Mechanism Analysis of Coix Seed in Gastric Cancer Treatment Based on Biological Network Modules

Abstract

Coix seed, the mature seed of Coix lacryma-jobi L., is a traditional herb widely used in various cancer adjuvant treatments; however, its mechanism is unknown. The aim of this study was to reveal the multitarget mechanisms of Coix seed in the treatment of gastric cancer (GC) by biological network and modular analysis methods. Forty-one ingredients and 482 targets of Coix seed and 165 GC-related genes were obtained from databases. Twelve on-target genes (AICDA, CASP3, EP300, ERBB2, FGFR2, IL12A, IL12B, IL1B, LOX, TJP1, TP53, and TRIB3) of Coix seed overlapped with GC-related genes. Using compound-target and protein–protein interaction network analyses, we discovered the core targets of Coix seed. Markov cluster algorithm-based modular analysis identified 5 potential module targets of Coix seed for GC. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated the vast actions of Coix seed, which involve pathways in cancer, the cell cycle, receptor signal transduction, deoxyribonucleic acid damage response, transcriptional regulation, apoptosis, and cell connections. This study elucidated the potential mechanisms of Coix seed on GC, which may lead to the development of an effective drug. Additionally, this study showed the feasibility of network and modular analysis methods to investigate traditional Chinese medicinal herbal mechanisms and may provide a new angle for further research in the field of anticancer mechanisms and multitarget drugs.

Keywords

biological network coix seed gastric cancer targeted module

Introduction

Gastric cancer (GC) is the fourth most common disease worldwide, with high global incidence and mortality rates.^1,2 Surgical treatment is limited by the low early diagnosis rate, and chemotherapy is limited by its side effects and drug resistance.^3,4 Although novel biochemical targeting agents have been used to improve the outcomes of patients with GC, the efficacy of these agents for advanced and recurrent GC remains unsatisfactory.⁵ Therefore, traditional Chinese medicine (TCM) is an important alternative treatment for patients with GC, especially for those in the advanced stage.⁶ Many TCM herbs and their extracts have antitumor effects; however, understanding the multitarget molecular mechanisms of the herbs is challenging.⁷

Coix seed (Semen Coicis), the mature seed of Coix lacryma-jobi L., is used as both a food and drug in TCM. As a drug, Coix seed has been traditionally used to treat cancers.⁸ The active components of Coix seed include sterols, triterpenoids, polysaccharides, oils, and starches.^9,10 These components have anticancer, anti-inflammatory, antiproliferative, and antiallergic activities.⁸ Extracts of Coix seed significantly suppress the growth of GC cells.¹¹ Kanglaite Injection, mainly composed of Coix seed oil, enhances the efficacy and reduces the side effects of chemotherapy in patients with GC.^12,13 Kanglaite Injection is efficacious in the treatment of various cancers and improves the quality of life^14
-16; however, its mechanisms are unclear.

The holistic philosophy of TCM shares basic ideas with network biology and network pharmacology.¹⁷ As a rational strategy, network-based methods are widely applied in TCM research, including studies on formula compatibility, target prediction, and pharmacology.^18
-20 In this study, biological network and modular analysis methods were used to explore the potential targets and action mechanisms of Coix seed for the treatment of GC to explain the potential mechanisms behind the antitumor activity of Coix seed from the system and modular biology network levels.

Materials and Methods

Datasets

The composite compounds of Coix seed were obtained from the Chinese Medicine and Chemical Composition Database (http://www.sioc.ac.cn/) and Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (http://lsp.nwsuaf.edu.cn/tcmsp.php), which are specifically designed to store information on the active components of Chinese herbs. In total, 41 compounds of Coix seed were collected after the removal of duplicated records. For each compound, we collected related gene targets from the TCMSP and Comparative Toxicogenomics Database (CTD, http://ctdbase.org). The targets obtained from TCMSP were either recorded in DrugBank (https://www.drugbank.ca/) or experimentally validated, and targets from CTD were bound to or regulated by Coix seed compounds, with at least 1 reference to show this. Then, GC-related genes were collected from Online Mendelian Inheritance in Man (www.omim.org/) and Human Phenotype Ontology (http://human-phenotype-ontology.github.io) databases. The Medical Subject Heading term “Stomach Neoplasms” was used as the keyword. Detailed information on Coix seed compounds and GC-related genes is listed in Supplemental Table S1.

