This article is part of the supplement: Proceedings of the 2009 AMIA Summit on Translational Bioinformatics
Network analysis of genes regulated in renal diseases: implications for a molecular-based classification
- Equal contributors
1 Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr., Domino's Farm, Lobby L, Ann Arbor, MI 48109-0738, USA
2 Michigan Institute for Clinical & Health Research, 24 Frank Lloyd Wright Dr., Domino's Farm, Lobby L, Ann Arbor, MI 48109-0738, USA
3 Dept. of Internal Medicine, Division of Nephrology, University of Michigan Medical School, 1150 W. Medical Center Drive, MSRB2, SPC 5676 Ann Arbor, MI 48109-5676, USA
4 Computer Science and Engineering, University of Michigan, 2260 Hayward, Ann Arbor, MI 48109-2121 USA
BMC Bioinformatics 2009, 10(Suppl 9):S3 doi:10.1186/1471-2105-10-S9-S3Published: 17 September 2009
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.
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.
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.