Integrated network pharmacology and cellular assay reveal the biological mechanisms of Limonium sinense (Girard) Kuntze against Breast cancer
BMC Complementary Medicine and Therapies volume 23, Article number: 408 (2023)
Limonium Sinense (Girard) Kuntze (L. sinense) has been widely used for the treatment of anaemia, bleeding, cancer, and other disorders in Chinese folk medicine. The aim of this study is to predict the therapeutic effects of L. sinense and investigate the potential mechanisms using integrated network pharmacology methods and in vitro cellular experiments.
The active ingredients of L. sinense were collected from published literature, and the potential targets related to L. sinense were obtained from public databases. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and DisGeNET enrichment analyses were performed to explore the underlying mechanisms. Molecular docking, cellular experiments, RNA-sequencing (RNA-seq) and Gene Expression Omnibus (GEO) datasets were employed to further evaluate the findings.
A total of 15 active ingredients of L. sinense and their corresponding 389 targets were obtained. KEGG enrichment analysis revealed that the biological effects of L. sinense were primarily associated with “Pathways in cancer”. DisGeNET enrichment analysis highlighted the potential role of L. sinense in the treatment of breast cancer. Apigenin within L. sinense showed promising potential against cancer. Cellular experiments demonstrated that the L. sinense ethanol extract (LSE) exhibited a significant growth inhibitory effect on multiple breast cancer cell lines in both 2D and 3D cultures. RNA-seq analysis revealed a potential impact of LSE on breast cancer. Additionally, analysis of GEO datasets verified the significant enrichment of breast cancer and several cancer-related pathways upon treatment with Apigenin in human breast cancer cells.
This study predicts the biological activities of L. sinense and demonstrates the inhibitory effect of LSE on breast cancer cells, highlighting the potential application of L. sinense in cancer treatment.
Limonium sinense (Girard) Kuntze (L. sinense), also known as Latouchea Fokiensis and Limoniumspp, is an endemic plant species with important commercial value in Chinese medicine . L. sinense mainly grows in the seashores and salt marshes regions of mainland China, western Taiwan, and Ryukyus islands in Japan [2, 3]. In Chinese folk medicine, L. sinense is commonly used for the treatment of bleeding, fever, hepatitis, haemostasis, anaemia, menorrhagia, irregular menstruation, and other disorders [4, 5]. Studies have reported that polysaccharides derived from the root of L. sinense exhibit potent anti-tumour and immunomodulatory activities with low toxicity, effectively inhibiting the growth of tumours in mice . Notably, LSP21, a polysaccharide separated from crude L. sinense polysaccharides, has demonstrated remarkable anti-tumour effects by inhibiting cell proliferation and inducing cell death . Furthermore, L. sinense extracts have shown hepatoprotective activity against carbon tetrachloride and D-galactosamine intoxication in rats, with the underlying mechanism being associated with mitochondrial protection [2, 8, 9]. Other studies revealed significant anti-viral activity of L. sinense, and compounds such as gallic acid, samarangenin B and myricetin derived from L. sinense exhibited significant inhibitory effects against viral infections [10,11,12]. Our previous study  has found a significant inhibitory effect of the water extract of L. sinense on multiple types of human breast cancer cells. However, further underlying mechanisms of the pharmacological activities of L. sinense still remain to be elucidated.
More than 40 chemical compounds have been identified from L. sinense, most of which are flavonoids [12, 14]. However, there are few studies investigating the biological activities of these active ingredients and their associated mechanisms in L. sinense. In this study, network pharmacology and in vitro cellular experiments were employed to gain insights into the molecular basis of the therapeutic effects of L. sinense. We first explored the active ingredients and possible targets of L. sinense. By constructing the Compound-Target-Pathway network, a hub connection was identified from the network. Molecular docking, cellular experiments, RNA-sequencing (RNA-seq) and Gene Expression Omnibus (GEO) datasets were further performed to verify earlier findings. The detailed technical strategy of this study is shown in Fig. 1.
Materials and methods
Collection and preparation of sample material
Plant collection and extraction were carried out as previously reported . Healthy whole plants of L. sinense were collected from the coastal region in Jiangsu, eastern China (33°09′33.0" N, 120°46′40.4" E). The collection of L. sinense was conducted with the oral approval of local authorities and in full compliance with China’s biodiversity rights and regulations. No specific license was required for the collection of L. sinense.
The whole plants were washed, and subsequently oven-dried at 60 °C until the weight was constant. After drying, the plants were crushed and extracted with 95% ethanol using a sonicator (Fisher Scientific, UK) at 1:400 (m/v) ratio with a processing time of 50 seconds and an elapsed time of 20 seconds, for a total extraction time of 30 minute. The extract process was repeated 3 times under the same conditions. The extracts were then put to suction filtration, rotary evaporated, and freeze-dried to powder. The powder was aliquoted and stored at -20 °C until future use when extracts were diluted with dimethylsulfoxide (DMSO) and filtered through a 0.45 μm filter (Millipore filter membranes, Merck, UK).
