Introduction

Cervical cancer is the fourth most common cancer in women worldwide1, and persistent human papillomavirus (HPV) infection is the main causative agent of cervical cancer, and despite the adoption of cervical cancer screening and cervical HPV vaccination, its morbidity and mortality remain high, with about 500,000 new cases and 300,000 deaths each year2. The high incidence age is 50–55 years old, and in recent years its incidence tends to be younger3. Cervical cancer often has no obvious symptoms and signs in the early stage, and the main treatment plan for patients in the middle and late stages is surgery combined with radiotherapy. Although this program is effective, it has many adverse reactions, cannot maintain the treatment for a long time, and the prognosis is usually poor4. And immunotherapy and targeted therapy have the disadvantages of expensive price, long preparation time, and big side effects. Therefore, in recent years, the advantages of traditional Chinese medicine in the treatment of cervical cancer have gradually appeared. Traditional Chinese medicine can play a role in the adjuvant treatment of cervical cancer to increase the efficacy and reduce the toxicity, and support the correctness and anticancer effects5. It can improve patients’ adaptability, reduce the pain of treatment, and help maintain the treatment. Rhubarb is the dried root and rhizome of Polygonum palmatum, Tangut rhubarb or medicinal rhubarb, which has anti-inflammatory, anti-tumor, hepatoprotective, nephroprotective, lipid-lowering, and anti-angiogenic effects6. Among them, anti-tumor effect is one of the important directions of modern pharmacological research on rhubarb, and studies have shown that rhubarb has anti-tumor effects on liver, lung, colorectal, breast, and cervical cancers7,8,9,10. However, rhubarb contains complex components, so the specific mechanisms in the treatment of cervical cancer has not been clarified. This study aims to investigate the relationship between drugs, diseases and pathways through network pharmacology and molecular docking techniques11,12, which provides reference and new ideas for the clinical application of rhubarb in the treatment of cervical cancer.

Materials and methods

Rhubarb active ingredients and targets

The TCMSP (https://old.tcmsp-e.com/tcmsp.php) and HERB (http://herb.ac.cn/) databases were searched for “rhubarb”. Statistical analysis and experience have shown that compounds with oral bioavailability (OB) ≥ 30% usually have better intestinal absorption and systemic exposure, and may have potential for drug formation; and natural ingredients with drug-likeness (DL) ≥ 0.18 are more likely to have drug-like properties13, so we utilized OB ≥ 30% and DL ≥ 0.18 as pharmacokinetic indexes for screening the active active ingredients of Rheum officinale. The target prediction of active ingredients was performed using SEA (https://sea.bkslab.org/) and Swiss TargetPrediction databases (http://www.swisstargetprediction.ch/), with species HUMAN, P < 0.5, and Tc > 0.8 as the screening criteria in SEA, and probability > mean as the screening criteria in Swiss TargetPrediction. We performed consolidation and duplicate data removal of the search results; active ingredients were removed which without SMILE numbers and corresponding targets, and target protein names were converted to gene names using the UniProt (https://www.uniprot.org/) database.

Cervical cancer-related targets

Four databases, OMIM (https://www.omim.org/), GeneCards (https://www.genecards.org/), CTD (https://ctdbase.org/), and GDA (https://disgenet.com/) were searched for “Cervical cancer”, integrating all targets and removing duplicates to obtain cervical cancer-related targets and converted to gene names in UniProt database.

Common target acquisition and drug-active ingredient-target network construction for rhubarb and cervical cancer

The related targets of rhubarb and cervical cancer were entered into Draw Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/) to obtain the common targets of rhubarb and cervical cancer respectively, and the Venn diagrams were drawn and saved. And the drug-active ingredient-target network diagram was constructed using Cytoscape 3.10.3 software.

Construction of PPI network and core gene screening

The common targets of rhubarb and cervical cancer were imported into the STRING database, the species was selected as Homo sapiens, the confidence threshold was set to moderate confidence 0.4, saved as TSV files, and imported into Cytoscape 3.10.3 software for visualization. Using CytoHubba plug-in, the top 20 targets of degree and MCC were filtered and intersected with Venn diagram to get the core targets. Cluster analysis was performed using MCODE plug-in to filter the most important modules.