Gene Set Comparison Analysis

The Coix seed targets and GC-related gene sets were compared and overlapping genes were detected, which were defined as the on-target genes. The similarity of the 2 gene sets was analyzed by Jaccard’s similarity coefficient, which is a pair-wise similarity tool defined as the intersection size divided by the union size of 2 sample sets. All targets were converted to the canonical gene symbol. A higher Jaccard’s similarity coefficient means a higher proportion of on-target genes.

Network Construction

Known protein–protein interactions (PPIs) were downloaded from the Human Protein Reference Database (http://www.hprd.org) and BioGRID (http://thebiogrid.org). The species was restricted to Homo sapiens and 216 938 existing PPIs were obtained. We obtained gene target networks by mapping Coix seed target genes to the PPI dataset with official gene symbols. Cytoscape 3.0.0 was used to visualize and analyze the Coix seed compound-gene network and gene target network and topological properties were analyzed by the NetworkAnalyzer plug-in.

Network Module Identification

The disease mechanism and drug action have a modular basis in network biology, as genes and proteins interact in a network to execute certain functions.^21,22 To understand better the underlying gene interactions and cellular functional mechanisms, we identified the densely connected multitarget modules of Coix seed. A Markov cluster algorithm (MCL)²³ was used to identify the target modules of Coix seed. The minimum module size was set as 3, all edges were assumed to be unidirectional, and the inflation value was set as 2.

Module Functional Enrichment Analysis

To characterize the function of the Coix seed target network and modules, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID 6.8)²⁴ to elucidate the pharmacological mechanisms of Coix seed. For the network and each module, an overrepresentation of a functionally relevant annotation was defined by a modified Fisher’s exact P-value with a Benjamini–Hochberg adjustment. The enrichment background was selected as “Homo sapiens”, and GO terms and pathways with a P < 0.05 were considered significant.

Results

Similarity of Coix Seed Target Genes and GC Gene Sets

Using the databases, 26 compounds and 482 gene targets of Coix seed and 165 GC-related genes were obtained. Among the target and GC-related genes, 12 overlapping genes were identified (AICDA, CASP3, EP300, ERBB2, FGFR2, IL12A, IL12B, IL1B, LOX, TJP1, TP53, and TRIB3), that is, the on-target genes. The Jaccard’s similarity coefficient of the 2 datasets was 0.019. This showed the multitargeted regulating effect of Coix seed in the treatment of GC.

Coix Seed Compound-Targets Network

Based on the compounds and their targets, we constructed the compound-target network of Coix seed, as shown in Figure 1. This network consisted of 523 nodes (26 components and 481 genes) and 858 edges. Among the gene nodes, GC-related CASP3 had the highest degree, as 7 compounds of Coix seed acted on it. The other high-degree genes included NCOA2 (14), PTGS2 (14), PTGS1 (12), TNF (10), ADH1C (8), and PPARA (8). These genes may have important roles in the pharmacological action of Coix seed.

Figure 1

Compound-target network of Coix seed. Green triangular nodes represent the components of Coix seed and red round nodes represent the target genes of each component.

PPI Network of Coix Seed Gene Targets

By mapping the Coix seed target genes to known PPI interactions, we obtained the Coix seed PPI network, which consisted of 120 nodes and 137 edges (Figure 2). The network density was 0.02, clustering coefficient was 0.01, connected components value was 13, network heterogeneity was 1.09, characteristic path length was 3.93, and average number of neighbors was 2.02. The hub nodes included RXRA, MAPK3, BCL2, and CDK1.

