# Neutrophil Transcriptional Deregulation by the Periodontal Pathogen Fusobacterium nucleatum in Gastric Cancer: A Bioinformatic Study

Authors: Ting Zhou; Xianhong Meng; Daxiu Wang; Weiran Fu; Xinrui Li
Journal: Disease Markers
 (2022)

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## Abstract

Background. Infection with the periodontal pathogen Fusobacterium nucleatum (F. nucleatum) has been associated with gastric cancer. The present study is aimed at uncovering the putative biological mechanisms underlying effects of F. nucleatum–mediated neutrophil transcriptional deregulation in gastric cancer. Materials and Methods. A gene expression dataset pertaining to F. nucleatum-infected human neutrophils was utilized to identify differentially expressed genes (DEGs) using the GEO2R tool. Candidate genes associated with gastric cancer were sourced from the “Candidate Cancer Gene Database” (CCGD). Overlapping genes among these were identified as link genes. Functional profiling of the link genes was performed using “g:Profiler” tool to identify enriched Gene Ontology (GO) terms, pathways, miRNAs, transcription factors, and human phenotype ontology terms. Protein-protein interaction (PPI) network was constructed for the link genes using the “STRING” tool, hub nodes were identified as key candidate genes, and functionally enriched terms were determined. Results. The gene expression dataset GEO20151 was downloaded, and 589 DEGs were identified through differential analysis. 886 candidate gastric cancer genes were identified in the CGGD database. Among these, 36 overlapping genes were identified as the link genes. Enriched GO terms included molecular function “enzyme building,” biological process “protein folding,’” cellular components related to membrane-bound organelles, transcription factors ER71 and Sp1, miRNAs miR580 and miR155, and several human phenotype ontology terms including squamous epithelium of esophagus. The PPI network contained 36 nodes and 53 edges, where the top nodes included PH4 and CANX, and functional terms related to intracellular membrane trafficking were enriched. Conclusion. F nucleatum-induced neutrophil transcriptional activation may be implicated in gastric cancer via several candidate genes including DNAJB1, EHD1, IER2, CANX, and PH4B. Functional analysis revealed membrane-bound organelle dysfunction, intracellular trafficking, transcription factors ER71 and Sp1, and miRNAs miR580 and miR155 as other candidate mechanisms, which should be investigated in experimental studies.


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## Body

## 1. Introduction

Gastric cancer is considered the sixth most common cancer globally [1]. A majority of gastric cancer cases occur in developing nations, and it is one of the chief causes of cancer-related morbidity and mortality [2]. Microbial factors are understood to play a central role in gastric cancer pathogenesis, and the best established among these is Helicobacter pylori (H. pylori) infection [3, 4]. An increasing number of studies have shown an association of several specific microbial species and the gastric microbial community or microbiome’s composition with gastric cancer [5–8].Recently, a meta-analysis of gastric mucosa and associated microbiota demonstrated the periodontal pathogensFusobacterium nucleatum (F. nucleatum), Parvimonas micra, and Peptostreptococcus stomatis as interacting and hub nodes associated with other gastric cancer-associated species and tumor status [9]. The periodontal pathogen F. nucleatum has been most strongly implicated in colorectal cancer (CRC) and is known to induce inflammation and suppress anticancer immune responses in CRC. F. nucleatum infection of neutrophils is known to induce NETosis [10]. In CRC, the circulatory transmission of F. nucleatum is the dominant mechanism [11], which suggests that systemic F. nucleatum and its immune signatures may be similarly relevant in other associated cancers. In particular, some F. nucleatum strains are shown to impede neutrophil-mediated oxidative killing [12], which could be implicated in its role in gastric cancer pathogenesis. In case of H. pylori, also a gram-negative pathogen, infection is also shown to promote N1 neutrophil subtype marked by nuclear hypersegmentation [13] but such mechanisms in case of F. nucleatum stimulated neutrophils are not yet investigated. As neutrophils play a central role in the tumor microenvironment [14], the role of F. nucleatum-induced neutrophil deregulation in gastric cancer merits further investigation. Tumor-activated neutrophils infiltrate the lesion and play a key role in the progression of gastric cancer via STAT3-related mechanisms [15], and the interaction of gastric cancer cells with tumor neutrophils promotes their migration, epithelial-mesenchymal transformation (EMT), and invasion [16]. Considering the paucity of research in this domain, bioinformatic approaches may reveal neutrophil transcriptional mechanisms relevant to gastric cancer. Therefore, the present study focused on uncovering neutrophil-related genes and molecular factors, which could be considered candidate mechanisms in gastric cancer via bioinformatic investigation.


