Identification of key biomarkers of the glomerulus in focal segmental glomerulosclerosis and their relationship with immune cell infiltration based on WGCNA and the LASSO algorithm.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37096442", "kind": "science-record", "revision": 3, "content_hash": "sha256:9bb85c4cb91b29376ea64dd5d47ae601cad87074c67448e69d2d20ae500d0afe", "created_at": "2026-11-01T19:01:46Z", "updated_at": "2026-11-01T22:09:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key biomarkers of the glomerulus in focal segmental glomerulosclerosis and their relationship with immune cell infiltration based on WGCNA and the LASSO algorithm.", "doi": "10.1080/0886022X.2023.2202264", "pmid": "37096442", "authors": ["Yun Xia Zhang", "Juan Lv", "Jun Yuan Bai", "XiaoWei Pu", "En Lai Dai"], "journal": "Renal failure", "year": 2023, "abstract": "OBJECTIVE: The aim of our study was to identify key biomarkers of glomeruli in focal glomerulosclerosis (FSGS) and analyze their relationship with the infiltration of immune cells.\nMETHODS: The expression profiles (GSE108109 and GSE200828) were obtained from the GEO database. The differentially expressed genes (DEGs) were filtered and analyzed by gene set enrichment analysis (GSEA). MCODE module was constructed. Weighted gene coexpression network analysis (WGCNA) was performed to obtain the core gene modules. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes. ROC curves were employed to explore their diagnostic accuracy. Transcription factor prediction of the key biomarkers was performed using the Cytoscape plugin IRegulon. The analysis of the infiltration of 28 immune cells and their correlation with the key biomarkers were performed.\nRESULTS: A total of 1474 DEGs were identified. Their functions were mostly related to immune-related diseases and signaling pathways. MCODE identified five modules. The turquoise module of WGCNA had significant relevance to the glomerulus in FSGS. TGFB1 and NOTCH1 were identified as potential key glomerular biomarkers in FSGS. Eighteen transcription factors were obtained from the two hub genes. Immune infiltration showed significant correlations with T cells. The results of immune cell infiltration and their relationship with key biomarkers implied that NOTCH1 and TGFB1 were enhanced in immune-related pathways.\nCONCLUSION: TGFB1 and NOTCH1 may be strongly correlated with the pathogenesis of the glomerulus in FSGS and are new candidate key biomarkers. T-cell infiltration plays an essential role in the FSGS lesion process.", "claims": [{"id": "claim-method", "statement": "Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37096442", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We collected the data of GSE200828 and GSE108109 with the help of the GEO (https://www.ncbi.nlm.nih.gov/geo/) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37096442", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "We collected the data of GSE200828 and GSE108109 with the help of the GEO (https://www.ncbi.nlm.nih.gov/geo/) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, TGFB1 and NOTCH1 were identified as potential key biomarkers.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37096442", "locator": "sections[18] \"Diagnostic value of hub gene expression levels\" paragraph 6", "quote": "Finally, TGFB1 and NOTCH1 were identified as potential key biomarkers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, TGFB1 and NOTCH1 were identified as potential key 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:37096442"], "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. 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