Exploring differential gene expression and biomarker potential in systemic lupus erythematosus: a retrospective study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40949741", "kind": "science-record", "revision": 4, "content_hash": "sha256:5bb4efd57f2e7dfb97a6cefed4ab8a8221006099b4ade594a25cb4ee9963ffe0", "created_at": "2026-11-01T20:31:02Z", "updated_at": "2026-11-01T22:57:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring differential gene expression and biomarker potential in systemic lupus erythematosus: a retrospective study.", "doi": "10.7717/peerj.19891", "pmid": "40949741", "authors": ["Jie Xiao", "Yuhong Luo", "Lina Duan", "Xinru Mao", "Lingyue Jin", "Haifang Wang", "Hongxia Wang", "Jie Pan", "Ying Gong", "Haixia Li"], "journal": "PeerJ", "year": 2025, "abstract": "Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by inflammation and immune-mediated multi-organ system damage, accompanied by clinical manifestations such as fever, hair loss, skin rash, oral ulcers, and joint pain and swelling. SLE has been reported to affect more than 3.4 million people worldwide, of which approximately 90% are women.\nPurpose: This study aims to identify and characterize key hub genes implicated in SLE through comprehensive bioinformatics analyses, providing a theoretical foundation for the development of more effective therapeutic strategies.\nMethods: Two datasets were procured from the Gene Expression Omnibus (GEO) database: GSE13887 and GSE10325. Differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis, protein-protein interaction (PPI) network construction, and receiver operating characteristic (ROC) curve analysis to evaluate potential hub genes. The top 20 significantly upregulated and downregulated DEGs, alongside the top 15 enriched Gene Ontology (GO) terms and five Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were screened from both datasets. Quantitative real-time PCR (RT-q PCR) was utilized to validate hub gene expression in CD3 + T cells from peripheral blood samples of SLE patients. Concurrently, flow cytometry was employed to quantify inflammatory cytokines in peripheral blood samples.\nResults: Bioinformatics analyses identified 1,912 DEGs in GSE13887 and 52 DEGs in GSE10325, with eight DEGs common to both datasets. Functional enrichment analysis underscored critical biological processes, notably cell-mediated cytotoxicity and cell killing. PPI network and enrichment analyses highlighted seven hub genes, among which FCER1A and RGS1 demonstrated consistent expression trends across datasets and clinical samples-FCER1A was significantly downregulated, while RGS1 was upregulated in SLE patients. ROC curve analysis confirmed their strong diagnostic potential (AUC > 0.7). Principal component analysis (PCA) further highlighted distinct gene expression profiles differentiating SLE patients from healthy controls. Clinical validation via RT-q PCR and flow cytometry corroborated these findings, demonstrating decreased FCER1A expression and increased RGS1 expression in CD3 + T cells from SLE patients. Moreover, elevated plasma levels of IL-6 and TNF-α, coupled with diminished IL-10 levels, were observed in SLE patients. These findings suggest that FCER1A and RGS1 are promising biomarkers for SLE diagnosis.\nConclusions: FCER1A and RGS1 are significantly associated with SLE and serve as potential biomarkers for distinguishing SLE patients from healthy individuals. Their involvement in SLE pathogenesis underscores their potential as targets for future diagnostic and therapeutic interventions.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) between SLE and HCs were identified using the limma package (version 3.64.1) in R (version 4.2.1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40949741", "locator": "sections[5] \"Identification of differentially expressed genes\" paragraph 2", "quote": "Differentially expressed genes (DEGs) between SLE and HCs were identified using the limma package (version 3.64.1) in R (version 4.2.1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Raw data is available in the Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40949741", "locator": "sections[30] \"Data Availability\" paragraph 2", "quote": "Raw data is available in the Supplemental Files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "FCER1A was significantly downregulated and RGS1 was upregulated in SLE patients, and both showed strong diagnostic potential with AUC values exceeding 0.7.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40949741", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "PPI network and enrichment analyses highlighted seven hub genes, among which FCER1A and RGS1 demonstrated consistent expression trends across datasets and clinical samples—FCER1A was significantly downregulated, while RGS1 was upregulated in SLE patients."}, {"asset": "source:pmid:40949741", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "ROC curve analysis confirmed their strong diagnostic potential (AUC > 0.7)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "FCER1A was significantly downregulated and RGS1 was upregulated in SLE patients, and both showed strong diagnostic potential with AUC values exceeding 0.7.", "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:40949741"], "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:GSE10325"], "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": {"fdr_threshold": "ed by adjusted p-value (false discovery rate) < 0"}, "inputs": ["data:geo:GSE10325"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40949741", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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Quantitative real-time PCR (RT-q PCR) was utilized to validate hub gene", "bead separation. RNA extraction followed by RT-qPCR revealed significant downregulation of FCER1", "ad separation. RNA extraction followed by RT-qPCR revealed significant downregulation of FCER1", "pp-1 Supplemental Information 2Raw data from qPCR experiments analyzing the expression levels"], "order_basis": "paper_text_order:1276", "id": "assay:1"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["blood samples of SLE patients. Concurrently, flow cytometry was employed to quantify inflammatory cytoki", "ntrols. Clinical validation via RT-q PCR and flow cytometry corroborated these findings, demonstrating d", "many). The purity of T cell were analyzed by Flow cytometry (CantoII, BD Bioscience, Franklin Lakes, NJ,", "ig. 7A). 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