Integrated multi-omics and machine learning approaches reveal the characteristics of macrophages in postmenopausal osteoporosis and identify SMAD7 as a KEY regulator
Abstract
Postmenopausal osteoporosis (OP) is a chronic bone disease characterized by low BMD and microstructural deterioration. While early research mainly focused on inhibiting osteoclast activity, recent perspectives highlight the interaction of bone resorption and formation. Given that macrophages serve as precursors to osteoclasts and secret cytokines that stimulate osteoblasts, they may be promising for identifying new dual-effect drug targets. In this study, we employed multi-omics and machine learning methods to investigate the molecular characteristics of macrophages in OP and identify some novel drug targets. We suggest that this research will help understand OP's molecular mechanisms and assist the development of new therapies.We integrated 6 scRNA-seq and 12 bulk RNA-seq samples as a discovery cohort to identify potential drug targets. Additionally, we utilized 20 paraffin tissue and 20 bulk RNA-seq samples for validation. Differential gene expression analysis was performed on scRNA-seq data, while Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to analyze bulk RNA-seq data. Furthermore, we employed Lasso regression and Support Vector Machine (SVM) models to identify core genes. Subsequently, we performed Protein-Protein Interaction (PPI) analysis, cellular communication inference, differentiation trajectory analysis, and network pharmacology analysis based on the identified genes.In discovery cohort, we observed a significant difference in the proportion…
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