Clear-cell renal cell carcinoma (ccRCC) is an increasingly common disease associated with dire prognosis. Identifying the appropriate treatment for this cancer still represents a relevant clinical challenge. Different molecular tests have been developed to understand abnormal molecular mechanisms of ccRCC that may impact drug response. Among these techniques, mass spectrometry imaging enables the discrimination of small cell subpopulations based on their different molecular profiles. We aim to identify relevant biomolecules associated with cancer cells and tumoral microenvironment, providing increasing biological knowledge. Potentially, this approach could discriminate regions that are indistinguishable at a microscopic level by pathologists. These findings can be combined with traditional histology images to generate molecular signatures with prognostic purposes. Here, we proposed a Bayesian model working under the assumption of separate exchangeability of the data to perform a biclustering analysis. This method allows us to segment a tissue section into regions based on their different molecular profiles while simultaneously identifying groups of molecules with similar activation, and consequently investigating molecular mapping within ccRCC tissue regions.