Screening of active compounds and targets of L. sinense
L. sinense is not included in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) or the Encyclopedia of Traditional Chinese Medicine (ETCM) platform, which allow users to explore the relationships or build networks among drugs, targets and diseases [15, 16]. The compounds of L. sinense were therefore obtained from the published literature. Active compounds were screened using the SwissADME database (http://swissadme.ch), based on the Lipinski’s rule of five  and Veber’s rule . Then, the corresponding targets of the active compounds of L. sinense were obtained from SwissTargetPrediction.
Construction of Protein–Protein interaction (PPI) network
The STRING (https://cn.string-db.org) database was utilized to analyse protein–protein interaction data. The species were limited to “Homo sapiens”, and medium confidence value > 0.4 was selected as the minimum required interaction score to construct the Protein–Protein interaction (PPI) network . Then, the obtained PPI network from the STRING database was visualized by using Cytoscape v3.9.1 software. The core nodes were selected based on the median values of three parameters in the interaction network: “Degree” which indicates the number of links to one node and reflects how often one node interacts with other nodes , “Betweenness Centrality” which quantifies the extent to which a node lies on paths between other nodes , and “Closeness Centrality” which measures the average distance from a node to other nodes . The level of the three parameters represents the topological importance of the nodes in the interaction network, with more important nodes outputting higher values in the network .
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis [24,25,26] was generated through GSEApy package, a comprehensive package for performing gene set enrichment analysis within the Python environment . The GSEApy source code is freely available at https://github.com/zqfang/GSEApy. Detailed information regarding the specific GSEApy codes employed in this study can be found in the Supplementary methods section of the article. Diseases associated with the hub targets were enriched by using the disgenet2r package (v0.99.3) in R , the specific R codes for performing the enrichment analysis of disgenet2r package are available in the Supplementary methods section of the article. The gene ontology (GO) Biological Process term analysis was visualized using Cytoscape v3.9.1 software. Groupings were facilitated by the Cytoscape AutoAnnotate plugin . The GO term fusion was selected based on a statistical significance threshold of P ≤ 0.05.
In this process, network construction was performed as follows: First, the Compound-Target network (CT network) was built based on the active compounds of L. sinense and their potential targets. Next, the Pathway-Target network (PT network) was constructed by selecting the top 20 enriched KEGG pathways and their associated targets. Finally, a Compound-Target-Pathway network (CTP network) was established by integrating the CT network, PT network and the core targets identified from the PPI network. All visualized network graphs were created using Cytoscape v3.9.1 software. The hub network was screened by using Cytohubba , a degree algorithm based Cytoscape plugin.
The crystal structure of AKT1 (PDB ID: 3O96), EGFR (PDB ID: 1XKK), SRC (PDB ID: 3EL8), ESR1 (PDB ID: 5ACC), GSK3B (PDB ID: 3I4B) and PTGS2 (PDB ID: 6COX) were downloaded from Protein Data Bank (https://www.rcsb.org/). Compound structure of Apigenin was downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/). The docking studies were performed by PyRx Autodock VINA tool (v0.8). A grid box was set to cover the active site of crystal structure with a default exhaustiveness value of 8 . The best compound with highest binding affinity (kcal/mol) was selected and visualized by using Discovery Studio (version 2021 Client) and PyMOL.
Cell culture and reagents
Sources of cell lines and culture conditions were reported earlier [32,33,34]. HCC1806, HCC1395 and HCC1937 cells were maintained in Roswell Park Memorial Institute (RPMI) 1640 medium, (Gibco® by Life Technology) with 10% fetal bovine serum (FBS) and 1% (v/v) penicillin/streptomycin, (Gibco® by Life Technology). BT20, MDA-MB-157, MDA-MB-231 and MDA-MB-468 cell lines were maintained in Dulbecco’s modified Eagle’s medium (DMEM) (Gibco® by Life Technology) with 10% FBS and 1% (v/v) penicillin/streptomycin. All cells were kept at 37 °C and 5% CO2. No mycoplasma contamination was detected in the cell lines used. In 3D culture, cells were seeded in 96-well ultralow attachment plate in 100 µl at plating densities between 3,000 and 7,000 cells/well. Cells were cultured in 1:1 DMEM:F12, (Gibco® by Life Technology) media plus 1% P/S, 2% B27 (Gibco® by Life Technology), 20 ng/ml epidermal growth factor (EGF) (PEPROTECH) and 20 ng/ml basic fibroblast growth factor (bFGF) (PEPROTECH) at 37 °C and 5% CO2 for 14 days. After the incubation period, the images were taken using with × 40 magnification.
Cell viability assay
Cell viability assays was performed as previously described . Cells were plated into 96-well plate with a density of 8000 cells/well. CellTiter-Glo® Luminescent cell viability assay (Promega) was performed 48 h after treatment according to the manufacturer’s protocol using GloMax® Discover Microplate Reader (Promega). For cell viability in 3D cultures, 100 µl of CellTiter-Glo® reagent was added into each well and incubated at room temperature for 1 h, followed by measurement.
For IC50 a serial dilution starting at 125 μg/mL was made in assay buffer. IC50 values were derived by a dose–response (variable slope) curve using GraphPad Prism v10.0.0 software. The reported data are average of at least three independent experiments.
