Rhaponticin inhibits the proliferation, migration, and invasion of head and neck squamous cell carcinoma (HNSCC) cells through modulation of the IL6/STAT3 signaling pathway

Rhaponticin inhibits the proliferation, migration, and invasion of head and neck squamous cell carcinoma (HNSCC) cells through modulation of the IL6/STAT3 signaling pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Rhaponticin inhibits the proliferation, migration, and invasion of head and neck squamous cell carcinoma (HNSCC) cells through modulation of the IL6/STAT3 signaling pathway Hongcheng Wei, Shanshan Wang, Jiayue Wan, Sicheng Li, Wei Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5917121/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Rhaponticin, a bioactive compound derived from rhubarb, has been demonstrated anti-tumor effects in various types of cancer. However, its impact on head and neck squamous cell carcinoma (HNSCC) remains unexplored. This study aims to investigate the specific molecular mechanisms by which Rhaponticin inhibits the invasion and metastasis of HNSCC cells. Method: The potential target genes that rhaponticin acts on in HNSCC were identified using online databases. The mechanisms by which rhaponticin influences the occurrence and progression of HNSCC were investigated through network pharmacology, molecular docking, bioinformatics analysis, and cellular experiments. Result: Using network pharmacology, we identified 40 hub genes from the collected gene set. Subsequently, by analyzing The Cancer Genome Atlas (TCGA) data with four machine learning algorithms, we identified IL-6 as a potential target associated with the occurrence and progression of head and neck squamous cell carcinoma (HNSCC). Based on the average expression level of IL-6, we classified the samples into high-expression and low-expression groups and conducted survival analysis. Our results indicated that IL-6 expression was significantly correlated with patient survival. Gene Set Enrichment Analysis (GSEA) revealed that Rhaponticin might influence HNSCC via the IL6/STAT3 signaling pathway. Using the CIBERSORT algorithm, we assessed the differences in infiltration levels of 22 immune cell types between the high and low IL-6 expression groups. Our findings suggest that multiple immune cells may be involved in the pathogenesis of HNSCC. Additionally, we analyzed single-cell RNA sequencing (scRNA-seq) data from the GEO database to compare IL6 expression levels in tumor and normal tissues and evaluated its prognostic impact using Receiver Operating Characteristic (ROC) curve analysis. Molecular docking studies demonstrated that Rhaponticin binds stably to IL6. In the experimental section, we used two HNSCC cell lines (CAL27 and SCC9) to investigate the effects of Rhaponticin. Our results showed that Rhaponticin effectively inhibited cell proliferation, invasion, and migration, and reduced the expression of proteins in the IL6/STAT3 signaling pathway. Conclusion: Rhaponticin shows promise in treating HNSCC by inhibiting the IL6/STAT3 signaling pathway. Biological sciences/Cancer/Head and neck cancer Biological sciences/Cancer/Tumour biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer globally. In 2022, approximately 950,000 new cases of HNSCC were reported worldwide, resulting in an estimated 480,000 deaths. [ 1 ] The incidence rate of HNSCC is consistently increasing and is projected to rise by 30% by 2030. [ 2 ] Currently, the primary etiological factors of HNSCC include the stimulatory effects of tobacco and alcohol, as well as the carcinogenic actions of viruses. [ 3 ] A minority of patients exhibit precancerous lesions, such as mucosal erythema and leukoplakia, in the early stages of HNSCC. However, most patients in advanced stages have no history of precancerous lesions. This poses significant challenges for the detection and diagnosis of HNSCC. [ 4 ] HNSCC is highly susceptible to recurrence and metastasis, leading to a relatively poor prognosis for patients. The median survival time is approximately one year. [ 5 ][ 6 ] Surgery combined with radiotherapy and chemotherapy remains the primary therapeutic modality for patients with advanced HNSCC. However, these conventional treatments can cause significant adverse effects, including xerostomia, loss of taste, dysphagia, and aspiration pneumonia, in post-surgical patients. [ 7 ][ 8 ] These adverse events highlight the need to explore new combined treatment modalities with fewer side effects and enhanced therapeutic efficacy. Traditional Chinese medicine (TCM) has been widely used as an effective adjunct in cancer therapy. Many active compounds in Chinese herbal medicines can directly or indirectly modulate the tumor microenvironment. [ 9 ][ 10 ] However, there are currently few reports on the use of Chinese herbal medicine for treating HNSCC, and the specific molecular mechanisms remain unclear. Further research is necessary to develop more effective treatment strategies and approaches for clinical practice. Rhubarb is a herbaceous plant widely distributed in China and South Korea. According to Traditional Chinese Medicine (TCM), rhubarb clears heat, detoxifies, and promotes purgation. [ 11 ] Rhaponticin, a stilbene compound extracted from the rhizomes of rhubarb, exhibits anti-tumor, antibacterial, and anti-inflammatory properties. [ 12 ] [ 13 ] Its anti-cancer effects have been verified in lung, gastric, and prostate cancers. Rhaponticin exerts its functions primarily through inducing apoptosis of cancer cells, inhibiting cancer cell invasion, metastasis, and angiogenesis, as well as suppressing the cell cycle. [ 14 ][ 15 ][ 16 ] A study using RT-PCR revealed that rhaponticin downregulates proteins involved in the PI3K-Akt-mTOR pathway, induces apoptosis of osteosarcoma cells, and reduces intercellular adhesion. [ 17 ] Rhaponticin increases reactive oxygen species (ROS) levels in lung cancer cells, leading to alterations in cell cycle-related proteins and subsequently inducing apoptosis. Additionally, it upregulates serum levels of immunoglobulins such as IgA and IgM in mice. [ 18 ] Rhaponticin inhibits the invasion and metastasis of human breast adenocarcinoma cells, and suppressing anti-angiogenic factor production exerts a similar effect. [ 19 ] In summary, we hypothesize that rhaponticin may effectively suppress the proliferation, invasion, and migration of HNSCC cells. In this study, we employed a network pharmacology approach to identify the targets of rhaponticin in HNSCC and screened potential targets by mining data from the TCGA and GEO databases. Based on these potential targets, we elucidated the effects of rhaponticin on the proliferation, invasion, and migration phenotypes of head and neck squamous cell carcinoma (HNSCC). This research provides a foundation for the clinical development of effective traditional Chinese medicine (TCM) agents for HNSCC treatment. 1. Materials and Methods 1.1 Data Acquisition The predicted targets of rhaponticin were retrieved from PharmMapper ( http://www.lilab-ecust.cn/pharmmapper ) and SEA ( http://sea.bkslab.org ). Disease-related target genes were obtained by searching the GeneCards database using the keyword "head and neck squamous cell carcinoma". Gene expression profiles and clinical information for HNSCC samples (n = 515) and normal samples (n = 44) were acquired from the TCGA database ( https://portal.gdc.cancer.gov ). The gene expression profile of the HNSCC dataset GSE30784 from GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) was used as a validation set. 1.2 Protein-Protein Interaction Network (PPI) and Target Screening The intersection targets of rhaponticin and HNSCC were imported into the STRING database, with a minimum interaction score threshold set at > 0.4, to generate a protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape software was used to analyze and identify the core targets within this PPI network. 1.3 KEGG and GO Enrichment Analyses The clusterProfiler package in R performed GO and KEGG enrichment analyses on the intersection gene target related to rhaponticin and HNSCC. 1.4 Molecular Docking The 2D structure of rhaponticin was retrieved from the PubChem database and imported into Chem3D software for minimum energy optimization before export. The 3D protein structure of IL-6 was downloaded from the PDB database and imported into PyMOL to remove water and ligand molecules. Subsequently, AutoDock Tools 1.5.6 was used for hydrogen addition, charge calculation, and other preprocessing steps. Finally, molecular docking was performed using AutoDock Vina, and the results were visualized with PyMOL 2.6. 1.5 Immune Infiltration Analysis We categorized the TCGA data into normal and tumor groups and used the CIBERSORT package to evaluate the infiltration levels of 22 immune cell types. 1.6 Univariate COX Regression Analysis Model We extracted the expression levels of core target genes and survival information of tumor patients from the TCGA database, merged these datasets, and subsequently established a prognosis model using univariate Cox regression analysis. A P value 1 suggested that the gene was a risk factor for poor prognosis, while an HR < 1 indicated that the gene was a protective factor for a better prognosis. 1.7 Screening and Validation of Potential Functional Targets We employed the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine - Recursive Feature Elimination (SVM-RFE) to analyze the core targets obtained from the initial screening. The randomForest package in R was used for random forest analysis, while the glmnet package was utilized for LASSO logistic regression, with the minimum lambda value selected as optimal. Genes with common characteristics identified by these three machine-learning algorithms were then selected for further investigation. Subsequently, the survival package in R was used to conduct a survival analysis on the selected diagnostic markers. To validate the utility of these markers, we utilized the GSE30784 dataset. Principal Component Analysis (PCA) was performed using the factoextra package in R, demonstrating significant differences between tumor and normal specimens. We then analyzed the expression differences of genes in normal and tumor tissues. We evaluatedtheir predictive capacity using Receiver Operating Characteristic (ROC) curves, calculating the Area Under the Curve (AUC) as a measure of algorithm performance. 1.8 Gene Set Enrichment Analysis (GSEA) We utilized R to perform GSEA analysis on the gene sets obtained from the TCGA database, aiming to gain a more intuitive understanding of the gene expression patterns in highly enriched pathways. 1.9 Cell Culture The human HNSCC-related cell lines CAL27, SCC9, and HOK were procured from [Wuhan Boyuan Science and Technology Group Co., Ltd]. They were cultivated in DMEM medium supplemented with 10% fetal bovine serum (FBS), penicillin (10,000 U/ml), and streptomycin (10 mg/ml). The cells were incubated in a humidified incubator at 37°C with 5% CO2. Cells in the logarithmic growth phase were selected for the experiments. 1.10 Cytotoxicity Test CAL27, SCC9, and HOK cells were each divided into eight groups. Each group was treated with rhaponticin at different final concentrations (0 µM, 10 µM, 25 µM, 50 µM, 100 µM, 200 µM, 300 µM, 400 µM) and cultured in a hypoxic environment (37°C, 1% O2). The OD values at 450 nm were measured using the CCK-8 assay. 