Bioinformatic mining of kinase inhibitors that regulate autophagy through kinase signaling pathways

Abstract The aim of this study was to predict the kinase inhibitors that may regulate autophagy. A total of 62 kinases were obtained through text mining by importing the keyword 'autophagy' and a 'protein kinase' Excel file to PubMed. Subsequently, 146 kinases were derivated through screening in the PubMed database by importing the 'autophagy‑associated gene' and 'protein kinase' files. Following intersection of the above two methods, 54 candidate autophagy‑associated kinases were obtained. Enrichment analysis indicated that these candidate autophagy‑associated kinases were mainly enriched in pathways such as the calcium, Wnt, HIF‑1 and mTOR signaling pathways. Among the 54 kinases, 24 were identified through text mining to have specific kinase inhibitors that regulate the corresponding functions; a total of 56 kinase inhibitors were found to be involved in the regulation of these 24 kinases. In total, nine of these 56 kinase inhibitors identified had been widely reported in autophagy regulation studies, 23 kinase inhibitors had been seldom reported and 24 had never been reported. Therefore, introducing these kinases into autophagy regulation analysis in subsequent studies may produce important results.