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Frontiers in Cardiovascular Medicine ·

Volume 13 - 2026 | https://doi.org/10.3389/fcvm.2026.1818551

Exploring the protective mechanism of Huangqishengmaiyin against anthracycline-induced cardiotoxicity in breast cancer with concomitant gut microbiota modulation

  • Yixuan Wang1,2
  • Yeying Jin1,3
  • Jiali Han4
  • Mingjia Ding5
  • Haoqing Hou4
  • Miaoyu Mei5
  • Rui Yang1*corresponding author
  1. 1Department of Breast Surgery, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
  2. 2Fifth Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, China
  3. 3First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, China
  4. 4College of Basic Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi, China
  5. 5College of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
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Abstract

To elucidate the bioactive compounds and cardioprotective mechanism of Huangqishengmaiyin (HQSMY) against anthracycline-induced cardiotoxicity (AIC) in breast cancer with concomitant gut microbiota modulation. Myocardial fibrosis was quantified using Masson's trichrome staining. Hematoxylin and eosin staining was used for general histopathological evaluation. Ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-Q Exactive HFX) was employed to comprehensively identify blood components in HQSMY. The regulatory effect of HQSMY on the gut microbiota of AIC mice was assessed by 16S rRNA microbial sequencing. Network pharmacology and molecular docking mapped the compound-target-pathway network. Western blot was performed to detect key proteins in the predicted pathways, including JAK1, STAT3, and HIF-1 α. HQSMY reduced myocardial fibrosis rates, enhanced gut microbiota diversity, abundance and health indices. Meanwhile, HQSMY increased the abundance of anti-inflammatory bacteria like Bacteroidetes and restored normal metabolic pathways in multiple gut microbes, including glycolysis. 42 blood components were identified, exhibiting antioxidant, anti-inflammatory, and gut microbiota-regulating activities. Network pharmacology analysis revealed that the JAK-STAT and HIF-1 signaling pathways are key mechanisms. Enrichment analysis focused on energy metabolism and inflammatory response. Western blot results confirmed that HQSMY significantly downregulated JAK1 and HIF-1 α protein expression; STAT3 was decreased without reaching statistical significance. The components of HQSMY exhibit anti-inflammatory effects in myocardial tissue, meanwhile enhancing beneficial gut bacteria in intestine. The regulatory effect of HQSMY may be exerted through JAK-STAT and HIF-1 pathways, as confirmed by altered expression of key proteins in these pathways.

Keywords

1 Introduction

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death among women worldwide (1). Currently, anthracycline drugs serve as the cornerstone chemotherapy for malignant breast tumors. However, their use in breast cancer treatment can lead to irreversible anthracycline-induced cardiotoxicity (AIC). The 2022 international guidelines on cardiac oncology define AIC based on the following criteria: a relative decrease in left ventricular ejection fraction (LVEF) > 15% or cTnI/cTnT > 99%, BNP ≥ 35 pg/mL, or NT-proBNP ≥ 125 pg/mL (2). Manifestations include heart failure, myocarditis, vascular toxicity, arrhythmias, and left ventricular dysfunction, posing a significant threat to patient survival (3). Notably, this drug-induced cardiomyopathy often prevents patients from completing full chemotherapy, ultimately leading to tumor progression. Clinically, anthracyclines are often combined with dexrazoxane (Dex) to prevent cardiotoxicity. However, Dex is associated with severe hematological adverse effects, including leukopenia, neutropenia, and thrombocytopenia, and requires bone marrow transplantation in some cases (4). Therefore, identifying effective strategies to treat AIC has become an urgent priority in breast cancer therapy.

AIC falls under Traditional Chinese Medicine (TCM) syndromes such as palpitations and chest-fluid obstruction (5). Sheng-Mai Yin (SMY), a classic TCM formulation for tonifying qi and nourishing yin that originated from YiXueQiYuan during the Yuan Dynasty, consists of three medicinal herbs: Radix Ginseng Rubra (Panax ginseng C. A. Mey.), Ophiopogon japonicus (Thunb.) Ker Gawl., and Schisandra chinensis (Turcz.) Baill. It is widely used in cardiovascular diseases with qi-yin deficiency syndrome (6). The efficacy of SMY on AIC has been evaluated by meta-analysis, showing that it can improve ST-T segment changes and creatine kinase isoenzyme, relieve prolonged QT interval, sinus tachycardia, and premature ventricular contractions in patients with AIC (7). With the evolution of science, it has progressively diversified into various dosage forms, while its prescription formulation has also been refined and modified. Huangqishengmaiyin (HQSMY) is an enhanced version of SMY, replacing Radix Ginseng Rubra with Pseudostellaria heterophylla (Miq.) Pax and incorporating Astragalus mongholicus Bunge as the sovereign drug. Given that Astragalus mongholicus has been demonstrated to alleviate myocardial fibrosis, pathological myocardial injury, and to inhibit oxidative stress and inflammatory responses, its incorporation into SMY can enhance the cardioprotective effects (8). Astragaloside IV reduces inflammatory cell infiltration in the myocardium while decreasing NLRP3 inflammasome expression and its downstream caspase-1, IL-18, and IL-1β protein levels (9). Pseudostellaria heterophylla, milder in nature than Radix Ginseng Rubra, is particularly suitable for patients with post-chemotherapy debility. It exhibits immunomodulatory, hypoglycemic, anti-inflammatory, cytoprotective, and tyrosinase-inhibiting effects (10). Ophiopogon japonicus is commonly used to treat palpitations and startle responses due to heart yin deficiency. Polysaccharides from Ophiopogon japonicus reduce doxorubicin (Dox)-induced myocardial ferroptosis by activating the Nrf2/GPX4 signaling pathway and mitigating iron accumulation (11). Schisandra regulates cardiovascular circulation, exhibiting significant blood pressure-elevating effects in circulatory failure patients. Schisandrin B prevents AIC by enhancing the glutathione redox cycle (12). Therefore, we speculate that HQSMY may exert superior clinical efficacy compared to SMY in the treatment of AIC.

Notably, numerous components in HQSMY have been demonstrated to exert cardioprotective effects through the gut microbiota and its metabolites. For example, Astragalus polysaccharides stimulate the proliferation of beneficial bacterial genera, such as Allobaculum and Lactobacillus, thereby strengthening the integrity of the intestinal barrier (13). TCM theory holds that the heart and small intestine are paired organs, closely linked in their meridian pathways and physiological functions. Modern gut microbiota research has revealed a close relationship between the cardiovascular system and the intestinal microecology. A balanced intestinal microecology promotes cardiovascular homeostasis. Conversely, an imbalanced gut microbiota can exert pathological effects on the cardiovascular system. This bidirectional regulation suggests a potential association between gut microbiota and cardiac function (14).

Given the intricate composition and multi-target nature of TCM, the bioactive components of HQSMY, which are absorbed into the bloodstream via the intestinal barrier following oral administration, were identified using UHPLC-Q Exactive HF-X. Meanwhile, this study evaluated the regulatory effect of HQSMY on the gut microbiota of AIC model mice through 16S rRNA microbial sequencing. Combined with network pharmacology methods, key signaling pathways and potential target molecules were identified. This study aims to elucidate the active components and mechanisms of HQSMY in treating AIC with concurrent assessment of gut microbiota changes.