## 2. Methods

### 2.1. Data Procurement and Link Gene Identification

Gene expression data forF. nucleatum-mediated regulation of neutrophil genes was sourced; the gene expression dataset GEO20151 [17] describing F. nucleatum-mediated regulation of neutrophil genes was downloaded from the Gene expression omnibus (GEO). Differential gene expression (DEG) analysis was performed using the GEO2R tool. Data were log transformed and normalized, and limma precision weights were applied. A significance level cut-off of p=0.05 with Benjamini and Hochberg (false discovery rate) correction was used to screen DEGs. Candidate human genes associated with gastric cancer from all available studies in the database were downloaded from the “Candidate Cancer Gene Database (CCGD)” [18]. The DEGs and candidate gastric cancer genes identified in the earlier step were overlapped using a Venn diagram, and shared genes were identified as “link” genes between F. nucleatum-mediated neutrophil transcriptome alteration and gastric cancer.


### 2.2. Functional Profiling of Link Genes

The link genes list was subjected to functional profiling analysis using the web-based tool “Gprofiler” [19]. Here, the organism of interest was selected as “Human,” only annotated genes were used as input, and the customized algorithm g:SCS significance threshold set at 0.05 was used for identification of enriched terms that was used.


### 2.3. Protein-Protein Interaction (PPI) Network and Functional Enrichment Analysis

PPI network construction with the link gene list as input was done using the STRING webtool [20]. A full STRING network with interaction sources including text mining, experiments, databases, coexpression, neighborhood, gene fusion, and co-occurrence was constructed. A minimum required interaction score was set as 0.15, and network edges represented the confidence measure. Network characteristics, “hub” genes, and functionally enriched terms in the network were determined.


## 2.1. Data Procurement and Link Gene Identification

Gene expression data forF. nucleatum-mediated regulation of neutrophil genes was sourced; the gene expression dataset GEO20151 [17] describing F. nucleatum-mediated regulation of neutrophil genes was downloaded from the Gene expression omnibus (GEO). Differential gene expression (DEG) analysis was performed using the GEO2R tool. Data were log transformed and normalized, and limma precision weights were applied. A significance level cut-off of p=0.05 with Benjamini and Hochberg (false discovery rate) correction was used to screen DEGs. Candidate human genes associated with gastric cancer from all available studies in the database were downloaded from the “Candidate Cancer Gene Database (CCGD)” [18]. The DEGs and candidate gastric cancer genes identified in the earlier step were overlapped using a Venn diagram, and shared genes were identified as “link” genes between F. nucleatum-mediated neutrophil transcriptome alteration and gastric cancer.


## 2.2. Functional Profiling of Link Genes

The link genes list was subjected to functional profiling analysis using the web-based tool “Gprofiler” [19]. Here, the organism of interest was selected as “Human,” only annotated genes were used as input, and the customized algorithm g:SCS significance threshold set at 0.05 was used for identification of enriched terms that was used.


## 2.3. Protein-Protein Interaction (PPI) Network and Functional Enrichment Analysis

PPI network construction with the link gene list as input was done using the STRING webtool [20]. A full STRING network with interaction sources including text mining, experiments, databases, coexpression, neighborhood, gene fusion, and co-occurrence was constructed. A minimum required interaction score was set as 0.15, and network edges represented the confidence measure. Network characteristics, “hub” genes, and functionally enriched terms in the network were determined.