RNA-sequencing (RNA-seq) analysis
RNA isolation and mRNA sequencing of samples were performed following the manufacturer’s instructions (Novogene, UK) as previously described [35, 36]. The MDA-MB-468 cells were treated with LSE for 48 h. Total RNA was isolated using RNeasy mini kit (Qiagen) according to manufacturer’s instructions and quantified using a Nanodrop Spectophotometer 2000c (Thermo Fisher Scientific). A total amount of 3 μg RNA per sample was used as input material for library construction. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, Ipswich, Massachusetts, USA) following manufacturer’s instruction. Libraries were pooled in equimolar and sequenced using the paired-end strategy (2 × 150) on the Illumina NovaSeq 6000 platform following the standard protocols (Novogene, UK). Raw read counts were imported into RStudio (v4.2.0) and analysed by using DESeq2 (v3.17) R package . Transcripts with low abundance (under 10 counts across all samples) were removed. The RNA-Seq data have been deposited in the Gene Expression Omnibus (GEO) database (accession code GSE244469). The R codes were provided in the R Scripts in the supplementary materials. Genes with |Log2FoldChange| above 2 and Padj values less than 0.05 were considered as differentially expressed genes (DEGs).
GSEA hallmark analysis
The collection of hallmark gene sets generated from the GSEA (Gene Set Enrichment Analysis) software (v4.1.0) (with registration) [38, 39]. The normalized counts generated in DESeq2 were put into GSEA software, and the analysis used h.all.v2023.1.Hs.symbols.gmt [Hallmarks] gene set database with the settings of perform: 1000 permutations, collapse/remap to gene symbol—no_collapse (use dataset ‘as is’ in the original format), permutation type—gene_set, enrichment statistic—weighted, metric for ranking genes—Signal2Noise, gene list sorting mode—real, gene list ordering mode—descending, max size of gene sets—500, and min size of gene sets—10.
GEO dataset analysis
Gene Expression Omnibus (GEO) dataset analysis was performed as previous described . GEO datasets of human breast cancer cells treated with Apigenin were screened, by searching the keywords “(breast cancer) AND (Apigenin)” and publication dates before 01/05/2023 in the National Centre for Biotechnology Information (NCBI) GEO platform. We only included datasets that met the following criteria: 1) mRNA expression data; 2) Homo sapiens samples; 3) Breast cancer cells; 4) minimum 3 biological replicates. Duplicate datasets were removed. Datasets with fewer than 10,000 genes were excluded to balance the number of analysed genes and sample size. Microarray probe IDs were translated to gene symbols according to the GPL annotation files provided in the GEO database. Probes mapped to multiple gene symbols were removed and genes mapped to multiple probe IDs were summarized by calculating the mean. Codes are available upon request. Genes with P value less than 0.05 and ∣Log2FoldChange∣ above 1 were considered as differentially expressed genes (DEGs). DEGs of each GEO datasets were obtained by GEO2R in the GEO platform.
Comparison of two groups was statistically calculated by two paired, unpaired Student’s t test in GraphPad Prism v10.0.3 (GraphPad Software Inc, San Diego, CA). Two-way analysis of variance (ANOVA) with Dunnett’s multiple comparison test was used for multiple comparisons. Results were considered significant if P < 0.05, where *P < 0.05, **P < 0.01, ***P < 0.001.
Identification of active compounds and targets of L. sinense
Based on the available published literature, a total of 42 natural compounds have been identified in L. sinense (Supplementary Table 1. The workflow for compound screening of L. sinense was shown in Supplementary Fig. 1). Out of these compounds, 15 have met the Lipinski’s rule of five and Veber’s rule, making them the active compounds of L. sinense. These active compounds include Gallic acid, Ethyl gallate, Apigenin, Naringenin, Luteolin, Kaempferol, Eriodictyol, ( +)-Catechin, N-trans-caffeoyltyramine, Quercetin, Morin, Homoeriodictyol, N-trans-feruloyltyramine, Isorhamnetin and Isodihydrosyringetin (Table 1). It is worth noting that the majority of these active compounds belong to the flavonoid group. Furthermore, 389 targets associated with these 15 active compounds were obtained after removing duplicate values (Supplementary Table 2). The Compound-Target network (CT network) revealed that all 15 bioactive compounds exhibited relatively high values of Degree, Betweenness Centrality (BC) and Closeness Centrality (CC) within the network (Fig. 2; Supplementary Table 3), showing significant connectivity and potential importance of these compounds in the overall network.
Construction of PPI network and the identification of core targets of L. sinense
A PPI network consisting of 389 nodes and 4,947 edges was established (Fig. 3A). Based on the threshold values of Degree, BC and CC, with the first screening using Degree ≥ 26, BC ≥ 0.00363 and CC ≥ 0.4228, the PPI network was pruned to 73 nodes and 1,049 edges (Fig. 3B). Subsequently, nodes with Degree ≥ 29, BC ≥ 0.0085 and CC ≥ 0.6324 were screened as the second screening threshold values and resulted in 20 core nodes and 178 edges (Fig. 3C). These 20 targets, namely AKT1, SRC, ALB, EGFR, HSP90AA, ESR1, VEGFA, TNF, MTOR, HIF1A, ERBB2, AR, SIRT1, BCL2L1, PPARG, GSK3B, PTGS2, NR3C1, PIK3CA and HPGDS, were defined as the core targets of L. sinense (Supplementary Table 4), which likely to contribute to its pharmacological activities.
