Exploring the key pathogenic mechanisms and potential intervention targets for Sophorae Flavescentis radix in managing bone metastasis of lung cancer based on network pharmacology and molecular docking techniques

Introduction

Lung cancer is characterized by its insidious onset, rapid progression, and poor prognosis. The average five-year survival rate of lung cancer patients is nearly 16% in the United States, and <10% in England; thus, it is one of the deadliest malignancies worldwide (1,2). In China, lung cancer is the most common cancer (21.9%) and the leading cause of cancer-related death (26.4%) among male cancer patients, while it is the second most common cancer (13.3%) (following breast cancer) and the leading cause of cancer-related death (11.1%) among female cancer patients (3). Thus, it negatively affects Chinese society.

Lung cancer frequently metastasizes to distant sites such as the brain, bones, and lymph nodes (4). The bones are a common site of distant metastasis (4). Bone metastasis, which often affects weight-bearing bones like the axial skeleton, is a primary cause of pain in advanced lung cancer. If it spreads to the bones, patients only live for 6–10 months, and even after treatment only 40–50% of patients survive (5). Currently, there is no curative treatment for bone metastasis in lung cancer, and research primarily focuses on the clinical efficacy of chemotherapy or targeted therapies to control the disease and alleviate symptoms (6). Traditional Chinese medicine (TCM) is used broadly in the treatment of cancer, as it can enhance drug sensitivity, regulate immune function, improve the efficacy of chemotherapy, and improve patient prognosis (7-10). The advantages of TCM are mainly reflected in its multi-target, multi-pathway actions, broad biological activity, and low toxicity, making it an alternative therapy for various cancers, including lung cancer.

Sophorae Flavescentis radix is a bitter, cold herb that was first recorded in the Shennong Ben Cao Jing. This herb has a clearing heat, drying dampness, kills insects, and encourages diuresis. The main chemical components of Sophorae Flavescentis radix are alkaloids, flavonoids, phenylpropanoids, terpenes, steroids, organic acids, and volatile oils. Among these, matrine and oxymatrine are the two characteristic components, accounting for 3% and 1.5% of Sophorae Flavescentis radix, respectively. These components have been shown to exert anti-inflammatory and anti-tumor pharmacological effects, demonstrating significant anti-tumor activity against many cancers (11). Previous research on the anti-lung cancer effects of Sophorae Flavescentis radix has mostly explored the mechanisms of specific active ingredients acting on particular targets or pathways and has not examined the overall mechanism of action (12). Thus, using network pharmacology, this study explored the effects of Sophorae Flavescentis radix on lung cancer bone metastasis from the perspective of the holistic theory of TCM and the modernization of TCM research. Additionally, it employed bioinformatics methods to conduct a preliminary investigation of the network of action mechanisms, and to provide a reference for the development of new anti-lung cancer bone metastasis drugs and clinical medication based on Sophorae Flavescentis radix. We present this article in accordance with the STREGA reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1947/rc).

Methods

Active-part finding and focus factors (genetic)

TCM Systems Pharmacology (TCMSP) (https://www.tcmsp-e.com/#/home) was used to screen the active components of the Sophorae Flavescentis radix, including the drug-likeness property (DLP) ≥0.18 and oral bioavailability (OB) ≥30%. The predicted targets of the compounds were screened from the DrugBank (https://www.drugbank.ca/) database. Additionally, UniProt (https://www.uniprot.org) was used to compared the target information.

Screening of differentially expressed genes (DEGs)

The DEGs of the primary and bone metastatic lung cancer tissue samples were obtained from the GSE175601 dataset in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The data from GSE175601 was processed and analyzed for differential expression using CHDTEPDB (13). The target genes of Sophorae Flavescentis radix were intersected with the DEGs from the GSE175601 dataset to identify the final candidate target genes. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Data examination using The Cancer Genome Atlas (TCGA) data

In the TCGA database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga), the expression of key genes in lung cancer tumor tissues was explored, and their correlation with the prognosis of lung cancer patients was analyzed.

Protein-protein interaction network analysis

Various tools, such as the GeneMANIA (http://www.genemania.org) database, and Search Tool for Recurring Instances of Neighbouring Genes (STRING) (https://string-db.org/) online database were used to study the MMP1 interaction.

