Network analysis of genes regulated in renal diseases: implications for a molecular-based classification.
BACKGROUND
Chronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not reliably predict the course of the disease and its response to therapy. In contrast, recent studies in diseases such as breast cancer suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. This article describes how we extracted gene expression profiles from biopsies of patients with chronic renal diseases, and used network visualizations and associated quantitative measures to rapidly analyze similarities and differences between the diseases.


RESULTS
The analysis revealed three main regularities: (1) Many genes associated with a single disease, and fewer genes associated with many diseases. (2) Unexpected combinations of renal diseases that share relatively large numbers of genes. (3) Uniform concordance in the regulation of all genes in the network.


CONCLUSIONS
The overall results suggest the need to define a molecular-based classification of renal diseases, in addition to hypotheses for the unexpected patterns of shared genes and the uniformity in gene concordance. Furthermore, the results demonstrate the utility of network analyses to rapidly understand complex relationships between diseases and regulated genes.
Bhavnani
Suresh K
SK
Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr, Domino's Farm, Lobby L, Ann Arbor, MI 48109-0738, USA. bhavnani@umich.edu
Eichinger
Felix
F
Martini
Sebastian
S
Saxman
Paul
P
Jagadish
H V
HV
Kretzler
Matthias
M
D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D006801:Humans; D007674:Kidney Diseases / Q000145:classification* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis