Investigation of the molecular mechanism of Xiangsha Liujun Pill in the treatment of gastritis based on network pharmacology and molecular docking

doi:10.21203/rs.3.rs-3214353/v1

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Investigation of the molecular mechanism of Xiangsha Liujun Pill in the treatment of gastritis based on network pharmacology and molecular docking

https://doi.org/10.21203/rs.3.rs-3214353/v1

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Xiangsha Liujun Pill (XSLJP) is a traditional Chinese medical complex prescription containing ten herbs and is widely used to treat gastrointestinal diseases. This study aims to investigate the mechanism of XSLJP treating gastritis. We first identified 327 targets based on 118 active components using the TCMSP, SwissTargetPrediction and STITCH databases and 180 gastritis-related targets using the DisGeNET database. Their intersection revealed that 26 common targets may play crucial roles in gastritis therapy by XSLJP. Then, we identified 12 key active components and eight hub proteins from the ‘drug-component-common target’ network. Finally, KEGG and GO enrichment analyses revealed that the therapeutic targets of XSLJP were mainly related to inflammation and cancer pathways, such as TNF, JAK/STAT and MAPK signalling pathway. This study not only provides new ideas for further exploring the specific mechanism of XSLJP treating gastritis but also offers a theoretical basis for future research on its active components.

Computational Biology

Drug Discovery, Design, & Development

Clinical Pharmacology

Xiangsha Liujun Pill (XSLJP)

Gastritis

Network pharmacology

Molecular docking

Action mechanism

Xiangsha Liujun Pill (XSLJP), which has been modified from Liujunzi decoction in the Orthodox Medical Record [1], the ancient herbal formulae of traditional Chinese medicine, is composed of 10 Chinese herbs, namely, Radix Aucklandiae (RA, muxiang), Amomum (AM, sharen), Ginger (GI, shengjiang), Codonopsis (CO, dangshen), Atractylodes (AT, baizhu), Poria (PO, fuling), Glycyrrhiza (GL, gancao), Tangerine Peel (TP, chenpi), Pinellia (PI, banxia), and Jujube (JU, dazao). In clinical therapeutics, XSLJP has been mainly used for dyspepsia, anorexia and abdominal distension [2], and combination treatments consisting of XSLJP and chemical drugs exert a significant therapeutic effect on the clinical treatment of chronic gastritis [3–5].

Many animal experiments and clinical trials have shown that XSLJP has a certain curative effect on gastritis treatment. For example, Lin et al. explored whether XSLJP can improve the clinical symptoms and pathological changes of the gastric mucosa in rats with Helicobacter pylori-associated gastritis [6]. Huang et al. showed that XSLJP can effectively relieve the symptoms of patients with chronic renal failure and spleen kidney qi deficiency syndrome by improving their renal function and reducing the levels of hormones such as gastrin, motilin and somatostatin [7]. Ok et al. found that Tangerine Peel combined with Pinellia in XSLJP can effectively reduce the level of inflammatory factors in rats with inflammation [8]. In addition, molecular biology experiments have indicated that some active components in XSLJP can efficiently relieve gastritis-related symptoms. For instance, Liu et al. found that CMP33, a component of Poria, can significantly inhibit the lipopolysaccharide (LPS)-stimulated expression of IL-6, TNF-α and IL-1β to attenuate inflammation in RAW264.7 murine macrophage cells [9]. Ji et al. indicated that atractylenolide I (ATL-1), an active component of Atractylodes, exerts an anti-inflammatory effect on RAW264.7 cells by inhibiting the production of TNF-α and IL-6, and its underlying mechanism might be related to inhibition of the NF-κB, ERK1/2 and p38 signaling pathways [10]. Due to the complex mixture of components in XSLJP, its active and adverse components and its pharmacological mechanism remain unclear. Thus, systematic explorations of the active components and potential mechanisms of action of XSLJP in the treatment of gastritis are needed at the cellular and molecular levels.

Network pharmacology is characterized by its integral emphasis on multicomponent and multitarget therapy, which is exactly coincident with the holistic view of traditional Chinese medicine (TCM) [11]. Due to the rapid development of modern TCM databases and analysis methods, network pharmacology has become a bridge for the effective combination of TCM and modern medicine [11]. The study of TCM prescriptions through network pharmacology can not only fully analyze their important active components and mechanisms, but also promote the modernization of TCM [12]. For example, Huang et al. predicted the presence of 34 components with biological activity in Yinchenhao decoction, and these components mainly act on 31 pathways, as determined by network pharmacology [13]. Furthermore, these researchers speculated that the anticancer effect of these 34 components might be induced through regulation of cell death, energy metabolism, anti-inflammation, and anti-angiogenesis and suppression of the immune response [13]. This finding indicates that network pharmacology might be a powerful analytical tool to study the mechanisms of various preparations of Chinese herbal medicines.

Therefore, based on network pharmacology, this study identified the active components of XSLJP and their therapeutic targets in the treatment of gastritis using four databases, namely, the TCMSP database [14] (http://tcmspw.com/tcmsp.php), the SwissTargetPrediction (STP) database [15] (http://www.swisstargetprediction.ch/), the STITCH database [16] (http://stitch.embl.de/) and the DisGeNET database [17] (https://www.disgenet.org/). The PubMed database (https://pubmed.ncbi.nlm.nih.gov/) was then utilized to retrieve experimental studies that confirm the identified active component-target pairs, and molecular docking simulations were performed using Discovery Studio (DS) software [18] to verify the putative active component-target pairs. Subsequently, a network diagram of the active components and disease targets was constructed using the STRING database [19] (https://string-db.org/) and Cytoscape [20] (https://cytoscape.org/) software, and from this network diagram, the key targets of and the important active components in XSLJP were determined according to various topology parameters. Furthermore, the important signaling pathways and biological processes of XSLJP were identified through KEGG pathway analysis and GO biological process analysis using the DAVID database [21] (https://david.ncifcrf.gov/). We then comprehensively analyzed and discuss the mechanism of action of XSLJP in the treatment of gastritis and provide a reference for further research on the development and utilization of traditional Chinese medicine resources. The entire workflow of this study is shown in Fig. 1.

Data collection

The TCMSP database [14] and the PubChem database [22] (https://pubchem.ncbi.nlm.nih.gov/), were used to collect the active chemical compounds of ten herbs in XSLJP and their structures, respectively. Based on their ADME parameters, the active components were identified according to the following criteria: oral bioavailability (OB) ≥ 30%, drug-like property (DL) ≥ 0.18, and half-life (HL) ≥ 4 h. Any duplicates were then removed.

The DisGeNET database [17] was searched using the keyword ‘gastritis’ to collect gastritis-related targets, and the sample ID used in the search was DisGeNET UMLS CUI: C0017152.

