Active compounds and potential targets of Shuganning injection in the treatment of hepatocellular carcinoma by network pharmacology and in vitro validation

Shuganning injection (SGNI), a TCM (traditional Chinese medicine) injection with good hepatoprotective effects, exerted therapeutic effects on hepatocellular carcinoma (HCC). However, the active compounds and effects of SGNI on HCC remain unclear. The objective of this study was to investigate the active compounds and potential targets of SGNI in the treatment of HCC, and explore the molecular mechanisms of main compounds. Network pharmacology was applied to predict the active compounds and targets of SGNI on cancer. The interactions between active compounds and target proteins were validated by drug affinity responsive target stability (DARTS), cellular thermal shift assay (CETSA), and pull-down assay. The in vitro test of the effects and mechanism of vanillin and baicalein was elucidated by MTT, western blot, immunofluorescence, and apoptosis analysis. According to compound characteristics, targets, etc., two typical active ingredients (vanillin and baicalein) were selected as representatives to explore the effects on HCC. Vanillin (an important food additive) bound to NF-κB1 and baicalein (a bioactive flavonoid) bound to FLT3 (FMS-like tyrosine kinase 3) were confirmed in this study. Vanillin and baicalein both inhibited cell viability and promoted apoptosis of Hep3B and Huh7 cells. In addition, both vanillin and baicalein could enhance the activation of the p38/MAPK (mitogen-activated protein kinase) signaling pathway, which may partially explain the anti-apoptosis effects of the two compounds. In conclusion, two active compounds of SGNI, vanillin and baicalein, promoted apoptosis of HCC cells via binding with NF-κB1 or FLT3, and regulating the p38/MAPK pathway. Baicalein and vanillin may be good candidates for HCC treatment on drug development.


Introduction
Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide with a poor prognosis (Villanueva 2019). In addition to being the largest organ of the human body, the liver appears to be a key immunological organ, which is closely related with inflammatory response during infection (Racanelli and Rehermann 2006;Kubes and Jenne 2018). Inflammation is the body's defense mechanism to remove harmful stimuli and is involved in the wound healing process. However, sustained inflammation and the corresponding regenerative wound healing response can induce the development of fibrosis, cirrhosis, and eventually HCC (Keenan et al. 2019). The majority of HCC arises from chronic inflammation or fibrosis, and many patients with HCC also have cirrhosis associated with local and systemic immune deficiency (Keenan et al. 2019). Although the treatment of HCC has made great progress in recent years, it still accounts for a large proportion of cancer-related deaths worldwide. The efficacy of chemotherapy, radiotherapy, antiviral therapy, and surgery remains limited (Ferlay et al. 2019). For example, sorafenib, an oral multikinase inhibitor used as a first-line drug for advanced HCC, still has limited and non-durable effects (Huang et al. 2020;Tang et al. 2020). Therefore, it is of great significance to elucidate the pathogenesis of HCC and develop specific and effective therapeutic drugs for HCC.
Traditional Chinese medicine (TCM) has long been used to treat hepatic diseases since ancient China. Chinese medicinal herbs (CMHs) exhibit hepatoprotective effects via blocking fibrogenesis, suppressing tumorigenesis, eliminating viruses, and inhibiting oxidative injury etc. (Lam et al. 2016;Ali et al. 2018). Shuganning injection (SGNI) is a TCM injection reformulated from the classical prescription Yinchenhao Decoction documented in Treatise on Cold Damage Diseases (in Chinese, Shang Han Lun). Yinchenhao Decoction, which consists of Artemisia capillaris Thunb, Gardenia jasminoides J. Ellis, and Rheum palmatum L., has been widely used to treat jaundice and liver disorders, including acute jaundice hepatitis, severe hepatitis, cholecystitis, cholelithiasis, and leptospirosis caused by leptospirosis (Chen et al. 2015). Modern research shows that Yinchenhao Decoction has good effect on tumor-bearing mice and in clinical adjuvant therapy of HCC (Xu 2016;Wu et al. 2017;Zhong et al. 2020). SGNI, which is composed of the extract of Ganoderma lucidum (Curtis) P. Karst, Strobilanthes cusia (Nees) Kuntze, Gardenia jasminoides J. Ellis, Artemisia capillaris Thunb, and a flavone glycoside baicalein, was approved by the Chinese Food and Drug Administration (CFDA) as a patented drug in China in 2002 and used to treat clinical hepatitis, high bilirubin hematic disease, liver function damage, fatty liver, and cholangitis (Du et al. 2021). Due to its hepatoprotective effects, it is also widely used as an adjuvant treatment for cancer, especially for HCC (Gao et al. 2013;Du et al. 2021). However, the effective components and molecular mechanism of SGNI in the treatment of HCC remain unclear.
Network pharmacology is an integrated network analysis of biological systems based on the theory of systems biology, which constructs an interaction network for drugs, targets, pathways, and diseases (Wang et al. 2021b). The ideas of holistic and systematic characteristics of network pharmacology are consistent with the holistic view of TCM and the principles of dialectical treatment . In this study, we aimed to screen the bioactive compounds of SGNI and investigate the potential mechanisms on cancer based on a network pharmacology-based strategy. In addition, the compound-target interactions and multiple pathway perturbations were validated on liver cancer cells in vitro.