Enrichment analysis of compound targets
The enrichment analysis was retrieved 202 KEGG pathways on the 389 compound targets with a screening threshold of a P value less than 0.05 (Supplementary Table 5). Among the top-ranked enrichment results, several cancer-related pathways were significantly identified in the KEGG pathways, including Pathways in cancer, Chemical carcinogenesis, Prostate cancer, Proteoglycans in cancer and Central carbon metabolism in cancer (Fig. 4A).
Within the context of Gene Ontology (GO) terms, we identified a total of 1,109 Biological Process terms (Supplementary Table 6), which were further grouped into 178 clusters using the AutoAnnotate v.1.2 Cytoscape plugin. Among these clusters, the most prominent ones included the regulation of metabolic process, ion transmembrane transporter, blood vessel development, leukocyte activation, and regulated vesicle exocytosis (Fig. 4B). Notably, the blood vessel development cluster encompassed terms such as angiogenesis, blood vessel development, tube development, and blood vessel morphogenesis, which could potentially relate to the blood-enriching properties of L. sinense (Supplementary Fig. 2). These findings suggest that L. sinense may play a role in the regulation of cancer and cancer-related pathways, offering insights into its potential mechanism for enhancing blood enrichment functions.
CTP network construction and the identification of hub connections
The top 20 KEGG pathways and their associated targets were selected to construct the TP network (Supplementary Fig. 3). Subsequently, a CTP network was built by incorporating the 15 active compounds, 20 core targets and the top 20 KEGG pathways, and in the end the CTP network consisted of 53 nodes and 195 edges (Fig. 5A). A hub network was identified from the CTP network which contains 20 nodes and 65 edges (Fig. 5B, Supplementary Table 7). Within the hub network, 6 hub targets, namely AKT1, EGFR, SRC, ESR1 and GSK3B, were found to be directly regulated by Apigenin. Additionally, 8 signalling pathways were involved in the hub network. Many of these signalling pathways were related with cancer processes, including Pathways in cancer, Chemical carcinogenesis, Proteoglycans in cancer and Prostate cancer. Furthermore, HIF-1 signalling pathway was also identified in the hub network, indicating a crucial role of HIF-1 signalling pathway in the biological activities of L. sinense.
To investigate the associated diseases with the hub targets, an analysis of the DisGeNET database (https://www.disgenet.org/ accessed on 15 May 2023) (version 7.0) was performed via the disgenet2r package (v0.99.3) in R. The results revealed that the top enriched diseases were primarily related to breast cancer, such as Breast carcinoma, Mammary neoplasms, and Mammary carcinoma (Fig. 5C; Supplementary Table 8). These findings suggest a potential therapeutic role of L. sinense in the treatment of breast cancer.
Molecular docking between Apigenin and hub targets
Molecular docking was employed to validate the interactions between Apigenin and 6 hub target proteins. The results presented that Apigenin exhibited a strong molecular docking affinity with 6 hub targets, as evidenced by docking scores of less than -5 kcal/mol (Table 2). AKT1 displayed the best binding activity with Apigenin (affinity = -9.6), followed by ESR1(affinity = -8.9), PTGS2 (affinity = -8.8), SRC (affinity = -8.6), EGFR (affinity = -8.5), and GSK3B (affinity = -8.4). The binding structures showed that Apigenin deeply entered the binding sites of each hub target, with abundant hydrogen-bond donors and acceptors around the binding cavity (Fig. 6). The binding sites and interaction bonds of Apigenin with each hub target protein were shown in Table 2. Taking AKT1 as an example, Apigenin bound to site of Ser205 in AKT1 via conventional hydrogen bond, while bound to sites of Asp292, Trp80, Leu210, Leu264, Lys268 and Val270 in AKT1 through pi-anion, pi-pi stacked and pi-alkyl bonds (Fig. 6A).
Effects of L. sinense ethanol extract (LSE) on breast cancer cells
In our previous study, we demonstrated that the water extract of L. sinense has a significant inhibitory effect on the growth of multiple breast cancer cell lines . Based on the network pharmacology results, we found that Apigenin may also play a role against cancer. These represent a new class of compounds, as unlike in our previous study, the Apigenin is almost insoluble in water and is present in the ethanol extract , we therefore chose ethanol as the solvent to extract compounds from L. sinense in this analysis.