Target prediction and verification of active components

The TCMSP database [14], STP database [15] and STITCH database [16] were used for predicting and screening the targets of the active components. The predicted targets from the TCMSP database were identified based on the following criteria: OB ≥ 30%, DL ≥ 0.18, and HL ≥ 4 h. The predicted targets from the STP database were retrieved based on a probability greater than 0.3, and those from the STITCH database had a confidence value greater than 0.4.

After integrating and removing duplicated targets, the gene symbol of the predicted targets were obtained in the UniProt ID format using the UniProt [23] (http://www.uniprot.org) database. The common targets from the list of predicted targets and that of gastritis-related targets were then obtained.

The PubMed database was used for literature mining to identify information on the protein targets of the various TCM components. Although the protein targets of some active components were confirmed by literature records, the remaining common targets were further verified by molecular docking.

Network construction

The common targets were analyzed using the STRING database [19], and a protein-protein interaction network diagram (PPI) was obtained and then imported into Cytoscape 3.7.1 software [20]. Subsequently, we calculated the topological parameters, such as the degree, betweenness centrality and closeness centrality, of the PPI diagram for network screening and rendering to determine an appropriate or the best layout. Using Cytoscape, the key proteins with topological parameters higher than the average of all the nodes in the network were identified [20].

In addition, the drugs, components and common targets were imported into Cytoscape 3.7.1 software to obtain the ‘drug-component-common target’ network diagram, and a topological analysis using the degree value as the main reference standard was conducted to identify the key active components [20].

Molecular docking

Discovery Studio software was used to simulate the interaction between molecules and to predict their binding pattern and affinity based on the receptor characteristics and the interaction modes between protein receptors and drug molecules [18]. The molecular structures of the active components were obtained from the PubChem database [22] (https://pubchem.ncbi.nlm.nih.gov/), and the crystal structures of the proteins were downloaded from the PDB database [24] (http://www.rcsb.org/).

Molecular docking between the active components and experimentally unverified targets was performed using the LibDock mode under the CHARMM force field [25]. The docking parameters were set as follows: docking tolerance, 0.25; energy threshold, 20.9; docking performance, high quality; construction method, FAST; minimization algorithm, intelligent minimum; and all other parameters, default values. Through computer calculation, a LibDock score is obtained, and a higher score indicates better docking. In addition, the molecular docking of key active components and hub protein targets was performed using the more accurate flexible docking mode, in which both the conformation of the active components and the amino acid residues at the binding sites of protein receptors can vary during the docking process [26]. In the flexible docking model, the result named -CDOCKER INTERACTION ENERGY is obtained [26]. A greater negative value of energy indicates a more stable docking interaction.

Pathway analysis

To obtain their enriched pathways and functions, the common target proteins were further investigated through Kyoto Encyclopedia of Genes and Genomics (KEGG) gene pathway analysis and Gene Ontology Database (GO) biological process analysis (BP) using the DAVID Biological Information Annotation database [21]. Using P < 0.05 as the screening criteria, the top 20 signaling pathways and top 20 biological processes with the lowest P-values obtained from the KEGG signaling pathway and GO analyses, respectively, were selected.

Active components in XSLJP and protein targets related to gastritis

In the TCMSP database, 122 compounds were screened through a search using the following keywords: Radix Aucklandiae (RA, muxiang), Amomum (AM, sharen), Ginger (GI, shengjiang), Codonopsis (CO, dangshen), Atractylodes (AT, baizhu), Poria (PO, fuling), Glycyrrhiza (GL, gancao), Tangerine peel (TP, chenpi), Pinellia (PI, banxia), and Jujube (JU, dazao). Because the main active ingredients of RA [27], AM [28] and AT [29] are volatile oils with important pharmacological activities, the screening conditions for these three medicines were relaxed and adjusted to “OB ≥ 30%, DL ≥ 0.1, and HL ≥ 1 h”. Subsequently, 19 compounds, including one in RA, namely, cynaropicrin, 11 in AM, such as junipene and isospathulenol, and seven in AT, including atractylenolide I, atractylenolide II and atractylenolide III, were reincorporated in this study, and these were marked in Table 1S. The active components include six in RA, 20 in AM, three in GI, five in CO, 12 in AT, 15 in PO, 49 in GL, four in TP, three in PI and five in JU. After the duplicate compounds were deleted, a total of 118 active components were obtained, and these were shown in Table 1S.

Gastritis-related targets were collected using the DisGeNET database, and as shown in Table 2S, 180 targets were obtained using the keyword ‘gastritis’.

Target prediction and literature-mining verification of active components

As shown in Table 3S, 327 targets were screened from the STP, STITCH and TCMSP databases based on the 118 active components using the standardization procedure and the UniProt database. According to their structures, the predicted targets can bind to the active component. The intersection among the predicted targets (Table 3S) and gastritis-related targets (Table 2S) yielded 26 common targets (Table 1), and these were considered potential targets of XSLJP in the treatment of gastritis.

As shown in Table 4S, a literature search of the PubMed database showed that 21 of the 26 common targets can interact with the components of XSLJP, and these were thus considered verified targets. The remaining five common targets had not been validated in the literature and were thus considered unverified targets. Using Discovery Studio software, 118 active components (Table 1S) and the five unverified targets (Table 1) were successfully docked with a reasonable score under the LibDock mode, as shown in Table 5S. As a result, all 26 targets have been verified. A network diagram of the active components in XSLJP and 21 experimentally verified targets was constructed (Fig. 2), and a network diagram of the active compounds and five computationally verified targets was also built (Fig. 3).

Table 1

Information on the 26 common targets.
Target Name	Gene Name
Matrix metalloproteinase-2	MMP2
Heme oxygenase 1	HMOX1
Interleukin-10	IL10
Signal transducer and activator of transcription 3	STAT3
Prostaglandin G/H synthase 2	PTGS2
Interleukin-1 β	IL1B
Proto-oncogene c-Fos	FOS
Interleukin-4	IL4
Transcription factor AP-1	JUN
Peroxisome proliferator-activated receptor gamma	PPARG
Tumor necrosis factor	TNF
Cytochrome P450 3A4	CYP3A4
Interleukin-6	IL-6
Cellular tumor antigen p53	TP53
Epidermal growth factor receptor erbB1	EGFR
Prostaglandin G/H synthase 1	PTGS1
Apoptosis regulator Bcl-2	BCL2
Matrix metalloproteinase-9	MMP9
Chemokine (C-C motif) ligand 2	CCL2
Hypoxia-inducible factor 1-α	HIF1A
P-glycoprotein 1	ABCB1
Telomerase reverse transcriptase*	TERT
Nitric oxide synthase, inducible*	NOS2
Myeloperoxidase*	MPO
Transforming growth factor β-1*	TGFB1
Ornithine decarboxylase*	ODC1
Note: The asterisk (*) indicates that the target has not been verified by previous experiments, as determined by a search of the PubMed database.

