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Research Article

Network Pharmacology, GEO Transcriptomics, and Molecular Docking of Zhenwu Decoction with Wuling Powder for Chronic Heart Failure

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September 11th, 2026

 ,  ,  ,  , 
1National Clinical Research Center for Traditional Chinese Medicine in Cardiovascular Diseases, Xiyuan Hospital

In This Article

Summary

This study integrates network-based target mapping, GEO expression data, and docking to rank candidates related to the Zhenwu-Wuling combination in chronic heart failure.

Abstract

Zhenwu Decoction combined with Wuling Powder is used in chronic heart failure (CHF), but its molecular basis is not fully defined. We integrated network-based target mapping, Gene Expression Omnibus (GEO) transcriptomic data, and molecular docking to prioritize candidate targets and pathways. Compound records for the eight herbs were obtained from pharmacological and chemical databases and subjected to compound and target screening. CHF-associated genes were collated from six disease resources, and their overlap with formula-related targets was examined using PPI network and functional enrichment analyses. GSE5406 provided myocardial expression data for failing and nonfailing samples. The workflow yielded 89 screened compounds, 314 nonredundant compound targets, and 2,515 CHF-related targets, with 143 shared genes. AKT1, IL6, TNF, SRC, and ESR1 ranked highest in the disease-target network. The expression analysis returned 1,348 differentially expressed genes (DEGs) and selected HSP90AA1 and STAT1 as transcriptomic hub candidates. Enriched terms clustered around hypoxia, circulation, TNF-related signaling, PI3K-Akt, and MAPK pathways. Docking produced negative predicted scores for representative constituent-target pairs; the most negative score was observed for STAT1 and alisol C (-9.2 kcal/mol). These findings generate a testable computational hypothesis and prioritize candidates for experimental validation.

Introduction

CHF comprises a heterogeneous syndrome arising from structural or functional myocardial abnormalities. Such changes can compromise ventricular filling or systolic ejection and are often accompanied by pulmonary or systemic congestion. Typical symptoms include dyspnea, fatigue, and fluid retention1. A retrospective analysis of China's Global Burden of Disease data from 1990 to 2023 showed that the national prevalence of newly diagnosed CHF increased by 208.4% over three decades2.

In traditional Chinese medicine (TCM), CHF corresponds to cardiac edema, dyspnea syndrome, and generalized edema. Its pathogenesis is commonly described as deficiency in the root and excess in the branch. Deficiency of yin, yang, and qi constitutes the underlying pathological basis, whereas internal fluid accumulation and blood stasis represent the manifest excess. Accordingly, TCM management emphasizes warming yang, removing retained fluid, promoting circulation, and resolving stasis.

The combined use of Zhenwu Decoction and Wuling Powder is a classical herbal strategy for clinical CHF management. Zhenwu Decoction warms yang and promotes diuresis, whereas Wuling Powder supports yang qi and regulates fluid metabolism. Together, the two formulas may reinforce yang and eliminate internal dampness. Clinical evidence suggests that this combination can relieve symptoms, improve cardiac function indicators, and slow CHF deterioration3. We first mapped formula constituents and candidate CHF targets using network pharmacology, then used docking to rank selected constituent-target pairs. GEO-derived expression profiles were then used to place the network candidates in a myocardial disease context and to examine downstream pathways. The study provides a hypothesis-generating computational framework for prioritizing candidates for subsequent experimental validation; it does not establish clinical efficacy or a validated mechanism.

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Protocol

This study analyzed public database resources and did not involve new human participants, human specimens, or animal experiments. All databases, web-based resources, software, analysis packages, visualization tools, molecular docking tools, and other computational resources used in this study, together with their details, are listed in the Table of Materials.

