Linkage of microRNA and proteome-based profiling data sets: a perspective for the priorization of candidate biomarkers in renal cell carcinoma?

Abstract

Despite recent advances in the understanding of the biology of renal cell carcinoma (RCC) and the implementation of novel targeted therapies, the overall 5 years' survival rate for RCC patients remains disappointing. Late presentation, tumor heterogeneity and in particular the lack of molecular biomarkers for early detection and classification represent major obstacles. Global, untargeted comparative analysis of RCC vs tumor adjacent renal epithelium (NN) samples by high throughput analyses both at the transcriptome and proteome level have identified signatures, which might further clarify the molecular differences of RCC subtypes and might allow the identification of suitable therapeutic targets and diagnostic/prognostic biomarkers, but none thereof has yet been implemented in routine clinical use. The increasing knowledge regarding the functional role of noncoding microRNA (miR) in physiological, developmental, and pathophysiological processes by shaping the protein expression profile might provide an important link to improve the definition of disease-relevant regulatory networks. Taking into account that miR profiling of RCC and NN provides robust signatures discriminating between malignant and normal tissues, the concept of evaluating and scoring miR/protein pairs might represent a strategy for the selection and prioritization of potential biomarkers and their translation into practical use.

Figure 1

Overview of influenced metabolic pathways. The scheme assigns the inverse correlated miRs and proteins to their respective metabolic pathways according to the KEGG database. The gray boxes represent metabolic parts. ↑, up-regulated; ↓, down-regulated.