Butein, a potential drug for the treatment of Bone Cancer Pain through bioinformatic and network pharmacology

doi:10.21203/rs.3.rs-2080214/v1

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Butein, a potential drug for the treatment of Bone Cancer Pain through bioinformatic and network pharmacology

https://doi.org/10.21203/rs.3.rs-2080214/v1

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Bone cancer pain (BCP) is a difficult-to-treat pathologic condition that impairs the patient's quality of life. The effective therapy options for BCP are restricted due to the unknown pathophysiology. Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database and differentially expressed gene (DEGs) extraction was performed. Differentially expressed genes analysis (DEGs) integrated with pathological targets found 68 genes in the study. Butein was discovered as a possible medication for BCP after the 68 genes were submitted to the Connectivity Map (CMap) 2.0 database for drug prediction. To collect the butein targets, we used the CTD, SEA, TargetNet, and Super-PRED databases. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed butein's pharmacological effects, indicating that butein may aid in treating BCP by altering the hypoxia-inducible factor, NF-kappa B, angiogenesis, and sphingolipid signaling pathways. Moreover, the pathological targets integrated with drug targets were obtained as the shared gene set A (GSA), which was analyzed by ClueGO and MCODE. Biological process analysis and MCODE algorithm further analyzed that BCP related targets were mainly involved in signal transduction process and ion channel-related pathways. Next, we integrated targets related to network topology parameters and targets of core pathways, identified PTGS2, EGFR, JUN, ESR1, TRPV1, AKT1 and VEGFA as butein regulated hub genes by molecular docking, which play a critical role in its analgesic effect. This study lays the scientific groundwork for elucidating the mechanism underlying butein's success in the treatment of BCP.

Activity drug screening

Butein

BCP

network pharmacology

molecular mechanisms

BCP is a symptom caused by tumor metastasis and can occur at any time during the disease course [1]. The prevalence of BCP is 50.7% in all cancer stages and 66.4% in advanced stages [2], so BCP is a severe problem worldwide. The migration of cancer to the bone can cause severe pain, and up to 85% of patients suffer from BCP [3]. Patients with BCP usually have persistent pain that can worsen as the disease progresses [4–6]. That is to say, patients with different pain experiences may have different potential pathology and need distinct therapeutic options [7]. Therefore, a more in-depth understanding of the molecular mechanism of BCP and the development of potential drugs are imminent, which is no less important than the treatment of cancer itself.

BCP is a complex pain with both neuropathic and inflammatory pain syndromes, therefore, it is difficult to treat clinically [8]. Mechanisms of BCP are complex, and the main mechanisms that have been reported so far including ion channels and receptors involved in sensing changes in temperature and acidic microenvironment [9, 10], immune system activation [11–13], sodium channels [14–16], protease-activated receptors [17] and the like.

Despite progress in medical science research, researchers have been unable to make significant progress in developing a reliable BCP treatment. Nonetheless, the World Health Organization has proposed a three-step analgesic ladder for BCP therapy, with non-opioid analgesics being prescribed first, followed by weak and strong opioids if the pain persists [18]. Opioids, such as morphine and pentazocine, and non-steroidal anti-inflammatory medicines (NSAIDs) such as diclofenac and indomethacin, are currently available for pain management. Both NSAIDs and opioids have various adverse effects, including respiratory depression, cardiovascular problems, gastrointestinal damage, nephrotoxicity and hepatotoxicity [19].

Throughout history, herbal plants have been widely used to relieve pain with lower side effects. For example, morphine and acetyl salicylic acid are two of the most important analgesics derived from plant sources [20, 21]. Butein (3,4,2′,4′-Tetrahydroxychalcone) is a chalcone chemical found in Toxicodendron vernicifluum, Dalbergia odorifera and Semecarpus odorifera [22, 23]. Butein has been demonstrated to have many therapeutic effects including anticancer, anti-inflammatory, antimicrobial, antioxidant properties [24], anti-depressive [25] and neuroprotective [26] characteristics. Although the pharmacological effects of butein had been widely reported, its analgesic effect on BCP remains to be further studied.