Formula composition and botanical provenance
The eight distinct crude drugs were aligned to current botanical and pharmacopoeial naming as follows: Aconiti Lateralis Radix Praeparata, the processed lateral root of Aconitum carmichaelii Debeaux; Poria, the sclerotium of Wolfiporia cocos (F.A.Wolf) Ryvarden & Gilb.; Atractylodis Macrocephalae Rhizoma, the rhizome of Atractylodes macrocephala Koidz.; Paeoniae Radix Alba, the root of Paeonia lactiflora Pall.; Zingiberis Rhizoma Recens, the fresh rhizome of Zingiber officinale Roscoe; Alismatis Rhizoma, the rhizome of Alisma orientale (Sam.) Juzep.; Polyporus, the sclerotium of Polyporus umbellatus (Pers.) Fr.; and Cinnamomi Ramulus, the young twig of Cinnamomum cassia (L.) J.Presl. The prescription comprised 9 g Aconiti Lateralis Radix Praeparata, 12 g Poria, 9 g Paeoniae Radix Alba, 12 g Atractylodis Macrocephalae Rhizoma (stir-fried Atractylodes), 9 g Zingiberis Rhizoma Recens, 9 g Polyporus, 15 g Alismatis Rhizoma, and 6 g Cinnamomi Ramulus; the corresponding ratio was 3:4:3:4:3:3:5:2. Aconiti Lateralis Radix Praeparata was treated as processed material. This was an entirely in silico study: no decoction batch was prepared, purchased, chemically assayed, or administered. Manufacturer, lot number, batch-specific processing records, and voucher specimens are therefore not applicable; the gram amounts define the formula composition and relative ratio, not experimental exposure. Poria and Atractylodis Macrocephalae Rhizoma are shared by the two formulae. Botanical names and medicinal-part terminology were checked against the 2025 Pharmacopoeia of the People’s Republic of China4.

Screening and target prediction of active ingredients in Zhenwu Decoction and Wuling Powder
TCMSP was queried separately for compounds recorded for Zhenwu Decoction and Wuling Powder, including Aconiti Lateralis Radix Praeparata, Poria, Atractylodis Macrocephalae Rhizoma, Paeoniae Radix Alba, Zingiberis Rhizoma Recens, Alismatis Rhizoma, Polyporus, and Cinnamomi Ramulus. Initial filtering required oral bioavailability (OB) of at least 30% and drug-likeness (DL) of at least 0.18. When a simplified molecular-input line-entry system (SMILES) record was unavailable, the corresponding structure was retrieved from a public chemical database. SMILES records were evaluated for physicochemical and drug-likeness properties. A compound passed the secondary filter when its molecular weight was ≤500, lipophilicity ≤5, hydrogen-bond donors ≤5, hydrogen-bond acceptors ≤10, at least three of the Lipinski, Ghose, Veber, Egan, and Muegge rule sets were positive, and predicted gastrointestinal absorption was high5. Eligible structures were subjected to target prediction, and only predictions with probability >0.02 were included in the target list. TCMSP and target-prediction records were combined at the herb-compound-target level, duplicate identifiers were collapsed, and gene names were standardized to official gene symbols.

Deduplication produced 646 candidate associations; 71 rows without a mapped official gene symbol were excluded, leaving 575 standardized associations. The independent herb-level integration yielded 314 nonredundant target genes. The compound and target screening criteria are supported by the TCMSP and SwissTargetPrediction source papers6,7.

Screening of disease targets for CHF
CHF-related gene entries were downloaded from GeneCards, OMIM, TTD, DisGeNET, DrugBank, and PharmGKB. The disease search used four phrases: heart failure, congestive heart failure, diastolic heart failure, and systolic heart failure. For GeneCards, entries with relevance scores below 0.20 were excluded. The six database-derived target sets were integrated after duplicate identifiers were removed, while preserving the source database for each record. All screened gene names were standardized to official gene symbols. The overlap between the formula and CHF target lists was visualized using a Venn diagram. The shared identifiers were treated as candidate formula-related targets for CHF.

TCMSP was searched on 7 February 2026; SwissTargetPrediction, GeneCards, OMIM, and DisGeNET on 10 February 2026; and PharmGKB, TTD, and DrugBank on 12 February 2026. Disease queries were entered as heart failure, congestive heart failure, heart failure, diastolic, and heart failure, systolic; the search was restricted to Homo sapiens where supported. Retrieved entries were standardized to official gene symbols before deduplication.

Construction of the protein-protein interaction (PPI) network and screening of core targets
The shared target identifiers were queried in STRING for PPI analysis. The query used Homo sapiens interactions and a confidence cutoff above 0.9. Isolated nodes were omitted, and the resulting STRING interaction data were used for network visualization and topological analysis. Candidate hubs were ranked by network topology. Betweenness, closeness, degree, and eigenvector centralities were calculated as network descriptors. Degree was the primary ranking variable, and the five highest-degree targets were retained as candidate hubs.

Official gene symbols were used for gene-level analyses, including AKT1, IL6, TNF, SRC, ESR1, BCL2, CASP3, PPARG, PTGS2, and MMP9; protein names are written in full only when needed for clarity. The initial PPI network contained 143 nodes and 584 interaction records before visualization-specific filtering; after network processing, the graph contained 143 nodes and 292 edges.

Construction of the drug-disease-component-target network
Once names were standardized, the network data were assembled and used to construct the drug-disease-component-target graph.

