The potential mechanism of ursolic acid in the treatment of bladder cancer based on network pharmacology and molecular docking

Abstract

Objective

This study explored the potential molecular mechanisms of ursolic acid (UA) in bladder cancer treatment using network pharmacology and molecular docking.

Methods

The Traditional Chinese Medicine Systems Pharmacology and UniProt databases were used to screen potential targets of UA. Relevant bladder cancer target genes were extracted using the GeneCards database. All data were pooled and intercrossed to obtain common target genes of UA and bladder cancer. Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. Molecular docking was conducted to verify the possible binding conformation between UA and bladder cancer cells. Then, in vitro experiments were performed to further validate the predicted results.

Results

UA exerts anti-tumor effects on bladder cancer through multiple targets and pathways. Molecular docking indicated that UA undergoes stable binding with the proteins encoded by the top six core genes (STAT3, VEGFA, CASP3, TP53, IL1B, and CCND1). The in vitro experiments verified that UA can induce bladder cancer cell apoptosis by regulating the PI3K/Akt signaling pathway.

Conclusions

Our study illustrated the potential mechanism of UA in bladder cancer based on network pharmacology and molecular docking. The results will provide scientific references for follow-up studies and clinical treatment.

Keywords

Network pharmacology molecular docking ursolic acid bladder cancer mechanism bioinformatics

Introduction

Recent statistics show that bladder cancer is the 10th most common malignancy worldwide and the most common malignancy of the urinary tract, and the fatality rate of bladder cancer ranks 13th among malignant tumors.¹ Currently, surgical treatment is the main therapy for bladder cancer. Because more than 90% of bladder cancers are urothelial carcinoma, which exhibits polycentric growth and vulnerability to recurrence,² most authoritative guidelines recommend postoperative chemotherapy.³ However, chemotherapy resistance and the side effects of chemotherapy drugs often affect the therapeutic effect. Therefore, an efficient treatment for bladder cancer with low toxicity is urgently needed. Some monomer components of Chinese herbs have been shown to have anti-tumor activity and few side effects, and they have become a current focus of research.^4,5 Nevertheless, because of the poor water solubility of most herbal monomer compounds, their bioavailability is low, which limits their clinical application.^6,7 In recent years, reduction of the size of compound particles to the nanometer level has been shown to significantly improve their water solubility and bioavailability, which may provide a potential method for the clinical use of monomer compounds extracted from Chinese herbs.^8,9 Ursolic acid (UA, C₃₀H₄₈O₃, molecular weight 456.78 Da, chemical structure shown in Figure 1) is a pentacyclic triterpenoid compound that exists in many natural medicinal plants, such as hedyotis diffusae herba, eriobotryae folium, and tripterygii radix.¹⁰ Because UA has few hydrophilic groups in its chemical structure, its water solubility is low, and it is easily soluble in methanol, ethanol, and dimethyl sulfoxide. Although the current research on UA is still in the experimental stage, studies have shown that UA has broad pharmacological functions, such as anti-oxidant, lipid-lowering, anti-inflammatory, and immune regulatory effects.^11,12 Additionally, UA can play an anti-tumor role by inducing tumor cell apoptosis, inhibiting tumor cell proliferation, and blocking the cell cycle.¹³ However, there are still few studies on the treatment of bladder cancer using UA. Therefore, systematic studies of the targets and molecular mechanisms by which UA inhibits bladder cancer are relatively scarce.

Figure 1.

Chemical structure of ursolic acid.

Network pharmacology is a powerful tool based on the achievements of systems biology, computer science, pharmacology, and bioinformatics for systematically exploring the pharmacological effects of drugs.¹⁴ Molecular docking is a theoretical simulation method used to predict binding patterns and affinity by studying the interactions between receptors and drug components.¹⁵ Our study aimed to explore the potential targets and mechanisms of UA in the treatment of bladder cancer through network pharmacology and molecular docking analysis. The results may provide a theoretical basis for follow-up experimental research on UA, and with the continuous improvement of pharmaceutical technology, we believe that UA will be used in clinical practice in the near future.

Methods

Ethics statement

This article does not contain any studies performed on human participants or animals, and therefore ethical approval was not obtained.

Extraction of UA targets

The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (https://tcmspw.com/tcmsp.php) was used to retrieve the target proteins of UA using “ursolic acid” as a keyword. The UniProt database (https://www.uniprot.org/) was used to normalize protein target information and screen targets classified as “Reviewed” and “Human”. Then, protein names were converted to gene names to obtain the relevant targets of UA.

