A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33430905", "kind": "science-record", "revision": 8, "content_hash": "sha256:ed61311f4b91399a77b25fb247f532042b6f1903114d77f19c933c3ec2af9cc3", "created_at": "2026-11-01T15:41:22Z", "updated_at": "2026-11-01T22:45:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning.", "doi": "10.1186/s13018-020-02180-w", "pmid": "33430905", "authors": ["Jianyong Liu", "Ningjie Chen"], "journal": "Journal of orthopaedic surgery and research", "year": 2021, "abstract": "BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. However, RA diagnosis is challenging, partly due to a lack of reliable biomarkers. Here, we aimed to explore the diagnostic signature and establish a predictive model of RA.\nMETHODS: The mRNA expression profiling data of GSE17755, containing blood samples of 112 RA patients and 53 healthy control patients, were obtained from the Gene Expression Omnibus (GEO) database, followed by differential expression, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. A PPI network was constructed to select candidate hub genes, then logistic regression and random forest models were established based on the identified genes.\nRESULTS: Significantly, we identified 52 differentially expressed genes (DEGs), including 16 upregulated genes and 36 downregulated genes in RA samples compared with control samples. GO and KEGG analysis showed that several immune-related cellular processes were particularly enriched. We identified nine hub genes in the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA. The logistic regression and random forest models based on the nine identified genes reliably distinguished the RA samples from the healthy samples with substantially high AUC.\nCONCLUSION: The diagnostic logistic regression and random forest models based on nine hub genes reliably predicted the occurrence of RA. Our findings could provide new insights into RA diagnostics.", "claims": [{"id": "claim-method", "statement": "The dataset of GSE17755 was normalized by robust multi-array (RMA) and the DEGs were analyzed by using a limma R package [18].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33430905", "locator": "sections[4] \"Identification of differentially expressed genes (DEGs)\" paragraph 2", "quote": "The dataset of GSE17755 was normalized by robust multi-array (RMA) and the DEGs were analyzed by using a limma R package [18]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in the [GEO] repository, [https://www.ncbi.nlm.nih.gov/geo/].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33430905", "locator": "sections[20] \"notes\" paragraph 1", "quote": "The datasets analyzed during the current study are available in the [GEO] repository, [https://www.ncbi.nlm.nih.gov/geo/]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33430905"], "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:GSE17755"], "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:GSE17755"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33430905", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s13018-020-02180-w", "sha256": 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