Docking of molecules

We sourced the 3D structures of the active chemicals from Pubchem (https://pubchem.ncbi.nlm.nih.gov/). The key targets’ receptor proteins were obtained using the Protein Data Bank (PDB) database (http://www.rcsb.org/pdb/home/home.do). For the molecular docking, the receptor proteins of the important targets and the 3D structures of the active ingredients were submitted to the DockThor (https://dockthor.lncc.br/v2/) online analysis platform. According to Dockthor calculations, the lower the binding energy required for the ligand to combine with the receptor, the more stable the binding and the better the effect. Finally, the CB-DOCK2 (https://cadd.labshare.cn/cb-dock2/) database was used to visualize the results.

Gene Set Enrichment Analysis (GSEA)

A functional analysis was conducted by uploading the DEGs to Metascape for investigation. This tool helped us to determine whether entire gene sets showed significant increases or decreases in biological processes. The identification of common signaling pathways and regulatory network modules extends understanding of the underlying mechanisms of biological differences between different samples.

Statistical analysis

R software (version 3.6.3) was used for the statistical analysis. The MMP1 mechanism was studied by a GSEA. The Kaplan-Meier method and log-rank test were used to examine patient survival and conduct significance testing, respectively. A P value or log-rank P value of <0.05 was considered statistically significant.

Results

Dynamic Sophorae Flavescentis radix’ genetic factors and parts

In the TCMSP database, 28 active compounds were identified from Sophorae Flavescentis radix as per the DLP and OB criteria (Table 1). Additionally, the target genes for these active compounds were screened from the DrugBank database. Among these compounds, (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one, 5,7-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one, and matrine each had 10 target genes; inermine had 17 target genes; 8-isopentenyl-kaempferol had 29 target genes; sophocarpine and sophoridine each had two target genes; inermin had 18 target genes; wighteone had 19 target genes; formononetin had 39 target genes; cis-Dihydroquercetin had six target genes; (2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one had 15 target genes; and luteolin had 50 target genes (Table S1).

DEG screening

The DEGs were obtained from the GSE175601 dataset in the GEO database as per the set criteria, and visualized in volcano plots, principal component analysis (PCA) plots, sample normalization box plots, and heatmaps (Figure 1A-1D). The (genetic) factors for the responsible constituents in Sophorae Flavescentis radix were intersected with the DEGs from the GSE175601 dataset, and a Venn diagram was generated, and the following five candidate target genes were identified: F10, JUN, AKR1B1, MMP1, and CCND1 (Figure 1E).

Figure 1 Screening of DEGs. (A-D) Volcano plot, PCA plot, sample normalization box plot, and heatmap of the GSE175601 dataset. In (A), The red dots represent significantly upregulated genes, the blue dots represent significantly downregulated genes, and the gray dots indicate non-significantly different genes. (E) Venn diagram of active compounds from Sophorae Flavescentis radix and DEGs from the GSE175601 dataset. DEGs, differentially expressed genes; PCA, principal component analysis.

Expression of DEG in lung cancer tissues in the TCGA database

The five target genes (i.e., F10, JUN, AKR1B1, MMP1, and CCND1) were analyzed in lung cancer tumor tissues using TCGA database. In both the non-paired and paired lung cancer tumor tissues, MMP1 expression was significantly upregulated, while F10 and JUN expression was significantly downregulated (Figure 2A,2B).

Figure 2 DEGs in various tumor tissues from TCGA data. (A) Expression of DEGs in non-paired lung cancer tissues (1,041 cancer tissues, and 108 adjacent normal tissues). (B) Expression of DEGs in paired lung cancer tissues (107 cancer tissues, 107 adjacent normal tissues). ns, P>0.05; **, P<0.01; ***, P<0.001. DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; TPM, transcripts per million.

Correlation between the expression of five DEGs and the prognosis of lung cancer patients in the TCGA database

The prognostic value of the expression of the five DEGs was evaluated in lung cancer patients using TCGA data. The areas under the curve for F10, JUN, and MMP1 were all >0.7, indicating good predictive and discriminative value for these genes. Further, the low expression of AKR1B1, MMP1, and CCND1 indicated good overall survival (OS) in lung cancer patients (Figure 3A,3B). Based on these findings and the expression patterns in lung cancer tissues, MMP1 was identified as the key gene of interest.

