Exploring the Common Genetic Signature and Molecular Mechanisms Between Gastritis and Gastric Cancer: A bioinformatics-coupled Network Pharmacology Analysis

doi:10.21203/rs.3.rs-2081262/v1

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Exploring the Common Genetic Signature and Molecular Mechanisms Between Gastritis and Gastric Cancer: A bioinformatics-coupled Network Pharmacology Analysis

https://doi.org/10.21203/rs.3.rs-2081262/v1

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Gastric cancer is a highly prevalent type of cancer among digestive system tumors. Early screening and intervention of gastric cancer can significantly improve the prognosis of patients. This paper aims to explore the driver genes associated with gastritis-gastric cancer progression and the therapeutic role of Chinese medicine based on bioinformatics analysis of microarray data. First, the microarray dataset GSE55696 of gastritis and gastric cancer was downloaded from the GEO database. The weighted gene co-expression network analysis was used to identify the gene modules associated with gastritis and gastric cancer. And the microarray dataset GSE130823 of gastritis and gastric cancer was downloaded for validation by differential gene analysis, and a total of 15 crossover genes were obtained. Second, the Kaplan-Meier plotter was used for survival analysis to determine the relationship between crossover genes and gastric cancer survival, resulting in CA1, CARNS1, CHAD, CLIC5, CXCL5, KRT6B, OSM, PEBP4, and RGL3 as biomarkers for the progression of chronic gastritis to early gastric cancer. Finally, the HERB database was used to search for compounds and herbs related to gastritis and gastric cancer progression, and to build a “target-compound-herb” network. And operating AutoDockTools 1.5.7 software for molecular docking of core components and core targets. Scopolamine alcohol, fraxetin, 6-aminopurine, citrulline and coumarin showed good docking activity with CA1, CARNS1, CXCL5, CHAD, and KRT6B. In conclusion, CA1, CARNS1, CHAD, CLIC5, CXCL5, KRT6B, OSM, PEBP4, and RGL3 may be used as biomarkers for the progression of chronic gastritis to early gastric cancer. Scopolamine, fraxetin, 6-aminopurine, citicoline, and coumarin may be novel agents against gastritis-gastric cancer progression.

gastritis

gastric cancer

WGCNA

Chinese herbs

bioinformatics

Gastric cancer (GC) was one of the most prevalent cancer, with estimated 1,089,000 new cases and 769,000 mortality cases in 2020 globally, ranked second in incidence and mortality rate of all malignant tumors of the digestive system[1]. About 50% of GC patients were in the advanced stage at the initial diagnosis. GC patients at the advanced stage have a poor prognosis, with a 5-year survival rate of 10%-30%. While the GC at the early stage has a better prognosis, and the 5-year survival rate after surgery or endoscopic resection was 90%[2–5]. The implementation of screening programs could dramatically improve the detection rate of GC, and the screening and intervention at the early stage of GC could substantially improve the prognosis of patients.

Non-cardia gastric adenocarcinoma is the final stage of a decades-long inflammatory process characterized by a sequence of histologic lesions known as Correa’s cascade[6]. The recognized development model of GC is as follows: normal mucosa or with non-active gastritis → Helicobacter pylori (H. pylori) infection → chronic-active gastritis → atrophic gastritis (AG) without intestinal metaplasia (IM) → IM → dysplasia → GC. Gastric precancerous lesions (GPL) are closely related to the occurrence of GC[7]. A population-based cohort study showed that 1.2% of patients with gastritis, 2.0% of AG, 2.6% of IM, and 5.3% with dysplasia would develop GC within 20 years[8]. Screening programs, which include endoscopy and radiographic screening, have been widely adopted in East Asia countries with a high incidence of GC[9]. However, the sensitivity of radiographic screening to detect GC was relatively low, especially in detecting early GC (32.1%)[10]. The sensitivity of endoscopy was significantly higher than radiographic screening. Still as an invasive and time-consuming approach, it was not the best option for massive screening strategy in countries with low incidence. Moreover, traditional tumor makers of carcinoembryonic antigen (CEA), carbohydrate-associated antigen 19 − 9 (CA19-9), alpha-fetoprotein (AFP), and cancer antigen 125 (CA125) were widely used in clinical practice, but the sensitivity of these markers to detect early GC was lower than 5%[11]. That makes it imperative to find out novel non-invasive biomarkers to diagnose GC at an early stage[12]. A variety of blood biomarkers have shown their potential in detecting early GC. Blood routine test was very widespread in clinical practice. Platelet-distribution width (PDW), neutrophil-lymphocyte ratio (NLR), and the platelet-lymphocyte ratio (PLR) had high diagnostic value in GC. The serum levels of pepsinogen I, pepsinogen II, and anti-H. pylori antibodies were associated with high sensitivity in East Asia[13, 14], but the same biomarkers did not reached satisfying sensitivity in a United States population[15]. More cross-population validation is necessary before these biomarkers are frequently used in daily practice. H. pylori eradication, and endoscopic mucosal resection (EMR) were accepted as standard treatments for GPL. A long-term cohort study showed the beneficial effect of H. pylori eradication against histological progression[16, 17]. EMR was restricted to treating severe dysplasia, and only accounts for a small part of GPL[18]. Therefore, it is important to seek effective complementary and alternative treatments for GPL[19].