## 3. Results

### 3.1. Link Gene Identification

The analysis of the gene expression dataset GEO20151 identified 589 annotated DEGs (TableS1). Table 1 displays the top 20 DEGs ranked by the adjusted p value.Table 1
Top 20 DEGs ranked by the adjustedp value.
Gene IDGene nameAdjustedp valueLog fold changeDNAJB1DnaJ heat shock protein family (Hsp40) member B10.003-2.13CXCL3C-X-C motif chemokine ligand 30.003-4.13FOSFos protooncogene, AP-1 transcription factor subunit0.003-2.44HMOX1Heme oxygenase 10.003-2.29HSPA1B///HSPA1AHeat shock protein family A (Hsp70) member 1B///heat shock protein family A (Hsp70) member 1A0.003-2.28HSPA1L///HSPA1B///HSPA1AHeat shock protein family A (Hsp70) member 1-like///heat shock protein family A (Hsp70) member 1B///heat shock protein family A (Hsp70) member 1A0.004-2.04OSMOncostatin M0.004-2.34VEGFAVascular endothelial growth factor A0.004-1.55MIR612///NEAT1MicroRNA 612///nuclear paraspeckle assembly transcript 1 (nonprotein coding)0.005-1.51HSP90AA1Heat shock protein 90 alpha family class A member 10.006-1.2CKS2CDC28 protein kinase regulatory subunit 20.006-2.33CXCL2C-X-C motif chemokine ligand 20.008-1.1BTG2BTG antiproliferation factor 20.008-1.4CSF1Colony-stimulating factor 10.008-2.12FFAR2Free fatty acid receptor 20.0081.48HILPDAHypoxia inducible lipid droplet associated0.0081.95IL1RNInterleukin 1 receptor antagonist0.0081.03LOC100129518///SOD2Uncharacterized LOC100129518///superoxide dismutase 2, mitochondrial0.008-1.09MARCKSMyristoylated alanine rich protein kinase C substrate0.0081.28MT1XMetallothionein 1X0.008-3.13Using the CCGD database, 886 annotated candidate gastric cancer human genes were identified (TableS2). Table 2 shows the top 20 candidate gastric cancer genes ranked by the number of supporting studies.Table 2
Top 20 candidate gastric cancer genes in the CCGD database ranked by the number of supporting studies.
Gene IDNumber of studiesPTEN45CREBBP32DYRK1A28GSK3B28KDM6A27WAC26ZMIZ126NF125SETD525PICALM24RAF124PPP1R12A23SFI123ERBB2IP22PPP6R322ANKRD1121CTNNA121TAOK121KANSL120PUM120A Venn diagram was constructed, and the overlapping genes were identified, which showed 36 link genes (Figure1). The 36 link genes are listed in Table 3.Figure 1
A Venn diagram depicting 589 annotatedF. nucleatum-stimulated netrophil DEGs, 886 annotated candidate gastric cancer genes, and 36 common “link” genes.Table 3
36 link genes shared byF. nucleatum-stimulated DEGs in neutrophils and gastric cancer candidate genes.
Gene IDGeneAdjustedp value∗Log fold change∗DNAJB1DnaJ heat shock protein family (Hsp40) member B10.003-2.13EHD1EH domain-containing 10.0111.16IER2Immediate early response 20.016-0.75SMARCE1SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily e, member 10.016-3.25GRIK1-AS2///BACH1GRIK1 antisense RNA 2///BTB domain and CNC homolog 10.0161.17RAB5ARAB5A, member RAS oncogene family0.0180.76RYBPRING1 and YY1 binding protein0.022-0.81P4HBProlyl 4-hydroxylase subunit beta0.022-0.99UQCR11Ubiquinol-cytochrome c reductase, complex III subunit XI0.022-0.96HSPE1Heat shock protein family E (Hsp10) member 10.026-1.42ATP5JATP synthase, H+ transporting, mitochondrial Fo complex subunit F60.027-1.07RRAGCRas-related GTP binding C0.027-0.67ARFIP1ADP ribosylation factor interacting protein 10.0281.06B3GALT2Beta-1,3-galactosyltransferase 20.028-3.68UBE2HUbiquitin conjugating enzyme E2 H0.0300.90GNB1G protein subunit beta 10.0340.62SETD5SET domain-containing 50.0370.74GALR2Galanin receptor 20.039-2.76TNPO3Transportin 30.039-2.70TM9SF2Transmembrane 9 superfamily member 20.039-0.73UBR4Ubiquitin protein ligase E3 component n-recognin 40.0400.69CANXCalnexin0.0410.69WNK1WNK lysine deficient protein kinase 10.042-0.83BMPR2Bone morphogenetic protein receptor type 20.043-3.06DICER1Dicer 1, ribonuclease III0.043-0.71ARGLU1Arginine and glutamate rich 10.046-0.84MOAP1Modulator of apoptosis 10.046-1.43AFTPHAftiphilin0.0460.62GARSGlycyl-tRNA synthetase0.047-0.75RABGAP1LRAB GTPase activating protein 1-like0.049-0.95SHBSH2 domain-containing adaptor protein B0.0492.33PBX1PBX homeobox 10.049-2.33PCM1Pericentriolar material 10.050-2.29GMFGGlia maturation factor gamma0.050-0.45TRAF3TNF receptor-associated factor 30.0500.93LYNLYN protooncogene, Src family tyrosine kinase0.0500.46∗Genes are ranked by adjusted p values for F. nucleatum-stimulated DEGs in neutrophils.


