Computational Drug Discovery in Ankylosing Spondylitis-Induced Osteoporosis Based on Data Mining and Bioinformatics Analysis

-BACKGROUND: Ankylosing spondylitis (AS) and osteo-porosis (OP) are both prevalent illnesses in spine surgery, with OP being a possible consequence of AS. However, the mechanism of AS-induced OP (AS-OP) remains unknown, limiting etiologic research and therapy of the illness. To mine targetable medicine for the prevention and treatment of AS-OP, this study analyzes public data sets using bio-informatics to identify genes and biological pathways relevant to AS-OP. -METHODS: First, text mining was used to identify com-mon genes associated with AS and OP, after which func-tional analysis was carried out. The STRING database and Cytoscape software were used to create protein -protein interaction networks. Hub genes and potential drugs were discovered using drug-gene interaction analysis and transcription factors-gene interaction analysis. -RESULTS: The results of text mining showed 241 genes common to AS and OP, from which 115 key symbols were sorted out by functional analysis. As options for treating AS-OP, protein -protein interaction analysis yielded 20 genes, which may be targeted by 13 medications. -CONCLUSIONS: Carlumab, bermekimab, rilonacept, rilo-tumumab, and ficlatuzumab were first identified as the potential drugs for the treatment of AS-OP, proving the value of text mining and pathway analysis in drug discovery.

Automated summary

By using text mining and pathway analysis, this study identified genes and biological pathways associated with ankylosing spondylitis (AS) and osteoporosis (OP), providing potential drug targets for the prevention and treatment of AS-induced OP.