Text mining‑based drug discovery in cutaneous squamous cell carcinoma

: Cutaneous squamous cell carcinoma (cSCC) is one of the most common skin cancers. However, the efficacy and utility of the available drug therapies are limited. The objective of the present study was to determine the genes and molecular pathways associated with cSCC by using computational tools and publicly available data, and to explore drugs targeting the relevant molecular pathways for cSCC treatment. In this study, we used text mining and GeneCodis to mine genes which were highly related to cSCC. Protein‑protein interaction (PPI) analysis was performed by using STRING and Cytoscape. By using the data analytical tool cBioPortal, we analyzed the characteristics of candidate genes for the purpose of drug selection. Based on the drug‑gene interaction analysis of the final genes, candidate drugs were then derived. Our analysis identified 121 genes related to cSCC from the text mining searches. Gene enrichment analysis yielded 11 genes representing 10 pathways, targetable by a total of 55 drugs as possible drug treatments for cSCC. The final list included 25 chemotherapy agents, 21 tyrosine kinase inhibitors (TKIs), 7 PI3K/AKT/mTOR inhibitors, 2 MAPK inhibitors, 2 cyclin‑dependent kinase (CDK) inhibitors, 1 histone deacetylase (HDAC) inhibitor, 3 nonsteroidal anti‑inflammatory drugs (NSAIDs) and 3 other drugs, which directly affect the most enriched pathways. In conclusions, drug discovery using in silico text mining and pathway analysis tools may be a method of exploring candidate drugs which target the genes/pathways relevant to cSCC, to identify potential treatments.