Identifying candidate agents for lung adenocarcinoma by walking the human interactome.

Despite recent advances in therapeutic strategies for lung cancer, mortality is still increasing. Therefore, there is an urgent need to identify effective novel drugs. In the present study, we implement drug repositioning for lung adenocarcinoma (LUAD) by a bioinformatics method followed by experimental validation. We first identified differentially expressed genes between LUAD tissues and nontumor tissues from RNA sequencing data obtained from The Cancer Genome Atlas database. Then, candidate small molecular drugs were ranked according to the effect of their targets on differentially expressed genes of LUAD by a random walk with restart algorithm in protein-protein interaction networks. Our method identified some potentially novel agents for LUAD besides those that had been previously reported (eg, hesperidin). Finally, we experimentally verified that atracurium, one of the potential agents, could induce A549 cells death in non-small-cell lung cancer-derived A549 cells by an MTT assay, acridine orange and ethidium bromide staining, and electron microscopy. Furthermore, Western blot assays demonstrated that atracurium upregulated the proapoptotic Bad and Bax proteins, downregulated the antiapoptotic p-Bad and Bcl-2 proteins, and enhanced caspase-3 activity. It could also reduce the expression of p53 and p21