Figure 2

Protein–protein interaction network of Coix seed gene targets. Node degree from high to low is labeled by deep to light red.

Target Module of Coix Seed in GC Treatment

Based on the Coix seed PPI network, the MCL algorithm was used to identify the target modules of Coix seed. Sixteen modules (nodes ≥3) were obtained, the largest consisting of 11 nodes. We labeled these 16 modules as Mod-1 to Mod-16. The detailed information is shown in Figure 3. Five modules included on-target genes related to GC. The 5 modules were 3, 4, 7, 12, and 14 and the on-target genes were CASP3, EP300, IL1B, TJP1, and TP53, respectively. Through a literature analysis, we found that 41 of 75 genes in the modules were reported to be risk factors of Helicobacter-induced GC.

Figure 3

Modules of the Coix seed protein–protein interaction network. Blue nodes represent target genes related to gastric cancer (GC). Red font represents genes associated with the development and progression of Helicobacter pylori-associated GC.

Biological Functions of Targeted Modules

To characterize the biological functions of the Coix seed target network and modules, we performed GO and KEGG pathway enrichment analysis for the 5 on-target modules that had overlapping genes. All significant 237 GO terms and 106 KEGG pathways of the Coix seed target network are listed in Supplemental Table S2. The most significant GO term and KEGG pathway were positive regulation of transcription from ribonucleic acid (RNA) polymerase II promoter and pathways in cancer, respectively. In Table 1, we show the 5 most significant GO functions and KEGG pathways of each target module. The compounds of Coix seed were mainly involved in the deoxyribonucleic acid (DNA) damage response, transcription factor binding, and the apoptotic process according to GO and pathways in cancer and the cell cycle, according to KEGG. In the 5 most significant pathways in Mod-4, Mod-14 genes were directly enriched in cancer-related pathways.

Table 1

Top 5 Significant Enriched GO and KEGG Biological Functions of Coix Seed On-Target Modules.

Moudle ID	GO functions	P-value	KEGG pathways	P-value
Mod-3	GO:0006977—DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest	2.52E−09	hsa04110: Cell cycle	2.96E−11
	GO:0034644—cellular response to ultraviolet	1.09E−04	hsa04115: p53 signaling pathway	1.19E−07
	GO:0005654—nucleoplasm	4.38E−04	hsa04010: mitogen-activated protein kinase signaling pathway	9.15E−04
	GO:0006461—protein complex assembly	6.97E−04	hsa04068: FoxO signaling pathway	5.32E−03
	GO:0005634—nucleus	7.03E−04	hsa05161: Hepatitis B	6.21E−03
Mod-4	GO:0003713—transcription coactivator activity	2.58E−07	hsa05211: Renal cell carcinoma	2.60E−04
	GO:0008134—transcription factor binding	4.44E−07	hsa04919: Thyroid hormone signaling pathway	8.01E−04
	GO:0045944—positive regulation of transcription from ribonucleic acid polymerase II promoter	6.99E−07	hsa04310: Wnt signaling pathway	1.17E−03
	GO:0003682—chromatin binding	1.64E−06	hsa05161: Hepatitis B	1.29E−03
	GO:0005667—transcription factor complex	1.27E−05	hsa05164: Influenza A	1.86E−03
Mod-7	GO:0001666—response to hypoxia	1.34E−04	hsa05134: Legionellosis	1.59E−02
	GO:0032611—interleukin-1 beta production	7.79E−03	hsa04621: NOD-like receptor signaling pathway	1.84E−02
	GO:0071310—cellular response to organic substance	5.66E−02	hsa04623: Cytosolic deoxyribonucleic acid-sensing pathway	2.39E−02
	GO:0033198—response to adenosine triphosphate	6.65E−02	hsa05133: Pertussis	2.16E−02
	GO:0007566—embryo implantation	5.24E−03	hsa05132: Salmonella infection	1.56E−02
Mod-12	GO:0016327—apicolateral plasma membrane	3.75E−05	hsa04530: Tight junction	3.90E−04
	GO:0005923—bicellular tight junction	2.14E−03	hsa04670: Leukocyte transendothelial migration	3.39E−02
	GO:0007043—cell cell junction assembly	3.28E−02	hsa05160: Hepatitis C	3.81E−02
	GO:0005911—cell cell junction	1.87E−02	hsa04514: Cell adhesion molecules	4.07E−02
	GO:0019904—protein domain specific binding	5.06E−02
Mod-14	GO:0034349—glial cell apoptotic process	7.15E−04	hsa05200: Pathways in cancer	3.23E−03
	GO:0032025—response to cobalt ion	9.53E−04	hsa05014: Amyotrophic lateral sclerosis	1.44E−02
	GO:0008635—activation of cysteine-type endopeptidase activity involved in apoptotic process by cytochrome c	1.07E−03	hsa05134: Legionellosis	1.56E−02
	GO:0097153—cysteine type endopeptidase activity involved in apoptotic process	1.59E−03	hsa05416: Viral myocarditis	1.64E−02
	GO:0030220—platelet formation	2.14E−03	hsa05210: Colorectal cancer	1.79E−02

GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Discussion

In this study, biological network and modular methods were used in the analysis of the multitarget pharmacological mechanisms of Coix seed in the treatment of GC. Twelve overlapping genes were identified between Coix seed compound targets and GC-related genes; thus, these may have important roles in GC development and therapy. Based on the Coix seed PPI network, 5 modules with GC-related genes were identified that may be Coix seed target modules for GC therapy. The functional analysis of the target modules revealed that the pharmacological actions of Coix seed in GC treatment may involve pathways in cancer, the DNA damage response, transcription factor binding, the apoptotic process, and the cell–cell junction. Our results may provide new clues for further exploration of the antitumor mechanism of Coix seed.

Among the module target genes, CASP3 was the highest degree node in the Coix seed compound-target network. CASP3 mutations play an important role in the development of several human tumors, including GC.²⁵ Another overlapping module gene, EP300, functions as histone acetyltransferase and is an important modulator of chromatin remodeling. Frameshift mutations of EP300 and its expressional loss may be a feature of gastric and colorectal cancers with high microsatellite instability.²⁶ Gene polymorphisms of IL1B, IL12A, and IL12B affect gastric mucosal carcinogenesis in patients with Helicobacter pylori infection.^27,28 The overlapping TJP1, TP53, and TRIB3 genes are also closely related to the treatment of GC.^29
-31 This suggests that Coix seed can regulate the development of GC through these targets.

Using the PPI network and functional enrichment analysis, several hub genes and important pathways of Coix seed were identified. Fifty-five percent of the identified module genes had important roles in the development and progression of H. pylori-associated GC (Figure 3). The target gene IL1B upregulates the hub gene RXRA via the activation of nuclear factor-kappa-light-chain-enhancer of activated B cells signaling,³² which suggests the possible clinical significance of hub genes in the diagnosis and treatment of GC. The hub gene MAPK3 is an independent prognostic marker for patients with resected GC.³³ Several enriched pathways are involved in the development or treatment of GC, such as pathways in cancer, cell adhesion, p53, and Wnt/beta-catenin.^34
-36 These indicate the efficacy of the identified key genes and pathways for GC treatment.

Systems biology and network research are holistic and systemic. TCM is guided by systems science to regulate disease via multiple components and targets. The study of systems biology, network research, and TCM has resulted in new concepts and strategies in the research of Chinese herbal compound action. Modular network analysis may also provide further insight into the multitarget pharmacological mechanisms of drugs at the network and systematic levels.

In conclusion, this study identified the gene targets of Coix seed in GC and potential pathways behind the therapeutic effect of Coix seed in GC. This showed the feasibility of network and modular analysis methods to investigate TCM herbal mechanisms and may provide a new angle for further research in the field of anticancer mechanisms and multitarget drugs.