Figure 3 Correlations between the expression of DEGs and the prognosis of lung cancer patients using TCGA data. (A) ROC curves of DEGs in TCGA database. (B) Correlation between the expression of DEGs and OS in lung cancer patients from TCGA database. DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; TPR, true positive rate; FPR, false positive rate; ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval; OS, overall survival.

Molecular docking studies

In the process of screening the DEGs, MMP1 was identified as a key gene found in the compound luteolin from Sophorae Flavescentis radix. The 3D structure of luteolin was obtained from Pubchem, and the receptor protein structure of the key target MMP1 was retrieved from the PDB. The 3D structures of the active compound and the receptor protein structure of the key target were uploaded to DockThor for the online molecular docking analysis. The results revealed that the binding affinity between MMP1 and luteolin was −8.9. Further visualization was performed using the CB-DOCK2 database (Figure 4). Based on the strength of the binding affinity, it was concluded that luteolin was indeed an effective compound exerting an action on MMP1.

Figure 4 Molecular docking of the active compound luteolin from Sophorae Flavescentis radix with the core target MMP1.

Identification of MMP1 interaction genes and proteins

The GeneMania data were used to build the gene-gene interaction network of MMP1 and its altered neighboring genes (Figure 5A). The STRING data were used to generate the protein-protein interaction network for MMP1 (Figure 5B).

Figure 5 MMP1 contact genes and proteins. (A) Gene-gene interaction network of MMP1 in the GeneMania database. (B) Protein-protein interaction network in the STRING database. STRING, Search Tool for Recurring Instances of Neighbouring Genes.

Pathway prediction by GSEA

A functional GSEA was conducted using the Metascape online tool. The analysis indicated that MMP1 might influence lung cancer biological processes through its involvement in several signaling pathways, including the chemokine signaling, apoptosis, Wingless/Integrated (Wnt) signaling, tumor protein p53 (TP53)-regulated cell cycle arrest, Hedgehog signaling, and mitogen-activated protein kinase (MAPK) signaling pathways. These pathways may contribute to the different prognoses observed in lung cancer patients (Figure 6).

Figure 6 Statistically significant pathways identified by the GSEA. The gene sets related to biological processes were sourced from the MSigDB. A total of 1,600 random samples were used for the analysis. GSEA, Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; NES, normalized enrichment score; FDR, false discovery rate.

Discussion

In recent years, network pharmacology has become an essential tool in Chinese medicine research, particularly in exploring the multi-target and multi-component nature of TCM. The application of network pharmacology allows for the identification of active compounds and the prediction of potential molecular targets, offering a more comprehensive understanding of TCM’s therapeutic effects. For instance, network pharmacology has been successfully applied to reveal the potential pharmacological mechanisms of Astragalus in the treatment of glioma (14). Moreover, advancements in bioinformatics and database technology have improved the predictive power of network pharmacology, enabling more accurate identification of drug-target interactions. However, there are several challenges that need to be addressed when applying network pharmacology to drug research. One of the main difficulties lies in the accuracy of predictive models, which depend heavily on the comprehensiveness and quality of existing databases. These databases may not fully capture all possible drug-target interactions or account for off-target effects. Additionally, while network pharmacology can reveal potential molecular targets and pathways, translating these findings into clinically effective treatments requires rigorous experimental validation and clinical trials, which can be time-consuming and costly. Addressing these challenges will be crucial for advancing drug innovation and realizing the full potential of network pharmacology in future research.

In TCM, bone metastasis can be classified under the categories of “bone paralysis”, “bone tumor”, “bone carbuncle”, and “bone erosion”. Patients with chronic illness and physical weakness may experience bone metastasis due to the accumulation of pathological factors in the bones. The primary pathogenesis is centered around “pain due to lack of nourishment” and “pain due to obstruction”. Tumors and sores on the body are often the result of pathogenic factors invading a body weakened by deficient vital energy, disrupting the flow of vital energy, and causing cold and heat to congeal within the body. Supporting the vital energy (Fu Zheng) and eliminating the pathogenic factors (Qu Xie) are two critical aspects of the comprehensive treatment of tumors by TCM. The herbs used to eliminate pathogenic factors primarily cleanse, soften, and remove hard masses, and promote blood circulation to remove blood stasis. They play various roles, including inducing cell differentiation, promoting apoptosis, reversing multidrug resistance, and inhibiting invasion and metastasis. Therefore, clearing heat and detoxifying, as part of eliminating pathogenic factors, is one of the primary treatment methods for lung cancer bone metastasis.

