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pmid:41544081
Identification of empagliflozin-related hub genes in atherosclerosis and their correlations with immune infiltration: Network pharmacology and bioinformatics analyses.
Yicheng Rong · Xinyu Liu · Yuanyuan Sun · Le Yang · Liming Chen
PloS one · 2026 doi:10.1371/journal.pone.0339956
Paper
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Experiment
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Data
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Analysis
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Complete file
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Recently, sodium-glucose cotransporter 2 (SGLT2) inhibitors stand out for their anti-atherosclerotic effects. The present study was conducted to explore the potential genetic and molecular mechanisms of empagliflozin, a selective SGLT2 inhibitor, in preventing AS, and the correlation of empagliflozin-related hub genes with immune cells.\nMETHODS: In our study, pharmacology platforms were accessed to identify the empagliflozin-related genes (ERGs). AS datasets GSE100927 and GSE43292 were downloaded from the public GEO database to identify AS-differentially expressed genes (AS-DEGs). Furthermore, the empagliflozin-related DEGs (ERDEGs) were obtained by intersecting AS-DEGs and ERGs. ERDEGs were further analyzed for Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The protein-protein interaction (PPI) network was constructed to screen for hub genes, which were subjected to regulatory network construction, ROC curve plotting, as well as validation analysis, correlation and Friends analysis, and CIBERSORT and ssGSEA analysis.\nRESULTS: A total of 33 genes were identified as ERDEGs, among which the empagliflozin-related hub genes were identified to be IL1B, EGFR, ERBB2, JAK2, SYK, and LGALS3 in AS. Furthermore, in terms of the characteristics of immune cells, there were significant correlations between ERBB2 and CD8 + T cells, IL1B and resting mast cells, LGALS3 and eosinophils, as well as JAK2 and CD56dim NK.\nCONCLUSION: Our network pharmacology and bioinformatics analyses provide a comprehensive understanding of the potential mechanisms of empagliflozin in AS at the genomic level, with the discovery of significant correlations between the screened hub genes and various immune cell subsets.", "claims": [{"id": "claim-method", "statement": "The samples in the combined dataset were divided into AS and control groups for inter-group differential gene analysis using the R package “limma”.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41544081", "locator": "sections[5] \"2.3. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:41544081", "data:geo:GSE100927"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE100927"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:10:19Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 14717, "completion_tokens": 1502, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:41544081", "type": "paper", "label": "pmid:41544081"}, {"id": "source:pmid:41544081", "type": "manuscript", "label": "PMID 41544081 full text"}, {"id": "data:geo:GSE100927", "type": "dataset", "label": "GEO GSE100927"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41544081", "type": "paper", "label": "pmid:41544081"}, {"id": "source:pmid:41544081", "type": "manuscript", "label": "PMID 41544081 full text"}, {"id": "data:geo:GSE100927", "type": "dataset", "label": "GEO GSE100927"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41544081", "type": "extraction", "used": ["source:pmid:41544081"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE100927", "type": "accession_resolution", "used": ["data:geo:GSE100927"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:41544081", "parent_hash": "sha256:52f910638b1289a73786d2efc398d588a0dc7898e960195d6add241af8bb6196", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE100927 licence not read", "asset:data:geo:GSE100927 not downloaded", "asset:data:geo:GSE28829 licence not read", "asset:data:geo:GSE28829 not downloaded", "asset:data:geo:GSE43292 licence not read", "asset:data:geo:GSE43292 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["The samples in the combined dataset were divided into AS and control groups for inter-group differential gene analysis using the R package “limma”."], "order_basis": "analysis_only", "id": "assay:1"}]}