Exploring the Molecular Mechanisms of Yinqiao Shihu Decoction in the Treatment of Hepatocellular Carcinoma Based on Network Pharmacology, Molecular Docking, and Single-Cell Sequencing Analysis ()
1. Introduction
Hepatocellular Carcinoma (HCC), the major pathological type of primary liver cancer, originates from hepatic epithelial cells and accounts for approximately 75-85% of primary liver cancers in Asia [1]. It is one of the most common malignant tumors of the liver. According to global cancer statistics, there were 18.1 million new cancer cases and 9.6 million cancer-related deaths worldwide in 2018, with approximately 782,000 deaths attributed to liver cancer [2]. China bears a disproportionately high burden of this disease.
Yinqiao is a traditional Chinese medicine formula primarily composed of Lonicera japonica (Yin Hua) and Forsythia suspensa (Lian Qiao), which are known for their heat-clearing and detoxifying effects. It is commonly used for the treatment of wind-heat type common cold and upper respiratory tract infections [3] [4]. Building on the Yinqiao formula, researchers have combined it with Liuwei Dihuang Wan and added Dendrobium officinale (Shi Hu) to develop Yinqiao Shihu Decoction, a compound formula that integrates the therapeutic principles of nourishing Yin, tonifying the kidneys, and clearing heat and detoxifying. Clinical practice has demonstrated that YSD is effective in alleviating symptoms associated with chronic urinary tract infections and kidney Yin deficiency, and it has also shown potential benefits in adjuvant treatment for chronic glomerulonephritis, postoperative recovery after renal carcinoma, and systemic lupus erythematosu.
However, research on YSD remains limited, and its potential role in tumor therapy, particularly in liver cancer, has yet to be fully elucidated. Research on Traditional Chinese Medicine (TCM) and compound formulas often faces challenges such as low detection sensitivity and insufficiently reliable evaluation indices, which hinder the systematic and comprehensive elucidation of their molecular mechanisms.
Therefore, this study employed network pharmacology to explore the potential mechanisms of YSD in the treatment of HCC, providing a theoretical foundation for subsequent experimental design and optimization.
2. Methods
2.1. Composition of Yinqiao Shihu Decoction (YSD)
Yinqiao Shihu Decoction (YSD) is derived from the Yinqiao Formula, Liuwei Dihuang Pill, and Dendrobium officinale, as documented in The Clinical Handbook of Chinese Prescriptions and previous literature. The decoction consists of nine herbal ingredients: Rehmannia glutinosa (Shudihuang), Cornus officinalis (Shanzhuyu), Dioscorea opposita (Shanyao), Poria cocos (Fuling), Alisma orientale (Zexie), Paeonia suffruticosa Andrews (Mudanpi), Lonicera japonica (Jinyinhua), Forsythia suspensa (Lianqiao), and Dendrobium nobile (Shihu).
2.2. Screening of Active Ingredients and Target Collection of Yinqiao Shihu Decoction
The Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) was used to retrieve the chemical constituents of each herb in the formula by searching the keywords “Lonicera japonica” (Jinyinhua), “Rehmannia glutinosa” (Shudihuang), “Paeonia suffruticosa” (Mudanpi), “Cornus officinalis” (Shanzhuyu), “Alisma orientale” (Zexie), “Poria cocos” (Fuling), “Dioscorea opposita” (Shanyao), and “Forsythia suspensa” (Lianqiao). The active components were screened based on pharmacokinetic parameters of Absorption, Distribution, Metabolism, and Excretion (ADME), with Oral Bioavailability (OB) and Drug-Likeness (DL) serving as key indicators. The constituents of Dendrobium (Shihu) were retrieved from the Traditional Chinese Medicine Integrated Database (TCMID) and cross-validated with the TCMSP database to identify bioactive compounds. The chemical structures of these active ingredients were obtained from the databases and redrawn using ChemDraw Ultra 8.0. The corresponding two-dimensional (2D) structures were converted into three-dimensional (3D) conformations using Chem3D Ultra 8.0 and saved in mol2 format. Potential targets of the active compounds were collected from the TCMSP database. Protein names were standardized and converted into corresponding gene names using Perl scripts and the UniProt database.
2.3. Identification of HCC-Related Targets
The GSE62232 dataset, containing data from 10 normal liver tissue samples and 81 Hepatocellular Carcinoma (HCC) tissue samples, was obtained from the Gene Expression Omnibus (GEO) database. Meanwhile, the TCGA-LIHC dataset, including 50 normal liver tissue samples and 374 primary HCC tissue samples, was downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression analyses were conducted on both datasets using the GEO2R web tool and R packages, and the overlapping Differentially Expressed Genes (DEGs) were identified based on the results. To further determine HCC-related targets, the Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), and GeneCards databases were queried using the keyword “Hepatocellular Carcinoma”, and the identified targets were considered as candidate genes. The DEGs and candidate targets were then integrated using the Venny 2.1.0 online tool and intersected with the targets of YSD. The overlapping genes, visualized in a Venn diagram, were identified as the potential key targets of YSD in the treatment of HCC.
