Predicting Novel Drug Candidates for Pancreatic Neuroendocrine Tumors via Gene Signature Comparison and Connectivity Mapping

Introduction There is a paucity of effective treatment options for advanced pancreatic neuroendocrine tumors (pNETs). Genome-wide analyses may allow for potential drugs to be identified based on differentially expressed genes (DEGs). Methods Oligo microarray data of RNA expression profiling of pNETs and normal pancreas tissues were downloaded from the Gene Expression Omnibus. Functional and pathway enrichment information of the DEGs was obtained using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Corresponding homologous proteins were analyzed and potential therapeutic drugs for pNETs were identified using the Connectivity Map and Drug-Gene Interaction Database. Results Assessment of raw data from 12,610 pNET genes demonstrated that 1082 and 380 genes were upregulated and downregulated, respectively, compared with normal pancreas tissue. Upregulated pathways were associated with nitrogen metabolism (i.e., GABAergic synapse, and graft-versus-host disease), whereas downregulated pathways included C-type leptin receptor signaling pathway, pertussis and AMPK signaling pathway. In particular, the protein–protein interaction analysis revealed 10 upregulated hub genes (DYNLL1, GNB5, GNB2, GNG4, GNAI2, GNAI1, HIST2H2BE, NUP107, NUP133, and SNAP25) and 10 downregulated hub genes (CXCL8, F2, CXCL2, GCG, SST, INS, GALR3, CCL20, ADRA2B, and CXCL6). Using the Drug-Gene Interaction Database, candidate drugs for pNETs treatment included 3 EGFR inhibitors (canertinib, erlotinib, WZ-4–145), as well as other cell-signaling pathway inhibitors such as AG-592, acarbose, lonidamine, azacytidine, rottlerin, and HU-211. Conclusion Using available genetic atlas data, potential drug candidates for treatment of pNETs were identified based on differentially expressed genes. These results may help focus efforts on identifying targeted agents with therapeutic efficacy to treat patients with pNETs.