GO and KEGG enrichment analyses
The candidate CHF target list was submitted to Metascape for functional annotation and pathway enrichment analysis. The analysis was run for Homo sapiens. The raw p-value cutoff was set at 0.01, a minimum overlap of 3, and a minimum enrichment score of 1.5. GO bar charts and KEGG bubble plots were generated for visualization. The 143-gene input set was used for Gene Ontology biological-process (GO-BP), molecular-function (GO-MF), and KEGG enrichment analyses. The analysis thresholds were raw p < 0.01, minimum overlap 3, and minimum enrichment 1.58. The GO-CC analysis was based on a 60-gene hit list against a 30,230-gene library. Because this input differed from the 143-gene input used for the BP, MF, and KEGG analyses, the GO-CC results were reported separately and were not quantitatively pooled with the other categories.

Screening of heart failure-related targets based on the GEO database
The GSE5406 dataset9 was used for differential-expression analysis. The series included 210 myocardial samples: 108 with ischemic cardiomyopathy, 86 with idiopathic dilated cardiomyopathy, and 16 nonfailing donor controls. The GEO record identifies the platform as GPL96, the Affymetrix Human Genome U133A Array. The series matrix was already robust multi-array average (RMA)-normalized. Probes without matched SYMBOL annotations were removed after matching to the platform annotation, and rowSums(expression > 0.5) ≥ 3 was applied before limma fitting. Group levels were defined as Control, DCM, and ICM, with a no-intercept design matrix (~0 + group) and the contrasts (DCM+ICM)/2-Control, DCM-Control, and ICM-Control. Differential expression was defined as adjusted p value < 0.05 and |log2(Fold Change)| > 0.3. Because all samples were processed within one GEO series and no separate batch variable was available, no batch-correction term was introduced. Differential-expression results were visualized using heatmaps and volcano plots. DEGs were subjected to PPI analysis followed by network-based hub ranking. Docking and pose visualization were conducted for the selected targets and prescription compounds.

The GEO record identifies the platform as GPL96, the Affymetrix Human Genome U133A Array. Group levels were defined as Control, DCM, and ICM, with a no-intercept design matrix (~0 + group) and the contrasts (DCM+ICM)/2-Control, DCM-Control, and ICM-Control. The series matrix was already robust multi-array average (RMA)-normalized; probes with missing SYMBOL annotations were removed after matching to the platform annotation, and the code applies rowSums(expression > 0.5) ≥ 3 before limma fitting. Because all samples were processed within one GEO series and no separate batch variable was available, no batch-correction term was introduced.

Molecular docking verification
A three-dimensional PDB structure for each receptor was obtained from the RCSB Protein Data Bank. Protein structures were prepared by removing crystallographic waters and non-receptor ligands. Structure-data file (SDF) structures for the five highest-degree compounds were obtained from a public chemical database.

The receptor structures used for docking were AKT1/1H10 (chain A, 1.40 Å, 4IP), IL6/1ALU (chain A, 1.90 Å, TLA), TNF/1A8M (chain A, 2.30 Å, no co-crystallized ligand), SRC/1A09 (chain A, 2.00 Å, no ligand), ESR1/1A52 (chain A, 2.80 Å, EST), STAT1/1BF5 (chain A, 2.90 Å, no ligand), and HSP90AA1/3OWB (chain A, 2.05 Å, BSM). The center, box dimensions, and random seed were specified for each tested pair. The search boxes were defined to encompass the receptor for exploratory global docking.

Receptor and ligand files were prepared with the docking-preparation tools. Waters were deleted and polar hydrogens were added to receptor structures. SDF ligands were converted to PDB, then dehydrated and hydrogenated before PDBQT export. The prepared receptor-ligand files were assigned to the docking models. PDBQT files were loaded into AutoDock Vina. Vina runs were executed and scores were recorded. Scores below -5 kcal/mol were treated as comparatively more favorable and scores below -7 kcal/mol as more strongly predicted interactions10; these are not experimental thresholds. The three complexes with the best predicted scores were selected for visualization.

No search-depth or output-mode override was specified; therefore, the documented command-line defaults were used: exhaustiveness 8 and at most 9 modes. Pair-specific random seeds were used, and mode 1 (the best-scoring returned pose) was selected for representative visualization. The HSP90AA1 output-file assignment was confirmed because each mode-1 score matched the corresponding HSP90AA1 value in the docking-score matrix. Molecular dynamics simulations were not performed, so the scores are exploratory computational predictions only.

