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pmid:35571384

Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis.

Ran You · Siyi Liu · Jinhai Tan

Annals of translational medicine · 2022 doi:10.21037/atm-22-1135

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:35571384
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2026-11-01T18:14:27Z
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2026-11-01T22:49:03Z
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paper object · 10 fields
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title
Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis.
doi
10.21037/atm-22-1135
pmid
35571384
authors array · 3 items
[0]
Ran You
[1]
Siyi Liu
[2]
Jinhai Tan
journal
Annals of translational medicine
year
2022
abstract
Background: Searching for the production mechanism of synovial lesions helps to find precise therapeutic targets and improve prognosis. The previous identification and screening of differential genes in osteoarthritis (OA) pathogenesis were well combined to further build a risk prognosis model of OA, which is beneficial to the diagnosis and treatment of patients with OA. Methods: The synovia-related chip data sets GSE82107, GSE12021, GSE55457, and GSE55235 were downloaded from the public database of Gene Expression Omnibus (GEO), and 40 cases of synovial tissues of OA and 36 cases of normal synovial tissues were included. R software was used to screen differentially expressed genes (DEGs), Gene Ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The STRING online analysis tool and Cytoscape software were used to further screen key genes, and a prognostic model of OA susceptibility risk was constructed. Results: The results showed 1,921 differential genes, including 762 upregulated genes and 1,159 downregulated genes, which were mainly involved cell growth, cell adhesion, skeletal muscle growth, iron ion binding, ubiquitin protein ligase binding, and hormone receptor binding. Co-acquisition based on 10 key target genes of the protein interaction network, containing CTNNB1, GSK3B, STAT1, RHOC, HDAC9, PSEN1, KDM5C, BACE1, JAK3, and CUL1. The area under the concentration-time curve (AUC) was used to evaluate the prognostic model of OA risk, and the curve results showed that the prognostic model had high accuracy and validity (AUC =0.690). Conclusions: Bioinformatics analysis was applied to screen out the DEG profiles of OA. This may provide functional predictions to provide new ideas for treatment of the disease and may be a biological marker for its diagnosis and a potential target for treatment. The construction of the risk and prognosis model is beneficial to the risk assessment of rehabilitation function recovery of patients with OA, the evaluation of the severity of the disease and the subsequent treatment guidance.
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Data from GSE12021, GSE55235, GSE55457, and GSE55584 datasets were merged, and batch corrected using the “sva” analysis package in R, then analyzed using “limma” analysis package 8, where DEGs were selected with P<0.05, |log2fold change (FC)| >1.
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sections[4] "Screening and analysis of DEGs" paragraph 2
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Data from GSE12021, GSE55235, GSE55457, and GSE55584 datasets were merged, and batch corrected using the “sva” analysis package in R, then analyzed using “limma” analysis package 8, where DEGs were selected with P<0.05, |log2fold change (FC)| >1.
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The data set of the GSE12021 chip was submitted by Huber in 2010 (16) and included 13 normal synovial tissue samples, 20 OA tissue samples, and 20 synovial tissue samples of rheumatoid arthritis (RA).
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sections[3] "Data collection and preprocessing" paragraph 2
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The data set of the GSE12021 chip was submitted by Huber in 2010 (16) and included 13 normal synovial tissue samples, 20 OA tissue samples, and 20 synovial tissue samples of rheumatoid arthritis (RA).
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The analysis identified 1,921 differentially expressed genes between OA and normal synovial tissues.
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The results showed 1,921 differential genes, including 762 upregulated genes and 1,159 downregulated genes, which were mainly involved cell growth, cell adhesion, skeletal muscle growth, iron ion binding, ubiquitin protein ligase binding, and hormone receptor binding.
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The synovia-related chip data sets GSE82107, GSE12021, GSE55457, and GSE55235 were downloaded from the public database of Gene Expression Omnibus (GEO), and 40 cases of synovial tissues of OA and 36 cases of normal synovial tissues were included.
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The analysis identified 1,921 differentially expressed genes between OA and normal synovial tissues.
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The data set of the GSE12021 chip was submitted by Huber in 2010 (16) and included 13 normal synovial tissue samples, 20 OA tissue samples, and 20 synovial tissue samples of rheumatoid arthritis (RA).
basis