2.4. Construction of the Protein-Protein Interaction (PPI) Network
To evaluate the potential protein-protein interactions among the identified key targets, these targets were imported into the database for analysis. The resulting interaction data were saved as a TSV file and subsequently imported into Cytoscape software to construct the protein-protein interaction network. The top five core target proteins were then identified based on their degree of connectivity within the network.
2.5. GO and KEGG Enrichment Analysis of Core Targets
Biological pathways perform specific physiological functions through the interactions among their constituent target proteins, providing the mechanistic basis for understanding the clinical manifestations of diseases [5]. The core target genes were imported into the DAVID database, and official gene symbols were used for identification. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then conducted, and the top 15 significantly enriched results were selected for visualization.
2.6. Construction of the “Active Compound-Key Target-Pathway” Network
Based on the KEGG enrichment analysis results, an “Active Compound-Key Target-Pathway” network was constructed using Cytoscape 3.7.2, reflecting the interactions between active compounds and key targets as well as between key targets and pathways. In this network, nodes represent active compounds, targets, and pathways, while edges indicate the interactions among them. The merge function was used to integrate this network with the Protein-Protein Interaction (PPI) network, and topological analysis was performed using the Network Analyzer plugin. Higher degree values indicate greater importance of the target in the therapeutic process of YSD against HCC. Based on the degree values, the top five core target proteins and corresponding core active compounds were identified.
2.7. Molecular Docking
The selected core target proteins were used as receptors, and their corresponding crystal structures were obtained from the Protein Data Bank (PDB). The active compounds of YSD-quercetin, kaempferol, luteolin, wogonin, and β-sitosterol-were employed as ligands, with the previously saved mol2 files uploaded. Molecular docking of the core target proteins and ligands was performed using AutoDockTools 1.5.6. Grid energy calculations were conducted using AutoGrid, and the Genetic Algorithm was applied for docking. Binding energy was used to assess the affinity between the receptor and ligand.
2.8. Prognostic and Single-Cell Analysis of Key Candidate Genes
To further elucidate the potential key genes linking YSD and Hepatocellular Carcinoma (HCC) progression, results from multiple analytical layers were integrated. Genes that were simultaneously ranked among the top nodes in the PPI network and included in the “Active Compound-Key Target-Pathway” network were first shortlisted. Among these, those exhibiting strong binding affinities to major active compounds (binding energy < −5.0 kcal/mol) in molecular docking and previously reported to be associated with tumorigenesis or HCC development were prioritized. Based on these criteria, five representative genes—NCOA2, PTGS1, RELA, CCND1, and RAF1—were selected for subsequent prognostic and single-cell analyses.
The prognostic significance of these targets was evaluated using the GEPIA2 online platform. A 95% confidence interval was applied, and the median expression level (50%) was used as the cut-off value to classify patients into high- and low-expression groups. The correlations between the expression levels of NCOA2, PTGS1, RELA, CCND1, and RAF1 and Overall Survival (OS) outcomes in HCC patients were then analyzed.
2.9. Single-Cell RNA Sequencing Analysis
We analysed three single-cell RNA sequencing (scRNA-seq) samples from the GEO database (GSM8825750, GSM8825751, GSM8825752) belonging to the series GSE290925 (“Single-Cell Transcriptomic Profiling of the Tumor Microenvironment in Treatment-Naive Hepatocellular Carcinoma Patients”). Each sample represents a surgical puncture tumour specimen obtained before any treatment from a Hepatocellular Carcinoma (HCC) patient. The scRNA libraries were constructed using the 10× Genomics Chromium Single Cell 3' Reagent v3 Kit and sequenced on an Illumina NextSeq 500 platform according to the manufacturer’s protocol. Cells with mitochondrial gene content exceeding 25% were filtered out, followed by data normalization using the LogNormalize method, and 2000 highly variable genes were selected. The Harmony algorithm was subsequently applied to correct batch effects. Cell clustering was performed based on Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Cell type annotation was conducted using the SingleR package in conjunction with a human primary cell atlas, and the expression patterns of core genes were visualized on t-SNE plots, providing a systematic characterization of single-cell heterogeneity.
3. Result
3.1. Screening of Active Compounds and Target Collection of Yinqiao Shihu Decoction
Through database retrieval and screening, 2 compounds of Rehmannia glutinosa (Shudihuang), 20 compounds of Cornus officinalis (Shanzhuyu), 16 compounds of Dioscorea opposita (Shanyao), 10 compounds of Alisma orientale (Zexie), 11 compounds of Paeonia suffruticosa (Mudanpi), 15 compounds of Poria cocos (Fuling), 23 compounds of Lonicera japonica (Jinyinhua), 23 compounds of Forsythia suspensa (Lianqiao), and 3 compounds of Dendrobium (Shihu) were identified (two additional Dendrobium compounds with drug-likeness < 0.18 were included due to limited matches across the two databases; see Appendix Table A1). Targets of these active compounds were collected from the TCMSP database and mapped to gene symbols using the UniProt database. After removing duplicates, a total of 120 unique targets were obtained.
