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. 2024 Feb 8;9(7):8055–8066. doi: 10.1021/acsomega.3c08320

Identifying the Main Components and Mechanisms of Action of Artemisia annua L. in the Treatment of Endometrial Cancer Using Network Pharmacology

Weikang Guo †, Wanyue Wang ‡, Fei Lei †, Ruxin Zheng †, Xinyao Zhao †, Yuze Gu †, Mengdi Yang †, Yunshun Tong §, Yaoxian Wang †,*
PMCID: PMC10882657  PMID: 38405483

Abstract

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Artemisia annua L. (A. annua), a Traditional Chinese Medicine (TCM) that has been utilized in China for centuries, is known for its potential anticancer properties. However, the main components and mechanism of action of A. annua on endometrial carcinoma have not been reported. We used the TCMSP database to identify the active components of A. annua and their corresponding gene targets. We then obtained the gene targets specific to endometrial cancer from The Cancer Genome Atlas (TCGA) and GeneCards databases. The gene targets common to three databases were selected, and a “component-target” network was constructed. Protein–protein interaction (PPI) network analysis and ranking of the target proteins identified the key protein PTGS2 network analysis, and ranking of the target proteins identified the key protein PTGS2. We also screened the active components of A. annua and found that quercetin, kaempferol, luteolin, isorhamnetin, artemisin, and stigmasterol had the most targets. Molecular docking models were established for these six components with PTGS2, revealing strong binding activity for all of them. Finally, we conducted validation experiments to assess the effects of quercetin, an active component of A. annua, on endometrial cancer cells (HEC-1-A and Ishikawa cells). Our findings demonstrate that quercetin has the potential to inhibit both cell growth and migration, while also suppressing the expression of PTGS2.

1. Introduction

Endometrial cancer is a malignancy that originates in the lining of the uterus. It is the most common gynecological cancer in developed countries, accounting for 6% of all cancers in women worldwide.1 Risk factors for endometrial cancer include obesity, early onset of menstruation, late onset of menopause, nulliparity, and hormone replacement therapy.2 The most common symptom of endometrial cancer is abnormal vaginal bleeding. Diagnosis is usually made through an endometrial biopsy or dilation and curettage. Treatment options include surgery, radiation therapy, and chemotherapy.3 In recent years, it has been discovered that extracts from Traditional Chinese Medicine (TCM) can prevent and treat cancer.4 Some studies have also reported that TCM can treat endometrial cancer, for example, tanshinone IIA extracted from salvia miltiorrhiza5 and shikonin extracted from Lithospermum erythrorhizon6 can inhibit the growth of endometrial cancer.

Artemisia annua L.(A. annua), also known as Qinghao in Chinese, is a natural plant. A. annua has been used as a medicine in China for thousands of years. It has the effect of clearing heat, relieving summer heat, and stopping bleeding.7,8A. annua is well known for its active component artemisinin, which has been recognized for its effectiveness in treating malaria.9 Importantly, many experiments have demonstrated the inhibitory effects of the active components in A. annua on various cancers, such as lung cancer,10 breast cancer,11 and colon cancer,12 and that A. annua extracts, dihydroartemisinin13 and artesunate,14 possess the capability to combat endometrial cancer. Since there are hundreds of components that can be extracted from A. annua, it is necessary to further explore the effect of A. annua on endometrial cancer.

The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) is a database platform for the study of pharmacology in TCM. It allows for the comprehensive exploration of the relationships among herbal medicines, their active ingredients, and disease targets. By utilizing this database, it becomes easier to elucidate the mechanisms of action of TCM in treating various diseases.15 This study utilized TCMSP to identify the target genes on which the active components of A. annua act and associated them with mutated gene targets in endometrial cancer. In this study, protein–protein interaction (PPI) network analysis, Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to identify key genes and pathways targeted by the active ingredients of A. annua in endometrial cancer. Molecular docking validation and cell experiments were conducted to further validate these findings. This study provides a theoretical foundation for elucidating the potential of A. annua in the treatment of endometrial cancer and lays the foundation for its clinical application.