In this study, in order to further select the potential drugs to treat BCP, the GEO dataset was used to obtain a list of DEGs. Then, through the CMap 2.0 database, we found that butein may be a potential drug. Next, we deeply analyzed the likely targets and molecular mechanisms behind butein's anti-BCP actions using the network pharmacology method. To begin, we used chemical similarity analysis and reversed docking to identify the molecular targets of butein. Various bioinformatics tools were used to identify pathological targets. After that, we completed KEGG enrichment analysis using KOBAS 3.0 database and formed a target-pathway network. Finally, the interactions between butein and its hub targets were verified using molecular docking.

The experimental flow is shown in Fig. 1.

2.1 Data collection and data processing

Genes were screened by using the GEO (http://www.ncbi.nlm.nih.gov/geo) database. We retrieved one GSE data (GSE149648) concerning the microarray changes in the Rattus norvegicus dorsal root ganglia (DRG) after BCP[27], 16 samples were analyzed from four groups, including tumor cell injection (TCI, Walker 256 mammary gland carcinoma cell) D1, TCI D7, TCI D14, and sham as control. Each group has four replicates. The microarray data was analyzed at various time points, including day 1, 7, and 14 following TCI. Following that, we looked at transcriptome changes in various stages of BCP development. Perl converted the names of microarray probes to gene symbols. The DEGs were obtained in each dataset using the limma package in R, with |log2 FoldChange(FC)|>1 and a P-value < 0.05 as the cutoff criteria. Next, we converted DEGs to human gene symbols as implemented in the “homologene” R package.

2.2 Identification of Pathological Targets

BCP-related targets were identified using Human Pain Genes Database (HPGD, https://humanpaingenetics.org/), the DrugBank database (https://go.drugbank.com/), Genecard (https://www.genecards.org), Open Targets Platform (OPT, https://platform.opentargets.org/), OMIM (https://www.omim.org/). These targets were supposed to be pathogenic targets after putting these targets together and removing the duplicates. Following that, the overlap of DEGs and pathogenic targets were established.

2.3 CMap 2.0 was used for drug prediction

The intersecting genes uploaded to the CMap 2.0 database (https://portals.broadinstitute.org/cmap/) to predict possible medicines for BCP. And the medication with a |enrichment|≥0.9 and a P value༜0.05 were chosen as BCP candidate medications.

2.4 Predict the pharmacological targets of butein and pathway enrichment analysis

Similarity ensemble approach (SEA, https://sea.bkslab.org/), Comparative Toxicogenomics Database (CTD, http://ctdbaseorg/), Swiss Target Prediction (http://swisstargetprediction.ch/) and Super-PRED (https://prediction.charite.de/) were used to predicted the potential therapeutic targets of butein. The candidate genes were identified and corrected by using the Uniprot database (https://www.uniprot.org/). The potential pharmacological targets of butein were selected and subjected to KEGG pathway analysis. For KEGG enrichment analysis (http://kobas.cbi.pku.edu.cn/), pathways were selected by their enrichment scores.

2.5 Identification of shared genes in butein and pathogenic targets

The shared gene set A (GSA) associated with butein and pathogenic targets were overlapped using Venn (https://bioinfogp.cnb.csic.es/tools/venny/). ClueGO is a Cytoscape plug-in, which could categorize the non-redundant Gene ontology (GO) terms and visualize them as a functionally grouped network. To explore potential roles of these shared genes in treatment of BCP, a biological analysis of these shared genes was performed by ClueGO. The biological process of GO analysis was focused. The p-value < 0.05 was considered significant. Next, the GSA is uploaded to Metascape (https://metascape.org/) for module functional analysis, the Molecular Complex Detection (MCODE) algorithm was applied to identify densely connected network components. Pathway and process enrichment analyses were applied to each MCODE component independently, while the three best-scoring terms by p-value were retained as the functional description of the corresponding components [28].