Prediction of the key targets of XSLJP in the treatment of gastritis.

As shown in Fig. 4, the protein-protein interaction network (PPI) composed of 26 potential gastritis-related targets (Table 1) of XSLJP contains 26 nodes with 218 edges, and the average node degree value was 16.8. The nodes represent the proteins, and the edges represent the interactions between proteins. A larger node size indicates a greater degree value.

The protein targets interact with each other to achieve therapeutic effects. We performed a topological parameter analysis of these protein targets and identified eight proteins located in the center of the PPI diagram. As shown in Table 2, these eight proteins might be the key targets of XSLJP in the treatment of gastritis.

Table 2

Information on the key targets located in the center of the PPI network.
Gene name	Protein Name	Degree	Betweenness Centrality	Closeness Centrality
TP53	Cellular tumor antigen p53	24	8.25E-02	9.62E-01
IL6	Interleukin-6	23	4.77E-02	9.26E-01
PTGS2	Prostaglandin G/H synthase 2	23	3.53E-02	9.26E-01
STAT3	Signal transducer and activator of transcription 3	22	4.16E-02	8.93E-01
JUN	Jun proto-oncogene	21	1.74E-02	8.62E-01
FOS	Fc fragment of IgG receptor Ⅲb	20	1.77E-02	8.33E-01
EGFR	Epidermal growth factor receptor ErbB1	19	2.73E-02	8.06E-01
HIF1A	Hypoxia-inducible factor 1-alpha	18	1.85E-02	7.81E-01

Prediction of the key active components in XSLJP

The ten TCM herbs (Table 1S), the 118 active components (Table 1S) and the 26 common targets (Table 1) were imported into Cytoscape 3.7.1 software to construct a ‘drug-component-common target’ network diagram that contained 154 nodes and 364 edges, as shown in Fig. 5. In this network diagram, the yellow square nodes represent the drugs, the green circular nodes represent the gastritis-related targets, and the remaining square nodes represent the active components. Among the latter nodes, the purple nodes represent the components bound to gastritis-related targets, and the blue nodes represent those that do not bind to gastritis-related targets, as determined by either experimental or computational validation.

XSLJP is a complex Chinese herbal medicine with various components, each of which can act on multiple disease targets and play a synergistic role in the treatment of diseases. As intuitively shown in the network diagram (Fig. 5), some nodes are more concentrated in the network than others. Therefore, the active components were ranked by degree value, which can be used as the main reference standard in a topological analysis. Using degree > 6 as the threshold, 12 important active components in this prescription were found, which are likely to be the key active components of this prescription in the treatment of this disease. Table 3 shows the gastritis-related target genes of these 12 active components.

Table 3

Information on the 12 key active components and their corresponding targets.
Mol ID	Degree	Active component	Herb	Target
Mol ID	Degree	Active component	Herb	Previously verified	Verified in the current study
MOL002714	12	Baicalein	PI	MMP9, HIF1A, EGFR, HMOX1, PTGS2, JUN, TNF, CYP3A4	TERT, ODC1, TGFB1
MOL005828	10	Nobiletin	TP	ABCB1, HIF1A, CCL2, HMOX1, STAT3	MPO, ODC1, TGFB1
MOL004328	8	Naringenin	TP	MMP9, CCL2, BCL2, MMP2, HMOX1, IL10, IL1B, IL4, PPARG, CYP3A4, IL6	TERT, MPO, ODC1, TGFB1
MOL008400	7	Glycitein	CO	/	TERT, NOS2, MPO, TGFB1, ODC1
MOL000449	7	Stigmasterol	RA/AM/GI	IL10, JUN, TNF	TERT, MPO, ODC1, TGFB1
MOL000392	7	Formononetin	GL	CCL2, EGFR, IL1B, PPARG, TNF, IL6	TERT, MPO, ODC1, TGFB1
MOL000358	7	Beta-Sitosterol	AM/GI	HIF1A, PTGS1, BCL2, PTGS2, PPARG, TNF	TERT, MPO, ODC1, TGFB1
MOL004828	6	Glepidotin A	GL	/	TERT, NOS2, MPO, TGFB1, ODC1
MOL004815	6	*	GL	/	TERT, NOS2, MPO, TGFB1, ODC1
MOL003896	6	#	CO/GL	CCL2, IL1B, IL6	TERT, MPO, ODC1
MOL001454	6	Berberine	JU	MMP9, ABCB1, HIF1A,CCL2, TP53, EGFR, BCL2, HMOX1, PTGS2, PPARG, TNF, CYP3A4	TERT, MPO, TGFB1, ODC1
MOL000497	6	Licochalcone α	GL	PTGS1, BCL2	TERT, MPO, NOS2, ODC1, TGBF1
Note: *: (E)-1-(2,4-Dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one
#: 7-Methoxy-2-methyl isoflavone

Molecular docking of the active components with the key targets

The flexible docking mode in DS software was used to conduct the molecular flexible docking of eight key targets (Table 2) and 12 key active components (Table 3), and the docking results are shown in Table 4. For each key target, the active components with the best flexible docking energies were selected as representatives to show the docking poses: 3D images of these docking poses are shown in Fig. 6, and 2D images of the interactions between the eight key targets and their best-docked active components are shown in Fig. 7.

Table 4

Docking energies between eight key targets and 12 key active components obtained by flexible docking.
Mol ID	Molecule Name	Flexible Docking Score
		(Gene Name/PDB ID, -CDOCKER INTERACTION ENERGY)
		TP53	IL6	PTGS2	STAT3	JUN	FOS	EGFR	HIF1A
		6GGA	1ALU	5KIR	6NUQ	5VPC	5VPF	3BEL	5OP8
MOL002714	Baicalein	36.0605	31.3986	34.6661	31.9157	35.2962	35.1087	38.0165	41.1303
MOL005828	Nobiletin	41.6575	39.0936	36.8723	37.9533	47.2618	39.4869	41.1759	44.9912
MOL004328	Naringenin	35.9583	39.2056	40.8041	34.5874	42.6349	37.5361	36.5803	41.2761
MOL008400	Glycitein	37.1968	40.2042	69.5127	42.7787	100.947	102.028	50.8868	60.6203
MOL000449	Stigmasterol	36.3805	43.0098	38.6033	34.2369	43.33	34.3709	40.422	50.7195
MOL000392	Formononetin	37.1416	39.8117	61.2005	33.982	80.7786	73.3408	46.0669	52.115
MOL000358	beta-Sitosterol	40.3303	40.3777	47.7691	37.7057	43.26	39.4006	42.4623	49.1203
MOL004828	Glepidotin A	50.7746	64.5126	90.9121	54.0966	92.7171	93.6863	57.3507	66.5643
MOL004815	*	45.5441	38.7195	46.8489	41.4946	44.0769	38.6819	41.3596	50.9278
MOL003896	#	38.1761	29.8178	40.6482	34.2941	38.2972	32.6945	36.6083	38.5724
MOL001454	Berberine	45.5441	42.0403	34.6332	36.1308	37.1438	25.349	45.693	46.7117
MOL000497	Licochalcone α	48.0094	49.7856	54.5744	52.5523	75.8474	70.7379	54.9272	60.7223
Note: *: (E)-1-(2,4-Dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one
#: 7-Methoxy-2-methyl isoflavone