SGNI compound collection, ADME analysis, and target prediction
The compounds of 4 herbs in SGNI were collected through multiple databases, including the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP, http:// lsp. nwu. edu. cn/), Traditional Chinese Medicine Integrated Database (TCMID, http:// 119.3. 41. 228: 8000/ tcmid/ search/), and Encyclopedia of Traditional Chinese Medicine (ETCM, http:// www. tcmip. cn/ ETCM/ index. php/ Home/ Index/). Combined with literature reports, 53 compounds were collected. Next, the Open Babel (http:// openb abel. org/ wiki/ Main_ Page) was used to predict the physical and chemical property of each compound by the name and SMILES codes of compound. The BOILED-Egg model (ChemMedChem 2016(ChemMedChem , 11, 1117(ChemMedChem -1121 was used to assess the lipid solubility and polarity of compounds and to predict the character of intestinal absorption and passing through the blood-brain barrier, as well as serving as a P-glycoprotein (p-gp) substrate to participate in active transport.
Seaware software was used to find the corresponding targets of 53 compounds to calculate and predict the potential targets. In addition, the targets of each compound were supplemented using the PubChem (https:// pubch em. ncbi. nlm. nih. gov) database. Integrate all the targets obtained by the above two methods and uniform the targets of all components to the official gene names through the UniProt database (https:// www. unipr ot. org/), and convert to human gene names.

Candidate targets collection
The GeneCards, KEGG, and DisGeNET databases were used to search with keywords such as cancer, tumor, neoplasm, oncology, tumorgenesis, carcinosis, and cancerous etcetera. The genes were unified through the UniProt database. The R software (version 3.6.1) was used to match the cancer-related target genes with the target genes, and those with correlation ≥ 1 were considered potential target genes for anti-cancer effects of SGNI and plotted in Venn diagrams.

Protein-protein interaction (PPI) network construction
Proteins tend to form complexes through interactions to perform biological functions, which was visualized by the PPI network. The targets of 53 compounds were inputted into the String database (https:// string-db. org/) to acquire the PPI, with the organism setting "Homo sapiens" and the scoring value (confidence) > 0.7. The compounds and anti-cancer targets were imported into Cytoscape 3.6.1 software to construct a comprehensive network diagram. The PPI network diagrams were analyzed to obtain the core target network diagrams by the cytoHubba module.

Enrichment analysis
In order to acquire a deeper understanding of the functions of target genes and their roles in the signaling pathway, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed through the metascape website (https:// metas cape. org/ gp/). GO enrichment analysis selected three modules, namely biological process (BP), molecular function (MF), and cellular component (CC). KEGG was selected for pathway analysis. The TOP20 of BP, MF, CC, and KEGG pathways were selected according to the p value and the results of the pathway enrichment analysis were visualized by R language.

