Identification of biomarkers associated with oxidative stress-related genes in postmenopausal osteoporosis

Osteoporosis (OP) is a prevalent metabolic disease, with aging and menopause being the major risk factors. Studies have shown that nearly one-third of postmenopausal women suffer from osteoporosis. However, there is a scarcity of research on antioxidant systems for the prevention and treatment of postmenopausal osteoporosis (PM-OP). To address this gap, we performed differential analysis using Limma to identify differentially expressed genes (DEGs) in PM-OP samples. We employed weighted correlation network analysis (WGCNA) to identify oxidative stress (OS)-related genes (OSRGs) highly correlated with PM-OP. The intersection of key modular genes and DEGs yielded differentially expressed OSRGs (DE-OSRGs) specific to PM-OP. We conducted GO and KEGG functional enrichment analyses on these genes. Additionally, we constructed a protein-protein interaction (PPI) network and utilized support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to identify signature genes. The diagnostic value of the signature genes was evaluated and validated using ROC curves. GSEA enrichment analysis was employed to explore the potential mechanisms associated with the signature genes. Finally, we constructed a regulatory network involving TF-miRNA-mRNA interactions for the signature genes and verified the biological roles of FOXO3 and DDIT3 in PM-OP and healthy groups using quantitative real-time PCR (qRT-PCR). Our analysis revealed 20 DE-OSRGs specific to PM-OP, obtained by intersecting modular and differential genes. The PPI network identified central genes (DDIT3, MAPK8, CDK2, SIRT1, and FOXO3) with more than 3 nodes. Through integration with machine learning algorithms, we identified DDIT3 and FOXO3 as signature genes. The ROC curve analysis indicated that the AUC value was greater than 0.7, suggesting the potential diagnostic value of these signature genes. Furthermore, GSEA results revealed their involvement in pathways related to the regulation of neutrophil activation, oxidative phosphorylation, MAPK signaling, mitochondrial matrix, and phagocytosis. Lastly, we constructed a regulatory network comprising 27 nodes (22 TFs, 3 miRNAs, and 2 mRNAs) and 28 edges. Additionally, qRT-PCR confirmed the significant up-regulation of FOXO3 and DDIT3 expressions in the PM-OP group compared to the healthy control group. In summary, this study employed bioinformatics analysis to identify OS-related biomarkers (DDIT3 and FOXO3) in PM-OP, providing new biological targets for clinical treatment.