Renal tubular gen e biomarkers identification based on immune infiltrates in focal segmental glomerulosclerosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35713363", "kind": "science-record", "revision": 5, "content_hash": "sha256:f4699ed96d17be6ea5c89b4476f5efd49f9b11be6a5b78b44d0a827dbe395589", "created_at": "2026-11-01T18:14:42Z", "updated_at": "2026-11-01T22:49:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Renal tubular gen e biomarkers identification based on immune infiltrates in focal segmental glomerulosclerosis.", "doi": "10.1080/0886022X.2022.2081579", "pmid": "35713363", "authors": ["JunYuan Bai", "XiaoWei Pu", "YunXia Zhang", "Enlai Dai"], "journal": "Renal failure", "year": 2022, "abstract": "OBJECTIVE: The present study identified novel renal tubular biomarkers that may influence the diagnosis and treatment of focal segmental glomerulosclerosis (FSGS) based on immune infiltration.\nMETHODS: Three FSGS microarray datasets, GSE108112, GSE133288 and GSE121211, were downloaded from the Gene Expression Omnibus (GEO) database. The R statistical software limma package and the combat function of the sva package were applied for preprocessing and to remove the batch effects. Differentially expressed genes (DEGs) between 120 FSGS and 15 control samples were identified with the limma package. Disease Ontology (DO) pathway enrichment analysis was conducted with statistical R software to search for related diseases. Gene set enrichment analysis (GSEA) was used to interpret the gene expression data and it revealed many common biological pathways. A protein-protein interaction (PPI) network was built using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and hub genes were identified by the Cytoscape (version 3.7.2) plug-in CytoHubba. The plug-in Molecular Complex Detection (MCODE) was used to screen hub modules of the PPI network in Cytoscape, while functional analysis of the hub genes and hub nodes involved in the submodule was performed by ClusterProfiler. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model. Receiver operating characteristic (ROC) curve analyses were used to investigate the logistic regression model and it was then validated by an external dataset GSE125779, which contained 8 FSGS samples and 8 healthy subjects. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to calculate the immune infiltration of FSGS samples.\nRESULTS: We acquired 179 DEGs, 79 genes with downregulated expression (44.1%) and 100 genes with upregulated expression (55.9%), in the FSGS samples. The DEGs were significantly associated with arteriosclerosis, kidney disease and arteriosclerotic cardiovascular disease. GSEA revealed that these gene sets were significantly enriched in allograft rejection signaling pathways and activation of immune response in biological processes. Fifteen genes were demonstrated to be hub genes by PPI, and three submodules were screened by MCODE linked with FSGS. Analysis by machine learning methodologies identified nuclear receptor subfamily 4 group A member 1 (NR4A1) and dual specificity phosphatase 1 (DUSP1) as sensitive tubular renal biomarkers in the diagnosis of FSGS, and they were selected as hub genes, as well as hub nodes which were enriched in the MAPK signaling pathway. Immune cell infiltration analysis revealed that the genetic biomarkers were both correlated with activated mast cells, which may amplify FSGS biological processes.\nCONCLUSION: DUSP1 and NR4A1 were identified as sensitive potential biomarkers in the diagnosis of FSGS. Activated mast cells have a decisive effect on the occurrence and development of FSGS through tubular lesions and tubulointerstitial inflammation, and they are expected to become therapeutic targets in FSGS.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35713363", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. All raw data are available in GEO datasets (GSE108112, GSE133288, GSE121211 and GSE125779).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35713363", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. All raw data are available in GEO datasets (GSE108112, GSE133288, GSE121211 and GSE125779)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified DUSP1 and NR4A1 as sensitive potential renal tubular biomarkers for diagnosing FSGS.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35713363", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "DUSP1 and NR4A1 were identified as sensitive potential biomarkers in the diagnosis of FSGS."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified DUSP1 and NR4A1 as sensitive potential renal tubular biomarkers for diagnosing FSGS.", "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:35713363"], "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:GSE108112"], "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:GSE108112"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35713363", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1080/0886022X.2022.2081579", "sha256": "sha256:c511bd6ec9c19c373832b00a65b78583399be57966e00e3e3123b7ffbcadbb5a", "bytes": 39807, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1080/0886022X.2022.2081579", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9225740", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE108112", "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=GSE108112", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE108112", "version": null, "note": "Publicly available datasets were analyzed in this study. 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METHODS: Three FSGS microarray datasets, GSE108112, GSE133288 and GSE121211"], "order_basis": "paper_text_order:282", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}