Ranking of SGNI effects on tumor types
The target genes of 53 compounds were imputed into the DisGeNET database (https:// www. disge net. org/) to obtain data on the relationship between genes and diseases, and the results were analyzed and visualized by R language.

Cell culture
Hep3B and Huh7 cells were purchased from the Procell Life Science & Technology Co., Ltd. The cells were cultured in Dulbecco's modified Eagle medium (dimethyl sulfoxide) supplemented with 10% (vol/vol) fetal bovine serum and 1% (vol/vol) penicillin/streptomycin. Cells were incubated at 37 °C in a humidified incubator containing 5% CO 2 .

Colony formation assay
Huh7 or Hep3B cells (500 cells/well) were seeded into 6-well plates with 2 mL complete medium. The colonies were fixed with methanol after a 2-week culture and then stained with 0.1% crystal violet solution (G1062, Solarbio, Beijing). After 15 min, the cells were gently washed 2 or 3 times with PBS (phosphate-buffered saline) and then air-dried overnight. Finally, the colony-forming units were observed and photographed. All experiments were performed in triplicate.

Cellular thermal shift assay (CETSA)
Huh7 and Hep3B cells were seeded in 10-cm dishes with a density of 4 × 10 5 cells/mL. After 24 h, cells were washed by ice-cold PBS. Cell pellets were resuspended with lysis buffer supplemented with protease inhibitor cocktail. After centrifuging at 12,000 g for 15 min, the supernatants were collected. Drugs or control (DMSO) was respectively incubated with cell lysate supernatant for 1 h at room temperature. Each lysate was divided into 6 aliquots and heated at different temperatures of 37, 40, 43, 46 and 49 °C for 5 min; one aliquot was controlled at room temperature (25 °C) for 5 min, and then cooled at room temperature for 3 min. The lysates were centrifuged at 12,000 g for 20 min at 4 °C to separate the soluble fractions from precipitates. The supernatants were transferred to new microtubes and analyzed by SDS-PAGE followed by western blot analysis.

Pull-down assay
Vanillin (1 mg) and baicalein (1 mg) were respectively dissolved in 1 mL of coupling buffer (0.1 M NaHCO 3 , pH11.0 containing 0.5 M NaCl) and conjugated with epoxy-activated Sepharose 6B. The epoxy-activated Sepharose 6B was swelled and washed in distilled water and then washed with the coupling buffer. The epoxy-activated Sepharose 6B beads were added to the drug-containing coupling buffer and rotated at 4 °C overnight. After washing, unoccupied binding sites were blocked with 0.1 M Tris-HCl (pH8.0) for 2 h at room temperature. The drug-conjugated Sepharose 6B was washed with three cycles of alternating pH wash buffers (buffer 1: 0.1 M acetate and 0.5 M NaCl, pH 4.0; buffer 2: 0.1 M Tris-HCl and 0.5 M NaCl, pH8.0). The control unconjugated epoxy-activated Sepharose 6B beads were prepared as described above in the absence of drug. The cell lysate was mixed with drug-conjugated Sepharose 6B or with Sepharose 6B at 4 °C overnight. The beads were then washed three times with TBST. The bound proteins were eluted with SDS loading buffer. The proteins were resolved by SDS-PAGE followed by immunoblotting with an antibody against NF-κB1 (1:1000 dilutions, Affinity Biosciences) or FLT3 (1:1000 dilutions, Affinity Biosciences).

