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BMC Med Genomics ; 17(1): 5, 2024 01 02.
Article in English | MEDLINE | ID: mdl-38169377

ABSTRACT

BACKGROUND: Osteoporosis is the most common metabolic bone disease in humans. Exploring the expression difference of iron metabolism-related genes in osteoporosis can provide a new target for diagnosis and treatment. METHODS: First, we used online databases to identify differentially expressed genes (DEGs) related to iron metabolism in patients with osteoporosis. The differential genes were comprehensively analyzed by bioinformatics method (GO, KEGG, GSEA, immune infiltration analysis, PPI). The expression levels of hub genes and important signaling pathways were verified by qRT-PCR and Western blotting. RESULTS: A total of 23 iron metabolism-related genes with significant differences were identified, which were enriched in "regulation of protein dephosphorylation" and "negative regulation of protein dephosphorylation". The GSEA results, heme metabolism and Myc targets v1 were among the top two pathways, both upregulated. The immune infiltration analysis revealed that the expressions of genes such as ABCA5, D2HGDH, GNAI2, and CTSW were correlated with the infiltration degree of significantly different cells. The PPI network contained 12 differentially expressed iron metabolism-related genes. Additionally, YWHAE, TGFB1, PPP1R15A, TOP2A, and CALR were mined as hub genes using the Cytoscape software. qRT PCR showed that the expression of TGF-ß1, YWHAE, TOP2A and CALR increased. We also verified the expression of related proteins and genes in the oxidative stress signaling pathway by qRT PCR and Western blotting. The results showed that Mob1, YAP and TAZ molecules were highly expressed at the gene and protein levels. CONCLUSIONS: These differentially expressed iron metabolism-related genes could provide new potential targets for the diagnosis and treatment of osteoporosis.


Subject(s)
Osteoporosis , Protein Interaction Maps , Humans , Protein Interaction Maps/genetics , Osteoporosis/genetics , Genes, Regulator , Signal Transduction , Iron , Computational Biology/methods , Gene Expression Profiling/methods
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