The potential drug for treatment in pancreatic adenocarcinoma: a bioinformatical study based on distinct drug databases

BACKGROUND: The prediction of drug-target interaction from chemical and biological data can advance our search for potential drug, contributing to a therapeutic strategy for pancreatic adenocarcinoma (PAAD). We aim to identify hub genes of PAAD and search for potential drugs from distinct databases. The docking simulation is adopted to validate our findings from computable perspective. METHODS: Differently expressed genes (DEGs) of PAAD were performed based on TCGA. With two Cytoscape plugins of CentiScaPe and MCODE, hub genes were analyzed and visualized by STRING analysis of Protein–protein Interaction (PPI). The hub genes were further selected with significant prognostic values. In addition, we examined the correlation between hub genes and immune infiltration in PAAD. Subsequently, we searched for the hub gene-targeted drugs in Connectivity map (Cmap) and cBioportal, which provided a large body of candidate drugs. The hub gene, which was covered in the above two databases, was estimated in Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Herbal Ingredients’ Targets (HIT) database, which collected natural herbs and related ingredients. After obtaining molecular structures, the potential ingredient from TCMSP was applied for a docking simulation. We finalized a network connectivity of ingredient and its targets. RESULTS: A total of 2616 DEGs of PAAD were identified, then we further determined and visualized 24 hub genes by a connectivity analysis of PPI. Based on prognostic value, we identified 5 hub genes including AURKA (p = 0.0059), CCNA2 (p = 0.0047), CXCL10 (p = 0.0044), ADAM10 (p = 0.00043), and BUB1 (p = 0.0033). We then estimated tumor immune correlation of these 5 hub genes, because the immune effector process was one major result of GO analysis. Subsequently, we continued to search for candidate drugs from Cmap and cBioportal database. BUB1, not covered in the above two databases, was estimated in TCMSP and HIT databases. Our results revealed that genistein was a potential drug of BUB1. Next, we generated two docking modes to validate drug-target interaction based on their 3D structures. We eventually constructed a network connectivity of BUB1 and its targets. CONCLUSIONS: All 5 hub genes that predicted poor prognosis had their potential drugs, especially our findings showed that genistein was predicted to target BUB1 based on TCMSP and docking simulation. This study provided a reasonable approach to extensively retrieve and initially validate putative therapeutic agents for PAAD. In future, these drug-target results should be investigated with solid data from practical experiments.