Drug affinity responsive target stability (DARTS)
Briefly, 6-well plates were plated with the cells (1.2 × 10 6 cells/well) for 18 h and the cells were washed once with cold PBS. Cells were lysed in RIPA (Radio-Immunoprecipitation Assay) lysis buffer on ice for 30 min. Lysates were collected and centrifuged at 12,000 g for 10 min. The cell lysates were incubated with 10 × TNC Buffer (500 mM Tris-HCl pH8.0, 500 mM NaCl, 100 mM CaCl 2 ) and mixed well. The protein concentration of the lysates was measured using the bicinchoninic acid (BCA) assay. The lysates were divided equally into two tubes. One tube was added with DMSO, while the other was incubated with the drug for 1 h at room temperature. Five aliquots were prepared from each of the two protein samples, lysate-treated with 1:1000 dilution of pronase, and kept at room temperature for 5 min, 10 min, 15 min, 20 min, and 30 min. 4 × SDS-PAGE loading buffer was mixed with cell lysates and suspended at 100 °C for 5 min. Through western blotting verification, we choose the 15 min as the optimal incubation time for subsequent experiments.

Western blot analysis
Cells were harvested and dissolved in RIPA lysis buffer, and the protein concentrations were determined using BCA protein assay. Whole cell lysates were fractionated and transferred to PVDF (polyvinylidene fluoride) membranes. Membranes were incubated at 4 °C overnight with specific primary antibodies. Proteins were then detected by an enhanced chemiluminescence system (ECL) reagent after incubation with secondary antibodies for 1 h at room temperature. The quantification was analyzed by Image Lab software.

Immunofluorescence staining analysis
Cell monolayers (5 × 10 3 per well) were cultured overnight in 24-well plates, and then, drugs were added for 24 h. Cells were fixed in 4% paraformaldehyde for 30 min, and permeabilized for 5 min with 0.5% Triton X-100. They were then blocked with 10% goat serum albumin for 1 h at room temperature (25 °C) and incubated overnight at 4 °C with the primary antibodies. Afterwards, the cells were incubated with the corresponding fluorescent-coupled secondary antibodies at 37 °C for 1 h. The nuclei were stained with DAPI (4,6-diamino-2-phenyl indole, Solarbio, Beijing, China). Respective images were taken by confocal microscopy.

Apoptosis analysis
Apoptosis was analyzed by an Annexin V/PI apoptosis detection kit (Sigma Aldrich, St. Louis, MO) according to the manufacturer's instructions. Briefly, cells receiving different treatments (1 × 10 5 ) were harvested and centrifuged (4 °C, 1000 rpm, 5 min), washed with PBS, and resuspended in a 1 × binding buffer. Next, the cells were incubated with 5 μL of Annexin V for 10 min and 5 μL of propidium iodide (PI) for 5 min at room temperature in the dark. Then, after terminating the staining reaction, the cells were immediately analyzed by flow cytometry.

Statistical analysis
All experiments were performed in triplicates and data was shown as mean ± standard deviation (SD) of three independent experiments. Statistical analysis was performed by one-way ANOVA with the Tukey multiple comparison test using GraphPad Prism 6 software. p < 0.05 was considered to be statistically significant between the control and treated groups.

Pharmacokinetics characteristics (ADME) and potential targets of SGNI
The BOILED-Egg model was used to predict the properties of the 53 compounds in SNGI. The predictions showed that 18 compounds were permeable to the blood-brain barrier (red dashed box), and 12 compounds were actively transferred through the intestine by P-gp as indicated by green stars (Fig. 1A; Supplementary Tab. 1). A total of 350 potential target genes were obtained of 53 compounds by Seaware software and the PubChem database, as shown in Fig. 1B. After the compounds are classified, the overlap between the target gene and its biological function is calculated ( Fig. 1C; Supplementary Tab. 2). The purple line represents the same target gene, and the blue line indicates the same biological function. The results showed that the target gene of different types of compounds overlapped greatly in biological functions. A total of 26,499 cancerrelated genes were collected in GeneCards and KEGG, and a total of 1970 genes were screened with a score > 10. A total of 112 common targets were obtained by intersection of component targets and cancer-related targets, which were regarded as potential anti-cancer targets for SGNI ( Fig. 1D; Supplementary Tab. 3).

