Proteomics in kidney allograft transplantation - Application of molecular pathway analysis for kidney allograft disease phenotypic biomarker selection.

PURPOSE: There is a need for accurate, robust, non-invasive methods to provide early diagnosis of graft lesions after kidney transplantation. A multitude of proteomic biomarkers for the major kidney allograft disease phenotypes defined by the BANFF classification criteria have been described in literature. None of these biomarkers have been established in the clinic. A key reason for this is the lack of clinical validation which is difficult, as even the gold standard of diagnosis, kidney biopsy, is often ambiguous.

EXPERIMENTAL DESIGN: We evaluated semantic clustering by ReviGO on top of transcriptomic pathway analysis to connect histological and transcriptomic kidney allograft disease characteristics with proteomic biomarker qualification.

RESULTS: By using public data generated in microarray studies of kidney allograft tissue, we were able to identify biological processes and key molecules specifically associated with the different kidney allograft disease phenotypes.

CONCLUSIONS AND CLINICAL RELEVANCE: Semantic clustering holds the promise to guide adaptation of proteomic marker panels to molecular pathology. This can support the development of noninvasive tests (e.g. in urine, by capillary electrophoresis mass spectrometry) that simultaneously detect diverse kidney allograft phenotypes with high accuracy and sensitivity. This article is protected by copyright. All rights reserved.