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pmid:37904457

Exploring the pathogenesis of osteomyelitis accompanied by diabetic foot ulcers using microarray data analysis.

Pan Fan · Huanhuan Ye · Chenhua Zhu · Hu Xie

Medicine · 2023 doi:10.1097/MD.0000000000033962

Paper

complete

Experiment

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Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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title
Exploring the pathogenesis of osteomyelitis accompanied by diabetic foot ulcers using microarray data analysis.
doi
10.1097/MD.0000000000033962
pmid
37904457
authors array · 4 items
[0]
Pan Fan
[1]
Huanhuan Ye
[2]
Chenhua Zhu
[3]
Hu Xie
journal
Medicine
year
2023
abstract
Although numerous studies have shown distinctive similarities between osteomyelitis and diabetic foot ulcers (DFU), the common pathogenesis of both is not fully understood. The current research focuses on an in-depth study of the molecular and pathway mechanisms involved in the complication of these 2 diseases. We downloaded clinical information on osteomyelitis (GSE30119) and DFU (GSE29221) from the GEO database, along with gene expression matrices. Differentially expressed genes (DEGs) among normal individuals and patients with osteomyelitis; normal individuals and patients with DFU were identified by R software, and thus common DEGs were confirmed. We then analyzed these differential genes, including the functional pathway analysis, protein-protein interaction (PPI), modules and hub genes establishment, and transcription factor regulatory networks. We identified 109 common DEGs (46 up-regulated and 63 down-regulated genes) for subsequent analysis. The results of PPI network and the functional pathway analysis revealed the importance of immune response and inflammatory response in both diseases. Among them, chemokines and cytokines were found to be closely related to both osteomyelitis and DFU. In addition, the tumor necrosis factor (TNF) pathway and Staphylococcus aureus infection were found to have more significant roles too. The 12 most essential key genes were later screened by cytoHubba, including matrix metalloproteinases (MMP) 1, MMP3, MMP9, IL8, C-X-C chemokine receptor (CXCR) 2, C-X-C motif chemokine ligand (CXCL) 9, CXCL10, CXCL13, FCGR3B, IL1B, LCN2, S100A12. CXCL10, and MMP1 were validated using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Osteomyelitis and DFU share similar molecular and pathway mechanisms. These common key genes and pathways may provide new directions toward the future study of osteomyelitis and DFU.
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Different gene expression analyses were completed by the “GEOquery” and “limma” packages.
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We identified 109 common DEGs (46 up-regulated and 63 down-regulated genes) for subsequent analysis.
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We identified 109 common DEGs (46 up-regulated and 63 down-regulated genes) for subsequent analysis.
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We identified 109 common DEGs (46 up-regulated and 63 down-regulated genes) for subsequent analysis.
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0.8
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pathway enrichment
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capability object · 3 fields
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capability:computational_analysis
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assay_families array · 9 items
[0]
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[2]
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[3]
statistical_regression
[4]
pca
[5]
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[6]
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[7]
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[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
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complete
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R1
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normalise_counts
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differential_expression_fit
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fdr_bh
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
capability object · 4 fields
tier
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capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
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[0]
GSE30119
[1]
GSE29221
[2]
GSE16129
[3]
GSE7014
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[0]
GSE30119
[1]
GSE29221
[2]
GSE16129
[3]
GSE7014
statements array · 1 item
[0]
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
basis
The paper names GSE30119, GSE29221, GSE16129, GSE7014 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 3 items
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normalise_counts
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differential_expression_fit
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fdr_bh
assets array · 2 items
[0]
source:pmid:37904457
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data:geo:GSE16129
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run_analysis
must_build object · 5 fields
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capabilities array · 0 items
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data:geo:GSE16129
gaps array · 0 items
review object · 5 fields
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partial
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kind
extraction_review
reader object · 5 fields
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MiniMax-M3.1-Flash
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minimax
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minimax
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2026-11-01T18:57:43Z
independence
single_reader
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verbatim
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12331
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264
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[0] object · 4 fields
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extraction
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provenance object · 7 fields
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pmid:37904457
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paper
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pmid:37904457
[1] object · 3 fields
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source:pmid:37904457
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manuscript
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PMID 37904457 full text
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data:geo:GSE16129
type
dataset
label
GEO GSE16129
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classification
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paper
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pmid:37904457
[1] object · 3 fields
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source:pmid:37904457
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manuscript
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PMID 37904457 full text
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data:geo:GSE16129
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dataset
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GEO GSE16129
agent
agent:corpus-assay-engine
[1] object · 4 fields
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agent:minimax-reader
[2] object · 4 fields
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activity-resolve-GSE16129
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used array · 1 item
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agent:corpus-assay-engine
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agent:corpus-assay-engine
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agent:minimax-reader
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dsl_catalogue_fingerprint
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[3]
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[7]
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evidence array · 1 item
[0]
is accompanied by diabetic foot ulcers using microarray data analysis.
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paper_text_order:41
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Different gene expression analyses were completed by the “GEOquery” and “limma” packages.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37904457", "kind": "science-record", "revision": 5, "content_hash": "sha256:3a51361bff11329d25ba4ea78488fb170b11d8a42f958f132b66ea8c794cf3a4", "created_at": "2026-11-01T19:02:46Z", "updated_at": "2026-11-01T22:52:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the pathogenesis of osteomyelitis accompanied by diabetic foot ulcers using microarray data analysis.", "doi": "10.1097/MD.0000000000033962", "pmid": "37904457", "authors": ["Pan Fan", "Huanhuan Ye", "Chenhua Zhu", "Hu Xie"], "journal": "Medicine", "year": 2023, "abstract": "Although numerous studies have shown distinctive similarities between osteomyelitis and diabetic foot ulcers (DFU), the common pathogenesis of both is not fully understood. The current research focuses on an in-depth study of the molecular and pathway mechanisms involved in the complication of these 2 diseases. We downloaded clinical information on osteomyelitis (GSE30119) and DFU (GSE29221) from the GEO database, along with gene expression matrices. Differentially expressed genes (DEGs) among normal individuals and patients with osteomyelitis; normal individuals and patients with DFU were identified by R software, and thus common DEGs were confirmed. We then analyzed these differential genes, including the functional pathway analysis, protein-protein interaction (PPI), modules and hub genes establishment, and transcription factor regulatory networks. We identified 109 common DEGs (46 up-regulated and 63 down-regulated genes) for subsequent analysis. The results of PPI network and the functional pathway analysis revealed the importance of immune response and inflammatory response in both diseases. Among them, chemokines and cytokines were found to be closely related to both osteomyelitis and DFU. In addition, the tumor necrosis factor (TNF) pathway and Staphylococcus aureus infection were found to have more significant roles too. The 12 most essential key genes were later screened by cytoHubba, including matrix metalloproteinases (MMP) 1, MMP3, MMP9, IL8, C-X-C chemokine receptor (CXCR) 2, C-X-C motif chemokine ligand (CXCL) 9, CXCL10, CXCL13, FCGR3B, IL1B, LCN2, S100A12. CXCL10, and MMP1 were validated using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Osteomyelitis and DFU share similar molecular and pathway mechanisms. These common key genes and pathways may provide new directions toward the future study of osteomyelitis and DFU.", "claims": [{"id": "claim-method", "statement": "Different gene expression analyses were completed by the “GEOquery” and “limma” packages.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37904457", "locator": "sections[4] \"2.2. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE16129"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE16129"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37904457", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000033962", "sha256": 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