Systematic bioinformatic approaches reveal novel gene expression signatures associated with acquired resistance to EGFR targeted therapy in lung cancer.

Objectives: Human non-small cell lung cancer (NSCLC) that harbors activating mutations in epidermal growth factor receptor (EGFR) initially responds to treatment with EGFR tyrosine kinase inhibitors (TKIs) such as gefitinib and erlotinib but eventually tumor cells acquire resistance. To date, several gene expression profiles have been reported in TKIs-resistant EGFR-mutant NSCLC. The objective of this study is to identify robust gene expression signatures, biological processes, and promising overcoming targets for TKIs-resistant EGFR-mutant NSCLC.

Materials And Methods: Five publicly available microarray datasets were integrated by performing two network-based meta-analyses following by protein-protein interaction (PPI) network and gene set enrichment analysis.

Results And Conclusion: According to our meta-analyses, 830 and 1286 genes were differentially expressed in the TKIs-resistant EGFR-mutant NSCLC cell lines compared to TKIs-sensitive EGFR-mutant NSCLC cell lines in the absence and presence of TKIs treatment, respectively. PPI network analysis identified ESR1 and ELAVL1 to be the most highly ranked hub genes involved in the NSCLC acquired TKI-resistance. Moreover, gene set enrichment analyses indicated that up-regulated genes are mainly distributed in hallmarks "Glycolysis", some "E2F targets". Down-regulated genes mainly contribute to hallmarks "interferon alpha response", "interferon gamma response", and also "E2F targets". For the first time, this study has demonstrated several robust candidate genes and pathways of the NSCLC acquired TKI-resistance. Further experimental verifications are highly recommended to examine our findings.