Elucidating Pingxiao Capsules Anti-Breast Cancer Mechanism via Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations

Elucidating Pingxiao Capsules Anti-Breast Cancer Mechanism via Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elucidating Pingxiao Capsules Anti-Breast Cancer Mechanism via Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations Xiaoyi Hou, Li Zhang, Peng Zhang, Xian Zhang, Xu Zhang, Hong Liao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722434/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Breast cancer is a prevalent malignancy threatening women's health globally. Pingxiao Capsules, a traditional Chinese medicine, exhibit promising efficacy in adjuvant breast cancer treatment, yet their molecular mechanisms remain unclear. Through network pharmacology, WGCNA and machine learning, drug targets related to the treatment of breast cancer with Pingxiao Capsules were screened, and the related functional effects of drug targets were explored through PPI, GO, KEGG enrichment analysis and GSEA. Molecular docking and dynamics simulations were conducted for verification. We identified 2,281 breast cancer-related genes and 256 potential targets of Pingxiao Capsules, with 53 overlapping targets. Functional enrichment revealed involvement in cell cycle, p53 signaling, tyrosine metabolism, and calcium signaling pathways. Machine learning pinpointed MAOA and ADH1C as core targets, validated by molecular docking with Stigmasterol, a key compound in Pingxiao Capsules. Subsequent molecular dynamics simulations confirmed stable binding conformations with low free energies, supporting these protein-ligand interactions as therapeutically relevant. GSEA highlighted pathway dynamics during breast cancer progression. Pingxiao Capsules may exert anti-tumor effects by modulating MAOA, ADH1C, and associated pathways (e.g., cell cycle, p53), offering novel insights into their molecular mechanisms. breast cancer Pingxiao capsules transcriptomics network pharmacology MAOA ADH1C Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Breast cancer describes a range of malignancies occurring in the mammary glands, which is the most common global malignancy and the leading cause of cancer deaths in females[ 1 ]. Breast cancer is a complex and heterogeneous disease in which multiple genetic, signal pathways and factors are involved and the exact mechanism is still unclear[ 2 ]. During the past recent years, various therapies have emerged in the era of breast cancer. Although serial screening detected during early stage of disease can decrease mortality, the prognosis of patients in the advanced stage remains poor[ 3 ]. Therefore, new strategies are urgently needed for the treatment of breast cancer. Traditional Chinese Medicine (TCM) has been employed in breast cancer treatment with a long-standing history, and herbal medicines have demonstrated significant advantages in the therapeutic management of breast cancer. Guided by traditional Chinese medical theory, TCM treatment demonstrates therapeutic advantages through its holistic approach, multi-target effects, and favorable safety profile[ 4 ]. Pingxiao Capsule (PXC), a standardized multi-herb formulation widely utilized in clinical oncology, integrates components such as Curcumae Rhizoma (Ezhu), Agrimoniae Herba (Xianhecao), and Trogopterus Dung (Wulingzhi) under TCM compatibility principles. As a first-line adjuvant therapy for breast cancer, it demonstrates measurable efficacy in tumor size reduction, symptom alleviation, and chemotherapy tolerance enhancement, supported by multicenter clinical trials[ 5 ]. Current pharmacological studies suggest its effects involve angiogenesis inhibition and apoptosis induction, yet its system-level mechanisms—particularly multi-target interactions and tumor microenvironment modulation—require further elucidation through network pharmacology and omics-based approaches. Transcriptomics technologies systematically identify biomarkers modulated by herbal formulations and pinpoint critical therapeutic targets by deciphering disease-specific molecular signatures and dynamic pharmacological responses[ 6 ]. Network pharmacology quantifies the polypharmacological synergy of multi-component formulations through multidimensional "herb-compound-target-pathway" network modeling, unveiling topologically defined mechanisms by which core functional modules exert pan-scale disease intervention[ 7 ]. Their integration innovatively bridges molecular evidence chains with systems-level regulatory logic: validated network modules not only elucidate the multi-target essence of herbal actions but also establish a theoretical framework for systems biology-driven formulation optimization and precision medicine applications (e.g., molecular subtype-guided personalized therapy in breast cancer). This study integrates transcriptomics and network pharmacology to systematically elucidate the molecular mechanisms underlying PXC's therapeutic effects against breast cancer. By employing differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify critical disease pathways, followed by compound-target network comparison to determine core active components and therapeutic targets, key interactions are further validated through molecular docking. The findings will clarify the multi-target mechanisms of PXC, guide personalized precision treatment, and provide theoretical foundations for biomarker screening, novel therapeutic target discovery, and lead compound optimization. 2 Materials and Methods 2.1 Transcriptome data download and preprocessing Breast cancer-related transcriptome datasets GSE10810 and GSE26910 were downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geoprofiles/ ). The GSE10810 dataset, based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), was used as the training set and contained transcriptome data from 27 normal breast samples and 31 breast cancer samples. The GSE26910 dataset, used as the validation set, included transcriptome data from 6 normal breast samples and 6 breast cancer samples. These datasets were preprocessed, including background correction, gene name conversion, and data normalization. 2.2 Screening of active compounds in Pingxiao capsules and construction of drug-target network This study obtained chemical component information of the eight herbs in Pingxiao Capsule from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://lsp.nwu.edu.cn/ ) and Herb database (HERB, http://herb.ac.cn/ ). Pingxiao Capsule consists of eight traditional Chinese herbs: Curcuma (Yujin), Agrimonia pilosa (Xianhecao), Faeces Trogopterori (Wulingzhi), Alumen (Baifan), Nitrum (Xiaoshi), processed Lacquer (Ganqi), wheat-processed Fructus Aurantii (Fuchao Zhiqiao), and Semen Strychni powder (Maqianzi fen). Active compounds were screened based on absorption, distribution, metabolism, and excretion (ADME) related parameters, with specific screening criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. Compounds meeting these screening criteria were defined as potential active compounds of Pingxiao capsules. Target prediction for the screened active compounds was then performed using the TCMSP and SwissTargetPrediction ( http://www.swisstargetprediction.ch/ ) platforms, and the predicted targets were mapped to the corresponding standardized gene names. Standardization of target genes was completed through the UniProt database ( http://www.uniprot.org/ ), retaining only human (Homo sapiens) derived target genes and removing gene entries with non-compliant species sources to ensure data accuracy and consistency. Finally, based on the screened active compounds and their corresponding target genes, a drug-compound-target network was constructed. 2.3 Identification of breast cancer-related targets Based on the downloaded training dataset, differential analysis was performed using the limma package, with |fold change| >1.5 and P value < 0.05 as thresholds, to screen for differentially expressed genes (DEGs) between BC and NC groups. Subsequently, weighted gene co-expression network analysis (WGCNA) was applied to further identify co-expression gene modules and hub genes closely related to the disease. Specifically, the gene expression data from the training dataset was inputted into the WGCNA framework. The "pickSoftThreshold" function was used to calculate the scale-free network fit index and determine the optimal soft threshold power (β value). This soft threshold was used to construct the adjacency matrix and convert it into a topological overlap matrix (TOM) to measure the co-expression similarity between genes. Based on the dissimilarity matrix of TOM, hierarchical clustering analysis and dynamic tree cut algorithm were used for module division, with parameters set to a minimum module gene number of 30, deep split of 3, and maximum module distance of 0.25. Subsequently, the module eigengenes (MEs) of each module were calculated, and the correlation between MEs and clinical features (disease group and normal group) was evaluated by Pearson correlation analysis. To screen for key targets in the significant modules, gene significance (GS) was defined as the absolute value of the correlation between individual genes and clinical traits, and module membership (MM) was defined as the correlation between gene expression profiles and corresponding module eigengenes. The screening criteria were |GS|>0.6 and |MM|>0.6, and hub genes were extracted from the significant modules. Finally, the intersection analysis of all genes in the significantly related modules was performed by the DEGs and WGCNA screening, and the targets closely related to breast cancer were identified. 2.4 Identification of breast cancer improvement targets of Pingxiao capsules and network construction To identify potential therapeutic targets of Pingxiao capsules for improving breast cancer, the previously screened breast cancer target gene set was intersected with the drug targets of Pingxiao capsules, yielding common targets (potential therapeutic targets). Based on these common targets, combined with the active compound and target data screened from Pingxiao capsules, a "active compound-therapeutic target" network was constructed using Cytoscape software to visually display the drug efficacy material basis and key therapeutic targets of Pingxiao capsules, providing theoretical support for subsequent mechanistic studies. 2.5 Functional enrichment analysis and PPI network analysis To explore the specific molecular mechanisms involved in the treatment of breast cancer by Pingxiao capsules, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed on the common targets (potential therapeutic targets). Functional enrichment analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses, was conducted using the DAVID database ( https://david.ncifcrf.gov ). GO annotations included three categories: biological processes (BPs), cellular components (CCs), and molecular functions (MFs). By calculating the enrichment degree of key targets in each KEGG pathway or GO term and performing Fisher's exact test for statistical significance, significantly enriched KEGG pathways and GO terms (P < 0.05) were identified, revealing the main biological functions and signaling pathways involved in the key drug targets related genes in childhood breast cancer. In addition, to compare the expression differences of pathways in the KEGG enrichment analysis results between the normal control (NC) and disease (BC) groups, gene set enrichment analysis (GSEA) was performed using the R software clusterProfiler package. Using the differential expression analysis results from the training set, GSEA analysis was conducted on the significant pathways from the KEGG enrichment analysis. By comparing the normalized enrichment score (NES) and significance P value of these pathways in the NC and BC groups, the dynamic changes of signaling pathways related to Pingxiao capsules' therapeutic targets during the occurrence and development of breast cancer were revealed. In the construction of the PPI network, interaction data between key target genes was obtained using the STRING database ( https://cn.string-db.org/ ). PPI network construction parameters were set with species origin as human (Homo sapiens) and confidence threshold selected as 0.4. The obtained interaction data was visualized using Cytoscape software, and the topological structure of the network was analyzed to assess the interaction relationships and potential regulatory mechanisms between target genes. 2.6 Screening of key therapeutic targets based on machine learning algorithms To screen for key therapeutic targets of Pingxiao capsules in the treatment of breast cancer, three classic machine learning algorithms were applied based on the transcriptome data: Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). These three methods were used to rank the feature importance and screen the 53 common targets, aiming to improve the robustness and reliability of feature selection. First, the "randomForest" software in R was used to perform feature importance analysis on the target genes using the random forest algorithm. Next, the SVM-RFE algorithm was used for recursive feature selection of genes, implemented through the "e1071" package in R. LASSO analysis was completed using the "glmnet" package in R, and the optimal regularization parameter (λ) was selected through cross-validation. The intersection of feature genes screened by random forest, SVM-RFE, and LASSO was taken to obtain the final set of key therapeutic target genes. In addition, to further validate the screening results, statistical tests were performed on the expression levels of key target genes in the training set and independent validation set, using the Wilcoxon rank-sum test to compare their differences in the breast cancer (BC) and normal control (NC) groups. Moreover, receiver operating characteristic curve (ROC curve) analysis was conducted, calculating the area under the curve (AUC) to evaluate the diagnostic efficacy of key gene expression levels for breast cancer. This study also constructed a risk prediction model based on the expression levels of key targets, assessing the accuracy and clinical application value of this prediction model by plotting calibration curves and decision curves. The calibration curve evaluated the consistency between the predicted probabilities of the model and the actual probabilities, while the decision curve measured the clinical net benefit of the model. Finally, based on the screened key therapeutic targets, their corresponding drug active compounds were identified to reveal the drug efficacy material basis and potential molecular mechanisms of Pingxiao capsules. 2.7 GSEA of key targets To explore the potential biological functions and enriched signaling pathways of key targets at different expression levels, single-gene GSEA analysis was performed on the screened key targets. Using the gene expression matrix and expression values of key targets, samples were divided into high expression group (samples with expression higher than the median) and low expression group (samples with expression lower than the median). The GSEA analysis tool (clusterProfiler package) was used for analysis, with enrichment results considered significant at p-value < 0.05. 2.8 Molecular Docking and Dynamics Simulation We used AutoDock Vina software to analyze the interaction strength and binding modes between small molecule drugs and key targets. Specifically, we first used AlphaFold3 to predict protein three-dimensional structures and obtained three-dimensional conformations of small molecule drugs from the PubChem database. The MOPAC program was utilized to optimize molecular structures and calculate PM3 atomic charges for subsequent molecular docking. AutoDock Tools 1.5.6 was employed to process ligand structures, add polar hydrogen atoms, calculate Gasteiger charges, and set rotatable bonds to generate pdbqt files for docking. Subsequent molecular docking was performed using AutoDock Vina, with the docking box center coordinates set to the protein center to ensure complete inclusion of the entire protein structure. The grid points in XYZ directions were set to 100×100×100, with 50 docking runs and a maximum of 20,000 iteration steps, while other parameters were kept at default values. The complex structures obtained from docking were then subjected to energy optimization using the Amber14 force field through a two-step optimization process: first, 1,000 steps of Steepest Descent Method optimization, followed by 5,000 steps of Conjugate Gradient Method for further fine adjustment to obtain energy-minimized stable conformations. Finally, we used Discovery Studio to perform visualization analysis of the optimized complex structures, identifying key interaction sites including hydrogen bonds, π-π stacking, electrostatic interactions, and hydrophobic interactions. Two-dimensional ligand interaction diagrams and three-dimensional complex structure diagrams were generated to highlight the binding modes and interaction characteristics between key residues and ligands. To validate the binding stability between key active compounds of Pingxiao capsules and target proteins, this study performed Molecular Dynamics (MD) simulations on complexes obtained from molecular docking. MD simulations were conducted using GROMACS version 2021.1, with the Amber14SB force field for target protein parameterization and General Amber Force Field (GAFF) all-atom force field parameters for ligand molecules. Subsequently, the ligand-target protein complexes were placed in cubic boxes with dimensions of 10 × 10 × 10 nm and solvated with TIP3P water molecules, with Na⁺ or Cl⁻ ions added to the system for charge neutralization. After energy minimization to remove spatial conflicts, equilibration simulations of 100 ps were performed separately under NVT and NPT ensembles, using the V-rescale thermostat to maintain temperature at 298 K and the Parrinello-Rahman barostat to maintain pressure at 1 atm. This was followed by 100 ns of production MD simulation with a time step of 2 fs, saving trajectories every 20 ps. Based on MD simulation trajectories, Root Mean Square Deviation (RMSD) of the complexes was analyzed to evaluate conformational stability of ligands and target proteins, Radius of Gyration (Rg) was calculated to measure the compactness of binding pockets, and the number of hydrogen bonds between ligands and target proteins along with their dynamic changes were analyzed. Additionally, the Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) method was utilized to calculate binding free energies of complexes, evaluating the binding affinity between ligands and target proteins. This demonstrated the dynamic behavior of ligands in target binding pockets and interaction patterns of key residues, providing important evidence for revealing the molecular mechanisms of action of active compounds in Pingxiao capsules. 3 Results 3.1 Active compounds of Pingxiao capsules and construction of drug-target network Based on the OB ≥ 30% and DL ≥ 0.18 criteria, a total of 27 compounds and 256 gene targets of Pingxiao capsules were screened from the TCMSP, HERB and PubChem platforms after converting the obtained drug targets into gene names and removing duplicates (Fig. 1 ). We found that some unique compounds interacted with multiple targets and participated in the regulation of multiple targets. This suggests that these compounds may have complementary and synergistic therapeutic effects in treating this disease. 3.2 Identification of breast cancer-related targets Differential analysis was performed on the transcriptome dataset to screen for differentially expressed genes (DEGs). Compared to normal samples (NC group), a total of 3,243 genes were significantly differentially expressed in the BC group, including 1,404 up-regulated genes and 1,839 down-regulated genes (Fig. 2 A, 2 B). WGCNA was then conducted to screen for gene modules and their genes that were significantly associated with disease grouping (BC and NC). Specifically, the optimal soft threshold (β = 3) was selected to construct the adjacency matrix (Fig. 3 A, 3 B). Based on hierarchical clustering and dynamic tree cutting algorithm, with the minimum module gene number set to 30, deep split set to 3, and maximum module distance set to 0.25, a total of 18 gene modules were generated (Fig. 3 C). Analysis of the connectivity of module eigengenes (MEs) showed that the distance between modules was greater than 0.25, indicating good independence between each module (Fig. 3 D). Among them, 4 modules (greenyellow, blue, cyan, and black) were significantly correlated (P < 0.05) (Fig. 3 E). To screen for potential targets in the significant modules, gene significance (GS) was defined as the absolute value of the correlation between individual genes and clinical traits, and module membership (MM) was defined as the correlation between gene expression profiles and corresponding module eigengenes. Using screening criteria of |GS| >0.6 and |MM| >0.6, hub genes were extracted from the significant modules, yielding a total of 2,703 genes. Combining the analysis of DEGs and genes obtained from WGCNA, 2,281 common genes were identified and considered as breast cancer-related targets (Fig. 3 F). 3.3 Identification of breast cancer improvement targets of Pingxiao capsules and network construction Comparing the drug-related targets and breast cancer disease-related targets, 53 common targets were obtained (Fig. 4 A). These common targets are the potential therapeutic targets of Pingxiao capsules for improving breast cancer. Based on these potential therapeutic targets, an "active compound-therapeutic target" network was constructed (Fig. 4 B), visually displaying the drug efficacy material basis and therapeutic targets of Pingxiao capsules for improving breast cancer. 3.4 Functional enrichment analysis and PPI network construction of common targets GO and KEGG functional enrichment analyses were performed on the above 53 common targets to explore the specific molecular mechanisms involved in the treatment of breast cancer by Pingxiao capsules. GO analysis results showed that these genes were mainly involved in biological processes such as response to steroid hormone, circadian rhythm, response to corticosteroid, and rhythmic process (Fig. 5 A), cellular components such as cyclin-dependent protein kinase holoenzyme complex, chromosomal region, basal plasma membrane, basal part of cell, and Bcl-2 family protein complex (Fig. 5 B), and molecular functions such as nuclear receptor activity, ligand-activated transcription factor activity, protein heterodimerization activity, and adrenergic receptor activity (Fig. 5 C). KEGG enrichment analysis showed that these genes were mainly enriched in pathways such as cell growth and death, drug resistance: antineoplastic, amino acid metabolism, and drug resistance: antineoplastic (Fig. 5 D, 5 E). At the same time, GSEA analysis was performed on all differentially expressed genes from the training set (Fig. 5 F). The results showed that cell cycle, p53 signaling pathway, tyrosine metabolism, calcium signaling pathway, and tryptophan metabolism pathways from the KEGG enrichment results were also significantly enriched. Among them, the cell cycle and p53 signaling pathways were significantly upregulated in the BC group (NES > 1), while the tyrosine metabolism, calcium signaling pathway, and tryptophan metabolism pathways were significantly upregulated in the NC group (NES < 1). In addition, interaction analysis was performed on these 53 common targets and a PPI network was constructed to explore the network regulatory mechanisms of these genes involved in disease progression. The PPI network included 51 nodes and 240 edges (Fig. 5 G), indicating 240 interaction relationships among 51 genes, suggesting that these common targets have good interaction relationships and that Pingxiao capsules may exert their effects on improving breast cancer through multi-target synergistic actions. 3.5 Screening of key therapeutic targets based on machine learning algorithms Based on the transcriptome data, three different machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were applied to further perform feature selection on the 53 common targets to explore key therapeutic targets related to the treatment of breast cancer by Pingxiao capsules. First, the random forest algorithm was used to analyze the feature importance of the 53 common targets, and 27 feature genes were screened (Fig. 6 A). These genes were considered to have high predictive power in the treatment of breast cancer. Second, the SVM-RFE algorithm was used to rank the importance of feature genes and screen 14 feature genes (Fig. 6 B, 6 C). Furthermore, based on the LASSO logistic regression model and regularization techniques, sparse selection of relevant genes was performed, ultimately yielding 2 breast cancer-related feature genes (Fig. 6 D, 6 E). To improve the reliability of feature selection, the intersection of feature genes screened by the three algorithms was taken, finally yielding 2 common genes: MAOA and ADH1C (Fig. 6 F). These common genes showed significant importance in all three algorithms, indicating that they may play key roles in the treatment of breast cancer by Pingxiao capsules. At the same time, based on expression profile data from the training and validation sets, the expression and diagnostic performance of key targets in breast cancer were further validated. Wilcoxon rank-sum test results showed that compared to the normal control group (NC), the two key targets MAOA and ADH1C were significantly downregulated in the BC group (P < 0.05) in both the training set (Fig. 7 A) and validation set (Fig. 7 B). ROC analysis results showed that the AUC values of MAOA and ADH1C for distinguishing between NC and BC groups were both greater than 0.9 (Fig. 7 C, 7 D). These results further confirmed the key roles of MAOA and ADH1C in breast cancer, suggesting that they may serve as key therapeutic targets for breast cancer. At the same time, single-gene GSEA analysis results showed that the MAOA gene was mainly enriched in signaling pathways such as DNA replication, proteasome, pyrimidine metabolism, PPAR signaling pathway, hormone signaling, and adipocytokine signaling pathway (Fig. 7 E, 7 F). Among them, cell cycle, p53 signaling pathway, calcium signaling pathway, tyrosine metabolism, tryptophan metabolism, and chemical carcinogenesis - receptor activation signaling pathways were enriched in both MAOA single-gene GSEA analysis and KEGG enrichment analysis (Fig. 5 ). Interestingly, however, the cell cycle and p53 signaling pathways were significantly upregulated in the low expression group divided by the median value (NES 1). In contrast, the calcium signaling pathway, tyrosine metabolism, and tryptophan metabolism signaling pathways were significantly upregulated in the high expression group divided by the median value (NES > 1), which was also opposite to the trend in the KEGG enrichment analysis results (Fig. 5 F, NES < -1). The ADH1C gene was mainly enriched in signaling pathways such as DNA replication, proteasome, base excision repair, regulation of lipolysis in adipocytes, PPAR signaling pathway, and hormone signaling (Fig. 7 F, Supplementary Table). Among them, cell cycle, p53 signaling pathway, calcium signaling pathway, tyrosine metabolism, tryptophan metabolism, HIF-1 signaling pathway, and chemical carcinogenesis - receptor activation signaling pathways were also enriched in both ADH1C single-gene GSEA analysis and KEGG enrichment analysis (Fig. 5 ). Similarly, the cell cycle and p53 signaling pathways were significantly upregulated in the low expression group divided by the median value (NES 1). In contrast, the calcium signaling pathway, tyrosine metabolism, and tryptophan metabolism signaling pathways were significantly upregulated in the high expression group divided by the median value (NES > 1), which was also opposite to the trend in the KEGG enrichment analysis results (Fig. 5 F, NES < -1). The reason for this trend is that the two genes MAOA and ADH1C were significantly downregulated in the BC group compared to the NC group. This downregulation led to the cell cycle and p53 signaling pathways being significantly upregulated in the low expression group (NES 1) in the KEGG enrichment analysis. In addition, a risk prediction model was constructed based on these 2 key targets to evaluate the risk of BC. The results showed that the expression of MAOA and ADH1C were risk factors for BC, with ADH1C contributing the most to the model (Fig. 8 A). Calibration curve analysis and DCA were used to evaluate the accuracy of this risk model. Calibration curve analysis results showed that the model had high sensitivity and specificity (Fig. 8 B). DCA results indicated that using this model for prediction yielded a net benefit superior to the two extreme cases of all patients or no patients over a relatively wide range of threshold probabilities, confirming the potential application value of this prediction model in clinical decision-making (Fig. 8 C). At the same time, this study examined the "active compound-key target" information based on key targets, and the results showed that both the key targets MAOA and ADH1C pointed to the compound Stigmasterol, indicating that Stigmasterol may be the active ligand of key targets, further revealing the key drug efficacy material basis of Pingxiao capsules. 3.6 Molecular docking and Dynamics Simulation To further validate the direct binding action between key compounds and key gene proteins, molecular docking analysis was conducted. Based on the "active compound-key target" association information, the compound Stigmasterol from Pingxiao capsules was selected to perform molecular docking simulation studies with two key target proteins (MAOA and ADH1C). As shown in Table 1 , the binding energies of key compounds with target proteins were low, suggesting good binding affinity between them. Specifically, the binding energy of Stigmasterol with ADH1C was − 7.768 kcal/mol, while the binding energy of Stigmasterol with MAOA was − 7.799 kcal/mol. These results indicate that Stigmasterol may be a direct ligand of MAOA and ADH1C. To more intuitively display the binding patterns of these two key ligand-receptor pairs, visual analysis of their docking conformations was further performed. As shown in Fig. 9 A, the Stigmasterol molecule can stably bind in the ADH1C protein cavity composed of amino acids Arg61, Cys60, Gly215, lle283, lle238, Arg383, Leu376, Gly213, Asp237, Val282, and Arg285. Further analysis of the interactions between the two showed that the molecule formed hydrophobic interactions and hydrogen bonds with the amino acids around the pocket to promote stable binding between the molecule and the protein. Specifically, the molecule formed a hydrogen bond with the amino acid Arg285 around the protein. At the same time, it also formed hydrophobic interactions with 10 amino acid molecules around the protein pocket, further enhancing the affinity between the molecule and the protein. The Stigmasterol molecule can also stably bind in the MAOA protein cavity composed of amino acids Trp116, Tyr121, Trp128, Pro107, Glu492, Phe108, Pro114, Phe112, Asn125, and Arg109 (Fig. 9 B). Further analysis of the interactions between the two showed that the molecule formed hydrophobic interactions and hydrogen bonds with the amino acids around the pocket to promote stable binding between the molecule and the protein. At the same time, the molecule formed a hydrogen bond with the amino acid Asn125 around the protein. It also formed hydrophobic interactions with 9 amino acid molecules around the protein pocket, further enhancing the affinity between the molecule and the protein. These results further confirm the possibility of Stigmasterol as a key direct ligand of MAOA and ADH1C, providing new clues for revealing the precise molecular mechanisms of Pingxiao capsules in improving breast cancer. Table 1 Docking results of key compound-target protein pairs Compound ID Compound Target Binding energy (-kcal/mol) MOL000449 Stigmasterol ADH1C 7.768 MOL000449 Stigmasterol MAOA 7.799 To further validate the binding stability between key compounds and key gene proteins, based on the results of molecular docking analysis, this study performed Molecular Dynamics (MD) simulations on Stigmasterol-ADH1C and Stigmasterol-MAOA. The simulations were conducted in the GROMACS 2021 environment using the Amber14SB force field for a duration of 100 ns to evaluate the dynamic behavior, binding stability, and interaction characteristics of the complexes. First, the Root Mean Square Deviation (RMSD) of the complexes was analyzed. As shown in Fig. 10 A, the Stigmasterol-ADH1C complex rapidly stabilized within the first 20 ns of the simulation, with RMSD stably maintained at approximately 0.30 nm, indicating that the ligand conformation in the ADH1C binding pocket was relatively stable without significant structural drift. Similarly, the RMSD of the Stigmasterol-MAOA complex also reached stability within the first 20 ns, ultimately maintaining around 0.50 nm, indicating that the conformation of Stigmasterol in the MAOA target was equally stable and likely formed a relatively stable binding mode. To further evaluate the compactness and overall conformational stability of the complex structures, we calculated the Radius of Gyration (Rg) of the systems (Fig. 10 B). The results showed that the Rg value of the Stigmasterol-ADH1C complex was stable at approximately 2.2 nm, indicating its overall compact structure. The Rg value of the Stigmasterol-MAOA complex was approximately 2.6 nm, which, although slightly higher than the former, also reflected that the binding state of the ligand in the MAOA binding pocket was relatively tight. Additionally, the Solvent Accessible Surface Area (SASA) was calculated to explore the degree of solvent exposure and its dynamic changes in the systems. As shown in Fig. 10 C, the SASA values of both complex systems showed a gradual decreasing trend during the simulation process, indicating that as simulation time progressed, the protein-ligand complexes tended toward more stable and closed conformational states, further supporting the stable binding of the systems. Finally, through analysis of the time evolution of hydrogen bond numbers (Fig. 10 D), it was found that the number of hydrogen bonds formed during the interaction between Stigmasterol and ADH1C as well as MAOA was relatively small. This might be related to the fact that the Stigmasterol molecule itself lacks polar groups (such as fluorine, oxygen, and nitrogen atoms) capable of forming hydrogen bonds. However, despite the relatively small number of hydrogen bonds, from the perspective of overall binding energy, both complexes exhibited relatively low free energies (Table 2 ), indicating that Stigmasterol can interact with target proteins with relatively high binding stability, possibly mainly relying on hydrophobic interactions and van der Waals forces to maintain the stability of its binding conformation. Table 2 The predicted binding free energy and the individual energy components (kJ/mol) Complex Van der Waals Energy (ΔE vdw ) Electrostatic Energy (ΔE elec ) Polar Solvation Energy (ΔG polar ) SASA Energy (ΔG nonpolar ) Binding Energy (ΔG bind ) Stigmasterol-ADH1C -201.424 ± 16.66 2.476 ± 4.877 70.882 ± 12.469 -21.394 ± 1.331 -149.46 ± 11.877 Stigmasterol-MAOA -122.352 ± 13.282 -5.958 ± 11.01 41.139 ± 20.513 -15.262 ± 1.595 -102.433 ± 11.918 4 Discussion Breast cancer remains one of the most prevalent malignant tumors among women globally, with persistently high incidence and mortality rates[ 1 ]. Current clinical management primarily employs comprehensive treatment modalities including surgery, chemoradiotherapy, endocrine therapy and targeted therapy[ 2 ]. However, challenges such as high risks of recurrence/metastasis and compromised quality of life persist[ 8 ]. Traditional Chinese Medicine (TCM) demonstrates unique advantages in adjuvant breast cancer therapy, exemplified by PXC, which exhibits favorable clinical efficacy. Nevertheless, its molecular mechanisms and pharmacodynamic material basis remain incompletely characterized. Therefore, elucidating PXC's active pharmacological components and multi-target interaction networks holds significant implications for guiding syndrome differentiation-based therapeutic strategies and advancing innovative drug development for breast cancer. In this study, differential expression analysis and WGCNA initially identified 2,281 common genes critically associated with breast carcinogenesis and progression. Besides, we systematically characterized 27 principal bioactive components and 256 potential therapeutic targets in PXC. Comparative analysis of disease-drug targets revealed 53 intersection targets, predominantly enriched in KEGG pathways including cell cycle regulation, p53 signaling, tyrosine metabolism, calcium signaling, and tryptophan metabolism. Further application of machine learning algorithms prioritized monoamine oxidase A (MAOA) and alcohol dehydrogenase 1C (ADH1C) as core therapeutic targets. These targets demonstrated significant downregulation in breast cancer tissues and exhibited strong binding affinities with stigmasterol – a key phytosterol in PXC – suggesting their direct involvement in mediating stigmasterol's anti-breast cancer effects. Dysregulated cell cycle control and p53 pathway dysfunction constitute critical pathological mechanisms driving breast cancer progression[ 9 ]. As a tumor suppressor, p53 maintains genomic stability by inducing cell cycle arrest and apoptosis[ 10 ]. Studies demonstrate aberrant expression of p53 pathway components (e.g., p21, BAX) in breast cancer tissues, leading to abnormal cell cycle progression and apoptotic resistance that fuel tumor proliferation[ 11 – 13 ]. Our findings identify multiple genes within these pathways as therapeutic targets of PXC, suggesting its anti-tumor effects may stem from dual pathway modulation. GSEA analysis further confirms significant upregulation of both pathways in breast cancer tissues, indicating their persistent activation during tumorigenesis. Notably, PXC contains bioactive compounds (e.g., curcumin, oleanolic acid) with documented capacity to induce G2/M phase arrest and apoptosis via p53 pathway activation[ 14 ]. These findings collectively propose that PXC may inhibit breast cancer progression by synergistically restoring cell cycle control and promoting apoptosis through coordinated regulation of these interconnected pathways. Aberrant tyrosine metabolism fuels tumor progression through catecholamine biosynthesis - tyrosine serves as the precursor for norepinephrine synthesis, with its rate-limiting enzyme tyrosine hydroxylase (TH) overexpressed in breast tumors. Elevated tyrosine derivatives activate oncogenic MAPK/PI3K cascades to drive malignant proliferation and invasion[ 15 ]. Conversely, suppressed tryptophan metabolism promotes immunosuppression via the kynurenine-NAD + axis. Tumor microenvironment upregulation of indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) depletes tryptophan, reducing NAD + levels and impairing T/NK cell cytotoxicity to facilitate immune evasion[ 16 – 18 ]. Furthermore, calcium signaling dysregulation, mediated by hyperactivated store-operated calcium entry (SOCE) and transient receptor potential (TRP) channels, induces cytosolic Ca²⁺ overload that sustains tumor-promoting PKC and Ras/Raf/MEK/ERK signaling[ 19 , 20 ]. Our multi-omics profiling identified these three pathways as potential therapeutic targets of PXC. GSEA revealed contrasting patterns: tyrosine/tryptophan metabolism pathways (normally tumor-suppressive) were downregulated in tumors, while calcium signaling exhibited pathological hyperactivity. Mechanistically, PXC may restore tyrosine/tryptophan metabolic homeostasis through bioactive components like β-sitosterol and ursolic acid, reactivating their tumor-inhibiting functions. Simultaneously, its triterpenoids (e.g., oleanolic acid) likely suppress oncogenic calcium fluxes by modulating TRPC6 and Orai1 channels, thereby disrupting Ca²⁺-dependent pro-survival signaling. This dual-directional regulation exemplifies PXC's systems-level intervention strategy - rectifying metabolic-ionic imbalances while reinvigorating endogenous tumor suppression mechanisms. Monoamine oxidase A (MAOA), a mitochondrial outer membrane enzyme, catalyzes the oxidative deamination of monoamine neurotransmitters including norepinephrine and serotonin[ 21 ]. Recent research results indicate that different types of cancer exhibit unique regulatory and functional patterns of MAOA. MAOA overexpression was observed in glioma[ 22 ], classical Hodgkin's lymphoma[ 23 ] and prostate cancer[ 24 ]. In contrast, it has been reported that the expression of MAOA shows a decreasing trend in pancreatic ductal adenocarcinoma[ 25 ], hepatocellular carcinoma (HCC)[ 21 ]and gastric cancer[ 26 ]. It is notable that previous studies have consistently shown that the expression of MAOA in invasive BC is significantly decreased compared with non-cancer cells and normal breast tissues [ 27 , 28 ], confirming our research results. Alcohol dehydrogenase 1C (ADH1C), a member of the ethanol dehydrogenase family, oxidizes ethanol to acetaldehyde while modulating retinoic acid signaling. ADH1C downregulation has been observed in colorectal and oral cancers, impairing tumor cell differentiation via disrupted retinoic acid biosynthesis[ 29 ]. Furthermore, existing studies have demonstrated that variants in the ADH1C gene (such as Arg272Gln) are associated with alcohol metabolism and may be closely linked to an elevated risk of breast cancer. Our findings reveal significant downregulation of both MAOA and ADH1C in breast cancer tissues with robust diagnostic potential (AUC > 0.85), nominating them as novel diagnostic biomarkers. Mechanistically, MAOA/ADH1C downregulation may promote tumor proliferation and dedifferentiation by dysregulating neurotransmitter metabolism and retinoic acid signaling. As a key active component of PXC, stigmasterol, a widely distributed phytosterol in soybeans and yams with high dietary safety profile, demonstrates promising potential in breast cancer prevention and treatment. Epidemiological studies reveal an inverse correlation between stigmasterol intake and breast cancer incidence risk, suggesting chemopreventive properties[ 30 ]. In vitro experiments confirm its capacity to inhibit breast cancer cell proliferation, invasion, epithelial-mesenchymal transition (EMT), while inducing cell cycle arrest and apoptosis[ 31 ]. Animal studies further demonstrate suppression of xenograft tumor growth and metastasis[ 32 ]. However, clinical evidence supporting stigmasterol's therapeutic efficacy remains scarce. Our molecular-level findings elucidate its novel anti-breast cancer mechanism via directly targeting MAOA/ADH1C, providing scientific rationale for clinical translation. This study pioneers the integration of transcriptomics and network pharmacology to systematically elucidate the molecular mechanisms underlying PXC's anti-breast cancer effects. We identified PXC's multi-target synergistic actions through regulating key pathways (cell cycle, p53 signaling, tyrosine/tryptophan metabolism, and calcium signaling), inhibiting tumor proliferation, inducing apoptosis, and restoring metabolic homeostasis. MAOA and ADH1C emerged as novel diagnostic biomarkers and therapeutic targets, with stigmasterol as a core active component. Molecular docking and dynamics simulations revealed stigmasterol's direct binding to MAOA/ADH1C. These findings provide a comprehensive "multi-component, multi-target, multi-pathway" mechanistic framework for PXC's clinical application in syndrome differentiation-based therapy and natural product drug development. The study's innovations include: (1) First integration of transcriptomics and network pharmacology to elucidate PXC's anti-breast cancer mechanisms from multi-omics and systems biomedicine perspectives, revealing its holistic network regulation; (2) Identification of MAOA/ADH1C as novel diagnostic biomarkers and therapeutic targets through machine learning algorithms; (3) Atomic-level characterization of stigmasterol-target interactions via molecular docking/dynamics simulations, providing a paradigm for TCM mechanism research; (4) Establish a reproducible methodology applicable to other herbal medicines and diseases, advancing TCM modernization. This study has several limitations. First, while bioinformatics analysis identified key targets/pathways, experimental validation through RNA interference, Western blot, and immunohistochemistry in breast cancer cell lines/animal models is needed to confirm MAOA/ADH1C regulation by PXC. Second, although stigmasterol's mechanism was preliminarily characterized, synergistic effects among other active components require further investigation using metabolomics and chemical probes. Our integrated multi-omics approach systematically elucidated the anti-breast cancer mechanisms of PXC, establishing a foundation for further investigation. Advances in omics and bioinformatics will accelerate the modernization of traditional Chinese medicine (TCM), enabling its integration into precision oncology to improve patient outcomes. Future research should prioritize clinical cohort studies to evaluate stigmasterol's efficacy as a dietary intervention or pharmacotherapy, focusing on tumor biomarkers, quality of life, and prognosis. Additionally, exploring synergistic combinations of TCM with endocrine or HER2-targeted therapies may optimize therapeutic strategies guided by TCM syndrome differentiation. Declarations Acknowledgements Not applicable. Author contributions Hou Xiaoyi and Zhang Peng: Writing-original draft. Hou Xiaoyi and Zhang Xu: Writing-review and editing. Zhang Li and Zhang Xu: Conceptualization. Hou Xiaoyi, Zhao Fen, and Zhang Xian:Validation. Zhang Peng: Formal analysis Zhang Xian and Liao Hong: Visualization. Zhang Xian and Pu Qiuyi: Investigation. Zhao Fen: Designed the methodology. Zhang Li: Acquired funding. All authors participated in reviewing and editing the manuscript and have read and agreed to the published version. Funding This work was supported by the Science and Technology Research Project of Sichuan Provincial Administration of Traditional Chinese Medicine, Grant No. 2023MS131. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Declarations Ethics approval and consent to participate Not applicable. Clinical Trial Number Not applicable. Consent for publication Not applicable. 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06:47:06","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109488,"visible":true,"origin":"","legend":"","description":"","filename":"2be55437ef29440c9c1984411f4832961structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/508482aa5ac37039a07a9d7d.xml"},{"id":92470490,"identity":"33184180-c2a4-42fb-aed7-a2433b1368ff","added_by":"auto","created_at":"2025-09-30 06:39:04","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118504,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/965492d0a547c724cfd68c00.html"},{"id":92470939,"identity":"f198e756-5e5a-4457-8b50-c145c949e157","added_by":"auto","created_at":"2025-09-30 06:47:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":268433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug-compound-target network of Pingxiao capsules. \u003c/strong\u003eRed triangles represent the main 8 herbs in Pingxiao capsules; yellow diamonds represent the main compounds; blue squares represent the predicted drug targets.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/03b0f2a29ec48a0f258c3ca4.png"},{"id":92470477,"identity":"9f5e6ed9-4977-41e2-ad0c-1ac970227a5b","added_by":"auto","created_at":"2025-09-30 06:39:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression between breast cancer and control groups. \u003c/strong\u003e(A) Volcano plot of differential analysis results. (B) Heatmap of top 20 up- and down-regulated differentially expressed genes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/8795a0f12ef9e128bc94c88c.png"},{"id":92470488,"identity":"3d1424ec-fb0d-45dd-8379-7c35e1d53c2e","added_by":"auto","created_at":"2025-09-30 06:39:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155804,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted gene co-expression network analysis (WGCNA). \u003c/strong\u003e(A) Trend of scale-free topology model fit R\u003csup\u003e2\u003c/sup\u003e with soft threshold β. (B) Trend of mean connectivity with β. (C) Gene clustering tree and dynamic tree cutting results. Different colors represent different gene co-expression modules. (D) Heatmap of correlations between module eigengenes (MEs), showing relationships between different modules. Blue indicates negative correlation, red indicates positive correlation, and color depth represents correlation strength. (E) Correlations between MEs and BC/NC groups. Color depth represents correlation strength, the number before the parentheses represents the correlation coefficient r, and the number inside the parentheses represents the significance P-value. (F) Venn diagram showing the intersection of WGCNA-identified hub genes and bulk DEGs to obtain genes closely related to BC development.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/ade0c0879ba31654844b0128.png"},{"id":92470480,"identity":"772fe39c-53ad-4f61-bbf7-1f107e5df5b6","added_by":"auto","created_at":"2025-09-30 06:39:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of breast cancer improvement targets of Pingxiao capsules.\u003c/strong\u003e (A) Venn diagram showing the intersection of Pingxiao capsule-related targets and breast cancer disease-related targets. (B) \"Active compound-therapeutic target\" network diagram. Red triangles represent main compound sources (ganqi, zhiqiao, yujin, maqianzifen, xianhecao, and wulingzhi) of Pingxiao capsules; yellow diamonds represent active compounds of Pingxiao capsules; blue squares represent 53 key targets related to breast cancer improvement.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/4f6987a73b06520302be6c39.png"},{"id":92470487,"identity":"db2cd14a-973c-4007-86e2-e954309be8a8","added_by":"auto","created_at":"2025-09-30 06:39:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":397613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis. \u003c/strong\u003e(A-C) Bubble plot of top 10 most significantly enriched GO terms in BP (A), CC (B), and MF (C) categories for candidate genes. (D) Bubble plot of KEGG pathways enriched by candidate genes. (E) Connectivity between potential targets and enriched KEGG pathways. (F) GSEA enrichment analysis. (G) PPI network analysis.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/edae7720d3e86a36ce6151ad.png"},{"id":92470485,"identity":"102b88e8-eb6e-47ec-bdb0-0046c70be24a","added_by":"auto","created_at":"2025-09-30 06:39:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":99440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of key therapeutic targets based on machine learning algorithms. \u003c/strong\u003e(A) Feature importance analysis of 53 common targets using the random forest algorithm, ranked by increased MSE. (B) Classification accuracy curve of the SVM-RFE algorithm, showing classification performance with different numbers of features. (C) Classification error rate curve of the SVM-RFE algorithm, selecting feature genes based on the optimal number of features. (D) Coefficient path plot of the LASSO regression model, showing coefficient changes of different genes under different regularization strengths. (E) Cross-validation error curve of the LASSO regression model, selecting feature genes based on the optimal λ value. (F) Venn diagram showing the intersection of feature genes screened by three algorithms (random forest, LASSO, and SVM-RFE).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/37544feb62132b622bba1831.png"},{"id":92470953,"identity":"598a7531-dbb9-42f0-bfb2-1765ab51b8a1","added_by":"auto","created_at":"2025-09-30 06:47:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":179077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression validation and diagnostic performance analysis of key targets in breast cancer. \u003c/strong\u003e(A) Expression level distribution of key targets in the training set (GSE10810). (B) Expression level distribution of key targets in the validation set (GSE26910). (C) ROC curve analysis of key targets in the training set. (D) ROC curve analysis of key targets in the validation set. (E-F) Single-gene GSEA analysis of key targets (E) MAOA and (F) ADH1C.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/ff8d24974b65d32358c03b4e.png"},{"id":92470481,"identity":"80cff8fb-5bc9-48d0-af82-e43c68e37b9a","added_by":"auto","created_at":"2025-09-30 06:39:03","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":43947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram risk prediction model and evaluation. \u003c/strong\u003e(A) BC risk prediction nomogram based on MAOA and ADH1C. (B) Calibration curve evaluating model accuracy. (C) DCA decision curve analysis.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/237f9361498694f253337952.png"},{"id":92470504,"identity":"5fc1930f-90d7-426d-8d7f-ab1cf6920237","added_by":"auto","created_at":"2025-09-30 06:39:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":257893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualization of key compound-target protein docking conformations.\u003c/strong\u003e (A) Docking conformation of Stigmasterol with ADH1C. Left: 3D model of Stigmasterol-ADH1C complex; middle: three-dimensional binding pattern of the molecule with the protein; right: two-dimensional binding pattern of the molecule with the protein, with green dashed lines representing hydrogen bonds and red gears representing hydrophobic interactions. (B) Docking conformation of Stigmasterol with MAOA. Left: 3D model of Stigmasterol-MAOA complex; middle: three-dimensional binding pattern of the molecule with the protein; right: two-dimensional binding pattern of the molecule with the protein, with green dashed lines representing hydrogen bonds and red gears representing hydrophobic interactions.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/4f82a70e7fa9e2d7d920252a.png"},{"id":92470942,"identity":"f83f96f1-fc82-45c5-a26f-eba6a3480632","added_by":"auto","created_at":"2025-09-30 06:47:03","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":164563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulation analysis of key compound-target protein complexes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Changes in RMSD values over simulation time for Stigmasterol-ADH1C (left) and Stigmasterol-MAOA (right) complex systems; (B) Changes in Rg values over simulation time for Stigmasterol-ADH1C (left) and Stigmasterol-MAOA (right) complex systems; (C) Changes in SASA values over simulation time for Stigmasterol-ADH1C (left) and Stigmasterol-MAOA (right) complex systems; (D) Changes in the number of hydrogen bonds between proteins and compounds over simulation time in Stigmasterol-ADH1C (left) and Stigmasterol-MAOA (right) complex systems.