Network construction and analysis
The PPI diagram was obtained from the String database, and the network contained 254 nodes and 2330 edges, as shown in Fig. 2A. The CytoHubba module in Cytoscape ranks nodes according to their attributes in the network, that is, assigns values or degrees to each target through topological network algorithm, and identifies key targets (hub genes) and sub-networks. In this research, TOP20 target genes were obtained by CytoHubba. The degree and related parameters of nodes were collected in the Network Analyzer website. The nodes changed from yellow to red, indicating the degree gradually increased from the outside to the inside ( Fig. 2B; Supplementary Tab. 4). The compound-target network was constructed by Cytoscape software, as shown in Fig. 2C. The network comprised 297 nodes and 652 edges, among which nodes represented compounds or corresponding target genes, and edges indicated the interactions. There are multiple compounds corresponding to one target or one compound modulating multiple targets, indicating that 53 compounds are assisted in the treatment of cancer through the synergistic action of multiple components and multiple targets. The compound-key target (top 20) network is shown in Fig. 2D, which contains 43 nodes and 67 straight lines.

Go and KEGG pathway enrichment analysis
To further investigate the biological functions of SGNI, the gene functional annotation and KEGG pathway enrichment analysis of key targets were conducted via the Metascape platform. The GO functional enrichment analysis yielded 772 biological processes (BP), 22 cellular components (CC), and 65 molecular functions (MF). The BP mainly involved the activation of apoptosis signal, the response to peptides, the production of cytokines, the metabolic process of monocarboxylic acid, and the cellular response to organic nitrogen-containing compounds. MF mainly involved transcription factor binding, ubiquitin protein ligase binding, RNA polymerase II transcription factor binding, specific protein region binding, etc. (Fig. 3A). The larger the dots in the figure, the more genes were enriched, and the redder the dots, the smaller the p value. Screening the TOP20 KEGG results according to the p value and visualized with the R software, a variety of pathways related to cancers were significantly enriched, especially HCC (Fig. 3B). In addition, according to p ≤ 0.05, the TOP30 of tumor-related diseases were selected for further analysis, and the visual analysis was carried out by R software and is shown in Fig. 3C and Supplementary Tab. 5. On the one hand, the results show that SGNI may have therapeutic effects on the occurrence and development of various liver-related diseases or tumors; on the other hand, flavonoids seem to have the greatest contribution to the overall disturbance, followed by phenols. By establishing a molecular library of active compounds of SGNI, screening potential anti-cancer targets, and performing pathway enrichment, we screened inflammation-HCC-related targets for in vitro validation. Finally, according to compound characteristics, targets, etc., two typical active ingredients (vanillin and baicalein) were selected as Fig. 2 The compound-target network for SGNI on cancer. A The network diagram of cancer-related targets, from the outside to the inside; yellow to red represent the increase of the degree value. The larger the degree value, the more important the target is. B TOP20 network map of key targets. C Component-target network diagram, in which the pink squares represent tumor-related targets and the diamonds represent individual compounds. D Component-key target network diagram, in which the red oval in the middle represents cancer, the pink squares represent tumor-related targets, and the diamonds outside represent different compounds representatives to explore the effects and molecular mechanism on HCC.

Vanillin targeted NF-κB1
Through the integrated network pharmacology, vanillin and baicalein were selected as representative drugs for further study about the effect on hepatoma cell lines Huh7 and Hep3B. Vanillin is a plant secondary metabolite used as an important food additive worldwide. The chemical structure of vanillin is shown in Fig. 4A. The corresponding target and target-related pathways of vanillin predicted by network pharmacology are shown in Fig. 4B. The MTT assay was applied to determine the effects of vanillin on cell viability, as shown in Fig. 4C). To further examine this inhibitory effect of vanillin, the colony assay was carried out. Similar to the MTT results, vanillin significantly suppressed colony numbers (Fig. 4D). As bioinformatic prediction showed that NF-κB1 was the potential target of vanillin, we further investigated the interaction of vanillin and NF-κB1/p50. Binding assay in vitro (pull-down assay) showed that NF-κB1 could be pulled down by vanillin-conjugated Sepharose 6, rather than unconjugated epoxy-activated Sepharose 6B (Fig. 4E). DARTS assay verified that vanillin could protect NF-κB1 protein from pronase damage (Fig. 4F). Furthermore, CETSA experiment showed that adding vanillin C Significance analysis of compound targets enriched in the Dis-GeNET database (p < 0.05). D Flowchart of the basis for screening drug candidates could effectively alleviate the degradation of NF-κB1 caused by temperature rise in Huh7 and Hep3B cells (Fig. 4G). In addition, vanillin significantly upregulated the expression of NF-κB1/p50 in Huh7 and Hep3B cells (Fig. 4H). Immunofluorescence experiments showed that NF-κB1 was mainly localized in the cytoplasm, and vanillin could enhance the expression of NF-κB1 (Fig. 4I).