2. Materials and Methods

2.1. Database and Software

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Disease target Database: TCGA data from cBioPortal,16 GeneCards,17 Venn diagram drawing,18 network analysis and mapping software: Cytoscape 3.8.0 and R-Studio 3.2.5,19 PPI network construction,20 KEGG and GO enrichment analysis: SRplot platform,21 molecular components: PubChem database,22 protein structures: Protein Data Bank (PDB),23 molecular docking software: AutoDock Vina 4.1, PyMOL 2.4.24

2.2. Prediction of the Main Components and Targets of A. annua

On the TCMSP platform, we entered “Qinghao” and filtered the identified components of A. annua based on criteria such as drug-likeness (DL) ≥ 0.18 and oral bioavailability (OB) ≥ 30%.25 Then, we constructed the protein targets corresponding to the active components and used the UniProt database to obtain the standardized gene symbols.26

2.3. Searching for the Target Points of A. annua on the Endometrium Cancer

After the endometrial cancer data were input on the GeneCards platform, we downloaded the data. Subsequently, we conducted a search in cBioPortal to download the corresponding TCGA data for endometrial cancer. The main component-target data of A. annua, as well as the endometrial data from GeneCards and TCGA, were inputted into an online Venn Diagram tool. The common target genes were obtained by taking the intersections of the data from each group.

2.4. Building a Component-Target Network

The pairwise relationships between the common target genes and component-target data of A. annua were matched, and the network was visualized by using Cytoscape software.

2.5. GO and KEGG Enrichment Analysis

GO analysis is an integrated analysis of target genes based on Gene Ontology database and finally obtains the enrichment results of cellular component (CC), molecular function (MF), and biological process (BP). KEGG analysis is an integrated analysis of target genes based on Kyoto Encyclopedia of Genes and Genomes database and then enrichment into related pathways. We employed the SRplot platform, an online analysis tool based on R software, for our analysis. We selected the GO and KEGG analysis modules and entered the common target genes into the platform. We specified the human species and applied a filter of a P-value <0.05 for both GO and KEGG analyses. Subsequently, we generated visual representations of the top 10 ranked results.

2.6. Construction of PPI Network and Analysis of PPI Network

PPI refers to the interaction between proteins. Through the construction and analysis of the PPI network, we can reveal the interaction and signal transmission path between proteins in the cell. To conduct the PPI network analysis, we accessed the STRING Web site and utilized its Multiple Proteins module to input the common target genes, specifying the human species for analysis. After excluding independent proteins, we downloaded the generated data. Subsequently, we ranked the number of other proteins linked to each target protein, selected the top 30 target proteins, and visualized them with R-Studio.

2.7. Molecular Docking

We download the structures of quercetin, kaempferol, luteolin, isorhamnetin, artemetin, and stigmasterol from the PubChem database. At the same time, we downloaded the PTGS2 protein structure file from the PDB web site and used PyMol software to remove the water and ligand structure. We used AutoDock Vina to combine quercetin, kaempferol, luteolin, isorhamnetin, artemetin, and stigmasterol; performed molecular docking with PTGS2; and obtained an affinity value. The affinity value (kilocalories per mole) represents the binding strength between the two molecules. A lower binding energy indicates a more stable binding between the ligand and the receptor. Generally, a docking energy value less than −4.25 kcal/mol signifies some binding activity, less than −5.0 kcal/mol denotes good binding activity, and less than −7.0 kcal/mol indicates strong binding activity.27 Finally, we used PyMol software to visualize the molecular docking.

2.8. Cell Culture

HEC-1-A cells and Ishikawa cells were purchased from Shanghai Fuyu Biotechnology Co., Ltd. HEC-1-A cells were cultured in McCoy’s 5A medium (Servicebio, Wuhan, China) supplemented with 10% fetal bovine serum (FBS) (Tianhang Biotechnology, Hangzhou, China) and 1% penicillin-streptomycin (Beyotime, Shanghai, China). Ishikawa cells were cultured in RPMI-1640 medium (Servicebio, Wuhan, China) supplemented with 10% FBS and 1% penicillin-streptomycin. The cells were incubated in a dedicated incubation chamber at 37 °C with 5% CO2.

