The novel diagnostic markers for systemic lupus erythematosus and periodontal disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40766309", "kind": "science-record", "revision": 3, "content_hash": "sha256:4f1e0ac3fa624f63fca5bc646654494dc363d0415ce04f1f0bce09024948dc99", "created_at": "2026-11-01T20:30:51Z", "updated_at": "2026-11-01T22:14:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The novel diagnostic markers for systemic lupus erythematosus and periodontal disease.", "doi": "10.3389/fimmu.2025.1614044", "pmid": "40766309", "authors": ["Xuedi Cheng", "Jinfeng Zhang", "Jiali Hou", "Xiaocui Han", "Bin Han", "Jun Zhou", "Zhongjun Wang", "Junzheng Wang"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Background and aims: Systemic lupus erythematosus (SLE) is one of the most prevalent systemic autoimmune diseases, characterized by aberrant activation of the immune system that leads to diverse clinical symptoms; periodontal disease (PD) is an inflammatory oral disorder caused by immune-mediated damage against subgingival microflora. Although clinical evidence suggests a potential association between SLE and PD, their shared pathogenic mechanisms remain unclear. This study aims to explore common genetic markers in SLE and PD that hold diagnostic and therapeutic implications.\nMethods: Microarray datasets for systemic lupus erythematosus (SLE) and periodontal disease (PD) were obtained from the Gene Expression Omnibus (GEO) database. Module genes between the two diseases were screened using Weighted Gene Co-expression Network Analysis (WGCNA), and module genes overlapping between the significant correlation modules of GSE61635 and GSE16134 were identified. Functional enrichment analyses of genes within overlapping modules and their significantly correlated associated modules were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Overlapping module genes underwent differential expression analysis in GSE16134. A diagnostic model was constructed using the Random Forest (RF) machine learning technique under Receiver Operating Characteristic (ROC) curve assessment, which top 10 key genes were screened and analyzed for differential expression across three datasets (GSE61635, GSE10334, and GSE50772) to identify hub genes. Protein-protein interaction (PPI) network analysis was conducted to explore relationships between hub genes. CIBERSORT and Gene Set Variation Analysis (GSVA) were used to evaluate the correlation between shared hub genes and immune infiltration patterns as well as metabolic pathways. Finally, hub genes were validated using additional datasets, single-cell RNA sequencing (scRNA-seq) data, and immunohistochemistry (IHC) experiments.\nResults: Using WGCNA, we identified significant correlation modules and overlapping module genes, which were subjected to differential expression analysis in different datasets. Further, 4 hub genes were screened and successfully used to build a prognostic model. Those shared hub genes were associated with immunological and metabolic processes in peripheral blood. The additional datasets, scRNA-seq and IHC results verified that LY96 and TMEM140, possessing the promising diagnostic and therapeutic performance.\nConclusion: LY96 andTMEM140 can be used as new diagnostic and therapeutic markers for SLE and PD.", "claims": [{"id": "claim-method", "statement": "WGCNA was conducted on the GSE61635 and GSE16134 datasets to identify gene modules.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40766309", "locator": "sections[4] \"Construction of weighted gene co−expression network analysis\" paragraph 2", "quote": "WGCNA was conducted on the GSE61635 and GSE16134 datasets to identify gene modules."}], "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 authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40766309", "locator": "sections[34] \"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 authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "WGCNA and follow-up differential-expression and machine-learning analyses of GSE61635 and GSE16134 identified four shared hub genes, with LY96 and TMEM140 further validated as diagnostic and therapeutic candidates.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40766309", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Using WGCNA, we identified significant correlation modules and overlapping module genes, which were subjected to differential expression analysis in different datasets."}, {"asset": "source:pmid:40766309", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Further, 4 hub genes were screened and successfully used to build a prognostic model."}, {"asset": "source:pmid:40766309", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The additional datasets, scRNA-seq and IHC results verified that LY96 and TMEM140, possessing the promising diagnostic and therapeutic performance."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "WGCNA and follow-up differential-expression and machine-learning analyses of GSE61635 and GSE16134 identified four shared hub genes, with LY96 and TMEM140 further validated as diagnostic and therapeutic candidates.", "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:40766309"], "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:GSE10334"], "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:GSE10334"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40766309", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1614044", "sha256": "sha256:e8601050ac00e740146443f7666da445c1a4fe35b2e94c8a3c604d4eb3f0bf2e", "bytes": 54729, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1614044", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12321837", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE10334", "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=GSE10334", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE10334", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary material. 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