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Identifying common genes and networks in multi-organ fibrosis
Kevin E Wenzke1, Carmen Cantemir-Stone, Jie Zhang
1Department of Biomedical Informatics, The Ohio State University, Columbus, OH;
Unlabelled:
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.
Insights
Organ fibrosis, including lung, heart, liver, and kidney diseases, shares common gene expression patterns. This discovery may lead to novel anti-fibrotic treatments targeting these shared regulatory factors.
Area of Science:
- Biomedical research
- Genomics
- Pathology
Background:
- Pathologic fibrosis affects multiple organs and lacks effective anti-fibrotic treatments.
- Understanding common regulatory factors in organ-based fibrotic diseases is crucial.
Purpose of the Study:
- To identify key regulatory factors common to various organ fibrotic diseases.
- To explore shared molecular mechanisms underlying different fibrotic conditions.
Main Methods:
- Analysis of 9 publicly available microarray datasets (489 samples total) from fibrotic lung, heart, liver, and kidney tissues.
- Differential gene expression analysis to identify commonly altered genes across fibrotic conditions.
- Bioinformatic analysis (IPA, DAVID) for gene network and molecular function identification.
Main Results:
- A set of 90 differentially expressed genes was identified across at least five fibrotic disease datasets.
- Network analysis revealed a highly active connective tissue disorders network common to all analyzed fibrosis types.
- Identification of shared molecular pathways and gene networks in organ fibrosis.
Conclusions:
- Despite distinct clinical manifestations, organ fibrotic diseases share a common genetic basis.
- These shared genes suggest a potential common origin or pathway for fibrotic diseases.
- Findings pave the way for developing pan-fibrotic anti-fibrotic therapies.
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