Analysis of common differential gene expression between rheumatoid arthritis and ulcerative colitis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41481752", "kind": "science-record", "revision": 4, "content_hash": "sha256:53b4e7220a19a94acd8f8f22b076d978b276fc3b26faa6c5838701d97326e91d", "created_at": "2026-11-01T20:31:27Z", "updated_at": "2026-11-01T22:57:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of common differential gene expression between rheumatoid arthritis and ulcerative colitis.", "doi": "10.1371/journal.pone.0339397", "pmid": "41481752", "authors": ["Peng-Fei Han", "Wei-Rong Cui", "Fang-Zheng He", "Tao Wu", "Chang-Sheng Liao"], "journal": "PloS one", "year": 2026, "abstract": "OBJECTIVE: This study employs bioinformatics analysis with the objective of identifying commonly differentially expressed genes (DEGs) in ulcerative colitis (UC) and rheumatoid arthritis (RA), as well as exploring their underlying molecular mechanisms. By doing so, it aims to provide a theoretical basis for investigating the potential associations between these two diseases and developing novel therapeutic strategies.\nMATERIALS AND METHODS: We downloaded multiple gene expression datasets for Rheumatoid Arthritis (RA) and Ulcerative Colitis (UC) from the Gene Expression Omnibus (GEO) database. For RA, GSE77298, GSE12021, and GSE55457 were selected as the training sets, with GSE89408 serving as the validation set. For UC, GSE36807, GSE87473, and GSE92415 were chosen as the training sets, and GSE13367 as the validation set.During data processing, we first merged the RA and UC data from each training set with standardized data, eliminated batch effects, and obtained combined datasets of differentially expressed genes (DEGs). Subsequently, we conducted a cross-analysis of the DEGs from RA and UC to identify commonly up-regulated and down-regulated genes. To gain a deeper understanding of these DEGs, we constructed a protein-protein interaction (PPI) network and identified hub genes.For further analysis of these hub genes, we utilized the GENEMANIA platform to obtain functional annotations and interaction information. Finally, we validated our analysis results using the GSE89408 and GSE13367 datasets.\nRESULTS: After a thorough analysis of the differentially expressed genes in the cells of patients with rheumatoid arthritis (RA) and ulcerative colitis (UC) we found that genes such as CCR7, CD19, CXCL13, CXCR4, and SELL were significantly up-regulated, suggesting their crucial roles in the pathology of both diseases. This discovery not only underscores the importance of these genes as biomarkers for the differential diagnosis of RA and UC, but also highlights key nodes worthy of further validation. In the future, it may be possible to slow or halt disease progression by modulating the expression of these genes.\nCONCLUSION: The results of this study reveal potential common molecular mechanisms underlying rheumatoid arthritis (RA) and ulcerative colitis (UC). The key target genes CCR7, CD19, CXCL13, CXCR4, and SELL highlight common underlying factors associated with both diseases. Further investigation and exploration of these findings can pave the way for new candidate targets and directions in therapeutic research aimed at treating RA and UC. This study emphasizes the importance of utilizing bioinformatics approaches to uncover the mechanisms of complex diseases, providing a promising pathway for the development of more effective and targeted treatments.", "claims": [{"id": "claim-method", "statement": "Based on these preprocessed data, we utilized the limma package, which runs within the RStudio environment, to conduct differential expression analyses on the RA and UC groups in comparison to the normal control group.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41481752", "locator": "sections[2] \"2. Materials and methods\" paragraph 3", "quote": "Based on these preprocessed data, we utilized the limma package, which runs within the RStudio environment, to conduct differential expression analyses on the RA and UC groups in comparison to the normal control group."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The intermediate analysis files and results generated during the above statistical procedures are available in S1 Data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41481752", "locator": "sections[2] \"2. Materials and methods\" paragraph 12", "quote": "The intermediate analysis files and results generated during the above statistical procedures are available in S1 Data."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Cross-analysis of batch-corrected RA and UC differential-expression results identified 77 commonly upregulated genes and 11 commonly downregulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41481752", "locator": "sections[3] \"3. Results\" paragraph 10", "quote": "By intersecting the two sets of DEGs, we precisely identified 77 upregulated genes and 11 downregulated genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Cross-analysis of batch-corrected RA and UC differential-expression results identified 77 commonly upregulated genes and 11 commonly downregulated genes.", "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:41481752"], "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:41481752", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0339397", "sha256": 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