### 3.2. Functional Profiling of the Link Genes

The functional enrichment analysis results from “G:profiler” are depicted in Figure2. These included 1 GO molecular function term (enzyme binding), 1 GO biological process term (protein folding), 3 GO cellular component terms (cytoplasm, intracellular membrane-bounded organelle, membrane-bounded organelle), 2 transcription factors (ER71 and Sp1), 2 miRNAs (miR 580, miR 155), and 10 human phenotype ontology terms (Table S3).Figure 2
Functional enrichment analysis of the link genes. (a) Bubble plot depicting -log 10p adjusted values of enriched terms. (b) Detailed results depicting 19 enriched terms.
(a)(b)


### 3.3. PPI Network and Functional Enrichment Analysis

The PPI network had 36 nodes and 54 edges with an average node degree of 3 and an average local clustering coefficient of 0.386 (Figure3). The top 5 enriched nodes included CANX, PH4B, ATP5J, DNAJB1, and EHD1. 32 enriched functional terms in 3 categories were identified (Table 4).Figure 3
PPI network analysis of the 36 link genes. The top 5 enriched nodes included CANX, PH4B, ATP5J, DNAJB1, and EHD1.Table 4
STRING functional enrichment analysis of 36 link gene PPI network∗.
CategoryTerm IDTerm descriptionStrengthFalse discovery rateCompartmentsGOCC:0070062Extracellular exosome0.950.012GOCC:0043230Extracellular organelle0.930.005GOCC:1903561Extracellular vesicle0.930.005GOCC:0098796Membrane protein complex0.610.018GOCC:0031982Vesicle0.540.010GOCC:0016020Membrane0.330.012GOCC:0043231Intracellular membrane-bounded organelle0.310.002GOCC:0043227Membrane-bounded organelle0.270.002GOCC:0005737Cytoplasm0.270.006GOCC:0043226Organelle0.250.002GOCC:0043229Intracellular organelle0.250.003GOCC:0005622Intracellular0.220.002GOCC:0110165Cellular anatomical entity0.150.002GO:0098805Whole membrane0.540.048GO:0031982Vesicle0.380.048GO componentGO:0043231Intracellular membrane-bounded organelle0.210.020GO:0005737Cytoplasm0.20.020GO:0043227Membrane-bounded organelle0.170.020GO:0043229Intracellular organelle0.170.020GO:0043226Organelle0.150.020GO:0005622Intracellular0.120.048TissuesBTO:0000132Blood platelet0.910.048BTO:0000580Blood cancer cell0.770.001BTO:0001271Leukemia cell0.760.004BTO:0000345Digestive gland0.460.021BTO:0000142Brain0.360.004BTO:0001491Viscus0.340.021BTO:0000282Head0.310.007BTO:0000083Female reproductive system0.310.016BTO:0003091Urogenital system0.290.015BTO:0001489Whole body0.180.003BTO:0000042Animal0.120.013∗The functional terms in each category are ranked by strength of enrichment.Functional enrichment analysis depicted multiple terms related to Extracellular exosomes, extracellular organelle, extracellular vesicle and membrane protein complex and tissues including blood cells and digestive glands (Table4).


