Integrated multi-omics and machine learning analysis identifies CDK1 as a key regulator of Dieda Qili Tablet-mediated osteogenic differentiation and immune microenvironment remodeling

Dieda Qili Tablet (DDQLT), a traditional Chinese medicine formula, has been clinically applied for fracture treatment; however, its molecular mechanisms underlying osteogenic effects remain unclear. This study aimed to elucidate the potential mechanisms of DDQLT in regulating osteoblast differentiation using an integrated bioinformatics and computational framework. Active compounds and potential targets of DDQLT were identified through network pharmacology. Transcriptomic datasets of MC3T3-E1 osteoblast differentiation were integrated for differential expression analysis and weighted gene co-expression network analysis (WGCNA). Candidate genes were screened by intersecting DDQLT-related targets with differentially expressed genes and WGCNA-derived hub genes. Protein–protein interaction (PPI) analysis combined with LASSO, random forest (RF), and support vector machine (SVM) algorithms was performed to identify key regulatory genes. Candidate targets were further validated using the independent human osteoblast dataset GSE157322. Molecular docking and immune infiltration analyses were conducted to explore potential mechanisms. A total of 127 active compounds and 780 potential targets of DDQLT were identified. Transcriptomic analysis revealed 1,165 differentially expressed genes, while WGCNA identified four osteogenesis-associated modules containing 5,628 genes. Functional enrichment analysis demonstrated that DDQLT-associated targets were primarily involved in cell-cycle regulation and osteogenic processes. CDK1 was consistently identified as a key hub gene across multiple approaches and was significantly upregulated during human osteoblast differentiation in the GSE157322 dataset. Molecular docking suggested potential interactions between CDK1 and DDQLT-derived flavonoids, including quercetin and kaempferol. Immune infiltration analysis revealed increased abundance of resting memory CD4⁺ T cells during osteogenic differentiation. This study suggests that DDQLT may promote osteoblast differentiation through a multi-component regulatory network involving CDK1-associated cell-cycle regulation and osteoimmune microenvironment modulation. These findings provide new insights into the molecular basis of DDQLT-mediated bone regeneration and highlight CDK1 as a potential target for osteogenic therapy.