In TCM, Sophorae Flavescentis radix is mainly used to treat damp-heat jaundice, diarrhea, dysentery, and skin itching. Modern pharmacological studies have shown that Sophorae Flavescentis radix possesses various pharmacological activities, including antioxidant, anti-inflammatory, anti-arrhythmic, anti-asthmatic, anti-ulcer, and anti-tumor effects (15). Research on its anti-tumor effects indicates that Sophorae Flavescentis radix and its preparations play significant roles in anti-tumor activity, reducing adverse reactions to chemotherapy, and reversing tumor drug resistance.

Using network pharmacology, this study identified 28 active compounds in Sophorae Flavescentis radix with a total of 227 target genes. By intersecting these target genes with the 952 DEGs related to lung cancer bone metastasis obtained from the GSE175601 dataset in the GEO database, we identified five common DEGs (i.e., F10, JUN, AKR1B1, MMP1, and CCND1). Based on the DEGs in lung cancer tumor tissues and their correlation with the prognosis of lung cancer patients, we ultimately identified MMP1 as the candidate gene for this study.

Matrix metalloproteinases (MMPs) are a family of proteases containing metal ions (zinc ions) that degrade various proteins in the extracellular matrix, including collagen, gelatin, elastin, and fibronectin (16). MMP1 was the first gene discovered in the MMP family. It encodes a collagenase with relatively high affinity for type III collagen, and it is located on the long arm of chromosome 11. Due to its fundamental role in collagen degradation, the aberrant expression of MMP1 is often associated with the local invasion and metastasis of tumor cells (17). Zhou et al. (18) conducted a bioinformatics analysis and found that MMP1 was related to non-small cell lung cancer (NSCLC). Gabasa et al. (19) confirmed that MMP1 can drive tumor progression in large cell lung carcinoma through fibroblast senescence. Additionally, MMP1 has been implicated in tumor bone metastasis; for example, Lu et al. (20) found that ADAMTS1 and MMP1 proteolysis are involved in the osteolytic signaling cascade of breast cancer bone metastasis. In molecular docking, the primary components of Sophorae Flavescentis radix showed strong binding interactions with the core target MMP1, which also validated the effective relationship between Sophorae Flavescentis radix and the treatment of lung cancer bone metastasis, demonstrating its potential research value.

Pathway prediction based on a GSEA suggested that MMP1 may influence lung cancer biological processes by participating in several signaling pathways, including the chemokine signaling, apoptosis, Wnt signaling, TP53-regulated cell cycle arrest, Hedgehog signaling, and MAPK signaling pathways. These mechanisms are also supported by related research in lung cancer bone metastasis. For instance, Wang et al. (21) established a bone metastasis xenograft model by implanting NCI-H460-luc2 lung cancer cells and found that the TCM Qilian formula inhibited tumor bone metastasis by suppressing osteoclastogenesis and promoting apoptosis. Xu et al. (22) detected blood samples from NSCLC patients and found that single nucleotide polymorphisms in the Wnt signaling pathway are associated with a reduced risk of bone metastasis and may serve as valuable biomarkers for bone metastasis in NSCLC patients. Xi et al. (23) also showed the role of anomalous activation of the Wnt signaling in lung cancer bone metastasis in malignant tumors, as well as the potential therapeutic application of targeting the Wnt pathway for the treatment of lung cancer and cancer-induced bone metastasis.

Conclusions

Sophorae Flavescentis radix may exert its therapeutic effects on lung cancer bone metastasis by inhibiting MMP1 expression and regulating the anomalous instigation of the Wnt signaling pathway. This hypothesis aligns with the observed molecular interactions and signaling pathways identified by our network pharmacology and bioinformatics analyses, providing a plausible mechanism for the anti-cancer activity of lung cancer bone metastasis. However, this study had certain limitations, including a lack of relevant preclinical experiments and the absence of large-sample, multicenter clinical data validation. Thus, future studies need to be conducted.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the STREGA reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1947/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1947/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1947/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: L. Huleatt)