2 Materials

2.1 Samples and instruments

Methanol, acetonitrile, formic acid, and isopropyl alcohol were purchased from ANPEL. All solvents were LC-MS grade. Ultra-pure water was prepared in-house using a Milli-Q water purification system (Millipore, Bedford, MA, USA). Masson's trichrome staining kit was purchased from Solarbio (Beijing, China). Primary antibodies against JAK1 (Proteintech, Cat No. 27284-1-AP, rabbit polyclonal, 1:1000), STAT3 (Proteintech, Cat No. 10253-2-AP, rabbit polyclonal, 1:1000), HIF-1α (Active Motif, Clone ESEE122, mouse monoclonal, 1:1000), and β-actin (Proteintech, Cat No. 81115-1-RR, rabbit recombinant, 1:2000) were used. Horseradish peroxidase-conjugated goat anti-rabbit IgG (H + L) secondary antibody (1:5000, #7074) and horseradish peroxidase-conjugated goat anti-mouse IgG (H + L) secondary antibody (1:5000, #7076) were purchased from Cell Signaling Technology (Danvers, MA, USA).

2.2 Drug preparation

HQSMY is composed of 12 g Ophiopogon japonicus (Thunb.) Ker Gawl. (Mai Dong), 6 g Schisandra chinensis (Turcz.) Baill. (Wu Wei Zi), 15 g Pseudostellaria heterophylla (Miq.) Pax (Tai Zi Shen), and 30 g Astragalus mongholicus Bunge (Huang Qi), purchased from the Traditional Chinese Medicine Pharmacy of Shanxi Cancer Hospital. HQSMY crude drugs were soaked in water for 30 min and decocted twice: the first decoction used 10 times the weight of water and simmered for 1 h; the liquid was filtered. The second decoction used 8 times the weight of water and simmered for 1 h; the filtered liquids were combined. The mixture was concentrated using a rotary evaporator, then placed in a freeze-dryer to produce lyophilized powder. The clinical human dose (63 g crude drugs/day for a 60-kg adult) was converted to the mouse equivalent dose using body surface area normalization (approximately 10-fold) (15). Administered doses were calculated as lyophilized extract (extraction yield: 16.2%, w/w). The medium dose (1.7 g/kg/day) corresponded to the clinical equivalent dose, and the high dose (3.4 g/kg/day) was 2-fold higher. For use, the lyophilized powder was dissolved in distilled water and administered by oral gavage once daily at the indicated doses.

2.3 Animals

Twenty-five SPF female BALB/c mice, 6 weeks old and weighing 18–20 g, were purchased from Shanghai Sileke Experimental Animal Co., Ltd. The living conditions were maintained in accordance with SPF standards, featuring a 12-hour light/12-hour dark cycle, an ambient temperature of 22 °C ± 2 °C, humidity levels of 50% ± 10%, and free access to water. Doxorubicin (Dox): GC16994; Dexrazoxane (Dex): HY-B0581. The 4T1 cells were obtained from the Center for Fine Resources at the Shanghai Institutes for Biological Sciences, which is affiliated with the Chinese Academy of Sciences.

2.4 Databases and software

GraphPad Prism 11.0; GeneCards (https://www.genecards.org); HERB (http://herb.ac.cn); SwissADME (http://www.swissadme.ch); SwissTargetPrediction (http://swisstargetprediction.ch); UniProt (https://www.uniprot.org); STRING (https://string-db.org); KEGG (https://www.genome.jp/kegg); TTD (https://db.idrblab.net/ttd); DrugBank (https://go.drugbank.com); PubChem (https://www.ncbi.nlm.nih.gov/pccompound); Omicshare (https://www.omicshare.com); PDB (https://www.rcsb.org); Cytoscape 3.7.2; CytoNCA software; MCODE application; PyMOL-2.5.2 software; AutoDockTools-1.5.7 software; Majorbio Bio-Pharm Technology cloud platform.

3 Methods

3.1 Animal model grouping and administration

Twenty-five BALB/c mice were acclimatized for one week and then randomly divided into five groups: Control, Model, HQSMY-M (1.7 g/kg/day), HQSMY-H (3.4 g/kg/day), and Positive Control-Dex (n = 5). Under aseptic conditions, 2 × 10⁵ 4T1 cells were injected into the second pair of right mammary fat pads of mice in the four groups (excluding the Control group) to establish a triple-negative breast cancer mouse model. Two days after tumor cell injection, mice in the four groups received intraperitoneal injections of Dox at 10 mg/kg/week to induce cardiotoxicity. The HQSMY groups were gavage-administered with the drug solution at 1.7 or 3.4 g/kg/day, while the Dex group received 100 mg/kg/week intraperitoneally. After 28 days, mice were euthanized, and cardiac tissue was harvested, fixed in 4% paraformaldehyde, embedded, and stained with H&E for general histopathological evaluation and Masson's trichrome for collagen deposition and fibrosis quantification. Masson's trichrome staining was performed on paraffin-embedded cardiac tissue sections (5 μm) according to the manufacturer's protocol. Collagen fibers were stained blue, while muscle fibers and cytoplasm were stained red. Images were captured under a light microscope (Olympus BX53, 200×). The percentage of fibrotic area was quantified using ImageJ software.

Mice were monitored twice daily for signs of distress throughout the study. Predefined humane endpoints included: (1) > 15% body weight loss sustained for 72 h, (2) severe lethargy or immobility, (3) dyspnea or cyanosis, (4) self-mutilation or prolonged convulsions. Animals reaching any of these endpoints were humanely euthanized by exposure to 1%–3% isoflurane until loss of the righting reflex (typically within 2 min), followed by cervical dislocation or bilateral pneumothorax to ensure death.

Subsequently, fecal microbiota 16S rRNA sequencing analysis was performed on 20 mice (n = 5 per group) from the Control, Model, HQSMY (1.7 g/kg/day), and Dex groups to examine gut microbiota changes under HQSMY intervention. Simultaneously, plasma samples were collected from 10 mice (n = 5 per group) in the Control and HQSMY (1.7 g/kg/day) groups. These samples were compared with herbal medicine samples (TCM-HQSMY) to identify HQSMY components entering the bloodstream. The medium dose (1.7 g/kg/day) was selected for 16S rRNA sequencing and plasma metabolomics based on comparable efficacy observed in the fibrosis assay and in accordance with the 3R principles.

3.2 Identification of HQSMY blood components

3.2.1 Sample preparation and extraction

Plasma samples were thawed slowly at 4 °C. An aliquot (1.0 mL) was transferred to a freeze-drying centrifuge tube, and an equal volume of extraction solution (methanol-acetonitrile, 1:1, v/v) containing 4 μg/mL L-2-chlorophenylalanine internal standard was added. The mixture was homogenized for 60 s and extracted by low-temperature ultrasonication for 30 min (twice), then incubated for 1 h to precipitate protein at −20 °C, followed by centrifugation at 12,000 rpm for 20 min at 4 °C. The supernatant was dried under vacuum, and 200 μL of 30% acetonitrile solution was added to re-dissolve. After homogenization and centrifugation at 14,000 rpm for 15 min at 4 °C, the supernatant was collected for detection. Quality control samples were prepared by mixing equal volumes of the supernatants from the HQSMY and Control groups, followed by centrifugation to obtain pooled samples.

3.3 Mass spectrometry detection method

The liquid chromatography parameters are as follows. Column: Waters HSS T3 (100 × 2.1 mm, 1.8 μm); Mobile phase: Phase A is ultrapure water solution (containing 0.1% formic acid), Phase B is acetonitrile solution (containing 0.1% formic acid); Flow rate: 0.3 mL/min; Column temperature: 40 °C; Injection volume: 2 μL; Elution gradient: 0 min A/B (100:0, v/v), 1 min A/B (100:0, v/v), 12 min A/B (5:95, v/v), 13 min A/B (5:95, v/v), 13.1 min A/B (100:0, v/v), 17 min A/B (100:0, v/v). Throughout the analysis, samples were maintained at 4 °C in the autosampler. Mass spectrometry conditions: Electrospray ionization source, sheath gas 40 arb, auxiliary gas 10 arb, ion spray voltage +3000 V/−2,800 V, temperature 350 °C, ion transfer tube temperature 320 °C. Scan mode: Full-ms-ddMS2, scan type: positive/negative ions. The primary mass spectrum scan range was 70–1,050 Da, with primary resolution of 70,000 and secondary resolution of 17,500.