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/fcf20decf89e00bc34675eb4.png"},{"id":93754580,"identity":"f3b392c6-3424-4eb7-af85-e798b1f02a03","added_by":"auto","created_at":"2025-10-17 08:32:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2811778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722434/v1/a36494a0-51a0-4bca-ab74-474c62f2fe73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating Pingxiao Capsules Anti-Breast Cancer Mechanism via Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBreast cancer describes a range of malignancies occurring in the mammary glands, which is the most common global malignancy and the leading cause of cancer deaths in females[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Breast cancer is a complex and heterogeneous disease in which multiple genetic, signal pathways and factors are involved and the exact mechanism is still unclear[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. During the past recent years, various therapies have emerged in the era of breast cancer. Although serial screening detected during early stage of disease can decrease mortality, the prognosis of patients in the advanced stage remains poor[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, new strategies are urgently needed for the treatment of breast cancer.\u003c/p\u003e\u003cp\u003eTraditional Chinese Medicine (TCM) has been employed in breast cancer treatment with a long-standing history, and herbal medicines have demonstrated significant advantages in the therapeutic management of breast cancer. Guided by traditional Chinese medical theory, TCM treatment demonstrates therapeutic advantages through its holistic approach, multi-target effects, and favorable safety profile[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Pingxiao Capsule (PXC), a standardized multi-herb formulation widely utilized in clinical oncology, integrates components such as Curcumae Rhizoma (Ezhu), Agrimoniae Herba (Xianhecao), and Trogopterus Dung (Wulingzhi) under TCM compatibility principles. As a first-line adjuvant therapy for breast cancer, it demonstrates measurable efficacy in tumor size reduction, symptom alleviation, and chemotherapy tolerance enhancement, supported by multicenter clinical trials[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Current pharmacological studies suggest its effects involve angiogenesis inhibition and apoptosis induction, yet its system-level mechanisms\u0026mdash;particularly multi-target interactions and tumor microenvironment modulation\u0026mdash;require further elucidation through network pharmacology and omics-based approaches.\u003c/p\u003e\u003cp\u003eTranscriptomics technologies systematically identify biomarkers modulated by herbal formulations and pinpoint critical therapeutic targets by deciphering disease-specific molecular signatures and dynamic pharmacological responses[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Network pharmacology quantifies the polypharmacological synergy of multi-component formulations through multidimensional \"herb-compound-target-pathway\" network modeling, unveiling topologically defined mechanisms by which core functional modules exert pan-scale disease intervention[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Their integration innovatively bridges molecular evidence chains with systems-level regulatory logic: validated network modules not only elucidate the multi-target essence of herbal actions but also establish a theoretical framework for systems biology-driven formulation optimization and precision medicine applications (e.g., molecular subtype-guided personalized therapy in breast cancer).\u003c/p\u003e\u003cp\u003eThis study integrates transcriptomics and network pharmacology to systematically elucidate the molecular mechanisms underlying PXC's therapeutic effects against breast cancer. By employing differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify critical disease pathways, followed by compound-target network comparison to determine core active components and therapeutic targets, key interactions are further validated through molecular docking. The findings will clarify the multi-target mechanisms of PXC, guide personalized precision treatment, and provide theoretical foundations for biomarker screening, novel therapeutic target discovery, and lead compound optimization.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Transcriptome data download and preprocessing\u003c/h2\u003e\u003cp\u003eBreast cancer-related transcriptome datasets GSE10810 and GSE26910 were downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geoprofiles/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geoprofiles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE10810 dataset, based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), was used as the training set and contained transcriptome data from 27 normal breast samples and 31 breast cancer samples. The GSE26910 dataset, used as the validation set, included transcriptome data from 6 normal breast samples and 6 breast cancer samples. These datasets were preprocessed, including background correction, gene name conversion, and data normalization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Screening of active compounds in Pingxiao capsules and construction of drug-target network\u003c/h2\u003e\u003cp\u003eThis study obtained chemical component information of the eight herbs in Pingxiao Capsule from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lsp.nwu.edu.cn/\u003c/span\u003e\u003cspan address=\"http://lsp.nwu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Herb database (HERB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://herb.ac.cn/\u003c/span\u003e\u003cspan address=\"http://herb.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pingxiao Capsule consists of eight traditional Chinese herbs: Curcuma (Yujin), Agrimonia pilosa (Xianhecao), Faeces Trogopterori (Wulingzhi), Alumen (Baifan), Nitrum (Xiaoshi), processed Lacquer (Ganqi), wheat-processed Fructus Aurantii (Fuchao Zhiqiao), and Semen Strychni powder (Maqianzi fen). Active compounds were screened based on absorption, distribution, metabolism, and excretion (ADME) related parameters, with specific screening criteria of oral bioavailability (OB)\u0026thinsp;\u0026ge;\u0026thinsp;30% and drug-likeness (DL)\u0026thinsp;\u0026ge;\u0026thinsp;0.18. Compounds meeting these screening criteria were defined as potential active compounds of Pingxiao capsules. Target prediction for the screened active compounds was then performed using the TCMSP and SwissTargetPrediction (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) platforms, and the predicted targets were mapped to the corresponding standardized gene names. Standardization of target genes was completed through the UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"http://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), retaining only human (Homo sapiens) derived target genes and removing gene entries with non-compliant species sources to ensure data accuracy and consistency. Finally, based on the screened active compounds and their corresponding target genes, a drug-compound-target network was constructed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Identification of breast cancer-related targets\u003c/h2\u003e\u003cp\u003eBased on the downloaded training dataset, differential analysis was performed using the limma package, with |fold change| \u0026gt;1.5 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as thresholds, to screen for differentially expressed genes (DEGs) between BC and NC groups. Subsequently, weighted gene co-expression network analysis (WGCNA) was applied to further identify co-expression gene modules and hub genes closely related to the disease. Specifically, the gene expression data from the training dataset was inputted into the WGCNA framework. The \"pickSoftThreshold\" function was used to calculate the scale-free network fit index and determine the optimal soft threshold power (β value). This soft threshold was used to construct the adjacency matrix and convert it into a topological overlap matrix (TOM) to measure the co-expression similarity between genes. Based on the dissimilarity matrix of TOM, hierarchical clustering analysis and dynamic tree cut algorithm were used for module division, with parameters set to a minimum module gene number of 30, deep split of 3, and maximum module distance of 0.25. Subsequently, the module eigengenes (MEs) of each module were calculated, and the correlation between MEs and clinical features (disease group and normal group) was evaluated by Pearson correlation analysis. To screen for key targets in the significant modules, gene significance (GS) was defined as the absolute value of the correlation between individual genes and clinical traits, and module membership (MM) was defined as the correlation between gene expression profiles and corresponding module eigengenes. The screening criteria were |GS|\u0026gt;0.6 and |MM|\u0026gt;0.6, and hub genes were extracted from the significant modules. Finally, the intersection analysis of all genes in the significantly related modules was performed by the DEGs and WGCNA screening, and the targets closely related to breast cancer were identified.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Identification of breast cancer improvement targets of Pingxiao capsules and network construction\u003c/h2\u003e\u003cp\u003eTo identify potential therapeutic targets of Pingxiao capsules for improving breast cancer, the previously screened breast cancer target gene set was intersected with the drug targets of Pingxiao capsules, yielding common targets (potential therapeutic targets). Based on these common targets, combined with the active compound and target data screened from Pingxiao capsules, a \"active compound-therapeutic target\" network was constructed using Cytoscape software to visually display the drug efficacy material basis and key therapeutic targets of Pingxiao capsules, providing theoretical support for subsequent mechanistic studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Functional enrichment analysis and PPI network analysis\u003c/h2\u003e\u003cp\u003eTo explore the specific molecular mechanisms involved in the treatment of breast cancer by Pingxiao capsules, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed on the common targets (potential therapeutic targets). Functional enrichment analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses, was conducted using the DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GO annotations included three categories: biological processes (BPs), cellular components (CCs), and molecular functions (MFs). By calculating the enrichment degree of key targets in each KEGG pathway or GO term and performing Fisher's exact test for statistical significance, significantly enriched KEGG pathways and GO terms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were identified, revealing the main biological functions and signaling pathways involved in the key drug targets related genes in childhood breast cancer.\u003c/p\u003e\u003cp\u003eIn addition, to compare the expression differences of pathways in the KEGG enrichment analysis results between the normal control (NC) and disease (BC) groups, gene set enrichment analysis (GSEA) was performed using the R software clusterProfiler package. Using the differential expression analysis results from the training set, GSEA analysis was conducted on the significant pathways from the KEGG enrichment analysis. By comparing the normalized enrichment score (NES) and significance P value of these pathways in the NC and BC groups, the dynamic changes of signaling pathways related to Pingxiao capsules' therapeutic targets during the occurrence and development of breast cancer were revealed.\u003c/p\u003e\u003cp\u003eIn the construction of the PPI network, interaction data between key target genes was obtained using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). PPI network construction parameters were set with species origin as human (Homo sapiens) and confidence threshold selected as 0.4. The obtained interaction data was visualized using Cytoscape software, and the topological structure of the network was analyzed to assess the interaction relationships and potential regulatory mechanisms between target genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Screening of key therapeutic targets based on machine learning algorithms\u003c/h2\u003e\u003cp\u003eTo screen for key therapeutic targets of Pingxiao capsules in the treatment of breast cancer, three classic machine learning algorithms were applied based on the transcriptome data: Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). These three methods were used to rank the feature importance and screen the 53 common targets, aiming to improve the robustness and reliability of feature selection. First, the \"randomForest\" software in R was used to perform feature importance analysis on the target genes using the random forest algorithm. Next, the SVM-RFE algorithm was used for recursive feature selection of genes, implemented through the \"e1071\" package in R. LASSO analysis was completed using the \"glmnet\" package in R, and the optimal regularization parameter (λ) was selected through cross-validation. The intersection of feature genes screened by random forest, SVM-RFE, and LASSO was taken to obtain the final set of key therapeutic target genes.\u003c/p\u003e\u003cp\u003eIn addition, to further validate the screening results, statistical tests were performed on the expression levels of key target genes in the training set and independent validation set, using the Wilcoxon rank-sum test to compare their differences in the breast cancer (BC) and normal control (NC) groups. Moreover, receiver operating characteristic curve (ROC curve) analysis was conducted, calculating the area under the curve (AUC) to evaluate the diagnostic efficacy of key gene expression levels for breast cancer.\u003c/p\u003e\u003cp\u003eThis study also constructed a risk prediction model based on the expression levels of key targets, assessing the accuracy and clinical application value of this prediction model by plotting calibration curves and decision curves. The calibration curve evaluated the consistency between the predicted probabilities of the model and the actual probabilities, while the decision curve measured the clinical net benefit of the model.