The paper names GSE12021, GSE55235, GSE55457, GSE55584, GSE82107 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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Data from GSE12021, GSE55235, GSE55457, and GSE55584 datasets were merged, and batch corrected using the “sva” analysis package in R, then analyzed using “limma” analysis package 8, where DEGs were selected with P<0.05, |log2fold change (FC)| >1.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35571384", "kind": "science-record", "revision": 5, "content_hash": "sha256:c7f7a0ce3088c231310c5fb12de9da077b90692ed72defdbd61047b27a94e5e3", "created_at": "2026-11-01T18:14:27Z", "updated_at": "2026-11-01T22:49:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis.", "doi": "10.21037/atm-22-1135", "pmid": "35571384", "authors": ["Ran You", "Siyi Liu", "Jinhai Tan"], "journal": "Annals of translational medicine", "year": 2022, "abstract": "Background: Searching for the production mechanism of synovial lesions helps to find precise therapeutic targets and improve prognosis. The previous identification and screening of differential genes in osteoarthritis (OA) pathogenesis were well combined to further build a risk prognosis model of OA, which is beneficial to the diagnosis and treatment of patients with OA.\nMethods: The synovia-related chip data sets GSE82107, GSE12021, GSE55457, and GSE55235 were downloaded from the public database of Gene Expression Omnibus (GEO), and 40 cases of synovial tissues of OA and 36 cases of normal synovial tissues were included. R software was used to screen differentially expressed genes (DEGs), Gene Ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The STRING online analysis tool and Cytoscape software were used to further screen key genes, and a prognostic model of OA susceptibility risk was constructed.\nResults: The results showed 1,921 differential genes, including 762 upregulated genes and 1,159 downregulated genes, which were mainly involved cell growth, cell adhesion, skeletal muscle growth, iron ion binding, ubiquitin protein ligase binding, and hormone receptor binding. Co-acquisition based on 10 key target genes of the protein interaction network, containing CTNNB1, GSK3B, STAT1, RHOC, HDAC9, PSEN1, KDM5C, BACE1, JAK3, and CUL1. The area under the concentration-time curve (AUC) was used to evaluate the prognostic model of OA risk, and the curve results showed that the prognostic model had high accuracy and validity (AUC =0.690).\nConclusions: Bioinformatics analysis was applied to screen out the DEG profiles of OA. This may provide functional predictions to provide new ideas for treatment of the disease and may be a biological marker for its diagnosis and a potential target for treatment. The construction of the risk and prognosis model is beneficial to the risk assessment of rehabilitation function recovery of patients with OA, the evaluation of the severity of the disease and the subsequent treatment guidance.", "claims": [{"id": "claim-method", "statement": "Data from GSE12021, GSE55235, GSE55457, and GSE55584 datasets were merged, and batch corrected using the “sva” analysis package in R, then analyzed using “limma” analysis package 8, where DEGs were selected with P<0.05, |log2fold change (FC)| >1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35571384", "locator": "sections[4] \"Screening and analysis of DEGs\" paragraph 2", "quote": "Data from GSE12021, GSE55235, GSE55457, and GSE55584 datasets were merged, and batch corrected using the “sva” analysis package in R, then analyzed using “limma” analysis package 8, where DEGs were selected with P<0.05, |log2fold change (FC)| >1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data set of the GSE12021 chip was submitted by Huber in 2010 (16) and included 13 normal synovial tissue samples, 20 OA tissue samples, and 20 synovial tissue samples of rheumatoid arthritis (RA).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35571384", "locator": "sections[3] \"Data collection and preprocessing\" paragraph 2", "quote": "The data set of the GSE12021 chip was submitted by Huber in 2010 (16) and included 13 normal synovial tissue samples, 20 OA tissue samples, and 20 synovial tissue samples of rheumatoid arthritis (RA)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified 1,921 differentially expressed genes between OA and normal synovial tissues.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35571384", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The results showed 1,921 differential genes, including 762 upregulated genes and 1,159 downregulated genes, which were mainly involved cell growth, cell adhesion, skeletal muscle growth, iron ion binding, ubiquitin protein ligase binding, and hormone receptor binding."}, {"asset": "source:pmid:35571384", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The synovia-related chip data sets GSE82107, GSE12021, GSE55457, and GSE55235 were downloaded from the public database of Gene Expression Omnibus (GEO), and 40 cases of synovial tissues of OA and 36 cases of normal synovial tissues were included."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified 1,921 differentially expressed genes between OA and normal synovial tissues.", "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:35571384"], "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:GSE12021"], "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:GSE12021"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35571384", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-22-1135", "sha256": 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{"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "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": {"tier": "R1", "reachable": true, "accessions": ["GSE12021", "GSE55235", "GSE55457", "GSE55584", "GSE82107"], "resolved_accessions": ["GSE12021", "GSE55235", "GSE55457", "GSE55584", "GSE82107"], "statements": ["The data set of the GSE12021 chip was submitted by Huber in 2010 (16) and included 13 normal synovial tissue samples, 20 OA tissue samples, and 20 synovial tissue samples of rheumatoid arthritis (RA)."], "basis": "The paper names GSE12021, GSE55235, GSE55457, GSE55584, GSE82107 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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