## 3.1. Link Gene Identification

The analysis of the gene expression dataset GEO20151 identified 589 annotated DEGs (TableS1). Table 1 displays the top 20 DEGs ranked by the adjusted p value.Table 1
Top 20 DEGs ranked by the adjustedp value.
Gene IDGene nameAdjustedp valueLog fold changeDNAJB1DnaJ heat shock protein family (Hsp40) member B10.003-2.13CXCL3C-X-C motif chemokine ligand 30.003-4.13FOSFos protooncogene, AP-1 transcription factor subunit0.003-2.44HMOX1Heme oxygenase 10.003-2.29HSPA1B///HSPA1AHeat shock protein family A (Hsp70) member 1B///heat shock protein family A (Hsp70) member 1A0.003-2.28HSPA1L///HSPA1B///HSPA1AHeat shock protein family A (Hsp70) member 1-like///heat shock protein family A (Hsp70) member 1B///heat shock protein family A (Hsp70) member 1A0.004-2.04OSMOncostatin M0.004-2.34VEGFAVascular endothelial growth factor A0.004-1.55MIR612///NEAT1MicroRNA 612///nuclear paraspeckle assembly transcript 1 (nonprotein coding)0.005-1.51HSP90AA1Heat shock protein 90 alpha family class A member 10.006-1.2CKS2CDC28 protein kinase regulatory subunit 20.006-2.33CXCL2C-X-C motif chemokine ligand 20.008-1.1BTG2BTG antiproliferation factor 20.008-1.4CSF1Colony-stimulating factor 10.008-2.12FFAR2Free fatty acid receptor 20.0081.48HILPDAHypoxia inducible lipid droplet associated0.0081.95IL1RNInterleukin 1 receptor antagonist0.0081.03LOC100129518///SOD2Uncharacterized LOC100129518///superoxide dismutase 2, mitochondrial0.008-1.09MARCKSMyristoylated alanine rich protein kinase C substrate0.0081.28MT1XMetallothionein 1X0.008-3.13Using the CCGD database, 886 annotated candidate gastric cancer human genes were identified (TableS2). Table 2 shows the top 20 candidate gastric cancer genes ranked by the number of supporting studies.Table 2
Top 20 candidate gastric cancer genes in the CCGD database ranked by the number of supporting studies.
Gene IDNumber of studiesPTEN45CREBBP32DYRK1A28GSK3B28KDM6A27WAC26ZMIZ126NF125SETD525PICALM24RAF124PPP1R12A23SFI123ERBB2IP22PPP6R322ANKRD1121CTNNA121TAOK121KANSL120PUM120A Venn diagram was constructed, and the overlapping genes were identified, which showed 36 link genes (Figure1). The 36 link genes are listed in Table 3.Figure 1
A Venn diagram depicting 589 annotatedF. nucleatum-stimulated netrophil DEGs, 886 annotated candidate gastric cancer genes, and 36 common “link” genes.Table 3
36 link genes shared byF. nucleatum-stimulated DEGs in neutrophils and gastric cancer candidate genes.
Gene IDGeneAdjustedp value∗Log fold change∗DNAJB1DnaJ heat shock protein family (Hsp40) member B10.003-2.13EHD1EH domain-containing 10.0111.16IER2Immediate early response 20.016-0.75SMARCE1SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily e, member 10.016-3.25GRIK1-AS2///BACH1GRIK1 antisense RNA 2///BTB domain and CNC homolog 10.0161.17RAB5ARAB5A, member RAS oncogene family0.0180.76RYBPRING1 and YY1 binding protein0.022-0.81P4HBProlyl 4-hydroxylase subunit beta0.022-0.99UQCR11Ubiquinol-cytochrome c reductase, complex III subunit XI0.022-0.96HSPE1Heat shock protein family E (Hsp10) member 10.026-1.42ATP5JATP synthase, H+ transporting, mitochondrial Fo complex subunit F60.027-1.07RRAGCRas-related GTP binding C0.027-0.67ARFIP1ADP ribosylation factor interacting protein 10.0281.06B3GALT2Beta-1,3-galactosyltransferase 20.028-3.68UBE2HUbiquitin conjugating enzyme E2 H0.0300.90GNB1G protein subunit beta 10.0340.62SETD5SET domain-containing 50.0370.74GALR2Galanin receptor 20.039-2.76TNPO3Transportin 30.039-2.70TM9SF2Transmembrane 9 superfamily member 20.039-0.73UBR4Ubiquitin protein ligase E3 component n-recognin 40.0400.69CANXCalnexin0.0410.69WNK1WNK lysine deficient protein kinase 10.042-0.83BMPR2Bone morphogenetic protein receptor type 20.043-3.06DICER1Dicer 1, ribonuclease III0.043-0.71ARGLU1Arginine and glutamate rich 10.046-0.84MOAP1Modulator of apoptosis 10.046-1.43AFTPHAftiphilin0.0460.62GARSGlycyl-tRNA synthetase0.047-0.75RABGAP1LRAB GTPase activating protein 1-like0.049-0.95SHBSH2 domain-containing adaptor protein B0.0492.33PBX1PBX homeobox 10.049-2.33PCM1Pericentriolar material 10.050-2.29GMFGGlia maturation factor gamma0.050-0.45TRAF3TNF receptor-associated factor 30.0500.93LYNLYN protooncogene, Src family tyrosine kinase0.0500.46∗Genes are ranked by adjusted p values for F. nucleatum-stimulated DEGs in neutrophils.