Screening of bladder cancer targets

Bladder cancer-related targets were retrieved from the GeneCards database (https://www.genecards.org/) using “bladder cancer” as a keyword and “Homo sapiens” as the species choice. The screening criteria were set to a relevance score ≥ 1. Venny 2.1 software (BioinfoGP, Spain) was used to identify common targets.

Protein–protein interaction (PPI) network construction and topological analysis

The common target genes of UA and bladder cancer were input into the STRING platform (https://string-db.org/cgi/input.pl). The PPI network was constructed under the condition of “Multiple proteins”, and the species was set to “Homo sapiens”. The results were input into Cytoscape 3.8.0 software (National Institute of General Medical Sciences, Bethesda, MD, USA) to construct the interaction network. The Cyot NCA plug-in was used to further perform topological analysis. The parameters of “degree”, “betweenness centrality”, and “closeness centrality” were used as reference indicators, and targets with all three parameters greater than the median value were screened.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses

To explore the biological function and pathways of UA in the treatment of bladder cancer, GO functional annotation and KEGG pathway enrichment analyses were carried out using the STRING platform. Items with a corrected q-value <0.05 or P-value <0.05 were selected and arranged in descending order according to the enrichment factor value. Then, the cluster profiler package of R 4.2.0 software (Lucent Technologies, Murray Hill, NJ, USA) was used to construct histograms and bubble charts.

Molecular docking verification

The core target genes were determined by topological analysis, and the proteins encoded by the top six core genes were selected for molecular docking with UA. The species was set to “Homo sapiens”, and the three-dimensional structures of the proteins encoded by the core target genes were downloaded from the Protein Data Bank database (https://www.rcsb.org/). AutoDock Tools 1.5.6 software (The Scripps Research Institute, San Diego, CA, USA) was used to remove water molecules and small molecule ligands from protein receptors, and output in the “pdbqt” format was obtained for receptors. The two-dimensional structure of UA was downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and saved in the “mol2” format. Chemoffice software (PerkinElmer Informatics, Shelton, CT, USA) was used to transform the two-dimensional structure of UA into the three-dimensional structure, and AutoDock Tools 1.5.6 software was used for hydrogenation, structure optimization, and to generate output in the “pdbqt” format for ligands. The active docking pocket was created using the GetBox plugin of Pymol 2.3 software (DeLano Scientific LLC, South San Francisco, CA, USA), and the spatial location and radius of the active pocket were recorded. Molecular docking of the receptors and ligands was conducted using AutoDock Vina 1.1.2 software, and vina scripts were used to calculate the binding energy. The docking results were visualized using Pymol 2.3 software.

Experimental validation

Cell culture

T24 human bladder cancer cells were purchased from the Chinese Academy of Sciences (Beijing, China). T24 cells were cultured in RPMI-1640 medium (Cytiva-HyClone, Logan, UT, USA) with 10% fetal bovine serum and 100 mg/mL penicillin‑streptomycin (Beyotime Institute of Biotechnology, Beijing, China) at 37°C in a humidified atmosphere with 5% CO₂.

UA preparation

UA was purchased from Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China), dissolved in dimethyl sulfoxide, and aliquoted and stored at −4°C. The final concentration of dimethyl sulfoxide in the culture was <0.1% in all experiments.

Cell viability

Cell viability was measured using a Cell Counting Kit-8 (CCK-8) assay (Beyotime Institute of Biotechnology). T24 cells were disseminated into 96-well plates with 5 × 10³ cells/well and incubated at 37°C with 5% CO₂ for 24 hours. Then, the cells were treated with different concentrations of UA for another 24 hours. CCK‑8 reagent was added to the medium in a ratio of 1:10, and the cells were incubated at 37°C for 2 hours. The absorbance at 450 nm was measured using a Tecan Infinite F200/M200 multifunction microplate reader (Tecan Group, Ltd., Männedorf, Switzerland). The inhibition rate of cells was 1− [optical density of the experimental group/optical density of the control group] × 100%.

Hoechst 33258 staining

T24 cells were disseminated into six-well plates with 5 × 10⁴ cells/well and incubated at 37°C with 5% CO₂ for 24 hours. The cells were then treated with UA under different conditions for another 24 hours. The cells were washed three times with phosphate-buffered saline and then treated with Hoechst 33258 reagent (10 μg/mL, Beyotime Institute of Biotechnology) in the dark at room temperature for 10 minutes. Cell morphology was observed using a fluorescence microscope with a blue filter.