Enrichment analysis of the potential targets of XSLJP in the treatment of gastritis

The 26 common targets (Table 1) that might play crucial roles in the treatment of gastritis were imported into the DAVID database for KEGG pathway enrichment analysis and GO biological function analysis with the species set to ‘Homo sapiens’. The analyses yielded 58 KEGG pathways and 230 GO biological processes. Table 5 lists the top 20 KEGG pathways with a P-value < 0.05, Fig. 8 shows their bubble plot drawn using R language software, Table 6 lists the top 20 GO biological processes with a P-value < 0.05, and Fig. 9 shows their bubble plot drawn using R language software.

Table 5

Information on the top 20 pathways identified from the KEGG pathway enrichment analysis.
Term	Count	P-Value	Genes
Pathways in cancer	14	1.58E-10	EGFR, IL6, PTGS2, MMP9, PPARG, TP53, MMP2, STAT3, TGFB1, FOS, HIF1A, JUN, BCL2, NOS2
Leishmaniasis	9	8.08E-11	IL4, FOS, TNF, PTGS2, JUN, IL1B, NOS2, IL10, TGFB1
Chagas disease (American trypanosomiasis)	9	1.82E-09	FOS, IL6, TNF, CCL2, JUN, IL1B, NOS2, IL10, TGFB1
Hepatitis B	9	2.57E-08	FOS, IL6, TNF, JUN, BCL2, MMP9, TP53, TGFB1, STAT3
Inflammatory bowel disease (IBD)	8	1.82E-09	IL4, IL6, TNF, JUN, IL1B, IL10, TGFB1, STAT3
TNF signaling pathway	8	6.90E-08	FOS, IL6, TNF, CCL2, PTGS2, JUN, MMP9, IL1B
Proteoglycans in cancer	8	4.87E-06	EGFR, TNF, HIF1A, MMP9, TP53, MMP2, TGFB1, STAT3
MicroRNAs in cancer	8	5.03E-05	EGFR, PTGS2, HMOX1, BCL2, MMP9, TP53, ABCB1, STAT3
Pertussis	7	2.06E-07	FOS, IL6, TNF, JUN, IL1B, NOS2, IL10
Rheumatoid arthritis	7	5.38E-07	FOS, IL6, TNF, CCL2, JUN, IL1B, TGFB1
HIF-1 signaling pathway	7	9.02E-07	EGFR, IL6, HIF1A, HMOX1, BCL2, NOS2, STAT3
Tuberculosis	7	3.14E-05	IL6, TNF, BCL2, IL1B, NOS2, IL10, TGFB1
Herpes simplex infection	7	3.79E-05	FOS, IL6, TNF, CCL2, JUN, TP53, IL1B
Cytokine-cytokine receptor interaction	7	1.84E-04	IL4, IL6, TNF, CCL2, IL1B, IL10, TGFB1
MAPK signaling pathway	7	2.29E-04	EGFR, FOS, TNF, JUN, TP53, IL1B, TGFB1
HTLV-I infection	7	2.34E-04	FOS, IL6, TNF, JUN, TP53, TGFB1, TERT
Malaria	6	7.10E-07	IL6, TNF, CCL2, IL1B, IL10, TGFB1
Amoebiasis	6	3.29E-05	IL6, TNF, IL1B, NOS2, IL10, TGFB1
Toxoplasmosis	6	3.93E-05	TNF, BCL2, NOS2, IL10, TGFB1, STAT3
Osteoclast differentiation	6	9.08E-05	FOS, TNF, JUN, PPARG, IL1B, TGFB1

As shown in Fig. 8, 26 targets of XSLJP in the treatment of gastritis were predominant in various pathways, including infectious disease pathways, immune inflammatory pathways, cancer pathways, signaling pathway, and cell growth and death pathways. The top 20 KEGG pathways included nine infectious disease pathways, namely, leishmaniasis, Chagas diseases, pertussis, malaria, tuberculosis, amoebiasis, herpes simplex infection, toxoplasmosis and hepatitis B; three immune inflammatory pathway, namely, inflammatory bowel disease, rheumatoid arthritis and HTLV-I infection; three cancer pathways, namely, pathways in cancer, proteoglycans in cancer and microRNAs in cancer; three cell growth and death pathways, namely, the TNF signaling pathway, osteoclast differentiation and cytokine-cytokine receptor interaction; and two signaling pathways, namely, the HIF-1 signaling pathway and the MAPK signaling pathway. The pathway with the smallest P-value of 8.08E-11 was leishmaniasis, whereas that with the largest count of 14 was pathways in cancer.

Table 6

Information on the top 20 biological processes identified from the GO functional enrichment analysis.
Term	Count	P-value	Genes
Positive regulation of transcription from RNA polymerase II promoter	13	3.79E-09	IL4, EGFR, IL6, TNF, PPARG, TP53, STAT3, TGFB1, IL10, FOS, HIF1A, JUN, IL1B
Positive regulation of transcription, DNA-templated	12	6.14E-11	IL4, FOS, IL6, TNF, HIF1A, JUN, PPARG, TP53, IL1B, IL10, TGFB1, STAT3
Response to drug	11	8.39E-12	IL4, FOS, IL6, PTGS2, JUN, BCL2, PPARG, ABCB1, IL10, TGFB1, STAT3
Inflammatory response	9	4.84E-08	FOS, IL6, TNF, CCL2, PTGS2, PTGS1, IL1B, IL10, TGFB1
Negative regulation of apoptotic process	9	1.97E-07	IL4, EGFR, IL6, BCL2, MMP9, TP53, MPO, IL10, STAT3
Aging	8	3.22E-09	FOS, IL6, CCL2, JUN, MPO, IL10, TGFB1, STAT3
Negative regulation of cell proliferation	8	1.28E-06	IL6, PTGS2, JUN, TP53, IL1B, IL10, TGFB1, STAT3
Positive regulation of gene expression	7	1.88E-06	IL6, TNF, HIF1A, TP53, IL1B, TGFB1, STAT3
Positive regulation of pri-miRNA transcription from RNA polymerase II promoter	6	7.30E-11	FOS, HIF1A, JUN, TGFB1, TERT, STAT3
Positive regulation of smooth muscle cell proliferation	6	2.47E-08	EGFR, IL6, TNF, PTGS2, JUN, HMOX1
Cellular response to hypoxia	6	2.67E-07	HIF1A, PTGS2, HMOX1, BCL2, TP53, TERT
Positive regulation of sequence-specific DNA-binding transcription factor activity	6	4.18E-07	IL4, IL6, TNF, PPARG, IL1B, IL10
Response to hypoxia	6	4.79E-06	HIF1A, CCL2, HMOX1, NOS2, MMP2, TGFB1
Positive regulation of ERK1 and ERK2 cascade	6	5.21E-06	EGFR, IL6, TNF, CCL2, JUN, TGFB1
Angiogenesis	6	1.69E-05	HIF1A, CCL2, PTGS2, JUN, HMOX1, MMP2
Positive regulation of apoptotic process	6	6.97E-05	IL6, TNF, PTGS2, HMOX1, TP53, TGFB1
Immune response	6	3.40E-04	IL4, IL6, TNF, CCL2, IL1B, IL10
Positive regulation of cell proliferation	6	5.41E-04	EGFR, ODC1, IL6, BCL2, TGFB1, STAT3
Negative regulation of transcription, DNA-templated	6	7.37E-04	IL4, TNF, JUN, PPARG, TP53, TGFB1
Signal transduction	6	2.61E-02	EGFR, HIF1A, CCL2, PPARG, IL1B, STAT3

