Probabilistic (Bayesian) Modeling of Gene Expression in Transplant Glomerulopathy

摘要

Transplant glomerulopathy (TG) is associated with rapid decline in glomerular filtration rate and poor outcome. We utilized low-density arrays with a novel probabilistic analysis to characterize relationships between gene transcripts and the development of TG in allograft recipients. Retrospective review identified TG in 10.8% of 963 core biopsies from 166 patients; patients with stable function (SF) were studied for comparison. The biopsies were analyzed for expression of 87 genes related to immune function and fibrosis using real-time PCR, and a Bayesian model was generated and validated to predict histopathology based on gene expression. A total of 57 individual genes were increased in TG compared with SF biopsies.