To study the effect of L. sinense ethanol extract (LSE) on breast cancer, 7 human breast cancer cell lines (BT20, MDA-MB-157, MDA-MB-231, MDA-MB-468, HCC1395, HCC1806 and HCC1937) were treated with LSE followed by a cell viability assay. The results showed that addition of LSE led to a strong inhibition of growth in multiple tested cell lines in a dose-dependent manner after 48 h treatment (Fig. 7A). The determination of IC50 values for each breast cancer cell line treated with LSE revealed significant inhibitory potency, particularly with notable effects observed on MDA-MB-468 cells (Fig. 7B). Furthermore, to confirm the effects of LSE on cell viability, a 3D mammosphere assay was performed on MDA-MB-468 cells. The results demonstrated a significant decrease in cell viability (P < 0.001) in LSE-treated MDA-MB-468 cells (Fig. 7C). These experiments provide further evidence of the growth inhibitory effect of L. sinense on breast cancer.
Global transcriptomic changes in MDA-MB-468 cells exposed to LSE
To assess the cellular response to LSE unbiasedly, we characterized the global transcriptomic changes in MDA-MB-468 cells exposed to LSE by utilizing RNA sequencing (RNA-Seq). Principal component analysis (PCA) clearly demonstrated a distinct separation between control and LSE-treated samples (Supplementary Fig. 4). Differentially expressed genes (DEGs) were identified based on an adjusted P value (Padj) less than 0.05 and an absolute ∣Log2FoldChange∣ greater than 2, resulting in a total of 1,306 DEGs, with 594 up-regulated and 712 down-regulated genes (Fig. 8A; Supplementary Table 9). The hierarchical clustering showed that DEGs were grouped into 2 major clusters (Fig. 8B).
Subsequently, we performed pathway enrichment analysis using Gene Set Enrichment Analysis (GSEA) (Fig. 8C; Supplementary Table 10), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (Fig. 8D, E; Supplementary Table 11) and Gene Ontology (GO) enrichment analysis (Fig. 8F, G; Supplementary Table 12). Interestingly, Hypoxia (NES = 1.721, FDR = 0.003), Heme_metabolism (NES = 1.409, FDR = 0.042) and G2M_checkpoint (NES =-1.967, FDR = 0.0003) were significantly enriched. In the context of KEGG analysis, Breast cancer pathway (P-value = 0.032) was significantly enriched by the up-regulated genes. Furthermore, GO biological process analysis highlighted the significant enrichment of “blood vessel development” among both up- and down-regulated genes. These findings reinforce the potential therapeutic application of L. sinense in breast cancer treatment and underscore its role in enhancing blood enrichment functions.
Effects of Apigenin on breast cancer cells
Finally, we utilized publicly available GEO datasets on human breast cancer cells with the treatment of Apigenin to gain further insights into the mechanisms of Apigenin against breast cancer. Two GEO datasets were selected based on predetermined screening criteria, which included a total of 14 samples derived from 2 different types of human breast cancer cell lines (Supplementary Table 13).
DEGs were screened from the obtained GEO datasets (Supplementary Fig. 5), and KEGG enrichment analysis was performed to identify enriched pathways. In MCF7 cell treated with Apigenin, both up- and down-regulated DEGs showed significant enrichment in the Breast cancer pathway (Fig. 9A, Supplementary Table 14), highlighting the crucial role of Apigenin in the treatment of breast cancer. Furthermore, the expression levels of genes associated with breast cancer were significantly altered by the treatment of Apigenin (Fig. 9B). In MDA-MB-231 cells, Cell cycle was commonly enriched from both up- and down-regulated DEGs upon Apigenin treatment (Fig. 9D, E, Supplementary Table 15). Additionally, we noticed that Pathways in cancer was significantly enriched in both Apigenin treated MCF7 and MDA-MB-231 cells, and genes involved in this pathway exhibited significant changes in expression levels (Fig. 9C, F), indicating the potential anti-cancer effects of Apigenin. These findings from the GEO dataset analysis further support the role of Apigenin in breast cancer treatment, especially its impact on breast cancer pathways, cell cycle regulation, and potential anti-cancer effects through modulation of genes involved in cancer-related pathways.
The anti-tumour activity of L. sinense has been previously discussed to be associated with its immunomodulatory activity . In line with these findings, our current analysis emphasizes the strong association between the biological activity of L. sinense and cancer-related pathways, suggesting a potential role of L. sinense in cancer treatment. Through network pharmacology analysis, we identified several enriched pathways related to cancer regulation, with “Pathways in cancer” being the top ranked KEGG pathway. Additional cancer-related signalling pathways, including Chemical carcinogenesis, Prostate cancer, Proteoglycans in cancer and Central carbon metabolism in cancer, were also significantly enriched by the targets of L. sinense. These findings underscore the importance of L. sinense in cancer regulation. Moreover, the significant enrichment of the Cell cycle pathway in our analysis further supports our previous findings that the water extract of L. sinense leads to cell cycle arrest at the G2/M phase . Additionally, the ethanol extract of L. sinense demonstrates a remarkable inhibitory effect on the growth of various breast cancer cell lines, suggesting the considerable potential of L. sinense as an adjuvant of therapeutic agent for the treatment of breast cancer.