As shown in Fig. 9, the gastritis-related targets are involved in a variety of biological processes, which mainly include the biological processes that regulate reactions, growth/apoptosis and gene expression. The top 20 biological processes include eight biological processes that regulate growth and apoptosis, namely, positive regulation of cell effort, positive regulation of smooth muscle cell effort, negative regulation of cell effort, aging, angiogenesis, signal transduction, positive regulation of apoptotic process, negative regulation of apoptotic process; seven biological processes that regulate gene expression, namely, positive regulation of transcription (DNA-templated), positive regulation of transcription from RNA polymerase II promoter, transcription of pri-miRNA transcription from RNA polymerase II promoter, positive regulation of link-specific DNA binding transcription factor activity, positive regulation of ERK1 and ERK2 cascade, and negative regulation of transcription (DNA-templated); and five biological processes that regulate reactions, namely, response to drug, response to hypoxia, cellular response to hypoxia, immune response, and inhibitory response. In addition, the biological process with the smallest P-value of 8.39E-12 was response to drug, whereas that with the largest count of 13 was positive regulation of transcription from RNA polymerase II promoter.

Gastritis, a general digestive system disease resulting from a variety of factors, is a type of infectious or autoimmune inflammation characterized by gastric mucosal damage [30]. At present, the first-line drugs for gastritis therapy are antibiotics and proton pump inhibitors, but these drugs are not sufficiently specific to this disease and have serious side effects [31, 32]. In addition, the nonsteroidal anti-inflammatory drugs (NSAIDs) commonly used in the clinic to reduce inflammation can easily cause gastric mucosal damage and subsequently induce peptic ulcers [33]. Significantly, long-term chronic inflammation of the gastric mucosa might lead to atrophy of the mucosa and glands and could even develop into gastric cancer [34]. Therefore, it is necessary to find safer and more effective drugs from natural drugs for the treatment of gastritis. TCM prescriptions have gradually developed through the processing of compatible Chinese herbal medicines according to long-term and rich experiences. TCMs are characterized by the synergistic effect of multiple components, and their advantages lie in long-lasting efficacy, mild medicinal properties, nourishing effects, and outstanding effects on the treatment of chronic diseases [35]. XSLJP is a complex TCM prescription with a complicated mechanism that remains unelucidated. Clearly exploring the mechanism of XSLJP and reasonably adjusting the drug dosage according to the drug characteristics will improve the application of XSLJP in the clinical treatment of gastritis.

Active components and corresponding targets of XSLJP in the treatment of gastritis

In this study, 26 potential targets of XSLJP in the treatment of gastritis were identified (Table 1), and 21 of these were verified by literature mining using the PubMed database, which demonstrated the reliability and validity of this study. In addition, molecular docking simulations demonstrated that the remaining five targets can be well docked with some of the active components of XSLJP (Table 5S and Fig. 3), which suggests that the 26 key targets are likely the main targets of XSLJP in the treatment of gastritis. Subsequently, we analyzed the topological parameters of these 26 targets according to their PPI diagram (Fig. 4) and obtained eight targets with a high degree of interaction (Table 2), and these eight targets, namely, TP53, IL6, PTGS2, STAT3, JUN, FOS, EGFR, and HIF1A, might be the key targets of XSLJP in the treatment of gastritis.

To date, most of the active components of XSLJP have been confirmed to have biological activity and various pharmacological effects. The interactions between different active components in a TCM preparation exert a therapeutic effect as a whole, and the preparation must have certain key active components that exert the major pharmacological effects. According to the ‘drug-compound-common target’ network (Fig. 5), we identified 12 potential key active components of XSLJP with a degree higher than 6 (Table 3). The targets related to these 12 key active components are consistent with 24 of the 26 potential targets of XSLJP in the treatment of gastritis (Table 1), which demonstrates that these 12 key active components are likely the key active components of XSLJP in the treatment of gastritis.

Based on the abovementioned identifications, flexible docking simulations between the 12 key active components and the eight key targets were conducted to further explore their mode of interactions. The results showed that all 12 key active components and eight key targets had good docking scores (Table 4) with satisfactory docking postures (Fig. 6 and Fig. 7). Moreover, according to the docking score results, a heatmap was drawn to show the correlation between the key components and key targets (Fig. 10). As shown in the figure, four key active components, namely, glycitein, formononetin, glepidotin A and licochalcone α, had relatively better docking scores with most of the eight hub targets. These findings are particularly interesting because they provide novel ideas as described below for further studies of this TCM medicine, including biological experimental verification, XSLIP prescription adjustment and single-component Western medicine development.

Potential key signaling pathways involved in the mechanisms of XSLJP in the treatment of gastritis

In our study, the GO biological function analysis (Table 6 and Fig. 9) showed that the abovementioned targets are related to a variety of biological processes, including positive regulation of transcription from the RNA polymerase II promoter and positive regulation of transcription. In addition, the KEGG enrichment analysis (Table 5 and Fig. 8) revealed that these targets are involved in infectious disease pathways and immune inflammatory pathways, among other pathways. These results indicate that the mechanism of XSLJP in the treatment of gastritis might be related to these extensive and intricate signaling pathways.

