Identification of key biomarkers in RF-negative polyarticular and oligoarticular juvenile idiopathic arthritis by bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38001449", "kind": "science-record", "revision": 5, "content_hash": "sha256:760dc8dbabb398e160e9c92e759888682dab0c2a08e3de91d1065d8b73d29684", "created_at": "2026-11-01T19:02:55Z", "updated_at": "2026-11-01T22:53:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key biomarkers in RF-negative polyarticular and oligoarticular juvenile idiopathic arthritis by bioinformatic analysis.", "doi": "10.1186/s12969-023-00926-4", "pmid": "38001449", "authors": ["Yun Liu", "Xuemei Tang"], "journal": "Pediatric rheumatology online journal", "year": 2023, "abstract": "OBJECTIVE: Juvenile idiopathic arthritis (JIA) is a broad term used to describe arthritis of unknown origin. JIA commonly persists into adulthood, often causing substantial morbidity such as restricted joint function, which can lead to challenges in employment and independence. This study aims to identify diagnostic biomarkers and investigate the role of immune cells in the pathogenesis of rheumatoid factor-negative polyarticular juvenile idiopathic arthritis (RF-negative pJIA) and oligoarticular juvenile idiopathic arthritis (oJIA).\nMETHODS: We retrieved a JIA dataset from the GEO database and conducted an analysis of differentially expressed genes (DEGs). Subsequently, functional enrichment analysis was performed on the DEGs. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key modules. Additionally, we constructed a protein‒protein interaction network to identify hub genes that serve as signature genes. Furthermore, we employed CIBERSORT to classify immune cell infiltration.\nRESULTS: From the GSE20307 dataset, we identified a total of 1438 DEGs in RF-negative pJIA and 688 DEGs in oJIA. WGCNA clustered the data into 6 modules in pJIA and 4 modules in oJIA. Notably, the ME5 and ME2 modules exhibited significant associations with pJIA and oJIA, respectively. In both pJIA and oJIA, we identified six hub genes, four of which demonstrated high diagnostic sensitivity and specificity in pJIA, while five showed high diagnostic sensitivity and specificity in oJIA. CIBERSORT analysis suggested the potential involvement of these signature genes in immune cell infiltration.\nCONCLUSION: In this study, we identified JUN, CXCL8, SOCS3, and KRAS as biomarkers for RF-negative pJIA and JUN, CXCL8, SOCS3, PTGS2, and NFKBIA as biomarkers for oJIA. Furthermore, our findings suggest that Tfh cells may play a role in the early onset of both RF-negative pJIA and oJIA.", "claims": [{"id": "claim-method", "statement": "The limma package [7] in the R software was utilized to analyze the DEGs between the RF-negative pJIA and oJIA cohorts and the control cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38001449", "locator": "sections[4] \"Identification of Differentially Expressed Genes (DEGs)\" paragraph 2", "quote": "The limma package [7] in the R software was utilized to analyze the DEGs between the RF-negative pJIA and oJIA cohorts and the control cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38001449", "locator": "sections[24] \"Availability of data and materials\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "From the GSE20307 dataset, we identified a total of 1438 DEGs in RF-negative pJIA and 688 DEGs in oJIA.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38001449", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "From the GSE20307 dataset, we identified a total of 1438 DEGs in RF-negative pJIA and 688 DEGs in oJIA."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "From the GSE20307 dataset, we identified a total of 1438 DEGs in RF-negative pJIA and 688 DEGs in oJIA.", "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:38001449"], "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:GSE13501"], "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:GSE13501"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38001449", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12969-023-00926-4", "sha256": "sha256:30d6a7107da57b2086ecbe358acfdedfa36bfb006785ee1f94120718f57a9cea", "bytes": 33866, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12969-023-00926-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10675924", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13501", "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=GSE13501", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13501", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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