3.2. Identification of HCC-Related Targets
Differential expression analysis was performed using sample data from the GEO and TCGA databases, combined with searches in the TTD, OMIM, and GeneCards databases. A total of 949 overlapping differentially expressed genes and 7100 disease-related targets were identified. In the TCGA dataset, 7821 genes were upregulated and 1493 were downregulated (Figure 1(A)). Based on the intersection with the targets of YSD, 101 key targets for HCC treatment were obtained (Figure 1(B), Figure 1(C)).
Figure 1. Screening of active compounds and identification of key targets related to Hepatocellular Carcinoma (HCC). (A) Differentially expressed genes in HCC vs. normal tissues from TCGA, including 7821 upregulated and 1493 downregulated genes. (B) (C) Venn diagram showing overlap between YSD targets and HCC-related genes; 101 intersecting genes were identified as key targets.
3.3. Construction of the Protein-Protein Interaction Network
The identified key targets were input into the STRING database to obtain the Protein-Protein Interaction (PPI) network, and the resulting interaction data were imported into Cytoscape for network construction (see Appendix Figure A1(A)). In the network, larger nodes indicate higher degree values. ALB, IL6, CASP3, EGFR, and VEGFA exhibited the highest degrees and were identified as the core target proteins.
3.4. GO Functional Annotation and KEGG Pathway Enrichment
GO functional enrichment analysis using the DAVID online database identified a total of 121 GO terms, including 79 Biological Process (BP) terms, 18 Cellular Component (CC) terms, and 24 Molecular Function (MF) terms. KEGG pathway enrichment analysis revealed 75 significantly enriched pathways. The major biological processes involved positive/negative regulation of RNA polymerase II promoter transcription, DNA transcription, negative regulation of apoptosis, and negative regulation of cell proliferation. Cellular components were primarily associated with the nucleus, cytoplasm, extracellular exosome, and cytosol. Molecular functions included transcription factor activity, sequence-specific DNA binding, zinc ion binding, and transcription activator activity. Significantly enriched pathways included PI3K-Akt, TNF, MAPK, cancer-related pathways, and hepatitis B signaling (see Appendix Figures A1(B)-(E)).
3.5. Construction and Analysis of the “Active Compound-Key Target-Pathway” Network
The constructed “Active Compound-Key Target-Pathway” network comprised 62 active compounds, 101 target proteins, and 20 pathways. In the network, red nodes represent active compounds, blue nodes represent key targets, and green nodes represent pathways (see Appendix Figure A2(A)). Based on node degree, the top five core targets were identified as MYC, ESR1, CCND1, ALB, and AR (in addition to the core targets identified from the PPI network), while the top five core active compounds were quercetin, kaempferol, luteolin, wogonin, and β-sitosterol, which were selected for subsequent analyses.
3.6. Molecular Docking and Analysis
The crystal structures of the 10 selected core targets were downloaded from the Protein Data Bank (PDB), and molecular docking was performed between the core targets and core active compounds (see Appendix Figure A1(B)). The results indicated that the five selected core active compounds exhibited low binding energies with most core targets, with β-sitosterol and wogonin showing the strongest binding affinities. Targets including RAF1, PTGS1, and NCOA2 demonstrated favorable binding with the core compounds; these proteins have been previously reported in HCC-related studies, and their molecular docking results showed relatively low binding energies (see Appendix Figures A1(C)-(F)).
3.7. Prognostic Analysis of Key Targets NCOA2, PTGS1, RELA, CCND1, and RAF1
Analysis revealed that the mRNA expression levels of PTGS1 and RELA were significantly associated with Overall Survival (OS) in patients (P < 0.05), indicating that both serve as adverse prognostic factors in HCC (Figure 2).
Figure 2. Survival analysis of RAF1 and EGFR in HCC patients.
3.8. Single-Cell Data Quality Control and Batch Correction
Cells with mitochondrial gene content exceeding 25% were filtered out from the single-cell dataset (Figure 3(A)). After filtering, the number of detected RNAs was highly positively correlated with the number of detected molecules (R = 0.91) (Figure 3(B)). Batch effects were subsequently corrected using the Harmony package, and t-SNE plots were generated before and after batch correction (Figure 3(C), Figure 3(D)). The first 10 Principal Components (PCs) were selected for clustering analysis (Figure 3(E)).
3.9. Expression Patterns of Key Genes NCOA2, PTGS1, RELA, CCND1, and RAF1 at the Single-Cell Level
Cell type annotation was performed using the SingleR package, classifying the cell populations into T cells, monocytes, macrophages, fibroblasts, tissue stem cells, and NK cells (Figure 4(A)). The results demonstrated that NCOA2, RELA, and RAF1 exhibited the highest average expression levels in monocytes, PTGS1 showed the highest expression in NK cells, and CCND1 displayed the highest expression in fibroblasts (Figure 4(B)).
Figure 3. Quality control and batch correction of single-cell data. (A) Cells with >25% mitochondrial gene content were removed. (B) A strong positive correlation was observed between the number of detected genes and UMIs (R = 0.91). (C) (D) t-SNE visualization before and after batch correction using Harmony. (E) The top 10 Principal Components (PCs) were selected based on variance. (F) Clustering was performed at a resolution of 1 for downstream analyses.