3.3.1 Data preprocessing

Raw data underwent preliminary processing using Progenesis QI for baseline filtering, peak identification, retention time correction, peak matching, and peak alignment. This yielded a data matrix containing mass-to-charge ratios, retention times, and peak intensities. Subsequently, qualitative analysis of metabolites in the samples was completed by matching against Huijun Bio's proprietary secondary mass spectrometry database of Chinese herbal medicines. Among the compounds identified through QI database searches, only those with a total score ≥50 were retained as identified components of HQSMY.

3.4 Fecal microbiota 16S rRNA sequencing

Bacterial DNA was extracted from fecal samples using the PowerSoil DNA Isolation Kit, and the V3-V4 region of the bacterial 16S rRNA gene was amplified using primers 338F and 806R. Pair-end sequencing of purified amplicons was performed by Majorbio Bio-Pharm Technology (Shanghai, China) on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA). Data analysis and visualization were conducted on the Majorbio BioCloud platform. All obtained raw sequence datasets have been uploaded to the NCBI Sequence Read Archive (SRA) with the accession number PRJNA1372457.

3.5 Network pharmacology-based prediction of HQSMY's AIC inhibition mechanism

Blood-penetrating component targets were collected via PubChem and SwissTargetPrediction. Genes associated with breast cancer and AIC were obtained from the GeneCards database. The STRING database was used to construct a protein-protein interaction network. Cytoscape 3.7.2 performed network topology analysis. The MCODE program calculated pivotal targets. Target IDs were converted to gene IDs via the Omicshare website. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were subsequently performed. Cytoscape 3.7.2 was used to construct a “Chinese medicine-compound-target-pathway-disease” interaction network and generate a Sankey diagram. Molecular docking validation followed: structural formulas were downloaded from PubChem, while protein structures were obtained from the PDB database. Water molecules were removed using PyMOL-2.5.2 software. Binding energy values were obtained using AutoDockTools-1.5.7. Visualization was performed with PyMOL-2.5.2.

3.6 Western blot analysis

Cardiac tissue samples were lysed in RIPA buffer containing protease and phosphatase inhibitors. Protein concentrations were determined using the bicinchoninic acid assay kit. Equal amounts of protein (30 μg) were separated by 10% SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% bovine serum albumin for 1 h at room temperature, then incubated overnight at 4 °C with primary antibodies against JAK1, STAT3, HIF-1α, and β-actin. After washing with TBST, membranes were incubated with the corresponding horseradish peroxidase-conjugated secondary antibodies [goat anti-rabbit or goat anti-mouse IgG (H + L), 1:5,000] for 1 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence reagent and captured with a chemiluminescence imaging system. Band intensities were quantified using ImageJ software and normalized to β-actin. Antibody dilutions are detailed in Section 2.1.

3.7 Statistical methods

Data processing and analysis were conducted using GraphPad Prism 11.0. All data are expressed as mean ± SD. Independent samples t-tests or one-way ANOVA were applied. A P < 0.05 was considered statistically significant.

4 Results

4.1 HQSMY reduced myocardial fibrosis rate of the AIC mice

To investigate the therapeutic potential of HQSMY against AIC, we established a Dox-induced cardiotoxicity model in female BALB/c mice bearing 4T1 breast tumors. The experimental workflow is depicted in Figure 1A. Histopathological analysis of H&E-stained myocardial sections revealed marked structural damage in the Model group compared to the Control group (Figure 1B). Masson's trichrome staining further demonstrated extensive collagen deposition (blue staining) in the Model group, reflecting severe Dox-induced myocardial fibrosis (Figure 1C). Remarkably, treatment with HQSMY (both medium and high doses) or Dex substantially reduced collagen accumulation. Quantitative analysis of the fibrotic area confirmed that the myocardial fibrosis rate was significantly decreased in the HQSMY-M, HQSMY-H, and Dex groups relative to the Model group (Figure 1D).

HQSMY reduced the incidence of doxorubicin-induced myocardial fibrosis. **(A)** Schematic illustration of the experimental design. Female BALB/c mice were inoculated with 4T1 cells on Day 1 and treated with doxorubicin (Dox, 10 mg/kg/week) from Day 2 to establish the cardiotoxicity model. Mice in the HQSMY-M and HQSMY-H groups were orally administered HQSMY at 1.7 g/kg/day and 3.4 g/kg/day, respectively, from Day 2. The Dex group received dexrazoxane (100 mg/kg/week). All mice were sacrificed on Day 28. **(B)** Representative images of hematoxylin and eosin (H&E) staining of myocardial tissue from each group. Scale ba*r* = 250 μm. **(C)** Representative images of Masson's trichrome staining of myocardial tissue from each group. Collagen fibers are stained blue. Scale ba*r* = 100 μm. **(D)** Quantitative analysis of myocardial fibrosis area based on Masson's trichrome staining. **P* < 0.05, ***P* < 0.01, ****P* < 0.001.
Figure 1. HQSMY reduced the incidence of doxorubicin-induced myocardial fibrosis. (A) Schematic illustration of the experimental design. Female BALB/c mice were inoculated with 4T1 cells on Day 1 and treated with doxorubicin (Dox, 10 mg/kg/week) from Day 2 to establish the cardiotoxicity model. Mice in the HQSMY-M and HQSMY-H groups were orally administered HQSMY at 1.7 g/kg/day and 3.4 g/kg/day, respectively, from Day 2. The Dex group received dexrazoxane (100 mg/kg/week). All mice were sacrificed on Day 28. (B) Representative images of hematoxylin and eosin (H&E) staining of myocardial tissue from each group. Scale bar = 250 μm. (C) Representative images of Masson's trichrome staining of myocardial tissue from each group. Collagen fibers are stained blue. Scale bar = 100 μm. (D) Quantitative analysis of myocardial fibrosis area based on Masson's trichrome staining. *P < 0.05, **P < 0.01, ***P < 0.001.

4.2 Analysis of HQSMY components and plasma components following administration

To determine the composition of HQSMY, we employed UHPLC-Q Exactive HFX for its component analysis. The base peak chromatogram of TCM-HQSMY is shown in Figure 2A. With a fragmentation score greater than or equal to 50 points, a total of 546 compounds in TCM-HQSMY were annotated. These included 145 lipids and lipid-like molecules, 99 phenylpropanoids and polyketides, 54 organic oxygen compounds, 49 organoheterocyclic compounds. 33 organic acids and derivatives, 18 benzenoids, 10 nucleosides, nucleotides, and analogues, 9 lignans, neolignans and related compounds, 4 alkaloids and derivatives, 2 organic nitrogen compounds, and 123 other compounds (Supplementary Table S1). Figure 2B shows the percentage of compound quantities by class in TCM-HQSMY.

Analysis of HQSMY components. **(A)** BPC of TCM-HQSMY. **(B)** Percentage distribution of compound quantities by class in TCM-HQSMY. **(C)** BPC of HQSMY plasma samples. **(D)** Venn diagram. **(E)** Blood component classification.
Figure 2. Analysis of HQSMY components. (A) BPC of TCM-HQSMY. (B) Percentage distribution of compound quantities by class in TCM-HQSMY. (C) BPC of HQSMY plasma samples. (D) Venn diagram. (E) Blood component classification.