Baicalein targeted FLT3
Baicalein also shows attractive anti-cancer effects on HCC, and network pharmacology analysis showed that baicalein targeted FLT3 and activated the MAPK (mitogen-activated protein kinase) signaling pathway. The chemical structure of baicalein is shown in Fig. 5A. Previous network pharmacology prediction result for baicalein is shown in Fig. 5B. Similarly, MTT and colony formation assays were used to verify the efficacy of baicalein on hepatoma cells. The results indicated that baicalein exerted a concentration-dependent inhibitory effect on the proliferation of Huh7 and Hep3B cells (Fig. 5C, D). The results of pull-down experiment prove that there is a clear combination between baicalein and FLT3 (Fig. 5E). In the DARTS assay, when pronase was added, baicalein could significantly inhibit the degradation of FLT3 by pronase compared with untreated, which further proved Fig. 4 Vanillin bound with NF-κB1 directly. A The chemical structure of vanillin. B Mapping of vanillin corresponding targets and related pathways. C Huh7 and Hep3B cells were treated with vanillin (0-8 mM) for 24 h, and the cell viability was determined by MTT assay (**p < 0.01, ***p < 0.001 vs control, Student's t-test). D Colony-forming assays in Huh7 and Hep3B cells, treated with different concentrations of vanillin. Graphs showing the colony numbers from 3 independent experiments. E An interaction between vanillin and NF-κB1 evaluated by pull-down assay. Vanillin was conjugated with epoxy-activated Sepharose 6B. F Cell lysates were incubated in the presence or absence of vanillin for 1 h at room temperature, followed by proteolysis with indicated ratios of pronase for 15 min, and the lysates were analyzed by western blotting. G CETSA indicate that vanillin increased the thermal stability of NF-κB1 compared with DMSO-treated cell lysates. Cell lysates were mixed with the indicated doses of vanillin at 37, 40, 43, 46, and 49 °C to evaluate the thermal stability of NF-κB1. H The protein expression of NF-κB1 in vanillin treated cells by western blotting. I Immunofluorescence staining NF-κB1 in Huh7 and Hep3B cells treated with vanillin, and the red signal represents NF-κB1 and blue signal represents nuclear DNA staining by DAPI the binding effect between baicalein and FLT3 (Fig. 5F). Furthermore, baicalein can bind FLT3 and slow down the degradation of FLT3 induced by the higher temperature (Fig. 5G). Western blot results showed that baicalein markedly decreased FLT3 levels compared to the control (Fig. 5H), which was confirmed by the immunofluorescence results (Fig. 5I).