4. Discussion
Yinqiao Shihu Decoction (YSD) originates from a modern formula, and there are few clinical or pharmacological studies available regarding its therapeutic effects, particularly against Hepatocellular Carcinoma (HCC). Therefore, this study aimed to explore the potential mechanisms by which YSD exerts its anti-HCC effects using an integrated approach combining network pharmacology, molecular docking, and single-cell transcriptomic analysis.
Figure 4. Cell type annotation and expression profiles of core genes. (A) Cell populations were annotated using the SingleR package. (B) NCOA2, RELA, and RAF1 were highly expressed in monocytes, PTGS1 in NK cells, and CCND1 in fibroblasts.
In this study, we first identified the active compounds and their corresponding targets of YSD from multiple databases and intersected them with HCC-related genes to obtain potential therapeutic targets. Protein-protein interaction (PPI) network analysis revealed that ALB, IL6, CASP3, EGFR, and VEGFA had high connectivity and interacted with multiple other targets, suggesting that they might play pivotal roles in the anti-HCC effects of YSD. GO and KEGG enrichment analyses further indicated that these targets were mainly enriched in PI3K-Akt, TNF, and MAPK signaling pathways. In the constructed “active compound-key target-pathway” network, quercetin, kaempferol, luteolin, wogonin, and β-sitosterol exhibited high degree values, suggesting that they are the major bioactive components of the decoction. Moreover, NCOA2, PTGS1, RELA, CCND1, and RAF1 were identified as hub genes in the network. Molecular docking analysis confirmed that these core compounds had favorable binding affinities with the corresponding key proteins. Molecular docking analysis further confirmed that these five core active compounds exhibited strong binding affinities with most hub targets, with β-sitosterol and wogonin showing the lowest binding energies. Among them, RAF1, PTGS1, and NCOA2 displayed particularly favorable binding interactions. These results suggest that YSD may exert therapeutic effects against HCC through multi-component, multi-target, and multi-pathway interactions.
Survival analysis revealed that high mRNA expression levels of PTGS1 and RELA were significantly associated with poorer overall survival in HCC patients, indicating that they might serve as adverse prognostic factors. Single-cell RNA sequencing analysis further showed that these key genes displayed cell-type-specific expression patterns: NCOA2, RELA, and RAF1 were mainly expressed in monocytes, PTGS1 showed the highest expression in NK cells, and CCND1 was predominantly expressed in fibroblasts. These findings suggest that YSD may regulate multiple cell types in the tumor microenvironment, thereby affecting HCC progression.
Mechanistically, Interleukin-6 (IL-6), a cytokine produced by various cell types, plays a crucial role in immune activation, signal transduction, and inflammation [6]. The IL-6/STAT3 signaling pathway has been shown to promote HCC cell growth and drug resistance, whereas its inhibition can polarize macrophages toward the M1 phenotype, thereby reducing the proliferation, invasion, and migration of HCC cells while promoting apoptosis. CASP3 is a critical executioner in apoptosis, and its downregulation suppresses apoptosis and promotes tumor growth [7]. The Epidermal Growth Factor Receptor (EGFR) pathway, particularly EGFR-PI3K-PDK1, is also crucial in hepatocarcinogenesis, activating YAP signaling to enhance cell proliferation [8]. VEGFA plays a central role in tumor angiogenesis by promoting endothelial cell proliferation and vascular permeability; inhibition of VEGFA signaling suppresses tumor growth and angiogenesis [9]. RAF1, acting as an oncogene, promotes HCC progression, and its inhibition can effectively halt tumor development [10].
The PI3K/Akt signaling pathway is widely recognized as a key regulator of cell proliferation, invasion, migration, and apoptosis in liver cancer, particularly Hepatocellular Carcinoma (HCC) [11]. Studies have shown that activation of the PI3K/Akt pathway can promote glucose uptake, glycolysis, proliferation, epithelial-mesenchymal transition, expression of matrix metalloproteinases, and angiogenesis in HCC cells, thereby enhancing their invasiveness and metastatic potential, while simultaneously inhibiting apoptosis and autophagy to facilitate tumor cell survival [12] [13]. Our findings indicate that several critical targets of YSD, such as EGFR, GSK3B, MTOR, and VEGFA, are enriched in the PI3K-Akt signaling pathway, implying that the decoction may interfere with HCC cell proliferation, invasion, and migration by modulating this pathway. Additionally, YSD may regulate the TNF signaling pathway through CASP3 and IL6, thereby affecting tumor growth and metastasis. The MAPK signaling pathway, which plays a vital role in cell proliferation, apoptosis, invasion, and angiogenesis, is also significantly involved in HCC pathogenesis; its inhibition can suppress tumor growth and angiogenesis [14]. Collectively, these results suggest that YSD exerts multi-component, multi-target, and multi-pathway synergistic effects to interfere with HCC progression.
