Exploring the prediction model of chronic renal failure based on serum proteomics / 基础医学与临床

ABSTRACT

Objective To Screen serum protein markers related to CRF and establish a diagnosis model,exploring and discussing its significance in serodiagnosis by comparing differences of serum protein spectrum expression between patients with chronic renal failure (CRF) and control group.Methods The trial included 62 CRF patients and 28 control ones.Serum samples were tested by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS).The data were analyzed to screen serum proteomic biomarkers.By bioinformatics analysis,decision classification tree models were to be established and tested.Results A total of 19 effective protein peaks were significantly different between CRF and normal control (P＜0.001) at m/z range of 1 500 to 30 000,among which 18 showed low expression and 1 showed high expression in CRF.CRF and normal control were obviously different in the clustering;By bioinformatics analysis,a CRF-normal controls of the diagnostic decision tree model was developed,which was 87.8% in with prediction accuracy rate of 87.8% sensitivity of 87.1% and a specificity of 89.3%.Condusion Diagnostic decision tree model provides a more accurate prediction and solid experimental evidence for early clinical diagnosis.