github link
Accession IconGSE9492

Analyses of heterogeneous renal allograft biopsies reveal conserved rejection signatures and molecular pathways II

Organism Icon Homo sapiens
Sample Icon 6 Downloadable Samples
Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Submitter Supplied Information

Description
Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining molecular Banff signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set here comprises a small validation batch of renal allograft biopsies for clinical indications plus control normal kidney samples from patients at Hpital Tenon, Paris (second batch) that complements the first batch of 60 samples.
PubMed ID
No associated PubMed ID
Publication Title
No associated publication
Total Samples
6
Submitter’s Institution
Authors
No associated authors

Samples

Show of 0 Total Samples
Filter
Add/Remove
Accession Code
Title
Subject
Processing Information
Additional Metadata
No rows found
Loading...