However, it should be noted that this study is entirely based on computational predictions. The predicted compound-target interactions and potential therapeutic effects of YSD still require experimental validation in vitro and in vivo to confirm their reliability. Furthermore, these findings provide guidance for future research and clinical translation. Key genes identified, such as PTGS1 and RELA, may serve as potential biomarkers to monitor the efficacy of YSD treatment in HCC, and could be considered as targets for subsequent mechanistic and therapeutic studies.
Building on these insights, this study systematically elucidated the potential mechanisms of YSD in treating hepatocellular carcinoma through an integrated approach combining network pharmacology, molecular docking, and single-cell transcriptomic analysis. The findings indicate that the decoction may act through major active components—quercetin, kaempferol, luteolin, wogonin, and β-sitosterol—to target multiple key genes such as ALB, IL6, CASP3, and VEGFA, thereby modulating signaling pathways including PI3K-Akt, TNF, and MAPK to suppress HCC development. Moreover, high expression levels of PTGS1 and RELA were significantly associated with poor prognosis in HCC patients, suggesting that these genes may serve as important prognostic biomarkers and potential therapeutic targets for YSD in the treatment of HCC.
Funding
Guangxi Natural Science Foundation (Grant No. 2025GXNSFHA069094), Baise City Science and Technology Plan Project (Science and Technology Infrastructure Support Program) (Grant No.ZJ252812), High-Level Talents Introduction Program of Youjiang Medical University for Nationalities (Grant No. YY2021sk02), National Undergraduate Innovation and Entrepreneurship Training Program (Grant No. 202310599005), Guangxi Undergraduate Innovation and Entrepreneurship Training Program (Grant Nos. 202410599001, S202410599049).
Author Contributions
Ya-Zhou Yang: Writing—review & editing, Writing—original draft, Software, Investigation, Formal analysis, Data curation, Conceptualization.
Yong-Le Li: review & editing, Software, Investigation, Formal analysis, Data curation, Conceptualization.
Xuan-Hua Chen: review & editing.
Xin-Ling Shang: review & editing.
Jun Tang: Conceptualization, Supervision, Methodology, Writing—review & editing, Visualization, Software, Resources, Funding acquisition, Formal analysis.
Li-He Jiang: Conceptualization, Supervision, Methodology, Writing—review & editing, Visualization, Software, Resources, Funding acquisition, Formal analysis.
Appendix
Table A1. Information on the active compounds of Yinqiao Shihu Decoction.
Mol ID |
Active compound |
OB (%) |
DL |
Single herb |
MOLO00359 |
Sitosterol |
36.91 |
0.75 |
Rehmannia glutinosa (Shu Dihuang) |
MOLO00449 |
Stigmasterol |
43.83 |
0.76 |
Rehmannia glutinosa (Shu Dihuang) |
MOLO01494 |
Mandenol |
42 |
0.19 |
Cornus officinalis (Shanzhuyu) |
MOLO01495 |
Ethyl linolenate |
46.1 |
0.2 |
Cornus officinalis (Shanzhuyu) |
MOLO01771 |
Poriferast-5-en-3beta-ol |
36.91 |
0.75 |
Cornus officinalis (Shanzhuyu) |
MOL002879 |
Diop |
43.59 |
0.39 |
Cornus officinalis (Shanzhuyu) |
MOLO02883 |
Ethyl oleate (NF) |
32.4 |
0.19 |
Cornus officinalis (Shanzhuyu) |
MOLO03137 |
Leucanthoside |
32.12 |
0.78 |
Cornus officinalis (Shanzhuyu) |
MOLO00358 |
Beta-sitosterol |
36.91 |
0.75 |
Cornus officinalis (Shanzhuyu) |
MOL000359 |
Sitosterol |
36.91 |
0.75 |
Cornus officinalis (Shanzhuyu) |
MOLO00449 |
Stigmasterol |
43.83 |
0.76 |
Cornus officinalis (Shanzhuyu) |
MOLO05360 |
Malkangunin |
57.71 |
0.63 |