Traditional Chinese medicine (TCM) was widely used as a complementary therapy in treating GPL and GC. Systematic reviews concluded that TCM was more effective in inhibiting H. Pylori and relieving the degree of AG, IM, and dysplasia of gastric mucosa[20, 21]. TCM combined with chemotherapy could improve clinical efficacy and reduce the relevant adverse effects in patients with GC[22]. The mechanisms of TCM against AG included alleviation of histological damage of gastric tissue, energy balance, intestinal microbiota and metabolites, amino acid and lipid metabolism, inflammatory suppression, tumor angiogenesis, and migration and invasion of tumor cells.[23–25]. However, as a multi-component mixture with multitarget, it is difficult to identify the vital targets, pathways, and genes that TCM plays important roles in inhibiting gastritis-GC progression.

Bioinformatics analysis of microarray data is a new approach for exploring disease-related gene expression, predicting molecular interactions, determining key regulatory pathways, and identifying targets for disease therapy[26, 27]. Weighted gene co-expression network analysis (WGCNA) is a systems biology approach used to describe gene correlations between different samples. By connecting related modules or external sample features, it can be used to find modules of highly correlated genes.[28]. Network pharmacology constructs multicomponent and multitarget models to clarify the complex interactions between genes, proteins, and metabolites related to diseases and herbs from a network perspective[29, 30]. This study identified driver genes associated with gastritis-GC progression through bioinformatics analysis. And coupling network pharmacology and molecular docking technology to screen natural compounds that prevent the progression of gastritis-GC, and build a “target-compound-herb” network.

Data source

Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) is a public functional genomics data repository, collecting array-and sequence-based data[31]. Download GSE55696 and GSE130823 datasets related to gastritis and early GC from the GEO database. The GSE55696 dataset came from the GPL6480 [Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Probe Name version)] includes 19 chronic gastritis samples, and 19 early GC samples (data submitted on Mar 07, 2014)[32]. The GSE130823 dataset came from the GPL17077 [Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381 (Probe Name version)] includes 47 chronic gastritis samples, and 16 early GC samples (May 07, 2019)[33]. The workflow chart is shown in Fig. 1.

Weighted correlation network analysis of key gene modules associated with gastritis-GC progression

WGCNA is a system biology approach to characterizing gene association patterns between different samples[28]. It can be used to identify highly synergistic gene sets and identify candidate biomarker genes or therapeutic targets based on the endogeneity of the gene set and the association between the gene set and the phenotype. Therefore, we performed WGCNA analysis using the “WGCNA” package in the R.4.0.3 software to obtain modules related to chronic gastritis and early GC. Hierarchical clustering analysis was performed using the Hclust function in R to exclude outlier samples. Then, the appropriate soft power β (ranging from 1 to 20) was selected using the “pickSoftThreshold” function in the WGCNA package according to the criteria of the scale-free network. Next, the correlation matrix between the soft power β and the paired genes calculated by Pearson analysis was used to build the adjacency matrix. Then, the topological overlap matrix (TOM) and the corresponding dissimilarity degree (1-TOM) were transformed from the adjacency matrix. A hierarchical clustering dendrogram was further built and similar gene expressions were classified into different modules. Genes with similar expression patterns were grouped into modules by average linkage hierarchical clustering, and different colors were used to label the modules. Those genes that were not co-expressed were placed in gray modules. To explore the correlation between gene modules and clinical traits, we used two different pathological stages of chronic gastritis and GC as clinical traits, representing the severity of pathology. Correlations between module eigengenes (ME) and clinical features were calculated using the Pearson correlation test and were bounded by a P-value <0.05. Those gene modules with the highest correlation with clinical characteristics were considered to be the critical gene modules associated with the progression of chronic gastritis to early GC.

