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pmid:40335993

Comprehensive bioinformatics analysis uncover molecular pathways shared between osteoarthritis and atherosclerosis.

Yingchao Jin · Hua Zhang

BMC musculoskeletal disorders · 2025 doi:10.1186/s12891-025-08563-6

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40335993
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science-record
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sha256:8cc90bfa1186a427eb486d19a627917745475adf8498b64d3d9b56e15f4b0952
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T20:30:30Z
updated_at
2026-11-01T22:14:02Z
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title
Comprehensive bioinformatics analysis uncover molecular pathways shared between osteoarthritis and atherosclerosis.
doi
10.1186/s12891-025-08563-6
pmid
40335993
authors array · 2 items
[0]
Yingchao Jin
[1]
Hua Zhang
journal
BMC musculoskeletal disorders
year
2025
abstract
BACKGROUND: There is growing evidence of an association between osteoarthritis (OA) and atherosclerosis (AS). However, their mechanisms are not yet fully understood. The aim of this study was to investigate the common genetic and molecular mechanisms underlying the common pathogenesis of OA and AS. METHODOLOGY: Gene expression profiles of OA (GSE51588) and AS (GSE100927) were obtained from the Gene Expression Omnibus (GEO) database. After identifying shared differentially expressed genes (DEGs) and hub genes, we performed multifaceted bioinformatics analyses, including functional annotation, co-expression analysis, TF-mRNA and ceRNA regulatory network construction, pharmacogenetic prediction, and receiver operator characteristic (ROC) curve assessment. In addition, the immune infiltration of OA and AS was analyzed and compared based on the ssGSEA algorithm, and the correlation between hub genes and infiltrating immune cells was evaluated in OA and AS, respectively. RESULT: A total of 48 up-regulated and 43 down-regulated public DEGs were screened between GSE51588 and GSE100927, and functional enrichment analysis emphasized the important role of immune and inflammatory pathways in OA and AS. After protein-protein interaction (PPI) network construction, a total of 9 hub genes (CCR5, IFIT2, MMP1, CXCL9, RSAD2, IFIH1, TNF, IFIT3, and TBX21) were identified as key genes. Targeting the key genes we identified several molecular drug candidates against OA combined with AS related. Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973). Immune infiltration study also revealed coordinated changes in immune cell profiles in OA and AS diseases. CONCLUSION: After a series of bioinformatics analysis and validation, CCR5, IFIT2, MMP1, CXCL9, RSAD2, IFIH1, TNF, IFIT3 and TBX21 were identified as common hub genes for the development of OA and AS. This study provides a new perspective on the common molecular mechanisms between OA and AS, and offers new insights into the potential pathogenesis of OA combined with AS and the direction of treatment.
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ROC curve analysis was performed using the “pROC” package of the R software and the results were visualized using the “ggplot2” package.
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sections[11] "Diagnostic performance analysis of hub genes" paragraph 2
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ROC curve analysis was performed using the “pROC” package of the R software and the results were visualized using the “ggplot2” package.
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sections[29] "Data availability" paragraph 2
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No datasets were generated or analysed during the current study.
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Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973).
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Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973).
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Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973).
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Atherosclerotic and control peripheral artery gene expression
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Genome-wide Expression Profiles of Subchondral Bone in Osteoarthritis
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40335993", "kind": "science-record", "revision": 3, "content_hash": "sha256:8cc90bfa1186a427eb486d19a627917745475adf8498b64d3d9b56e15f4b0952", "created_at": "2026-11-01T20:30:30Z", "updated_at": "2026-11-01T22:14:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive bioinformatics analysis uncover molecular pathways shared between osteoarthritis and atherosclerosis.", "doi": "10.1186/s12891-025-08563-6", "pmid": "40335993", "authors": ["Yingchao Jin", "Hua Zhang"], "journal": "BMC musculoskeletal disorders", "year": 2025, "abstract": "BACKGROUND: There is growing evidence of an association between osteoarthritis (OA) and atherosclerosis (AS). However, their mechanisms are not yet fully understood. The aim of this study was to investigate the common genetic and molecular mechanisms underlying the common pathogenesis of OA and AS.\nMETHODOLOGY: Gene expression profiles of OA (GSE51588) and AS (GSE100927) were obtained from the Gene Expression Omnibus (GEO) database. After identifying shared differentially expressed genes (DEGs) and hub genes, we performed multifaceted bioinformatics analyses, including functional annotation, co-expression analysis, TF-mRNA and ceRNA regulatory network construction, pharmacogenetic prediction, and receiver operator characteristic (ROC) curve assessment. In addition, the immune infiltration of OA and AS was analyzed and compared based on the ssGSEA algorithm, and the correlation between hub genes and infiltrating immune cells was evaluated in OA and AS, respectively.\nRESULT: A total of 48 up-regulated and 43 down-regulated public DEGs were screened between GSE51588 and GSE100927, and functional enrichment analysis emphasized the important role of immune and inflammatory pathways in OA and AS. After protein-protein interaction (PPI) network construction, a total of 9 hub genes (CCR5, IFIT2, MMP1, CXCL9, RSAD2, IFIH1, TNF, IFIT3, and TBX21) were identified as key genes. Targeting the key genes we identified several molecular drug candidates against OA combined with AS related. Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973). Immune infiltration study also revealed coordinated changes in immune cell profiles in OA and AS diseases.\nCONCLUSION: After a series of bioinformatics analysis and validation, CCR5, IFIT2, MMP1, CXCL9, RSAD2, IFIH1, TNF, IFIT3 and TBX21 were identified as common hub genes for the development of OA and AS. This study provides a new perspective on the common molecular mechanisms between OA and AS, and offers new insights into the potential pathogenesis of OA combined with AS and the direction of treatment.", "claims": [{"id": "claim-method", "statement": "ROC curve analysis was performed using the “pROC” package of the R software and the results were visualized using the “ggplot2” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40335993", "locator": "sections[11] \"Diagnostic performance analysis of hub genes\" paragraph 2", "quote": "ROC curve analysis was performed using the “pROC” package of the R software and the results were visualized using the “ggplot2” package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "No datasets were generated or analysed during the current study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40335993", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "No datasets were generated or analysed during the current study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40335993", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973).", "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:40335993"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE100927"], "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:GSE100927"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40335993", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12891-025-08563-6", "sha256": 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