Discovery of potential drugs to treat aggressive pituitary adenomas through text mining

Abstract Background Aggressive growth hormone-secreting pituitary adenomas (GHSPAs) account for 20–45% of GHSPAs. Although they are benign, treatment of GHSPAs is usually unsatisfactory.We wished to identify existing gene–drug interactions and expand the potential indications for new drugs to treat GHSPAs. Methods We used text mining with the keywords “growth hormone” and “visual disturbance” to obtain a common set of genes. These genes were analyzed using Genome Ontology and Kyoto Encyclopedia of Genes and Genomes databases, as well as protein–protein interaction networks. Finally, important genes clustered in PPI networks were selected for analyses of gene–drug interactions to identify potential drugs. Results Through text mining, we obtained 2848 genes related to growth hormone and 220 genes related to visual disturbance, and 157 genes were common to both. Common genes were clustered in significant gene modules. Finally, 12 of the 17 genes were targeted against 46 existing drugs: APOE, TRH, MMP2, ACE, CXCL8, MMP1, ALB, VEGFA, EDN1, TNF, PTH, and VWF. Conclusion Drug–gene interactions increase our understanding of the pathogenesis of invasive GHSPAs, and could be used in their prevention and treatment.