern of RCC of clear cell subtype, panel B that of chromophilic subtype, panel C that of chromophobic subtype and panel D renal cell adenomas of oncocytic type. Light grey segments indicate strong positive, dark grey segments intermediate, black segments weak staining and blank segments indicate no staining. The relative staining frequencies are indicated by providing the respective percentage for each segment.