The chromatograms of HQSMY plasma samples are shown in Figure 2C. Using Venn diagram analysis of plasma and herbal medicine components, 42 absorbed components were identified (Figure 2D). The pie chart classifying absorbed components according to Chinese subcategories is shown in Figure 2E. Detailed information on the 42 absorbed components is provided in Table 1. Among these, flavonoids (16), tannins (17), isoflavonoids (18), coumarins and derivatives (19) have been verified to have anti-inflammatory activity. This may be the key reason why HQSMY reduced myocardial inflammation and alleviated AIC by improving both intestinal and myocardial conditions.

Table 1. HQSMY components in the plasma samples.

Class AClass BNumberComponentsAdductsm/zFormulaClass C
Class AClass BNumberMetaboliteAdductsm/zFormulaClass C
Lipids and lipid-like moleculesPrenol lipids1Eucommiol2[M+K]+415.174C9H16O4Monoterpenoids
2Calealactone C[M+H-2H2O]+371.148C21H26O8Terpene lactones
3Teucroxide[M+Na]+401.159C20H26O7Terpene lactones
4Wedeliatrilolactone A[M+H-2H2O]+417.190C23H32O9Terpene lactones
58alpha-Methacryloyloxybalchanin[M-H2O-H]-313.145C19H24O5Terpene lactones
6Caesalmin E[M+Na]+515.227C26H36O9Diterpenoids
7Phorbol 13-acetate[M-H2O-H]-387.182C22H30O7Diterpenoids
8Chaetopenoid A[M+Na]+437.195C24H30O6Sesquiterpenoids
9Notopterol[M+CH3OH+H]+387.179C21H22O5Terpene lactones
10Tanshinone IIA[M+H]+, [M+Na]+295.132C19H18O3Diterpenoids
11Caleurticolide 2-methylacrylate[M+H-2H2O]+397.164C23H28O8Terpene lactones
12Dihydrotanshinone I[M+H]+279.101C18H14O3Diterpenoids
13Methyl 6-hydroxyangolensate[M+FA-H]-531.223C27H34O8Triterpenoids
14Yomogiartemin[M+H-2H2O]+301.107C17H20O7Terpene lactones
Fatty Acyls15Myriocin[M+2Na-H]+446.252892825416C21H39NO6Fatty acids and conjugates
16Virginiaebutanolide C[M+FA-H]-261.134C11H20O4Fatty alcohols
17Methyl hydrogen azelate[M+FA-H]-247.119C10H18O4Fatty acids and conjugates
Steroids and derivatives18Juncuenin D[M+CH3OH+H]+315.158431165091C18H18O3Oxosteroids
Phenylpropanoids and polyketidesFlavonoids19Wogonoside[M-H]-459.094C22H20O11Flavonoid glycosides
20Tectochrysin[M+H]+269.080C16H12O4O-methylated flavonoids
21Homoplantaginin[M+H]+463.123C22H22O11Flavonoid glycosides
22Herbacetin[M+ACN+H]+344.075C15H10O7Flavones
Tannins23Heteroclitin G[M+H-H2O]+, [M+H]+401.159C22H24O7Hydrolyzable tannins
24Schisandrin B[M+H]+, [M+Na]+, [M+NH4]+401.195C23H28O6Hydrolyzable tannins
25Angeloylgomisin H[M+H-H2O]+, [M+NH4]+, [M+K]+, [M+Na]+, [M+H]+483.237C28H36O8Hydrolyzable tannins
Isoflavonoids26Dehydroglyasperin C[M+CH3OH+H]+387.179C21H22O5O-methylated isoflavonoids
27calycosin[M+H]+285.075C16H12O5O-methylated isoflavonoids
Cinnamic acids and derivatives28Xanthohumol L[M+CH3OH+H]+403.174C21H22O6Hydroxycinnamic acids and derivatives
Diarylheptanoids29Isotriuvaretin[M+H-2H2O]+555.220C37H34O7Linear diarylheptanoids
Coumarins and derivatives307-Amino-4-methylcoumarin[M+Cl]-268.038C12H11NO4-
Organoheterocyclic compoundsPyridines31Nicotinic acid[M+FA-H]-168.030C6H5NO2Pyridinecarboxylic acids
Pyrrolizidines32Procerine[M+NH4]+289.175C13H21NO5-
Pyrrolizines33Crosemperine[M+Cl]-403.177C19H30NO6+-
Benzopyrans34Eupatoriochromene[M+H]+219.101C13H14O31-benzopyrans
Carboxylic acids and derivatives358alpha-Tigloyloxyhirsutinolide 13-O-acetate[M+H-2H2O]+385.164C22H28O8Tricarboxylic acids and derivatives
362,6-Diaminoheptanedioic acid[M-H]-189.088C7H14N2O4Amino acids, peptides, and analogues
37L-Homocystine[M+H]+269.065C8H16N2O4S2Amino acids, peptides, and analogues
Organic oxygen compoundsOrganooxygen compounds38Simmondsin[M+ACN+H]+417.190C16H25NO9Carbohydrates and carbohydrate conjugates
OthersOthers39Schisantherin A[M+NH4]+554.238C30H32O9-
40Homoharringtonine[M+CH3OH+H]+578.295C29H39NO9-
41Lentinan[M-2H]-575.185C42H72O36-
42Licoagrochalcone B[M+CH3OH+H]+369.169C21H20O4-

4.3 HQSMY improved gut microbiota in AIC mice

4.3.1 HQSMY altered intestinal microbe diversity and microbial health index in AIC mice

We performed diversity testing from different dimensions, including α-diversity and β-diversity analysis. α-Diversity showed that compared to the Control group, the Model group exhibited reduced Shannon and Chao indices. Following intervention, the Shannon and Chao indices increased in both the HQSMY and Dex groups (Figures 3A,B). Figure 3C illustrates the reasonableness of sequencing data volume via a dilution curve; Figure 3D displays species richness at the operational taxonomic unit level across the four groups, with the HQSMY group's richness closest to the Control group. β-Diversity analysis revealed that the Control group and both intervention groups are distant from the Model group, while the HQSMY and Dex groups are closer (Figure 3E). Compared to the Model group, the HQSMY group exhibited a higher microbial health index (Figure 3F) and a lower dysbiosis index (Figure 3G).

Diversity analysis and microbial community characterization indices. **(A)** α-Diversity analysis: Shannon index reflecting both species richness and evenness **(B)** α-Diversity analysis: Chao index, reflecting species richness (estimated total number of species) **(C)** Dilution curve analysis **(D)** Inter-group difference test (Kruskal–Wallis H-rank test) **(E)** PcoA analysis **(F)** Microbial health index **(G)** Microbial dysbiosis index.
Figure 3. Diversity analysis and microbial community characterization indices. (A) α-Diversity analysis: Shannon index reflecting both species richness and evenness (B) α-Diversity analysis: Chao index, reflecting species richness (estimated total number of species) (C) Dilution curve analysis (D) Inter-group difference test (Kruskal–Wallis H-rank test) (E) PcoA analysis (F) Microbial health index (G) Microbial dysbiosis index.

4.3.2 HQSMY adjusted the structure of gut microbes and increased the abundance of beneficial bacteria

A total of 404 operational taxonomic units were obtained from the four sample groups, of which 333 were shared among all groups (Figure 4A). The gut microbiota composition of each group was ranked in descending order of abundance at the phylum and genus levels, as shown in Figures 4B,C.