Protein-protein interaction analysis and enrichment analysis of key module genes

The STRING database (https://cn.string-db.org) is a searchable database of known protein-protein interactions and predicted protein-protein interactions[34]. Studying the interaction network between proteins helps to uncover core regulatory genes. The gene modules with the highest correlation from chronic gastritis to early GC were selected for protein-protein interaction (PPI) analysis. The PPI network diagram was visualized using Cytoscape software, and topological analysis was performed using “Analyze network”. Gene Set Enrichment (GSE) analysis plays a vital role in extracting biological insights from genome-scale experiments. KEGG orthology-based annotation system (KOBAS, http://kobas.cbi.pku.edu.cn/genelist/) database is gene or protein function annotation and function enrichment[35]. We used the KOBAS 3.0 database for GO and KEGG enrichment analysis of genes in key modules.

Differential expression analysis of GSE130823

We performed differential expression analysis (DEGs) of chronic gastritis and early GC datasets (GSE130823) for differential expression genes in chronic gastritis and early GC using GEO2R software. The cut-off value was set as P<0.05, |logFC|>1. Then, the crossover genes of crucial module from the GSE55696 with DEGs from the GSE130823 dataset were obtained using Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/), and GO and KEGG enrichment analyses were performed using the KOBAS database.

Kaplan-Meier analysis of crossover genes

The Kaplan-Meier plotter (http://kmplot.com/analysis/) is based on a meta-analysis to discover and validate a database of survival biomarkers. Data were obtained from the GEO, EGA, and TCGA databases and included correlations between the expression of 30,000 genes and survival in 21 tumors[36]. Among the crossover genes of crucial genes in the GSE55696 and GSE130823 datasets, we regarded that the new genes should be differentially expressed between tumors and adjacent normal tissues and significantly correlated with prognosis. Therefore, we used Kaplan-Meier plotter for survival analysis with a Log-rank P<0.05 as the cut-off to determine the relationship between crossover genes and GC survival.

Candidate compound screening and compound-target network construction

HERB database is a natural medicine database platform that integrates high-throughput experimental data and reference mining data[37]. We used the HERB database (http://herb.ac.cn/) to collect relevant compounds that may prevent the progression of chronic gastritis to early GC. Swiss Target Prediction database (STP, http://swisstargetprediction.ch/), an online prediction platform for protein-small molecule targets, could predict the target information of known compounds based on the similarity of two-dimensional and three-dimensional chemical structures[38]. STP was used to predict ADME parameters of compounds[39], and candidate compounds with oral bioavailability (OB) ≥ 30% were selected according to Lipinski rule. The component information parameters provided by STP include the results of computer prediction. After preliminary screening, the deleted components are checked, the relevant active components are added, and final compounds are obtained as candidate compounds. Cytoscape (https://cytoscape.org/) integrates biomolecular interaction networks with high-throughput gene expression data and other molecular state information. The core of Cytoscape is to provide basic functional layout and query networks, and combine the primary data into a visual network[40]. The candidate compounds and targets were introduced into Cytoscape 3.9.1 to construct the target-compound network and analyze the topological attribute values of network nodes[41], thus obtaining the potential active compounds related to the progression of chronic gastritis into early GC.