1.11 Cell Proliferation Formation Assay CAL27 and SCC9 cells were incubated in a 5% CO2, 37°C constant temperature incubator. When the cells were in the logarithmic growth phase, they were digested with trypsin, collected after centrifugation, and evenly inoculated into 6-well plates at a density of 1×105 cells/well. Twenty-four hours later, the two types of cells were grouped (divided into 3 groups according to the drug concentration of 0 and 25, with 3 replicate wells in each group) for drug administration. The medium was changed every 3 days, and the cells were continuously cultured until the cell clone density was visible to the naked eye. Subsequently, the culture medium was removed, and the cells were washed with PBS. The cells were fixed with 4% paraformaldehyde, stained with crystal violet at room temperature for 30 minutes, washed with PBS, photographed, and the proliferation rates of the two types of cells were calculated. 1.12 Cell Scratch Assay Logarithmic growth phase CAL27 and SCC9 cells were harvested, rinsed twice with PBS, and digested with 0.25% trypsin. After centrifugation, the cells were resuspended in a culture medium and seeded at a density of 100,000 cells per well in six-well plates. The plates were then incubated overnight in a humidified incubator at 37°C with 5% CO2. When the cells reached 100% confluence the next day, a scratch was made using a 200 µl pipette tip. The original medium was discarded, and the cells were rinsed twice with PBS to remove floating cells. Serum-free DMEM medium was added, and the cells were photographed under a 200x microscope. The plates were then incubated for 24 hours under the same conditions. After this period, the medium was again discarded, the cells were rinsed twice with PBS, fresh serum-free DMEM medium was added, and the cells were re-photographed under a 200x microscope. 1.13 Cell Invasion Assay Matrigel was diluted to 1 mg/ml, and 70 µl was evenly spread onto the upper chamber of the transwell insert (purchased from Corning). The coated chambers were incubated at 37°C with 5% CO2 for 3 hours. Excess Matrigel was aspirated, and 100 µl of DMEM medium was added for activation. Subsequently, CAL27 and SCC9 cells in the logarithmic growth phase were harvested, trypsinized, resuspended in serum-free DMEM medium, and adjusted to a concentration of 5.0 × 10^5 cells/mL. A 100 µl aliquot of the single-cell suspension was seeded into the upper chamber, while DMEM medium containing 10% fetal bovine serum (FBS) was added to the lower chamber. The cells were then incubated in a hypoxic environment (37°C, 1% O2) for 24 hours. After incubation, the cells were fixed with 0.25% glutaraldehyde for 20 minutes and stained with 0.1% crystal violet for 30 minutes. The Matrigel in the upper chamber was carefully removed, and the cells were counted in five random fields under a 200x microscope. The experiment was repeated three times, and the average value was calculated. 1.14 Western Blot The CAL27 and SCC9 cells from each group were collected. To each well, 100 µl of PMSF-containing cell lysis buffer with protease inhibitors was added, and the cells were lysed on ice for 30 minutes. The lysates were then transferred to 1.5 ml centrifuge tubes and centrifuged at 12,000 rpm for 5 minutes at 4°C, and the supernatants were collected. Electrophoresis was performed using 10% SDS-PAGE, and proteins were transferred to PVDF membranes via the semi-dry method. The membranes were blocked with 5% skim milk in TBST for 2 hours at room temperature, washed three times with PBST for 15 minutes each, and incubated overnight at 4°C with primary antibodies diluted to their working concentrations. The next day, the membranes were again washed three times with PBST for 15 minutes each, and incubated with secondary antibodies diluted to their working concentrations for 1 hour at room temperature. Protein bands were visualized using a chemiluminescence imager, and quantitative analysis of the band intensities was conducted using ImageJ software. 2. Results 2.1 Flow Diagram In this study, we investigated the molecular mechanisms of rhaponticin, an extract from the rhizomes of Rheum officinale, on HNSCC cells using a combination of network pharmacology, multiple machine learning algorithms, and molecular docking. The detailed workflow is illustrated in Fig. 1 . 2.2 Network Pharmacology Studies and Molecular Docking The SMILES structure of rhaponticin was obtained from PubChem. Subsequently, 364 potential targets of rhaponticin were predicted using the PharmMapper ( https://www.lilab-ecust.cn/pharmmapper/ ) and SEA ( http://sea.bkslab.org ) databases. A total of 7,175 targets associated with head and neck squamous cell carcinoma (HNSCC) were retrieved from the GeneCards database. The intersection of these two sets of targets, totaling 262 common targets, was identified using the Venn package in R (Fig. 2 A), and a drug-target network was constructed (Fig. 2 B). The gene targets of rhaponticin for HNSCC treatment were imported into the STRING database, where isolated protein targets were hidden to generate the protein-protein interaction (PPI) network for rhaponticin in HNSCC treatment. The PPI data were downloaded in TSV format and imported into Cytoscape v.3.10.0. Topological properties of the network were analyzed using the NetworkAnalyzer tool in Cytoscape 3.10.0. Six topological parameters of the nodes were calculated using the cytoNCA plugin: betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), eigenvector centrality (EC), network centrality (NC), and local average connectivity (LAC). Nodes with parameter values greater than the median were selected as core targets, resulting in the identification of 40 key gene targets (Fig. 2 C-D). KEGG and GO enrichment analyses were performed on the intersection targets, and the top 10 results with the smallest q-values were visualized (Fig. 2 E-F). KEGG enrichment analysis indicated that the common targets of rhaponticin and HNSCC were primarily enriched in pathways related to lipid metabolism and atherosclerosis, prostate cancer, hepatitis B, T cell receptor signaling, and JAK-STAT signaling. GO enrichment analysis revealed that these targets were mainly involved in responses to steroid hormones, cellular responses to steroid hormone stimuli, membrane rafts, membrane microdomains, nuclear receptor activity, and estrogen response element binding. Molecular docking methods were employed to predict direct interactions between rhaponticin and its corresponding target genes. The three-dimensional binding mode of the active compound and its targets is shown in (Fig. 2 G). As presented in Supplementary Table 1, rhaponticin exhibits high binding affinity with IL-6. 2.3 Screening of potential target sites by machine algorithms We employed four machine learning algorithms to identify potential genes. Univariate COX analysis was conducted on 16 core genes using survival time as the indicator. A total of 14 genes had P < 0.05, including HSP90AA1, IL-2, ANXA5, ESR1, APP, ALB, IL-6, AKT1, EGFR, CCL5, KIT, PPARG, IGF1, and IGF1R (Fig. 3 A). Subsequently, COX LASSO analysis further screened out 12 genes: HSP90AA1, IL-2, ANXA5, ESR1, APP, ALB, IL-6, EGFR, CCL5, KIT, PPARG, and IGF1 (Figs. 3 B-C). Random forest with feature selection was used to determine the optimal number of classification trees, error rate, and relationships among the 16 genes. Six predictive genes were selected based on a mean decrease Gini score greater than 5: IL-6, PPARG, IGF1R, CCL5, KIT, and ALB (Figs. 3 D-E). SVM-RFE analysis identified three predictive genes: IL-6, PPARG, and IGF1 (Fig. 3 F). 2.4 Survival Analysis and Gene Set Enrichment Analysis (GSEA) We identified IL6 and PPARG as two potential target sites by intersecting the results from three distinct machine learning algorithms(Fig. 4 A).We classified all samples into high-risk and low-risk groups based on the average expression levels of IL-6 and PPARG, and subsequently conducted Kaplan-Meier (K-M) survival analysis. The results showed that the expression level of IL-6 was significantly correlated with patient survival (p < 0.05), whereas PPARG exhibited no significant association with patient survival (Fig. 4 B). Subsequently, we performed Gene Set Enrichment Analysis (GSEA) on the high-risk and low-risk groups. In the HALLMARK gene set, the high-risk group was predominantly enriched in pathways such as the IL6-JAK-STAT signaling pathway, inflammatory response, allograft rejection, and angiogenesis (Fig. 4 C-D and Supplementary Table 2). Simultaneously, we analyzed the enrichment of the high and low-risk groups in the c7 immune-related gene set. We found that 845 gene sets were enriched in the high-risk group, while only 12 gene sets were enriched in the low-risk group (Fig. 4 E-F and Supplementary Table 3). These findings suggest that IL-6 may serve as a potential indicator influencing the development and prognosis of head and neck squamous cell carcinoma (HNSCC). 2.5 Validation in the GEO Database and Analysis of Immune Cell Infiltration Firstly, we performed principal component analysis (PCA) on the GSE30784 dataset. The results, as shown in (Fig. 5 A), indicated that HNSCC samples and normal samples originated from distinct populations, suggesting the reliability of the sample sources. Subsequently, we evaluated the expression levels of IL-6 in this dataset. The results revealed that IL-6 expression was significantly elevated in HNSCC tissues compared to normal tissues (Fig. 5 B). The ROC curve analysis indicated that the AUC value for IL-6 as a diagnostic marker for HNSCC was 0.868 (Fig. 5 C). We performed a comprehensive analysis of 22 types of immune infiltrating cells in HNSCC samples. The bar chart illustrates the relative proportions of these immune cell populations(Fig. 5 D), while the heatmap elucidates the correlations among them(Fig. 5 E).Next, we divided the HNSCC samples into high-IL-6 and low-IL-6 expression groups based on the average expression level of IL-6. Using the CIBERSORT algorithm, we analyzed the differences in the infiltration of 22 types of immune cells between the two groups. The results showed that the high-IL-6 expression group had a higher proportion of resting CD4 memory T cells, regulatory T cells (Tregs), monocytes, M0 macrophages, M2 macrophages, and eosinophils (Fig. 5 F). These findings suggest that the occurrence and progression of HNSCC may be associated with immune cell infiltration. 2.6 Rhaponticin glycoside influences the phenotypic manifestations of HNSCC cells and suppresses the protein expression of the IL-6/STAT3 pathway. To further substantiate the inhibitory effect of rhaponticin on HNSCC cells, a series of experimental validations were conducted. We cultivated two cell lines, CAL27 and SCC9. Firstly, the CCK8 assay was employed to analyze the cytotoxic effects of different concentrations of rhaponticin (0, 10, 25, 50, 100, 200, 300, 400 µM) on CAL27, SCC9, and HOK cells under hypoxic conditions for 24 hours. As shown in Fig. 6 A, the cell survival rate decreased with increasing concentration. The IC50 values were 46.09 µM for CAL27 cells and 54.79 µM for SCC9 cells. In subsequent experiments, two concentrations, 25 µM and 50 µM, were selected for further investigation. We initially examined the expression of EMT-related proteins in HNSCC cells treated with varying concentrations of rhaponticin. It was found that as the drug concentration increased, the protein levels of N-cadherin decreased while those of E-cadherin increased (Fig. 6 B). Subsequently, the scratch wound healing assay and Transwell migration/invasion assays were used to evaluate the impact of different concentrations of rhaponticin on the migration and invasion of CAL27 and SCC9 cells under hypoxic conditions. Based on the IC50 values, cells were divided into three groups with rhaponticin concentrations of 0, 25, and 50 µM. The results indicated that the scratch widths were similar at 0 h across all groups. After 24 hours of culture at 37°C and 1% O2 (hypoxia), the migration ability of the cells was significantly inhibited with increasing concentration (Figs. 6 C-D). The colony formation assay was utilized to validate the proliferation capacity of HNSCC cells. With increasing concentration, the proliferation ability of HNSCC cells was markedly suppressed (Fig. 6 E). In the Transwell invasion assay, the invasion ability of the cells also declined significantly as the concentration of rhaponticin increased (Fig. 6 F) 2.7 Rhaponticin suppresses the activation of the IL-6/STAT3 pathway. Similarly, we conducted immunofluorescence experiments on SCC9 and CAL27 cells. The results indicated that as the concentration of rhaponticin increased, the expression level of IL-6 decreased (Fig. 7 A-B). We further validated the effect of rhaponticin on the expression of IL-6/STAT3 axis-related proteins in HNSCC cells using Western blotting. As shown in Fig. 7 C, compared to the control group, the expression levels of proteins involved in the IL-6/STAT3 pathway were significantly reduced in cells treated with rhaponticin. 