Cellular processes involved in lung cancer cells exposed to direct current electric field

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Abstract
With the rapid breakthrough of electrochemical treatment of tumors, electric field (EF)-sensitive genes, previously rarely exploited, have become an emerging field recently. Here, we reported our work for the identification of EF-sensitive genes in lung cancer cells. The gene expression profile (GSE33845), in which the human lung cancer CL1-0 cells were treated with a direct current electric field (dcEF) (300 mV/mm) for 2 h, was retrieved from GEO database. Differentially expressed genes (DEGs) were acquired, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) and protein-protein interaction (PPI) analysis. Hub genes were acquired and analyzed by various tools including the Human Protein Atlas, Kaplan-Meier analysis, Cytoscape, FunRich, Oncomine and cBioPortal. Subsequently, three-dimensional protein models of hub genes were modeled by Modeller 9.20 and Rosetta 3.9. Finally, a 100 ns molecular dynamics simulation for each hub protein was performed with GROMACS 2018.2. A total of 257 DEGs were acquired and analyzed by GO, KEGG and PPI. Then, 10 hub genes were obtained, and the signal pathway analysis showed that two inflammatory pathways were activated: the FoxO signaling pathway and the AGE-RAGE signaling pathway. The molecular dynamic analysis including RMSD and the radius of gyration hinted that the 3D structures of hub proteins were built. Overall, our work identified EF-sensitive genes in lung cancer cells and identified that the inflammatory state of tumor cells may be involved in the feedback mechanism of lung cancer cells in response to electric field stimulation. In addition, qualified three-dimensional protein models of hub genes were also constructed, which will be helpful in understanding the complex effects of dcEF on human lung cancer CL1-0 cells.
Funding Information
  • National Natural Science Foundation of China (No. 61873209, No. 61704139)
  • China Postdoctoral Science Foundation (No. 2017M623250)

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