Systematic identification of key extracellular proteins as the potential biomarkers in lupus nephritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35967373", "kind": "science-record", "revision": 5, "content_hash": "sha256:40e7034090eafa5fd9709bf4e21b69df5943f2d0a4dc9285481f2d0601322f19", "created_at": "2026-11-01T18:15:05Z", "updated_at": "2026-11-01T22:49:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Systematic identification of key extracellular proteins as the potential biomarkers in lupus nephritis.", "doi": "10.3389/fimmu.2022.915784", "pmid": "35967373", "authors": ["Xue Zhou", "Yuefeng Zhang", "Ning Wang"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Lupus nephritis (LN) is the most common and severe clinical manifestation of systemic lupus erythematosus (SLE) with considerable morbidity/mortality and limited treatment options. Since kidney biopsy is a relative hysteretic indicator, it is indispensable to investigate potential biomarkers for early diagnosis and predicting clinical outcomes of LN patients. Extracellular proteins may become the promising biomarkers by the secretion into body fluid. Our study linked extracellular proteins with lupus nephritis to identify the emerging biomarkers.\nMethods: The expression profiling data were acquired from the Gene Expression Omnibus (GEO) database. Meanwhile, the two gene lists encoding extracellular proteins were collected from the Human Protein Atlas (HPA) and UniProt database. Subsequently, the extracellular protein-differentially expressed genes (EP-DEGs) were screened out, and the key EP-DEGs were determined by MCODE, MCC, and Degree methods via the protein-protein interaction (PPI) network. The expression level, immune characteristics, and diagnostic value of these candidate biomarkers were investigated. Finally, the Nephroseq V5 tool was applied to evaluate the clinical significance of the key EP-DEGs.\nResults: A total of 164 DEGs were acquired by comparing LN samples with healthy controls based on GSE32591 datasets. Then, 38 EP-DEGs were screened out through the intersection between DEGs and extracellular protein gene lists. Function enrichment analysis indicated that these EP-DEGs might participate in immune response and constitute the extracellular matrix. Four key EP-DEGs (LUM, TGFBI, COL1A2, and POSTN) were eventually identified as candidate biomarkers, and they were all overexpressed in LN samples. Except that LUM expression was negatively correlated with most of the immune regulatory genes, there was a positive correlation between the remaining three biomarkers and the immune regulatory genes. In addition, these biomarkers had high diagnostic value, especially the AUC value of the LUM-TGFBI combination which reached almost 1 (AUC = 0.973), demonstrating high accuracy in distinguishing LN from controls. Finally, we found a meaningful correlation of these biomarkers with sex, WHO class, and renal function such as glomerular filtration rate (GFR), serum creatinine level, and proteinuria.\nConclusion: In summary, our study comprehensively identified four key EP-DEGs exerting a vital role in LN diagnosis and pathogenesis and serving as promising therapeutic targets.", "claims": [{"id": "claim-method", "statement": "DEGs between the LN sample and control groups were identified utilizing the “Limma” package setting the criteria “adjusted P < 0.05 and Fold Change > 2,” which were exhibited via volcano plots and heatmaps.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35967373", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "DEGs between the LN sample and control groups were identified utilizing the “Limma” package setting the criteria “adjusted P < 0.05 and Fold Change > 2,” which were exhibited via volcano plots and heatmaps."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32591.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35967373", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32591."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:35967373"], "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:GSE32591"], "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:GSE32591"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35967373", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.915784", "sha256": "sha256:315fc19c119f10a8bc3dba4a790615dc268a3a95e8dfee6fa09e86326e91a9f1", "bytes": 30672, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.915784", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9366080", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE32591", "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=GSE32591", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE32591", "version": null, "note": "Publicly available datasets were analyzed in this study. 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