Western blot analysis

The total proteins of T24 cells were extracted using radioimmunoprecipitation assay lysis buffer (Beyotime Institute of Biotechnology) containing 1 mmol/L phenylmethane-sulfonyl fluoride (Beyotime Institute of Biotechnology). The protein concentration was calculated using a BCA kit (Beyotime Institute of Biotechnology). Sodium dodecyl-sulfate polyacrylamide gel electrophoresis on 10%–12% gels was used to separate protein samples in equal amounts (40 μg), and the gels were transferred to polyvinylidene difluoride membranes. The membranes were blocked with 5% skim milk solution for 2 hours at room temperature and then incubated with primary antibodies overnight at 4°C. The phospho-PI3K (#4228), phospho-AKT (#13038), and cleaved poly-ADP‑ribosepolymerase (PARP) (#9548) antibodies were purchased from Cell Signaling Technology (Boston, MA, USA). The anti-PI3K (#20584-1-AP) and anti-AKT (60203-2-Ig) antibodies were purchased from Proteintech Group (Wuhan, China). The cleaved caspase-3 (#9664) antibody was purchased from BD Biosciences (Franklin Lakes, NJ, USA), and the anti-β-actin (# D124905) antibody was obtained from Sangon Biotech (Shanghai, China). The polyvinylidene difluoride membranes were then incubated for 2 hours with horseradish peroxidase-conjugated secondary antibodies (Cell Signaling Technology) at room temperature, and chemiluminescence was performed using an electrochemiluminescence system (Amersham Biosciences, USA).

Statistical analysis

The data are presented as the mean ± standard deviation. SPSS 22.0 statistical software (IBM Corp., Armonk, NY, USA) was used for all statistical analyses. Data from each group were obtained from three independent experiments. One-way analysis of variance was used to compare multiple groups. In all analyses, a value of P < 0.05 was considered statistically significant.

Results

Common targets in the screening results of UA and bladder cancer

A total of 55 target genes of UA were obtained by TCMSP database retrieval and UniProt database transformation. Additionally, 8693 target genes of bladder cancer were retrieved by setting the screening criterion to “relevance score ≥1” in the GeneCards database. The two groups of target genes were input into Venny 2.1 software, and 51 common targets were obtained (Figure 2).

Figure 2.

Venn diagram of potential targets of ursolic acid (UA) and bladder cancer. The intersection represents the 51 common target genes between UA and bladder cancer.

PPI network construction and core target gene screening

The common target genes of UA and bladder cancer were imported into the STRING database, and the protein interaction relationship was obtained (Figure 3a). The PPI network map was saved in TSV format and then imported into Cytoscape 3.8.0 software to create a visual network diagram (Figure 3b). One isolated node was removed from the network. The results showed 50 nodes in the network interacting through 453 edges, and the average node degree value was 18.12. The core proteins included in the PPI network had degree, betweenness centrality, and closeness centrality values that all exceeded the median values of nodes (degree >19, betweenness centrality >12.638105, closeness centrality >0.6163765) based on the topological analysis. In total, 21 core targets were detected (Table 1). Targets with a higher degree value played a more important role in this network.

Figure 3.

Protein–protein interaction (PPI) network analysis of ursolic acid and bladder cancer-related targets. (a) Fifty common target genes were added to the PPI network complex using the STRING database. Colored circles indicate the nodes in the PPI graph that are target genes corresponding to proteins, and the lines between the nodes represent different interactions and (b) The interaction diagram of the 50 common target genes was visualized using Cytoscape 3.8.0 software.

Table 1.

Topological analysis of the 21 common core targets between ursolic acid and bladder cancer.

Target name	Degree	Betweenness centrality	Closeness centrality
STAT3	38	154.7920803	0.816666667
VEGFA	37	148.1270579	0.803278689
CASP3	36	143.3331248	0.790322581
TP53	36	156.7387687	0.790322581
IL1B	36	231.3461189	0.790322581
CCND1	33	124.9168192	0.742424242
MMP9	33	47.59119332	0.753846154
PTGS2	32	94.11463683	0.742424242
FOS	29	65.30508991	0.710144928
NFKBIA	28	41.07745975	0.700000000
MMP2	28	24.09313046	0.700000000
CASP8	26	37.72208203	0.680555556
ICAM1	26	19.10712251	0.680555556
MAPK8	25	23.45634000	0.662162162
MCL1	25	21.43247728	0.671232877
CASP9	24	16.67748174	0.653333333
CSF2	24	107.8721869	0.662162162
RELA	23	21.33668184	0.653333333
CREB1	23	53.93082658	0.644736842
FGF2	23	34.82059394	0.653333333
NOS3	20	29.28673927	0.620253165

