Identification of a potential signature to predict the risk of postmenopausal osteoporosis

Abstract

BackgroundPostmenopausal osteoporosis (PMOP) is related to the elevated risk of fracture in postmenopausal women. Thus, to effectively predict the occurrence of PMOP, we explored a novel gene signature for the prediction of PMOP risk. MethodsThe WGCNA analysis was conducted to identify the PMOP-related gene modules based on the data from GEO database (GSE56116 and GSE100609). The “limma” R package was applied for screening differentially expressed genes (DEGs) based on the data from GSE100609 dataset. Next, LASSO Cox algorithm were applied to identify valuable PMOP-related risk genes and construct a risk score model. GSEA was then conducted to analyze potential signaling pathways between high-risk (HR) score and low-risk (LR) score groups. ResultsA novel risk model with five PMOP-related risk genes (SCUBE3, TNNC1, SPON1, SEPT12 and ULBP1) was developed for predicting PMOP risk status. RT-qPCR and western blot assays validated that compared to postmenopausal non-osteoporosis (non-PMOP) patients, SCUBE3, ULBP1, SEPT12 levels were obviously elevated, and TNNC1 and SPON1 levels were reduced in blood samples from PMOP patients. Additionally, PMOP-related pathways such as MAPK signaling pathway, PI3K-Akt signaling pathway and HIF-1 signaling pathway were significantly activated in the HR-score group compared to the LR-score group. The circRNA-gene-miRNA and gene-transcription factor networks showed that 533 miRNAs, 13 circRNAs and 40 TFs might be involved in regulating the expression level of these five PMOP-related genes. ConclusionCollectively, we developed a PMOP-related gene signature based on SCUBE3, TNNC1, SPON1, SEPT12 and ULBP1 genes, and higher risk score indicated higher risk suffering from PMOP.