Identification of biomarkers associated with diagnosis of postmenopausal osteoporosis patients based on bioinformatics and machine learning

Background: Accumulating evidence suggests that postmenopausal osteoporosis (PMOP) is a common chronic systemic metabolic bone disease, but its specific molecular pathogenesis remains unclear. This study aimed to identify novel genetic diagnostic markers for PMOP. Methods: In this paper, we combined three GEO datasets differentially expressed genes (DEGs) and performed functional enrichment analysis of PMOP-related differential genes. Key were analyzed using two machine learning algorithms, namely, LASSO the Gaussian mixture model, candidate biomarkers found after taking intersection. After further ceRNA network construction, methylation analysis, immune infiltration ACACB WWP1 finally selected as markers. Twenty-four clinical samples collected, expression levels in PMOP detected by qPCR. Results: We identified 34 DEG was mainly related amino acid synthesis, inflammatory response, apoptosis. The construction XIST—hsa-miR-15a-5p/hsa-miR-15b-5p/hsa-miR-497-5p hsa-miR-195-5p—WWP1/ACACB may be RNA regulatory pathways regulating disease progression. PMOP, validated sample experiments. addition, these also significantly associated with cells, such T, B, NK cells. Conclusion: Overall, vital responsible results help provide potential immunotherapeutic targets