Figure 11 shows a schematic diagram of the possible mechanism of XSLJP based on the 26 common targets and their main enriched signals. In the figure, the boxes represent the corresponding proteins encoded by the gene targets, and the green and yellow boxes represent the experimentally verified targets and the key targets, respectively. The schematic diagram shows the positions of the proteins encoded by the 26 genes in the signal pathways and their upstream and downstream relationships. On the one hand, Fig. 11 shows the existence of several target clusters, and these are mainly concentrated in the TNF, JAK/STAT, MAPK (ERK/JNK/p38), p53 and NO/VEGF signaling pathways. On the other hand, most of the predicted targets of the active components are closely related to immune regulation and influence various inflammatory cytokines, including IL-1β, IL-6/8/10/12, TNF-α, COX-2, iNOS, NO, TGF-β, NF-κB, and IFN-γ, to regulate the immune process of the body. The following discussion will focus on the key active components, the key targets and their corresponding signaling pathways related to XSLJP in the treatment of gastritis.

XSLJP and the TNF signaling pathway

Figure 1S shows detailed information on the TNF signaling pathway. TNF-α can be regulated by some gene promoters, such as NF-κB, c-Jun, and AP1, and subsequently induces apoptosis by TNF-α family proteins, such as FasL, TNF-α and TRAIL, whose expression is mediated by linker proteins containing death domains, such as FADD [36]. By mediating linker proteins without the death domain, such as TRAF, TNF-α can induce Toll-like receptor (TLR), IL-1 receptor (IL1R) or other members of the TNF family to activate NF-κB, JNK, p38 and other downstream signaling pathways [36]. Due to its pivotal location with respect to the TLR downstream signaling pathway, NF-κB can induce chemotactic granulocytes and macrophages, increase capillary permeability and cause lymphocyte infiltration to participate in the early immune response and inflammatory reaction by initiating target genes to express inflammatory factors, such as TNF-α, IL-1/6/8/12 and IFN [37]. The activation of this pathway can not only directly stimulate the synthesis of ICAF and IL-1 to promote cell adhesion, proliferation and inflammation but also indirectly stimulate other cells to produce vascular growth factor and promote angiogenesis and the apoptosis of transformed cells [38]. Formononetin, naringenin, berberine and other active components of XSLJP can regulate the expression of TNF, IL-1B, IL-10 and other cytokines in the TNF signaling pathway. For instance, formononetin can reduce intracellular calcium, restrict the activation of NF-κB, the phosphorylation of its upstream protein IκKα and the activity of caspase-1, and further inhibit the release of histamine and the secretion of TNF-α, IL-1β and IL-6, which ultimately results in the exertion of antioxidant, anti-inflammatory, proapoptotic and antitumor activities [39]. Naringenin can reduce the lung injury of mice with mycoplasma pneumonia to exhibit anti-inflammatory activity by inhibiting the expression of autophagy-mediated proinflammatory cytokines, such as IL-6, IL-1β, TNF-α and TGF-β, as well as pulmonary fibrosis [36]. Berberine can significantly inhibit the expression of IL-6 and TNF-α in human HK-2 cells damaged by palmitic acid (PA) and can attenuate PA-induced lipotoxicity through the PPAR-α pathway [40]. In addition, our calculations revealed that glepidotin A showed the best docking to IL-6 with the lowest docking energy, which suggests that glepidotin A might exert a series of downstream biological effects by binding with the IL-6 protein to mediate its protein expression level and biological function, but this hypothesis needs to be verified by future experiments. It is worth noting that glepidotin A can also combine tightly with all seven other hub targets, particularly PTGS2, JUN and FOS, with very low docking energies, which implies that this compound might significantly contribute to pill therapy for gastritis and has the potential to be developed into a single component-multiple target agent.

XSLJP and the JAK/STAT signaling pathway

Figure 2S shows detailed information on the JAK/STAT signaling pathway. Cytokines such as STAT3, CCL2, IFN-α/β/γ, IL-6/10 and MMP-9 can activate the JAK/STAT signaling pathway in host cells [41]. These cytokines can phosphorylate JAK to activate STAT3, which can enter the cell nucleus and further combine with its target gene promoters to stimulate transcription [41]. STAT3 protein plays an important role in selectively inducing and maintaining the oncogenic inflammatory microenvironment at the beginning of malignant transformation and cancer development [42]. In addition, activated JAKs can also upregulate the expression of STAT3 via the Grb2/mTOR or Grb2/Ras/Raf/MEK/p-ERK/TF pathways [43]. As a crucial activator of STAT3 [44], IL-6 can indirectly promote the production of the proinflammatory factor CCL2 in monocytes to drive downstream Ca²⁺ signal transduction and thereby lead to enhanced expression of MMP-9, which can degrade a variety of extracellular matrix components and further promote tumor cell infiltration and metastasis [45]. In addition, CCL2 can trigger the secretion of CCL2/CCR2 to promote and maintain the generation of an immunosuppressive microenvironment [45]. IL-10, a major immunomodulatory cytokine, can activate STAT3 phosphorylation directly in a positive feedback manner due to the STAT3 binding site in its promoter after JAK activation [45, 46]. IL-10 can also downregulate the production of IL-12 and the expression of costimulatory molecules in macrophages and thereby reduces the generation of the Th1 response [47]. Ergosterol peroxide (EP), nobiletin, naringenin and other active components of XSLJP can affect the expression of cytokines in the JAK/STAT signaling pathway, such as STAT3, CCL2, IFN-α/β/γ, IL-6/10 and MMP-9. For instance, EP can significantly inhibit the phosphorylation of STAT3 and the expression and secretion of VEGF-C, and as a result, EP can counteract tumor angiogenesis and thereby suppress ovarian tumor growth[48]. Similarly, by reducing the activity and expression of STAT3, nobiletin can inhibit the angiogenesis of CD36-dependent breast cancer cells [49] and estrogen receptor-positive (ER⁺) breast cancer cells [50]. In addition, nobiletin can significantly and dose-dependently inhibit the secretion of inflammatory mediators such as NO, TNF-α and CCL2 in a coculture of adipocytes and macrophages [51]. Another active component, naringenin, can upregulate the expression of IL-6/10/18R1/18RAP, MMP-2/-3/-9, IFG1R and BCL2 and thus exerts anti-inflammatory and regenerative effects in degenerated human NP cells [52]. As demonstrated by our calculations, licochalcone α, in addition to glepidotin A as described above, exhibits high binding affinity to the active pocket of STAT3, which pending experimental verification, suggests that licochalcone α might combine with STAT3 and inhibit the expression of STAT3 or the biological effects of STAT3 against gastritis.