Community structure and species-level differential analysis. **(A)** OTU sample network diagram between groups. **(B)** Phylum-level bacterial composition. **(C)** Genus-level bacterial composition. **(D)** Multiple group difference test. **(E)** Lefse multistage species difference discriminant analysis. **(F)** norank_f_Muribaculaceae. **(G)** norank_o_Clostridia_UCG-014. **(H)** Candidatus Arthromitus. **(I)** Lachnospiraceae_FCS020_group. **(J)** UCG_005. **(K)** NK4A214_group. **(L)** Anaeroplasma.
Figure 4. Community structure and species-level differential analysis. (A) OTU sample network diagram between groups. (B) Phylum-level bacterial composition. (C) Genus-level bacterial composition. (D) Multiple group difference test. (E) Lefse multistage species difference discriminant analysis. (F) norank_f_Muribaculaceae. (G) norank_o_Clostridia_UCG-014. (H) Candidatus Arthromitus. (I) Lachnospiraceae_FCS020_group. (J) UCG_005. (K) NK4A214_group. (L) Anaeroplasma.

At the phylum level, compared to the Model group, HQSMY increased the relative abundance of Bacteroidota and Verrucomicrobiota (Figure 4B). At the genus level, HQSMY increased the relative abundance of norank_f_Muribaculaceae (a genus-level unclassified group within the family Muribaculaceae) and decreased Lachnospiraceae_NK4A136_group, Lactobacillus, and Ligilactobacillus (Figure 4C).

Figures 4D,E present the relative abundance and intergroup differences of microbial taxa via stacked bar charts and LEfSe multi-level species differential analysis, respectively. Compared with the Model group, the HQSMY group exhibited increased abundance of norank_f_Muribaculaceae (family Muribaculaceae, reported at the genus level), norank_o_Clostridia_UCG-014 (order level), Lachnospiraceae_FCS020_group (genus), UCG_005 (genus), NK4A214_group (genus), and Anaeroplasma (genus), while showing decreased abundance of Candidatus_Arthromitus (genus). These microbial taxa may represent the core bacterial species involved in the therapeutic effects of HQSMY treatment for AIC. Figures 4F–L present box plots illustrating differences in these taxa across groups.

4.3.3 HQSMY restored the amino acid and energy metabolism activities in model group to control group

The PICRUSt2 functional prediction enables identification of microbial community functions, including metabolic pathways, biochemical pathways, and orthologous groups involved in disease processes. Figure 5A shows the functional classification of protein sequences in the HQSMY sample. The results indicated that expression levels were relatively high in categories such as amino acid transport and metabolism, carbohydrate transport and metabolism, translation, ribosomal structure and biogenesis, and transcription. In multiple pathways, HQSMY mitigated metabolic pathways activated in the Model group, restoring them to Control group levels. Figure 5B shows a heatmap of KEGG pathway level 2, where HQSMY affected pathways including carbohydrate metabolism, amino acid metabolism, and energy metabolism. Combined with deeper (Level 3) KEGG information, purine metabolism, amino sugar and nucleotide sugar metabolism, quorum sensing, and glycolysis/gluconeogenesis pathways may be involved in energy metabolism and intestinal barrier function (Figure 5C). In the MetaCyc pathways, HQSMY also downregulated energy metabolic pathways in gut microbes, such as glycolysis III, as well as the pentose phosphate pathway. Additionally, HQSMY exerted inhibitory effects on nucleotide metabolism, amino acid metabolism, and lipid metabolism of the microbial community, which helped to better maintain gut homeostasis, such as adenosine deoxyribonucleotides de novo biosynthesis II, L-isoleucine biosynthesis I, and CDP-diacylglycerol biosynthesis I (Figure 5D).

PICRUSt2 function prediction analysis. **(A)** The function classification of protein sequences in the HQSMY sample. **(B)** Heatmap of KEGG pathway level 2. **(C)** Heatmap of KEGG pathway level 3 (20–22). **(D)** Heatmap of MetaCyc pathways.
Figure 5. PICRUSt2 function prediction analysis. (A) The function classification of protein sequences in the HQSMY sample. (B) Heatmap of KEGG pathway level 2. (C) Heatmap of KEGG pathway level 3 (20–22). (D) Heatmap of MetaCyc pathways.

4.4 Prediction of HQSMY's mechanism for treating AIC based on network pharmacology analysis

4.4.1 Target screening and enrichment analysis

Based on the aforementioned 42 bioactive compounds, a “herb-compound-target” network was constructed (Figure 6A). Using breast cancer, AIC, and drug targets as queries, 121 intersecting targets were identified (Figure 6B). Figure 6C shows the protein-protein interaction network, containing 121 nodes and 2,440 edges. Core targets included AKT1, HIF1A, ESR1, IL-6, BCL2, JUN, HSP90AA1, STAT3, and CCND1 (Figure 6D).

Target screening and enrichment analysis. **(A)** 42 blood active ingredients and the “herb-compound-target” network of targets. **(B)** Venn diagram. **(C)** PPI diagram. **(D)** Network diagram of 9 key targets. **(E)** GO analysis. **(F)** KEGG analysis (20–22). **(G)** A Sankey diagram illustrating the core genes and active components associated with the HIF-1 and JAK-STAT signaling pathways.
Figure 6. Target screening and enrichment analysis. (A) 42 blood active ingredients and the “herb-compound-target” network of targets. (B) Venn diagram. (C) PPI diagram. (D) Network diagram of 9 key targets. (E) GO analysis. (F) KEGG analysis (20–22). (G) A Sankey diagram illustrating the core genes and active components associated with the HIF-1 and JAK-STAT signaling pathways.

Functional enrichment analysis was performed on these 9 key target genes. Figure 6E illustrates the GO enrichment analysis, including Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). GO-BP analysis indicated that HQSMY may affect energy metabolism through multiple biological processes, including positive regulation of cell population proliferation, response to oxygen-containing compounds, regulation of phosphorylation, response to reactive oxygen species, and regulation of pri-miRNA transcription by RNA polymerase II. GO-MF analysis revealed that HQSMY can regulate molecular functions such as nitric-oxide synthase regulator activity, protein phosphatase binding, ubiquitin protein ligase binding, and RNA polymerase II-specific DNA-binding transcription factor binding, which might indirectly influence gut microbiota. Additionally, GO-CC analysis suggested regulation of the cell cycle and inflammatory response through cellular components including the cyclin D1-CDK4 complex, BAD-BCL-2 complex, and interleukin-6 receptor complex. Figure 6F displays the top 20 enriched KEGG pathways, including lipid and atherosclerosis, Th17 cell differentiation, HIF-1 signaling pathway, and JAK-STAT signaling pathway. After an extensive review of the literature and considering the functional enrichment analysis (23–26), we predicted the HIF-1 and JAK-STAT signaling pathways as key pathways for HQSMY treatment of AIC. A Sankey diagram was generated using core genes and active components corresponding to the HIF-1 and JAK-STAT signaling pathways (Figure 6G).

The core targets predicted through the dendrogram were AKT1, HIF1A, IL-6, BCL2, STAT3, and CCND1. The active ingredients were isotriuvaretin, chaetopenoid A, methyl 6-hydroxyangolensate, 8alpha-tigloyloxyhirsutinolide 13-O-acetate, herbacetin, dihydrotanshinone I, simmondsin, eupatoriochromene, dehydroglyasperin C, angeloylgomisin H, notopterol, schisantherin A, caleurticolide 2-methylacrylate, and wedeliatrilolactone A. Subsequently, we performed molecular docking to validate target-component binding affinity and refined selections based on binding energy.

4.4.2 Validation of the JAK-STAT and HIF-1 signaling pathways

Molecular docking was performed between the six selected core targets and their corresponding active components, and all binding energies were ≤ −5.0 kcal/mol. Basic information on the molecular docking between HQSMY's main components and target proteins is presented in Table 2, where the key interactions included hydrogen bonds. The core components were dihydrotanshinone I, isotriuvaretin, dehydroglyasperin C, schisantherin A, methyl 6-hydroxyangolensate, and notopterol. The docking poses are shown in Figure 7A. Western blot analysis was performed to detect key proteins in the JAK-STAT and HIF-1 pathways. HQSMY treatment downregulated the expression of JAK1 and HIF-1α compared to the Model group. STAT3 expression was elevated in the Model group compared to Control, but the decrease after HQSMY treatment did not reach statistical significance.

