Topological network analysis of differentially expressed genes in cancer cells with acquired gefitinib resistance.

BACKGROUND/AIM Despite great effort to elucidate the process of acquired gefitinib resistance (AGR) in order to develop successful chemotherapy, the precise mechanisms and genetic factors of such resistance have yet to be elucidated. MATERIALS AND METHODS We performed a cross-platform meta-analysis of three publically available microarray datasets related to cancer with AGR. For the top 100 differentially expressed genes (DEGs), we clustered functional modules of hub genes in a gene co-expression network and a protein-protein interaction network. We conducted a weighted correlation network analysis of total DEGs in microarray dataset GSE 34228. The identified DEGs were functionally enriched by Gene Ontology (GO) function and KEGG pathway. RESULTS We identified a total of 1,033 DEGs (510 up-regulated, 523 down-regulated, and 109 novel genes). Among the top 100 up- or down-regulated DEGs, many genes were found in different types of cancers and tumors. Through integrative analysis of two systemic networks, we selected six hub DEGs (Pre-B-cell leukemia homeobox1, Transient receptor potential cation channel subfamily C member 1, AXL receptor tyrosine kinase, S100 calcium binding protein A9, S100 calcium binding protein A8, and Nucleotide-binding oligomerization domain containing 2) associated with calcium homeostasis and signaling, apoptosis, transcriptional regulation, or chemoresistance. We confirmed a correlation of expression of these genes in the microarray dataset. CONCLUSION Our study may lead to comprehensive insights into the complex mechanism of AGR and to novel gene expression signatures useful for further clinical studies.