GO biofunction and KEGG pathway enrichment analysis

The common target and core genes of rhubarb and cervical cancer were entered into the DAVID (https://david.ncifcrf.gov/) and REACTOME (https://reactome.org/) databases, and the selected species was Homo sapiens for gene ontology (GO) biological enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis14. The top 10 of biological process (BP), cellular component (CC), and molecular function (MF) in the GO enrichment analysis and the top 20 in the KEGG enrichment analysis, were ranked according to P-Value from smallest to largest conditioned by P < 0.05, and the data were visualized on the Microbiome platform (http://www.bioinformatics.com.cn/) for visualization. The top 15 pathways of the signaling pathway results obtained from the REACTOME database for the core genes were visualized with Sankey bubble plots.

Constructing topological networks

Rheum palmatum, active ingredients, common targets and their corresponding top 20 pathways were organized into data files and attribute files. Data files refer to their two-by-two associations and represent edges in a graph. Attribute files refer to the properties of the four components and represent nodes in the graph. The two files were imported into Cytoscape 3.10.3 software, and the “drug-active ingredient-target-pathway” network was established, topologically analyzed and visualized. The top 5 core active ingredients and targets were ranked according to the degree.

Molecular Docking validation

The five core active ingredients obtained in 2.6 were used as small molecule ligands, and the intersection of the core targets ranked in the top 18 of degree in the PPI network and the targets ranked in the top 5 of degree in the drug-active ingredient-target-pathway network in cervical cancer was taken by Venn diagram to obtain the common core target as the receptor, and two-by-two docking between the ligand and the receptor was performed for validation. In order to make the protein receptor structure more accurate, we first searched the core target gene name in the Uniprot database, selected human, and obtained the protein ID sequence number, entered the ID into the RCSB PDB (https://www.rcsb.org/) database to obtain the protein 3D structure, and saved it as pdb format by removing the water molecules and the small-molecule ligands in the PyMOL. The 3D structure of the core constituents of rhubarb was retrieved from the PubChem (https://pubchem.ncbi.nlm.nih.gov/) database, saved in sdf format, converted to pdb format by inputting the sdf format into openBabel or PyMOL. The pdb format will be imported into Auto Dock Tools 1.5.6 software for pre-processing such as hydrogenation, determination of active pockets, etc., and finally saved as a pdbqt file. Set parameters to energy_range = 5, exhaustiveness = 40, num_mode = 8. Apply Auto Dock Vina software for molecular docking, record the molecular docking binding energy to draw a heat map, and import the docking results into PyMOL software for visualization such as displaying the hydrogen bonds and the amino acid residues they act on.

Results

Screening of rhubarb active compounds and targets

A total of 92 active ingredients were obtained from the public database TCMSP, and 16 active ingredients were screened based on the criteria of OB ≥ 30% and DL ≥ 0.18. A total of 150 active ingredients were obtained from the HERB database, and the active ingredients in this data that were redundant to TCMSP were screened and the corresponding SMILE numbers were obtained in HERB, and the active ingredients without SMILE numbers were deleted. The obtained SMILE number was then entered into Swissadme and 12 active ingredients were screened with Druglikeness showing YES. The results of the two databases were taken as a concatenation and 28 active ingredients were obtained. The active ingredient targets were obtained in SEA and Swiss TargetPrediction databases and duplicate targets were removed, and active ingredients without corresponding targets were also removed. Finally, 23 active ingredients and 173 potential targets of rhubarb were obtained, and 2967 cervical cancer-related targets were also obtained from public databases. 106 common targets were obtained after taking the intersection of the two (Fig. 1A). A “drug-active ingredient-target” network consisting of one herb, 23 compounds and 106 targets was constructed in Cytoscape 3.10.3 (Fig. 1B), including 129 nodes and 610 edges, to reveal the multi-targeting mechanism of rhubarb in the treatment of cervical cancer.

Fig. 1
figure 1

(A) Venn diagram of rhubarb active ingredient targets and cervical cancer genes. (B) Rhubarb-active ingredient-common target interaction network: purple ovals represent rhubarb, yellow diamonds represent active ingredients, and blue rectangles represent common targets.

