Uncovering the Key Targets and Therapeutic Mechanisms of Qizhen Capsule in Gastric Cancer through Network Pharmacology and Bioinformatic Analyses

Abstract

Objective. This study is aimed at screening out effective active compounds of Qizhen capsule (QZC) and exploring the underlying mechanisms against gastric cancer (GACA) by combining both bioinformatic analysis and experimental approaches. Weighted gene coexpression network analysis (WGCNA), network pharmacology, molecular docking simulation, survival analysis, and data-based differential gene and protein expression analysis were employed to predict QZC's potential targets and explore the underlying mechanisms. Subsequently, multiple experiments, including cell viability, apoptosis, and protein expression analyses, were conducted to validate the bioinformatics-predicted therapeutic targets. The results indicated that luteolin, rutin, quercetin, and kaempferol were vital active compounds, and TP53, MAPK1, and AKT1 were key targets. Molecular docking simulation showed that the four abovementioned active compounds had high binding affinities to the three main targets. Enrichment analysis showed that vital active compounds exerted therapeutic effects on GACA through regulating the TP53 pathway, MAPK pathway, and PI3K/AKT pathway. Furthermore, data-based gene expression analysis revealed that TP53 and JUN genes were not only differentially expressed between normal and GACA tissues but also correlated with clinical stages. In parallel, in vitro experimental results suggested that QZC exerted therapeutic effects on GACA by decreasing IC₅₀ values, downregulating AKT expression, upregulating TP53 and MAPK expression, and increasing apoptosis of SGC-7901 cells. This study highlights the potential candidate biomarkers, therapeutic targets, and basic mechanisms of QZC in treating GACA, providing a foundation for new drug development, target mining, and related animal studies in GACA.

Figure 1

The technical strategy flowchart for exploring potential mechanisms of QZC against GACA.

Figure 2

Analysis of network topology with different soft-thresholding powers.

Figure 3

Cluster gene dendrogram with dissimilarity based on topological overlap, together with assigned module colors calculated by WGCNA.

Figure 4

Module-trait associations. Each row corresponded to a module eigengene and each column to a trait. Each cell contained the corresponding correlation and P value.

Figure 5

The drug-compound-target network of QZC. Orange arrows represented herb nodes, green squares represented active compound nodes, and blue circles represented target nodes.

Figure 6

Potential targets of QZC in treating GACA. (a) Targets of QZC against GACA in Venn. (b) The PPI network of QZC against GACA. Darker circles represented that these targets had greater correlation degrees with higher degree values.

Figure 7

Bar graph of GO enrichment analysis of important genes.

Figure 8

Bubble chart of KEGG enrichment analysis of important genes.

Figure 9

The network of targets and top 30 pathways. Green squares represented pathways, pink squares represented pathways with high count values, and orange circles represented important targets.

Figure 10

The most prominent pathways in GACA. Important targets in pathways were highlighted in pink, and the most relevant targets were highlighted in red. The PI3K/AKT and MAPK signaling pathways were marked in red, enriched significantly, and related to GACA.

Figure 11

The heat map of 72 compound-target docking scores. Darker color represented lower molecular docking score. Pro-lig was used as the control.

Figure 12

The sketch map of ten compound-target pairs: (a) MAPK1-luteolin, (b) SRC-luteolin, (c) EP300-rutin, (d) AKT1-rutin, (e) SRC-rutin, (f) HSP-rutin, (g) TP53-rutin, (h) MAPK1-que, (i) SRC-quercetin, and (j) SRC-kaempferol.

Figure 13

Kaplan-Meier curves of top eight PPI genes in GACA: (a) TP53, (b) EP300, (c) AKT1, (d) MAPK1, (e) SRC, (f) RELA, (g) JUN, and (h) HSP90AA1.

Figure 14

The mRNA expression levels of the top eight PPI genes in GACA patients: (a) mRNA expression levels between normal and GACA tissues; (b) mRNA expression levels in four clinical stages (Stages I-IV).

Figure 15

Protein expression levels of TP53, EP300, AKT1, MAPK1, SRC, RELA, JUN, and HSP90AA1 in normal and GACA tissues based on immunohistochemistry data from HPA.

Figure 16

Inhibitory effects of four active compounds on SGC-7901 cells' proliferation: (a) quercetin; (b) kaempferol; (c) luteolin; (d) rutin; (e) time-dependent inhibitory effects of four active compounds on SGC-7901 cells' proliferation. The data were expressed as mean ± SD (n = 3).

Figure 17

Apoptosis of SGC-7901 cells treated with four active compounds for 24 h: (a) control, (b) quercetin, (c) rutin, (d) kaempferol, and (e) luteolin.

Figure 18

Effects of four active compounds on protein levels of p-AKT, AKT, p-P53, P53, p-MAPK, and MAPK in SGC-7901 cells: (a) control, (b) quercetin, (c) rutin, (d) kaempferol, and (e) luteolin. ^∗P < 0.05, ^∗∗P < 0.01, and ^∗∗∗P < 0.001 vs. control group.