Traditional Chinese medicine (TCM) acquisition and “target-compound-herb” network construction

TCM herbs that contain candidate compounds were collected by STP, and the candidate compounds-herb networks were constructed. Combined with the compound target network, the target-compound-herb networks were constructed by using Cytoscape 3.9.1. Through the “network analyzer” module function of Cytoscape 3.9.1, topological parameters of each node in the network were calculated to find out the key nodes, the effect intensity of TCM herbs and their compounds on the progression of chronic gastritis to early GC were evaluated.

Molecular docking of target-candidate compound

To further verify the credibility of the interaction relationship between core targets and core components in the “target-compound-herb” network, we used compound targets in this network as receptors for molecular docking. The Protein Data Bank (PDB, http://www.rcsb.org/pdb/)[42] and The Universal Protein Resource (UniProt, https://www.uniprot.org/)[43] are comprehensive resource databases of protein sequence and annotation data. The crystal structures of five proteins were selected and saved in PDB format by the PDB database and UniProt database. The candidate compound MOL2 files were downloaded using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP; https://tcmsp-e.com/tcmsp.php).

AutoDock is an automated docking tool designed to predict how small molecules (such as substrates or drug candidates) will be binded to receptors with known 3D structures[44]. AutoDockTools 1.5.7 was used to remove water molecules in the ligand, disperse ligand and receptor, add hydrogen atoms, calibrate Gasteiger charges, and store them as PDBQT files. AutoDockVina1.1.2 was used to calculate Affinity and evaluate the matching degree and docking activity between target and ligand. Affinity <-4.25 were considered to have binding activity between the ligand and the target, Affinity < -5.0 indicates better binding activity, and Affinity < -7.0 indicates strong docking activity[45].

Key gene modules associated with the progression of chronic gastritis to early GC

A total of 1,8340 genes in GSE55696 were used for WGCNA analysis. β=9 (scale-free R2 0.85) was the lowest power fit scale-free indices over 0.85 and determined as the soft thresholding power parameters to ensure a scale-free network in GSE55696. Eventually, genes with similar expression modules had 5 co-expression modules in GSE55696, brown module (R=0.59, P=1e-04), turquoise module (R=0.58, P=1e-04), blue module (R=-0.77, P=1e-08), yellow module (R=0.77, P=1e-08) and gray module (R=0.38, P=0.02), respectively (Fig. 2). The gray module is a set of genes that cannot be aggregated to any module. We identified the blue module in GSE55696 (R=0.05, P=0.8) as a key gene module associated with the progression of chronic gastritis to early GC. The blue module in GSE55696 contained 140 genes and defines this gene set as GS1 for the next step of analysis.

Key module gene protein interaction network construction and enrichment analysis

The 137 genes in GS1 were analyzed for PPI protein interactions using the STRING database, and the PPI protein interaction network was mapped using Cytoscape 3.8.0 software, with 67 nodes and 64 edges (Fig. 3A). KOBAS database analysis showed that they were mainly enriched in lipid metabolic pathways such as fat digestion and absorption, cholesterol metabolism, and associated with H. pylori, such as NOD-like receptor signaling pathway, epithelial cell signaling in H. pylori infection and so on (Fig. 3B, C).

Validated cohort: The differential genes analysis in chronic gastritis and early GC

To validate our results, we performed differential gene analysis on the GSE130823 dataset. A total of 1,494 DEGs were identified in GSE130823, including 758 up-regulated genes and 736 down-regulated genes. The DEGs in GSE130823 were defined as GS2 and drawn volcano map with R ggplot2 package (Fig. 4A). There are 15 crossover genes in GS1 and GS2, namely PEBP4, PTPRZ1, CARNS1, PLA2G2A, CHI3L1, RGL3, OSM, CLIC5, CHAD, HOTAIR, IFI44L, CXCL5, CIRBP, KRT6B and CA1 (Fig. 4B). GO and KEGG analyses showed that these crossover genes were enriched in fat digestion and absorption, epithelial cell signaling in H. pylori infection, and so on. The results were consistent with GS1 enrichment analysis (Fig. 4C, D).

Survival analysis and validation of crossover genes

To investigate the prognostic value of these 15 crossover genes, we used the Kaplan Meier plotter platform to analyze the results showing that of the 15 crossover genes, 9 were associated with unfavorable OS in GC patients. This suggests that CA1, CARNS1, CHAD, CLIC5, CXCL5, KRT6B, OSM, PEBP4, and RGL3 can be used as biomarkers for the progression of chronic gastritis to early GC (Fig. 5). The mRNA expression levels of nine survival-related genes were validated between normal and GC samples using the GEPIA database.

