Elucidating the mechanism of plasticizer induced osteoporosis through network toxicology and molecular docking

Target genes associated with osteoporosis were identified through differential expression analysis of various datasets by machine learning, enrichment analysis was performed to understand their biological functions, and the binding of plasticizers to target proteins was investigated using network toxicology and molecular docking techniques. Plasticizers may affect OP progression and alter expression levels in OP samples. Machine learning analysis identified six core target genes - CKM, TACR3, SOAT2, SGK1, ERAP2, and MMP12 - critical for plasticizer-induced OP. Molecular docking revealed specific binding interactions between plasticizers and target proteins.

通过机器学习对多种数据集进行差异表达分析，识别出与骨质疏松症相关的靶基因，进而通过富集分析解析其生物学功能。采用网络毒理学和分子对接技术，探究了塑化剂与靶蛋白的结合情况。塑化剂可能影响骨质疏松症的进展并改变骨质疏松症样本中的表达水平。机器学习分析确定了六个核心靶基因——CKM、TACR3、SOAT2、SGK1、ERAP2 和 MMP12——这些基因对于塑化剂诱导的骨质疏松症至关重要。分子对接揭示了塑化剂与靶蛋白之间的特异性结合作用。