Back to Search
Start Over
Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis
- Source :
- Molecular Medicine, Vol 26, Iss 1, Pp 1-19 (2020)
- Publication Year :
- 2020
- Publisher :
- BMC, 2020.
-
Abstract
- Abstract Background asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious pulmonary diseases that contain common and unique characteristics. Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing misdiagnosis. In this regard, the present study aimed to identify the disorders at the early stages, based on lung transcriptomics data and drug-target interactions. Methods To this end, the differentially expressed genes were found in each disease. Then, WGCNA was utilized to find specific and consensus gene modules among the three diseases. Finally, the disease-disease similarity was analyzed, followed by determining candidate drug-target interactions. Results The results confirmed that the asthma lung transcriptome was more similar to COPD than IPF. In addition, the biomarkers were found in each disease and thus were proposed for further clinical validations. These genes included RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF, LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF and LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, analyzing drug-target networks suggested anti-inflammatory candidate drugs for treating the above mentioned diseases. Conclusion In general, the results revealed the unique and common biomarkers among three chronic lung diseases. Eventually, some drugs were suggested for treatment purposes.
Details
- Language :
- English
- ISSN :
- 10761551 and 15283658
- Volume :
- 26
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Molecular Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.03560ac5177d4ee8be0d0ef9f1f6f7e0
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s10020-019-0135-9