Identification of Immune-Related Genes in Patients with Osteoporosis by Gene Expression Profiling of Monocyte Samples from Microarray Datasets

Abstract

Background: Although studies have highlighted the potential role of monocytes in osteoporosis, the detailed mechanism of action of monocytes in the microenvironment and their diagnostic value in osteoporosis have not been fully elucidated. This study identified immune-related genes in osteoporosis using transcriptional data from monocyte samples obtained from public datasets. Methods: Microarray datasets associated with osteoporosis were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the low bone mineral density (BMD) and high BMD groups were screened, with a false-discovery rate (FDR) <0.05 and |log₂fold change (FC)| > 0.263 as thresholds. The type of immune infiltration was assessed in monocyte samples using the single-sample gene set enrichment (ssGSEA) algorithm, and immune-related DEGs were identified. Enrichment analysis was performed on immune-related DEGs. Subsequently, optimal immune-related DEGs were screened using least absolute shrinkage and selection operator (LASSO) and recursive feature elimination (RFE) algorithms. Finally, a risk diagnostic model was constructed based on optimal immune-related DEGs. Results: A total of 428 DEGs were identified in patients with low BMD compared with those in the control group. Four types of immune cells with significant differences and 310 immune-related DEGs were identified. These immune-related DEGs were mainly enriched in the peroxisome, metabolic pathways, and alanine, aspartate, and glutamate metabolism. In addition, seven optimal immune-related genes were identified as immune-related markers for osteoporosis, and a diagnostic model was constructed. The risk diagnostic model of the seven signature genes showed high area under the curve (AUC) values, suggesting a reliable predictive ability for osteoporosis. Conclusions: Our results suggest a potential role for these seven optimal genes in osteoporosis progression and immune cell infiltration. Risk models based on the seven optimal genes are useful tools for osteoporosis diagnosis and osteoporotic fracture risk prediction.