Candidate compound screening and compound-target network related to progression of chronic gastritis into early GC

There were 9 targets that related to poor OS in patients with GC, and were eligible to be used as biomarkers for progression of chronic gastritis into early GC. Among these targets, those could be matched with small molecular compounds by HERB database include CA1, CARNS1, CHAD, CXCL5, KRT6B, OSM, PEBP4 and RGL3 were defined as potential targets.

87 target-related components were screened through HERB database, and 59 candidate compounds were further screened according to ADME parameters and Lipinski rule. (Table 1). These compounds correspond to 5 potential targets. The target-compound network with potential targets was established, which consists of 34 nodes and 33 edges (Fig. 6A). The greater degree means the stronger hub of the node, and the greater regulation role in the whole network. The top 2 compounds (including the tied for second) of degree value are glycerin, immune globulin, D-malic acid (DMR), and aminobutyric acid (D-2-Aminobutyrate), corresponding to 3, 2, 2, 2 targets respectively. These compounds can affect the progression of multiple chronic gastritis to early GC-related target proteins. The top three targets in degree value were CXCL5, CA1 and CARNS1.

Table 1. Abbreviations corresponding to compounds and OB values.

NO.	Ingredient name	OB	NO.	Ingredient name	OB
HBIN1	1-methoxyindole-3-carbaldehyde	0.55	HBIN28	pedatisectine b	0.55
HBIN2	2-Diazo-2,3-dihydro-3-methyl-1H-inden-1-one	0.55	HBIN29	cis-9-octadecenoic acid	0.85
HBIN3	2-Methyl-N-phenylmaleimide	0.55	HBIN30	oleic acid	0.85
HBIN4	(2R)-3-oxo-2-phenylbutanenitrile	0.55	HBIN31	absinthin	0.55
HBIN5	3-methoxy-1H-quinazoline-2,4-dione	0.55	HBIN32	acetylcholine	0.55
HBIN6	8-dihydroxy-4,5-dimethyl-3,4-dihydronaphthalen-1(2H) -one	0.55	HBIN33	allyl isothiocyanate	0.55
HBIN7	7-hydroxy-8-(2-hydroxyethyl)coumarin	0.55	HBIN34	cascarillin	0.55
HBIN8	Acetovanillin	0.55	HBIN35	cascarillin c	0.55
HBIN9	FMT	0.85	HBIN36	colchine	0.55
HBIN10	fraxetin	0.55	HBIN37	coumari	0.55
HBIN11	methyl (2S)-3-hydroxy-2-phenylpropanoate	0.55	HBIN38	coumarin	0.55
HBIN12	Methyl 4-hydroxyphenylacetate	0.55	HBIN39	ethylpyrazine	0.55
HBIN13	noradrenaline	0.55	HBIN40	evodin	0.55
HBIN14	Phlegmariuine-N	0.55	HBIN41	grosheimin	0.55
HBIN15	procurcumadiol	0.55	HBIN42	grossheimin	0.55
HBIN16	Scopoletol	0.55	HBIN43	isohumulone a	0.56
HBIN17	(S)-tropic acid	0.85	HBIN44	limonin	0.55
HBIN18	Tuberosine B	0.85	HBIN45	mustard oil	0.55
HBIN19	3-methylhistidine	0.55	HBIN46	nigakilactone d	0.55
HBIN20	6-aminopurine	0.55	HBIN47	(-)-noradrenaline	0.55
HBIN21	aminobutyric acid	0.55	HBIN48	obakulactone	0.55
HBIN22	D-malic acid	0.56	HBIN49	Propanoic acid	0.85
HBIN23	glycerin	0.55	HBIN50	Pyrazine	0.55
HBIN24	Immune globulin from	0.55	HBIN51	serotonine	0.55
HBIN25	L-lysine	0.55	HBIN52	uccinic acid	0.85
HBIN26	lysine acid	0.55	HBIN53	17-beta-estradiol	0.55
HBIN27	n-thirteen(carbon)alkyl	0.55	HBIN54	(-)-nicotine	0.55

