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pmid:41496131

Identification of diagnostic biomarkers for ischemic stroke and drug targets of Buyang Huanwu decoction: A bioinformatics and network pharmacology study.

Shun Fu · Wensheng Tang · Shisui Luo · Wei Wang · Yin Huang · Hao Li · Guixue Wang

Medicine · 2026 doi:10.1097/MD.0000000000046966

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:41496131
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title
Identification of diagnostic biomarkers for ischemic stroke and drug targets of Buyang Huanwu decoction: A bioinformatics and network pharmacology study.
doi
10.1097/MD.0000000000046966
pmid
41496131
authors array · 7 items
[0]
Shun Fu
[1]
Wensheng Tang
[2]
Shisui Luo
[3]
Wei Wang
[4]
Yin Huang
[5]
Hao Li
[6]
Guixue Wang
journal
Medicine
year
2026
abstract
Buyang Huanwu decoction (BHD) is extensively employed in the management of ischemic stroke (IS), yet its drug targets and mechanisms of action in IS remain obscure. Thus, in this investigation, network pharmacology and computational biology techniques were employed to investigate the drug targets of BHD in the management of IS. This study aims to identify diagnostic biomarkers for IS and to elucidate the potential drug target network of BHD using integrated bioinformatics and network pharmacology approaches. mRNA expression profiles (GSE16561, GSE58294) were retrieved from the Gene Expression Omnibus (GEO). BHD compounds were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (oral bioavailability ≥ 30 %, drug-likeness ≥ 0.18). Predicted compound targets (SwissTargetPrediction) were mapped to UniProt IDs. Differentially expressed genes were identified with limma (|log_2 FC| > 0.5, adjusted P < .05). Key coexpression modules were extracted by weighted gene coexpression network analysis (WGCNA; |cor| ≥ 0.4). Intersecting differentially expressed genes, WGCNA genes, and compound targets yielded candidate genes, refined by LASSO regression. Functional enrichment (Gene Ontology/Kyoto Encyclopedia of Genes and Genomes), gene set enrichment analysis, protein-protein interaction (STRING), and regulatory network analysis (miRDB, JASPAR) were performed. Diagnostic value was evaluated by receiver operating characteristic curves; a nomogram was constructed and calibrated. The analysis revealed several key targets of BHD that are potentially involved in the treatment of IS, including ARG1, CYP1B1, PAK1, and PYGL. These targets were enriched in pathways related to neuroprotection and anti-inflammatory responses, suggesting that BHD may exert therapeutic effects through these mechanisms. This study provides insights into the molecular targets and pathways through which BHD may alleviate IS, providing a foundation for future experimental validation and potential therapeutic strategies for stroke management.
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Differential gene expression analysis between the normal and IS groups in the GSE16561 dataset was performed via the limma package[6] (|log_2 FC| > 0.5 and P.adjust < .05) to screen for differentially expressed genes (DEGs).
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sections[7] "2.5. Difference analysis" paragraph 2
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Differential gene expression analysis between the normal and IS groups in the GSE16561 dataset was performed via the limma package[6] (|log_2 FC| > 0.5 and P.adjust < .05) to screen for differentially expressed genes (DEGs).
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All data generated or analyzed during this study are included in this published article [and its supplementary information files].
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Furthermore, on the basis of the LASSO model, a total of 4 key genes, including ARG1, CYP1B1, PAK1, and PYGL, were identified (Fig. 12).
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sections[19] "3.3. Identification of candidate key genes and PPIs in IS" paragraph 2
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Furthermore, on the basis of the LASSO model, a total of 4 key genes, including ARG1, CYP1B1, PAK1, and PYGL, were identified (Fig. 12).
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Furthermore, on the basis of the LASSO model, a total of 4 key genes, including ARG1, CYP1B1, PAK1, and PYGL, were identified (Fig. 12).
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Gene expression analysis of peripheral whole blood RNA following ischemic stroke
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Gene Expression Following Cardioembolic Stroke