Cornus officinalis (Shanzhuyu) |
MOL005481 |
2,6,10,14,18-pentamethylicosa-2,6,10,14,18-pentaene |
33.4 |
0.24 |
Cornus officinalis (Shanzhuyu) |
MOLO05486 |
3,4-Dehydrolycopen-16-al |
46.64 |
0.49 |
Cornus officinalis (Shanzhuyu) |
MOLO05489 |
3,6-Digalloylglucose |
31.42 |
0.66 |
Cornus officinalis (Shanzhuyu) |
MOLO05503 |
Cornudentanone |
39.66 |
0.33 |
Cornus officinalis (Shanzhuyu) |
MOLO05530 |
Hydroxygenkwanin |
36.47 |
0.27 |
Cornus officinalis (Shanzhuyu) |
MOLO05531 |
Telocinobufagin |
69.99 |
0.79 |
Cornus officinalis (Shanzhuyu) |
MOLO08457 |
Tetrahydroalstonine |
32.42 |
0.81 |
Cornus officinalis (Shanzhuyu) |
MOLOO0554 |
Gallic acid-3-0-(6’-0-galloyl)-glucoside |
30.25 |
0.67 |
Cornus officinalis (Shanzhuyu) |
MOLO05552 |
gemin D |
68.83 |
0.56 |
Cornus officinalis (Shanzhuyu) |
MOLO05557 |
Lanosta-8,24-dien-3-ol,3-acetate |
44.3 |
0.82 |
Cornus officinalis (Shanzhuyu) |
MOLO01559 |
Piperlonguminine |
30.71 |
0.18 |
Dioscorea opposita (Shanyao) |
MOL001736 |
(-)-taxifolin |
60.51 |
0.27 |
Dioscorea opposita (Shanyao) |
MOLO00310 |
Denudatin B |
61.47 |
0.38 |
Dioscorea opposita (Shanyao) |
MOLO00322 |
Kadsurenone |
54.72 |
0.38 |
Dioscorea opposita (Shanyao) |
MOL005429 |
Hancinol |
64.01 |
0.37 |
Dioscorea opposita (Shanyao) |
MOLO05430 |
Hancinone C |
59.05 |
0.39 |
Dioscorea opposita (Shanyao) |
MOL005435 |
24-Methylcholest-5-enyl-3belta-0-glucopyranoside_qt |
37.58 |
0.72 |
Dioscorea opposita (Shanyao) |
MOLO05438 |
Campesterol |
37.58 |
0.71 |
Dioscorea opposita (Shanyao) |
MOLO05440 |
Isofucosterol |
43.78 |
0.76 |
Dioscorea opposita (Shanyao) |
MOLO00449 |
Stigmasterol |
43.83 |
0.76 |
Dioscorea opposita (Shanyao) |
MOL005458 |
DioscoresideC_qt |
36.38 |
0.87 |
Dioscorea opposita (Shanyao) |
MOLO00546 |
Diosgenin |
80.88 |
0.81 |
Dioscorea opposita (Shanyao) |
MOLO05461 |
Doradexanthin |
38.16 |
0.54 |
Dioscorea opposita (Shanyao) |
MOLO05463 |
Methylcimicifugoside_qt |
31.69 |
0.24 |
Dioscorea opposita (Shanyao) |
MOLO05465 |
AIDS180907 |
45.33 |
0.77 |
Dioscorea opposita (Shanyao) |
MOL000953 |
CLR |
37.87 |
0.68 |
Dioscorea opposita (Shanyao) |
MOLO00359 |
Sitosterol |
36.91 |
0.75 |
Alisma orientale (Zexie) |
MOLO00830 |
Aliso1 B |
34.47 |
0.82 |
Alisma orientale (Zexie) |
MOL000831 |
Alisol B monoacetate |
35.58 |
0.81 |
Alisma orientale (Zexie) |
MOLO00832 |
Alisol, b,23-acetate |
32.52 |
0.82 |
Alisma orientale (Zexie) |
MOL000849 |
16β-methoxyalisol B monoacetate |
32.43 |
0.77 |
Alisma orientale (Zexie) |
MOL000853 |
Alisol B |
36.76 |
0.82 |
Alisma orientale (Zexie) |
MOLO00854 |
Alisol C |
32.7 |
0.82 |
Alisma orientale (Zexie) |
MOLO00856 |
Alisol C monoacetate |
33.06 |
0.83 |
Alisma orientale (Zexie) |
MOLO02464 |
1-Monolinolein [(1S,3R)-1-[(2R)-3,3-dimethyloxiran-2-yl]-3-[(5R,8S,9S, 10S,11S,14R)-11 |
37.18 |
0.3 |
Alisma orientale (Zexie) |
MOL000862 |
-Hydroxy-4, 4,8, 10, 14-pentamethy1-3-oxo-1,2,5, 6, 7,9,11,12,15,16-decahyd rocyclopenta[a]phenanthren-17-y1]bu tyl] acetate |
35.58 |
0.81 |
Alisma orientale (Zexie) |
MOL001925 |
paeoniflorin_qt |
68.18 |
0.4 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO00211 |
Mairin |
55.38 |
0.78 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO00359 |
Sitosterol |
36.91 |
0.75 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO00422 |
Kaempferol |
41.88 |
0.24 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOL000492 |
(+)-catechin |
54.83 |
0.24 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO07003 |
Benzoyl paeoniflorin |
31.14 |
0.54 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO07369 |
4-0-methylpaeoniflorin_qt |
