Analysis of the potential correlation between gastric cancer and gastrointestinal microbiota via in-silico data mining

Emerging evidence shows the gastrointestinal microbiome might play an important role in the carcinogenesis of gastric cancer. While Helicobactor pylori has been reported to be a specific risk factor of gastric cancer, it is still controversial whether significant difference of non- H. pylori microbiota exists between gastric cancer patients and healthy control. In this study, we employed multiple bioinformatic databases to excavate the potential correlation between gastrointestinal microbiome and gastric cancer. The databases involved in this investigation include HMDB, STITCH, OMIM, GWAS Catalog, WebGestalt, Toppgene, GeneMANIA. In addition, the network diagrams were built by use of Cytoscape software. Notably, our results showed that 33 common genes participate in both gastrointestinal microbiome and gastric cancer. The further analysis of these common genes suggested that there was a wide array of interactions and pathways in which the correlation between gastrointestinal microbiome and gastric cancer is involved. Our present study gives a bioinformatic insight into possible pathways in which the gastrointestinal microbiome play roles in gastric cancer. Future efforts are necessary to be paid to elicit the exact mechanisms as well as potential therapeutic targets of gastric cancer. protein binding, hydrolase activity, nucleotide binding, ion binding, nucleic acid binding, transferase activity and molecular transducer activity. The main cellular components that these genes enriched in include membrane, macromolecular complex, vesicle, cell projection, vacuole, cytosol, nucleus, endomembrane system and cytoskeleton. Heterotrimeric G-protein signaling pathway-rod outer segment phototransduction, G-protein beta:gamma signaling, Aquaporin-mediated transport, GABA receptor activation, Thyrotropin-releasing hormone receptor signaling pathway, Histamine H1 receptor mediated signaling pathway, G alpha (s) signalling events, Neurotransmitter Receptor Binding And Downstream Transmission In The Postsynaptic Cell, 5HT2 type receptor mediated signaling pathway, G alpha (q) signalling events, G alpha (i) signalling events, Glucagon signaling in metabolic regulation, Histamine H2 receptor mediated signaling pathway, GABA-B receptor II signaling, G protein gated Potassium channels, Inhibition of voltage gated Ca2 + channels via Gbeta/gamma subunits, Activation of G protein gated Potassium channels, Oxytocin receptor mediated signaling pathway, Beta3 adrenergic receptor signaling pathway, Alcoholism, Beta-catenin independent WNT signaling, 5HT4 type receptor mediated signaling pathway, Inwardly rectifying K + channels, Platelet homeostasis, Class B/2 (Secretin family receptors), Transmission across Chemical Synapses, Chemokine signaling pathway, Muscarinic acetylcholine receptor 1 and 3 signaling pathway, Heterotrimeric G-protein signaling pathway-Gq alpha and Go alpha mediated pathway, Pathways in cancer, Beta1 adrenergic receptor signaling pathway, Beta2 adrenergic receptor signaling pathway, GPCR downstream signaling, Signaling by GPCR, Signaling by Wnt, Neuronal System, Hemostasis, Gastrin-CREB signalling pathway via PKC and MAPK, PI3 kinase pathway, Angiotensin II-stimulated signaling through G proteins and beta-arrestin, Ras signaling pathway, Metabotropic glutamate receptor group III pathway, Potassium Channels, Heterotrimeric G-protein signaling pathway-Gi alpha and Gs alpha mediated pathway, GPCR ligand binding, Dopamine receptor mediated signaling pathway, Wnt signaling pathway, Metabolism of proteins, Sphingosine 1-phosphate (S1P) pathway, Transmembrane transport of triphosphate binding, purine ribonucleoside binding, ribonucleoside binding, nucleoside binding, purine ribonucleotide binding, purine nucleotide binding and ribonucleotide binding. amplification and pyrosequencing analysis.

Introduction polymorphism and 16S rRNA gene cloning and sequencing, they found no significant difference between the gastric bacterial community from two groups. Sequencing of 140 clones revealed 102 phylotypes, with representatives from five bacterial phyla (Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria and Fusobacteria), while the abundance of H.pylori is relatively low (24).In non-H.pylori gastric cancer cases, Helicobacter cinaedi, Helicobacter mustelae and Campylobacter hyointestinalis were detected (25), while no significant role of these non-H.pylori bacteria in gastric cancer (26).
Some research suggest that a gradual shift of gastric microbiota community is likely to take place in the progression from precancerous lesions to gastrointestinal cancer, while different studies do not reach a consensus on the nature of the shift.
Both increases (27,28) and decreases (29) in the bacterial diversity have been reported through comparing samples in each step of the gastric carcinogenesis, from chronic gastritis or intestinal metaplasia to gastric cancer. The increase in lachnospiraceae and members of the lactobaccillaceae and streptococcacaea family during the carcinogenesis were turned out to be the most consistent finding among the studies. Nevertheless, the exact role of these non-H.pylori bacteria in gastric carcinogenesis as well as the factors regulating the microbiota diversity are still remains to be determined. The discrepancy between the methods of different studies make it difficult to directly compare the results.
In this study, we employed multiple bioinformatics databases to further explore the possible association between gastrointestinal microbiota and gastric cancer in the level of interaction genes. Our data will help to provide several new insights for further investigation of the role of the gastrointestinal microbiota in the development of gastric cancer.

1.
Analysis of the common genes shared by GI microbiota and GC 1.1 The identification of the genes related to GI microbiota 10 metabolites of GI microbiota associated with gastric diseases were identified according to the HMDB database. These metabolites included Trimethylamine Noxide, Butyric acid, Indoleacetic acid, Propionic acid, Indoxyl sulfate, p-Cresol, Acetone, Methane, 2-Methylerythritol and Glycine. 313 human genes associated with the 10 metabolites were identified by searching the STITCH database. The number of genes related to each metabolite is shown in Table 1. The identification of the genes related to GC 464 genes from OMIM database and 34 genes from GWAS Catalog were shown to associated with GC. No overlap was found in genes obtained from the two databases.