Target-compound-herb network construction

87 candidate compounds were matched out of 248 TCM herbs. According to the relationship between compounds and herbs, compound-herb network was constructed, which contained 337 nodes and 405 edges. The top 13 in degree value were Fructus Hippophae, Radix Bupleuri, Radix Peucedani, Radix Isatidis, Cortex Phellodendri Chinrnsis, Fructus Mori, Arum Ternatum Thunb., Rhizoma Typhonii, Radix Stemonae, Radix Saposhnikoviae, Herba Portulacae, Fructus Amomi, Fructus Evodiae (Fig. 6B). The targets of each herbs were collected through the bridging of candidate compounds. The results showed that Radix Bupleuri, Radix Isatidis, Fructus Mori, Fructus Hippophae and Rhizoma Typhonii could be associated with 5, 4, 4, 4, 4 targets, respectively. These five herbs have strong modulating effects on the progression of chronic gastritis to early GC, which can provide a basis for the selection of herbs in clinical or experimental settings. Ramulus Cinnamomi, Cortex Phellodendri Chinrnsis, Radix Stemonae, Herba Ephedra, Radix Saposhnikoviae, Herba Portulacae, Radix Angelicae Dahuricae, Cortex Periplocae Radicis, Arum Ternatum Thunb., Silybum Marianum, Lilii Bulbus, Lygodii Spora, Fructus Chaenomelis, Radix Aucklandiae, Folium Ginseng, Fructus Amomi could all be associated with 2 targets (Fig. 6C). The compound degree value>6 was a potential core compound including oleic acid, coumarin, scopoletol, limonin, glycerin, fraxetin, noradrenaline, 6-aminopurine, pedatisectine b, (-)-nicotine, acetylcholine, FMT, Propanoic acid (PPI), evodin, obakulactone (Fig. 6D). The comparison table of Chinese medicine abbreviations is shown in Appendix 1.

The results of molecular docking of core compounds and targets

25 sets of docking results between the five potential core compounds and the five core targets CA1 (PDB ID: 7Q0D), CARNS1 (Uniprot ID: A5YM72), CXCL5 (PDB ID: 2MGS), CHAD (PDB ID:5LFN), KRT6B (Uniprot ID: P04259), 20 groups (80%) had Affinity <-5 and 8 groups (32%) had Affinity <-7 (Fig. 7). Each ligand is embedded in the active pocket of the target, and interacts with multiple residues of the target through hydrophobicity and hydrogen bonding. The highest Affinity was CXCL5-limonin (-8.7 kcal/mol), and the lowest was CXCL5-coumarin (-5.7 kcal/mol). It indicates that the potential core compounds screened may have good binding activity with the core target. There were 20 new groups outside the network, the top 3 of Affinity were CHAD-limonin (-11.3 kcal/mol), CA1-limonin (-9.8 kcal/mol), and CARNS1-limonin (-9.1 kcal/mol). There were 15 off-network groups with Affinity<-5kcal/mol.

Chronic gastritis is considered to be the most important risk factor for GC [46]. Chronic inflammation causes changes in the stomach and creates a favorable environment for the development of GC. As a preventable disease, GC could be detected early by regular endoscopic surveillance, and be treated by surgery or endoscopic radical resection [47]. However, the sensitivity and specificity of conventional tumor markers are insufficient, affecting the detection of early GC non-invasively. Therefore, an in-depth understanding of the mechanism of transformation of chronic gastritis into early GC is essential for the development of novel strategies for cancer treatment.

In this study, we explored the mechanism of progression of chronic gastritis to GC using WGCNA, screened genes associated with clinical traits, and obtained gene co-expression modules of high biological significance. A total of 15 genes were associated with the progression of gastritis to early GC, then GO and KEGG pathway enrichment analysis was performed to identify key pathways for both conversions. The results suggested that the two are closely related in fat digestion and absorption, epithelial cell signaling in H. pylori infection, IL-17 signaling pathway. CA1, CARNS1, CHAD, CXCL5, KRT6B, OSM, PEBP4 and RGL3 are associated with unfavorable survival in patients with GC and are biomarkers of progression from chronic gastritis to early GC.