2.9. Cell Viability

Cell viability was assessed through the cultivation of cells within 96-well plates. Subsequent to cellular adhesion, varying concentrations of quercetin (10, 25, 50, and 100 μm) were introduced, accompanied by the establishment of a negative control group. The cells were then cultured within a 5% CO2 incubation chamber at a temperature of 37 °C. Cell viability was determined using the Cell Counting Kit-8 (CCK-8) (Wanlei Bio, Shenyang, China) assay at 24 and 48 h. Finally, the IC50 of quercetin on HEC-1-A and Ishikawa cells at 24 and 48 h were calculated.

2.10. Cell Scratch Experiment

Cells were seeded onto a 6-well plate and allowed to grow until they formed a confluent monolayer. The confluent cell monolayer was wounded using a sterile pipet tip, and cells were gently washed with phosphate-buffered saline (PBS) 3 times to remove any debris. Subsequently, a blank control group and various quercetin groups (25, 50, 100 μm). Scratch sizes were recorded at 0 and 48 h. We used ImageJ software to measure the area of the scratches and then calculated the wound closure percentage using the formula: wound closure (%) = (area at 48 h – area at 0 h)/area at 0 h.

2.11. Western Blot

Cells from each group were lysed using a cell lysis buffer (Wanleibio, Shenyang, China), and the protein concentration in each group was quantified with a BCA protein quantification kit (Wanleibio, Shenyang, China). The proteins were then separated on a 14% sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) (Wanleibio, Shenyang, China) and transferred onto a poly(vinylidene difluoride) (PVDF) membrane (Millipore, Billerica, MA). To block nonspecific binding, the PVDF membrane was subsequently incubated with 5% skim milk for 1 h. Primary antibodies, including anti-PTGS2 (Wanleibio, Shenyang, China) and anti-GAPDH (Wanleibio, Shenyang, China), were added and left to incubate overnight at 4 °C. Following that, the membrane was incubated with a secondary antibody, goat antirabbit IgG-HRP (Wanleibio, Shenyang, China), at 37 °C for 45 min. Lastly, an ECL detection system (Wanleibio, Shenyang, China) was used to expose and scan the membrane, and ImageJ software was employed to analyze the band intensities and obtain the optical density values.

2.12. Statistical Analysis

The results of our experiments were analyzed using GraphPad Prism 8.0 software. To assess the differences between two groups, we used the Student t test, while for comparisons involving three or more groups, we utilized ANOVA. A significance level of P < 0.05 was considered statistically significant.

3. Results

3.1. Active Ingredients of A. annua and Target Genes of Active Ingredients

We screened and obtained 22 active components based on drug-likeness (DL) ≥ 0.18 and oral bioavailability (OB) ≥ 30% (Table 1). These 22 active components corresponded to 510 targets. We used R programming to annotate these targets with gene symbols. In the end, we obtained 448 corresponding gene symbols (Table 1s).

Table 1. Main Components of A. annua.

mol ID molecule name OB (%) DL
MOL002235 EUPATIN 50.8 0.41
MOL000354 isorhamnetin 49.6 0.31
MOL000359 sitosterol 36.91 0.75
MOL004083 tamarixetin 32.86 0.31
MOL004112 patuletin 53.11 0.34
MOL000422 kaempferol 41.88 0.24
MOL000449 stigmasterol 43.83 0.76
MOL004609 areapillin 48.96 0.41
MOL005229 artemetin 49.55 0.48
MOL000006 luteolin 36.16 0.25
MOL007274 skrofulein 30.35 0.3
MOL007389 artemisitene 54.36 0.31
MOL007400 vicenin-2_qt 45.84 0.21
MOL007401 cirsiliol 43.46 0.34
MOL007404 vitexin_qt 52.18 0.21
MOL007412 DMQT 42.6 0.37
MOL007415 [(2S)-2-[[(2S)-2-(benzoylamino)-3-phenylpropanoyl]amino]-3-phenylpropyl] acetate 58.02 0.52
MOL007423 6,8-di-c-glucosylapigenin_qt 59.85 0.21
MOL007424 artemisinin 49.88 0.31
MOL007425 dihydroartemisinin 50.75 0.3
MOL007426 deoxyartemisinin 54.47 0.26
MOL000098 quercetin 46.43 0.28
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3.2. Target Genes of A. annua Active Ingredients in Endometrial Cancer