2.6 Identifying hub targets of butein against BCP

The hub targets were constructed by intersecting the butein and pathogenic targets, and Protein Protein Interactions (PPI) networks were constructed through the STRING (https://string-db.org/) platform using the target proteins selected above. A high confidence score of greater than 0.4 was used in the present study to obtain PPI data. Cytoscape v3.7.1 was used to show the PPI network. The network topology parameters were calculated using Network Analyzer [29], with the network being viewed as undirected. We calculated the topological scores for the nodes using the CytoNCA plugin (version 2.1.6), including betweenness, closeness, degree, and subgraph centrality.

2.7 Molecular docking

The binding situation and interaction force of proteins and small molecules can be anticipated and acquired via molecular docking research. The Traditional Chinese Medicine Database and Analysis Platform database (TCMSP, https://tcmsp-e.com/) was used to find the molecular structure of butein. The Protein Data Bank (http://www.rcsb.org/) provided the X-ray crystal structures of the proteins. The proteins were then stripped of their water molecules and heteroatoms using PyMOL 2.4. We handled the protein receptor and ligand files with AutoDock Tools1.5.6, including inserting hydrogen atoms, computing Gasteiger charges, and integrating nonpolar hydrogen atoms, and making rotatable torsion bonds. A pdbqt file was then created from them. The Lamarckian genetic algorithm was used to perform molecular docking between butein and the core targets using AutoDock software[30]. PyMOL 2.4 and Discovery Studio Visualizer 2016 are used for visualization.

3.1 Identification of DEGs

The transcriptome alterations at various stages of BCP development were analyzed through GEO database. DEGs were screened for further functional analysis using cut-offs of |log2 FoldChange (FC)|>1 and P-value < 0.05 between two comparable groups. The volcanos in Figs. 2A-C depict representative distribution of DEGs. One day after TCI treatment, 68 genes were up-regulated, while 44 genes were down-regulated. In comparison with the sham-operated group, differential gene expression peaked on day 7 following TCI with 809 genes up-regulated and 203 genes down-regulated. Comparing the TCI group to the sham-operated group, 601 genes were up-regulated on day 14 and 148 genes were down-regulated. Therefore, these DEGs might play a crucial role in regulating the genesis of BCP. Finally, 701 up-regulated genes and 239 down-regulated genes were obtained by converting these DEGs to human orthologs (Supplementary Table 1).

3.2 Identification of potential therapeutic targets

To obtain potential therapeutic targets, 844 targets associated with BCP have been identified by using HPGD, Drugbank, Genecard, OMIM, and Open Targets Platform (Supplementary Table 2). Next, 68 intersection genes of DEGs with 844 targets were displayed by using Venn diagram, of which 48 targets were up-regulated and 20 targets were down-regulated (Fig. 2D).

3.3 Butein is a candidate drug for BCP

In order to search for potential drugs to treat BCP, CMap 2.0 was used to perform drug prediction on 68 DEGs, and two potential drugs, butein and phenazone, were obtained.

Previous research has shown that butein has anti-inflammatory and antioxidative stress properties, and therefore has a therapeutic effect on various inflammatory disorders [22, 31]. Recent research found that butein could significantly reduce pain in mice caused by thermal and chemical stimulation, and can significantly reduce carrageenan-induced paw edema and inflammatory cell infiltration in mice [21]. Therefore, butein was selected for further network pharmacology study.

3.4 The prediction of the pharmacological targets of butein and pathway enrichment analysis

A total of 275 targets of butein were obtained from CTD, SEA, TargetNet, Super-PRED, and Literature supplement [31] (Supplementary Table 3). Besides, these targets were uploaded to the KOBAS 3.0 database for KEGG enrichment analysis. Then, the top 20 related pathways with the highest enrichment of BCP-related genes were selected to construct the target pathway network by using Sangerbox 3.0 (http://vip.sangerbox.com/home.html) cloud platform (Fig. 3A). The results showed that the remarkably enriched 275 potential targets were mainly belonged to “HIF-1 signaling pathway, VEGF signaling pathway, Sphingolipid signaling pathway, NF-kappa B signaling pathway.” According to the annotation of KEGG, 128 of the 275 (46.5%) targets were dramatically enriched in 20 pathways with BCP association (Supplementary Table 4). According to the statistical analysis, 12 targets were engaged in the top 20 pathways (Fig. 3B) extremely frequently (≥ 7 times), indicating that they have important roles in the enriched pathways. The 12 core targets were PIK3R1, MAP2K2, RELA, NFKB1, PRKACA, FOS, JUN, IKBKG, AKT1, TNF, PRKCG, and EGFR.