## 3.2. Functional Profiling of the Link Genes

The functional enrichment analysis results from “G:profiler” are depicted in Figure2. These included 1 GO molecular function term (enzyme binding), 1 GO biological process term (protein folding), 3 GO cellular component terms (cytoplasm, intracellular membrane-bounded organelle, membrane-bounded organelle), 2 transcription factors (ER71 and Sp1), 2 miRNAs (miR 580, miR 155), and 10 human phenotype ontology terms (Table S3).Figure 2
Functional enrichment analysis of the link genes. (a) Bubble plot depicting -log 10p adjusted values of enriched terms. (b) Detailed results depicting 19 enriched terms.
(a)(b)


## 3.3. PPI Network and Functional Enrichment Analysis

The PPI network had 36 nodes and 54 edges with an average node degree of 3 and an average local clustering coefficient of 0.386 (Figure3). The top 5 enriched nodes included CANX, PH4B, ATP5J, DNAJB1, and EHD1. 32 enriched functional terms in 3 categories were identified (Table 4).Figure 3
PPI network analysis of the 36 link genes. The top 5 enriched nodes included CANX, PH4B, ATP5J, DNAJB1, and EHD1.Table 4
STRING functional enrichment analysis of 36 link gene PPI network∗.
CategoryTerm IDTerm descriptionStrengthFalse discovery rateCompartmentsGOCC:0070062Extracellular exosome0.950.012GOCC:0043230Extracellular organelle0.930.005GOCC:1903561Extracellular vesicle0.930.005GOCC:0098796Membrane protein complex0.610.018GOCC:0031982Vesicle0.540.010GOCC:0016020Membrane0.330.012GOCC:0043231Intracellular membrane-bounded organelle0.310.002GOCC:0043227Membrane-bounded organelle0.270.002GOCC:0005737Cytoplasm0.270.006GOCC:0043226Organelle0.250.002GOCC:0043229Intracellular organelle0.250.003GOCC:0005622Intracellular0.220.002GOCC:0110165Cellular anatomical entity0.150.002GO:0098805Whole membrane0.540.048GO:0031982Vesicle0.380.048GO componentGO:0043231Intracellular membrane-bounded organelle0.210.020GO:0005737Cytoplasm0.20.020GO:0043227Membrane-bounded organelle0.170.020GO:0043229Intracellular organelle0.170.020GO:0043226Organelle0.150.020GO:0005622Intracellular0.120.048TissuesBTO:0000132Blood platelet0.910.048BTO:0000580Blood cancer cell0.770.001BTO:0001271Leukemia cell0.760.004BTO:0000345Digestive gland0.460.021BTO:0000142Brain0.360.004BTO:0001491Viscus0.340.021BTO:0000282Head0.310.007BTO:0000083Female reproductive system0.310.016BTO:0003091Urogenital system0.290.015BTO:0001489Whole body0.180.003BTO:0000042Animal0.120.013∗The functional terms in each category are ranked by strength of enrichment.Functional enrichment analysis depicted multiple terms related to Extracellular exosomes, extracellular organelle, extracellular vesicle and membrane protein complex and tissues including blood cells and digestive glands (Table4).