1.3
The common genes shared by GI microbiota and GC 33 genes were identified to be the common genes shared by GI microbiota and GC.
The symbol and description of each gene is shown in Table 2.  Dopamine receptor mediated signaling pathway, Wnt signaling pathway, Metabolism of proteins, Sphingosine 1-phosphate (S1P) pathway, Transmembrane transport of small molecules, S1P2 pathway, S1P3 pathway, Inflammation mediated by chemokine and cytokine signaling pathway, PLC beta mediated events, G-protein mediated events, S1P5 pathway, Alzheimer disease, S1P4 pathway, Long-term depression, Phototransduction, CXCR3-mediated signaling events, Thrombin signaling and protease-activated receptors, Activation of the phototransduction cascade, Bioactive Peptide Induced Signaling Pathway, Chagas disease (American trypanosomiasis), Inhibition of adenylate cyclase pathway, Adenylate cyclase inhibitory pathway, S1P1 pathway, Synthesis, secretion, and inactivation of

4.
Analysis of interaction networks of the common genes shared by GI microbiota and GC The 33 common genes shared by GI microbiota and GC were further analyzed by GeneMANIA, and the interaction networks were mapped by Cytoscape software (Fig. 3). Each node represents a particular gene. The color of the gene represents a The detail of the interactions is in Table 3. Table 3 The interaction patterns and their proportions of protein

Databases and Methods
Multiple bioinformatic approaches were used to analyze the correlation between gastrointestinal microbiota and gastric cancer. All analyses were updated on May Cytoscape (www.cytoscape.org/) is designed to facilitate the visualization of molecular network and biological pathways. The software is able to depict large networks with more than ten thousand nodes and edges and display interactions between distinct molecular network constituents. The generation of biological networks are supported by the application of the editor module (37).

Discussion
The overlap of the genes related to GI microbiota and gastric cancer indicates that a probable correlation between GI microbiota and gastric cancer might exist. The further GO and pathway analysis suggest that the correlation is likely to be involved in widespread aspects which contain sophisticated interactions and mechanisms.
The analysis of the 33 common genes will provide insight into further investigation into the mechanism of the gastric carcinogenesis and the potential diagnostic and therapeutic target of gastric cancer.
The main functions of the 33 common genes shared by GI microbiota and GC include GTPase activity, nucleoside-triphosphatase activity, pyrophosphatase activity, hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides, hydrolase activity, acting on acid anhydrides, G-protein beta/gamma-subunit complex binding, signal transducer activity, G-protein coupled receptor binding, Gprotein coupled serotonin receptor binding, GTP binding, guanyl ribonucleotide binding, guanyl nucleotide binding, signaling receptor binding, protein-containing complex binding, GTPase activating protein binding, purine ribonucleoside triphosphate binding, purine ribonucleoside binding, ribonucleoside binding, nucleoside binding, purine ribonucleotide binding, purine nucleotide binding and ribonucleotide binding.
The main pathways that 33 common genes enriched in mainly focus on G protein related pathways, several cell signal transduction pathways and synapse involving pathways. What is more, several rare pathways attracted our attention, such as opioid signaling and morphine addiction, the regulation of insulin secretion and inhibition as well as protein folding.
Opioids such as morphine have been taken advantage in analgesia for centuries.  (39). Some other studies connected the morphine administration with gastric microbiota. It was reported that opioid induce gastric microbial disruption and bile dysregulation which further compromise the gastric barrier (40). Opioid is also reported to involve in the exacerbation of gram-positive sepsis which could be rescued by IL-17A neutralization (41). While the morphine and other narcotics have been noted to alter the composition of GI microbiota and promote the translocation of GI microbiota, it is reported that GI microbiota was indicated to play an essential role in modulating the response to chronic morphine administration (42). The opioids along with their effects on GI microbiota is also reported to have a role in the neuroinflammation in the central nervous system that is mediated by a gut-brain signaling axis, which further contributed to several inflammation-related psychopathologies. The difference in the opioid regimen significantly influence the GI microbiota as well as opioid dependence-related behaviors (43).
The endocannabinoid signaling is another pathway that the common genes significantly enriched in. In addition to be acknowledged to participate in the adipogenesis and obesity, the endocannabinoid system is currently under attention due to its potential therapeutic effects on a broad variety of disease including cancer (44). Drugs targeting the endocannabinoid system may have anti-metastatic effect to function against various types of tumors (45). Other functions of cannabinoid include anti-apoptotic, anti-angiogenic and anti-proliferative in the cancer cell growth through multiple pathways such as ERK, Akt, MAPK, PI3K and HIF-1 (46). The endocannabinoid signaling is also reported to play a role in inflammation (47), energy balance (47) There is also evidence supports that GI microbiota is specifically related to the regulation of the apelinergic system in the type 2 diabetes(57). However, it is still remains to be determined whether apelin participate in the effect of GI microbiota on gastric carcinogenesis.

Conclusions:
Our data suggest that the GI microbiota might take part in the development of

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Availability of data and materials
All data generated or analyzed during this study are included in this published article.

Conflicts of Interest:
No conflicts of interest declared.

Funding:
Research in the authors' lab is supported by Chinese National Natural Science Multiple bioinformatic approaches were used to analyze the potential interaction between ga Figure 2 Gene ontology analysis of 33 common genes shared by GI microbiota and GC conducted by W Figure 3 The 33 common genes shared by GI microbiota and GC were further analyzed by GeneMANIA

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