3.5 The common genes in butein and pathogenic targets

To obtain therapeutic targets associated with BCP, there were 65 genes overlapped in related of butein and pathogenic targets, which was defined as gene set A (GSA) (Fig. 4A). To explore the potential functions of GSA, we performed the GO enrichment analysis with GlueGo. The top three significantly enriched GO terms about biological process (BP) were “monoamine transport”, “regulation of blood circulation” and “chemical synaptic transmission postsynaptic”. The three terms accounted for 76.47% of total GO terms, and was associated with 49 genes (Figs. 4B, C), confirming that these terms maybe of vital importance for butein treatment of BCP. It was further constructed the PPI network with the 68 potential targets by STRING and searched the similar function clusters of the PPI network by MCODE analysis based on topology (Fig. 5A). Similar function subnetworks were constructed, and function analysis showed that MCODE 1 mainly participated in the GPCR ligand binding. MCODE 2 specifically regulates the brain-derived neurotrophic factor (BDNF) signaling pathway. MCODE 3 mainly regulates the regulation of miRNA transcription. MCODE 4 mainly regulates the Calcium signaling pathway (Fig. 5B).

3.6 Identification of hub targets and PPI network construction

In order to obtain essential targets from GSA, the Network Analyzer evaluated the topological characteristics of the node degree, and the CytoNCA plugin computed the four centralities (betweenness, closeness, degree, and subgraph) to identify the critical nodes and key targets in the PPI network, as shown in Fig. 6A (Supplementary Table 5). Based on the overlap of the top ten targets in each of the four different categories, AKT1, TNF, CASP3, FOS, EGFR, PTGS2, VEGFA, ESR1, ACHE, and TRPV1 were considered as critical proteins with significant centrality values based on network topology analysis. Combining the 12 core targets and 10 essential targets, 18 hub targets were obtained, which were submitted to STRING database to obtain the protein network relationship composed of 18 nodes and 98 edges. Cytoscape v3.8.2 was used to establish the protein interaction results (Fig. 6B).

3.7 Molecular docking

Docking analyses were carried out between butein and 18 hub targets. To measure the bonding strength between the docked molecules, the binding energy (Vina score) was employed, and the results showed that butein might have potential binding effect with these proteins. Since previous studies have found that butein specifically inhibits EGFR [32], it is used as a positive control for molecular docking. Therefore, seven hub targets, namely EGFR, JUN, VEGFA, TRPV1, PTGS2, AKT1, and ESR1, were finally obtained according to the binding energy with butein less than that of EGFR (Supplementary Table 6). The docking mode and binding site of butein and hub targets were shown in Fig. 7.

Cancer is the world's second leading cause of mortality, particularly in low- and middle-income nations. According to the World Health Organization's cancer data page, around 10 million people died of cancer worldwide in 2020 [33]. The majority of cancer patients suffer from BCP, and almost 75–90% of metastatic cancer patients report it as a challenging clinical problem [34]. In view of the complex mechanism of BCP, despite decades of extensive research and continuous efforts, the underlying cellular and molecular pathways remain unclear, and clinical options for BCP treatment are limited [35, 36]. Therefore, exploring the underlying mechanism of BCP and drug development are crucial for the treatment of BCP [37].

In this study, DEGs analysis integrated with the CMap 2.0 database indicated butein as a prospective medicine for BCP, and network pharmacology results obtained butein related molecular functions and pharmacological targets for BCP treatment. In order to explore the complex mechanism of 275 anti-BCP targets, these targets were submitted to KOBAS 3.0 database for pathway enrichment analysis, and the interaction between butein and hub targets were evaluated by molecular docking technology. The results showed that HIF-1 signaling pathway, Sphingolipid signaling pathway, VEGF signaling pathway, and NF-kappa B signaling pathway were significantly enriched, and 7 targets were identified as the hub targets. The findings could help researchers better understand the effects of butein on the treatment of BCP, as well as direct future research into the development of butein as an analgesic drug.