By integrating TCGA data and Genecards data through a Venn diagram, we obtained a total of 3771 common genes. Furthermore, by combining the target genes of A. annua active ingredients, we identified 149 common genes (Figure 1 and Table 2s).

Figure 1.

Figure 1

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Common genetic targets of endometrial cancer and the active components of A. annua.

3.3. Building a Network Targeting Endometrial Cancer Using A. annua Active Ingredients

We constructed a “component-target” network using Cytoscape. In this network, A. annua and endometrial cancer are represented by green and red ellipses, respectively. The yellow nodes represent the active ingredients of A. annua, and the blue nodes represent the ingredient targets of A. annua active ingredients for endometrial cancer (Figure 2 and Table 3s).

Figure 2.

Figure 2

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“Component-target” network of the active components of A. annua and genetic targets of endometrial cancer.

3.4. GO Enrichment Analysis

We performed GO analysis on the 149 common genes and obtained 4020 biological processes (BP) (Table 4s), 291 cellular components (CC) (Table 5s), and 472 molecular functions (MF) (Table 6s). The top 10 enriched terms for each group of BP, CC, and MF (with P-value <0.05) are displayed in the figure (Figure 3A–C). We found that whether BP, CC, or MF, they are closely associated with the occurrence and development of endometrial cancer.

Figure 3.

Figure 3

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GO analysis of the common genetic targets of the active components of A. annua and the genetic targets associated with endometrial cancer. (A) Biological processes (BP), (B) cellular components (CC), (C) molecular functions (MF).

3.5. KEGG Enrichment Analysis

In addition, KEGG analysis was performed on the 149 common genes obtained, revealing enrichment in 252 pathways, (Table 7s). The figure below displays the top 10 pathways identified through the analysis (Figure 4A). Among these 10 pathways, chemical carcinogenes-receptor activation and TNF signaling pathway are closely related to the occurrence and development of endometrial cancer.28 Furthermore, we obtained relevant pathways involved in endometrial cancer (Figure 4B). In the figure, it can be found that the common target genes fall on the key pathways of the endometrium including the cytokine–cytokine receptor interaction, ErbB signaling pathway, cell cycle, and p53 signaling pathway.

Figure 4.

Figure 4

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KEGG analysis of the common genetic targets of the active components of A. annua and the genetic targets associated with endometrial cancer. (A) The top 10 KEGG pathways for the common target genes. (B) Common target genes are in endometrial cancer-related pathways. Red squares represent collections of homologous genes in which a common target gene acts on the pathway.

3.6. PPI Network

We input the 149 common genes into String, set the species to human, and left the remaining parameters as default. Subsequently, we obtained the PPI network (Figure 5A). The generated data was then downloaded (Table 8s), and the proteins were ranked based on the number of associated proteins for each target protein. The following are the top 30 ranked target proteins (Figure 5B).

Figure 5.

Figure 5

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(A) PPI networks of 149 common genes. (B) Top 30 ranked proteins in the PPI network. X-axis represents the number of proteins associated with a target protein, and Y-axis represents the name of the target protein.