Supplemental Material

Table S1 - Supplemental material for Mechanism Analysis of Coix Seed in Gastric Cancer Treatment Based on Biological Network Modules

Supplemental material, Table S1, for Mechanism Analysis of Coix Seed in Gastric Cancer Treatment Based on Biological Network Modules by Fengbin Zhang, Xiaoyan Liu, Bingjie Huo, Bing Li and Ruixing Zhang in Natural Product Communications

Supplemental Material

Table S2 - Supplemental material for Mechanism Analysis of Coix Seed in Gastric Cancer Treatment Based on Biological Network Modules

Supplemental material, Table S2, for Mechanism Analysis of Coix Seed in Gastric Cancer Treatment Based on Biological Network Modules by Fengbin Zhang, Xiaoyan Liu, Bingjie Huo, Bing Li and Ruixing Zhang in Natural Product Communications

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors’ work was funded by the National Natural Science Foundation of China (81803966) and the Natural Science Foundation of Hebei province (H2015206461).

ORCID iD

Bing Li

References

Carcas

. Gastric cancer review. J Carcinog. 2014;13:14 doi:10.4103/1477-3163.146506

http://www.ncbi.nlm.nih.gov/pubmed/25589897

Jemal

Bray

Center

et al. Global cancer statistics. CA Cancer J Clin. 2011;61(2):69-90.doi:10.3322/caac.20107

http://www.ncbi.nlm.nih.gov/pubmed/21296855

Shi

W-J.

Gao

J-B

. Molecular mechanisms of chemoresistance in gastric cancer. World J Gastrointest Oncol. 2016;8(9):673-681.doi:10.4251/wjgo.v8.i9.673

http://www.ncbi.nlm.nih.gov/pubmed/27672425

Liu

Luo

et al. Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification. Expert Rev Proteomics. 2017;14(4):351-362.doi:10.1080/14789450.2017.1299006

http://www.ncbi.nlm.nih.gov/pubmed/28276747

Aoyagi

Kouhuji

Kizaki

et al. Molecular targeting to treat gastric cancer. World J Gastroenterol. 2014;20(38):13741-13755.doi:10.3748/wjg.v20.i38.13741

http://www.ncbi.nlm.nih.gov/pubmed/25320512

Liu

Xiu

L-J.

Jiao

J-P

et al. Traditional Chinese medicine integrated with chemotherapy for stage IV non-surgical gastric cancer: a retrospective clinical analysis. J Integr Med. 2017;15(6):469-475.doi:10.1016/S2095-4964(17)60377-7

http://www.ncbi.nlm.nih.gov/pubmed/29103417

Qiu

Jia

. Research advances on TCM anti-tumor effects and the molecular mechanisms. J Cancer Res Ther. 2014;10 Suppl 1(1):8-13.doi:10.4103/0973-1482.139744

http://www.ncbi.nlm.nih.gov/pubmed/25207899

Gao

Zeng

Liu

C-X

. Inhibition of Coix seed extract on fatty acid synthase, a novel target for anticancer activity. J Ethnopharmacol. 2008;119(2):252-258.doi:10.1016/j.jep.2008.07.015

http://www.ncbi.nlm.nih.gov/pubmed/18691644

Cui

Rong

Gong

. Optimization of ultrasound-assisted acid extraction of polysaccharide from Coix seed. Chin Arch Tradit Chin Med. 2015;33:2146-2163.

10.

Han

Zhao

et al. Mechanisms of Coix seed compositions in the treatment of spleen deficiency and wet dampness zheng. Afr J Tradit Complement Altern Med. 2017;14(4):239-246.doi:10.21010/ajtcam.v14i4.26

http://www.ncbi.nlm.nih.gov/pubmed/28638886

11.

Chung

C-P.

Hsia

S-M.

Lee

M-Y

et al. Gastroprotective activities of adlay (Coix lachryma-jobi L. var. ma-yuen Stapf) on the growth of the stomach cancer AGS cell line and indomethacin-induced gastric ulcers. J Agric Food Chem. 2011;59(11):6025-6033.doi:10.1021/jf2009556

http://www.ncbi.nlm.nih.gov/pubmed/21517098

12.