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Differential gene expression analysis between the normal and IS groups in the GSE16561 dataset was performed via the limma package[6] (|log_2 FC| > 0.5 and P.adjust < .05) to screen for differentially expressed genes (DEGs).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41496131", "kind": "science-record", "revision": 4, "content_hash": "sha256:9b2d3c0b1b7717713903b8a962af661deba31b670b497cb9475481539592bb62", "created_at": "2026-11-01T20:31:28Z", "updated_at": "2026-11-01T22:57:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of diagnostic biomarkers for ischemic stroke and drug targets of Buyang Huanwu decoction: A bioinformatics and network pharmacology study.", "doi": "10.1097/MD.0000000000046966", "pmid": "41496131", "authors": ["Shun Fu", "Wensheng Tang", "Shisui Luo", "Wei Wang", "Yin Huang", "Hao Li", "Guixue Wang"], "journal": "Medicine", "year": 2026, "abstract": "Buyang Huanwu decoction (BHD) is extensively employed in the management of ischemic stroke (IS), yet its drug targets and mechanisms of action in IS remain obscure. Thus, in this investigation, network pharmacology and computational biology techniques were employed to investigate the drug targets of BHD in the management of IS. This study aims to identify diagnostic biomarkers for IS and to elucidate the potential drug target network of BHD using integrated bioinformatics and network pharmacology approaches. mRNA expression profiles (GSE16561, GSE58294) were retrieved from the Gene Expression Omnibus (GEO). BHD compounds were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (oral bioavailability ≥ 30 %, drug-likeness ≥ 0.18). Predicted compound targets (SwissTargetPrediction) were mapped to UniProt IDs. Differentially expressed genes were identified with limma (|log_2 FC| > 0.5, adjusted P < .05). Key coexpression modules were extracted by weighted gene coexpression network analysis (WGCNA; |cor| ≥ 0.4). Intersecting differentially expressed genes, WGCNA genes, and compound targets yielded candidate genes, refined by LASSO regression. Functional enrichment (Gene Ontology/Kyoto Encyclopedia of Genes and Genomes), gene set enrichment analysis, protein-protein interaction (STRING), and regulatory network analysis (miRDB, JASPAR) were performed. Diagnostic value was evaluated by receiver operating characteristic curves; a nomogram was constructed and calibrated. The analysis revealed several key targets of BHD that are potentially involved in the treatment of IS, including ARG1, CYP1B1, PAK1, and PYGL. These targets were enriched in pathways related to neuroprotection and anti-inflammatory responses, suggesting that BHD may exert therapeutic effects through these mechanisms. This study provides insights into the molecular targets and pathways through which BHD may alleviate IS, providing a foundation for future experimental validation and potential therapeutic strategies for stroke management.", "claims": [{"id": "claim-method", "statement": "Differential gene expression analysis between the normal and IS groups in the GSE16561 dataset was performed via the limma package[6] (|log_2 FC| > 0.5 and P.adjust < .05) to screen for differentially expressed genes (DEGs).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41496131", "locator": "sections[7] \"2.5. Difference analysis\" paragraph 2", "quote": "Differential gene expression analysis between the normal and IS groups in the GSE16561 dataset was performed via the limma package[6] (|log_2 FC| > 0.5 and P.adjust < .05) to screen for differentially expressed genes (DEGs)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article [and its supplementary information files].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41496131", "locator": "sections[30] \"(untitled)\" paragraph 1", "quote": "All data generated or analyzed during this study are included in this published article [and its supplementary information files]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, on the basis of the LASSO model, a total of 4 key genes, including ARG1, CYP1B1, PAK1, and PYGL, were identified (Fig. 12).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41496131", "locator": "sections[19] \"3.3. Identification of candidate key genes and PPIs in IS\" paragraph 2", "quote": "Furthermore, on the basis of the LASSO model, a total of 4 key genes, including ARG1, CYP1B1, PAK1, and PYGL, were identified (Fig. 12)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Furthermore, on the basis of the LASSO model, a total of 4 key genes, including ARG1, CYP1B1, PAK1, and PYGL, were identified (Fig. 12).", "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:41496131"], "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:GSE16561"], "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:GSE16561"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41496131", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000046966", "sha256": 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