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Profiling the proteome in renal transplantation
Author(s) -
Sigdel Tara K.,
Lee Sangho,
Sarwal Minnie M.
Publication year - 2011
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201000117
Subject(s) - biomarker discovery , transplantation , proteomics , biomarker , proteome , profiling (computer programming) , computational biology , identification (biology) , kidney transplantation , genomics , bioinformatics , medicine , biology , computer science , genome , gene , biochemistry , botany , operating system
Improved monitoring of transplanted solid organs is one of the next crucial steps leading to an increase in both patient and allograft survival. This can be facilitated through one or a set of surrogate biomarker molecules that accurately and precisely indicate the health status of the transplanted organ. Recent developments in the field of high throughput “omic” methods including genomics and proteomics have facilitated robust and comprehensive analysis of genes and proteins. This development has stimulated efforts in the identification of effective and clinically applicable gene and protein biomarkers in solid organ transplantation, including kidney transplantation. Some achievements have been made through proteomics in terms of profiling proteins and identification of potential biomarkers. However, the road to a successful biomarker discovery and its clinical implementation has proved to be challenging, requiring a number of key issues to be addressed. Such issues are: the lack of widely accepted protocols, difficulty in sample processing and transportation and a lack of collaborative efforts to achieve significant sample sizes in clinical studies. In this review using our area of expertise, we describe the current strategies used for proteomic‐based biomarker discovery in renal transplantation, discuss inherent issues associated with these efforts and propose better strategies for successful biomarker discovery.