Vanillin and baicalein enhance apoptosis via the p38/ MAPK signaling pathway
Previous predictions indicated that both vanillin and baicalein targeted the corresponding proteins to activate the MAPK signaling pathway. The MAPK signaling pathway is involved in multiple biological processes, including cell for 24 h (*p < 0.05, ** p < 0.01, *** p < 0.001 vs. control, Student's t-test). D Colony-forming assays in Huh7 and Hep3B cells, treated with baicalein. Graphs showing the colony numbers from 3 independent experiments. E Pull-down assay was conducted to identify the interaction between baicalein-FLT3. F Cells were collected to perform DARTS assay. G The baicalein-FLT3 binding was examined by CETSA and western blot. H Western blot analysis of the protein levels of FLT3 in baicalein-treated cells. I Immunofluorescence staining FLT3 in Huh7 and Hep3B cells treated with baicalein or the control; the green signal represents FLT3 and blue signal represents nuclear DNA staining by BP apoptosis. Thus, we examined the cell apoptosis and related protein expression after being treated with baicalein and vanillin. The result showed that both vanillin and baicalein increased the expression of the pro-apoptotic protein Bax, but decreased the expression of Bcl-2 (anti-apoptotic protein) and p-p38 in Huh7 and Hep3B cells (Fig. 6A, B). The Annexin V-FITC apoptosis assay (flow cytometry) showed that vanillin and baicalein promoted cell apoptosis in a dosedependent manner in Huh7 and Hep3B cells (Fig. 6C, D), which was also consistent with the above MTT results. To sum up, the results indicated that vanillin and baicalein may induce apoptosis partially through the p38/MAPK signaling pathway to inhibit the development of HCC.