3. Discussion To explore the potential targets of Rhaponticin in HNSCC, we collected 7,175 HNSCC-related genes and 364 Rhaponticin-targeted genes, resulting in 262 intersection genes. GO and KEGG enrichment analyses revealed that these intersection genes were predominantly enriched in tumor-related signaling pathways, including lipid metabolism and atherosclerosis, prostate cancer, hepatitis B, T cell receptor signaling, and JAK-STAT signaling. These findings suggest that using network pharmacology to identify potential targets of Rhaponticin against HNSCC is a reasonable approach. Topological analysis was employed to screen 40 core genes from the intersection genes. Subsequently, four machine learning algorithms were utilized to identify potential target sites. We used the univariate COX regression model to analyze the influence of these genes on patient survival time. Additionally, SVM-RFE (Support Vector Machine Recursive Feature Elimination) was applied as a supervised learning method aimed at maximizing the margin between data points of different categories. [ 20 ] The Random Forest (RF) model is composed of multiple decision trees, with each tree making independent predictions. This ensemble approach reduces the risk of overfitting. [ 21 ] The LASSO regression algorithm enhances the interpretability and predictive accuracy of the model by identifying the combination of independent variables that have the most significant explanatory power for the dependent variable. [ 22 ] Ultimately, we identified IL-6 and PPARG as candidate targets. Survival analyses of these two genes revealed that IL-6 was significantly associated with patient survival. Consequently, we confirmed IL-6 as a potential therapeutic target. Verification using the GEO database further supported this conclusion. Research indicates that IL-6 and related cytokines promote immune cell infiltration and inflammatory responses in the tumor microenvironment. [ 23 ][ 24 ] These cytokines further activate a variety of signaling pathways, including the JAK/STAT3 and TNF signaling pathways, playing a highly significant role in tumor recurrence and metastasis. [ 25 ][ 26 ] We divided patients with head and neck squamous cell carcinoma (HNSCC) from the TCGA database into high and low IL-6 expression groups based on the average IL-6 expression level, and analyzed the differences in immune cell infiltration between the two groups. The results revealed that the infiltration of various immune cells, including resting memory CD4 + T cells, regulatory T cells (Tregs), monocytes, macrophages, M2 macrophages, and eosinophils, was significantly augmented in the high IL-6 expression group. This suggests that rhaponticin may target IL-6 to influence the tumor microenvironment of HNSCC tissues. To further explore the signaling pathways through which rhaponticin acts, we performed GSEA enrichment analysis. In the HALLMARK collection, the high IL-6 expression group was predominantly enriched in pathways such as the IL-6/JAK/STAT signaling pathway, inflammatory response pathways, and apoptosis. In the C7 collection, the high IL-6 expression group was enriched in 846 immune-related pathways, while the low IL-6 expression group was enriched in only 11 immune-related pathways. A considerable body of evidence indicates that the IL-6/STAT3 axis plays a significant role in many cancers. Studies have demonstrated that IL-6 secreted by M1-like tumor-associated macrophages (TAMs) activates STAT3, thereby promoting the genesis and progression of oral squamous cell carcinoma. [ 27 ] The upregulation of IL-6 autocrine by RAB3C in colon cancer cells and the subsequent activation of the JAK2/STAT3 signaling pathway play a crucial role in the invasion and metastasis of colon cancer. [ 28 ] Consequently, we hypothesized that rhaponticin might exert an anti-HNSCC effect via the IL-6/STAT3 axis. We validated this hypothesis through in vitro experiments. CCK8 and colony formation assays demonstrated that rhaponticin at a concentration of 50 µM significantly inhibited the proliferation of HNSCC cells while exhibiting relatively low toxicity to human oral mucosal keratinocytes (HOK). Wound healing and Transwell assays indicated that rhaponticin could suppress the migration and invasion capabilities of HNSCC cells. Additionally, the upregulation of E-cadherin and downregulation of N-cadherin, both EMT-related proteins, further confirmed that rhaponticin inhibits the migration and invasion of HNSCC cells. Western blotting results showed that rhaponticin significantly repressed the expression of IL-6 and STAT3, and immunofluorescence analysis revealed reduced IL-6 expression in Cal27 and SCC9 cells following rhaponticin treatment. However, our study has several limitations. First, we focused primarily on validated drug and disease targets, potentially overlooking unverified or undocumented targets. Second, we did not conduct in vivo experiments to validate the therapeutic efficacy of Rhaponticin. Therefore, further in vivo and in vitro studies are necessary to comprehensively elucidate the molecular mechanisms underlying Rhaponticin's effects on HNSCC. 4. Conclusion Our study demonstrates that Rhaponticin may inhibit the proliferation, invasion, and migration of HNSCC cells by modulating the IL-6/STAT3 axis. Furthermore, our findings suggest that IL-6 expression in HNSCC is associated with multiple immune cell types, providing new insights for the clinical application of traditional Chinese medicine in HNSCC treatment. Declarations Funding This work was supported by [National Natural Science Foundation of China and Natural Science Foundation of Jiangxi Province] (Grant numbers [82460954] and [20232BAB206153]). Ethics declarations Ethical approcal This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent Not Applicable Conflict of interest The authors declare no competing interests. Data availability The predicted targetsof rhaponticin during the current study are available in the PharmMapper and SEA repository, [http://www.lilab-ecust.cn/pharmmapper and http://sea.bkslab.org]. Disease-related target genes during the current study are available in the GeneCards database using the keyword “head and neck squamous cell carcinoma” [http://www.genecards.org]. Gene expression profiles and clinical information for HNSCC samples and normal samples were acquired from the TCGA database [https://portal.gdc.cancer.gov]. The gene expression profile of the HNSCC dataset GSE30784 was acquired from GEO [https://www.ncbi.nlm.nih.gov/geo/]. Author Contribution Hongcheng Wei:Methodology,Writing original draft,Formal analysis,Conceptualization,Validation.Shanshan Wang:Formal analysis,Conceptualization,Validation.Jiayue Wan:Methodology,Validation.Sicheng Li:Conceptualization,Validation.Wei Wang:Methodology.Jiajun Zhu:Validation.Lin Jiang:Formal analysis.Yisen Shao:Conceptualization,Methodology,Funding acquisition,Writing-review&editing,Supervision,Data curation,Project,administration.Yuan WU:Conceptualization,Methodology,Funding acquisition,Writing-review&editing,Supervision,Data curation,Project,administration. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. PMID: 33538338. Johnson DE, Burtness B, Leemans CR, Lui VWY, Bauman JE, Grandis JR. Head and neck squamous cell carcinoma. Nat Rev Dis Primers. 2020 Nov 26;6(1):92. doi: 10.1038/s41572-020-00224-3. Erratum in: Nat Rev Dis Primers. 2023 Jan 19;9(1):4. doi: 10.1038/s41572-023-00418-5. PMID: 33243986; PMCID: PMC7944998. Hashibe M, Brennan P, Benhamou S, Castellsague X, Chen C, Curado MP, Dal Maso L, Daudt AW, Fabianova E, Fernandez L, Wünsch-Filho V, Franceschi S, Hayes RB, Herrero R, Koifman S, La Vecchia C, Lazarus P, Levi F, Mates D, Matos E, Menezes A, Muscat J, Eluf-Neto J, Olshan AF, Rudnai P, Schwartz SM, Smith E, Sturgis EM, Szeszenia-Dabrowska N, Talamini R, Wei Q, Winn DM, Zaridze D, Zatonski W, Zhang ZF, Berthiller J, Boffetta P. Alcohol drinking in never users of tobacco, cigarette smoking in never drinkers, and the risk of head and neck cancer: pooled analysis in the International Head and Neck Cancer Epidemiology Consortium. J Natl Cancer Inst. 2007 May 16;99(10):777-89. doi: 10.1093/jnci/djk179. Erratum in: J Natl Cancer Inst. 2008 Feb 6;100(3):225. Fernandez, Leticia [added]. PMID: 17505073. Hunter KD, Parkinson EK, Harrison PR. Profiling early head and neck cancer. Nat Rev Cancer. 2005 Feb;5(2):127-35. doi: 10.1038/nrc1549. PMID: 15685196. Magnes T, Wagner S, Kiem D, Weiss L, Rinnerthaler G, Greil R, Melchardt T. Prognostic and Predictive Factors in Advanced Head and Neck Squamous Cell Carcinoma. Int J Mol Sci. 2021 May 7;22(9):4981. doi: 10.3390/ijms22094981. PMID: 34067112; PMCID: PMC8125786.。 Bhat AA, Yousuf P, Wani NA, Rizwan A, Chauhan SS, Siddiqi MA, Bedognetti D, El-Rifai W, Frenneaux MP, Batra SK, Haris M, Macha MA. Tumor microenvironment: an evil nexus promoting aggressive head and neck squamous cell carcinoma and avenue for targeted therapy. Signal Transduct Target Ther. 2021 Jan 12;6(1):12. doi: 10.1038/s41392-020-00419-w. Erratum in: Signal Transduct Target Ther. 2021 Feb 26;6(1):93. doi: 10.1038/s41392-021-00503-9. PMID: 33436555; PMCID: PMC7804459. Tang E, Lahmi L, Meillan N, Pietta G, Albert S, Maingon P. Treatment Strategy for Distant Synchronous Metastatic Head and Neck Squamous Cell Carcinoma. Curr Oncol Rep. 2019 Nov 14;21(11):102. doi: 10.1007/s11912-019-0856-5. PMID: 31728650. Gong Y, Bao L, Xu T, Yi X, Chen J, Wang S, Pan Z, Huang P, Ge M. The tumor ecosystem in head and neck squamous cell carcinoma and advances in ecotherapy. Mol Cancer. 2023 Apr 6;22(1):68. doi: 10.1186/s12943-023-01769-z. PMID: 37024932; PMCID: PMC10077663. Luo H, Vong CT, Chen H, Gao Y, Lyu P, Qiu L, Zhao M, Liu Q, Cheng Z, Zou J, Yao P, Gao C, Wei J, Ung COL, Wang S, Zhong Z, Wang Y. Naturally occurring anti-cancer compounds: shining from Chinese herbal medicine. Chin Med. 2019 Nov 6;14:48. doi: 10.1186/s13020-019-0270-9. PMID: 31719837; PMCID: PMC6836491. Wang Y, Zhang Q, Chen Y, Liang CL, Liu H, Qiu F, Dai Z. Antitumor effects of immunity-enhancing traditional Chinese medicine. Biomed Pharmacother. 2020 Jan;121:109570. doi: 10.1016/j.biopha.2019.109570. Epub 2019 Nov 9. PMID: 31710893. Xiang H, Zuo J, Guo F, Dong D. What we already know about rhubarb: a comprehensive review. Chin Med. 2020 Aug 26;15:88. doi: 10.1186/s13020-020-00370-6. PMID: 32863857; PMCID: PMC7448319. Chen D, Liu JR, Cheng Y, Cheng H, He P, Sun Y. Metabolism of Rhaponticin and Activities of its Metabolite, Rhapontigenin: A Review. Curr Med Chem. 2020;27(19):3168-3186. doi: 10.2174/0929867326666190121143252. PMID: 30666906. Liudvytska O, Bandyszewska M, Skirecki T, Krzyżanowska-Kowalczyk J, Kowalczyk M, Kolodziejczyk-Czepas J. Anti-inflammatory and antioxidant actions of extracts from Rheum rhaponticum and Rheum rhabarbarum in human blood plasma and cells in vitro. Biomed Pharmacother. 