This study has identified 15 natural components as the main active compounds in L. sinense, with the majority of them demonstrating potent anti-cancer properties. For instance, gallic acid (3,4,5-trihydroxybenzoic acid), a major phenolic acid commonly found in plants and fruits, exhibits a wide range of biological activities, including antioxidant, anti-microbial, anti-inflammatory and anti-cancer effects . It was showed that gallic acid effectively against metastasis in various cancer cell types such as myeloid leukemia, breast cancer and ovarian cancer by modulating multiple signalling pathways, including Akt/mTOR, ERK, MMPs, NFκB, PTEN/Akt/HIF-1α/VEGF pathways [42,43,44]. Apigenin, a naturally occurring plant flavone, has gained recognition as a promising cancer chemopreventive agent. It exhibits remarkable antioxidant, anti-mutagenic, anti-inflammatory, antibacterial and antiviral effects [45,46,47,48]. Recent research has extensively investigated Apigenin for its anti-cancer activities, showing broad-spectrum effects across various cancer types, including colorectal cancer, breast cancer, liver cancer, lung cancer, melanoma, prostate cancer, and osteosarcoma [49,50,51,52,53,54]. Studies have revealed that Apigenin exerts its anti-cancer properties potentially by inducing cell cycle arrest, triggering apoptosis, inducing autophagy, inhibiting migration/invasion, attenuating drug resistance, or stimulating immune responses in various cancer types both in vitro and in vivo [45, 55]. Luteolin, another flavone compound found in various plants and medicinal herbs, exhibits diverse biological effects, such as antioxidant and anti-inflammatory properties. Luteolin has also demonstrated anti-cancer activity against multiple types of human cancers, including lung cancer, breast cancer, glioblastoma cancer, prostate cancer, colon cancer, and pancreatic cancer . In the context of carcinogenesis, luteolin impedes cancer progression through various mechanisms, including the suppression of kinases, regulation of the cell cycle, induction of apoptotic cell death, and reduction of transcription factors, thereby inhibiting cell transformation, metastasis, invasion, and angiogenesis .
The hub network analysis revealed that Apigenin as the key component in L. sinense that can directly interact with 6 hub targets, including AKT1, EGFR, SRC, ESR1, GSK3B and PTGS2. By using molecular docking method, we observed that Apigenin binding with these hub targets tightly and forming various interaction bonds at the binding sites. We further checked the mRNA expressions of these 6 hub targets in the TCGA cohort using UCSCXenaShiny (v1.1.9) (A comprehensive description of UCSCXenaShiny methodology can be found in reference ). The results revealed significant up- or down-regulation of these hub targets in most of the 33 TCGA cancer types in mRNA levels (Supplementary Fig. 6A-F). In addition, these target genes also predicted overall survival in multiple cancer types (Supplementary Fig. 6G). These results suggesting the Apigenin in L. sinense may play a key role in regulating cancer process and disease-related signalling pathways.
L. sinense has traditionally been used to replenish blood in the body, and treat conditions such as haemostasis, anaemia, bleeding and menorrhagia in Chinese folk medicine . However, there is no direct evidence to prove the blood-enriching function of L. sinense so far. Our network pharmacology and RNA-seq analyses yielded a noteworthy observation that the biological process “blood vessel development” was significantly enriched in both L. sinense targets and the DEGs identified in MDA-MB-468 breast cancer cells treated with LSE. These findings provide valuable novel insights into the mechanisms underlying L. sinense potential to boost blood vessels. It is well known that anaemia is a common diagnosis in patients with cancer that may affect both quality of life and survival . Cancer can directly cause or exacerbate anaemia either by suppressing haematopoiesis, cytokine-induced iron sequestration, or reduced red blood cell production . Treatment of anaemia can significantly improve patients’ quality of life and potentially enhance clinical outcomes. Therefore, the blood-enriching property of L. sinense may help alleviate cancer-related symptoms. Additionally, the immunomodulatory activity of L. sinense may also play a critical role in this process. Further exploration and verification using high-performance liquid chromatography (HPLC) and disease models are required to elucidate the mechanisms underlying these effects and to better understand the blood-enriching and anti-tumour activities of L. sinense.
Our study predicts that the biological activities of L. sinense are strongly associated with breast cancer and multiple cancer-related pathways. The active compound Apigenin in L. sinense appears to have a crucial role against cancer. Experimental assessments of the ethanol extract of L. sinense have demonstrated a notable growth inhibitory effect on multiple breast cancer cell lines. Additionally, RNA-seq analysis unveiled global transcriptomic alterations in breast cancer cells treated with LSE potentially linked to pathways associated with breast cancer and hematopoietic cell lineage. GEO datasets validation indicates the involvement of breast cancer pathway upon treatment with Apigenin in human breast cancer cells. These findings highlight the potential of L. sinense as a promising therapeutic agent for the treatment of breast cancer. Further research and clinical investigations are warranted to explore the full therapeutic potential of L. sinense in combating breast cancer.
Availability of data and materials
All data generated or analysed in this study are included in the article and its supplementary information files. The RNA-Seq data have been deposited in the Gene Expression Omnibus (GEO) database (accession code GSE244469).