3.7. Molecular Docking

We ranked the number of components corresponding to the same target in our constructed “component-target” set and found that PTGS2 corresponded to the largest number of components (Table 9s), with PTGS2 corresponding to 16 components. Additionally, PTGS2 is among the top ten proteins ranked by the PPI network. Subsequently, we sorted the number of targets corresponding to each component and found that quercetin, kaempferol, luteolin, isorhamnetin, artemetin, and stigmasterol were the top six components, and quercetin-associated target proteins were the most, corresponding to 117 target proteins (Table 10s). Furthermore, we performed molecular docking using AutoDock Vina (Figure 6A–F) and obtained the affinity (kcal/mol) values of −9.4, −9.6, −9.3, −9.8, −9.2, and −8.6 for PTGS2-isorhamnetin, PTGS2-kaempferol, PTGS2-stigmasterol, PTGS2-luteolin, PTGS2-quercetin, and PTGS2-artemetin, respectively. A value below −7.0 kcal/mol indicates a strong binding activity. These results demonstrate the strong binding activity between the aforementioned proteins and the A. annua active compounds.

Figure 6.

Figure 6

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Molecular docking models. (A) PTGS2-isorhamnetin, (B) PTGS2-kaempferol, (C) PTGS2-stigmasterol, (D) PTGS2-luteolin, (E) PTGS2-quercetin, and (F) PTGS2-artemetin.

3.8. Quercetin Inhibition of Endometrial Cancer Cells Viability

We tested the activity of the cells using CCK-8. We observed a decrease in the activity of HEC-1-A and Ishikawa cells with increasing concentrations of quercetin. Additionally, the activity of cells decreased with longer times (Figure 7A,B). We also calculated the 50% inhibiting concentration (IC50) of quercetin on HEC-1-A and Ishikawa cells at 24 and 48 h. The IC50 of quercetin on HEC-1-A cells was 100.7 at 24 h and 60.7 μm at 48 h. The IC50 of quercetin on Ishikawa cells was 151.1 at 24 h and 100.8 μm at 48 h.

Figure 7.

Figure 7

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Effects of different concentrations of quercetin on endometrial cancer cells at 24 and 48 h. (A) Results of the measurement on HEC-1-A cells. (B) Results of the measurement on Ishikawa cells. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

3.9. Quercetin Inhibition of Endometrial Cancer Cells Migration Ability

We evaluated the effect of quercetin on the migration ability of HEC-1-A and Ishikawa cells by using a scratch assay. Compared to the control group, treatment with 25 μm, 50 μm, and 100 μm of quercetin significantly inhibited the migration ability of HEC-1-A cells at 48h (Figure 8A,B). Similarly, the migration ability of Ishikawa cells was inhibited with the increase in quercetin concentration (Figure 8C,D).

Figure 8.

Figure 8

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Effects of different concentrations of quercetin on the migration ability of endometrial cancer cells. (A, C) Size of cell scratch area at 0 and 48 h under the microscope. (B, D) Wound closure percentage of cell scratches at different concentrations after 48 h. *P < 0.05; ***P < 0.001; ****P < 0.0001.

3.10. Quercetin Inhibition of PTGS2 Expression in Endometrial Cancer Cells

We measured the expression levels of PTGS2 in HEC-1-A and Ishikawa cells treated with different concentrations of quercetin using Western blot analysis. The experimental results revealed that compared to the control group, the expression of PTGS2 in HEC-1-A and Ishikawa cells cultured with quercetin was decreased, suggesting that quercetin can suppress PTGS2 expression in HEC-1-A cells (Figure 9A,B) and Ishikawa cells (Figure 9C,D).

Figure 9.

Figure 9

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(A, B) Expression of PTGS2 after treatment of HEC-1-A cells with different concentrations of quercetin. (C, D) Expression of PTGS2 after treatment of Ishikawa cells with different concentrations of quercetin.*P < 0.05; ***P < 0.001; ****P < 0.0001.

