Identification of osteoporosis ferroptosis-related markers and potential therapeutic compounds based on bioinformatics methods and molecular docking technology.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38650009", "kind": "science-record", "revision": 4, "content_hash": "sha256:b03fca373a7b3246516fdb1bdd58be6bc0f020f73358939b3c58b2957ea8fc46", "created_at": "2026-11-01T19:20:37Z", "updated_at": "2026-11-01T22:54:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of osteoporosis ferroptosis-related markers and potential therapeutic compounds based on bioinformatics methods and molecular docking technology.", "doi": "10.1186/s12920-024-01872-0", "pmid": "38650009", "authors": ["Shi-Wei Long", "Shi-Hong Li", "Jian Li", "Yang He", "Bo Tan", "Hao-Han Jing", "Wei Zheng", "Juan Wu"], "journal": "BMC medical genomics", "year": 2024, "abstract": "RESEARCH BACKGROUND AND PURPOSE: Osteoporosis (OP) is one of the most common bone diseases worldwide, characterized by low bone mineral density and susceptibility to pathological fractures, especially in postmenopausal women and elderly men. Ferroptosis is one of the newly discovered forms of cell death regulated by genes in recent years. Many studies have shown that ferroptosis is closely related to many diseases. However, there are few studies on ferroptosis in osteoporosis, and the mechanism of ferroptosis in osteoporosis is still unclear. This study aims to identify biomarkers related to osteoporosis ferroptosis from the GEO (Gene Expression Omnibus) database through bioinformatics technology, and to mine potential therapeutic small molecule compounds through molecular docking technology, trying to provide a basis for the diagnosis and treatment of osteoporosis in the future.\nMATERIALS AND METHODS: We downloaded the ferroptosis-related gene set from the FerrDb database ( http://www.zhounan.org/ferrdb/index.html ), downloaded the data sets GSE56815 and GSE7429 from the GEO database, and used the R software \"limma\" package to screen differentially expressed genes (DEGs) from GSE56815, and intersected with the ferroptosis gene set to obtain ferroptosis-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by the R software \"clusterProfiler\" package. The random forest model was further screened to obtain essential ferroptosis genes. R software \"corrplot\" package was used for correlation analysis of essential ferroptosis genes, and the Wilcox test was used for significance analysis. The lncRNA-miRNA-mRNA-TF regulatory network was constructed using Cytoscape software. The least absolute shrinkage and selection operator (LASSO) was used to construct a disease diagnosis model, and a Receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic performance, and then GSE7429 was used to verify the reliability of the diagnosis model. Molecular docking technology was used to screen potential small molecule compounds from the Drugbank database. Finally, a rat osteoporosis model was constructed, and peripheral blood mononuclear cells were extracted for qRT-PCR detection to verify the mRNA expression levels of crucial ferroptosis genes.\nRESULT: Six DEGs related to ferroptosis were initially screened out. GO function and KEGG pathway enrichment analysis showed that ferroptosis-related DEGs were mainly enriched in signaling pathways such as maintenance of iron ion homeostasis, copper ion binding function, and ferroptosis. The random forest model identified five key ferroptosis genes, including CP, FLT3, HAMP, HMOX1, and SLC2A3. Gene correlation analysis found a relatively low correlation between these five key ferroptosis genes. The lncRNA-miRNA-mRNA-TF regulatory network shows that BAZ1B and STAT3 may also be potential molecules. The ROC curve of the disease diagnosis model shows that the model has a good diagnostic performance. Molecular docking technology screened out three small molecule compounds, including NADH, Midostaurin, and Nintedanib small molecule compounds. qRT-PCR detection confirmed the differential expression of CP, FLT3, HAMP, HMOX1 and SLC2A3 between OP and normal control group.\nCONCLUSION: This study identified five key ferroptosis genes (CP, FLT3, HAMP, HMOX1, and SLC2A3), they were most likely related to OP ferroptosis. In addition, we found that the small molecule compounds of NADH, Midostaurin, and Nintedanib had good docking scores with these five key ferroptosis genes. These findings may provide new clues for the early diagnosis and treatment of osteoporosis in the future.", "claims": [{"id": "claim-method", "statement": "We used the “limma” package in R software to perform differential genetic analysis on the processed data, and the screening threshold was set to an adjusted p-value < 0.05 and |log2FC| >0.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38650009", "locator": "sections[4] \"Identification of differentially expressed genes related to ferroptosis\" paragraph 2", "quote": "We used the “limma” package in R software to perform differential genetic analysis on the processed data, and the screening threshold was set to an adjusted p-value < 0.05 and |log2FC| >0."}], "state": "complete"}, {"id": "claim-data-access", "statement": "This study downloaded the GSE56815 dataset from the GEO database as a training set and the GSE7429 dataset from the website as a validation set for model diagnostic efficacy. Among them, GSE56815 contains blood samples of 40 women with high BMD (bone mineral density) and 40 samples of women with low BMD, while GSE7429 contains blood samples of 10 women with high BMD and ten blood samples of women with low BMD.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38650009", "locator": "sections[3] \"Data collection and organization\" paragraph 2", "quote": "This study downloaded the GSE56815 dataset from the GEO database as a training set and the GSE7429 dataset from the website as a validation set for model diagnostic efficacy. Among them, GSE56815 contains blood samples of 40 women with high BMD (bone mineral density) and 40 samples of women with low BMD, while GSE7429 contains blood samples of 10 women with high BMD and ten blood samples of women with low BMD."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using limma on the GSE56815 dataset, the analysis screened 400 differentially expressed genes and identified six ferroptosis-related differentially expressed genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38650009", "locator": "sections[16] \"Identification of differentially expressed genes related to ferroptosis\" paragraph 2", "quote": "Through differential expression analysis, a total of 400 DEGs were screened from the dataset GSE56815, including 118 up-regulated genes and 282 down-regulated genes (Fig. 1A and B)."}, {"asset": "source:pmid:38650009", "locator": "sections[16] \"Identification of differentially expressed genes related to ferroptosis\" paragraph 2", "quote": "The obtained 376 ferroptosis-related genes were intersected with 400 DGEs to obtain six ferroptosis-related DEGs, as shown in the Venn diagram and heat map of ferroptosis-related DEGs (Fig. 1C and D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using limma on the GSE56815 dataset, the analysis screened 400 differentially expressed genes and identified six ferroptosis-related differentially expressed genes.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38650009"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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