3.1 Identification of JPQHSYD potential targets for REG treatment
The active components of the JPQHSYD formula, comprising multiple herbs, including Codonopsis Radix, Radix Salviae, Herba Artemisiae Scopariae, Atractylodes macrocephala, Lablab Semen Album, Poria Cocos, Pinellia ternata, Cortex Magnoliae Officinalis, Coptidis Rhizoma, Curcumae Rhizoma, Amomum villosum, Licorice, and Citrus Reticulata, were identified based on TCMSP with oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. In addition, the effective components of Carapax Trionycis were screened in the BATMAN database with a setting score cutoff of 20 or more and a p-value of 0.05 or less. Furthermore, 245 JPQHSYD candidate ingredients were selected from databases (Supplementary).
Meanwhile, the targets of these active ingredients were also obtained, including 185 targets of Codonopsis Radix ingredients, 881 targets of Radix Salviae ingredients, 343 targets of Herba Artemisiae Scopariae ingredients, 23 targets of Atractylodes Macrocephala ingredients, 2 targets of Carapax Trionycis ingredients, 22 targets of Lablab Semen Album ingredients, 149 targets of Poria Cocos ingredients, 168 targets of Pinellia Ternata ingredients, 33 targets of Cortex Magnoliae Officinalis ingredients, 278 targets of Coptidis Rhizoma ingredients, 24 targets of Curcumae Rhizoma ingredients, 75 targets of Amomum Villosum ingredients, 1504 targets of Licorice ingredients, and 73 targets of Citrus Reticulata ingredients. Finally, there were 320 targets left after removing duplicates.
A total of 523 REG-related targets were obtained from the GeneCards and OMIM database. The intersection of 320 drug targets and 523 disease targets yielded 94 common targets (Fig. 2A), which were used as JPQHSYD prediction targets in REG treatment.
3.2 Network analysis and key components
The D-I-G-D network constructed using the Cytoscape 3.7.2 software obtained the JPQHSYD core components and ranked them based on degree values; the higher the value, the greater the importance (Fig. 2B). This contributes to a better understanding of the potential pharmacodynamic substances and targets of JPQHSYD in REG treatment. The top ten were chosen for subsequent analysis (Table 1). Among these bioactive components, Quercetin had the highest correlation with REG targets based on the degree value, followed by Luteolin, Kaempferol, Naringenin, Beta-sitosterol, and so on.
Table 1. Top active ingredients of JPQHSYD identified in the component-target network.
MOL ID
|
Name
|
Degree
|
OB (%)
|
DL
|
MOL000098
|
Quercetin
|
210
|
46.43
|
0.28
|
MOL000006
|
Luteolin
|
62
|
36.16
|
0.25
|
MOL000422
|
Kaempferol
|
25
|
41.88
|
0.24
|
MOL004328
|
Naringenin
|
21
|
59.29
|
0.21
|
MOL000358
|
Beta-sitosterol
|
20
|
36.91
|
0.75
|
MOL002773
|
Beta-carotene
|
18
|
37.18
|
0.58
|
MOL000354
|
Isorhamnetin
|
18
|
49.60
|
0.31
|
MOL003896
|
7-Methoxy-2-methyl isoflavone
|
18
|
42.56
|
0.20
|
MOL007154
|
Tanshinone iia
|
17
|
49.89
|
0.40
|
MOL005828
|
Nobiletin
|
15
|
61.67
|
0.52
|
3.3 PPI network analysis of the core genes
The 94 common targets were imported into the STRING database to construct the PPI network, which had 94 nodes and 422 edges, an average node degree of 8.98, and a PPI enrichment p‑value of less than 1.0e-16 (Fig. 3A). The degree, PPI network topological eigenvalues, degree of node color, size of the reaction center, edge thickness, and color depth were then analyzed using Cytoscape Version 3.7.2 to yield a combined score (Fig. 3B). The average degree value of the PPI network was 9.48. Topological analysis of the PPI networks identified 35 genes with scores greater than the average as core targets. The top ten targets in terms of degree value were STAT3, JUN, AKT1, EP300, tumor necrosis factor (TNF), TP53, interleukin 6 (IL6), mitogen-activated protein kinase 14 (MAPK14), CTNNB1, and MAPK1 (Table 2).
Table 2. The ranking of core targets, betweenness centrality, closeness centrality, and degree values.