2023 Sep;165:115111. doi: 10.1016/j.biopha.2023.115111. Epub 2023 Jul 6. PMID: 37421780. Li Y, Zhang Y, Tang J. Rhaponticin suppresses the stemness phenotype of gastric cancer stem-like cells CD133+/CD166 + by inhibiting programmed death-ligand 1. BMC Gastroenterol. 2024 Nov 21;24(1):423. doi: 10.1186/s12876-024-03512-4. PMID: 39573998; PMCID: PMC11583647. Kim A, Ma JY. Piceatannol-3-O-β-D-glucopyranoside (PG) exhibits in vitro anti-metastatic and anti-angiogenic activities in HT1080 malignant fibrosarcoma cells. Phytomedicine. 2019 Apr;57:95-104. doi: 10.1016/j.phymed.2018.12.017. Epub 2018 Dec 11. PMID: 30668328. Hibasami H, Takagi K, Ishii T, Tsujikawa M, Imai N, Honda I. Induction of apoptosis by rhapontin having stilbene moiety, a component of rhubarb (Rheum officinale Baillon) in human stomach cancer KATO III cells. Oncol Rep. 2007 Aug;18(2):347-51. PMID: 17611655. Mickymaray S, Alfaiz FA, Paramasivam A, Veeraraghavan VP, Periadurai ND, Surapaneni KM, Niu G. Rhaponticin suppresses osteosarcoma through the inhibition of PI3K-Akt-mTOR pathway. Saudi J Biol Sci. 2021 Jul;28(7):3641-3649. doi: 10.1016/j.sjbs.2021.05.006. Epub 2021 May 8. PMID: 34220214; PMCID: PMC8241634. Wang X, Priya Veeraraghavan V, Krishna Mohan S, Lv F. Anticancer and immunomodulatory effect of rhaponticin on Benzo(a)Pyrene-induced lung carcinogenesis and induction of apoptosis in A549 cells. Saudi J Biol Sci. 2021 Aug;28(8):4522-4531. doi: 10.1016/j.sjbs.2021.04.052. Epub 2021 Apr 24. PMID: 34354438; PMCID: PMC8324936. Kim A, Ma JY. Rhaponticin decreases the metastatic and angiogenic abilities of cancer cells via suppression of the HIF‑1α pathway. Int J Oncol. 2018 Sep;53(3):1160-1170. doi: 10.3892/ijo.2018.4479. Epub 2018 Jul 11. PMID: 30015877; PMCID: PMC6065401. Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics Proteomics. 2018 Jan-Feb;15(1):41-51. doi: 10.21873/cgp.20063. PMID: 29275361; PMCID: PMC5822181. Hu J, Szymczak S. A review on longitudinal data analysis with random forest. Brief Bioinform. 2023 Mar 19;24(2):bbad002. doi: 10.1093/bib/bbad002. PMID: 36653905; PMCID: PMC10025446. Fernández-Delgado M, Sirsat MS, Cernadas E, Alawadi S, Barro S, Febrero-Bande M. An extensive experimental survey of regression methods. Neural Netw. Rhaponticin2019 Mar;111:11-34. doi: 10.1016/j.neunet.2018.12.010. Epub 2018 Dec 21. PMID: 30654138. Wang G, Zhang M, Cheng M, Wang X, Li K, Chen J, Chen Z, Chen S, Chen J, Xiong G, Xu X, Wang C, Chen D. Tumor microenvironment in head and neck squamous cell carcinoma: Functions and regulatory mechanisms. Cancer Lett. 2021 Jun 1;507:55-69. doi: 10.1016/j.canlet.2021.03.009. Epub 2021 Mar 17. PMID: 33741424. Yang J, Xie K, Li C. Immune-related genes have prognostic significance in head and neck squamous cell carcinoma. Life Sci. 2020 Sep 1;256:117906. doi: 10.1016/j.lfs.2020.117906. Epub 2020 Jun 3. PMID: 32504750. Mei Z, Huang J, Qiao B, Lam AK. Immune checkpoint pathways in immunotherapy for head and neck squamous cell carcinoma. Int J Oral Sci. 2020 May 28;12(1):16. doi: 10.1038/s41368-020-0084-8. PMID: 32461587; PMCID: PMC7253444. Ralli M, Grasso M, Gilardi A, Ceccanti M, Messina MP, Tirassa P, Fiore M, Altissimi G, A Salzano F, De Vincentiis M, Greco A. The role of cytokines in head and neck squamous cell carcinoma: A review. Clin Ter. 2020 May-Jun;171(3):e268-e274. doi: 10.7417/CT.2020.2225. PMID: 32323717. You Y, Tian Z, Du Z, Wu K, Xu G, Dai M, Wang Y, Xiao M. M1-like tumor-associated macrophages cascade a mesenchymal/stem-like phenotype of oral squamous cell carcinoma via the IL6/Stat3/THBS1 feedback loop. J Exp Clin Cancer Res. 2022 Jan 6;41(1):10. doi: 10.1186/s13046-021-02222-z. PMID: 34991668; PMCID: PMC8734049. Chang YC, Su CY, Chen MH, Chen WS, Chen CL, Hsiao M. Secretory RAB GTPase 3C modulates IL6-STAT3 pathway to promote colon cancer metastasis and is associated with poor prognosis. Mol Cancer. 2017 Aug 7;16(1):135. doi: 10.1186/s12943-017-0687-7. PMID: 28784136; PMCID: PMC5547507. Additional Declarations No competing interests reported. Supplementary Files Table1.xls Table2.xls Table3.xls GelsandBlotsimages.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5917121","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411514747,"identity":"ecdb228b-3307-42d4-9f60-c603f552cfbc","order_by":0,"name":"Hongcheng Wei","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Hongcheng","middleName":"","lastName":"Wei","suffix":""},{"id":411514748,"identity":"5dc33f35-88e5-4ac8-ab33-34b62eaf0384","order_by":1,"name":"Shanshan Wang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Wang","suffix":""},{"id":411514749,"identity":"bd7615f6-9a0b-43b3-8470-1775dd013f79","order_by":2,"name":"Jiayue Wan","email":"","orcid":"","institution":"JiangXi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiayue","middleName":"","lastName":"Wan","suffix":""},{"id":411514750,"identity":"ed826318-edfa-4220-937a-195a9efe3239","order_by":3,"name":"Sicheng Li","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Sicheng","middleName":"","lastName":"Li","suffix":""},{"id":411514751,"identity":"649dbdec-342b-4dbf-9bea-5d262f53d010","order_by":4,"name":"Wei Wang","email":"","orcid":"","institution":"Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":411514752,"identity":"9d0962d4-33f1-4a57-a35d-e33b96e6865a","order_by":5,"name":"Jiajun Zhu","email":"","orcid":"","institution":"Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiajun","middleName":"","lastName":"Zhu","suffix":""},{"id":411514753,"identity":"12781247-1440-430c-9e1f-b623b739b952","order_by":6,"name":"Lin Jiang","email":"","orcid":"","institution":"Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Jiang","suffix":""},{"id":411514754,"identity":"a32ae67f-4132-46ad-976d-a90ccc1b32f0","order_by":7,"name":"Yisen Shao","email":"","orcid":"","institution":"Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yisen","middleName":"","lastName":"Shao","suffix":""},{"id":411514755,"identity":"691259c2-4192-4a74-a7a7-6fa66e0144a0","order_by":8,"name":"Yuan Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYPCC/3Js7O0HHyRU1BCthdmYj+dMssGDM8eI15I4TyLBTPJhCzNhtQY3cswkfu5gM2ZjSEirSGxgY+Bv704gqEWy9wyPHBvDwWM3EnfIMEicObsBrxYzkC28bRLGbIwNaTcSz7AxGEjkEtYi+bfNILGNmcGsAEQSpUWaty0hsY2NwYyBKC32Z54VW8u2HTBm4+FJlkg4c4yHoF8k25M33nzbdkBOfv7zgx9/VNTI8bf34tfCwMBhgMLlIaAcBNgfEKFoFIyCUTAKRjQAAN4PR4o243uPAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-01-28 08:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5917121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5917121/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75705919,"identity":"979a46d6-f331-4076-a155-64e9f189bc11","added_by":"auto","created_at":"2025-02-07 10:21:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145419,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Rhein in Head and Neck Squamous Cell Carcinoma (HNSCC) Research\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/ccc63d971d2ae8a171ed82ed.png"},{"id":75707072,"identity":"f640eaa6-82ca-45dc-96fb-99062f738b83","added_by":"auto","created_at":"2025-02-07 10:29:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":649516,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment of PPI protein interaction diagrams and analysis of compound-protein binding stability. A: The Venn diagram shows 262 common targets between rhaponticin and HNSCC; B: A total of 364 rhaponticin-related targets are visualized as blue circular rectangles; C: Construction of the protein-protein interaction (PPI) network; D: Screening of 40 core targets using topological methods; E: GO enrichment analysis; F: KEGG enrichment analysis; G: Molecular docking technology is employed to assess the binding stability of rhaponticin with IL-6.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/5ec6ba2ebb63b8aa4cd4bd85.png"},{"id":75705916,"identity":"34e661f4-8b68-495a-af9e-49fdd8065085","added_by":"auto","created_at":"2025-02-07 10:21:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163636,"visible":true,"origin":"","legend":"\u003cp\u003ePotential diagnostic markers were identified using machine learning algorithms. A: Univariate COX regression was employed to analyze the relationship between gene expression levels and patient survival; B, C: The COX LASSO algorithm was utilized to screen potential diagnostic markers further; D, E: Diagnostic markers were selected based on the Random Forest (RF) algorithm; F: Diagnostic markers were identified using Support Vector Machine with Recursive Feature Elimination (SVM-RFE).\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/6729d75155c3df03faf13314.png"},{"id":75705930,"identity":"7b10f992-8112-491d-8121-9c2243d7e331","added_by":"auto","created_at":"2025-02-07 10:21:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":340890,"visible":true,"origin":"","legend":"\u003cp\u003eStudy the relationship with patient survival and identify associated pathways. A: A Venn diagram shows the intersection of potential target sites obtained from multiple algorithms; B: Survival analysis demonstrates the correlation between gene expression and patient survival; C: HALLMARK analysis reveals all enriched pathways, with a focus on the IL6-JAK-STAT signaling pathway; D: Some immune-related pathways enriched in the high-risk group within the c7 gene set; E: Immune-related pathways enriched in the low-risk group within the c7 gene set.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/1c43a48162446576fee64a8b.png"},{"id":75705928,"identity":"7b97932b-3b6e-44ef-b760-2d3e675db305","added_by":"auto","created_at":"2025-02-07 10:21:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":270245,"visible":true,"origin":"","legend":"\u003cp\u003eFurther verification in the GEO database and CIBERSORT immune infiltration analysis: A. Principal Component Analysis (PCA) of the GSE30784 dataset; B. Comparison of IL-6 expression between normal and HNSCC samples, ****P \u0026lt; 0.0001; C. The ROC curve indicates that in the GSE30784 dataset, the AUC value is 0.868 (95% CI: 0.825 - 0.914); D. A bar graph shows the proportions of 22 types of immune cells in HNSCC samples; E. A heatmap illustrates the correlations among the 22 types of immune cells. The numbers in the small squares represent P values, while the depth of the color reflects the correlation coefficient between cell types; F. Violin plots depict the differences in expression levels of the 22 types of immune cells between the high-IL-6 expression group and the low-IL-6 expression group.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/99e0a674f110addc4214cf66.png"},{"id":75705936,"identity":"34255e20-5298-441e-afb9-74a4b97b150b","added_by":"auto","created_at":"2025-02-07 10:21:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":575478,"visible":true,"origin":"","legend":"\u003cp\u003eThe Impact of Rhaponticin Glycoside on the Phenotype of HNSCC Cells: A. Selection of Safe and Effective Concentrations of Rhaponticin Glycoside; B. Expression of EMT-Related Proteins; C, D. Effects of Rhaponticin Glycoside on the Migration Capacity of HNSCC Cells; E. Colony Formation Assay Demonstrating Inhibition of HNSCC Cell Proliferation by Rhaponticin Glycoside; F. Transwell Assay Measuring the Effects of Rhaponticin Glycoside on the Invasion Capacity of HNSCC Cells. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/6ac6191ca1b63b20d1eb2b77.png"},{"id":75705924,"identity":"287c315d-381f-4c92-a855-f104f5f400c5","added_by":"auto","created_at":"2025-02-07 10:21:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":387278,"visible":true,"origin":"","legend":"\u003cp\u003eRhaponticin suppressed the expression of IL-6/STAT3 pathway proteins. (A) and (B) Immunofluorescence was used to assess the effect of different concentrations of rhaponticin on IL-6 expression; (C) Western blotting analysis was employed to evaluate the impact of varying concentrations of rhaponticin on the expression of IL-6/STAT3 pathway-related proteins.