The activity of HIF-1 signaling pathway is closely related to the occurrence and progression of BCP. Hypoxia inducible factor-1 α (HIF-1 α) is a regulatory subunit of hypoxia inducible factor-1 (HIF-1) [38], which can activate downstream pathways and further affect autophagy, while autophagy deficiency will further affect several pain transmission elements, such as Spinal GABAergic interneurons [39], Spinal microglia [40], Schwann cells [41], and dorsal root ganglion neurons [42], In addition, HIF-1 can also affect the contact between tumor cells and neurons[43], VEGF signal transduction[44]. Therefore, Butein may inhibit BCP by affecting HIF-1 signaling pathway and initiating multiple downstream pathways. It is the complex pathway network composed of these individual pathways and the cross-talk between them that enables multiple complementary functions for treating BCP with butein.

Sphingolipids were found to play a part in increasing the pain response by affecting several critical pathways, including triggering an immunological response, activating spinal astrocytes, and activating the TRPM3 channel [45]. Recently, the role of sphingolipid signaling in BCP was further discovered. Many types of pain diseases, including neuropathic pain, chemotherapy-induced peripheral neuropathy (CIPN), diabetic neuropathy, and complex regional pain syndrome (CRPS), are mediated by S1P, a sphingosine-derived inflammatory product, its signal-mediated. [46]. Therefore, butein may further regulate the production of SIP by affecting inflammatory mediators such as TNF and NLRP2, and prevent the further development of BCP. In summary, we performed pathway enrichment analysis on 275 butein targets, and finally screened out 20 pathways related to BCP, with the top 3 pathways described in detail (Fig. 8).

To better understand the ameliorative role of butein in the treatment of BCP, we performed pathway and process enrichment analysis for the GSA. The two enrichment methods demonstrate each other, further demonstrating that butein may play a role in the treatment of BCP through various biological processes and pathways. Besides, to find the hub targets, two methodologies were utilized separately: PPI centrality analysis and KEGG pathway analysis. We identified the essential proteins based on the centrality and node degree of the PPI network, which included AKT1, TNF, CASP3, FOS, EGFR, PTGS2, VEGFA, ESR1, ACHE, and TRPV1. The most vital parts in the network are frequently played by nodes with high centralities and node degrees. Because of their high frequency of participation, AKT1, PIK3R1, MAP2K2, RELA, NFKB1, PRKACA, FOS, JUN, IKBKG, TNF, PRKCG, and EGFR were regarded as necessary among the top 20 enriched KEGG pathways. Subsequently, the potential interaction between butein and the obtained 18 targets was further verified by molecular docking. The binding energies of both the active component and the target protein were all below than − 4.5 kcal/moL, ensuring better docking and binding activity [47]. Using EGFR as a positive control, we finally obtained seven hub targets, namely EGFR, JUN, VEGFA, TRPV1, PTGS2, AKT1 and ESR1, which might play a crucial role in the pathogenesis of BCP. In addition, further investigation through the literature found that among the seven hub targets, TRPV1 and ESR1 might become new targets for butein therapy of BCP.

TRPV1 exists in many tissues [48], especially in dorsal root ganglions (DRGs), and its role in various pain states is well established [49], including inflammatory pain and cancer pain[50]. In addition, TRPV1 signaling is reportedly reinforced by a variety of inflammatory mediators, including NO, H₂O₂, serotonin (5-HT), protease-activated receptor (PAR) activators, adenosine triphosphate (ATP), histamine, calcitonin-gene-related peptide (CGRP), endocannabinoids, nerve growth factor (NGF), prostaglandins, tumor necrosis factor α (TNFα), and granulocyte colony-stimulating factor (G-CSF), resulting in painful hypersensitivity [51–53].