Carnosine synthase 1 (CARNS1) is protein 1 of the ATP-grasp family, which catalyzes the carnosine and homocarnosine, distributing in vertebrate skeletal muscle and the central nervous system [48]. CARNS1 is an oncogene that is significantly downregulated in breast cancer and may serve as an independent prognostic indicator for breast cancer patients [49]. It has not been reported that CARNS1 is associated with gastritis or GC. However, carnosine has great potential in protecting the gastrointestinal mucosa and preventing the development of GC. Polaprezinc, a chelating compound containing L-carnosine and zinc, attenuated the development of dextran sodium sulfate-induced ulcerative colitis in mice [50]. Polaprezinc inhibited the proliferation of human GC SGC-7901 cells through the mitochondrial respiratory and glycolytic pathways and decreased the expression of mitochondrial PHB-1 [51, 52]. Polaprezinc reduced chemotherapy drug-induced damage to the stomach, small intestine and brain, and lowered blood levels of DA, 5-HT and NF-κB [53, 54]. There is evidence that polaprezinc also reduces radiation toxicity from radiotherapy and improves chemotherapy-induced esophagitis and gastrointestinal mucositis [55]. CARNS1 is a carnosine synthase that catalyzes the formation of carnosine. Modulation of CARNS1 may have great potential in protecting gastric mucosa, anti-GC, and improving the efficacy and safety of radiotherapy and chemotherapy.

C-X-C motif chemokine 5 (CXCL5) is a member of the CXC chemokine family and is highly associated with inflammatory infiltration [56]. The CXCL family plays an important role in the pathogenesis of GC and can be used as a marker for the development of GC. The level of chemokines is associated with the number of tumor-infiltrating immune cells and plays a central role in GC immunity [57]. CXCL5 high-expression has been identified to be closely associated with the development of GC [58]. CXCL5 is upregulated in both gastritis and GC, and its release is induced by COX-2/PGE2 pathway-dependent manner to promote malignant progression of GC [59].

Carbonic anhydrases (CAs) are a family of mostly zinc metalloenzymes that catalyze the reversible hydration/dehydration of CO₂ into bicarbonate and a proton [60]. Carbonic anhydrases 1 (CA1) is mutated in peritoneal metastasis of gastric adenocarcinoma, driving cancer cell phenotypic transformation and metastasis[61]. Caudal-related homeobox transcription factor 2 (CDX2) was associated with the upregulation of CA1 [62]. Overexpression of CDX2 in the gastric mucosa leads to intestinal chemotaxis [63]. CDX2 has been reported to be associated with susceptibility to GC in children with gastritis [64].

Natural products with favorable pharmacokinetics and pharmacodynamics offer new therapeutic approaches to overcome cross-resistance to antineoplastic drugs. Natural products with multiple pharmacological profiles have proven to be promising agents for the development of oncology drugs[65]. Natural medicines have been used for thousands of years to prevent and treat various diseases[66]. Prevention of disease is an important idea in the treatment of disease in Chinese medicine. Developing effective agents of natural products by targeting mutated cancer genes, finding safer and more effective treatments, improving the efficiency of cancer treatment and reducing the cost of treatment are emerging means to prevent or cure cancer.

The HERB database was used to collect important compounds related to the progression of gastritis to GC and to build a “target-compound-herb” network. Radix Bupleuri, Radix Isatidis, Fructus Mori, Fructus Hippophae and Rhizoma Typhonii have strong modulating effects on the progression of chronic gastritis to early GC, which can provide a basis for the selection of herbs in clinical or experimental settings. We also used AutoDockTools 1.5.7 software to perform molecular docking of core components and core targets to explore new drugs for the prevention and treatment of gastritis-GC transformation. The results showed that scopoletol, fraxetin, 6-aminopurine, limonin, and coumarin had good docking activity with five targets (CA1, CARNS1, CXCL5, CHAD, and KRT6B).

