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pmid:38509535

Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes.

Xiaoxuan You · Yanbo Ye · Shufeng Lin · Zefeng Zhang · Huiyang Guo · Hui Ye

BMC musculoskeletal disorders · 2024 doi:10.1186/s12891-024-07347-8

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Experiment

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Data

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Analysis

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Execution

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Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes.
doi
10.1186/s12891-024-07347-8
pmid
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[0]
Xiaoxuan You
[1]
Yanbo Ye
[2]
Shufeng Lin
[3]
Zefeng Zhang
[4]
Huiyang Guo
[5]
Hui Ye
journal
BMC musculoskeletal disorders
year
2024
abstract
BACKGROUND: Osteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes. METHODS: We obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website. RESULTS: We identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified. CONCLUSION: 4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.
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sections[9] "Validation of hub genes’ diagnostic capability" paragraph 2
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The predictive capability of Hub genes was assessed by generating ROC curves and calculating the area under the curve (AUC) using the pROC R package on the GSE169077 dataset and subsequently validated on the GSE55235 dataset.
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The four hub genes demonstrated good diagnostic performance for osteoarthritis, with GSE55235 validation-set AUCs of 0.96, 0.61, 0.84, and 0.84 for MMP9, MMP2, MMP3, and MMP13, respectively.
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The results indicated that the AUCs for MMP9, MMP2, MMP3, and MMP13 in the GSE169077 dataset were 0.933, 1, 0.833, and 1, respectively.
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In the validation dataset GSE55235, the AUCs were 0.96, 0.61, 0.84, and 0.84, respectively (Fig. 5C-D).
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sections[16] "Validation of hub gene diagnostic performance" paragraph 2
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In summary, Hub genes demonstrated good diagnostic performance in OA patients.
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The four hub genes demonstrated good diagnostic performance for osteoarthritis, with GSE55235 validation-set AUCs of 0.96, 0.61, 0.84, and 0.84 for MMP9, MMP2, MMP3, and MMP13, respectively.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38509535", "kind": "science-record", "revision": 3, "content_hash": "sha256:31b86aacfac96f51395517577887e6a239ee83f86332c69cf8f264c265608fa5", "created_at": "2026-11-01T19:20:27Z", "updated_at": "2026-11-01T22:11:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes.", "doi": "10.1186/s12891-024-07347-8", "pmid": "38509535", "authors": ["Xiaoxuan You", "Yanbo Ye", "Shufeng Lin", "Zefeng Zhang", "Huiyang Guo", "Hui Ye"], "journal": "BMC musculoskeletal disorders", "year": 2024, "abstract": "BACKGROUND: Osteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes.\nMETHODS: We obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website.\nRESULTS: We identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified.\nCONCLUSION: 4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.", "claims": [{"id": "claim-method", "statement": "The predictive capability of Hub genes was assessed by generating ROC curves and calculating the area under the curve (AUC) using the pROC R package on the GSE169077 dataset and subsequently validated on the GSE55235 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38509535", "locator": "sections[9] \"Validation of hub genes’ diagnostic capability\" paragraph 2", "quote": "The predictive capability of Hub genes was assessed by generating ROC curves and calculating the area under the curve (AUC) using the pROC R package on the GSE169077 dataset and subsequently validated on the GSE55235 dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data and materials in the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38509535", "locator": "sections[22] \"Data availability\" paragraph 2", "quote": "The data and materials in the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The four hub genes demonstrated good diagnostic performance for osteoarthritis, with GSE55235 validation-set AUCs of 0.96, 0.61, 0.84, and 0.84 for MMP9, MMP2, MMP3, and MMP13, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38509535", "locator": "sections[16] \"Validation of hub gene diagnostic performance\" paragraph 2", "quote": "The results indicated that the AUCs for MMP9, MMP2, MMP3, and MMP13 in the GSE169077 dataset were 0.933, 1, 0.833, and 1, respectively."}, {"asset": "source:pmid:38509535", "locator": "sections[16] \"Validation of hub gene diagnostic performance\" paragraph 2", "quote": "In the validation dataset GSE55235, the AUCs were 0.96, 0.61, 0.84, and 0.84, respectively (Fig. 5C-D)."}, {"asset": "source:pmid:38509535", "locator": "sections[16] \"Validation of hub gene diagnostic performance\" paragraph 2", "quote": "In summary, Hub genes demonstrated good diagnostic performance in OA patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The four hub genes demonstrated good diagnostic performance for osteoarthritis, with GSE55235 validation-set AUCs of 0.96, 0.61, 0.84, and 0.84 for MMP9, MMP2, MMP3, and MMP13, respectively.", "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:38509535"], "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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