4. Discussion

In recent years, endometrial cancer has gained attention as a focal point in gynecological cancer treatment.29 With the progress of precision medicine, advancements in genomic research, and the promotion of related technologies, molecular classification has emerged as a valuable source of additional tumor biological information for endometrial cancer. This classification allows for personalized treatment approaches, resulting in improved treatment outcomes and significant benefits for patients.30

TCM has garnered increasing recognition as a complementary therapy for cancer. Specifically, certain extracts from TCM sources, such as berberine,31 ferulic acid,32 and hyperoside,33 have been substantiated for their potent inhibitory effects on various cancer. Furthermore, the utilization of extracted compounds from numerous TCM and compound Chinese medicine has found practical application in clinic. This application has notably contributed to enhancing the survival times of patients. However, the intricate composition of TCM and the potential interactions among its various components present a formidable challenge in providing substantial evidence and elucidating the precise mechanisms underlying its anticancer effects. In 2007, Hopkins proposed network pharmacology as a solution to this problem.34 The basic research approach of network pharmacology is to obtain the chemical components of herbs through online databases such as TCMSP,15 TCMID,35 BATMAN-TCM,36 etc. Then, the corresponding gene targets of the chemical components are predicted through public databases or high-throughput sequencing data. This allows for the prediction of the interactions between effective drug components and disease-related gene targets. Through these methods, the underlying mechanism of A. annua in the treatment of abdominal aortic aneurysm was found.25 In addition, a large number of articles on network pharmacology have been published, which are devoted to elaborating the mechanism of action of TCM.

In China, A. annua has been used as herb for treating various diseases for thousands of years.37 One of its extracted components, artemisinin, has gained significant recognition for its efficacy in treating malaria, leading to the award of the Nobel Prize.38 It is important to note, however, that artemisinin is not the sole active component found in the A. annua. Presently, more than 600 components, including sesquiterpenoids, flavonoids, coumarins, triterpenoids, and phenolics, have been identified in A. annua. Many of these components exhibit properties associated with antiparasitic, anti-inflammatory, and antitumor effects.7 Therefore, we boldly speculate whether the antitumor and anti-inflammatory components of A. annua could be effective in the treatment of endometrial cancer.

Initially, we performed an intersection analysis between the target genes associated with the active components of A. annua and the target genes of endometrial cancer. Subsequently, we conducted GO and KEGG pathway analyses of the shared genes. The GO analysis revealed significant associations with molecular function (MF), cellular component (CC), and biological process (BP) related to cellular antioxidant activity, oxidative stress, and drug response. Some literature reviews have confirmed a close correlation between oxidative stress, antioxidant activity, and endometrial cancer.39 In addition, through KEGG pathway analysis, we identified 14 key genes (EGFR, MAPK1, MYC, CDKN1A, AKT1, EGF, TP53, BAX, ERBB2, ELK1, CASP9, GSK3B, CCND1, and RAF1) that are involved in the endometrial cancer pathway. We proceeded with subsequent experiments.

We screened 22 active components of A. annua using the TCMSP database and ranked the number of shared target proteins among the component-target interactions in the constructed network. We discovered that PTGS2 is associated with 16 components and is also among the top 10 key proteins in the PPI network, making it a focal point of our study. PTGS2, also known as cyclooxygenase-2 or COX-2, plays a crucial role in triggering inflammatory responses.40 In recent years, a growing body of evidence has shown a close connection between COX-2 and tumor development.41 A meta-analysis demonstrated that overexpression of COX-2 in endometrial cancer may promote the occurrence of malignant tumors and increase the susceptibility to endometrial cancer. Therefore, assessing the expression level of COX-2 can contribute to the improvement of diagnosis, treatment, and prognosis of endometrial cancer.42 Additionally, a review article highlighted the ability of anti-COX-2 drugs to accelerate apoptosis in endometrial cancer cells, inhibit tumor growth and metastasis, and modulate the immune function of the body.43 Considering the numerous reports on the anti-inflammatory properties of A. annua, we daringly hypothesize that the main components of A. annua may inhibit the expression of PTGS2, thereby restraining the progression of endometrial cancer.