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Constructing PPI network and core gene screening

The 106 common targets were entered into the STRING database to create a PPI network and visualized in Cytoscape 3.10.3, which showed 104 nodes and 1886 edges, with the removal of two unrelated targets (Fig. 2A). Using CytoHubba plugin, the top 20 targets were filtered by Degree and MCC as filtering conditions to take the intersection and draw the Wayne diagram, and 18 core targets were obtained, which were ESR1, GSK3B, KDR, HSP90AB1, HSP90AA1, MMP9, AR, AKT1, PGR, EGFR, PARP1, IGF1R, RELA, SRC, PTGS2, MET, MMP2, MCL1. (Fig. 2B) And the targets were classified into 7 categories using the MCODE plug-in for cluster analysis (Fig. 2C).

Fig. 2
figure 2

(A) Common target PPI network. Nodes represent proteins, edges represent protein associations, and node size and color shades are positively correlated with degree values. (B) Venn diagram of core target screening by CytoHubba. (C) Network graph of the 7 most important modules obtained by MCODE.

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Results of GO and KEGG enrichment analysis

The 106 common targets were imported into the DAVID database, and GO enrichment analysis revealed 266 BP, 63 CC, and 122 MF. Ranking was based on P-Value. The top 3 in BP were phosphorylation, protein phosphorylation, protein autophosphorylation, the top 3 in CC were cytoplasm, protein-containing complex, plasma membrane, and in MF, ATP binding, protein tyrosine kinase activity, protein kinase activity, and the top 10 in each category were visualized. (Fig. 3).The KEGG analysis revealed a total of 126 signaling pathways, and the top 20 pathways were taken according to P-Value for visualized (Fig. 4). Similarly, 18 core targets were imported into the REACTOME database for signaling pathway enrichment analysis, and the he results showed that ESR-mediated signalin ranked first with a P-value of 3.89E-15, revealing that it may be a key mechanism of rhubarb in the treatment of cervical cancer, in which HSP90AA1, HSP90AB1, SRC, MMP2, AKT1, PGR, MMP9, ESR1, EGFR, and IGF1R play a central role. The results of top 15 were visualized in a Sankey bubble map (Fig. 5).

Fig. 3
figure 3

Histograms of GO analysis for common targets of rhubarb and cervical cancer ((A) horizontal axis represents GO terms including BP, CC, MF; vertical axis represents -log10 (P-Value)) and bubble plots ((B) horizontal axis represents multiplicity of enrichment, vertical axis represents GO terms, size of the bubbles represents the number of genes, and color shade represents -log10 (P-Value)).

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Fig. 4
figure 4

Bubble plots ((A) horizontal axis represents fold enrichment, vertical axis represents KEGG pathway, bubble size represents number of genes, and color shade represents -log10 (P-Value)) and category histograms ((B) horizontal axis represents number of genes, vertical axis represents KEGG pathway) of the top 20 pathways in the results of the common target KEGG pathway enrichment analysis15.

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Fig. 5
figure 5

Sankey bubble plots of the top 15 pathways in the signaling pathway enrichment analysis results obtained in the REACTOME database for the core target. (Horizontal axis represents Gene Ratio, vertical axis represents targets and pathways, bubble size represents the number of genes, and color shade represents -log10 (P-Value)).

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Drug-active ingredient-common target-pathway analysis and identification of core targets in cervical cancer

The network of drugs, compounds, common targets and pathways was constructed using Cytoscape 3.10.3, which included 149 nodes and 898 edges, and degree showed that 3,5,3’-trihydroxy-6,7,4’-trimethoxyflavone, eupatin, 5-carboxy-7- hydroxy-2-methyl-benzopyran-γ-one, rhapontigenin, and chrysophanol were the top 5 ranked active ingredients, and PIK3CA, PIK3CB, EGFR, IGF1R, PIK3R1, AKT1, MMP9, MET, and SRC were the co-ranked and tied top 5 targets (Fig. 6).

Fig. 6
figure 6

Drug-active ingredient-common target-pathway network. Red quadrilateral nodes represent herbs, purple triangles represent active ingredients, green rectangles represent common targets, and yellow diamonds represent pathways.