Table 2. Basic information on principal component and target protein molecular docking.

TargetsMolecular nameSitesBinding energy (kcal/mol)
STAT3Dihydrotanshinone IASP-369, HIS-437, LEU-438, THR-440−8.8
BCL2IsotriuvaretinVAL-133−8.4
HIF1ADehydroglyasperin CASP-256, GLU-257, HIS−378−7.4
CCND1Schisantherin AARG-26, LYS-33−7.3
AKT1Methyl 6-hydroxyangolensateLYS-14, GLY-16, LYS-17, ARG−25−6.8
IL6NotopterolARG-169−5.4
Molecular docking of HQSMY active components with target proteins and experimental validation of the JAK-STAT and HIF-1 pathways in cardiac tissue. **(A)** Molecular docking patterns of six active components with their corresponding targets. The 3D binding modes are shown as global views (left) and local amplified views (right) for each pair (from left to right, top to bottom): STAT3–dihydrotanshinone I, BCL2–isotriuvaretin, HIF1A–dehydroglyasperin C, CCND1–schisantherin A, AKT1–methyl 6-hydroxyangolensate, and IL6–notopterol. **(B)** Representative Western blot images of JAK1, STAT3, HIF-1*α*, and *β*-actin. This experiment was repeated independently three times with similar results. **(C–E)** Quantitative analysis of HIF-1*α*, JAK1, and STAT3 protein expression normalized to β-actin. Data are presented as individual values with mean ± SD (n = 3 independent experiments). ***P* < 0.01 vs. the Control group; <sup>##</sup>*P* < 0.01 vs. the Model group (one-way ANOVA followed by Tukey's *post-hoc* test).
Figure 7. Molecular docking of HQSMY active components with target proteins and experimental validation of the JAK-STAT and HIF-1 pathways in cardiac tissue. (A) Molecular docking patterns of six active components with their corresponding targets. The 3D binding modes are shown as global views (left) and local amplified views (right) for each pair (from left to right, top to bottom): STAT3–dihydrotanshinone I, BCL2–isotriuvaretin, HIF1A–dehydroglyasperin C, CCND1–schisantherin A, AKT1–methyl 6-hydroxyangolensate, and IL6–notopterol. (B) Representative Western blot images of JAK1, STAT3, HIF-1α, and β-actin. This experiment was repeated independently three times with similar results. (C–E) Quantitative analysis of HIF-1α, JAK1, and STAT3 protein expression normalized to β-actin. Data are presented as individual values with mean ± SD (n = 3 independent experiments). **P < 0.01 vs. the Control group; ##P < 0.01 vs. the Model group (one-way ANOVA followed by Tukey's post-hoc test).

5 Discussion

AIC is triggered by multiple complex mechanisms, including oxidative stress, inflammation, autophagy, apoptosis, and energy metabolism disorder (27). Among these, inflammation is considered a fundamental cause of AIC progression. Excessive and prolonged inflammation leads to tissue destruction and progressive fibrosis, impairing cardiac function (28, 29). According to TCM theory, “The turbid qi returns to the heart, and the excessive essence pervades the blood vessels.” This concept is consistent with the fact that anthracyclines not only affect the heart but also damage the intestinal barrier, promoting the translocation of endotoxins and microbes into systemic circulation, thereby causing systemic inflammation and cardiovascular complications (30). Notably, a study showed that fecal microbiota transplantation from Dox-treated mice to germ-free mice induced cardiotoxicity, highlighting the critical role of gut microbiota dysbiosis in AIC (31).

Based on the association between gut microbiota and cardiovascular health (32), protecting the gut microbiota may represent a promising strategy to mitigate cardiovascular complications from chemotherapy. The bioactive components of HQSMY are absorbed into the bloodstream via the intestinal barrier following oral administration. This study demonstrated that HQSMY restored α-diversity, exhibited a higher microbial health index, and a lower dysbiosis index. Combined with principal coordinate analysis, these findings suggested that the beneficial effect of HQSMY on intestinal flora was equivalent to or superior to that of Dex. Members of the phylum Bacteroidota (formerly Bacteroidetes) are key contributors to intestinal homeostasis (33). At the phylum level, HQSMY increased the abundance of Bacteroidota and restored the abundance of Verrucomicrobiota while decreasing that of Bacillota. At the genus level, HQSMY increased the abundance of norank_f_Muribaculaceae and decreased Lachnospiraceae_NK4A136_group, Lactobacillus, and Ligilactobacillus. Taxonomic differential analysis revealed that HQSMY increased the abundance of norank_f_Muribaculaceae, norank_o_Clostridia_UCG-014, Lachnospiraceae_FCS020_group, UCG_005, NK4A214_group, and Anaeroplasma, while decreasing that of Candidatus_Arthromitus. These microbial taxa may represent the core bacterial groups involved in the therapeutic effects of HQSMY for AIC.

Members of the family Muribaculaceae have a strong capacity to metabolize endogenous (mucin glycans) and exogenous (dietary fibre) polysaccharides, can produce SCFAs, and have cross-feeding relationships with Bifidobacterium and Lactobacillus (34). An increased abundance of Muribaculaceae modulates lipid metabolism and improves cardiomyocyte survival rates (35). Functional prediction analysis indicated that HQSMY restored multiple intestinal microbial metabolic pathways to normal levels, focusing on energy metabolism and the synthesis and transport of amino acids and fatty acids. This may be linked to the production of acetate, propionate, and butyrate (36). Notably, SCFAs can suppress lipopolysaccharide-induced inflammation by inhibiting inducible nitric oxide synthase activity (37).

The JAK-STAT pathway is recognized as a central signaling hub in cellular communication. In cardiovascular diseases, inhibiting this pathway can alleviate inflammation and myocardial fibrosis (24, 38). Butyrate enhances H3K27ac enrichment around the SOCS1 gene promoter region, thereby increasing SOCS1 protein expression and ultimately suppressing JAK-STAT signaling activation (39). HQSMY normalized energy metabolic pathways in gut microbes, such as glycolysis III and the pentose phosphate pathway. Increased microbial oxygen consumption and inflammation caused by dysbiosis jointly lead to local intestinal hypoxia and disrupt the integrity of the intestinal barrier (40). Furthermore, IL-6 activates the JAK-STAT signaling pathway, particularly STAT3, playing a crucial role in Th17 cell differentiation and inflammatory responses in the myocardium (41). Systemic inflammation driven by gut dysbiosis may exacerbate this local inflammatory cascade in cardiac tissue (42, 43).We hypothesize that anthracycline-induced gut dysbiosis may increase intestinal permeability and facilitate LPS translocation, thereby contributing to myocardial inflammatory responses and HIF-1α activation. HQSMY restored multiple microbial metabolic pathways, including glycolysis and amino acid metabolism, which may be associated with improved intestinal barrier integrity and reduced systemic inflammatory burden. Consequently, the decreased inflammatory milieu in the myocardium may contribute to the attenuation of HIF-1α stabilization observed in cardiac tissue (Figure 7). However, direct evidence connecting specific microbial taxa or metabolites to cardiac HIF-1α regulation was not established in the present study.