STAT3, Signal transducer and activator of transcription 3; VEGFA, Vascular endothelial growth factor A; CASP3, Caspase-3; TP53, Cellular tumor antigen p53; IL1B, Interleukin-1 beta; CCND1, G1/S-specific cyclin-D1; MMP9, Matrix metalloproteinase-9; PTGS2, Prostaglandin G/H synthase 2; FOS, Proto-oncogene c-Fos; NFKBIA, NF-kappa-B inhibitor alpha; MMP2, 72 kDa type IV collagenase; CASP8, Caspase-8; ICAM1, Intercellular adhesion molecule 1; MAPK8, Mitogen-activated protein kinase 8; MCL1, Induced myeloid leukemia cell differentiation protein Mcl-1; CASP9, Caspase-9; CSF2, Granulocyte-macrophage colony-stimulating factor; RELA, Transcription factor p65; CREB1, Cyclic AMP-responsive element-binding protein 1; FGF2, Heparin-binding growth factor 2; NOS3, Nitric oxide synthase, endothelial.

GO functional enrichment analysis

According to a corrected P value <0.05, 976 GO functional items were enriched, including 894 in the biological process, 20 in the cellular component, and 62 in the molecular function categories. The items were ranked in order of significance, and the top 10 items of each module were selected for visual presentation using R 4.2.0 software (Figure 4). The biological process targets mainly included lipopolysaccharide response, cell oxidative stress, and cell chemical stress. The cellular component targets mainly involved membrane rafts, membrane microdomain, and transcription regulator complex. The molecular function targets were classified into regulation of apoptosis, endopeptidase activity, and cytokine receptor binding.

Figure 4.

Gene Ontology (GO) functional enrichment analysis of ursolic acid in bladder cancer. The y-axis indicates GO categories including biological processes (BP), cellular components (CC), and molecular functions (MF), and the x-axis represents the number of enriched genes. Colors closer to red have greater significance and smaller q-values.

KEGG pathway enrichment analyses

Items with a corrected P value <0.05 were selected, and 122 signaling pathways were screened by KEGG pathway enrichment analysis. The top 20 pathways according to the P value were visualized using a bubble diagram (Figure 5). The result showed that UA exerted anti-cancer effects on bladder cancer through multiple signaling pathways, such as pathways involved in cancer, regulation of blood lipids and atherosclerosis, the tumor necrosis factor (TNF) signaling pathway, and the PI3K/Akt signaling pathway.

Figure 5.

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of ursolic acid in bladder cancer. The y-axis indicates the KEGG terms for enriched pathways, and the x-axis shows the KEGG enrichment ratio. The sizes of circles represent the number of genes enriched in the corresponding pathways. Colors closer to red have greater significance and smaller P-values.

Molecular docking results

According to the ranking of degree values, the proteins encoded by the top six core genes (STAT3, VEGFA, CASP3, TP53, IL1B, and CCND1) were selected for molecular docking with UA (Figure 6). The Protein Database IDs of these target proteins and their binding energies are shown in Table 2. The binding energies between the proteins encoded by the top six core genes and UA were lower than −5.0 kcal/mol. These results suggested that UA exhibits strong binding activity with the proteins encoded by these core genes.¹⁶

Figure 6.

Three-dimensional visualization models of molecular docking between ursolic acid and the top six core target proteins. (a) STAT3; (b) VEGFA; (c) CASP3; (d) TP53; (e) IL1B and (f) CCND1.

Table 2.

Binding energies between ursolic acid and the proteins encoded by the top six core genes.

Target name	Protein Data Bank ID	Binding energy (kcal/mol)
STAT3	6NJS	−6.3
VEGFA	1BJ1	−7.7
CASP3	1NMS	−8.6
TP53	1TSR	−5.8
IL1B	5R7W	−7.6
CCND1	2W96	−6.9

Cytotoxicity of UA in T24 cells

T24 cells were treated with a series of concentrations of UA (0, 10, 20, 30, 40, and 50 μM) for 24 hours, and then cell viability was measured using a CCK-8 assay. The results showed that UA inhibited proliferation of the T24 bladder cancer cell line in a concentration-dependent manner (Figure 7). The 50% inhibitory concentration of UA in T24 cells was 27.61 μM.