XSLJP and the MAPK (ERK/JNK/p38) signaling pathway

Figure 3S shows detailed information on the MAPK (ERK/JNK/p38) signaling pathway. Activation of the ERK1/2 pathway mainly amplifies the signal, whereas activation of the p38 pathway induces the expression of IL-1β/6/8, TNF-α, COX-2, iNOS, and prostaglandins, which participate in the proliferation and differentiation of immune cells [53, 54]. In the JNK/SAPK pathway, JNK can be activated by cytokines such as VEGF, IFN-α, IL-1, TRAF2, TGF-β and TNF-α and can then be phosphorylated and incorporated into the nucleus through the MAPK cascade reaction to act on downstream transcription factors such as c-Jun, c-Fos, AP-1, Elk1, p53, c-Myc, and Smad4 [53–55]. In the presence of IL-6, TGFβ1 drives the differentiation of T helper 17 (Th17) cells and then promotes further inflammation and enhances autoimmune conditions [56]. After binding to epidermal growth factor (EGF) and other ligands, EGFR autophosphorylates and stimulates Ras protein, which triggers gene transcription and control cell proliferation, differentiation and survival by activating signaling pathways such as the MAPK and PI3K/Akt pathways [57]. When the gastric mucosa is injured, the expression of EGFR in gastric parietal cells and related tissues is significantly increased [58], which might enhance the expression or activity of EGFR downstream signaling molecules such as ERK2, MEK1, AP1 and plasma membrane RTK [57]. In addition, EGFR can activate STAT3 and induce a series of biological effects downstream of STAT3, as discussed above discussion. Berberine, formononetin, and other active components of XSLJP can affect the expression of TGFB1, IL-6, TNF, EGFR and other cytokines in the MAPK signaling pathway. For instance, berberine can greatly reduce the expression of EGFR, further inhibit the phosphorylation of Raf, MEK and ERK, and thereby induce glioblastoma senescence [59]. Formononetin, another key component, has been identified as an EGFR inhibitor that can inhibit the tumor growth of nonsmall cell lung cancer through the EGFR/Akt/Mcl-1 pathway [60]. In this study, naringenin was the best-docked compound to TGFB1 in the LibDock mode (Table 5S), which suggests that this compound might directly bind to TGFB1, but its induction of the expression of TGFB1 and further downstream effects on gastritis need to be verified by biological experiments. In addition, glycitein exhibited significant binding affinity within the active site of EGFR, c-Jun and c-Fos, which suggests that glycitein might also target these three proteins to inhibit the expression of EGFR and the downstream transcription factors c-Jun and c-Fos or influence its downstream biological effects in gastritis therapy, but these effects remain to be confirmed by further experiments.

XSLJP and the p53 signaling pathway

Figure 4S shows detailed information on the p53 signaling pathway. As a key node in the p53 signaling pathway, p53, a transcription factor, can interact with a variety of functional proteins to induce a series of biological effects, such as blocking the cell cycle, promoting apoptosis, repairing DNA and inhibiting tumor angiogenesis and inflammation [61]. As an important downstream protein of p53 and a cyclin-dependent protein kinase inhibitor, p21 can bind to a series of cyclin-CDK complexes to inhibit the activity of corresponding protein kinases and thereby affects the phosphorylation of its downstream protein, interferes with the transcriptional regulation of CDK/cyclin complexes and ultimately causes cell cycle arrest [62, 63]. Moreover, the p53 protein triggers cell apoptosis unless it applies its own exonuclease activity to repair damaged DNA by forming a complex with p21 and GADD45 [64]. Under most conditions, regulated by the p53 protein concurrently with the apoptotic protein Bax, Bcl-2 exerts an anti-apoptotic effect by preventing the release of apoptosis-forming factors such as cytochrome C from the mitochondria, regulating the transcription factors NF-κB, AP-1, CRE and NFAT by blocking their entry into the nucleus, and thereby jointly promoting apoptosis [65]. Di-n-butyl phthalate, atractylenolide II, naringenin, berberine and other active components of XSLJP can affect the expression of p53, Bcl-2, and other cytokines in the p53 signaling pathway. For instance, di-n-butyl phthalate can effectively induce the apoptosis of mouse osteoblasts through the downstream effect mediated by p53 activation [66]. Atractylenolide II can decrease the expression of CDK2, p-AKT, p-ERK and Bcl-2, increase the phosphorylation of p38 and p53, p21 and p27, and further activate caspase-3/-8/-9, which results in the induction of G1 cell cycle arrest and apoptosis in B16 melanoma cells [67]. Naringenin can upregulate the expression of IL6/10, MMP-2/-3/-9 and Bcl-2 and downregulate the expression of IL1A and CASP3 in degenerated human nucleus pulposus cells, and thus, this compound exhibits anti-inflammatory and regenerative effects [52]. Berberine can trigger the release of cytochrome C from the mitochondrial membrane space into the cytoplasm, significantly increase the expression of caspase-3/-7/-9, cleaved PARP and Bax and reduce that of Bcl-2, which ultimately results in activation of the apoptotic pathway [68]. Among the key predicted components identified in this study, berberine exhibited low binding energies with TP53, which suggests that berberine might affect a series of downstream biological effects by binding to the p53 protein to induce apoptosis in gastritis cells, but this hypothesis needs to be verified by further experiments.

XSLJP and the NO/VEGF signaling pathway

Figure 5S shows detailed information on the NO/VEGF signaling pathway. COX-1 encoded by the PTGS1 gene mainly exists in normal tissue cells and is responsible for the production of endogenous prostaglandins (PGs) involved in mucosal protection, whereas COX-2 encoded by the PTGS2 gene is generally expressed in inflammatory environments and cancer tissues rather than normal tissues [69]. In most cases of inflammation and cancer, COX-2 and NF-κB are often activated simultaneously, whereas inactivated EGFR triggers the EGFR signaling pathway through negative feedback to promote cancer cell proliferation [70]. Inflammatory factors such as NF-κB, IL-6/-8, and IFN-γ exert a certain therapeutic effect on gastric ulcers by stimulating inflammatory cells to express iNOS and produce NO, and the enhancement of this compounds might promote the protection of mucosal glycoproteins and reduce oxidative stress, which is speculated to be the therapeutic mechanism for gastric ulcers [71]. Under hypoxic conditions, an increase in HIF-1, a cell hypoxia receptor molecule encoded by the HIF1A gene, can stimulate the activation of VEGF, a basic factor for vascular growth regulation, and stimulate angiogenesis to promote oxygen delivery [72]. Berberine, mairin, nobiletin, licochalcone α, and other active components of XSLJP can affect the expression of COX-1/2 and iNOS in the NO/VEGF signaling pathway. For instance, berberine can downregulate the expression of HIF-1α and VEGF, inhibit the phosphorylation of Akt and ERK, and suppress the growth of NSCLC cells through main pathways, such as NF-κB/COX-2, HIF-1α/VEGF, PI3K/Akt, and RAR/MEK/ERK [68]. Mairin can inhibit the expression and transcriptional activity of HIF-1α under hypoxic conditions, inhibit the nuclear accumulation of STAT3 and the expression of key factors such as VEGF, and protect against angiogenesis in hypoxic PC-3 prostate cells [73]. Nobiletin can reduce the expression of NF-κB and HIF-1α and the phosphorylation of Akt in both PC-3 and DU-145 cells and further downregulate the expression of VEGF and c-Myc respectively to suppress the viability of these two cell lines [74]. Licochalcone α exerts a strong inhibitory effect on the production of thromboxane A2, arachidonic acid and collagen-induced platelet aggregation and thereby inhibits the expression of COX-1 and COX-2 against inflammation [75]. Among the key predicted components identified in this study, formononetin showed good binding affinity to both PTGS2, Jun and HIF1A, which suggests that formononetin is likely to integrate with these proteins to affect their expression level or induce their downstream function in suppressing the inflammatory process in gastritis. However, the existence of a direct interaction between this active component and its target proteins remains to be further verified by experiments.

