Details

Autor(en) / Beteiligte

• Wang, Huimei
• Xie, Qiqi
• Ou‐Yang, Wen
• Zhang, Mingwei

Titel

Integrative analyses of genes associated with idiopathic pulmonary fibrosis

Ist Teil von

• Journal of cellular biochemistry, 2019-05, Vol.120 (5), p.8648-8660

Ort / Verlag

United States: Wiley Subscription Services, Inc

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Wiley Online Library Journals Frontfile Complete

Beschreibungen/Notizen

• Idiopathic pulmonary fibrosis (IPF), characterized by irreversible scarring and progressive destruction of the lung tissue, is one of the most common types of idiopathic interstitial pneumonia worldwide. However, there are no reliable candidates for curative therapies. Hence, elucidation of the mechanisms of IPF genesis and exploration of potential biomarkers and prognostic indicators are essential for accurate diagnosis and treatment of IPF. Recently, efficient microarray and bioinformatics analyses have promoted an understanding of the molecular mechanisms of disease occurrence and development, which is necessary to explore genetic alternations and identify potential diagnostic biomarkers. However, high false‐positive rates results have been observed based on single microarray datasets. In the current study, we performed a comprehensive analysis of the differential expression, biological functions, and interactions of IPF‐related genes. Three publicly available microarray datasets including 54 IPF samples and 34 normal samples were integrated by performing gene set enrichment analysis and analyzing differentially expressed genes (DEGs). Our results identified 350 DEGs genetically associated with IPF. Gene ontology analyses revealed that the changes in the modules were mostly enriched in the positive regulation of smooth muscle cell proliferation, positive regulation of inflammatory responses, and the extracellular space. Kyoto encyclopedia of genes and genomes enrichment analysis of DEGs revealed that IPF involves the TNF signaling pathway, NOD‐like receptor signaling pathway, and PPAR signaling pathway. To identify key genes related to IPF in the protein‐protein interaction network, 20 hub genes were screened out with highest scores. Our results provided a framework for developing new pathological molecular networks related to specific diseases in silico. Here we used a comprehensive analysis concentrated on differential expression, biological function, and interactions of IPF‐related genes. Our results provide a framework for developing new pathological molecular and gene network related to a specific disease in silico.