Figure 7.

Ursolic acid (UA) inhibited the proliferation of T24 cells. The cell inhibition rate was detected using a cell counting kit-8 assay. T24 cells were treated with different concentrations of UA for 24 hours. Data were obtained from three independent experiments and are represented as the mean ± standard deviation. *P < 0.05 versus the control group.

UA can induce apoptosis in T24 cells by downregulating the PI3K/Akt signaling pathway

UA concentrations of 20 and 40 μM were selected for the follow-up experiments. T24 cells were treated with UA (0, 20, 40 μM) for 24 hours. Fluorescence microscopy of Hoechst 33258 staining revealed that UA can induce apoptosis of T24 cells (Figure 8a). Consistent with the predicted results, UA can exert an anti-bladder cancer effect through the PI3K/Akt signaling pathway. Furthermore, we detected proteins involved in this signaling pathway in T24 cells by western blot analysis. The results showed that although the protein expression levels of p-PI3K and p-Akt were downregulated, activation of PARP and Caspase 3 cleavage was increased (Figure 8b), which indicated that UA can promote apoptosis of T24 cells by downregulating the PI3K/Akt signaling pathway.

Figure 8.

Ursolic acid (UA) induced apoptosis in T24 cells by down-regulating the PI3K/Akt signaling pathway. (a) T24 cells were treated with UA (0, 20, 40 μM) for 24 hours and then stained with Hoechst 33258 and observed under a fluorescence microscope (original magnification, 100×) and (b) T24 cells were treated with UA for 24 hours, and the indicated proteins were detected by western blot. β-Actin was detected as a loading control, *P < 0.05 versus the control group.

Discussion

The incidence of bladder cancer, which is a serious threat to human health, has increased in recent years.¹ Chemotherapy plays an important role in the treatment of bladder cancer. However, the side effects of chemotherapy drugs and chemotherapy resistance seriously affect its clinical therapeutic effect. Therefore, identification of a highly efficient treatment for bladder cancer with low toxicity has become an urgent issue. Some monomer components of Chinese herbal medicines have powerful anti-cancer activity, as well as the advantages of low toxicity and few side effects.¹⁷ UA is a triterpenoid compound that is found in a variety of Chinese herbs. Studies have shown that UA exhibits potent anti-tumor activity against multiple cancers, such as pancreatic, colorectal, and breast cancer.^18,19 Additionally, UA has the advantage of extremely low toxicity and is widely accepted as non-toxic to normal cells. In tests on mice, doses of UA up to 1000 mg/kg body weight have shown no adverse effects.²⁰ However, there are few studies on the treatment of bladder cancer with UA. Therefore, it is important to investigate unexplored targets and the potential mechanisms of UA in bladder cancer, which may provide a theoretical basis for follow-up experimental research and clinical treatment.

In recent years, network pharmacological analysis has become a popular research tool to determine the pharmacological effects of Chinese herbal medicine on disease. In our study, the PPI network showed that UA had 50 targets for bladder cancer treatment. Twenty-one core targets were identified in the topological analysis, including STAT3, VEGFA, CASP3, and TP53. These core target proteins likely play an important role in the effect of UA against bladder cancer. The signal transducer and activator of transcription (STAT) protein family is closely associated with tumor cell survival and proliferation.²¹ Activation of STAT3 has been found in a wide variety of human cancer cell lines.²² Liu et al.²³ showed that inhibition of STAT3 phosphorylation contributed to the antiproliferative effect of UA on hepatocellular carcinoma. VEGFA has been shown to have anti-tumor angiogenesis effects and inhibit tumor growth.²⁴ Studies have reported that VEGFA inhibition can promote the apoptosis of bladder cancer.²⁵ CASP3 is a key enzyme in the cell apoptosis pathway and is involved in the downstream reactions of the various apoptotic protease cascades.²⁶ As a tumor suppressor gene, TP53 is highly expressed in a variety of tumors.²⁷ A study by Ciccares indicated that TP53 could be a potentially important target for the treatment of bladder cancer.²⁸

According to the GO functional enrichment analysis, UA can exert various functional anti-cancer effects on bladder cancer, such as regulating apoptosis and responding to oxidative stress. Cell apoptosis is a highly regulated physiological mechanism of cell death that requires the activation, expression, and regulation of the involved genes, and it is an important response to anti-tumor therapy.²⁹ Studies have shown that oxidative stress plays a pivotal role in the development of solid malignant tumors.³⁰ Moreover, UA was reported to activate intracellular oxidative stress and mediate ERK1/2 MAPK-associated apoptosis in human osteosarcoma cells.³¹

