Identification of differentially expressed genes and signaling pathways using bioinformatics in interstitial lung disease due to tyrosine kinase inhibitors targeting the epidermal growth factor receptor.

ABSTRACT

Interstitial lung disease (ILD) is a rare but lethal adverse effect of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) treatment. The specific mechanism of this disease is not fully understood. To systematically analyze genes associated with EGFR-TKI induced ILD, gene data of EGFR-TKI induced ILD were extracted initially using text mining, and then the intersection between genes from text mining and Gene Expression Omnibus (GEO) dataset was taken for further protein-protein interaction (PPI) analysis using String-bd database. Go ontology (GO) and pathway enrichment analysis was also conducted based on Database of Annotation, Visualization and Integrated Discovery (DAVID) platform. The PPI network generated by STRING was visualized by Cytoscape, and the topology scores, functional regions and gene annotations were analyzed using plugins of CytoNCA, molecular complex detection (MCODE) and ClueGo. 37 genes were identified as EGFR-TKI induced ILD related. Gene enrichment analysis yield 18 enriched GO terms and 12 associated pathways. A PPI network that included 199 interactions for a total of 35 genes was constructed. Ten genes were selected as hub genes using CytoNCA plugin, and four highly connected clusters were identified using MCODE plugin. GO and pathway annotation analysis for the cluster one revealed that five genes were associated with either response to dexamethasone or with lung fibrosis, including CTGF, CCL2, IGF1, EGFR and ICAM1. Our data might be useful to reveal the pathological mechanisms of EGFR-TKI induced ILD and provide evidence for the diagnosis and treatment in the future.