In conclusion, the components of HQSMY may exert anti-inflammatory effects in myocardial tissue, while concomitantly altering gut microbiota composition. These changes may be associated with increased SCFA production, thereby contributing to intestinal barrier protection and attenuation of LPS-induced inflammation. It should be noted that compound identification was based on database matching of MS/MS spectra and retention times; confirmation with reference standards was not performed. Although these findings suggest a potential association between gut microbiota modulation and cardiac protection, a direct causal relationship was not established. The relatively small sample size may compromise the robustness of the findings. Future studies will prioritize GC-MS-based SCFA quantification and multi-omics integration to further elucidate the mechanisms of HQSMY in AIC. It should be noted that histological assessment was performed on residual cardiac tissue sections; unbiased whole-section evaluation was not feasible due to sample availability. Direct measurement of SCFA concentrations was not performed; the association between HQSMY and SCFA production was inferred from changes in the abundance of SCFA-producing bacterial taxa.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2026.1818551/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Evgenia Gerasimovskaya, University of Colorado Denver, United States

Reviewed by: Boyu Pan, Tianjin Medical University Cancer Institute and Hospital, China

Kai Zhang, Shanghai Jiao Tong University, China

AbbreviationsAIC, anthracycline-induced cardiotoxicity; Dex, dexrazoxane; Dox, doxorubicin; GO, Gene Ontology; H&E, hematoxylin and eosin; HIF-1, hypoxia-inducible factor 1; HQSMY, Huangqishengmaiyin; KEGG, Kyoto Encyclopedia of Genes and Genomes; SCFA, short-chain fatty acid; SMY, Sheng-Mai Yin; TCM, traditional Chinese medicine.

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by National Natural Science Foundation of China (NO. 82205132). Youth Science Research Project of Shanxi Basic Research Program (NO. 202203021212065). Cancer Hospital of Chinese Academy of Medical Sciences, Shanxi Hospital, Doctoral Talents Foundation (NO. QH2023006). Shanxi Province Traditional Chinese Medicine Research (NO. 2024ZYY2D025). Shanxi Cancer Hospital Doctoral Research Foundation (NO. Dr202312). National Undergraduate Training Program for Innovation (202610114042).

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Ethics approval

The study was reported in accordance with ARRIVE guidelines. Animal experiments were conducted in accordance with protocols approved by the Institutional Animal Care and Use Committee at Shanxi Provincial Cancer Hospital (IACUC 2024021).

Author contributions

YW: Software, Validation, Visualization, Writing – original draft. YJ: Validation, Writing – original draft. JH: Software, Writing – original draft. MD: Validation, Writing – original draft. HH: Software, Writing – original draft. MM: Software, Writing – original draft. RY: Funding acquisition, Project administration, Writing – review & editing.

