Comprehensive bioinformatics analysis reveals novel potential biomarkers associated with aging and mitochondria in osteoporosis.

Osteoporosis (OP) is a prevalent age-related bone metabolic disease. Aging and mitochondrial dysfunction are involved in the onset and progression of OP, but the specific mechanisms have not been elucidated. The aim of this study was to identify novel potential biomarkers associated with aging and mitochondria in OP. In this study, based on GEO database, aging-related and mitochondria-related differentially expressed genes (AR&MRDEGs) were screened. The AR&MRDEGs were enriched in mitochondrial structure and function. Then, 6 key genes were identified by WGCNA and multiple machine learning, and a novel diagnostic model was constructed. The efficacy of diagnostic model was validated using external datasets. The results showed that diagnostic model had favorable diagnostic prediction ability. Next, key gene regulatory networks were constructed and single-gene GSEA analysis was performed. In addition, based on a single-cell dataset from OP, single-cell differentially expressed genes (scDEGs) were identified. The results revealed that aging-related and mitochondria-related genes (AR&MRGs) were enriched in the ERK pathway in tissue stem cells (TSCs), and in mitochondrial membrane potential depolarization in monocytes. Cellular communication analysis showed that TSCs were active, with numerous signaling interactions with monocytes, macrophages and immune cells. Finally, the expression of key gene was verified by quantitative real-time PCR (qRT-PCR). This study is expected to provide strategies for the diagnosis and treatment of OP targeting aging and mitochondria.