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Results

Active ingredients and targets of Zhenwu Decoction combined with Wuling Powder
Constituent-target records were obtained for each herb. The corresponding per-herb target counts were Aconiti Lateralis Radix Praeparata, 178; Poria, 21; Atractylodis Macrocephalae Rhizoma, 18; Paeoniae Radix Alba, 86; Zingiberis Rhizoma Recens, 127; Alismatis Rhizoma, 96; Polyporus, 4; and Cinnamomi Ramulus, 45. After symbol standardization and deduplication, the integrated formula-related set contained 314 nonredundant t...

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Discussion

CHF requires long-term clinical management and often involves complex medication regimens. Studies by Wang JX and Zhang S11 and other studies indicate that long-term combined conventional pharmacotherapy may reduce medication adherence. Poor adherence can increase the risk of readmission and adverse emotional outcomes. Available drugs largely focus on symptom control and slowing ventricular remodeling. The 2023 National Guidelines for the Management of Heart Failure in China12

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Disclosures

The authors report no competing interests related to this study.

Acknowledgements

Not applicable.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
AutoDock VinaCCSB-Scrippsv1.2.6Command-line version used; current official release v1.2.7; configurations used CLI defaults exhaustiveness 8 and num_modes 9. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockToolsThe Scripps Research Institutev1.5.7Receptor/ligand preparation; version used in analysis. https://autodock.scripps.edu/
BioconductorBioconductor Project3.16 compatibility releaseR 4.2-compatible environment; current release 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape DesktopCytoscape Consortiumv3.7.1Network visualization/analysis version used; current release v3.10.4. https://cytoscape.org/download.html
DisGeNETMedBioinformatics Solutionsv25.4CHF-associated gene retrieval; searched 2026-02-10. https://disgenet.com/
DrugBankOMx Personal Health Analyticsv6.0CHF-associated gene retrieval; searched 2026-02-12. https://go.drugbank.com/
GeneCardsWeizmann Institute of ScienceWeb databaseCHF-associated gene retrieval; searched 2026-02-10. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406; GPL96RMA-normalized human left-ventricular microarray series. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu133a.dbBioconductorBioconductor 3.16 environmentAffymetrix Human Genome U133A probe annotation; exact patch version not retained. https://bioconductor.org/packages/hgu133a.db/
limmaBioconductorBioconductor 3.16 environmentDifferential-expression analysis; exact patch version not retained. https://bioconductor.org/packages/limma/
MacBookAppleM4 processorLocal computational workstation; memory specification not retained. https://www.apple.com/mac/
MetascapeMetascapev3.5GO/KEGG enrichment; exact access timestamp not retained. https://metascape.org/
Microsoft ExcelMicrosoftDesktop applicationData curation/export; application build not retained. https://www.microsoft.com/microsoft-365/excel
OMIMJohns Hopkins UniversityWeb databaseCHF-associated gene retrieval; searched 2026-02-10. https://www.omim.org/
Open BabelOpen Babel ProjectVersion not retainedStructure conversion in docking workflow. https://openbabel.org/
PharmGKBPharmGKBWeb databaseCHF-associated gene retrieval; searched 2026-02-12. https://www.pharmgkb.org/
PubChemNCBIWeb databaseSMILES and structure completion. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrödingerVersion not retainedProtein preprocessing and pose visualization. https://www.pymol.org/
RR Foundation4.2.0+Version family recorded for analysis; current release 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDBWeb archiveProtein crystal structures. https://www.rcsb.org/
RStudioPosit2026.04.0+526Analysis IDE recorded in revision note. https://posit.co/download/rstudio-desktop/
STRINGSTRING ConsortiumWeb serviceHomo sapiens; confidence >0.9; release/session metadata not retained. https://string-db.org/
SwissADMESIB Swiss Institute of BioinformaticsWeb serviceADME and Lipinski-rule screening; local version not applicable. https://www.swissadme.ch/
SwissTargetPredictionSIB Swiss Institute of Bioinformatics2019 major updateHomo sapiens; probability >0.02; searched 2026-02-10. https://www.swisstargetprediction.ch/
TCMSPNorthwest A&F Universityv2.3Compound retrieval; searched 2026-02-07. https://tcmsp-e.com/
TTDTherapeutic Target Database2026 releaseCHF-associated gene retrieval; searched 2026-02-12. https://ttd.idrblab.cn/
UniProtKBUniProt ConsortiumRelease 2026_01Reviewed Homo sapiens gene-symbol standardization. https://www.uniprot.org/
VennyCNB-CSICv2.1.0Target-set intersection visualization. https://bioinfogp.cnb.csic.es/tools/venny/

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