\u003c/p\u003e\u003cp\u003eFinally, based on the screened key therapeutic targets, their corresponding drug active compounds were identified to reveal the drug efficacy material basis and potential molecular mechanisms of Pingxiao capsules.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 GSEA of key targets\u003c/h2\u003e\u003cp\u003eTo explore the potential biological functions and enriched signaling pathways of key targets at different expression levels, single-gene GSEA analysis was performed on the screened key targets. Using the gene expression matrix and expression values of key targets, samples were divided into high expression group (samples with expression higher than the median) and low expression group (samples with expression lower than the median). The GSEA analysis tool (clusterProfiler package) was used for analysis, with enrichment results considered significant at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Molecular Docking and Dynamics Simulation\u003c/h2\u003e\u003cp\u003eWe used AutoDock Vina software to analyze the interaction strength and binding modes between small molecule drugs and key targets. Specifically, we first used AlphaFold3 to predict protein three-dimensional structures and obtained three-dimensional conformations of small molecule drugs from the PubChem database. The MOPAC program was utilized to optimize molecular structures and calculate PM3 atomic charges for subsequent molecular docking. AutoDock Tools 1.5.6 was employed to process ligand structures, add polar hydrogen atoms, calculate Gasteiger charges, and set rotatable bonds to generate pdbqt files for docking. Subsequent molecular docking was performed using AutoDock Vina, with the docking box center coordinates set to the protein center to ensure complete inclusion of the entire protein structure. The grid points in XYZ directions were set to 100\u0026times;100\u0026times;100, with 50 docking runs and a maximum of 20,000 iteration steps, while other parameters were kept at default values. The complex structures obtained from docking were then subjected to energy optimization using the Amber14 force field through a two-step optimization process: first, 1,000 steps of Steepest Descent Method optimization, followed by 5,000 steps of Conjugate Gradient Method for further fine adjustment to obtain energy-minimized stable conformations. Finally, we used Discovery Studio to perform visualization analysis of the optimized complex structures, identifying key interaction sites including hydrogen bonds, π-π stacking, electrostatic interactions, and hydrophobic interactions. Two-dimensional ligand interaction diagrams and three-dimensional complex structure diagrams were generated to highlight the binding modes and interaction characteristics between key residues and ligands.\u003c/p\u003e\u003cp\u003eTo validate the binding stability between key active compounds of Pingxiao capsules and target proteins, this study performed Molecular Dynamics (MD) simulations on complexes obtained from molecular docking. MD simulations were conducted using GROMACS version 2021.1, with the Amber14SB force field for target protein parameterization and General Amber Force Field (GAFF) all-atom force field parameters for ligand molecules. Subsequently, the ligand-target protein complexes were placed in cubic boxes with dimensions of 10 \u0026times; 10 \u0026times; 10 nm and solvated with TIP3P water molecules, with Na⁺ or Cl⁻ ions added to the system for charge neutralization. After energy minimization to remove spatial conflicts, equilibration simulations of 100 ps were performed separately under NVT and NPT ensembles, using the V-rescale thermostat to maintain temperature at 298 K and the Parrinello-Rahman barostat to maintain pressure at 1 atm. This was followed by 100 ns of production MD simulation with a time step of 2 fs, saving trajectories every 20 ps. Based on MD simulation trajectories, Root Mean Square Deviation (RMSD) of the complexes was analyzed to evaluate conformational stability of ligands and target proteins, Radius of Gyration (Rg) was calculated to measure the compactness of binding pockets, and the number of hydrogen bonds between ligands and target proteins along with their dynamic changes were analyzed. Additionally, the Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) method was utilized to calculate binding free energies of complexes, evaluating the binding affinity between ligands and target proteins. This demonstrated the dynamic behavior of ligands in target binding pockets and interaction patterns of key residues, providing important evidence for revealing the molecular mechanisms of action of active compounds in Pingxiao capsules.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Active compounds of Pingxiao capsules and construction of drug-target network\u003c/h2\u003e\n \u003cp\u003eBased on the OB\u0026thinsp;\u0026ge;\u0026thinsp;30% and DL\u0026thinsp;\u0026ge;\u0026thinsp;0.18 criteria, a total of 27 compounds and 256 gene targets of Pingxiao capsules were screened from the TCMSP, HERB and PubChem platforms after converting the obtained drug targets into gene names and removing duplicates (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). We found that some unique compounds interacted with multiple targets and participated in the regulation of multiple targets. This suggests that these compounds may have complementary and synergistic therapeutic effects in treating this disease.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Identification of breast cancer-related targets\u003c/h2\u003e\n \u003cp\u003eDifferential analysis was performed on the transcriptome dataset to screen for differentially expressed genes (DEGs). Compared to normal samples (NC group), a total of 3,243 genes were significantly differentially expressed in the BC group, including 1,404 up-regulated genes and 1,839 down-regulated genes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). WGCNA was then conducted to screen for gene modules and their genes that were significantly associated with disease grouping (BC and NC). Specifically, the optimal soft threshold (\u0026beta;\u0026thinsp;=\u0026thinsp;3) was selected to construct the adjacency matrix (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Based on hierarchical clustering and dynamic tree cutting algorithm, with the minimum module gene number set to 30, deep split set to 3, and maximum module distance set to 0.25, a total of 18 gene modules were generated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Analysis of the connectivity of module eigengenes (MEs) showed that the distance between modules was greater than 0.25, indicating good independence between each module (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). Among them, 4 modules (greenyellow, blue, cyan, and black) were significantly correlated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\n \u003cp\u003eTo screen for potential targets in the significant modules, gene significance (GS) was defined as the absolute value of the correlation between individual genes and clinical traits, and module membership (MM) was defined as the correlation between gene expression profiles and corresponding module eigengenes. Using screening criteria of |GS| \u0026gt;0.6 and |MM| \u0026gt;0.6, hub genes were extracted from the significant modules, yielding a total of 2,703 genes. Combining the analysis of DEGs and genes obtained from WGCNA, 2,281 common genes were identified and considered as breast cancer-related targets (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Identification of breast cancer improvement targets of Pingxiao capsules and network construction\u003c/h2\u003e\n \u003cp\u003eComparing the drug-related targets and breast cancer disease-related targets, 53 common targets were obtained (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). These common targets are the potential therapeutic targets of Pingxiao capsules for improving breast cancer. Based on these potential therapeutic targets, an \u0026quot;active compound-therapeutic target\u0026quot; network was constructed (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB), visually displaying the drug efficacy material basis and therapeutic targets of Pingxiao capsules for improving breast cancer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Functional enrichment analysis and PPI network construction of common targets\u003c/h2\u003e\n \u003cp\u003eGO and KEGG functional enrichment analyses were performed on the above 53 common targets to explore the specific molecular mechanisms involved in the treatment of breast cancer by Pingxiao capsules. GO analysis results showed that these genes were mainly involved in biological processes such as response to steroid hormone, circadian rhythm, response to corticosteroid, and rhythmic process (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), cellular components such as cyclin-dependent protein kinase holoenzyme complex, chromosomal region, basal plasma membrane, basal part of cell, and Bcl-2 family protein complex (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB), and molecular functions such as nuclear receptor activity, ligand-activated transcription factor activity, protein heterodimerization activity, and adrenergic receptor activity (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). KEGG enrichment analysis showed that these genes were mainly enriched in pathways such as cell growth and death, drug resistance: antineoplastic, amino acid metabolism, and drug resistance: antineoplastic (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\n \u003cp\u003eAt the same time, GSEA analysis was performed on all differentially expressed genes from the training set (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF). The results showed that cell cycle, p53 signaling pathway, tyrosine metabolism, calcium signaling pathway, and tryptophan metabolism pathways from the KEGG enrichment results were also significantly enriched. Among them, the cell cycle and p53 signaling pathways were significantly upregulated in the BC group (NES\u0026thinsp;\u0026gt;\u0026thinsp;1), while the tyrosine metabolism, calcium signaling pathway, and tryptophan metabolism pathways were significantly upregulated in the NC group (NES\u0026thinsp;\u0026lt;\u0026thinsp;1).\u003c/p\u003e\n \u003cp\u003eIn addition, interaction analysis was performed on these 53 common targets and a PPI network was constructed to explore the network regulatory mechanisms of these genes involved in disease progression. The PPI network included 51 nodes and 240 edges (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG), indicating 240 interaction relationships among 51 genes, suggesting that these common targets have good interaction relationships and that Pingxiao capsules may exert their effects on improving breast cancer through multi-target synergistic actions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Screening of key therapeutic targets based on machine learning algorithms\u003c/h2\u003e\n \u003cp\u003eBased on the transcriptome data, three different machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were applied to further perform feature selection on the 53 common targets to explore key therapeutic targets related to the treatment of breast cancer by Pingxiao capsules. First, the random forest algorithm was used to analyze the feature importance of the 53 common targets, and 27 feature genes were screened (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). These genes were considered to have high predictive power in the treatment of breast cancer. Second, the SVM-RFE algorithm was used to rank the importance of feature genes and screen 14 feature genes (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Furthermore, based on the LASSO logistic regression model and regularization techniques, sparse selection of relevant genes was performed, ultimately yielding 2 breast cancer-related feature genes (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e\n \u003cp\u003eTo improve the reliability of feature selection, the intersection of feature genes screened by the three algorithms was taken, finally yielding 2 common genes: MAOA and ADH1C (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF). These common genes showed significant importance in all three algorithms, indicating that they may play key roles in the treatment of breast cancer by Pingxiao capsules. At the same time, based on expression profile data from the training and validation sets, the expression and diagnostic performance of key targets in breast cancer were further validated. Wilcoxon rank-sum test results showed that compared to the normal control group (NC), the two key targets MAOA and ADH1C were significantly downregulated in the BC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in both the training set (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA) and validation set (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). ROC analysis results showed that the AUC values of MAOA and ADH1C for distinguishing between NC and BC groups were both greater than 0.9 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC, \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD). These results further confirmed the key roles of MAOA and ADH1C in breast cancer, suggesting that they may serve as key therapeutic targets for breast cancer.