Zhan

Y-P.

Huang

X-E.

Cao

et al. Clinical safety and efficacy of Kanglaite® (Coix seed oil) injection combined with chemotherapy in treating patients with gastric cancer. Asian Pac J Cancer Prev. 2012;13(10):5319-5321.doi:10.7314/APJCP.2012.13.10.5319

http://www.ncbi.nlm.nih.gov/pubmed/23244156

13.

Xie

Huang

et al. Efficacy and safety of Huachansu combined with chemotherapy in advanced gastric cancer: a meta-analysis. Med Hypotheses. 2013;81(2):243-250.doi:10.1016/j.mehy.2013.04.038

http://www.ncbi.nlm.nih.gov/pubmed/23692970

14.

Zhang

Hong

Wang

. Effects of Kanglaite injection on serum miRNA-21 in patients with advanced lung cancer. Med Sci Monit. 2018;24:2901-2906.doi:10.12659/MSM.909719

http://www.ncbi.nlm.nih.gov/pubmed/29735968

15.

Shen

W-S.

Shu

Z-Q.

Deng

L-C

. Curative effect of 3D-CRT combined with gemcitabine concurrently with addition of Kanglaite injection in treatment of locally advanced pancreatic. Zhongguo Zhong Xi Yi Jie He Za Zhi. 2012;32(7):902-905.http://www.ncbi.nlm.nih.gov/pubmed/23019943

16.

Guo

HY.

Cai

Yang

et al. Randomized phase II trial on mitomycin-C/cisplatin +/- KLT in heavily pretreated advanced breast cancer. Am J Chin Med. 2008;36(4):665-674.doi:10.1142/S0192415X08006132

http://www.ncbi.nlm.nih.gov/pubmed/18711764

17.

Zhang

. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med. 2013;11(2):110-120.doi:10.3724/SP.J.1009.2013.00110

http://www.ncbi.nlm.nih.gov/pubmed/23787177

18.

Zhang

Zhu

Bai

Ning

. Network pharmacology databases for traditional Chinese medicine: review and assessment. Front Pharmacol. 2019;10(10):123 doi:10.3389/fphar.2019.00123

http://www.ncbi.nlm.nih.gov/pubmed/30846939

19.

Hao

DC.

Xiao

. Network pharmacology: a Rosetta stone for traditional Chinese medicine. Drug Dev Res. 2014;75(5):299-312.doi:10.1002/ddr.21214

http://www.ncbi.nlm.nih.gov/pubmed/25160070

20.

Jarrell

JT.

Gao

Cohen

DS.

Huang

. Network medicine for Alzheimer’s disease and traditional Chinese medicine. Molecules. 2018;23(5):1143.doi:10.3390/molecules23051143

http://www.ncbi.nlm.nih.gov/pubmed/29751596

21.

Barabási

A-L.

Gulbahce

Loscalzo

. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56-68.doi:10.1038/nrg2918

http://www.ncbi.nlm.nih.gov/pubmed/21164525

22.

Beltrao

Cagney

Krogan

. Quantitative genetic interactions reveal biological modularity. Cell. 2010;141(5):739-745.doi:10.1016/j.cell.2010.05.019

http://www.ncbi.nlm.nih.gov/pubmed/20510918

23.

Shih

Y-K.

Parthasarathy

. Identifying functional modules in interaction networks through overlapping Markov clustering. Bioinformatics. 2012;28(18):i473-i479.doi:10.1093/bioinformatics/bts370

http://www.ncbi.nlm.nih.gov/pubmed/22962469

24.

Huang

DW.

Sherman

BT.

Lempicki

. Systematic and integrative analysis of large gene Lists using David bioinformatics resources. Nat Protoc. 2009;4(1):44-57.doi:10.1038/nprot.2008.211

http://www.ncbi.nlm.nih.gov/pubmed/19131956

25.