In BCP, ESR1 activation and enhanced expression were detected in the DRG. Studies of gene polymorphisms suggest that ESR1 polymorphisms affect downstream COMT activity, which in turn affects pain score and worse self-reported health [54]. Recently, research has shown that estrogen is essential for the IL-23/IL-17A/TRPV1 axis to evoke mechanical pain in females and further enables IL-23 and IL-17A to produce mechanical pain in males via ESR1. And importantly, ESR1 expression by TRPV1⁺ nociceptors is necessary for inducing female mechanical pain by IL-23, IL-17A, and capsaicin [55]. Therefore, we hypothesized that butein may have different mechanisms for its analgesic effect in male and female individuals.

The network analysis results provide a theoretical foundation as well as critical details that may help understanding the mechanisms underlying butein's therapeutic efficacy and discovering potential targets, but further experimental validation of the hub genes and interactions will be required in the future, and the clinical effect of butein on BCP awaits further investigation.

Our findings indicated butein as a novel therapeutic candidate for BCP. Bioinformatics and network analysis were used to study the hub genes and molecular mechanisms behind butein's hypoxia, anti-inflammatory, and anti-oxidant stress on BCP. The critical pathway involved in the management of BCP of butein is the HIF-1 signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, Sphingolipid signaling pathway,” and seven hub targets were identified, including EGFR, JUN, VEGFA, TRPV1, PTGS2, AKT1, and ESR1. In conclusion, the findings provided here may serve as a basis for future pharmacological research into the effects of butein on BCP.

BCP: Bone cancer pain

DEGs: Differentially expressed genes

CMap: Connectivity map

KEGG: Kyoto Encyclopedia of Genes and Genome

GEO: Gene Expression Omnibus

PPI: Protein Protein interactions

NSAIDs: Non-steroidal anti-inflammatory drugs

DRG: Dorsal root ganglia

TCI: Tumor cell injection

PPI: Protein-protein interaction

TCMSP: Traditional Chinese Medicine Database and Analysis Platform

PDB: Protein Data Bank

EGFR: Epidermal growth factor receptor

JUN: Transcription factor AP-1

VEGFA: Vascular endothelial growth factor A,

TRPV1: Transient receptor potential cation channel subfamily V member 1

PTGS2: Prostaglandin G/H synthase 2

AKT1: RAC-alpha serine/threonine-protein kinase

ESR1: Estrogen receptor 1

GO: Gene ontology

BP: Biological process

Acknowledgment

This work was supported by the General project of Education Department of Liaoning Province (LJKZ0947, LJKZ0952); and the cultivation Fund Project of National Natural Science Foundation of Shenyang Pharmaceutical University (PYJJ2021106).

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose

Author Contributions

Xin Liu, Yijia Xu, Mingyi Zhao conceived and designed the experiments. Xin Liu, Shangfeng Zhao, Yiwei Chen conducted the databases prediction and draft the paper. Qianqian Zhao, Shubing Jia, Rongwu Xiang, Jinghai Zhang analyzed the data. All authors revised the paper and approved the final manuscript. All authors agree to be accountable for all aspects of work ensuring integrity and accuracy.

Data Availability

The datasets generated during and analysed during the current study are available in the supplementary documents.

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Butein, a potential drug for the treatment of Bone Cancer Pain through bioinformatic and network pharmacology

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Abstract

Figures

1. Introduction

2. Method

2.1 Data collection and data processing

2.2 Identification of Pathological Targets

2.3 CMap 2.0 was used for drug prediction

2.4 Predict the pharmacological targets of butein and pathway enrichment analysis

2.5 Identification of shared genes in butein and pathogenic targets

2.6 Identifying hub targets of butein against BCP

2.7 Molecular docking

3. Results

3.1 Identification of DEGs

3.2 Identification of potential therapeutic targets

3.3 Butein is a candidate drug for BCP

3.4 The prediction of the pharmacological targets of butein and pathway enrichment analysis

3.5 The common genes in butein and pathogenic targets

3.6 Identification of hub targets and PPI network construction

3.7 Molecular docking

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Supplementary Files

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