Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38260098", "kind": "science-record", "revision": 3, "content_hash": "sha256:92b4966010033dd03df2009bb8185c495bd43a5e4a7a27a95a8e1e8aed7d415e", "created_at": "2026-11-01T19:20:11Z", "updated_at": "2026-11-01T22:11:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model.", "doi": "10.3389/fphys.2023.1289976", "pmid": "38260098", "authors": ["Yu Su", "Gangying Yu", "Dongchen Li", "Yao Lu", "Cheng Ren", "Yibo Xu", "Yanling Yang", "Kun Zhang", "Teng Ma", "Zhong Li"], "journal": "Frontiers in physiology", "year": 2023, "abstract": "Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, including GO and KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = -0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP.", "claims": [{"id": "claim-method", "statement": "We used the Wilcoxon test method in the R package to analyze the genetic data and get individual p-values after we completed the data download and collation (Cheng L. et al., 2022).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38260098", "locator": "sections[4] \"2.2 Identification of differentially expressed genes\" paragraph 2", "quote": "We used the Wilcoxon test method in the R package to analyze the genetic data and get individual p-values after we completed the data download and collation (Cheng L. et al., 2022)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38260098", "locator": "sections[34] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using the Wilcoxon test on GSE56815, the paper identified 548 differentially expressed genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38260098", "locator": "sections[4] \"2.2 Identification of differentially expressed genes\" paragraph 2", "quote": "We used the Wilcoxon test method in the R package to analyze the genetic data and get individual p-values after we completed the data download and collation (Cheng L. et al., 2022)."}, {"asset": "source:pmid:38260098", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In total, 548 DEGs were identified in the GSE56815 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using the Wilcoxon test on GSE56815, the paper identified 548 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:38260098"], "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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Results: In total, 548 DEGs were identifie", "to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consist", "e quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identifie", "be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consist"], "order_basis": "paper_text_order:1567", "id": "assay:1"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We used the Wilcoxon test method in the R package to analyze the genetic data and get individual p-values after we completed the data download and collation (Cheng L. et al., 2022)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}