Background:Bone giant cell tumor (BGCT) is one of the world’s major disease types of locally aggressive bone tumors. In recent years, denosumab treatment has been introduced before curettage surgery. However, the current therapeutic was practical only sometimes, given the local recurrence effects after discontinuation of denosumab. Due to the complex nature of BGCT, this study aims to use bioinformatics to identify potential genes and drugs associated with BGCT.Methods:The genes that integrate BGCT and fracture healing were determined by text mining. The gene was obtained from the pubmed2ensembl website. We filtered out common genes for the function, and signal pathway enrichment analyses were implemented. The protein–protein interaction (PPI) networks and the hub genes were screened by MCODE built-in Cytoscape software. Lastly, the confirmed genes were queried in the Drug Gene Interaction Database to determine potential genes and drugs.Results:Our study finally identified 123 common specific genes in bone giant cell tumors and fracture healing text mining concepts. The GO enrichment analysis finally analyzed 115 characteristic genes in BP, CC, and MF. We selected 10 KEGG pathways and identified 68 characteristic genes. We performed protein–protein interaction analysis (PPI) on 68 selected genes and finally identified seven central genes. In this study, these seven genes were substituted into drug–gene interactions, and there were 15 antineoplastic drugs, 1 anti-involving drug, and 1 anti-influenza drug.Conclusion:The 7 genes (includingANGPT2,COL1A1,COL1A2,CTSK,FGFR1,NTRK2, andPDGFB) and 17 drugs, which have not been used in BGCT, but 6 of them approved by the FDA for other diseases, could be potential genes and drugs, respectively, to improve BGCT treatment. In addition, the correlation study and analysis of potential drugs through genes provide great opportunities to promote the repositioning of drugs and the study of pharmacology in the pharmaceutical industry.