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• W4311307788 abstract "Objective To identify key pathological hub genes, micro RNAs (miRNAs), and circular RNAs (circRNAs) of osteoporosis (OP) and construct their ceRNA network in an effort to explore the potential biomarkers and drug targets for OP therapy. Methods GSE7158, GSE201543, and GSE161361 microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by comparing OP patients with healthy controls and hub genes were screened by machine learning algorithms. Target miRNAs and circRNAs were predicted by FunRich and circbank, then ceRNA network were constructed by Cytoscape. Pathways affecting OP were identified by functional enrichment analysis. The hub genes were verified by receiver operating characteristic (ROC) curve and real time quantitative PCR (RT‐qPCR). Potential drug molecules related to OP were predicted by DSigDB database and molecular docking was analyzed by autodock vina software. Results A total of 179 DEGs were identified. By combining three machine learning algorithms, BAG2 , MME , SLC14A1, and TRIM44 were identified as hub genes. Three OP‐associated target miRNAs and 362 target circRNAs were predicted to establish ceRNA network. The ROC curves showed that these four hub genes had good diagnostic performance and their differential expression was statistically significant in OP animal model. Benzo[a]pyrene was predicted which could successfully bind to protein receptors related to the hub genes and it was served as the potential drug molecules. Conclusion An mRNA‐miRNA‐circRNA network is reported, which provides new ideas for exploring the pathogenesis of OP. Benzo[a]pyrene, as potential drug molecules for OP, may provide guidance for the clinical treatment." @default.
• W4311307788 created "2022-12-25" @default.
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• W4311307788 date "2022-12-13" @default.
• W4311307788 modified "2023-09-30" @default.
• W4311307788 title "Mining Potential Drug Targets for Osteoporosis Based on <scp>CeRNA</scp> Network" @default.
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• W4311307788 doi "https://doi.org/10.1111/os.13617" @default.
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