Identifying functional subtypes of IgA nephropathy based on three machine learning algorithms and WGCNA.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38395835", "kind": "science-record", "revision": 4, "content_hash": "sha256:a92e75603a32acab3b3453a03ceed2f80be1aa0aff523f777b2acb5eedfd77eb", "created_at": "2026-11-01T19:20:20Z", "updated_at": "2026-11-01T22:53:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying functional subtypes of IgA nephropathy based on three machine learning algorithms and WGCNA.", "doi": "10.1186/s12920-023-01702-9", "pmid": "38395835", "authors": ["Hongbiao Ren", "Wenhua Lv", "Zhenwei Shang", "Liangshuang Li", "Qi Shen", "Shuai Li", "Zerun Song", "Xiangshu Cheng", "Xin Meng", "Rui Chen", "Ruijie Zhang"], "journal": "BMC medical genomics", "year": 2024, "abstract": "BACKGROUND: IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis, which is a significant cause of renal failure. At present, the classification of IgAN is often limited to pathology, and its molecular mechanism has not been established. Therefore we aim to identify subtypes of IgAN at the molecular level and explore the heterogeneity of subtypes in terms of immune cell infiltration, functional level.\nMETHODS: Two microarray datasets (GSE116626 and GSE115857) were downloaded from GEO. Differential expression genes (DEGs) for IgAN were screened with limma. Three unsupervised clustering algorithms (hclust, PAM, and ConsensusClusterPlus) were combined to develop a single-sample subtype random forest classifier (SSRC). Functional subtypes of IgAN were defined based on functional analysis and current IgAN findings. Then the correlation between IgAN subtypes and clinical features such as eGFR and proteinuria was evaluated by using Pearson method. Subsequently, subtype heterogeneity was verified by subtype-specific modules identification based on weighted gene co-expression network analysis(WGCNA) and immune cell infiltration analysis based on CIBERSORT algorithm.\nRESULTS: We identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type. We screened seventeen genes specific to viral hormonal type (ATF3, JUN and FOS etc.), and seven genes specific to bacterial immune type (LIF, C19orf51 and SLPI etc.). The subtype-specific genes showed significantly high correlation with proteinuria and eGFR. The WGCNA modules were in keeping with functions of the IgAN subtypes where the MEcyan module was specific to the viral-hormonal type and the MElightgreen module was specific to the bacterial-immune type. The results of immune cell infiltration revealed subtype-specific cell heterogeneity which included significant differences in T follicular helper cells, resting NK cells between viral-hormone type and control group; significant differences in eosinophils, monocytes, macrophages, mast cells and other cells between bacterial-immune type and control.\nCONCLUSION: In this study, we identified three functional subtypes of IgAN for the first time and specific expressed genes for each subtype. Then we constructed a subtype classifier and classify IgAN patients into specific subtypes, which may be benefit for the precise treatment of IgAN patients in future.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) between patients with IgAN and healthy controls were identified by setting adjusted P value less than 0.05 and the absolute logarithmic fold change greater than 1.7 using the limma package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38395835", "locator": "sections[7] \"Differential expression analysis between patients with IgAN and healthy controls and functional enrichment analysis\" paragraph 2", "quote": "Differentially expressed genes (DEGs) between patients with IgAN and healthy controls were identified by setting adjusted P value less than 0.05 and the absolute logarithmic fold change greater than 1.7 using the limma package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets employed in our study can be acquired in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). The accession numbers are GSE115857, and GSE116626.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38395835", "locator": "sections[34] \"Data Availability\" paragraph 2", "quote": "The datasets employed in our study can be acquired in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). The accession numbers are GSE115857, and GSE116626."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38395835", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type.", "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:38395835"], "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:GSE115857"], "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:GSE115857"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38395835", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-023-01702-9", "sha256": "sha256:3a418b5faf604cc9629a7dd41e93d3d97acd9171397c11c232ceaab3b2f3766d", "bytes": 66995, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12920-023-01702-9", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10893719", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE115857", "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=GSE115857", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE115857", "version": null, "note": "The datasets employed in our study can be acquired in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). 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METHODS: Two microarray datasets (GSE116626 and GSE115857) were down"], "order_basis": "paper_text_order:500", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Differentially expressed genes (DEGs) between patients with IgAN and healthy controls were identified by setting adjusted P value less than 0.05 and the absolute logarithmic fold change greater than 1.7 using the limma package."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}