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Molecular Docking results

Molecular docking was used to screen the interaction of active ingredients with core targets to validate the results of network pharmacology. The 18 core targets obtained in Result 3.2 were intersected with the 6 core targets that ranked in the top 5 in 3.4 by taking the intersection through Venn diagrams, and 6 common core targets were obtained, which corresponded to six receptor proteins, including EGFR (PDB ID:4HZR), IGF1R (PDB ID:1P4O), AKT1 (PDB ID:7NH5), MMP9 (PDB ID:5TH6), MET (PDB ID:3EDG), SRC (PDB ID:6E6E ). Their corresponding active pocket parameters were 4HZR (X:10.89,Y:12.9,Z:14.0,diameter:28), 1P4O (X:11.45,Y:67.94,Z:-17.19,diameter:47), 7NH5 (X:12.26,Y:-18.19,Z:-15.23,diameter:42), 5TH6 (X:11.34,Y:12.29,Z:38.0,Diameter:34), 3EDG (X:1.4,Y:5.5,Z:5.08, Diameter:23), 6E6E (X:-5.69,Y:-54.03,Z:107.13,Diameter:23). Two-by-two docking with the five core active ingredients was performed in Auto Dock Vina software to record the minimum free energy of binding, hydrogen bond length, and key amino acids (Table 1). Combined energy scores between 0 and −5 indicate fair results, while scores between −5 and −10 indicate good results. The average free energy value of −7.74 kcal/mol was found in the 30 sets of active ingredient-target protein docking results, indicating that these core targets have a strong binding capacity to the active compounds in rhubarb, which is in line with the network pharmacological predictions. (Fig. 7A, B) The top 6 binding modes with the highest compound-target interactions and free binding energy scores were visualized using PyMOL (Fig. 8).

Table 1 Molecular Docking scores for core targets.
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Fig. 7
figure 7

(A) Venn diagram of common core targets. (B) Heatmap of binding energies.

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Fig. 8
figure 8

Docking patterns of core targets and active ingredients.Pink represents proteins, light blue represents ligands, dark blue represents key amino acids, and yellow dashed lines represent hydrogen bonds.

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Conclusion

Rhubarb, which was first published in Shennong’s Classic of the Materia Medica, has a long history and wide application in China, and its role in the treatment of cervical cancer has gradually emerged in recent years.In this study, we investigated the mechanism of rhubarb in the treatment of cervical cancer from the aspects of active ingredients, targets and signaling pathways. The results showed that among the common targets of rhubarb and cervical cancer, ESR1 and AKT1 were included, and chrysophanol, the active component in rhubarb, had a strong binding ability with AKT1. The results of KEGG enrichment analysis showed that the two common targets were highly enriched in the ESR-mediated signaling pathway and the PI3K/AKT signaling pathway, which seems to suggest that there may be a close connection between rhubarb, ESR-mediated signaling, and PI3K/AKT. Studies have shown that rhubarb, the main component of rhubarb, has been found to act on the PI3K/AKT signaling pathway and to be involved in the pathogenic process of a variety of tumors such as cervical, lung, and breast cancers16,17. Estrogen Receptor 1 is a transcription factor belonging to the nuclear receptor superfamily that encodes Estrogen Receptor α (ER-α), which is able to be activated by estrogen and growth factors in a ligand-dependent manner, and is involved in the regulation of cell proliferation, differentiation and apoptosis18,19,20. The estrogen signaling-activated PI3K/AKT/mTOR pathway has been found to be overactive in up to 70% of breast cancers. Targeting the PI3K-AKT-mTOR pathway has been shown to be beneficial in both neoadjuvant and advanced stages of ERα + breast cancer21,22. However, in cervical cancer, the studies between rhubarb, ESR-mediated signaling, and the PI3K/AKT signaling pathway are scarce, innovative, and need to be further explored in the future.

This study combines network pharmacology and molecular docking techniques to reveal the potential value of rhubarb in the treatment of cervical cancer, and points out possible molecular mechanisms and key targets for future research and clinical applications.However, there are some limitations in this study, such as the molecular docking technique only reflects static interactions, and future studies should perform molecular dynamics simulations to further validate the stability and kinetics of these ligand-protein complexes under physiological conditions; and the reliability of the screened key targets and pathways also needs to be verified by in vitro and in vivo experiments.