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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

complete

Experiment

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Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:41544081
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2026-11-01T20:31:30Z
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2026-11-01T22:57:45Z
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title
Identification of empagliflozin-related hub genes in atherosclerosis and their correlations with immune infiltration: Network pharmacology and bioinformatics analyses.
doi
10.1371/journal.pone.0339956
pmid
41544081
authors array · 5 items
[0]
Yicheng Rong
[1]
Xinyu Liu
[2]
Yuanyuan Sun
[3]
Le Yang
[4]
Liming Chen
journal
PloS one
year
2026
abstract
BACKGROUND: Atherosclerosis (AS) is by far the most frequent underlying cause of atherosclerotic cardiovascular disease. 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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The samples in the combined dataset were divided into AS and control groups for inter-group differential gene analysis using the R package “limma”.
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sections[5] "2.3. Identification of ERDEGs" paragraph 2
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The samples in the combined dataset were divided into AS and control groups for inter-group differential gene analysis using the R package “limma”.
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Using the R package “GEOquery”, AS datasets GSE100927, [21] GSE43292, [22] and GSE28829 [21] were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) [23,24].
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sections[4] "2.2. Data acquisition and preparation" paragraph 2
quote
Using the R package “GEOquery”, AS datasets GSE100927, [21] GSE43292, [22] and GSE28829 [21] were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) [23,24].
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The combined dataset contained 3,503 AS-DEGs, and intersection with the 76 empagliflozin-related genes identified 33 ERDEGs.
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sections[20] "3.3. Identification of ERDEGs" paragraph 2
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Based on |logFC| > 0.25 and adj.p < 0.05, the combined dataset included 3,503 AS-DEGs.
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sections[20] "3.3. Identification of ERDEGs" paragraph 2
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After overlapping between all AS-DEGs and the 76 ERGs, 33 genes were identified as ERDEGs (Fig 4B).
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The combined dataset contained 3,503 AS-DEGs, and intersection with the 76 empagliflozin-related genes identified 33 ERDEGs.
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The samples in the combined dataset were divided into AS and control groups for inter-group differential gene analysis using the R package “limma”.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41544081", "kind": "science-record", "revision": 4, "content_hash": "sha256:1c294b60435d3400550fcb6b6d26606208d36c0cd3a9b530a379911d55615413", "created_at": "2026-11-01T20:31:30Z", "updated_at": "2026-11-01T22:57:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of empagliflozin-related hub genes in atherosclerosis and their correlations with immune infiltration: Network pharmacology and bioinformatics analyses.", "doi": "10.1371/journal.pone.0339956", "pmid": "41544081", "authors": ["Yicheng Rong", "Xinyu Liu", "Yuanyuan Sun", "Le Yang", "Liming Chen"], "journal": "PloS one", "year": 2026, "abstract": "BACKGROUND: Atherosclerosis (AS) is by far the most frequent underlying cause of atherosclerotic cardiovascular disease. 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. Identification of ERDEGs\" paragraph 2", "quote": "The samples in the combined dataset were divided into AS and control groups for inter-group differential gene analysis using the R package “limma”."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Using the R package “GEOquery”, AS datasets GSE100927, [21] GSE43292, [22] and GSE28829 [21] were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) [23,24].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41544081", "locator": "sections[4] \"2.2. Data acquisition and preparation\" paragraph 2", "quote": "Using the R package “GEOquery”, AS datasets GSE100927, [21] GSE43292, [22] and GSE28829 [21] were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) [23,24]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The combined dataset contained 3,503 AS-DEGs, and intersection with the 76 empagliflozin-related genes identified 33 ERDEGs.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Based on |logFC| > 0.25 and adj.p < 0.05, the combined dataset included 3,503 AS-DEGs."}, "evidence": [{"asset": "source:pmid:41544081", "locator": "sections[20] \"3.3. Identification of ERDEGs\" paragraph 2", "quote": "Based on |logFC| > 0.25 and adj.p < 0.05, the combined dataset included 3,503 AS-DEGs."}, {"asset": "source:pmid:41544081", "locator": "sections[20] \"3.3. Identification of ERDEGs\" paragraph 2", "quote": "After overlapping between all AS-DEGs and the 76 ERGs, 33 genes were identified as ERDEGs (Fig 4B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The combined dataset contained 3,503 AS-DEGs, and intersection with the 76 empagliflozin-related genes identified 33 ERDEGs.", "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:41544081"], "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:GSE100927"], "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:GSE100927"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41544081", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0339956", "sha256": 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A run that reproduces the effect is judged against this threshold."}], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE100927", "GSE43292", "GSE28829"], "resolved_accessions": ["GSE100927", "GSE43292", "GSE28829"], "statements": ["Using the R package “GEOquery”, AS datasets GSE100927, [21] GSE43292, [22] and GSE28829 [21] were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) [23,24]."], "basis": "The paper names GSE100927, GSE43292, GSE28829 in GEO and the accession resolves in the repository. 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"}]}