KEGG pathway enrichment analysis showed that UA had anti-cancer effects on bladder cancer involving multiple signaling pathways. The main signaling pathways included pathways involved in cancer, lipid metabolism, and atherosclerosis. The pathways involved in cancer included signaling pathways that were enriched with the most targets. It was further revealed that UA had anti-tumor activity against bladder cancer. Recent studies have found that lipids and atherosclerosis are risk factors for cancer and can promote the appearance and progression of cancer.^32,33 Related studies have found that lipid metabolism was activated in bladder cancer in contrast to that in the normal bladder, and the lipid level was increased in bladder cancer compared with that in paracancerous tissues. Inhibition of lipid metabolism can reduce ATP and nicotinamide adenine dinucleotide phosphate levels, suppressing human bladder cancer cell growth.³⁴ UA has been experimentally shown to enhance macrophage autophagy and attenuate atherogenesis in vivo and in vitro.³⁵ Therefore, UA has the potential to treat cancers through an anti-atherosclerosis effect. In addition, our study showed that the TNF and PI3K/Akt signaling pathways were also important for UA treatment of bladder cancer. TNF-α is a member of the TNF family, which can promote cells to release a variety of inflammatory factors and activate downstream nuclear factor-kappa B to inhibit proliferation and induce apoptosis in human bladder cancer cells.³⁶ The PI3K/Akt signaling pathway is recognized as a crucial signaling pathway involved in apoptosis, invasion, cell survival, and protein synthesis.³⁷ Moreover, the PI3K/Akt signaling pathway is involved in UA-induced apoptosis in multiple cancers, such as prostate and pancreatic cancer.^38,39

STAT3, VEGFA, CASP3, TP53, IL1B, and CCND1 are the most likely core targets of UA for bladder cancer treatment. In this study, we verified the binding activity of UA and the top six core genes that encode these proteins using molecular docking techniques. The results showed that UA has good affinity for these targets. Meanwhile, the molecular docking results preliminarily validated the reliability of the network pharmacological predictions. In addition, both the GO functional enrichment analysis and the pathway analysis suggested that UA regulates cell apoptosis in bladder cancer. We used the T24 human bladder cancer cell line to validate the results in vitro, and the Hoechst 33258 staining results were consistent with the network pharmacology predictions. Furthermore, the PI3K/Akt signaling pathway was selected from the predicted pathways for validation. Our western blot analysis results indicated that UA can induce apoptosis in T24 cells by downregulating the PI3K/Akt signaling pathway.

UA possesses great potential in the treatment of bladder cancer. However, the low water solubility of UA hinders its further clinical application. With the development of nanotechnology, nano-drug delivery systems have been widely applied to deliver various agents such as drugs, phytochemicals, and biomolecules.⁴⁰ In recent years, a variety of UA nanosystems have emerged, and the results suggest that UA-based nano-delivery systems could ameliorate the water solubility and bioavailability issues of UA.^41,42 Therefore, nanotechnology may be a promising strategy to overcome the low water solubility of UA, which may promote clinical use of UA in the treatment of bladder cancer.

Conclusion

In summary, UA is a monomer component extracted for Chinese herbal medicine that possesses potent anti-cancer activity and the advantages of low toxicity and few side effects. On the basis of network pharmacology and molecular docking analysis, we demonstrated that UA ameliorated bladder cancer through multiple targets and pathways. Our in vitro experiments verified that UA induced apoptosis of T24 cells, and the PI3K/Akt signaling pathway was confirmed to be involved in the effect of UA against bladder cancer. Nevertheless, the other prediction results still require further experimental verification. Our study may provide a theoretical reference for subsequent research and clinical treatment using UA.

Footnotes

Acknowledgements

The authors thank the Central Laboratory of The First Affiliated Hospital of Chongqing Medical University (Chongqing, China) for their technical support.

Author contributions

Wei-yang He and Xiao-long Huang designed the research, Xiao-long Huang and Yan Sun conducted the experiments, Jun-cheng Pan and Peng Wen analyzed the data, and Xiao-long Huang wrote the paper.

Declaration of conflicting interests

The authors declare that they have no conflicts of interest.

Funding

This study was funded by the Natural Science Foundation of China (No. 81874092).

ORCID iD

Xiao-Long Huang

Supplemental material

The supplemental material for this article is available online.

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