Later, we sorted the number of targets corresponding to each component and found that the top six components were quercetin, kaempferol, luteolin, isorhamnetin, artemetin, and stigmasterol. Interestingly, six components were all found to target PTGS2. Subsequently, we reviewed relevant papers on these six components and found that they are all associated with the treatment of endometrial cancer. Quercetin has been found to inhibit tumor proliferation by promoting ferroptosis in HEC-1-A cells.44 Additionally, some studies have shown that kaempferol induces G2/M phase cell cycle arrest and apoptosis, inhibits cell invasion, and upregulates the m-TOR/PI3K signaling pathway, thus contributing to the suppression of endometrial cancer.45 Furthermore, luteolin,25 isorhamnetin,46 artemetin,25 and stigmasterol47 have been found to inhibit the proliferation of endometrial cancer cells. Subsequently, we performed molecular docking of PTGS2 with quercetin, kaempferol, luteolin, isorhamnetin, artemetin, and stigmasterol and found that all of these components exhibited strong binding activity with PTGS2, with an affinity value smaller than −7.0 kcal/mol.

We also found that quercetin corresponds to 117 target proteins of endometrial cancer; therefore, we suspected that quercetin may be an important component of A. annua in the treatment of endometrial cancer. We further focused on investigating the effects of quercetin on HEC-1-A and Ishikawa cells. To demonstrate the ability of quercetin to inhibit the activity of HEC-1-A and Ishikawa cells, we conducted CCK-8 experiments to measure cell proliferation.48 Additionally, we performed a scratch assay to evaluate cell migration49 and Western blot analysis to assess PTGS2 expression.50 The results showed that as the concentration of quercetin increased, the proliferation and migration abilities of HEC-1-A and Ishikawa cells decreased. Furthermore, the expression of PTGS2 decreased with an increasing concentration of quercetin.

Previous experimental studies have demonstrated that quercetin can regulate the activity of colonic cancer cells by reducing the production of reactive oxygen species (ROS) through modulating PTGS2 expression.51 There is also evidence linking PTGS2 expression in tumor cells to apoptosis,52 autophagy,53 and immune function.54 Additionally, the active components of A. annua have been found to regulate the expression of tumor-related proteins through modulation of various noncoding RNAs.55 Therefore, we are interested in investigating whether the active components of A. annua regulate the occurrence and development of endometrial cancer through these specific mechanisms.

While the study employs network pharmacology to investigate the components of A. annua, it does not delve into the potential interactions between these components. Such interactions could significantly affect the therapeutic outcomes, especially when patients consume complex mixtures rather than isolated compounds.56 Moreover, we used only two cell lines, HEC-1-A and Ishikawa, to validate the results of the pharmacological network. Validation results are limited. We need to experiment with a wider range of endometrial cancer cell lines to ensure a greater clinical relevance. The precise mechanism of action of A. annua on endometrial carcinoma still requires further investigation.

5. Conclusions

This study used network pharmacology to establish the relationship between the active components of A. annua and the targets in endometrial cancer. The key gene PTGS2, which is targeted by anaerobic annua, was identified. Additionally, cell experiments were conducted to validate that quercetin, an effective component of the Anodebacter annua, can inhibit the proliferation and migration of endometrial cancer cells as well as the expression of PTGS2. These findings provide a theoretical basis for the clinical application of A. annua.

Acknowledgments

WU JIEPING Medical Foundation Special Fund for Clinical Research (no. 320.6750.2023-05-4).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.3c08320.

  • Gene targets corresponding to the active components of A. annua (Table 1s); common genetic targets of endometrial cancer and the active components of A. annua (Table 2s); details of the “component-target” network (Table 3s); details of BP (Table 4s); details of CC (Table 5s); details of MF (Table 6s); details of KEGG analysis (Table 7s); details of PPI network analysis (Table 8s); ranking of the number of components corresponding to the same target (Table 9s); and ranking of the number of target corresponding to the same component (Table 10s) (PDF)

Author Contributions

∥ W.G. and W.W. contributed equally to this work. Experiment design: Y.W., W.G. Experiment operation: W.G., F.L., R.Z., X.Z., Y.T. Data statistics: Y.G., M.Y., Y.T. Writing: W.G., W.W. All authors have read and approved the final version.

The authors declare no competing financial interest.

Supplementary Material

ao3c08320_si_001.pdf (8.5MB, pdf)

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