*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Picture7.png","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/5787acf9eed9e915b1833e3b.png"},{"id":78494468,"identity":"a46d5c18-b491-4f6f-a9e8-6c75808d9e9b","added_by":"auto","created_at":"2025-03-14 03:46:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3356541,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/3e4168a9-3bba-48a5-825b-c8c0f648fc5e.pdf"},{"id":75707071,"identity":"1325e6ae-6dde-4028-ae82-012d291db4f5","added_by":"auto","created_at":"2025-02-07 10:29:24","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21504,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xls","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/45b46f63ca743298d4266ba3.xls"},{"id":75705915,"identity":"489de5cc-3d58-4af8-8c0f-1ee1620e0409","added_by":"auto","created_at":"2025-02-07 10:21:24","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47616,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xls","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/a66064e81b30ae3c91b94436.xls"},{"id":75707075,"identity":"a2e494db-427f-400c-a99d-9d36e4b7b944","added_by":"auto","created_at":"2025-02-07 10:29:24","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1066496,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.xls","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/a18a2e508680c691a919dfb2.xls"},{"id":75705951,"identity":"2a52187c-d090-4b0f-a67c-76872ae6b036","added_by":"auto","created_at":"2025-02-07 10:21:32","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":123263461,"visible":true,"origin":"","legend":"","description":"","filename":"GelsandBlotsimages.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5917121/v1/9a66f42aca638ea661e4b0a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rhaponticin inhibits the proliferation, migration, and invasion of head and neck squamous cell carcinoma (HNSCC) cells through modulation of the IL6/STAT3 signaling pathway","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHead and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer globally. In 2022, approximately 950,000 new cases of HNSCC were reported worldwide, resulting in an estimated 480,000 deaths. \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e The incidence rate of HNSCC is consistently increasing and is projected to rise by 30% by 2030. \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e Currently, the primary etiological factors of HNSCC include the stimulatory effects of tobacco and alcohol, as well as the carcinogenic actions of viruses. \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003eA minority of patients exhibit precancerous lesions, such as mucosal erythema and leukoplakia, in the early stages of HNSCC. However, most patients in advanced stages have no history of precancerous lesions. This poses significant challenges for the detection and diagnosis of HNSCC. \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003eHNSCC is highly susceptible to recurrence and metastasis, leading to a relatively poor prognosis for patients. The median survival time is approximately one year. \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003eSurgery combined with radiotherapy and chemotherapy remains the primary therapeutic modality for patients with advanced HNSCC. However, these conventional treatments can cause significant adverse effects, including xerostomia, loss of taste, dysphagia, and aspiration pneumonia, in post-surgical patients.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003eThese adverse events highlight the need to explore new combined treatment modalities with fewer side effects and enhanced therapeutic efficacy. Traditional Chinese medicine (TCM) has been widely used as an effective adjunct in cancer therapy. Many active compounds in Chinese herbal medicines can directly or indirectly modulate the tumor microenvironment. \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003eHowever, there are currently few reports on the use of Chinese herbal medicine for treating HNSCC, and the specific molecular mechanisms remain unclear. Further research is necessary to develop more effective treatment strategies and approaches for clinical practice.\u003c/p\u003e \u003cp\u003eRhubarb is a herbaceous plant widely distributed in China and South Korea. According to Traditional Chinese Medicine (TCM), rhubarb clears heat, detoxifies, and promotes purgation. \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e Rhaponticin, a stilbene compound extracted from the rhizomes of rhubarb, exhibits anti-tumor, antibacterial, and anti-inflammatory properties. \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e Its anti-cancer effects have been verified in lung, gastric, and prostate cancers. Rhaponticin exerts its functions primarily through inducing apoptosis of cancer cells, inhibiting cancer cell invasion, metastasis, and angiogenesis, as well as suppressing the cell cycle. \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003eA study using RT-PCR revealed that rhaponticin downregulates proteins involved in the PI3K-Akt-mTOR pathway, induces apoptosis of osteosarcoma cells, and reduces intercellular adhesion. \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003eRhaponticin increases reactive oxygen species (ROS) levels in lung cancer cells, leading to alterations in cell cycle-related proteins and subsequently inducing apoptosis. Additionally, it upregulates serum levels of immunoglobulins such as IgA and IgM in mice. \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003eRhaponticin inhibits the invasion and metastasis of human breast adenocarcinoma cells, and suppressing anti-angiogenic factor production exerts a similar effect. \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003eIn summary, we hypothesize that rhaponticin may effectively suppress the proliferation, invasion, and migration of HNSCC cells.\u003c/p\u003e \u003cp\u003eIn this study, we employed a network pharmacology approach to identify the targets of rhaponticin in HNSCC and screened potential targets by mining data from the TCGA and GEO databases. Based on these potential targets, we elucidated the effects of rhaponticin on the proliferation, invasion, and migration phenotypes of head and neck squamous cell carcinoma (HNSCC). This research provides a foundation for the clinical development of effective traditional Chinese medicine (TCM) agents for HNSCC treatment.\u003c/p\u003e"},{"header":"1. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Data Acquisition\u003c/h2\u003e \u003cp\u003eThe predicted targets of rhaponticin were retrieved from PharmMapper (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lilab-ecust.cn/pharmmapper\u003c/span\u003e\u003cspan address=\"http://www.lilab-ecust.cn/pharmmapper\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and SEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sea.bkslab.org\u003c/span\u003e\u003cspan address=\"http://sea.bkslab.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Disease-related target genes were obtained by searching the GeneCards database using the keyword \"head and neck squamous cell carcinoma\". Gene expression profiles and clinical information for HNSCC samples (n\u0026thinsp;=\u0026thinsp;515) and normal samples (n\u0026thinsp;=\u0026thinsp;44) were acquired from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The gene expression profile of the HNSCC dataset GSE30784 from GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used as a validation set.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2 Protein-Protein Interaction Network (PPI) and Target Screening\u003c/h3\u003e\n\u003cp\u003eThe intersection targets of rhaponticin and HNSCC were imported into the STRING database, with a minimum interaction score threshold set at \u0026gt;\u0026thinsp;0.4, to generate a protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape software was used to analyze and identify the core targets within this PPI network.\u003c/p\u003e\n\u003ch3\u003e1.3 KEGG and GO Enrichment Analyses\u003c/h3\u003e\n\u003cp\u003eThe clusterProfiler package in R performed GO and KEGG enrichment analyses on the intersection gene target related to rhaponticin and HNSCC.\u003c/p\u003e\n\u003ch3\u003e1.4 Molecular Docking\u003c/h3\u003e\n\u003cp\u003eThe 2D structure of rhaponticin was retrieved from the PubChem database and imported into Chem3D software for minimum energy optimization before export. The 3D protein structure of IL-6 was downloaded from the PDB database and imported into PyMOL to remove water and ligand molecules. Subsequently, AutoDock Tools 1.5.6 was used for hydrogen addition, charge calculation, and other preprocessing steps. Finally, molecular docking was performed using AutoDock Vina, and the results were visualized with PyMOL 2.6.\u003c/p\u003e\n\u003ch3\u003e1.5 Immune Infiltration Analysis\u003c/h3\u003e\n\u003cp\u003eWe categorized the TCGA data into normal and tumor groups and used the CIBERSORT package to evaluate the infiltration levels of 22 immune cell types.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.6 Univariate COX Regression Analysis Model\u003c/h2\u003e \u003cp\u003eWe extracted the expression levels of core target genes and survival information of tumor patients from the TCGA database, merged these datasets, and subsequently established a prognosis model using univariate Cox regression analysis. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, indicating that the gene expression influenced patient survival. Specifically, an HR\u0026thinsp;\u0026gt;\u0026thinsp;1 suggested that the gene was a risk factor for poor prognosis, while an HR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicated that the gene was a protective factor for a better prognosis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.7 Screening and Validation of Potential Functional Targets\u003c/h3\u003e\n\u003cp\u003eWe employed the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine - Recursive Feature Elimination (SVM-RFE) to analyze the core targets obtained from the initial screening. The randomForest package in R was used for random forest analysis, while the glmnet package was utilized for LASSO logistic regression, with the minimum lambda value selected as optimal. Genes with common characteristics identified by these three machine-learning algorithms were then selected for further investigation. Subsequently, the survival package in R was used to conduct a survival analysis on the selected diagnostic markers. To validate the utility of these markers, we utilized the GSE30784 dataset. Principal Component Analysis (PCA) was performed using the factoextra package in R, demonstrating significant differences between tumor and normal specimens. We then analyzed the expression differences of genes in normal and tumor tissues. We evaluatedtheir predictive capacity using Receiver Operating Characteristic (ROC) curves, calculating the Area Under the Curve (AUC) as a measure of algorithm performance.\u003c/p\u003e\n\u003ch3\u003e1.8 Gene Set Enrichment Analysis (GSEA)\u003c/h3\u003e\n\u003cp\u003eWe utilized R to perform GSEA analysis on the gene sets obtained from the TCGA database, aiming to gain a more intuitive understanding of the gene expression patterns in highly enriched pathways.