67.24 |
0.43 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO07374 |
5-[[5-(4-methoxyphenyl)-2-furyl] met hylene] barbituric acid |
43.44 |
0.3 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO07382 |
Mudanpioside-h_qt 2 |
42.36 |
0.37 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO07384 |
Paeonidanin_qt |
65.31 |
0.35 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOL000098 |
Quercetin (2R)-2-[(3S,5R.10S.13R.14R,16R.17R) -3,16-dihydroxy-4,4,10,13, 14-pentam |
46.43 |
0.28 |
Paeonia suffruticosa Andrews (Mudanpi) |
MOLO00273 |
Ethyl-2,3, 5, 6, 12, 15, 16, 17-octahydro-1H-cyclopenta[a]phenanthren-17-y1]-6-methylhept-5-enoic acid |
30.93 |
0.81 |
Poria cocos (Fuling) |
MOLO00275 |
Trametenolic acid |
38.71 |
0.8 |
Poria cocos (Fuling) |
MOLO00276 |
7,9(11)-dehydropachymic acid |
35.11 |
0.81 |
Poria cocos (Fuling) |
MOL000279 |
Cerevisterol (2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13, 14-pentam |
37.96 |
0.77 |
Poria cocos (Fuling) |
MOL000280 |
Ethyl-2,3,5, 6, 12,15, 16, 17-octahydro-1H-cyclopenta[a]phenanthren-17-y1]-5-isopropyl-hex-5-enoic acid |
31.07 |
0.82 |
Poria cocos (Fuling) |
MOL000282 |
Ergosta-7,22E-dien-3beta-ol |
43.51 |
0.72 |
Poria cocos (Fuling) |
MOL000283 |
Ergosterol peroxide (2R)-2-[(5R,10S,13R,14R,16R,17R)-16-hydroxy-3-keto-4,4, 10,13, 14-pentam |
40.36 |
0.81 |
Poria cocos (Fuling) |
MOLO00285 |
Ethy1-1,2,5, 6, 12, 15, 16, 17-oc tahydro cyclopenta[a]phenanthren-17-yl]-5-i sopropyl-hex-5-enoic acid |
38.26 |
0.82 |
Poria cocos (Fuling) |
MOL000287 |
3beta-Hydroxy-24-methylene-8-lanost ene-21-oic acid |
38.7 |
0.81 |
Poria cocos (Fuling) |
MOLO00289 |
Pachymic acid |
33.63 |
0.81 |
Poria cocos (Fuling) |
MOLO00290 |
Poricoic acid A |
30.61 |
0.76 |
Poria cocos (Fuling) |
MOLO00291 |
Poricoic acid B |
30.52 |
0.75 |
Poria cocos (Fuling) |
MOLO00292 |
Poricoic acid C |
38.15 |
0.75 |
Poria cocos (Fuling) |
MOLO00296 |
Hederagenin |
36.91 |
0.75 |
Poria cocos (Fuling) |
MOLO00300 |
Dehydroeburicoic acid |
44.17 |
0.83 |
Poria cocos (Fuling) |
MOLO01494 |
Mandenol |
42 |
0.19 |
Lonicera japonica (Jinyinhua) |
MOLO01495 |
Ethyl linolenate |
46.1 |
0.2 |
Lonicera japonica (Jinyinhua) |
MOLO02707 |
Phytofluene |
43.18 |
0.5 |
Lonicera japonica (Jinyinhua) |
MOLO02914 |
Eriodyctiol (flavanone) (-)-(3R,8S,9R,9aS,10aS)-9-etheny1-8 -(beta-D-glucopyranosyloxy)-2,3,9,9 |
41.35 |
0.24 |
Lonicera japonica (Jinyinhua) |
MOLO03006 |
a,10,10a-hexahydro-5-oxo-5H,8H-pyra no[4,3-d] oxazolo[3,2-a] pyridine-3-c arboxylic acid_qt |
87.47 |
0.23 |
Lonicera japonica (Jinyinhua) |
MOLO03014 |
secologanic dibutylacetal_qt |
53.65 |
0.29 |
Lonicera japonica (Jinyinhua) |
MOL002773 |
Beta-carotene |
37.18 |
0.58 |
Lonicera japonica (Jinyinhua) |
MOLO03036 |
ZINC03978781 |
43.83 |
0.76 |
Lonicera japonica (Jinyinhua) |
MOLO03044 |
Chryseriol |
35.85 |
0.27 |
Lonicera japonica (Jinyinhua) |
MOLO03059 |
kryptoxanthin |
47.25 |
0.57 |
Lonicera japonica (Jinyinhua) |
MOL003062 |
4,5'-Retro-. beta., beta.-Carotene-3,3'-dione,4',5'-didehydro- |
31.22 |
0.55 |
Lonicera japonica (Jinyinhua) |
MOL003095 |
5-hydroxy-7-methoxy-2-(3,4, hoxyphenyl) chromone |
51.96 |
0.41 |
Lonicera japonica (Jinyinhua) |
MOLO03101 |
7-epi-Vogeloside |
46.13 |
0.58 |
Lonicera japonica (Jinyinhua) |
MOLO03108 |
Caeruloside C |
55.64 |
0.73 |
Lonicera japonica (Jinyinhua) |
MOLO03111 |
Centauroside_qt |
55.79 |
0.5 |
Lonicera japonica (Jinyinhua) |
MOLO03117 |
Ioniceracetalides B_qt |
61.19 |
0.19 |
Lonicera japonica (Jinyinhua) |
MOLO03124 |
XYLOSTOSIDINE |
43.17 |
0.64 |
Lonicera japonica (Jinyinhua) |
MOLO03128 |
