Analysis of m6A-regulated genes and subtype classification in lupus nephritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38570749", "kind": "science-record", "revision": 3, "content_hash": "sha256:c49fb62b5314d43eb2fa8be16fbbc2722f26372e46f309f6f3f292a5d5b96b55", "created_at": "2026-11-01T19:20:32Z", "updated_at": "2026-11-01T22:11:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of m6A-regulated genes and subtype classification in lupus nephritis.", "doi": "10.1186/s12882-024-03549-3", "pmid": "38570749", "authors": ["Diangeng Li", "Yanchun Li", "Kaiyi Zhu", "Yuqing Yuan", "Zheng He", "Qianmei Sun", "Meiling Jin"], "journal": "BMC nephrology", "year": 2024, "abstract": "BACKGROUND: Lupus nephritis (LN) is the most common and severe clinical manifestation of systemic lupus erythematosus (SLE). N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, the roles of m6A regulators in LN are not fully demonstrated.\nMETHODS: We downloaded the kidney tissue transcriptome dataset of LN patients and normal controls from the GEO database and extracted the expression levels of m6A regulators. We constructed and compared Random Forest (RF) and Support Vector Machine (SVM) models, and subsequently selected featured genes to develop nomogram models. The m6A subtypes were identified based on significantly differentially expressed m6A regulators, and the m6A gene subtypes were identified based on m6A-associated differential genes, and the two m6A modification patterns were comprehensively evaluated.\nRESULTS: We obtained the GSE32591 and GSE112943 datasets from the GEO database, including 78 LN samples and 36 normal control samples. We extracted the expression levels of 20 m6A regulators. By RF analysis we identified 7 characteristic m6A regulators and constructed nomogramh models with these 7 genes. We identified two m6A subtypes based on these seven important m6A regulators, and the immune cell infiltration levels of the two subtype clusters were significantly different. We identified two more m6A gene subtypes based on m6A-associated DEGs. We calculated the m6A scores using the principal component analysis (PCA) algorithm and found that the m6A scores of m6A cluster A and gene cluster A were lower than those of m6A cluster B and gene cluster B. In addition, we found that the levels of inflammatory factors were also significantly different between m6A clusters and gene clusters.\nCONCLUSION: This study confirms that m6A regulators are involved in the LN process through different modes of action and provide new diagnostic and therapeutic targets for LN.", "claims": [{"id": "claim-method", "statement": "Using the “ConsensusClusterPlus” package in R, a consensus clustering method was used to identify different m6A patterns based on 7 important m6A regulators, and 2 m6A patterns (cluster A, cluster B) were identified", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38570749", "locator": "sections[15] \"Identification of different m6A patterns\" paragraph 2", "quote": "Using the “ConsensusClusterPlus” package in R, a consensus clustering method was used to identify different m6A patterns based on 7 important m6A regulators, and 2 m6A patterns (cluster A, cluster B) were identified"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38570749", "locator": "sections[24] \"notes\" paragraph 1", "quote": "The original datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We obtained the GSE32591 and GSE112943 datasets from the GEO database, including 78 LN samples and 36 normal control samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38570749", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We obtained the GSE32591 and GSE112943 datasets from the GEO database, including 78 LN samples and 36 normal control samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We obtained the GSE32591 and GSE112943 datasets from the GEO database, including 78 LN samples and 36 normal control samples.", "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:38570749"], "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:GSE112943"], "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:GSE112943"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38570749", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12882-024-03549-3", "sha256": "sha256:139d2292c32c87731e583faeb1df2227077df6f98fa72199376655d82a9b2f13", "bytes": 25481, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12882-024-03549-3", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10988804", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE112943", "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=GSE112943", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE112943", "version": null, "note": "The original datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). 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