## 4. Discussion

The present identified key molecular mechanisms, which may linkF. nucleatum-stimulated neutrophil transcriptomic alterations with the development of gastric cancer. Among the DEGs in F. nucleatum-stimulated neutrophils, 36 genes were documented as gastric cancer candidate genes. The most significant genes among these included DNAJB1, EHD1, and IER2. DnaJ/Hsp40 (heat shock protein 40) proteins are key proteins for protein biology via stimulation of ATPase and are shown to play a role in p53 ubinquination to promote cancer cells in vitro [21]. EHD1 (Eps15 homology (EH) domain-containing protein 1) plays an important role in receptor-mediated endocytic recycyling [22], shows to promote tumor growth, and is implicated in resistance to cisplatin in case of non-small-cell lung cancer [23]. Human immediate early response 2 (IER2) is a nuclear protein that is implicated in cancer via transcriptional regulation of endothelial motility and adhesion via a FAK-dependent mechanism [24], thereby regulating tumor angiogenesis. Apart from DNAJB1 and EHD1, the PPI network analysis showed CANX and PH4B as the top hub genes. Calnexin or CANX is an ER stress chaperone transmembrane protein involved in glycoprotein folding, is considered a prognostic indicator and therapeutic target in CRC [25], and is found to restrict antitumor CD4+ and CD8+ T cells [26] in oral cancer. The protein disulfide-isomerase P4HB also acts as a chaperone protein involved in protein folding and the ER stress response and is shown to be a prognostic marker of glioma [27]. In gastric cancer, HIF-1 is found to suppress P4HB and promote cancer cell proliferation [28]. PH4B is also linked to chemoresistance [29–31]. Emerging evidence indicates that neutrophil NETosis is a central contributor to cancer proliferation and chemoresistance [32]. Overall, the identified candidate genes may serve as molecular mechanisms underlying F. nucleatum neutrophil-stimulated NETosis with gastric cancer. Of note, NETosis has been documented in relation to Helicobacter pylori via NADPH oxidase activation through several kinases [33], which is well established in its association with gastric cancer [34]. Inflammatory mechanisms leading to NETosis activation via F. nucleatum in gastric cancer should be investigated. In addition, emerging evidence shows F. nucleatum as a factor increasing the chemoresistance in CRC by modulating the tumor microenvironment and autophagy [35, 36]. The plausible role of F. nucleatum infection in the chemoresistance of gastric cancer remains to be investigated.Functional enrichment analysis of the link genes and PPI network was conducted, and consistency in the findings was evident. Several extracellular processes including exosome, membrane protein complex, vesicles, and intracellular membrane-bound organelle were seen as enriched components in the PPI network. Protein folding and associated cellular components were evident as enriched, underscoring the potential relevance of the ER stress response as a linkage mechanism [37]. The 2 enriched transcription factors included ER71 and Sp1. The Ets transcription factor Er71 is a key regulator in endothelial and hematopoietic stem cell development [38] and recently has been reported as a valuable target to block tumor angiogenesis [39]. SP1 is shown to transcriptionally regulate oncostatin M receptor in gastric cancer and thereby contribute to cancer progression [40]. SP1 is also implicated in neutrophil