Identifying common genes and networks in multi-organ fibrosis

Abstract

Fibroproliferative diseases of organs are poorly understood and generally lack effective anti-fibrotic treatments. Our goal was to identify the key regulatory factors in pathologic fibrosis, common between organ-based fibrotic disease. We analyzed 9 microarray datasets publicly available in the GEO datasets from lung, heart, liver and kidney fibrotic disease tissue (489 microarrays total, disease and control). We identified a set of 90 genes differentially expressed in at least five microarray datasets. We used IPA and DAVID analysis to identify gene networks and their molecular functions. A mutual information based network work activity analysis showed that a connective tissue disorders network was the most active for all types of fibrosis included in this analysis.

Conclusion: Our analysis indicates that despite different disease manifestation, organ fibrosis share a specific set of genes suggesting the potential for a common origin.

Figure 1.

Workflow for identifying common genes and networks among multiple types of fibrosis.

Figure 2:

Histogram showing the number of microarray datasets in which a gene shows differential expression (p < 0.05 and mean fold change > 1.5).

Figure 3:

A) Network graph for Network 1, involved in connective tissue disorders. B) Network graph for Network 2, involved in genetic and skeletal and muscular disorders.