Multi-Omics Analysis Identifies TTYH3 and MPG as Candidate Osteoporosis-Associated Biomarkers in Osteoblasts and...

Background: Osteoporosis (OP) arises from dysregulated bone metabolism driven by genetic and epigenetic factors. Osteoblasts are central to bone formation, and their functional heterogeneity-shaped by genetic background and receptor expression profiles-may critically influence OP susceptibility. This study aimed to identify osteoblast-specific genes robustly associated with OP and elucidate their underlying pathogenic mechanisms. Methods: We analyzed single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (bulk RNA-seq), and spatial transcriptomics (ST) datasets of osteoblasts. Differentially expressed genes (DEGs) were identified from scRNA-seq and bulk RNA-seq datasets, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Three machine learning methods and an artificial neural network (ANN)-based weighting analysis, together with weighted gene co-expression network analysis (WGCNA), were used to prioritize candidate OP-associated genes. ST data were integrated with CellChat analysis based on scRNA-seq data to investigate spatial expression patterns and potential cell-cell communication. Candidate genes were validated by immunohistochemical staining in human femoral head samples and quantitative real-time PCR (qRT-PCR) in MC3T3-E1 cells. Results: The intersecting DEGs from the scRNA-seq and bulk RNA-seq datasets were potentially related to inhibition of ossification. Three machine learning methods identified five osteoporosis-associated candidate genes: TTYH3, NRBP2, MPG, HSPG2, and GPR153, which were subsequently evaluated using ANN-based weighting analysis. Among these, TTYH3 and MPG were identified as OP-related genes by WGCNA. Integration of the scRNA-seq, ST, and CellChat results suggested that SPP1 was highly expressed in osteoblasts from the OP sample and exhibited a spatially heterogeneous expression pattern. Immunohistochemical staining of human femoral head tissues from individuals with normal bone mass and osteoporosis, together with qRT-PCR analysis in MC3T3-E1 cells, further validated the differential expression of TTYH3 and MPG. Conclusions: This study identified candidate osteoporosis-associated biomarkers and provided insights into potential pathogenic mechanisms, thereby establishing a basis for future mechanistic and clinical validation.