Inhibitory Effects of Rabdosia rubescens in Esophageal Squamous Cell Carcinoma: Network Pharmacology and Experimental Validation

Abstract

Esophageal squamous cell carcinoma (ESCC) is one of the most frequently occurring diseases in the world. Rabdosia rubescens (RR) has been demonstrated to be effective against ESCC; however, the mechanism is unknown. The primary gene modules related to the clinical characteristics of ESCC were initially investigated in this research using weighted gene co-expression network analysis (WCGNA) and differential expression gene (DEG) analysis. We employed network pharmacology to study the hub genes linked with RR therapy on ESCC. A molecular docking simulation was achieved to identify the binding activity of central genes to RR compounds. Lastly, a chain of experimentations was used to verify the inhibitory effect of RR water extract on the ESCC cell line in vitro. The outcomes revealed that CCNA2, TOP2A, AURKA, CCNB2, CDK2, CHEK1, and other potential central targets were therapeutic targets for RR treatment of ESCC. In addition, these targets are over-represented in several cancer-related pathways, including the cell cycle signaling pathway and the p53 signaling pathway. The predicted targets displayed good bonding activity with the RR bioactive chemical according to a molecular docking simulation. In vitro experiments revealed that RR water extracts could inhibit ESCC cells, induce cell cycle arrest, inhibit cell proliferation, increase P53 expression, and decrease CCNA2, TOP2A, AURKA, CCNB2, CDK2, and CHEK1. In conclusion, our study reveals the molecular mechanism of RR therapy for ESCC, providing great potential for identifying effective compounds and biomarkers for ESCC therapy.

Figure 1

The technical strategy for this article.

Figure 2

WGCNA analysis for ESCC genes. (a) Scale-free soft threshold distribution diagram. The horizontal axis represents the power value of the weight parameter, the vertical axis in the left figure represents the square of the correlation coefficient between log(k) and log(p(k)) in the corresponding network, and the vertical axis in the right figure represents the mean value of all gene adjacency functions in the corresponding gene module. (b) Clustering dendrogram. (c) Heat map of correlation between modules and clinical traits. The ordinate is different module, and the abscissa is different trait. Each square represents the correlation coefficient and significance p value of a certain module and a certain trait. (d) Network topological overlap measure heatmap plot. The upper part of the figure is the selected gene cluster tree, the lower part is the heat map, and the horizontal and vertical colors represent different modules. (e) Green module MM and GS scatter diagrams. (f) Turquoise module MM and GS scatter diagrams.

Figure 3

DEG analysis of GEO161533. The abscissa represents the multiple of difference, and the ordinate represents −log 10 (p value). The red dots are upregulated genes and the blue dots are downregulated genes.

Figure 4

Venn diagram after WGCNA and DEG analysis.

Figure 5

Venn diagram of related targets of RR and ESCC.

Figure 6

Drug-component-target-disease network and PPI network construction. (a) Drug-component-target-disease network. The red diamond represents ESCC, the green triangle represents bioactive components, the blue rectangle represents genes, and the purple rectangle represents Rabdosia rubescens. (b) PPI network. The node's size, color, and shade variations correspond to the degree value's size.

Figure 7

Bubble chart of analysis of GO and pathway enrichment. (a) BP results. (b) MF results. (c) CC results. (d) KEGG function enrichment.

Figure 8

Molecular docking results of key targets and bioactive molecules. (a) AURKA-sideritiflavone. (b) CCNA2-rubiadin. (c) CCNB2-sideritiflavone. (d) TTK-rubiadin. (e) TOP2A-rubiadin. (f) CHEK1-rubiadin. (g) MELK-sideritiflavone.

Figure 9

Heat map of binding capacity between key targets and the bioactive compounds.

Figure 10

RR reduced the proliferation of ESCC cells by CCK8 assay. (a) Time and dose-dependent effects of extracts of RR treatment on the viability of ESCC cells. (b) Bar charts of OD values of each group at 24 h and 48 h (n = 3, mean ± SD; ^∗p < 0.05, ^∗∗p < 0.01, and ^∗∗∗p < 0.001 vs. the nontreated group).

Figure 11

RR reduced the proliferation of ESCC cells by soft agar clone formation assay. (a) Representative image of each group. (b) Bar charts of clone cell number of each group (n = 3, mean ± SD; ^∗p < 0.05, ^∗∗p < 0.01, and ^∗∗∗p < 0.001 vs. the nontreated group).

Figure 12

RR inhibits the proliferation of KYSE150 cells in the G0/G1 phase. (a) Cell cycle was assayed by flow cytometry. (b) Quantitative results of (a) (^∗p < 0.05, ^∗∗p < 0.01, and ^∗∗∗p < 0.001 vs. the nontreated group).

Figure 13

RR regulates the expression of key target genes at mRNA level. (a–c) The key target genes at mRNA level were detected by qRT-PCR. (a) KYSE150. (b) ECA109. (c) KYSE510 (^∗p < 0.05, ^∗∗p < 0.01, and ^∗∗∗p < 0.001 vs. the nontreated group).

Figure 14

RR regulates the expression of key target genes at protein level. (a) Western blot analysis of key targets in three cell lines. (b) The quantitative analysis of gene expression in KYSE150 (^∗p < 0.05, ^∗∗p < 0.01, and ^∗∗∗p < 0.001 vs. the nontreated group).