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e1.9 Cell Culture\u003c/h2\u003e \u003cp\u003eThe human HNSCC-related cell lines CAL27, SCC9, and HOK were procured from [Wuhan Boyuan Science and Technology Group Co., Ltd]. They were cultivated in DMEM medium supplemented with 10% fetal bovine serum (FBS), penicillin (10,000 U/ml), and streptomycin (10 mg/ml). The cells were incubated in a humidified incubator at 37°C with 5% CO2. Cells in the logarithmic growth phase were selected for the experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e1.10 Cytotoxicity Test\u003c/h2\u003e \u003cp\u003eCAL27, SCC9, and HOK cells were each divided into eight groups. Each group was treated with rhaponticin at different final concentrations (0 µM, 10 µM, 25 µM, 50 µM, 100 µM, 200 µM, 300 µM, 400 µM) and cultured in a hypoxic environment (37°C, 1% O2). The OD values at 450 nm were measured using the CCK-8 assay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e1.11 Cell Proliferation Formation Assay\u003c/h2\u003e \u003cp\u003eCAL27 and SCC9 cells were incubated in a 5% CO2, 37°C constant temperature incubator. When the cells were in the logarithmic growth phase, they were digested with trypsin, collected after centrifugation, and evenly inoculated into 6-well plates at a density of 1×105 cells/well. Twenty-four hours later, the two types of cells were grouped (divided into 3 groups according to the drug concentration of 0 and 25, with 3 replicate wells in each group) for drug administration. The medium was changed every 3 days, and the cells were continuously cultured until the cell clone density was visible to the naked eye. Subsequently, the culture medium was removed, and the cells were washed with PBS. The cells were fixed with 4% paraformaldehyde, stained with crystal violet at room temperature for 30 minutes, washed with PBS, photographed, and the proliferation rates of the two types of cells were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e1.12 Cell Scratch Assay\u003c/h2\u003e \u003cp\u003eLogarithmic growth phase CAL27 and SCC9 cells were harvested, rinsed twice with PBS, and digested with 0.25% trypsin. After centrifugation, the cells were resuspended in a culture medium and seeded at a density of 100,000 cells per well in six-well plates. The plates were then incubated overnight in a humidified incubator at 37°C with 5% CO2. When the cells reached 100% confluence the next day, a scratch was made using a 200 µl pipette tip. The original medium was discarded, and the cells were rinsed twice with PBS to remove floating cells. Serum-free DMEM medium was added, and the cells were photographed under a 200x microscope. The plates were then incubated for 24 hours under the same conditions. After this period, the medium was again discarded, the cells were rinsed twice with PBS, fresh serum-free DMEM medium was added, and the cells were re-photographed under a 200x microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e1.13 Cell Invasion Assay\u003c/h2\u003e \u003cp\u003eMatrigel was diluted to 1 mg/ml, and 70 µl was evenly spread onto the upper chamber of the transwell insert (purchased from Corning). The coated chambers were incubated at 37°C with 5% CO2 for 3 hours. Excess Matrigel was aspirated, and 100 µl of DMEM medium was added for activation. Subsequently, CAL27 and SCC9 cells in the logarithmic growth phase were harvested, trypsinized, resuspended in serum-free DMEM medium, and adjusted to a concentration of 5.0 × 10^5 cells/mL. A 100 µl aliquot of the single-cell suspension was seeded into the upper chamber, while DMEM medium containing 10% fetal bovine serum (FBS) was added to the lower chamber. The cells were then incubated in a hypoxic environment (37°C, 1% O2) for 24 hours. After incubation, the cells were fixed with 0.25% glutaraldehyde for 20 minutes and stained with 0.1% crystal violet for 30 minutes. The Matrigel in the upper chamber was carefully removed, and the cells were counted in five random fields under a 200x microscope. The experiment was repeated three times, and the average value was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e1.14 Western Blot\u003c/h2\u003e \u003cp\u003eThe CAL27 and SCC9 cells from each group were collected. To each well, 100 µl of PMSF-containing cell lysis buffer with protease inhibitors was added, and the cells were lysed on ice for 30 minutes. The lysates were then transferred to 1.5 ml centrifuge tubes and centrifuged at 12,000 rpm for 5 minutes at 4°C, and the supernatants were collected. Electrophoresis was performed using 10% SDS-PAGE, and proteins were transferred to PVDF membranes via the semi-dry method. The membranes were blocked with 5% skim milk in TBST for 2 hours at room temperature, washed three times with PBST for 15 minutes each, and incubated overnight at 4°C with primary antibodies diluted to their working concentrations. The next day, the membranes were again washed three times with PBST for 15 minutes each, and incubated with secondary antibodies diluted to their working concentrations for 1 hour at room temperature.\u003c/p\u003e \u003cp\u003eProtein bands were visualized using a chemiluminescence imager, and quantitative analysis of the band intensities was conducted using ImageJ software.\u003c/p\u003e "},{"header":"2. Results","content":"\u003ch2\u003e2.1 Flow Diagram\u003c/h2\u003e\n\u003cp\u003eIn this study, we investigated the molecular mechanisms of rhaponticin, an extract from the rhizomes of Rheum officinale, on HNSCC cells using a combination of network pharmacology, multiple machine learning algorithms, and molecular docking. The detailed workflow is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch2\u003e2.2 Network Pharmacology Studies and Molecular Docking\u003c/h2\u003e\n\u003cp\u003eThe SMILES structure of rhaponticin was obtained from PubChem. Subsequently, 364 potential targets of rhaponticin were predicted using the PharmMapper (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.lilab-ecust.cn/pharmmapper/\u003c/span\u003e\u003c/span\u003e) and SEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sea.bkslab.org\u003c/span\u003e\u003c/span\u003e) databases. A total of 7,175 targets associated with head and neck squamous cell carcinoma (HNSCC) were retrieved from the GeneCards database. The intersection of these two sets of targets, totaling 262 common targets, was identified using the Venn package in R (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA), and a drug-target network was constructed (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The gene targets of rhaponticin for HNSCC treatment were imported into the STRING database, where isolated protein targets were hidden to generate the protein-protein interaction (PPI) network for rhaponticin in HNSCC treatment. The PPI data were downloaded in TSV format and imported into Cytoscape v.3.10.0. Topological properties of the network were analyzed using the NetworkAnalyzer tool in Cytoscape 3.10.0. Six topological parameters of the nodes were calculated using the cytoNCA plugin: betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), eigenvector centrality (EC), network centrality (NC), and local average connectivity (LAC). Nodes with parameter values greater than the median were selected as core targets, resulting in the identification of 40 key gene targets (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). KEGG and GO enrichment analyses were performed on the intersection targets, and the top 10 results with the smallest q-values were visualized (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). KEGG enrichment analysis indicated that the common targets of rhaponticin and HNSCC were primarily enriched in pathways related to lipid metabolism and atherosclerosis, prostate cancer, hepatitis B, T cell receptor signaling, and JAK-STAT signaling. GO enrichment analysis revealed that these targets were mainly involved in responses to steroid hormones, cellular responses to steroid hormone stimuli, membrane rafts, membrane microdomains, nuclear receptor activity, and estrogen response element binding. Molecular docking methods were employed to predict direct interactions between rhaponticin and its corresponding target genes. The three-dimensional binding mode of the active compound and its targets is shown in (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). As presented in Supplementary Table 1, rhaponticin exhibits high binding affinity with IL-6.\u003c/p\u003e\n\u003ch2\u003e2.3 Screening of potential target sites by machine algorithms\u003c/h2\u003e\n\u003cp\u003eWe employed four machine learning algorithms to identify potential genes. Univariate COX analysis was conducted on 16 core genes using survival time as the indicator. A total of 14 genes had P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, including HSP90AA1, IL-2, ANXA5, ESR1, APP, ALB, IL-6, AKT1, EGFR, CCL5, KIT, PPARG, IGF1, and IGF1R (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, COX LASSO analysis further screened out 12 genes: HSP90AA1, IL-2, ANXA5, ESR1, APP, ALB, IL-6, EGFR, CCL5, KIT, PPARG, and IGF1 (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB-C). Random forest with feature selection was used to determine the optimal number of classification trees, error rate, and relationships among the 16 genes. Six predictive genes were selected based on a mean decrease Gini score greater than 5: IL-6, PPARG, IGF1R, CCL5, KIT, and ALB (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). SVM-RFE analysis identified three predictive genes: IL-6, PPARG, and IGF1 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\n\u003ch2\u003e2.4 Survival Analysis and Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e\n\u003cp\u003eWe identified IL6 and PPARG as two potential target sites by intersecting the results from three distinct machine learning algorithms(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA).We classified all samples into high-risk and low-risk groups based on the average expression levels of IL-6 and PPARG, and subsequently conducted Kaplan-Meier (K-M) survival analysis. The results showed that the expression level of IL-6 was significantly correlated with patient survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas PPARG exhibited no significant association with patient survival (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Subsequently, we performed Gene Set Enrichment Analysis (GSEA) on the high-risk and low-risk groups. In the HALLMARK gene set, the high-risk group was predominantly enriched in pathways such as the IL6-JAK-STAT signaling pathway, inflammatory response, allograft rejection, and angiogenesis (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC-D and Supplementary Table 2). Simultaneously, we analyzed the enrichment of the high and low-risk groups in the c7 immune-related gene set. We found that 845 gene sets were enriched in the high-risk group, while only 12 gene sets were enriched in the low-risk group (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE-F and Supplementary Table 3). These findings suggest that IL-6 may serve as a potential indicator influencing the development and prognosis of head and neck squamous cell carcinoma (HNSCC).\u003c/p\u003e\n\u003ch2\u003e2.5 Validation in the GEO Database and Analysis of Immune Cell Infiltration\u003c/h2\u003e\n\u003cp\u003eFirstly, we performed principal component analysis (PCA) on the GSE30784 dataset. The results, as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), indicated that HNSCC samples and normal samples originated from distinct populations, suggesting the reliability of the sample sources. Subsequently, we evaluated the expression levels of IL-6 in this dataset. The results revealed that IL-6 expression was significantly elevated in HNSCC tissues compared to normal tissues (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). The ROC curve analysis indicated that the AUC value for IL-6 as a diagnostic marker for HNSCC was 0.868 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). We performed a comprehensive analysis of 22 types of immune infiltrating cells in HNSCC samples. The bar chart illustrates the relative proportions of these immune cell populations(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD), while the heatmap elucidates the correlations among them(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE).Next, we divided the HNSCC samples into high-IL-6 and low-IL-6 expression groups based on the average expression level of IL-6. Using the CIBERSORT algorithm, we analyzed the differences in the infiltration of 22 types of immune cells between the two groups. The results showed that the high-IL-6 expression group had a higher proportion of resting CD4 memory T cells, regulatory T cells (Tregs), monocytes, M0 macrophages, M2 macrophages, and eosinophils (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF). These findings suggest that the occurrence and progression of HNSCC may be associated with immune cell infiltration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Rhaponticin glycoside influences the phenotypic manifestations of HNSCC cells and suppresses the protein expression of the IL-6/STAT3 pathway.