jct Journal of Cancer Therapy 2151-1934 2151-1942 Scientific Research Publishing 10.4236/jct.2024.1510034 jct-137001 Articles Medicine Healthcare Identification of MUC1 as a Novel Oncogene of Fusobacterium nucleatum-Associated Colorectal Cancer by a Combined Bioinformatics and Experimental Approach Xinli Ye 1 Shouru Zhang 1 Zhaoli Zhang 2 Jie Zou 3 Xiaojie Gao 1 aDepartment of Gastrointestinal Tumor Center, Chongqing University Cancer Hospital, Chongqing, China aNursing Department, Chongqing Cancer Hospital, Chongqing, China aDepartment of Hepatobiliary Surgery, Children’s Hospital of Chongqing Medical University, Chongqing, China 28 10 2024 15 10 362 380 29, September 2024 27, September 2024 27, October 2024 © Copyright 2014 by authors and Scientific Research Publishing Inc. 2014 This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Background: Fusobacterium nucleatum can cause opportunistic and chronic infections and has recently been shown to be involved in colorectal cancer. However, the specific mechanism by which F. nucleatum induces colorectal carcinoma remains unclear. Methods: We downloaded the GSE110223, GSE110224, GSE113513 and GSE122183 microarray datasets from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) related to F. nucleatum in CRC by overlapping data sets was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome pathway (KEGG) analyses were used for enrichment analysis. Moreover, Cytoscape software constructed a protein-protein interaction (PPI) network of differentially expressed genes. Finally, western blot and RT-qPCR analysis identified the relative protein and mRNA expression of hub genes in the cell model. Results: In total, 118 DEGs in F. nucleatum-associated CRC were screened from nonoverlapping microarray data, among which 20 upregulated and 98 downregulated DEGs were identified. The 118 DEGs were significantly correlated with diverse functions and pathways. The hub gene MUC1 had higher centrality scores in the PPI network, and the top 5 closely interacting hub genes, SLC7A11, AGR2, KRT18, CARTPT and TSPYL5, were identified. Conclusion: Our evidence suggests that the identified DEGs associated with F. nucleatum enhance our comprehension of the molecular Mechanisms underlying the tumorigenesis and development of CRC and might be used as molecular targets and diagnostic biomarkers for the treatment of CRC. CRC F. nucleatum DEGs MUC1 1. Introduction

Colorectal carcinoma (CRC) is a major and severe health problem worldwide. Over 1.8 million new colorectal carcinoma cases and 881,000 deaths were estimated to occur in 2018, accounting for approximately 1/10 of cancer morbidities and mortalities. In general, colorectal carcinoma ranked third in incidence and second in mortality [1] . