Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38771826", "kind": "science-record", "revision": 4, "content_hash": "sha256:bf424474081fb2768239c5dd70c2c572398dd5acb34252704f77269878846d53", "created_at": "2026-11-01T19:20:43Z", "updated_at": "2026-11-01T22:54:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis.", "doi": "10.1371/journal.pone.0303506", "pmid": "38771826", "authors": ["Chang-Sheng Liao", "Fang-Zheng He", "Xi-Yong Li", "Yan Zhang", "Peng-Fei Han"], "journal": "PloS one", "year": 2024, "abstract": "OBJECTIVE: To elucidate potential molecular mechanisms differentiating osteoarthritis (OA) and rheumatoid arthritis (RA) through a bioinformatics analysis of differentially expressed genes (DEGs) in patient synovial cells, aiming to provide new insights for clinical treatment strategies.\nMATERIALS AND METHODS: Gene expression datasets GSE1919, GSE82107, and GSE77298 were downloaded from the Gene Expression Omnibus (GEO) database to serve as the training groups, with GSE55235 being used as the validation dataset. The OA and RA data from the GSE1919 dataset were merged with the standardized data from GSE82107 and GSE77298, followed by batch effect removal to obtain the merged datasets of differential expressed genes (DEGs) for OA and RA. Intersection analysis was conducted on the DEGs between the two conditions to identify commonly upregulated and downregulated DEGs. Enrichment analysis was then performed on these common co-expressed DEGs, and a protein-protein interaction (PPI) network was constructed to identify hub genes. These hub genes were further analyzed using the GENEMANIA online platform and subjected to enrichment analysis. Subsequent validation analysis was conducted using the GSE55235 dataset.\nRESULTS: The analysis of differentially expressed genes in the synovial cells from patients with Osteoarthritis (OA) and Rheumatoid Arthritis (RA), compared to a control group (individuals without OA or RA), revealed significant changes in gene expression patterns. Specifically, the genes APOD, FASN, and SCD were observed to have lower expression levels in the synovial cells of both OA and RA patients, indicating downregulation within the pathological context of these diseases. In contrast, the SDC1 gene was found to be upregulated, displaying higher expression levels in the synovial cells of OA and RA patients compared to normal controls.Additionally, a noteworthy observation was the downregulation of the transcription factor PPARG in the synovial cells of patients with OA and RA. The decrease in expression levels of PPARG further validates the alteration in lipid metabolism and inflammatory processes associated with the pathogenesis of OA and RA. These findings underscore the significance of these genes and the transcription factor not only as biomarkers for differential diagnosis between OA and RA but also as potential targets for therapeutic interventions aimed at modulating their expression to counteract disease progression.\nCONCLUSION: The outcomes of this investigation reveal the existence of potentially shared molecular mechanisms within Osteoarthritis (OA) and Rheumatoid Arthritis (RA). The identification of APOD, FASN, SDC1, TNFSF11 as key target genes, along with their downstream transcription factor PPARG, highlights common potential factors implicated in both diseases. A deeper examination and exploration of these findings could pave the way for new candidate targets and directions in therapeutic research aimed at treating both OA and RA. This study underscores the significance of leveraging bioinformatics approaches to unravel complex disease mechanisms, offering a promising avenue for the development of more effective and targeted treatments.", "claims": [{"id": "claim-method", "statement": "The analysis tool selected was DESeq2, a widely used and validated R package for count data differential expression analysis. DESeq2 fits the gene expression data using a negative binomial distribution and employs the Wald test to assess differences between gene expression levels. Following the correction, we conducted differential expression gene analysis using the limma package to identify statistically significant differentially expressed genes within the consolidated dataset. All statistical analyses were conducted within the R environment. A two-sample t-test analyzed gene expression differences between groups. The Benjamini-Hochberg method was applied for multiple testing correction to control the false discovery rate (FDR).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38771826", "locator": "sections[4] \"2. Data preprocessing and identification of differentially expressed genes\" paragraph 2", "quote": "The analysis tool selected was DESeq2, a widely used and validated R package for count data differential expression analysis."}, {"asset": "source:pmid:38771826", "locator": "sections[4] \"2. Data preprocessing and identification of differentially expressed genes\" paragraph 2", "quote": "DESeq2 fits the gene expression data using a negative binomial distribution and employs the Wald test to assess differences between gene expression levels."}, {"asset": "source:pmid:38771826", "locator": "sections[5] \"3. Batch effect correction and differential analysis\" paragraph 2", "quote": "Following the correction, we conducted differential expression gene analysis using the limma package to identify statistically significant differentially expressed genes within the consolidated dataset."}, {"asset": "source:pmid:38771826", "locator": "sections[13] \"11. Statistical methods, software, and tools\" paragraph 2", "quote": "All statistical analyses were conducted within the R environment."}, {"asset": "source:pmid:38771826", "locator": "sections[13] \"11. Statistical methods, software, and tools\" paragraph 2", "quote": "A two-sample t-test analyzed gene expression differences between groups."}, {"asset": "source:pmid:38771826", "locator": "sections[13] \"11. Statistical methods, software, and tools\" paragraph 2", "quote": "The Benjamini-Hochberg method was applied for multiple testing correction to control the false discovery rate (FDR)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All relevant data are within the manuscript and its Supporting Information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38771826", "locator": "sections[27] \"Data Availability\" paragraph 2", "quote": "All relevant data are within the manuscript and its Supporting Information files."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:38771826"], "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:GSE1919"], "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:GSE1919"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38771826", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0303506", "sha256": "sha256:f6ab9ee64f222b8f35bd384730496371919f2286cda1afffce56831139e76685", "bytes": 54696, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1371/journal.pone.0303506", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11108184", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE1919", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1919", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE1919", "version": null, "note": "All relevant data are within the manuscript and its Supporting Information files.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All relevant data are within the manuscript and its Supporting Information files.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM34379", "GSM34383", "GSM34385", "GSM34388", "GSM34391", "GSM34393", "GSM34394", "GSM34395", "GSM34396", "GSM34397", "GSM34398", "GSM34399", "GSM34400", "GSM34401", "GSM34402"], "sample_count": 15, "group_sizes": null, "group_sizes_basis": null, "platform": "91", "taxon": "Homo sapiens", "title": "Rheumatoid arthritis", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE55235", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55235", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE55235", "version": null, "note": "All relevant data are within the manuscript and its Supporting Information files.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All relevant data are within the manuscript and its Supporting Information files.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1332201", "GSM1332202", "GSM1332203", "GSM1332204", "GSM1332205", "GSM1332206", "GSM1332207", "GSM1332208", "GSM1332209", "GSM1332210", "GSM1332211", "GSM1332212", "GSM1332213", "GSM1332214", "GSM1332215", "GSM1332216", "GSM1332217", "GSM1332218", "GSM1332219", "GSM1332220", "GSM1332221", "GSM1332222", "GSM1332223", "GSM1332224", "GSM1332225", "GSM1332226", "GSM1332227", "GSM1332228", "GSM1332229", "GSM1332230"], "sample_count": 30, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE77298", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77298", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE77298", "version": null, "note": "All relevant data are within the manuscript and its Supporting Information files.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All relevant data are within the manuscript and its Supporting Information files.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2048265", "GSM2048266", "GSM2048267", "GSM2048268", "GSM2048269", "GSM2048270", "GSM2048271", "GSM2048272", "GSM2048273", "GSM2048274", "GSM2048275", "GSM2048276", "GSM2048277", "GSM2048278", "GSM2048279", "GSM2048280", "GSM2048281", "GSM2048282", "GSM2048283", "GSM2048284", "GSM2048285", "GSM2048286", "GSM2048287"], "sample_count": 23, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Synovial biopsies of rheumatoid arthritis and healthy controls", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE82107", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82107", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE82107", "version": null, "note": "All relevant data are within the manuscript and its Supporting Information files.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All relevant data are within the manuscript and its Supporting Information files.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2183532", "GSM2183533", "GSM2183534", "GSM2183535", "GSM2183536", "GSM2183537", "GSM2183538", "GSM2183539", "GSM2183540", "GSM2183541", "GSM2183542", "GSM2183543", "GSM2183544", "GSM2183545", "GSM2183546", "GSM2183547", "GSM2183548"], "sample_count": 17, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Synovial biopsies of osteoarthritis patients", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE1919 not downloaded", "asset:data:geo:GSE1919 licence not read", "asset:data:geo:GSE55235 not downloaded", "asset:data:geo:GSE55235 licence not read", "asset:data:geo:GSE77298 not downloaded", "asset:data:geo:GSE77298 licence not read", "asset:data:geo:GSE82107 not downloaded", "asset:data:geo:GSE82107 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "deseq2", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "reordered", "quote": "The analysis tool selected was DESeq2, a widely used and validated R package for count data differential expression analysis. DESeq2 fits the gene expression data using a negative binomial distribution and employs the Wald test to assess differences between gene expression levels. Following the correction, we conducted differential expression gene analysis using the limma package to identify statistically significant differentially expressed genes within the consolidated dataset. All statistical analyses were conducted within the R environment. A two-sample t-test analyzed gene expression differences between groups. The Benjamini-Hochberg method was applied for multiple testing correction to control the false discovery rate (FDR)."}}, {"id": "method-2", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-3", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-4", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}, {"id": "method-5", "family": "network_analysis", "specific": "centrality", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "centrality"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"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: deseq2."}, "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": ["GSE1919", "GSE82107", "GSE77298", "GSE55235"], "resolved_accessions": ["GSE1919", "GSE82107", "GSE77298", "GSE55235"], "statements": ["All relevant data are within the manuscript and its Supporting Information files."], "basis": "The paper names GSE1919, GSE82107, GSE77298, GSE55235 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:38771826", "data:geo:GSE1919"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE1919"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:19:04Z", "independence": "single_reader"}, "evidence_check": {"method": "reordered", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14094, "completion_tokens": 2519, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38771826", "type": "paper", "label": "pmid:38771826"}, {"id": "source:pmid:38771826", "type": "manuscript", "label": "PMID 38771826 full text"}, {"id": "data:geo:GSE1919", "type": "dataset", "label": "GEO GSE1919"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38771826", "type": "paper", "label": "pmid:38771826"}, {"id": "source:pmid:38771826", "type": "manuscript", "label": "PMID 38771826 full text"}, {"id": "data:geo:GSE1919", "type": "dataset", "label": "GEO GSE1919"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38771826", "type": "extraction", "used": ["source:pmid:38771826"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE1919", "type": "accession_resolution", "used": ["data:geo:GSE1919"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38771826", "parent_hash": "sha256:ef47278d36ee17c5cf52fbca2b5f305df1aa2e59992bf2b1f2205fcb05247cdc", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE1919 licence not read", "asset:data:geo:GSE1919 not downloaded", "asset:data:geo:GSE55235 licence not read", "asset:data:geo:GSE55235 not downloaded", "asset:data:geo:GSE77298 licence not read", "asset:data:geo:GSE77298 not downloaded", "asset:data:geo:GSE82107 licence not read", "asset:data:geo:GSE82107 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The analysis tool selected was DESeq2, a widely used and validated R package for count data differential expression analysis. DESeq2 fits the gene expression data using a negative binomial distribution and employs the Wald test to assess differences between gene expression levels. Following the correction, we conducted differential expression gene analysis using the limma package to identify statistically significant differentially expressed genes within the consolidated dataset. All statistical analyses were conducted within the R environment. A two-sample t-test analyzed gene expression differences between groups. The Benjamini-Hochberg method was applied for multiple testing correction to control the false discovery rate (FDR)."], "order_basis": "analysis_only", "id": "assay:1"}]}