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pmid:41327695

Analysis of key genes and pathways of knee osteoarthritis based on GEO data mining.

Yuan Gao

Medicine · 2025 doi:10.1097/MD.0000000000046250

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

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:41327695
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title
Analysis of key genes and pathways of knee osteoarthritis based on GEO data mining.
doi
10.1097/MD.0000000000046250
pmid
41327695
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[0]
Yuan Gao
journal
Medicine
year
2025
abstract
This study aims to give significant information for the research and treatment of knee osteoarthritis (KOA) targets, using bioinformatics methods to uncover the associated genes of KOA and explore their expression and clinical importance. GEO database was used to analyze differential genes of KOA, R was used to perform pathway enrichment analysis and functional annotation of differential genes, a protein-protein interaction network of differential genes was constructed, core modules of the protein-protein interaction network of relationship targets were screened, and common genes of 2 protein relationships were obtained. After combining, correcting, and screening the GSE51588, GSE55457, and GSE82107 data sets, 488 differential genes and 297 disease intersection targets were identified. The gene ontology study primarily focused on neutrophil activation and neutrophil activation in immunological response. Depending on Kyoto encyclopedia of genes and genomes analysis, signal pathways mostly included MAPK, IL-17, Wnt, TNF, and other information pathways. Biomarkers and treatment targets of KOA are likely to be PPBP, FPR2, PF4, ELANE, ORM1FPR1, PENK, POMC, RETN, ARG1, HIST1H2BB, and HIST1H2BJ. KOA can be treated clinically by regulating the expression of the above targets, activating or inhibiting related signal pathways, anti-inflammation, regulating apoptosis, and lowering oxidative stress.
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Then the Bioconductor software package was used for data processing and statistical analysis, and the expression data at the probe level were converted to gene symbol level data using the Perl language, and log2FC > 1 or log2FC < ‐1 and P < .05 were used as the screening conditions for candidate differential genes, and the limma package of the R software was used to analyze KOA tissues and normal human tissues for the microarray data for differential genes and performed DEG screening.
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sections[3] "2.1. Knee osteoarthritis differentially expressed genes (DEGs) screening, dataset integration, and batch normalize correction" paragraph 2
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Then the Bioconductor software package was used for data processing and statistical analysis, and the expression data at the probe level were converted to gene symbol level data using the Perl language, and log2FC > 1 or log2FC < ‐1 and P < .05 were used as the screening conditions for candidate differential genes, and the limma package of the R software was used to analyze KOA tissues and normal human tissues for the microarray data for differential genes and performed DEG screening.
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After combining, correcting, and screening the GSE51588, GSE55457, and GSE82107 data sets, 488 differential genes and 297 disease intersection targets were identified.
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After combining, correcting, and screening the GSE51588, GSE55457, and GSE82107 data sets, 488 differential genes and 297 disease intersection targets were identified.
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After combining, correcting, and screening the GSE51588, GSE55457, and GSE82107 data sets, 488 differential genes and 297 disease intersection targets were identified.
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Then the Bioconductor software package was used for data processing and statistical analysis, and the expression data at the probe level were converted to gene symbol level data using the Perl language, and log2FC > 1 or log2FC < ‐1 and P < .05 were used as the screening conditions for candidate differential genes, and the limma package of the R software was used to analyze KOA tissues and normal human tissues for the microarray data for differential genes and performed DEG screening.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41327695", "kind": "science-record", "revision": 4, "content_hash": "sha256:5bf65fa5c7e9953e891b6dd2e77feec9bc2c7f5b490633777f735d52760689fd", "created_at": "2026-11-01T20:31:21Z", "updated_at": "2026-11-01T22:57:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of key genes and pathways of knee osteoarthritis based on GEO data mining.", "doi": "10.1097/MD.0000000000046250", "pmid": "41327695", "authors": ["Yuan Gao"], "journal": "Medicine", "year": 2025, "abstract": "This study aims to give significant information for the research and treatment of knee osteoarthritis (KOA) targets, using bioinformatics methods to uncover the associated genes of KOA and explore their expression and clinical importance. GEO database was used to analyze differential genes of KOA, R was used to perform pathway enrichment analysis and functional annotation of differential genes, a protein-protein interaction network of differential genes was constructed, core modules of the protein-protein interaction network of relationship targets were screened, and common genes of 2 protein relationships were obtained. After combining, correcting, and screening the GSE51588, GSE55457, and GSE82107 data sets, 488 differential genes and 297 disease intersection targets were identified. The gene ontology study primarily focused on neutrophil activation and neutrophil activation in immunological response. Depending on Kyoto encyclopedia of genes and genomes analysis, signal pathways mostly included MAPK, IL-17, Wnt, TNF, and other information pathways. Biomarkers and treatment targets of KOA are likely to be PPBP, FPR2, PF4, ELANE, ORM1FPR1, PENK, POMC, RETN, ARG1, HIST1H2BB, and HIST1H2BJ. 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