Soung

YH.

Lee

JW.

Kim

et al. Somatic mutations of CASP3 gene in human cancers. Hum Genet. 2004;115(2):112-115.doi:10.1007/s00439-004-1129-3

http://www.ncbi.nlm.nih.gov/pubmed/15127291

26.

Kim

MS.

Lee

SH.

Yoo

NJ.

Lee

. Frameshift mutations of tumor suppressor gene ep300 in gastric and colorectal cancers with high microsatellite instability. Hum Pathol. 2013;44(10):2064-2070.doi:10.1016/j.humpath.2012.11.027

http://www.ncbi.nlm.nih.gov/pubmed/23759652

27.

El-Omar

EM.

Carrington

Chow

et al. Interleukin-1 polymorphisms associated with increased risk of gastric cancer. Nature. 2000;404(6776):398-402.doi:10.1038/35006081

http://www.ncbi.nlm.nih.gov/pubmed/10746728

28.

Navaglia

Basso

Zambon

C-F

et al. Interleukin 12 gene polymorphisms enhance gastric cancer risk in H pylori infected individuals. J Med Genet. 2005;42(6):503-510.doi:10.1136/jmg.2004.022723

http://www.ncbi.nlm.nih.gov/pubmed/15937086

29.

Amieva

MR.

Vogelmann

Covacci

et al. Disruption of the epithelial apical-junctional complex by Helicobacter pylori CagA. Science. 2003;300(5624):1430-1434.doi:10.1126/science.1081919

http://www.ncbi.nlm.nih.gov/pubmed/12775840

30.

891961 Bellini

MF.

Cadamuro

ACT.

Succi

Proença

MA.

Silva

. Alterations of the TP53 gene in gastric and esophageal carcinogenesis. J Biomed Biotechnol. 2012;2012-13.doi:10.1155/2012/891961

http://www.ncbi.nlm.nih.gov/pubmed/22919278

31.

Smith

SM.

Moran

AP.

Duggan

et al. Tribbles 3: a novel regulator of TLR2-mediated signaling in response to Helicobacter pylori lipopolysaccharide. J Immunol. 2011;186(4):2462-2471.doi:10.4049/jimmunol.1000864

http://www.ncbi.nlm.nih.gov/pubmed/21220698

32.

Ren

H-Y.

Huang

G-L.

Liu

W-M

et al. IL-1β induced RXRα overexpression through activation of NF-κB signaling in gastric carcinoma. Biomed Pharmacother. 2016;78:329-334.doi:10.1016/j.biopha.2016.01.033

http://www.ncbi.nlm.nih.gov/pubmed/26898458

33.

Kim

JG.

Lee

SJ.

Chae

et al. Association between phosphorylated AMP-activated protein kinase and MAPK3/1 expression and prognosis for patients with gastric cancer. Oncology. 2013;85(2):78-85.doi:10.1159/000351234

http://www.ncbi.nlm.nih.gov/pubmed/23860205

34.

Yang

H-B.

Song

Cheng

M-D

et al. Deoxycholic acid inhibits the growth of BGC-823 gastric carcinoma cells via a p53‑mediated pathway. Mol Med Rep. 2015;11(4):2749-2754.doi:10.3892/mmr.2014.3004

http://www.ncbi.nlm.nih.gov/pubmed/25434397

35.

Chiurillo

. Role of the Wnt/β-catenin pathway in gastric cancer: an in-depth literature review. World J Exp Med. 2015;5(2):84-102.doi:10.5493/wjem.v5.i2.84

http://www.ncbi.nlm.nih.gov/pubmed/25992323

36.

Liu

J-P

et al. Analysis of key genes and signaling pathways involved in Helicobacter pylori-associated gastric cancer based on The Cancer Genome Atlas database and RNA sequencing data. Helicobacter. 2018;23(5):e12530 doi:10.1111/hel.12530

http://www.ncbi.nlm.nih.gov/pubmed/30175534

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.09 MB

0.35 MB

0.00 MB