Aims: This study aimed to find new biomarkers and establish urine protein fingerprint model for diagnosis of renal allograft subclinical rejection (SCR).
Methods: A total of 73 urine samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) combined with bioinformatics tools.
Results: Firstly, 22 urine samples from recipients of stable graft function proved by protocol biopsies and 27 from subclinical rejection gruop were analyzed by SELDI-TOF-MS and Zhejiang University Cancer Institute-ProteinChip Data Analysis System (ZUCI-PDAS). The diagnostic pattern comprised of 4 biomarkers could differentiate SCR group from stable group with sensitivity of 81.5% and specificity of 81.8%. The remaining 14 samples from stable group and 10 samples from SCR were analyzed on the second day as an independent test set. The independent tests yielded a specificity of 71.4% and sensitivity of 90%.
Conclusions: Urine protein fingerprint analysis by SELDI-TOF-MS combined with bioinformatics can help to discover new biomarkers and provide a non-invasive tool to diagnosis of SCR.