Gene Expression Omnibus
Kyoto Encyclopedia of Genes and Genomes
- L. sinense :
Limonium Sinense (Girard) Kuntze
L. sinense ethanol extract
Dong B. Research on the conservation of Limonium sinense in the coast of Jiangsu. Chin Wild Plant Res. 2005;24(06):28–30.
Chaung SS, et al. The hepatoprotective effects of Limonium sinense against carbon tetrachloride and beta-D-galactosamine intoxication in rats. Phytother Res. 2003;17(7):784–91.
Wu ZYPHR. Flora of China, ed. Z.Y.P.H.R. Wu. Vol. 15. 1996: Science Press (Beijing) & Missouri Botanical Garden Press (St. Louis).
Li H. Plumbaginaceae Flora of Taiwan. 1998;4:79–82.
Editorial Committee of Chinese Herbal, the State Administration of Traditional Chinese Medicine. Chinese Herbal. Vol XVI. Shanghai: Shanghai Science and Technology Publishers; 1999. p. 130.
Tang XH, et al. Antitumor and immunomodulatory activity of polysaccharides from the root of Limonium sinense Kuntze. Int J Biol Macromol. 2012;51(5):1134–9.
Tang XH, et al. Isolation and identification of anti-tumor polysaccharide LSP21 from Limonium sinense (Girard) Kuntze. Int J Biol Macromol. 2014;70:138–42.
Tang XH, et al. Mitochondrial modulation is involved in the hepatoprotection of Limonium sinense extract against liver damage in mice. J Ethnopharmacol. 2008;120(3):427–31.
Tang XH, et al. Preservation on calcium homeostasis is involved in mitochondrial protection of Limonium sinense against liver damage in mice. Pharmacogn Mag. 2010;6(23):191–7.
Hsu WC, et al. Limonium sinense and gallic acid suppress Hepatitis C virus Infection by blocking early viral entry. Antiviral Res. 2015;118:139–47.
Kuo YC, et al. Samarangenin B from Limonium sinense suppresses herpes simplex virus type 1 replication in Vero cells by regulation of viral macromolecular synthesis. Antimicrob Agents Chemother. 2002;46(9):2854–64.
Lin LC, Kuo YC, Chou CJ. Anti-herpes simplex virus type-1 flavonoids and a new flavanone from the root of Limonium sinense. Planta Med. 2000;66(4):333–6.
Zhao H, et al. Integrated analysis reveals effects of bioactive ingredients from Limonium Sinense (Girard) Kuntze on hypoxia-inducible factor (HIF) activation. Front Plant Sci. 2022;13: 994036.
Lin LC, Chou CJ. Flavonoids and phenolics from Limonium sinense. Planta Med. 2000;66(4):382–3.
Ru J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6: 13.
Xu HY, et al. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019;47(D1):D976–982.
Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999;1(1):55–68.
Veber DF, et al. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615–23.
von Mering C, et al. STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 2005;33(Database issue):D433–437.
Missiuro PV, et al. Information flow analysis of interactome networks. PLoS Comput Biol. 2009;5(4): e1000350.
Raman K, Damaraju N, Joshi GK. The organisational structure of protein networks: revisiting the centrality-lethality hypothesis. Syst Synth Biol. 2014;8(1):73–81.
Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.
Wan Y, et al. Utilising network pharmacology to explore the underlying mechanism of Wumei Pill in treating pancreatic Neoplasms. BMC Complement Altern Med. 2019;19(1):158.
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947–51.
Kanehisa M, et al. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587–592.
Fang Z, Liu X, Peltz G. GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics. 2023;39(1):btac757.
Pinero J, et al. The DisGeNET knowledge platform for Disease genomics: 2019 update. Nucleic Acids Res. 2020;48(D1):D845–855.
Kucera M, et al. AutoAnnotate: a Cytoscape app for summarizing networks with semantic annotations. F1000Res. 2016;5:1717.
Chin CH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.
Agarwal R, Smith JC. Speed vs accuracy: effect on ligand pose accuracy of varying Box size and exhaustiveness in AutoDock Vina. Mol Inf. 2023;42(2):e2200188.
Ertay A, et al. WDHD1 is essential for the survival of PTEN-inactive triple-negative Breast cancer. Cell Death Dis. 2020;11(11):1001.
Liu H, et al. SGLT1 is required for the survival of triple-negative Breast cancer cells via potentiation of EGFR activity. Mol Oncol. 2019;13(9):1874–86.
Wang Y, et al. ASPP2 controls epithelial plasticity and inhibits Metastasis through beta-catenin-dependent regulation of ZEB1. Nat Cell Biol. 2014;16(11):1092–104.
Yao L, et al. Bidirectional epithelial-mesenchymal crosstalk provides self-sustaining profibrotic signals in pulmonary fibrosis. J Biol Chem. 2021;297(3):101096.
Brereton CJ, et al. Pseudohypoxic HIF pathway activation dysregulates collagen structure-function in human lung fibrosis. Elife. 2022;11:11.
Love MI, Huber W, Anders S. Moderated estimation of Fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
Subramanian A, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.
Mootha VK, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human Diabetes. Nat Genet. 2003;34(3):267–73.