\u003c/p\u003e\n \u003cp\u003eAt the same time, single-gene GSEA analysis results showed that the MAOA gene was mainly enriched in signaling pathways such as DNA replication, proteasome, pyrimidine metabolism, PPAR signaling pathway, hormone signaling, and adipocytokine signaling pathway (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE, \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eF). Among them, cell cycle, p53 signaling pathway, calcium signaling pathway, tyrosine metabolism, tryptophan metabolism, and chemical carcinogenesis - receptor activation signaling pathways were enriched in both MAOA single-gene GSEA analysis and KEGG enrichment analysis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Interestingly, however, the cell cycle and p53 signaling pathways were significantly upregulated in the low expression group divided by the median value (NES \u0026lt; -1) in the single-gene GSEA analysis, which was opposite to the trend in the KEGG enrichment analysis results (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF, NES\u0026thinsp;\u0026gt;\u0026thinsp;1). In contrast, the calcium signaling pathway, tyrosine metabolism, and tryptophan metabolism signaling pathways were significantly upregulated in the high expression group divided by the median value (NES\u0026thinsp;\u0026gt;\u0026thinsp;1), which was also opposite to the trend in the KEGG enrichment analysis results (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF, NES \u0026lt; -1).\u003c/p\u003e\n \u003cp\u003eThe ADH1C gene was mainly enriched in signaling pathways such as DNA replication, proteasome, base excision repair, regulation of lipolysis in adipocytes, PPAR signaling pathway, and hormone signaling (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eF, Supplementary Table). Among them, cell cycle, p53 signaling pathway, calcium signaling pathway, tyrosine metabolism, tryptophan metabolism, HIF-1 signaling pathway, and chemical carcinogenesis - receptor activation signaling pathways were also enriched in both ADH1C single-gene GSEA analysis and KEGG enrichment analysis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Similarly, the cell cycle and p53 signaling pathways were significantly upregulated in the low expression group divided by the median value (NES \u0026lt; -1) in the single-gene GSEA analysis, which was opposite to the trend in the KEGG enrichment analysis results (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF, NES\u0026thinsp;\u0026gt;\u0026thinsp;1). In contrast, the calcium signaling pathway, tyrosine metabolism, and tryptophan metabolism signaling pathways were significantly upregulated in the high expression group divided by the median value (NES\u0026thinsp;\u0026gt;\u0026thinsp;1), which was also opposite to the trend in the KEGG enrichment analysis results (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF, NES \u0026lt; -1). The reason for this trend is that the two genes MAOA and ADH1C were significantly downregulated in the BC group compared to the NC group. This downregulation led to the cell cycle and p53 signaling pathways being significantly upregulated in the low expression group (NES \u0026lt; -1) in the single-gene GSEA analysis, which was opposite to the trend in the high expression group (NES\u0026thinsp;\u0026gt;\u0026thinsp;1) in the KEGG enrichment analysis.\u003c/p\u003e\n \u003cp\u003eIn addition, a risk prediction model was constructed based on these 2 key targets to evaluate the risk of BC. The results showed that the expression of MAOA and ADH1C were risk factors for BC, with ADH1C contributing the most to the model (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). Calibration curve analysis and DCA were used to evaluate the accuracy of this risk model. Calibration curve analysis results showed that the model had high sensitivity and specificity (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB). DCA results indicated that using this model for prediction yielded a net benefit superior to the two extreme cases of all patients or no patients over a relatively wide range of threshold probabilities, confirming the potential application value of this prediction model in clinical decision-making (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC). At the same time, this study examined the \u0026quot;active compound-key target\u0026quot; information based on key targets, and the results showed that both the key targets MAOA and ADH1C pointed to the compound Stigmasterol, indicating that Stigmasterol may be the active ligand of key targets, further revealing the key drug efficacy material basis of Pingxiao capsules.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Molecular docking and Dynamics Simulation\u003c/h2\u003e\n \u003cp\u003eTo further validate the direct binding action between key compounds and key gene proteins, molecular docking analysis was conducted. Based on the \u0026quot;active compound-key target\u0026quot; association information, the compound Stigmasterol from Pingxiao capsules was selected to perform molecular docking simulation studies with two key target proteins (MAOA and ADH1C). As shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the binding energies of key compounds with target proteins were low, suggesting good binding affinity between them. Specifically, the binding energy of Stigmasterol with ADH1C was \u0026minus;\u0026thinsp;7.768 kcal/mol, while the binding energy of Stigmasterol with MAOA was \u0026minus;\u0026thinsp;7.799 kcal/mol. These results indicate that Stigmasterol may be a direct ligand of MAOA and ADH1C.\u003c/p\u003e\n \u003cp\u003eTo more intuitively display the binding patterns of these two key ligand-receptor pairs, visual analysis of their docking conformations was further performed. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA, the Stigmasterol molecule can stably bind in the ADH1C protein cavity composed of amino acids Arg61, Cys60, Gly215, lle283, lle238, Arg383, Leu376, Gly213, Asp237, Val282, and Arg285. Further analysis of the interactions between the two showed that the molecule formed hydrophobic interactions and hydrogen bonds with the amino acids around the pocket to promote stable binding between the molecule and the protein. Specifically, the molecule formed a hydrogen bond with the amino acid Arg285 around the protein. At the same time, it also formed hydrophobic interactions with 10 amino acid molecules around the protein pocket, further enhancing the affinity between the molecule and the protein.\u003c/p\u003e\n \u003cp\u003eThe Stigmasterol molecule can also stably bind in the MAOA protein cavity composed of amino acids Trp116, Tyr121, Trp128, Pro107, Glu492, Phe108, Pro114, Phe112, Asn125, and Arg109 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB). Further analysis of the interactions between the two showed that the molecule formed hydrophobic interactions and hydrogen bonds with the amino acids around the pocket to promote stable binding between the molecule and the protein. At the same time, the molecule formed a hydrogen bond with the amino acid Asn125 around the protein. It also formed hydrophobic interactions with 9 amino acid molecules around the protein pocket, further enhancing the affinity between the molecule and the protein.\u003c/p\u003e\n \u003cp\u003eThese results further confirm the possibility of Stigmasterol as a key direct ligand of MAOA and ADH1C, providing new clues for revealing the precise molecular mechanisms of Pingxiao capsules in improving breast cancer.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDocking results of key compound-target protein pairs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinding energy (-kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMOL000449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStigmasterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADH1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMOL000449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStigmasterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo further validate the binding stability between key compounds and key gene proteins, based on the results of molecular docking analysis, this study performed Molecular Dynamics (MD) simulations on Stigmasterol-ADH1C and Stigmasterol-MAOA. The simulations were conducted in the GROMACS 2021 environment using the Amber14SB force field for a duration of 100 ns to evaluate the dynamic behavior, binding stability, and interaction characteristics of the complexes.\u003c/p\u003e\n \u003cp\u003eFirst, the Root Mean Square Deviation (RMSD) of the complexes was analyzed. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eA, the Stigmasterol-ADH1C complex rapidly stabilized within the first 20 ns of the simulation, with RMSD stably maintained at approximately 0.30 nm, indicating that the ligand conformation in the ADH1C binding pocket was relatively stable without significant structural drift. Similarly, the RMSD of the Stigmasterol-MAOA complex also reached stability within the first 20 ns, ultimately maintaining around 0.50 nm, indicating that the conformation of Stigmasterol in the MAOA target was equally stable and likely formed a relatively stable binding mode.\u003c/p\u003e\n \u003cp\u003eTo further evaluate the compactness and overall conformational stability of the complex structures, we calculated the Radius of Gyration (Rg) of the systems (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eB). The results showed that the Rg value of the Stigmasterol-ADH1C complex was stable at approximately 2.2 nm, indicating its overall compact structure. The Rg value of the Stigmasterol-MAOA complex was approximately 2.6 nm, which, although slightly higher than the former, also reflected that the binding state of the ligand in the MAOA binding pocket was relatively tight.\u003c/p\u003e\n \u003cp\u003eAdditionally, the Solvent Accessible Surface Area (SASA) was calculated to explore the degree of solvent exposure and its dynamic changes in the systems. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eC, the SASA values of both complex systems showed a gradual decreasing trend during the simulation process, indicating that as simulation time progressed, the protein-ligand complexes tended toward more stable and closed conformational states, further supporting the stable binding of the systems.\u003c/p\u003e\n \u003cp\u003eFinally, through analysis of the time evolution of hydrogen bond numbers (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eD), it was found that the number of hydrogen bonds formed during the interaction between Stigmasterol and ADH1C as well as MAOA was relatively small. This might be related to the fact that the Stigmasterol molecule itself lacks polar groups (such as fluorine, oxygen, and nitrogen atoms) capable of forming hydrogen bonds. However, despite the relatively small number of hydrogen bonds, from the perspective of overall binding energy, both complexes exhibited relatively low free energies (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that Stigmasterol can interact with target proteins with relatively high binding stability, possibly mainly relying on hydrophobic interactions and van der Waals forces to maintain the stability of its binding conformation.\u003c/p\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe predicted binding free energy and the individual energy components (kJ/mol)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComplex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVan der Waals Energy (\u0026Delta;E\u003csub\u003evdw\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElectrostatic Energy (\u0026Delta;E\u003csub\u003eelec\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolar Solvation Energy (\u0026Delta;G\u003csub\u003epolar\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSASA Energy (\u0026Delta;G\u003csub\u003enonpolar\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinding Energy (\u0026Delta;G\u003csub\u003ebind\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStigmasterol-ADH1C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-201.424\u0026thinsp;\u0026plusmn;\u0026thinsp;16.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.476\u0026thinsp;\u0026plusmn;\u0026thinsp;4.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.882\u0026thinsp;\u0026plusmn;\u0026thinsp;12.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-21.394\u0026thinsp;\u0026plusmn;\u0026thinsp;1.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-149.46\u0026thinsp;\u0026plusmn;\u0026thinsp;11.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStigmasterol-MAOA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-122.352\u0026thinsp;\u0026plusmn;\u0026thinsp;13.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.958\u0026thinsp;\u0026plusmn;\u0026thinsp;11.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.139\u0026thinsp;\u0026plusmn;\u0026thinsp;20.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.262\u0026thinsp;\u0026plusmn;\u0026thinsp;1.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-102.433\u0026thinsp;\u0026plusmn;\u0026thinsp;11.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBreast cancer remains one of the most prevalent malignant tumors among women globally, with persistently high incidence and mortality rates[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Current clinical management primarily employs comprehensive treatment modalities including surgery, chemoradiotherapy, endocrine therapy and targeted therapy[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, challenges such as high risks of recurrence/metastasis and compromised quality of life persist[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Traditional Chinese Medicine (TCM) demonstrates unique advantages in adjuvant breast cancer therapy, exemplified by PXC, which exhibits favorable clinical efficacy. Nevertheless, its molecular mechanisms and pharmacodynamic material basis remain incompletely characterized. Therefore, elucidating PXC's active pharmacological components and multi-target interaction networks holds significant implications for guiding syndrome differentiation-based therapeutic strategies and advancing innovative drug development for breast cancer.