Fraxetin is a coumarin compound, a natural product isolated from the herb Cortex Fraxini, which has been shown to have various functions such as antibacterial, antioxidant, neuroprotective, anti-fibrotic and anti-tumor. Fraxetin stimulates the anti-inflammatory M2 phenotype by inhibiting MAPKs and IκBα in LPS-activated macrophages and increasing the expression of arginase 1 and KLF4, showing anti-inflammatory properties [67]. Fraxetin has good anti-cancer effects on liver[68], colon[69], pancreatic[70] and other gastrointestinal tract tumors. Fraxetin regulates the expression of apoptotic proteins in HT29 and HCT116 colon cancer cells, induces reactive oxygen species levels and calcium inward flow, and regulates G2/M phase block. In addition, fraxetin exerts antitumor effects in concert with 5-FU or irinotecan and reverses 5-FU resistance[71]. These studies demonstrate the powerful potential of fraxetin in anti-inflammatory and anti-cancer activities. However, whether fraxetin exhibits anti-GC effects remains to be elucidated.

Limonin is a natural tetracyclic triterpenoid derived from Fructus Evodiae and Cortex Phellodendri Chinrnsis, which has been shown anticancer effects in a variety of human cancer cells[72]. Limonin bitters effectively treats gastric ulcer in rats by reducing pepsin activity[73]. Limonin can be used as an effective chemopreventive agent to significantly reduce benzo[a]pyrene-induced forestomach cancer in ICR/Ha mice[74], and has highly cytotoxic to human GC N-87 cells[75]. In vitro and in vivo studies revealed that citicoline modulates BCL2/Bax, MCP-1, p53, p21, miR-216a-3p, TNF-α, iNOS, IL-1β, IL-2, IFN-γ, Wnt5/β-linked protein, TLR4/NF-κB, OST and TLR signaling pathways [76].

Coumarins are widely found in herbal medicines such as Fraxini Cortex, Cnidii fructus, Psoraleae Fructus, Angelicae Dahuricae Radix. Coumarins have been shown to possess various biological activities such as anticoagulant, anti-inflammatory, antibacterial and anticancer[77]. Styrene substituted biscoumarin significantly induced apoptosis in gastric adenocarcinoma AGS cells and acted through an endogenous pathway to downregulate Bcl-2 and upregulate the expression of Bax and cytochrome C[78]. Numerous studies have shown that coumarins exhibit gastroprotective and anti-ulcer activity[79–81].

In conclusion, our work suggests a possible mechanism of gastritis secondary to GC, and reveals that CA1, CARNS1, CHAD, CLIC5, CXCL5, KRT6B, OSM, PEBP4, and RGL3 may be important susceptibility factors for GC. We also identified scopolamine alcohol, fraxetin, 6-aminopurine, citrulline and coumarin as potential therapeutic agents for the regulation of CA1, CARNS1, CXCL5, CHAD, and KRT6B targets to prevent the progression of gastritis to GC. The shortcoming of this study is that it is only a predictive analysis and we plan to conduct clinical, in vivo and in vitro studies for validation afterwards to explore the preventive effect of natural drugs on gastritis-GC transformation.

In summary, the present analysis suggests that CA1, CARNS1, CHAD, CLIC5, CXCL5, KRT6B, OSM, PEBP4, and RGL3 may be drivers of gastritis to GC, and regulate fat digestion and absorption, epithelial cell signaling in H. pylori infection, IL-17 signaling pathway. Scopolamine alcohol, fraxetin, 6-aminopurine, citrulline and coumarin may be new drugs to reverse gastritis-GC progression.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China (82074402) and Scientific and technological innovation project of China Academy of Chinese Medical Sciences (CI2021A01802).

Authors’ contributions

Jie Li conceived and designed the analysis, Xiaotong Song and Xiaoyan Qin analyzed the data and wrote the manuscript, Heping Wang wrote the manuscript, and Manman Xu, Xinmiao Wang, Baoyi Ni and Guanghui Zhu reviewed the literature.

Availability of data and materials

The GSE55696 and GSE130823 data supporting this bioinformatic analysis are from previously datasets, which have been cited.

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Exploring the Common Genetic Signature and Molecular Mechanisms Between Gastritis and Gastric Cancer: A bioinformatics-coupled Network Pharmacology Analysis

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