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

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