\u003c/p\u003e\u003cp\u003eIn this study, differential expression analysis and WGCNA initially identified 2,281 common genes critically associated with breast carcinogenesis and progression. Besides, we systematically characterized 27 principal bioactive components and 256 potential therapeutic targets in PXC. Comparative analysis of disease-drug targets revealed 53 intersection targets, predominantly enriched in KEGG pathways including cell cycle regulation, p53 signaling, tyrosine metabolism, calcium signaling, and tryptophan metabolism. Further application of machine learning algorithms prioritized monoamine oxidase A (MAOA) and alcohol dehydrogenase 1C (ADH1C) as core therapeutic targets. These targets demonstrated significant downregulation in breast cancer tissues and exhibited strong binding affinities with stigmasterol \u0026ndash; a key phytosterol in PXC \u0026ndash; suggesting their direct involvement in mediating stigmasterol's anti-breast cancer effects.\u003c/p\u003e\u003cp\u003eDysregulated cell cycle control and p53 pathway dysfunction constitute critical pathological mechanisms driving breast cancer progression[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As a tumor suppressor, p53 maintains genomic stability by inducing cell cycle arrest and apoptosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies demonstrate aberrant expression of p53 pathway components (e.g., p21, BAX) in breast cancer tissues, leading to abnormal cell cycle progression and apoptotic resistance that fuel tumor proliferation[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our findings identify multiple genes within these pathways as therapeutic targets of PXC, suggesting its anti-tumor effects may stem from dual pathway modulation. GSEA analysis further confirms significant upregulation of both pathways in breast cancer tissues, indicating their persistent activation during tumorigenesis. Notably, PXC contains bioactive compounds (e.g., curcumin, oleanolic acid) with documented capacity to induce G2/M phase arrest and apoptosis via p53 pathway activation[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings collectively propose that PXC may inhibit breast cancer progression by synergistically restoring cell cycle control and promoting apoptosis through coordinated regulation of these interconnected pathways.\u003c/p\u003e\u003cp\u003eAberrant tyrosine metabolism fuels tumor progression through catecholamine biosynthesis - tyrosine serves as the precursor for norepinephrine synthesis, with its rate-limiting enzyme tyrosine hydroxylase (TH) overexpressed in breast tumors. Elevated tyrosine derivatives activate oncogenic MAPK/PI3K cascades to drive malignant proliferation and invasion[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Conversely, suppressed tryptophan metabolism promotes immunosuppression via the kynurenine-NAD\u0026thinsp;+\u0026thinsp;axis. Tumor microenvironment upregulation of indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) depletes tryptophan, reducing NAD\u0026thinsp;+\u0026thinsp;levels and impairing T/NK cell cytotoxicity to facilitate immune evasion[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, calcium signaling dysregulation, mediated by hyperactivated store-operated calcium entry (SOCE) and transient receptor potential (TRP) channels, induces cytosolic Ca\u0026sup2;⁺ overload that sustains tumor-promoting PKC and Ras/Raf/MEK/ERK signaling[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our multi-omics profiling identified these three pathways as potential therapeutic targets of PXC. GSEA revealed contrasting patterns: tyrosine/tryptophan metabolism pathways (normally tumor-suppressive) were downregulated in tumors, while calcium signaling exhibited pathological hyperactivity. Mechanistically, PXC may restore tyrosine/tryptophan metabolic homeostasis through bioactive components like β-sitosterol and ursolic acid, reactivating their tumor-inhibiting functions. Simultaneously, its triterpenoids (e.g., oleanolic acid) likely suppress oncogenic calcium fluxes by modulating TRPC6 and Orai1 channels, thereby disrupting Ca\u0026sup2;⁺-dependent pro-survival signaling. This dual-directional regulation exemplifies PXC's systems-level intervention strategy - rectifying metabolic-ionic imbalances while reinvigorating endogenous tumor suppression mechanisms.\u003c/p\u003e\u003cp\u003eMonoamine oxidase A (MAOA), a mitochondrial outer membrane enzyme, catalyzes the oxidative deamination of monoamine neurotransmitters including norepinephrine and serotonin[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recent research results indicate that different types of cancer exhibit unique regulatory and functional patterns of MAOA. MAOA overexpression was observed in glioma[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], classical Hodgkin's lymphoma[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and prostate cancer[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, it has been reported that the expression of MAOA shows a decreasing trend in pancreatic ductal adenocarcinoma[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], hepatocellular carcinoma (HCC)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]and gastric cancer[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is notable that previous studies have consistently shown that the expression of MAOA in invasive BC is significantly decreased compared with non-cancer cells and normal breast tissues [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], confirming our research results. Alcohol dehydrogenase 1C (ADH1C), a member of the ethanol dehydrogenase family, oxidizes ethanol to acetaldehyde while modulating retinoic acid signaling. ADH1C downregulation has been observed in colorectal and oral cancers, impairing tumor cell differentiation via disrupted retinoic acid biosynthesis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, existing studies have demonstrated that variants in the ADH1C gene (such as Arg272Gln) are associated with alcohol metabolism and may be closely linked to an elevated risk of breast cancer. Our findings reveal significant downregulation of both MAOA and ADH1C in breast cancer tissues with robust diagnostic potential (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.85), nominating them as novel diagnostic biomarkers. Mechanistically, MAOA/ADH1C downregulation may promote tumor proliferation and dedifferentiation by dysregulating neurotransmitter metabolism and retinoic acid signaling.\u003c/p\u003e\u003cp\u003eAs a key active component of PXC, stigmasterol, a widely distributed phytosterol in soybeans and yams with high dietary safety profile, demonstrates promising potential in breast cancer prevention and treatment. Epidemiological studies reveal an inverse correlation between stigmasterol intake and breast cancer incidence risk, suggesting chemopreventive properties[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In vitro experiments confirm its capacity to inhibit breast cancer cell proliferation, invasion, epithelial-mesenchymal transition (EMT), while inducing cell cycle arrest and apoptosis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Animal studies further demonstrate suppression of xenograft tumor growth and metastasis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, clinical evidence supporting stigmasterol's therapeutic efficacy remains scarce. Our molecular-level findings elucidate its novel anti-breast cancer mechanism via directly targeting MAOA/ADH1C, providing scientific rationale for clinical translation. This study pioneers the integration of transcriptomics and network pharmacology to systematically elucidate the molecular mechanisms underlying PXC's anti-breast cancer effects. We identified PXC's multi-target synergistic actions through regulating key pathways (cell cycle, p53 signaling, tyrosine/tryptophan metabolism, and calcium signaling), inhibiting tumor proliferation, inducing apoptosis, and restoring metabolic homeostasis. MAOA and ADH1C emerged as novel diagnostic biomarkers and therapeutic targets, with stigmasterol as a core active component. Molecular docking and dynamics simulations revealed stigmasterol's direct binding to MAOA/ADH1C. These findings provide a comprehensive \"multi-component, multi-target, multi-pathway\" mechanistic framework for PXC's clinical application in syndrome differentiation-based therapy and natural product drug development.\u003c/p\u003e\u003cp\u003eThe study's innovations include: (1) First integration of transcriptomics and network pharmacology to elucidate PXC's anti-breast cancer mechanisms from multi-omics and systems biomedicine perspectives, revealing its holistic network regulation; (2) Identification of MAOA/ADH1C as novel diagnostic biomarkers and therapeutic targets through machine learning algorithms; (3) Atomic-level characterization of stigmasterol-target interactions via molecular docking/dynamics simulations, providing a paradigm for TCM mechanism research; (4) Establish a reproducible methodology applicable to other herbal medicines and diseases, advancing TCM modernization.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, while bioinformatics analysis identified key targets/pathways, experimental validation through RNA interference, Western blot, and immunohistochemistry in breast cancer cell lines/animal models is needed to confirm MAOA/ADH1C regulation by PXC. Second, although stigmasterol's mechanism was preliminarily characterized, synergistic effects among other active components require further investigation using metabolomics and chemical probes.\u003c/p\u003e\u003cp\u003eOur integrated multi-omics approach systematically elucidated the anti-breast cancer mechanisms of PXC, establishing a foundation for further investigation. Advances in omics and bioinformatics will accelerate the modernization of traditional Chinese medicine (TCM), enabling its integration into precision oncology to improve patient outcomes. Future research should prioritize clinical cohort studies to evaluate stigmasterol's efficacy as a dietary intervention or pharmacotherapy, focusing on tumor biomarkers, quality of life, and prognosis. Additionally, exploring synergistic combinations of TCM with endocrine or HER2-targeted therapies may optimize therapeutic strategies guided by TCM syndrome differentiation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHou Xiaoyi and Zhang Peng: Writing-original draft.\u003c/p\u003e\n\u003cp\u003eHou Xiaoyi and Zhang Xu: Writing-review and editing.\u003c/p\u003e\n\u003cp\u003eZhang Li and Zhang Xu: Conceptualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHou Xiaoyi, Zhao Fen, and Zhang Xian:Validation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhang Peng: Formal analysis\u003c/p\u003e\n\u003cp\u003eZhang Xian and Liao Hong: Visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhang Xian and Pu Qiuyi: Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhao Fen: Designed the methodology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhang Li: Acquired funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors participated in reviewing and editing the manuscript and have read and agreed to the published version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Science and Technology Research Project of Sichuan Provincial Administration of Traditional Chinese Medicine, Grant No. 2023MS131.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, et al. 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BMC Pharmacology and Toxicology. 2022,23(1)：42.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, Pingxiao capsules, transcriptomics, network pharmacology, MAOA, ADH1C","lastPublishedDoi":"10.21203/rs.3.rs-7722434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7722434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer is a prevalent malignancy threatening women's health globally. Pingxiao Capsules, a traditional Chinese medicine, exhibit promising efficacy in adjuvant breast cancer treatment, yet their molecular mechanisms remain unclear. Through network pharmacology, WGCNA and machine learning, drug targets related to the treatment of breast cancer with Pingxiao Capsules were screened, and the related functional effects of drug targets were explored through PPI, GO, KEGG enrichment analysis and GSEA. Molecular docking and dynamics simulations were conducted for verification. We identified 2,281 breast cancer-related genes and 256 potential targets of Pingxiao Capsules, with 53 overlapping targets. Functional enrichment revealed involvement in cell cycle, p53 signaling, tyrosine metabolism, and calcium signaling pathways. Machine learning pinpointed MAOA and ADH1C as core targets, validated by molecular docking with Stigmasterol, a key compound in Pingxiao Capsules. Subsequent molecular dynamics simulations confirmed stable binding conformations with low free energies, supporting these protein-ligand interactions as therapeutically relevant. GSEA highlighted pathway dynamics during breast cancer progression. Pingxiao Capsules may exert anti-tumor effects by modulating MAOA, ADH1C, and associated pathways (e.g., cell cycle, p53), offering novel insights into their molecular mechanisms.\u003c/p\u003e","manuscriptTitle":"Elucidating Pingxiao Capsules Anti-Breast Cancer Mechanism via Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 06:38:25","doi":"10.21203/rs.3.rs-7722434/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cf8b273-14eb-4f18-b524-ff15d35a0842","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T08:24:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 06:38:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7722434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7722434","identity":"rs-7722434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}