Purpose: Here we aimed to explore the possible mechanism and potential regulatory relationships in which the non.small.cell lung cancer. (NSCLC)-resisted epidermal growth factor receptor. (EGFR) tyrosine kinase inhibitor erlotinib.
Materials and methods: GSE38310, the gene expression profiles of NSCLC cell lines treated with dimethylsulfoxide or erlotinib, including HCC827, ER3, and T15-2, were downloaded from Gene Expression Omnibus database and preprocessed by normalization. Basing on the regulatory relationships of transcriptional factors obtained from University of California Santa Cruz. (UCSC) database, the differentially expressed genes. (DEGs) were screened using limma package in R with. |logFC| >1 and P < 0.05, and regulatory networks of these DEGs were built with supervised inference of regulatory networks (SIRENE). Subsequently, differentially regulatory networks were compared basing on Limit Fold Change. (LFC) method.
Results: Totally 24,380 genes were obtained, 1,531 DEGs were identified in HCC827 cell lines, 37 DEGs in ER3 cell lines, 156 DEGs in T15-2 cell lines. After removing the redundancy genes, 1,575 differentially expressed genes were got at last. Basing on three regulatory networks of HCC827 cell lines, ER3 cell lines and T15-2 cell lies, sex-determining region Y (SRY).related high mobility group-box gene 9. (SOX9) and Suppressor of cytokine signaling 3 (STAT3) were identified by comparing with HCC827 and ER3 networks. And suppressor of cytokine signaling 5 B (STAT5B), early growth response-1 (EGR1) and STAT6 were obtained in comparison of HCC827 and T15-2 networks.
Conclusions: The regulatory edges with remarkable changes between HCC827 and ER3, HCC827 and T15.2 included some transcription factors and genes. (e. g., STAT3 and SOX9). STAT3, SOX9, STAT5B, EGR1, and STAT6 might affect the resistance of NSCLC to erlotinib.