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

Abstract

Background: Rhaponticin, a bioactive compound derived from rhubarb, has been demonstrated anti-tumor effects in various types of cancer. However, its impact on 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 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 (CAL 27 and SCC-9) 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.

Keywords: Biomarkers; HNSCC; IL6/STAT3 signaling pathway; Inflammatory cytokines; Tumor immune infiltration.

Fig. 1

Flowchart of Rhein in HNSCC Research

Fig. 2

Establishment 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

Fig. 3

Potential 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)

Fig. 4

Study 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

Fig. 5

Further 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 < 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

Fig. 6

The 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 < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Fig. 7

Rhaponticin suppressed the expression of IL-6/STAT3 pathway proteins. A, 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 < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001