CLEC4E as a molecular biomarker in systemic lupus erythematosus: integrating bioinformatics and clinical data to assess its prognostic value.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41098736", "kind": "science-record", "revision": 4, "content_hash": "sha256:792e479c2dc3ae1526f36d849c5438448132d76bb8941d4c5d19bf8a701bead2", "created_at": "2026-11-01T20:31:09Z", "updated_at": "2026-11-01T22:57:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "CLEC4E as a molecular biomarker in systemic lupus erythematosus: integrating bioinformatics and clinical data to assess its prognostic value.", "doi": "10.3389/fimmu.2025.1617878", "pmid": "41098736", "authors": ["Xiaoxia Ma", "Huan Zhang", "Jiana Li"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Objective: This study explores the prognostic value of the CLEC4E gene in systemic lupus erythematosus (SLE) through bioinformatics analysis and evaluates its role in disease diagnosis and progression.\nMethods: Gene expression datasets related to SLE (GSE17755, GSE50772, and GSE61635) were obtained from the GEO (Gene Expression Omnibus) database. Intersection analysis was performed using the Jvenn tool with a screening threshold of |log2FC| > 1 and P< 0.05 to identify differentially expressed genes (DEGs). The resulting DEGs were then cross-referenced with immune-related genes in the GeneCards database (relevance score > 8) to further prioritize candidates with immunological relevance. Peripheral blood from 360 SLE patients and 360 healthy controls was collected for CLEC4E expression analysis via RT-qPCR. Disease activity was evaluated using the SLEDAI score, and patients were grouped accordingly. Pearson and Spearman correlation analysis to investigate the relationship between CLEC4E and immune indicators. Logistic regression and ROC analyses were conducted to assess diagnostic and prognostic value. Kaplan-Meier analysis evaluated survival outcomes.\nResults: Bioinformatics analysis identified six SLE-related DEGs, namely ISG15, HERC5, TNFAIP6, IFIT3, OASL, and CLEC4E. Further intersection with immune-related genes from the GeneCards database (relevance score > 8) ultimately highlighted CLEC4E as the key gene for clinical validation. The expression level of CLEC4E was significantly higher in SLE patients compared with healthy controls. ROC analysis showed good diagnostic performance (AUC = 0.7744). CLEC4E expression was higher in active SLE, and multivariate analysis identified CLEC4E, C3, C4, ANA, and anti-dsDNA as independent predictors of disease activity. CLEC4E demonstrated moderate diagnostic value for distinguishing active from inactive disease (AUC = 0.6360). Higher CLEC4E expression was associated with worse prognosis (P = 0.0002). The combined diagnostic performance with other biomarkers (C3, C4, ANA, anti-dsDNA) showed a remarkable AUC of 0.9407.\nConclusion: CLEC4E is a potential biomarker for SLE diagnosis, disease activity assessment, and prognosis evaluation.", "claims": [{"id": "claim-method", "statement": "Logistic regression and ROC analyses were conducted to assess diagnostic and prognostic value. Kaplan-Meier analysis evaluated survival outcomes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41098736", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Logistic regression and ROC analyses were conducted to assess diagnostic and prognostic value. Kaplan-Meier analysis evaluated survival outcomes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41098736", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CLEC4E showed diagnostic value for SLE, with an AUC of 0.7744 for distinguishing SLE patients from healthy controls and an AUC of 0.9407 when combined with other biomarkers.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41098736", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "ROC analysis showed good diagnostic performance (AUC = 0.7744)."}, {"asset": "source:pmid:41098736", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The combined diagnostic performance with other biomarkers (C3, C4, ANA, anti-dsDNA) showed a remarkable AUC of 0.9407."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "CLEC4E showed diagnostic value for SLE, with an AUC of 0.7744 for distinguishing SLE patients from healthy controls and an AUC of 0.9407 when combined with other biomarkers.", "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:41098736"], "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:41098736", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1617878", "sha256": "sha256:d486b0ba10b6e312b3012187013fe73fe1b201a7df8594ca1416e1423a82c60c", "bytes": 30798, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1617878", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12518280", "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": "The original contributions presented in the study are included in the article/supplementary material. 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