Network analysis of genes regulated in renal diseases

Implications for a molecular-based classification

Suresh Bhavnani, Felix Eichinger, Sebastian Martini, Paul Saxman, HV V. Jagadish, Matthias Kretzler

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

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. Conclusion: 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.

Original languageEnglish (US)
Article number1471
JournalBMC Bioinformatics
Volume10
Issue numberSUPPL. 9
StatePublished - Sep 17 2009
Externally publishedYes

Fingerprint

Gene Regulatory Networks
Network Analysis
Electric network analysis
Genes
Gene
Kidney
Chronic Disease
Concordance
Chronic Renal Insufficiency
Gene Expression Profile
Breast Cancer
Transcriptome
Uniformity
Biopsy
Therapy
Visualization
Gene expression
Regularity
Breast Neoplasms
Predict

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

Cite this

Bhavnani, S., Eichinger, F., Martini, S., Saxman, P., Jagadish, HV. V., & Kretzler, M. (2009). Network analysis of genes regulated in renal diseases: Implications for a molecular-based classification. BMC Bioinformatics, 10(SUPPL. 9), [1471].

Network analysis of genes regulated in renal diseases : Implications for a molecular-based classification. / Bhavnani, Suresh; Eichinger, Felix; Martini, Sebastian; Saxman, Paul; Jagadish, HV V.; Kretzler, Matthias.

In: BMC Bioinformatics, Vol. 10, No. SUPPL. 9, 1471, 17.09.2009.

Research output: Contribution to journalArticle

Bhavnani, S, Eichinger, F, Martini, S, Saxman, P, Jagadish, HVV & Kretzler, M 2009, 'Network analysis of genes regulated in renal diseases: Implications for a molecular-based classification', BMC Bioinformatics, vol. 10, no. SUPPL. 9, 1471.
Bhavnani S, Eichinger F, Martini S, Saxman P, Jagadish HVV, Kretzler M. Network analysis of genes regulated in renal diseases: Implications for a molecular-based classification. BMC Bioinformatics. 2009 Sep 17;10(SUPPL. 9). 1471.
Bhavnani, Suresh ; Eichinger, Felix ; Martini, Sebastian ; Saxman, Paul ; Jagadish, HV V. ; Kretzler, Matthias. / Network analysis of genes regulated in renal diseases : Implications for a molecular-based classification. In: BMC Bioinformatics. 2009 ; Vol. 10, No. SUPPL. 9.
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