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further substantiate the inhibitory effect of rhaponticin on HNSCC cells, a series of experimental validations were conducted. We cultivated two cell lines, CAL27 and SCC9. Firstly, the CCK8 assay was employed to analyze the cytotoxic effects of different concentrations of rhaponticin (0, 10, 25, 50, 100, 200, 300, 400 \u0026micro;M) on CAL27, SCC9, and HOK cells under hypoxic conditions for 24 hours. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, the cell survival rate decreased with increasing concentration. The IC50 values were 46.09 \u0026micro;M for CAL27 cells and 54.79 \u0026micro;M for SCC9 cells. In subsequent experiments, two concentrations, 25 \u0026micro;M and 50 \u0026micro;M, were selected for further investigation. We initially examined the expression of EMT-related proteins in HNSCC cells treated with varying concentrations of rhaponticin. It was found that as the drug concentration increased, the protein levels of N-cadherin decreased while those of E-cadherin increased (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Subsequently, the scratch wound healing assay and Transwell migration/invasion assays were used to evaluate the impact of different concentrations of rhaponticin on the migration and invasion of CAL27 and SCC9 cells under hypoxic conditions. Based on the IC50 values, cells were divided into three groups with rhaponticin concentrations of 0, 25, and 50 \u0026micro;M. The results indicated that the scratch widths were similar at 0 h across all groups. After 24 hours of culture at 37\u0026deg;C and 1% O2 (hypoxia), the migration ability of the cells was significantly inhibited with increasing concentration (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). The colony formation assay was utilized to validate the proliferation capacity of HNSCC cells. With increasing concentration, the proliferation ability of HNSCC cells was markedly suppressed (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE). In the Transwell invasion assay, the invasion ability of the cells also declined significantly as the concentration of rhaponticin increased (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Rhaponticin suppresses the activation of the IL-6/STAT3 pathway.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilarly, we conducted immunofluorescence experiments on SCC9 and CAL27 cells. The results indicated that as the concentration of rhaponticin increased, the expression level of IL-6 decreased (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). We further validated the effect of rhaponticin on the expression of IL-6/STAT3 axis-related proteins in HNSCC cells using Western blotting. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC, compared to the control group, the expression levels of proteins involved in the IL-6/STAT3 pathway were significantly reduced in cells treated with rhaponticin.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eTo explore the potential targets of Rhaponticin in HNSCC, we collected 7,175 HNSCC-related genes and 364 Rhaponticin-targeted genes, resulting in 262 intersection genes. GO and KEGG enrichment analyses revealed that these intersection genes were predominantly enriched in tumor-related signaling pathways, including lipid metabolism and atherosclerosis, prostate cancer, hepatitis B, T cell receptor signaling, and JAK-STAT signaling. These findings suggest that using network pharmacology to identify potential targets of Rhaponticin against HNSCC is a reasonable approach. Topological analysis was employed to screen 40 core genes from the intersection genes. Subsequently, four machine learning algorithms were utilized to identify potential target sites. We used the univariate COX regression model to analyze the influence of these genes on patient survival time. Additionally, SVM-RFE (Support Vector Machine Recursive Feature Elimination) was applied as a supervised learning method aimed at maximizing the margin between data points of different categories. \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003eThe Random Forest (RF) model is composed of multiple decision trees, with each tree making independent predictions. This ensemble approach reduces the risk of overfitting. \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003eThe LASSO regression algorithm enhances the interpretability and predictive accuracy of the model by identifying the combination of independent variables that have the most significant explanatory power for the dependent variable. \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003eUltimately, we identified IL-6 and PPARG as candidate targets. Survival analyses of these two genes revealed that IL-6 was significantly associated with patient survival. Consequently, we confirmed IL-6 as a potential therapeutic target. Verification using the GEO database further supported this conclusion. Research indicates that IL-6 and related cytokines promote immune cell infiltration and inflammatory responses in the tumor microenvironment. \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e][\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003eThese cytokines further activate a variety of signaling pathways, including the JAK/STAT3 and TNF signaling pathways, playing a highly significant role in tumor recurrence and metastasis. \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003eWe divided patients with head and neck squamous cell carcinoma (HNSCC) from the TCGA database into high and low IL-6 expression groups based on the average IL-6 expression level, and analyzed the differences in immune cell infiltration between the two groups. The results revealed that the infiltration of various immune cells, including resting memory CD4 + T cells, regulatory T cells (Tregs), monocytes, macrophages, M2 macrophages, and eosinophils, was significantly augmented in the high IL-6 expression group. This suggests that rhaponticin may target IL-6 to influence the tumor microenvironment of HNSCC tissues. To further explore the signaling pathways through which rhaponticin acts, we performed GSEA enrichment analysis. In the HALLMARK collection, the high IL-6 expression group was predominantly enriched in pathways such as the IL-6/JAK/STAT signaling pathway, inflammatory response pathways, and apoptosis. In the C7 collection, the high IL-6 expression group was enriched in 846 immune-related pathways, while the low IL-6 expression group was enriched in only 11 immune-related pathways. A considerable body of evidence indicates that the IL-6/STAT3 axis plays a significant role in many cancers. Studies have demonstrated that IL-6 secreted by M1-like tumor-associated macrophages (TAMs) activates STAT3, thereby promoting the genesis and progression of oral squamous cell carcinoma.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003eThe upregulation of IL-6 autocrine by RAB3C in colon cancer cells and the subsequent activation of the JAK2/STAT3 signaling pathway play a crucial role in the invasion and metastasis of colon cancer. \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003eConsequently, we hypothesized that rhaponticin might exert an anti-HNSCC effect via the IL-6/STAT3 axis. We validated this hypothesis through in vitro experiments. CCK8 and colony formation assays demonstrated that rhaponticin at a concentration of 50 µM significantly inhibited the proliferation of HNSCC cells while exhibiting relatively low toxicity to human oral mucosal keratinocytes (HOK). Wound healing and Transwell assays indicated that rhaponticin could suppress the migration and invasion capabilities of HNSCC cells. Additionally, the upregulation of E-cadherin and downregulation of N-cadherin, both EMT-related proteins, further confirmed that rhaponticin inhibits the migration and invasion of HNSCC cells. Western blotting results showed that rhaponticin significantly repressed the expression of IL-6 and STAT3, and immunofluorescence analysis revealed reduced IL-6 expression in Cal27 and SCC9 cells following rhaponticin treatment. However, our study has several limitations. First, we focused primarily on validated drug and disease targets, potentially overlooking unverified or undocumented targets. Second, we did not conduct in vivo experiments to validate the therapeutic efficacy of Rhaponticin. Therefore, further in vivo and in vitro studies are necessary to comprehensively elucidate the molecular mechanisms underlying Rhaponticin's effects on HNSCC.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eOur study demonstrates that Rhaponticin may inhibit the proliferation, invasion, and migration of HNSCC cells by modulating the IL-6/STAT3 axis. Furthermore, our findings suggest that IL-6 expression in HNSCC is associated with multiple immune cell types, providing new insights for the clinical application of traditional Chinese medicine in HNSCC treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by [National Natural Science Foundation of China and Natural Science Foundation of Jiangxi Province] (Grant numbers [82460954] and [20232BAB206153]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approcal\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predicted targetsof rhaponticin during the current study are available in the PharmMapper and SEA repository, [http://www.lilab-ecust.cn/pharmmapper and http://sea.bkslab.org].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisease-related target genes during the current study are available in the GeneCards database using the keyword \u0026ldquo;head and neck squamous cell carcinoma\u0026rdquo; [http://www.genecards.org].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene expression profiles and clinical information for HNSCC samples and normal samples were acquired from the TCGA database [https://portal.gdc.cancer.gov].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe gene expression profile of the HNSCC dataset GSE30784 was acquired from GEO [https://www.ncbi.nlm.nih.gov/geo/].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHongcheng Wei:Methodology,Writing original draft,Formal analysis,Conceptualization,Validation.Shanshan Wang:Formal analysis,Conceptualization,Validation.Jiayue Wan:Methodology,Validation.Sicheng Li:Conceptualization,Validation.Wei Wang:Methodology.Jiajun Zhu:Validation.Lin Jiang:Formal analysis.Yisen Shao:Conceptualization,Methodology,Funding acquisition,Writing-review\u0026amp;editing,Supervision,Data curation,Project,administration.Yuan WU:Conceptualization,Methodology,Funding acquisition,Writing-review\u0026amp;editing,Supervision,Data curation,Project,administration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. PMID: 33538338.\u003c/li\u003e\n\u003cli\u003eJohnson DE, Burtness B, Leemans CR, Lui VWY, Bauman JE, Grandis JR. Head and neck squamous cell carcinoma. Nat Rev Dis Primers. 