dinethylsecologanoside |
48.46 |
0.48 |
Lonicera japonica (Jinyinhua) |
MOLO00358 |
Beta-sitosterol |
36.91 |
0.75 |
Lonicera japonica (Jinyinhua) |
MOL000422 |
Kaempferol |
41.88 |
0.24 |
Lonicera japonica (Jinyinhua) |
MOLO00449 |
Stigmasterol |
43.83 |
0.76 |
Lonicera japonica (Jinyinhua) |
MOLO00006 |
Luteolin |
36.16 |
0.25 |
Lonicera japonica (Jinyinhua) |
MOL000098 |
Quercetin |
46.42 |
0.28 |
Lonicera japonica (Jinyinhua) |
MOLO00173 |
Wogonin |
30.68 |
0.23 |
Forsythia suspensa (Lianqiao) |
MOL003281 |
20(S)-dammar-24-ene-3 β,20-diol-3-acetate (2R,3R,4S)-4-(4-hydroxy-3-methoxy-p |
40.23 |
0.82 |
Forsythia suspensa (Lianqiao) |
MOL003283 |
henyl)-7-methoxy-2,3-dimethylol-tet ralin-6-ol |
66.51 |
0.39 |
Forsythia suspensa (Lianqiao) |
MOL003290 |
(3R,4R)-3,4-bis[(3,4-dimethoxypheny1) methyl] oxolan-2-one |
52.3 |
0.48 |
Forsythia suspensa (Lianqiao) |
MOL003295 |
（+)-pinoresinol monomethyl ether |
53.08 |
0.57 |
Forsythia suspensa (Lianqiao) |
MOLO03305 |
PHILLYRIN |
36.4 |
0.86 |
Forsythia suspensa (Lianqiao) |
MOLO03306 |
ACon1_001697 |
85.12 |
0.57 |
Forsythia suspensa (Lianqiao) |
MOL003308 |
(+)-pinoresinol monomethyl ether-4-D-beta-glucoside_qt |
61.2 |
0.57 |
Forsythia suspensa (Lianqiao) |
MOL003315 |
3beta-Acety1-20,25-epoxydammarane-24alpha-ol |
33.07 |
0.79 |
Forsythia suspensa (Lianqiao) |
MOLO00211 |
Mairin |
55.38 |
0.78 |
Forsythia suspensa (Lianqiao) |
MOLO03322 |
FORSYTHINOL |
81.25 |
0.57 |
Forsythia suspensa (Lianqiao) |
MOL003330 |
（-)-Phillygenin |
95.04 |
0.57 |
Forsythia suspensa (Lianqiao) |
MOL003344 |
β-amyrin acetate |
42.06 |
0.74 |
Forsythia suspensa (Lianqiao) |
MOL003347 |
Hyperforin |
44.03 |
0.6 |
Forsythia suspensa (Lianqiao) |
MOLO03348 |
Adhyperforin |
44.03 |
0.61 |
Forsythia suspensa (Lianqiao) |
MOL003365 |
Lactucasterol |
40.99 |
0.85 |
Forsythia suspensa (Lianqiao) |
MOLO03370 |
Onjixanthone I |
79.16 |
0.3 |
Forsythia suspensa (Lianqiao) |
MOL000358 |
Beta-sitosterol |
36.91 |
0.75 |
Forsythia suspensa (Lianqiao) |
MOL000422 |
Kaempferol |
41.88 |
0.24 |
Forsythia suspensa (Lianqiao) |
MOLO00522 |
Arctiin |
34.45 |
0.84 |
Forsythia suspensa (Lianqiao) |
MOL000006 |
Luteolin |
36.16 |
0.25 |
Forsythia suspensa (Lianqiao) |
MOL000791 |
Bicuculline |
69.67 |
0.88 |
Forsythia suspensa (Lianqiao) |
MOL000098 |
Quercetin |
46.43 |
0.28 |
Forsythia suspensa (Lianqiao) |
MOL004793 |
Nodakenetin |
84.77 |
0.18 |
Dendrobium nobile (Shihu) |
MOLO01999 |
Scoparone |
74.75 |
0.09 |
Dendrobium nobile (Shihu) |
MOLO05074 |
Shikimic acid |
46.24 |
0.04 |
Dendrobium nobile (Shihu) |
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Figure A1. Construction of the Protein-Protein Interaction (PPI) network and functional enrichment analysis. (A) PPI network constructed via STRING and visualized in Cytoscape; larger nodes indicate higher degree. Core targets: ALB, IL6, CASP3, EGFR, VEGFA. (B) - (D) GO enrichment for Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). (E) KEGG pathway enrichment highlighting PI3K-Akt, TNF, and MAPK signaling pathways.
Figure A2. Construction and analysis of the “Active Compound-Key Target-Pathway” network and molecular docking results. (A) The “Active Compound-Key Target-Pathway” network included 62 active compounds, 101 target proteins, and 20 pathways. Red nodes represent active compounds, blue nodes represent key targets, and green nodes represent pathways. (B) Molecular docking binding energy heat map. (C) Docking of quercetin and RAF1. (D) Docking of beta-sitosterol and RAF1. (E) Docking of kaempferol and RAF1. (F) Docking of beta-sitosterol and EGFR.