Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37426641", "kind": "science-record", "revision": 2, "content_hash": "sha256:a67b55f4e9e6e904fbb76830bc8ccba9b7ab9187e7d4ff442e7b494639fbc917", "created_at": "2026-11-01T19:02:11Z", "updated_at": "2026-11-01T21:31:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning.", "doi": "10.3389/fimmu.2023.1204652", "pmid": "37426641", "authors": ["Yuchen Liu", "Haixu Jiang", "Tianlun Kang", "Xiaojun Shi", "Xiaoping Liu", "Chen Li", "Xiujuan Hou", "Meiling Li"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background and aim: Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers.\nMethods: We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC).\nResults: The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979.\nConclusion: We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.", "claims": [{"id": "claim-method", "statement": "We then used the LASSO algorithm to develop a diagnostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37426641", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We then used the LASSO algorithm to develop a diagnostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were also analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/ under the accession numbers GSE93272 and GSE17755.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37426641", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were also analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/ under the accession numbers GSE93272 and GSE17755."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37426641", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979.", "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:37426641"], "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:37426641", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1204652", "sha256": "sha256:45a4272c058d9558276b20e6fd7632571a467458ea8a96255b074e50589214a1", "bytes": 23152, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1204652", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10327425", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17755", "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=GSE17755", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17755", "version": null, "note": "Publicly available datasets were also analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/ under the accession numbers GSE93272 and GSE17755.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were also analyzed in this study. 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