The pharmacological effects of active components in TCM prescriptions are complementary to each other. As shown in Fig. 2 and Fig. 3, the following finding was obtained for all the targets, regardless of their experimental verification: either one active component corresponds to multiple targets or multiple components act on the same target, which indicates that the TCM prescription XSLJP treats gastritis through a multitarget and multichannel approach. Attentionally, the signaling pathways mainly included the TNF, JAK/STAT, MAPK, p53 and NO/VEGF signaling pathways, and the regulation of some active compounds on these pathways had been verified, which indicates the scientificity and reliability of this study. Notably, this study showed that the core active ingredients glepidotin A, licochalcone α, glycitein, berberine and formononetin interact with most of the core targets at the optimal level, which implies the importance of these core active ingredients in gastritis therapy, but the interaction between these compounds and core targets requires experimental verification. As indicated by the discussion above, this research has the potential to provide basic ideas for and guide follow-up studies on the mechanism of XSLJP in the treatment of gastritis.

Outlook/Prospects

In addition to exploring the mechanism of XSLJP in the treatment of gastritis, our research can also provide certain reference ideas for the future optimization of prescription compositions, dosage adjustment, clinical applications and the adjustment of combination schemes. For example, lauric acid, an active component in Codonopsis, possesses antibacterial, anti-inflammatory and anticancer properties [76, 77] but might also cause inflammation [78]. In addition, from a broad perspective, this study found that certain active components might exert a wide range of pharmacological effects by targeting multiple targets, as shown in Fig. 2 and Fig. 3, which provides a theoretical basis for the streamlining of TCM prescriptions. The heat map clearly shows the likely mutual interaction between key components and hub targets determined by molecular docking, which provides a clue for the optimization of prescription compositions in the development of multitarget TCM medicines.

The focus of this research was to explore the mechanism of XSLJP in the treatment of gastritis, but as observed in many studies, inflammatory conditions can promote cancerous development, which in turn induces an inflammatory microenvironment that is more conducive to cancer progression [79]. Normal gastric cells often undergo inflammation after being invaded by infectious Helicobacter pylori and further develop chronic inflammation, which might deteriorate into gastric cancer if no treatments are applied in time and properly. The correlation between gastritis and gastric cancer is obvious because gastritis meets the characteristics of cancer-related inflammation regarding specific inflammatory mediators, including cytokines and chemokines [79]. The above-described analysis on predicting targets of the active components of XSLJP and their related pathways confirms the importance of cytokines and chemokines in gastritis drug therapy. Therefore, our study also indicates that XSLJP might have the potential to be further studied for the treatment of gastric cancer.

In this study, we identified 12 key active components and eight hub therapeutic targets of XSLJP in the treatment of gastritis through network pharmacology. Based on our comprehensive analysis of these active components and targets, we propose that the mechanism of XSLJP in the treatment of gastritis involves multiple targets and multiple pathways, which mainly include the TNF, JAK/STAT, MAPK, p53 and NO/VEGF signaling pathways. The potential mutual interaction between core active ingredients, such as glepidotin A, glycitein, licochalcone α and formononetin, and hub targets, such as PTGS2, JUN, FOS and HIF1A, are worth further experimental research. This study provides new ideas for further exploring the detailed mechanism of XSLJP in the treatment of gastritis as well as a certain theoretical basis for further in-depth study of these ingredients, the optimization of compound prescriptions, the adjustment of clinical application schemes and even the development of new drugs.

Authorship contributions

ZH, JW, ZL and YL conceived and designed the experiments; ZL, TT, RY and XC collected the data; RY and XC performed the target prediction and the verification of the active components; TT performed the molecular docking simulations; ZL and TT constructed the network; JW, RY and XC prepared the figures; JW, ZL, TT, RY and XC analyzed the data and prepared the tables; JW and TT performed the pathway analyses; JW, ZL and TT wrote the manuscript; YL and ZH guided the writing of the manuscript; and ZH revised the manuscript and provided financial support. Additionally, all the authors read and approved the final manuscript.

Conflict of interest

The authors declare no conflict of interest.

Data Availability

The data that support the findings of this study are openly available in the TCMSP database (http://tcmspw.com/tcmsp.php), the SwissTargetPrediction (STP) database (http://www.swisstargetprediction.ch/), the STITCH database (http://stitch.embl.de/) and the DisGeNET database (https://www.disgenet.org/).

Funding sources

This work was supported by the Key Discipline Construction Project of Guangdong Medical University [4SG22004G] and the Higher Education Reform Project of Guangdong Province [2019268].

Acknowledgements

We thank American Journal Experts for their help in revising the English grammar.

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Investigation of the molecular mechanism of Xiangsha Liujun Pill in the treatment of gastritis based on network pharmacology and molecular docking

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Abstract

Figures

Introduction

Materials & Methods

Data collection

Target prediction and verification of active components

Network construction

Molecular docking

Pathway analysis

Results

Active components in XSLJP and protein targets related to gastritis

Target prediction and literature-mining verification of active components

Prediction of the key active components in XSLJP

Molecular docking of the active components with the key targets

Enrichment analysis of the potential targets of XSLJP in the treatment of gastritis

Discussion

Active components and corresponding targets of XSLJP in the treatment of gastritis

Potential key signaling pathways involved in the mechanisms of XSLJP in the treatment of gastritis

XSLJP and the TNF signaling pathway

XSLJP and the JAK/STAT signaling pathway

XSLJP and the MAPK (ERK/JNK/p38) signaling pathway

XSLJP and the p53 signaling pathway

XSLJP and the NO/VEGF signaling pathway

Outlook/Prospects

Conclusions

Declarations

Authorship contributions

Conflict of interest

Data Availability

Funding sources

Acknowledgements

References

Supplementary Files

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