References

  1. 1. Sung H, Filho AM, Laversanne M, Ferlay J, Siegel RL, Soerjomataram I. Global cancer statistics 2024: gLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries. CA Cancer J Clin. (2026) 76 (4): e70090. doi:10.3322/caac.70090
  2. 2. Lyon AR, López-Fernández T, Couch LS, Asteggiano R, Aznar MC, Bergler-Klein J. 2022 ESC guidelines on cardio-oncology developed in collaboration with the European hematology association (EHA), the European society for therapeutic radiology and oncology (ESTRO) and the international cardio-oncology society (IC-OS). Eur Heart J. (2022) 43 (41): 4229 – 361. doi:10.1093/eurheartj/ehac244
  3. 3. Fabiani I, Chianca M, Cipolla CM, Cardinale DM. Anthracycline-induced cardiomyopathy: risk prediction, prevention and treatment. Nat Rev Cardiol. (2025) 22 (8): 551 – 63. doi:10.1038/s41569-025-01126-1
  4. 4. Upshaw JN, Parson SK, Buchsbaum RJ, Schlam I, Ruddy KJ, Durani U. Dexrazoxane to prevent cardiotoxicity in adults treated with anthracyclines: JACC: cardioOncology controversies in cardio-oncology. JACC CardioOncol. (2024) 6 (2): 322 – 4. doi:10.1016/j.jaccao.2024.02.004
  5. 5. Hao W, Shi YY, Qin YN, Sun CP, Chen LY, Wu CY. Cardioprotective effect of Chinese herbal medicine for anthracycline-induced cardiotoxicity in cancer patients: a meta-analysis of prospective studies. Medicine (Baltimore). (2022) 101 (30): e29691. doi:10.1097/MD.0000000000029691
  6. 6. Zheng Y, Tian C, Fan C, Xu N, Xiao J, Zhao X. Sheng-Mai Yin exerts anti-inflammatory effects on RAW 264.7 cells and zebrafish. J Ethnopharmacol. (2021) 267: 113497. doi:10.1016/j.jep.2020.113497
  7. 7. Yang L, Liu X, Yang W, Wang S, Li Z, Lei Y. Effect of Shenmai injection on anthracycline-induced cardiotoxicity: a systematic review and meta-analysis. Complement Ther Med. (2024) 83: 103053. doi:10.1016/j.ctim.2024.103053
  8. 8. Ren C, Zhao X, Liu K, Wang L, Chen Q, Jiang H. Research progress of natural medicine Astragalus mongholicus bunge in treatment of myocardial fibrosis. J Ethnopharmacol. (2023) 305: 116128. doi:10.1016/j.jep.2022.116128
  9. 9. Zhang X, Qu H, Yang T, Liu Q, Zhou H. Astragaloside IV attenuate MI-induced myocardial fibrosis and cardiac remodeling by inhibiting ROS/caspase-1/GSDMD signaling pathway. Cell Cycle. (2022) 21 (21): 2309 – 22. doi:10.1080/15384101.2022.2093598
  10. 10. Lei Z, Luan F, Zou J, Zhang X, Zhai B, Xin B. Traditional uses, phytochemical constituents, pharmacological properties, and quality control of Pseudostellaria heterophylla (Miq.) Pax. J Ethnopharmacol. (2025) 337 (Pt 2): 118871. doi:10.1016/j.jep.2024.118871
  11. 11. Chen Y, Ma L, Yan Y, Wang X, Cao L, Li Y. Ophiopogon japonicus polysaccharide reduces doxorubicin-induced myocardial ferroptosis injury by activating Nrf2/GPX4 signaling and alleviating iron accumulation. Mol Med Rep. (2025) 31 (2): 36. doi:10.3892/mmr.2024.13401
  12. 12. Li L, Pan Q, Han W, Liu Z, Li L, Hu X. Schisandrin B prevents doxorubicin-induced cardiotoxicity via enhancing glutathione redox cycling. Clin Cancer Res. (2007) 13 (22 Pt 1): 6753 – 60. doi:10.1158/1078-0432.CCR-07-1579
  13. 13. Chen X, Chen C, Fu X. Hypoglycemic effect of the polysaccharides from Astragalus membranaceus on type 2 diabetic mice based on the “gut microbiota-mucosal barrier”. Food Funct. (2022) 13 (19): 10121 – 33. doi:10.1039/d2fo02300h
  14. 14. Kamo T, Akazawa H, Suzuki JI, Komuro I. Novel concept of a heart-gut axis in the pathophysiology of heart failure. Korean Circ J. (2017) 47 (5): 663 – 9. doi:10.4070/kcj.2017.0028
  15. 15. Reagan-Shaw S, Nihal M, Ahmad N. Dose translation from animal to human studies revisited. FASEB J. (2008) 22 (3): 659 – 61. doi:10.1096/fj.07-9574LSF
  16. 16. Al-Khayri JM, Sahana GR, Nagella P, Joseph BV, Alessa FM, MQ A-M. Flavonoids as potential anti-inflammatory molecules: a review. Molecules. (2022) 27 (9): 2901. doi:10.3390/molecules27092901
  17. 17. Jing W, Xiaolan C, Yu C, Feng Q, Haifeng Y. Pharmacological effects and mechanisms of tannic acid. Biomed Pharmacother. (2022) 154: 113561. doi:10.1016/j.biopha.2022.113561
  18. 18. Danciu C, Avram S, Pavel IZ, Ghiulai R, Dehelean CA, Ersilia A. Main isoflavones found in dietary sources as natural anti-inflammatory agents. Curr Drug Targets. (2018) 19 (7): 841 – 53. doi:10.2174/1389450118666171109150731
  19. 19. Saadati F, M Chahardehi A, Jamshidi N, Jamshidi N, Ghasemi D Coumarin: a natural solution for alleviating inflammatory disorders. Curr Res Pharmacol Drug Discov. 2024; 7: 100202. doi:10.1016/j.crphar.2024.100202
  20. 20. Kanehisa M, Furumichi M, Sato Y, Matsuura Y, Ishiguro-Watanabe M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. (2025) 53 (D1): D672 – 7. doi:10.1093/nar/gkae909
  21. 21. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. (2019) 28 (11): 1947 – 51. doi:10.1002/pro.3715
  22. 22. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. (2000) 28 (1): 27 – 30. doi:10.1093/nar/28.1.27
  23. 23. Fu X, Sun Z, Long Q, Tan W, Ding H, Liu X. Glycosides from Buyang Huanwu decoction inhibit atherosclerotic inflammation via JAK/STAT signaling pathway. Phytomedicine. (2022) 105: 154385. doi:10.1016/j.phymed.2022.154385
  24. 24. Pang Q, You L, Meng X, Li Y, Deng T, Li D. Regulation of the JAK/STAT signaling pathway: the promising targets for cardiovascular disease. Biochem Pharmacol. (2023 Jul) 213: 115587. doi:10.1016/j.bcp.2023.115587
  25. 25. Chen Z, Jiang P, Su D, Zhao Y, Zhang M. Therapeutic inhibition of the JAK-STAT pathway in the treatment of inflammatory bowel disease. Cytokine Growth Factor Rev. (2024) 79: 1 – 15. doi:10.1016/j.cytogfr.2024.07.008
  26. 26. Li C, Fu C, Zhou W, Li H, Liu Z, Wu G. Lactylation modification of HIF-1 α enhances its stability by blocking VHL recognition. Cell Commun Signal. (2025) 23 (1): 364. doi:10.1186/s12964-025-02366-x
  27. 27. Huang C, Li X, Li H, Chen R, Li Z, Li D. Role of gut microbiota in doxorubicin-induced cardiotoxicity: from pathogenesis to related interventions. J Transl Med. (2024) 22 (1): 433. doi:10.1186/s12967-024-05232-5
  28. 28. Hutchins E, Yang EH, Stein-Merlob AF. Inflammation in chemotherapy-induced cardiotoxicity. Curr Cardiol Rep. (2024) 26 (12): 1329 – 40. doi:10.1007/s11886-024-02131-5
  29. 29. Suthahar N, Meijers WC, Silljé HHW, De Boer RA. From inflammation to fibrosis-molecular and cellular mechanisms of myocardial tissue remodelling and perspectives on differential treatment opportunities. Curr Heart Fail Rep. (2017) 14 (4): 235 – 50. doi:10.1007/s11897-017-0343-y
  30. 30. Wei L, Wen XS, Xian CJ. Chemotherapy-induced intestinal microbiota dysbiosis impairs mucosal homeostasis by modulating toll-like receptor signaling pathways. Int J Mol Sci. (2021) 22 (17): 9474. doi:10.3390/ijms22179474
  31. 31. An L, Wuri J, Zheng Z, Li W, Yan T. Microbiota modulate doxorubicin induced cardiotoxicity. Eur J Pharm Sci. (2021) 166: 105977. doi:10.1016/j.ejps.2021.105977
  32. 32. Tonon CR, Pereira AG, Ferreira NF, Monte MG, Vieira NM, Fujimori ASS. The gut-heart axis and its role in doxorubicin-induced cardiotoxicity: a narrative review. Microorganisms. (2025) 13 (4): 855. doi:10.3390/microorganisms13040855
  33. 33. Gibiino G, Lopetuso LR, Scaldaferri F, Rizzatti G, Binda C, Gasbarrini A. Exploring bacteroidetes: metabolic key points and immunological tricks of our gut commensals. Dig Liver Dis. (2018) 50 (7): 635 – 9. doi:10.1016/j.dld.2018.03.016
  34. 34. Zhu Y, Chen B, Zhang X, Akbar MT, Wu T, Zhang Y. Exploration of the muribaculaceae family in the gut microbiota: diversity, metabolism, and function. Nutrients. (2024 Aug 12) 16 (16): 2660. doi:10.3390/nu16162660
  35. 35. Zhong X, Zhao Y, Huang L, Liu J, Wang K, Gao X. Remodeling of the gut microbiome by lactobacillus johnsonii alleviates the development of acute myocardial infarction. Front Microbiol. (2023) 14: 1140498. doi:10.3389/fmicb.2023.1140498
  36. 36. He J, Zhang P, Shen L, Niu L, Tan Y, Chen L. Short-chain fatty acids and their association with signalling pathways in inflammation, glucose and lipid metabolism. Int J Mol Sci. (2020) 21 (17): 6356. doi:10.3390/ijms21176356
  37. 37. Filippone A, Lanza M, Campolo M, Casili G, Paterniti I, Cuzzocrea S. The anti-inflammatory and antioxidant effects of sodium propionate. Int J Mol Sci. (2020) 21 (8): 3026. doi:10.3390/ijms21083026
  38. 38. Feng R, Liu H, Chen Y. Baricitinib represses the myocardial fibrosis via blocking JAK/STAT and TGF- β 1 pathways in vivo and in vitro. BMC Cardiovasc Disord. (2025) 25 (1): 65. doi:10.1186/s12872-025-04517-x
  39. 39. Shi Z, Li M, Zhang C, Li H, Zhang Y, Zhang L. Butyrate-producing Faecalibacterium prausnitzii suppresses natural killer/T-cell lymphoma by dampening the JAK-STAT pathway. Gut. (2025) 74 (4): 557 – 70. doi:10.1136/gutjnl-2024-333530
  40. 40. Jyoti Z, Dey P. Mechanisms and implications of the gut microbial modulation of intestinal metabolic processes. NPJ Metab Health Dis. (2025) 3 (1): 24. doi:10.1038/s44324-025-00066-1
  41. 41. Lv Y, Qi J, Babon JJ, Cao L, Fan G, Lang J. The JAK-STAT pathway: from structural biology to cytokine engineering. Signal Transduct Target Ther. (2024) 9 (1): 221. doi:10.1038/s41392-024-01934-w
  42. 42. Violi F, Castellani V, Menichelli D, Pignatelli P, Pastori D. Gut barrier dysfunction and endotoxemia in heart failure: a dangerous connubium? Am Heart J. (2023) 264: 40 – 8. doi:10.1016/j.ahj.2023.06.002
  43. 43. Abdulaal R, Afara I, Harajli A, Al Mashtoub E, Tarchichi A, Hassan K. Gut microbiome and chemotherapy-induced cardiotoxicity: a systematic review of evidence and emerging therapies. World J Biol Chem. (2025) 16 (4): 112221. doi:10.4331/wjbc.v16.i4.112221

Citation

Wang, Y., et al. (2026). Exploring the protective mechanism of Huangqishengmaiyin against anthracycline-induced cardiotoxicity in breast cancer with concomitant gut microbiota modulation. Frontiers in Cardiovascular Medicine, Volume 13. https://doi.org/
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13 - 2026

Copyright

© 2026 Wang, Jin, Han, Ding, Hou, Mei and Yang.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Rui Yang

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