Gene
|
Betweenness centrality
|
Closeness centrality
|
Degree
|
STAT3
|
0.13
|
0.56
|
33
|
JUN
|
0.13
|
0.55
|
33
|
AKT1
|
0.10
|
0.51
|
28
|
EP300
|
0.08
|
0.51
|
28
|
TNF
|
0.08
|
0.50
|
26
|
TP53
|
0.07
|
0.48
|
25
|
IL6
|
0.05
|
0.49
|
24
|
MAPK14
|
0.04
|
0.51
|
23
|
CTNNB1
|
0.05
|
0.49
|
22
|
MAPK1
|
0.09
|
0.51
|
22
|
STAT3, signal transducer and activator of transcription 3; AKT1, protein kinase B;TNF, tumor necrosis factor; TP53, tumor protein P53; IL6,interleukin 6 ; MAPK14, mitogen-activated protein kinase 14; CTNNB1,beta-Catenin; MAPK1,mitogen-activated protein kinase 1.
3.4 GO and KEGG enrichment analyses of common target genes
The 94 common targets were analyzed using GO and KEGG pathway enrichment, which identified the potential molecular mechanisms for JPQHSYD in REG. GO enrichment analysis yielded cellular component (CC), molecular function (MF), and biological process (BP) results. CC analysis revealed a higher proportion of protein in the cytoplasm, nucleus, and extracellular space. The MFs were mainly related to “protein binding,” “identical protein binding,” and “enzyme binding.” The main BPs were associated with “cell proliferation,” “cell cycle regulation,” and “inflammatory response.” Furthermore, GO analysis revealed the top 20 enriched conditions in the CC, MF, and BP categories (Figs. 4A–C).
To investigate the signaling pathways and functions of these target genes, a KEGG pathway functional enrichment analysis was performed (Fig. 4D). The signaling pathways were identified by screening their statistical significance (p < 0.01), and the resulting target genes were found to primarily interact with the IL17, TNF, MAPK, phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), hypoxia-inducible factor-1, FoxO, and T-cell receptor signaling pathways. Thus, these signaling pathways appear to be closely related to the potential impact of JPQHSYD on REG.
3.5 Experimental validation using in vitro CCK-8 assay
First, the effects of various IGF-1 doses on GES-1 cell viability were determined using the CCK-8 assay (Fig. 5A). An IGF-1 concentration of 2.5 ng/mL resulted in high cell viability. Therefore, a concentration of 2.5 ng/mL was selected for subsequent experiments. The effect of JPQHSYD on GES-1 cell viability was investigated. GES-1 cells were incubated with JPQHSYD at concentrations of 0, 3.125, 6.25, 12.5, 25, 50, 100, 200, and 400 µg/mL for 24 hours. The results revealed that JPQHSYD treatment at 200 µg/mL for 24 hours significantly reduced GES-1 cell viability, while lower concentrations had no significant effect (Fig.5B). Therefore, the concentrations of 25, 50, and 100 µg/mL were selected for subsequent experiments. After treatment with 50 and 100 µg/mL of JPQHSYD for 24 hours, the number of GES-1 cells was significantly lower than that in the control group (p< 0.05; Figs.5C–D). IGF-1 stimulation was shown to promote GES-1 cell proliferation, while medium and high JPQHSYD concentrations were able to inhibit IGF-1-induced cell proliferation.
3.7 JPQHSYD inhibited cell proliferation via PI3K/Akt pathway in vitro
To validate the underlying signaling pathway for JPQHSYD action on GES-1 cells predicted by network pharmacology, the expression changes in key genes in the PI3K/Akt signaling pathway were detected using the network pharmacology enrichment index (Figs. 6A–B). Compared to the control group, IFG-1 significantly upregulated p-AKT, Bcl2, CDK4, and CyclinD1 protein expression (p < 0.05), while AKT and Bax protein expression were not significantly different, and Bax/Bcl was downregulated (p < 0.05). Compared to the IGF-1 group, JPQHSYD treatment at 50 and 100 µg/mL significantly inhibited p-AKT, Bcl2, CDK4, and CyclinD1 protein expression (p < 0.05), with no significant difference in AKT or Bax protein expression and upregulation of Bax/Bcl2 (p < 0.05). In contrast, JPQHSYD at 25 µg/mL had no significant effect. These findings suggest that IGF-1 may promote the expression of its downstream proteins Bcl2, CDK4, and CyclinD1 by activating the AKT signaling pathway. JPQHSYD promoted apoptosis by inhibiting the AKT signaling pathway and suppressing AKT phosphorylation, which in turn inhibited its downstream Bcl2 expression. In addition, it suppressed cell cycle transition and proliferation by inhibiting CDK4 and CyclinD1 expression. Overall, we found that JPQHSYD could inhibit cell proliferation by inhibiting the PI3K/Akt pathway.