elastase-mediated increase in mucin gene receptors [41] and thus may play a role in stimulated neutrophil-mediated deregulation of the mucous barrier [42].The role ofF. nucleatum in CRC is well studied. It has multiple adhesins, and Fap2-mediated adhesion of F. nucleatum to epithelial cells is shown to induce a proinflammatory cascade, whereas Fap2-independent mechanisms are demonstrated in CRC neutrophils and macrophages, which together increase proinflammatory signaling to increase tumor invasion, seeding, and metastatsis [43]. In the colon, F. nucleatum is shown to disrupt epithelial barrier integrity by damage to tight junctions and induction of cytokines of helper T cells [44]. Pathogenic strains of F. nucleatum are shown to induce MUC2 and TNF secretion from colonic cells [45]. The interaction of F. nucleatum with mucins warrants further investigation in the context of gastric cancer. The 2 enriched miRNAs included miR 580 and miR 155. miR 580 has been shown to inhibit chemokine ligand 2 (CCL2) production in the hepatocellular carcinoma tumor microenvironment [46]. miR-155 is involved in neutrophil NETosis [47] and is considered a key factor interlinking inflammation with cancer [48]. miR-155 was found to play a tumor suppressor role in gastric cancer [49]. The enriched GO terms and compartments in the PPI network supported the role of intracellular membrane trafficking as a key cancer mechanism harnessed by F. nucleatum stimulation of neutrophils [50].Taken together, the findings of this bioinformatic analysis revealed several possible molecular mechanisms byF. nucleatum-induced neutrophil gene deregulation that may promote gastric carcinogenesis. At the same time, these findings are limited by the small sample number in the analyzed gene expression dataset and the lack of validation experiments to support the relevance of the highlighted candidate genes, transcription factors, cellular processes, and miRNAs. Furthermore, the effects of F. nucleatum are likely to be subspecies or strain-specific and should be investigated in future research. F. nucleatum strains with higher invasive capacity have been identified in inflamed colonic tissues as compared to those from healthy tissues [51], which raises the need for phylotype and functional characterization in context of its role gastric cancer. The present findings should be verified in experimental research models that investigate the candidate link genes and functional mechanisms involved in F. nucleatum-mediated neutrophil plasticity relevant to gastric cancer pathogenesis. Cell model experiments, animal experiments, and clinical examination of the theoretical premises established in this study are warranted. The present investigation focused on the role of F. nucleatum-stimulated neutrophils alone in gastric cancer but the tumor microenvironment constitutes of varied immune cell populations that may be deregulated by F. nucleatum and also warrant deeper investigation.


## 5. Conclusion

F nucleatum-induced neutrophil transcriptional activation may be implicated in gastric cancer via several candidate genes including DNAJB1, EHD1, IER2, CANX, and PH4B among the top genes of interest. Putative key functional mechanisms included membrane-bound organelle dysfunction and intracellular trafficking along with the modulation of transcription factors ER71 and Sp1 and miRNAs miR580 and miR155.



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*Source: 9584507-2022-08-18.xml*