Eroglu Ozkan E, et al. The therapeutic potential of Ethnomedicinally important Anatolian Thyme species: a Phytochemical and Biological Assessment. Front Pharmacol. 2022;13: 923063.
Fernandes FH, Salgado HR. Gallic acid: review of the methods of determination and quantification. Crit Rev Anal Chem. 2016;46(3):257–65.
Chen YJ, et al. Gallic acid-capped gold nanoparticles inhibit EGF-induced MMP-9 expression through suppression of p300 stabilization and NFkappaB/c-Jun activation in Breast cancer MDA-MB-231 cells. Toxicol Appl Pharmacol. 2016;310:98–107.
Gu R, et al. Gallic acid targets acute Myeloid Leukemia via Akt/mTOR-dependent mitochondrial respiration inhibition. Biomed Pharmacother. 2018;105:491–7.
He Z, et al. Gallic acid, a phenolic compound, exerts anti-angiogenic effects via the PTEN/AKT/HIF-1alpha/VEGF signaling pathway in Ovarian cancer cells. Oncol Rep. 2016;35(1):291–7.
Madunic J, et al. Apigenin: a dietary flavonoid with diverse anticancer properties. Cancer Lett. 2018;413:11–22.
Yang CS, et al. Inhibition of carcinogenesis by dietary polyphenolic compounds. Annu Rev Nutr. 2001;21:381–406.
Wang YC, Huang KM. In vitro anti-inflammatory effect of apigenin in the Helicobacter pylori-infected gastric adenocarcinoma cells. Food Chem Toxicol. 2013;53:376–83.
Ozcelik B, Kartal M, Orhan I. Cytotoxicity, antiviral and antimicrobial activities of alkaloids, flavonoids, and phenolic acids. Pharm Biol. 2011;49(4):396–402.
Xu M, et al. Apigenin suppresses Colorectal cancer cell proliferation, migration and invasion via inhibition of the Wnt/beta-catenin signaling pathway. Oncol Lett. 2016;11(5):3075–80.
Huang C, et al. Chrysin, Abundant in Morinda citrifolia Fruit Water-EtOAc extracts, combined with apigenin synergistically Induced apoptosis and inhibited Migration in Human breast and Liver Cancer cells. J Agric Food Chem. 2016;64(21):4235–45.
Lee YM, et al. Inhibition of glutamine utilization sensitizes Lung cancer cells to apigenin-induced apoptosis resulting from metabolic and oxidative stress. Int J Oncol. 2016;48(1):399–408.
Zhao G, et al. Apigenin inhibits proliferation and invasion, and induces apoptosis and cell cycle arrest in human Melanoma cells. Oncol Rep. 2017;37(4):2277–85.
Gupta S, Afaq F, Mukhtar H. Involvement of nuclear factor-kappa B, Bax and Bcl-2 in induction of cell cycle arrest and apoptosis by apigenin in human prostate carcinoma cells. Oncogene. 2002;21(23):3727–38.
Angulo P, et al. Natural compounds targeting major cell signaling pathways: a novel paradigm for osteosarcoma therapy. J Hematol Oncol. 2017;10(1):10.
Yan X, et al. Apigenin in cancer therapy: anti-cancer effects and mechanisms of action. Cell Biosci. 2017;7:50.
Imran M, et al. Luteolin, a flavonoid, as an anticancer agent: a review. Biomed Pharmacother. 2019;112:108612.
Birt DF, Hendrich S, Wang W. Dietary agents in cancer prevention: flavonoids and isoflavonoids. Pharmacol Ther. 2001;90(2–3):157–77.
Wang S, et al. UCSCXenaShiny: an R/CRAN package for interactive analysis of UCSC Xena data. Bioinformatics. 2022;38(2):527–9.
Anand S, Burkenroad A, Glaspy J. Workup of anemia in cancer. Clin Adv Hematol Oncol. 2020;18(10):640–6.
Dicato M, Plawny L, Diederich M. Anemia in cancer. Ann Oncol. 2010;21(Suppl 7):vii167–72.
We thank all authors for their contributions and support.
This project was funded in part by Yancheng Teachers' University and the University of Southampton. YW was supported by the Medical Research Council [grant no: MR/S025480/1] and an Academy of Medical Sciences/the Wellcome Trust Springboard Award [grant no: SBF002\1038]. SW was supported by China Scholarship Council. For the purpose of open access, the authors have applied a CC-BY public copyright license to any Author Accepted Manuscript version arising from this submission.
Ethics approval and consent to participate
The authors declared that the collection of L. sinense was conducted with the oral approval of local authorities and in full compliance with China’s biodiversity rights and regulations. The authors confirm that no specific license was required for the collection of L. sinense. The plants were identified by Professor Tongxing Sun, School of Marine and Biological Engineering, Yancheng Teachers University.
Consent for publication
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Zhao, H., Wang, S., Williamson, P.T. et al. Integrated network pharmacology and cellular assay reveal the biological mechanisms of Limonium sinense (Girard) Kuntze against Breast cancer. BMC Complement Med Ther 23, 408 (2023). https://doi.org/10.1186/s12906-023-04233-z