Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40438384", "kind": "science-record", "revision": 3, "content_hash": "sha256:13a57812943e3ae51cb9750f1f16eb7b61337d9b9c789400e16f1de5f6ff482a", "created_at": "2026-11-01T20:30:34Z", "updated_at": "2026-11-01T22:14:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning.", "doi": "10.3389/fmed.2025.1557307", "pmid": "40438384", "authors": ["Su Zhang", "Weitao Hu", "Yuchao Tang", "Hongjie Lin", "Xiaoqing Chen"], "journal": "Frontiers in medicine", "year": 2025, "abstract": "Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that involves multiple systems. SLE is characterized by the production of autoantibodies and inflammatory tissue damage. This study further explored the role of immune-related genes in SLE. We downloaded the expression profiles of GSE50772 using the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) in SLE. The DEGs were also analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The gene modules most closely associated with SLE were then derived by Weighted Gene Co-expression Network Analysis (WGCNA). Differentially expressed immune-related genes (DE-IRGs) in SLE were obtained by DEGs, key gene modules and IRGs. The protein-protein interaction (PPI) network was constructed through the STRING database. Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. Then, we constructed a diagnostic model. The model was validated by external cohort GSE61635 and peripheral blood mononuclear cells (PBMC) from SLE patients. Immune cell abundance assessment was achieved by CIBERSORT. The hub DE-IRGs and miRNA networks were made accessible through the NetworkAnalyst database. We screened 945 DEGs, which are closely related to the type I interferon pathway and NOD-like receptor signaling pathway. Machine learning identified a total of five hub DE-IRGs (CXCL2, CXCL8, FOS, NFKBIA, CXCR2), and validated in GSE61635 and PBMC from SLE patients. Immune cell abundance analysis showed that the hub genes may be involved in the development of SLE by regulating immune cells (especially neutrophils). In this study, we identified five hub DE-IRGs in SLE and constructed an effective predictive model. These hub genes are closely associated with immune cell in SLE. These may provide new insights into the immune-related pathogenesis of SLE.", "claims": [{"id": "claim-method", "statement": "Pearson analysis was applied to investigate the correlation between hub genes and immune cells, and p < 0.05 was considered to be statistical significance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40438384", "locator": "sections[14] \"Statistical analysis\" paragraph 2", "quote": "Pearson analysis was applied to investigate the correlation between hub genes and immune cells, and p < 0.05 was considered to be statistical significance."}], "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/Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40438384", "locator": "sections[28] \"Data availability statement\" 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/Supplementary material."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified five hub DE-IRGs in SLE and constructed an effective predictive model.", "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:40438384"], "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:40438384", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2025.1557307", "sha256": "sha256:562b701c8c35203a71c01c6427184d3ae838a7b706c693606415e5aaca07253a", "bytes": 46199, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2025.1557307", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12116674", "source": "europe_pmc_fulltext", "coverage": "whole_body", "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 datasets presented in this study can be found in online repositories. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["pearson_r"], "assets": ["source:pmid:40438384", "data:geo:GSE17755"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE17755"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:53:38Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14074, "completion_tokens": 2042, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:40438384", "type": "paper", "label": "pmid:40438384"}, {"id": "source:pmid:40438384", "type": "manuscript", "label": "PMID 40438384 full text"}, {"id": "data:geo:GSE17755", "type": "dataset", "label": "GEO GSE17755"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40438384", "type": "paper", "label": "pmid:40438384"}, {"id": "source:pmid:40438384", "type": "manuscript", "label": "PMID 40438384 full text"}, {"id": "data:geo:GSE17755", "type": "dataset", "label": "GEO GSE17755"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40438384", "type": "extraction", "used": ["source:pmid:40438384"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE17755", "type": "accession_resolution", "used": ["data:geo:GSE17755"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40438384", "parent_hash": "sha256:45053e7a5edeb15cd768a50966d3bbae6000e67b5ca0bcd15aa73990fb5d50fc", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE17755 licence not read", "asset:data:geo:GSE17755 not downloaded", "asset:data:geo:GSE50772 licence not read", "asset:data:geo:GSE50772 not downloaded", "asset:data:geo:GSE61635 licence not read", "asset:data:geo:GSE61635 not downloaded", "asset:data:geo:GSE84844 licence not read", "asset:data:geo:GSE84844 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "correlation_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pearson_r"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Pearson analysis was applied to investigate the correlation between hub genes and immune cells, and p < 0.05 was considered to be statistical significance."], "order_basis": "analysis_only", "id": "assay:1"}]}