2020 Nov 26;6(1):92. doi: 10.1038/s41572-020-00224-3. Erratum in: Nat Rev Dis Primers. 2023 Jan 19;9(1):4. doi: 10.1038/s41572-023-00418-5. PMID: 33243986; PMCID: PMC7944998.\u003c/li\u003e\n\u003cli\u003eHashibe M, Brennan P, Benhamou S, Castellsague X, Chen C, Curado MP, Dal Maso L, Daudt AW, Fabianova E, Fernandez L, W\u0026uuml;nsch-Filho V, Franceschi S, Hayes RB, Herrero R, Koifman S, La Vecchia C, Lazarus P, Levi F, Mates D, Matos E, Menezes A, Muscat J, Eluf-Neto J, Olshan AF, Rudnai P, Schwartz SM, Smith E, Sturgis EM, Szeszenia-Dabrowska N, Talamini R, Wei Q, Winn DM, Zaridze D, Zatonski W, Zhang ZF, Berthiller J, Boffetta P. Alcohol drinking in never users of tobacco, cigarette smoking in never drinkers, and the risk of head and neck cancer: pooled analysis in the International Head and Neck Cancer Epidemiology Consortium. J Natl Cancer Inst. 2007 May 16;99(10):777-89. doi: 10.1093/jnci/djk179. Erratum in: J Natl Cancer Inst. 2008 Feb 6;100(3):225. Fernandez, Leticia [added]. PMID: 17505073.\u003c/li\u003e\n\u003cli\u003eHunter KD, Parkinson EK, Harrison PR. Profiling early head and neck cancer. Nat Rev Cancer. 2005 Feb;5(2):127-35. doi: 10.1038/nrc1549. PMID: 15685196.\u003c/li\u003e\n\u003cli\u003eMagnes T, Wagner S, Kiem D, Weiss L, Rinnerthaler G, Greil R, Melchardt T. Prognostic and Predictive Factors in Advanced Head and Neck Squamous Cell Carcinoma. Int J Mol Sci. 2021 May 7;22(9):4981. doi: 10.3390/ijms22094981. PMID: 34067112; PMCID: PMC8125786.。\u003c/li\u003e\n\u003cli\u003eBhat AA, Yousuf P, Wani NA, Rizwan A, Chauhan SS, Siddiqi MA, Bedognetti D, El-Rifai W, Frenneaux MP, Batra SK, Haris M, Macha MA. Tumor microenvironment: an evil nexus promoting aggressive head and neck squamous cell carcinoma and avenue for targeted therapy. Signal Transduct Target Ther. 2021 Jan 12;6(1):12. doi: 10.1038/s41392-020-00419-w. Erratum in: Signal Transduct Target Ther. 2021 Feb 26;6(1):93. doi: 10.1038/s41392-021-00503-9. PMID: 33436555; PMCID: PMC7804459.\u003c/li\u003e\n\u003cli\u003eTang E, Lahmi L, Meillan N, Pietta G, Albert S, Maingon P. Treatment Strategy for Distant Synchronous Metastatic Head and Neck Squamous Cell Carcinoma. Curr Oncol Rep. 2019 Nov 14;21(11):102. doi: 10.1007/s11912-019-0856-5. PMID: 31728650.\u003c/li\u003e\n\u003cli\u003eGong Y, Bao L, Xu T, Yi X, Chen J, Wang S, Pan Z, Huang P, Ge M. The tumor ecosystem in head and neck squamous cell carcinoma and advances in ecotherapy. Mol Cancer. 2023 Apr 6;22(1):68. doi: 10.1186/s12943-023-01769-z. PMID: 37024932; PMCID: PMC10077663.\u003c/li\u003e\n\u003cli\u003eLuo H, Vong CT, Chen H, Gao Y, Lyu P, Qiu L, Zhao M, Liu Q, Cheng Z, Zou J, Yao P, Gao C, Wei J, Ung COL, Wang S, Zhong Z, Wang Y. Naturally occurring anti-cancer compounds: shining from Chinese herbal medicine. Chin Med. 2019 Nov 6;14:48. doi: 10.1186/s13020-019-0270-9. PMID: 31719837; PMCID: PMC6836491.\u003c/li\u003e\n\u003cli\u003eWang Y, Zhang Q, Chen Y, Liang CL, Liu H, Qiu F, Dai Z. Antitumor effects of immunity-enhancing traditional Chinese medicine. Biomed Pharmacother. 2020 Jan;121:109570. doi: 10.1016/j.biopha.2019.109570. Epub 2019 Nov 9. PMID: 31710893.\u003c/li\u003e\n\u003cli\u003eXiang H, Zuo J, Guo F, Dong D. What we already know about rhubarb: a comprehensive review. Chin Med. 2020 Aug 26;15:88. doi: 10.1186/s13020-020-00370-6. PMID: 32863857; PMCID: PMC7448319.\u003c/li\u003e\n\u003cli\u003eChen D, Liu JR, Cheng Y, Cheng H, He P, Sun Y. Metabolism of Rhaponticin and Activities of its Metabolite, Rhapontigenin: A Review. Curr Med Chem. 2020;27(19):3168-3186. doi: 10.2174/0929867326666190121143252. PMID: 30666906.\u003c/li\u003e\n\u003cli\u003eLiudvytska O, Bandyszewska M, Skirecki T, Krzyżanowska-Kowalczyk J, Kowalczyk M, Kolodziejczyk-Czepas J. Anti-inflammatory and antioxidant actions of extracts from Rheum rhaponticum and Rheum rhabarbarum in human blood plasma and cells in vitro. Biomed Pharmacother. 2023 Sep;165:115111. doi: 10.1016/j.biopha.2023.115111. Epub 2023 Jul 6. PMID: 37421780.\u003c/li\u003e\n\u003cli\u003eLi Y, Zhang Y, Tang J. Rhaponticin suppresses the stemness phenotype of gastric cancer stem-like cells CD133+/CD166\u0026thinsp;+\u0026thinsp;by inhibiting programmed death-ligand 1. BMC Gastroenterol. 2024 Nov 21;24(1):423. doi: 10.1186/s12876-024-03512-4. PMID: 39573998; PMCID: PMC11583647.\u003c/li\u003e\n\u003cli\u003eKim A, Ma JY. Piceatannol-3-O-\u0026beta;-D-glucopyranoside (PG) exhibits in vitro anti-metastatic and anti-angiogenic activities in HT1080 malignant fibrosarcoma cells. Phytomedicine. 2019 Apr;57:95-104. doi: 10.1016/j.phymed.2018.12.017. Epub 2018 Dec 11. PMID: 30668328.\u003c/li\u003e\n\u003cli\u003eHibasami H, Takagi K, Ishii T, Tsujikawa M, Imai N, Honda I. Induction of apoptosis by rhapontin having stilbene moiety, a component of rhubarb (Rheum officinale Baillon) in human stomach cancer KATO III cells. Oncol Rep. 2007 Aug;18(2):347-51. PMID: 17611655.\u003c/li\u003e\n\u003cli\u003eMickymaray S, Alfaiz FA, Paramasivam A, Veeraraghavan VP, Periadurai ND, Surapaneni KM, Niu G. Rhaponticin suppresses osteosarcoma through the inhibition of PI3K-Akt-mTOR pathway. Saudi J Biol Sci. 2021 Jul;28(7):3641-3649. doi: 10.1016/j.sjbs.2021.05.006. Epub 2021 May 8. PMID: 34220214; PMCID: PMC8241634.\u003c/li\u003e\n\u003cli\u003eWang X, Priya Veeraraghavan V, Krishna Mohan S, Lv F. Anticancer and immunomodulatory effect of rhaponticin on Benzo(a)Pyrene-induced lung carcinogenesis and induction of apoptosis in A549 cells. Saudi J Biol Sci. 2021 Aug;28(8):4522-4531. doi: 10.1016/j.sjbs.2021.04.052. Epub 2021 Apr 24. PMID: 34354438; PMCID: PMC8324936.\u003c/li\u003e\n\u003cli\u003eKim A, Ma JY. Rhaponticin decreases the metastatic and angiogenic abilities of cancer cells via suppression of the HIF‑1\u0026alpha; pathway. Int J Oncol. 2018 Sep;53(3):1160-1170. doi: 10.3892/ijo.2018.4479. Epub 2018 Jul 11. PMID: 30015877; PMCID: PMC6065401.\u003c/li\u003e\n\u003cli\u003eHuang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics Proteomics. 2018 Jan-Feb;15(1):41-51. doi: 10.21873/cgp.20063. PMID: 29275361; PMCID: PMC5822181.\u003c/li\u003e\n\u003cli\u003eHu J, Szymczak S. A review on longitudinal data analysis with random forest. Brief Bioinform. 2023 Mar 19;24(2):bbad002. doi: 10.1093/bib/bbad002. PMID: 36653905; PMCID: PMC10025446.\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Delgado M, Sirsat MS, Cernadas E, Alawadi S, Barro S, Febrero-Bande M. An extensive experimental survey of regression methods. Neural Netw. Rhaponticin2019 Mar;111:11-34. doi: 10.1016/j.neunet.2018.12.010. Epub 2018 Dec 21. PMID: 30654138.\u003c/li\u003e\n\u003cli\u003eWang G, Zhang M, Cheng M, Wang X, Li K, Chen J, Chen Z, Chen S, Chen J, Xiong G, Xu X, Wang C, Chen D. Tumor microenvironment in head and neck squamous cell carcinoma: Functions and regulatory mechanisms. Cancer Lett. 2021 Jun 1;507:55-69. doi: 10.1016/j.canlet.2021.03.009. Epub 2021 Mar 17. PMID: 33741424.\u003c/li\u003e\n\u003cli\u003eYang J, Xie K, Li C. Immune-related genes have prognostic significance in head and neck squamous cell carcinoma. Life Sci. 2020 Sep 1;256:117906. doi: 10.1016/j.lfs.2020.117906. Epub 2020 Jun 3. PMID: 32504750.\u003c/li\u003e\n\u003cli\u003eMei Z, Huang J, Qiao B, Lam AK. Immune checkpoint pathways in immunotherapy for head and neck squamous cell carcinoma. Int J Oral Sci. 2020 May 28;12(1):16. doi: 10.1038/s41368-020-0084-8. PMID: 32461587; PMCID: PMC7253444.\u003c/li\u003e\n\u003cli\u003eRalli M, Grasso M, Gilardi A, Ceccanti M, Messina MP, Tirassa P, Fiore M, Altissimi G, A Salzano F, De Vincentiis M, Greco A. The role of cytokines in head and neck squamous cell carcinoma: A review. Clin Ter. 2020 May-Jun;171(3):e268-e274. doi: 10.7417/CT.2020.2225. PMID: 32323717.\u003c/li\u003e\n\u003cli\u003eYou Y, Tian Z, Du Z, Wu K, Xu G, Dai M, Wang Y, Xiao M. M1-like tumor-associated macrophages cascade a mesenchymal/stem-like phenotype of oral squamous cell carcinoma via the IL6/Stat3/THBS1 feedback loop. J Exp Clin Cancer Res. 2022 Jan 6;41(1):10. doi: 10.1186/s13046-021-02222-z. PMID: 34991668; PMCID: PMC8734049.\u003c/li\u003e\n\u003cli\u003eChang YC, Su CY, Chen MH, Chen WS, Chen CL, Hsiao M. Secretory RAB GTPase 3C modulates IL6-STAT3 pathway to promote colon cancer metastasis and is associated with poor prognosis. Mol Cancer. 2017 Aug 7;16(1):135. doi: 10.1186/s12943-017-0687-7. PMID: 28784136; PMCID: PMC5547507.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5917121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5917121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Rhaponticin, a bioactive compound derived from rhubarb, has been demonstrated anti-tumor effects in various types of cancer. However, its impact on head and neck squamous cell carcinoma (HNSCC) remains unexplored. This study aims to investigate the specific molecular mechanisms by which Rhaponticin inhibits the invasion and metastasis of HNSCC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e The potential target genes that rhaponticin acts on in HNSCC were identified using online databases. The mechanisms by which rhaponticin influences the occurrence and progression of HNSCC were investigated through network pharmacology, molecular docking, bioinformatics analysis, and cellular experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eUsing network pharmacology, we identified 40 hub genes from the collected gene set. Subsequently, by analyzing The Cancer Genome Atlas (TCGA) data with four machine learning algorithms, we identified IL-6 as a potential target associated with the occurrence and progression of head and neck squamous cell carcinoma (HNSCC). Based on the average expression level of IL-6, we classified the samples into high-expression and low-expression groups and conducted survival analysis. Our results indicated that IL-6 expression was significantly correlated with patient survival. Gene Set Enrichment Analysis (GSEA) revealed that Rhaponticin might influence HNSCC via the IL6/STAT3 signaling pathway. Using the CIBERSORT algorithm, we assessed the differences in infiltration levels of 22 immune cell types between the high and low IL-6 expression groups. Our findings suggest that multiple immune cells may be involved in the pathogenesis of HNSCC. Additionally, we analyzed single-cell RNA sequencing (scRNA-seq) data from the GEO database to compare IL6 expression levels in tumor and normal tissues and evaluated its prognostic impact using Receiver Operating Characteristic (ROC) curve analysis. Molecular docking studies demonstrated that Rhaponticin binds stably to IL6. In the experimental section, we used two HNSCC cell lines (CAL27 and SCC9) to investigate the effects of Rhaponticin. Our results showed that Rhaponticin effectively inhibited cell proliferation, invasion, and migration, and reduced the expression of proteins in the IL6/STAT3 signaling pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eRhaponticin shows promise in treating \u0026nbsp;HNSCC by inhibiting the IL6/STAT3 signaling pathway.\u003c/p\u003e","manuscriptTitle":"Rhaponticin inhibits the proliferation, migration, and invasion of head and neck squamous cell carcinoma (HNSCC) cells through modulation of the IL6/STAT3 signaling pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 10:21:19","doi":"10.21203/rs.3.rs-5917121/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ceb365a0-8362-465e-985a-aafae2d2ebdc","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43878606,"name":"Biological sciences/Cancer/Head and neck cancer"},{"id":43878607,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2025-03-14T03:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-07 10:21:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5917121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5917121","identity":"rs-5917121","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}