Discussion
Cancer, characterized by abnormal cell proliferation and differentiation, continues to be a leading cause of mortality. The main western medicine therapies include surgery, radiation therapy, chemotherapy, targeted therapy, and immunotherapy, but the toxicity on the normal cells and other adverse effects of cancer patients are still serious problems (Mun et al. 2018;Tsimberidou et al. 2020). In recent years, traditional Chinese medicine (TCM) or integrated traditional Chinese and western medicine therapy has achieved good effects in the treatment of cancers, including assisting against cancer, reducing toxic side effects, improving chemo-sensitivity, and regulating tumor microenvironment (Tao et al. 2016;Wang et al. , 2021a. HCC is one of the most lethal malignancies worldwide due to great challenges in prevention, diagnosis, and treatment (Wang and Wei 2020). The efficacy of TCM on the treatment of HCC is well documented. Due to the complex compound system of Chinese medicine formulae or modern preparation, TCM exhibits extensive pharmacological activities targeting various targets and pathways (Zhou et al. 2014). Network pharmacology explains the occurrence and development of diseases from the perspective of systems biology and biological network balance, and demonstrates the interaction between drugs and the body from the overall perspective, which is consistent with the holistic view of TCM. The present research used the network pharmacology to clarify the mechanism of SGNI for treating HCC.
SGNI, a TCM injection reformulated from Yinchenhao Decoction, has been approved for liver protection in Chinese Pharmacopoeia since 2002. SGNI is widely used in Chinese clinics for the treatment of high bilirubin hematic disease, liver function damage, fatty liver, and cholangitis, etc. The efficacy of SGNI in the treatment of HCC has been reported clinically (Dou et al. 2013). This paper constructed the cancer-targets-drug network diagram, and the multi-component, multi-target, and multi-pathway of SGNI was clearly visualized through network pharmacology. Fifty-three compounds, including flavonoids, phenols, coumarin, terpenoids, and benzenes, were screened by the ADME characteristic and the potential cancer-related targets were predicted. According to the network and enrichment analysis, flavonoids and phenols seem to have great contribution to the overall disturbance. The BP mainly involved the apoptosis signal, the response to peptides, and the production of cytokines, and a variety of pathways related to cancers were significantly enriched, especially HCC, which confirmed that SGNI could treat HCC through multiple targets and pathways.
Combining the results of network pharmacology predictions and previous literature reports, vanillin and baicalein were selected for further in vitro verification. The present study showed that vanillin and baicalein could significantly inhibit the cell viability and colony formation, and promoted the apoptosis of Huh7 and Hep3B cells. Consistent with the results of bioinformatics analysis, the binding between vanillin and target protein NF-κB1 was verified by pull-down assay, CETSA, and DARTS. Vanillin could enhance the expression of NF-κB1 localized in the cytoplasm. The NF-κB family, with 5 distinct subunits, namely RelA, RelB, Rel, NF-κB1, and NF-κB2, plays vital roles in immune response, tumorigenesis, and the progress of malignancy. Although the mRNAs of RelA, RelB, NF-κB1, and NF-κB2 were significantly elevated in HCC tissues, levels of Rel and NF-κB1 expression had no significant effect on the overall survival of HCC patients .
Although reports indicate that NF-κB signaling generally exerts tumor-promoting activity, it has also been demonstrated that loss of NF-κB1 (p50) is associated with an increased risk of inflammatory disease and epithelial cancer. NF-κB1 exerts tumor suppressor function in both epithelial and hematopoietic cells (O'Reilly et al. 2018). The complex function of NF-kB1 is attributed to its regulatory effects on multiple downstream signaling pathways. For example, p50 induces cell apoptosis through mediating the accumulation of growth arrest and DNA damage 45α (GADD45α) protein, which suggests a novel function of the p50 (Yu et al. 2009). The pro-apoptotic effect of p50 partly explains the present result that vanillin interacts with p50 and inhibits tumor cell growth.
The binding between baicalein and FLT3 was also verified and baicalein could decrease the expression of FLT3. FLT3, a type III receptor tyrosine kinase, plays an important role in the regulation of normal hematopoietic cell function and hematopoietic malignancy (Daver et al. 2019). FLT3 mutation is a common characteristic of acute myeloid leukemia (AML) patients and predicts a poor prognosis (Daver et al. 2021). Recent research found that HCC patients with high FLT3 levels exhibited a longer overall survival after sorafenib treatment ).
The present result showed that baicalein could bind with FLT3 and increase the expression of FLT3, which indicated that baicalein may be a potential candidate for HCC adjuvant treatment.
The p38/MAPK family members integrate signals that affect proliferation, differentiation, survival, and migration, which is consistent with these events in tumorigenesis (Coulthard et al. 2009). The p38/MAPK signaling pathway is vital Fig. 6 Baicalein and vanillin promoted apoptosis through the p38 MAPK signaling pathway. A Cell lysates of Huh7 and Hep3B cells were immunoblotted for p38, p-p38, Bcl-2, and Bax, β-actin as a loading control. B Western blotting for apoptosis marker in Huh7 and Hep3B cells treated with baicalein for 24 h. C Determination of the effect of vanillin on apoptosis using FACS based on PI staining in Huh7 and Hep3B cells after 48 h of treatment. D The effects of baicalein on Huh7 and Hep3B cell apoptosis examined by Annexin V/PI for the growth and survival of cancer cells. Generally, p38 activation is associated with anti-proliferative functions in various cells, including hepatocytes, fibroblasts, hematopoietic cells, and lung cells (Wagner and Nebreda 2009). The results showed that both vanillin and baicalein could activate p38/MAPK to induce apoptosis of HCC. In addition, a previous report showed that FLT3 and NF-κB appeared to regulate the p38-MAPK pathway (Hoesel and Schmid 2013;Sun et al. 2020). Whether the binding of baicalein to FLT3 and the binding of vanillin to NF-κB1 is the main factor that causes the activation of the p38/MAPK pathway needs to be further studied.
To sum up, this study investigated the pharmacological mechanism of SGNI in treating HCC based on the network pharmacology prediction and experimental validation. The results indicated that two active compounds, baicalein and vanillin, could decrease the viability and promote apoptosis of HCC cells in vitro via binding with NF-κB1 or FLT3, and regulating the p38/MAPK pathway. Baicalein and vanillin may be good candidates for HCC treatment on drug development. The potential effects of SGNI or active compounds on HCC need further studies in vivo and on clinical trials.

Conclusion
In this study, the mechanism of SGNI inhibiting HCC was revealed through network pharmacology analysis and was verified in vitro. Fifty-three active compounds and cancerrelated targets were identified in SNGI. GO enrichment studies showed that SGNI could inhibit the occurrence and development of HCC by activating the apoptosis signaling pathway. Two active compounds of SGNI, baicalein and vanillin, promoted apoptosis of HCC cells via binding with NF-κB1 or FLT3, and regulating the p38/MAPK pathway. This study provided a basis for further mechanistic study and clinical application of SGNI and indicated that baicalein and vanillin may be good candidates for HCC treatment on drug development.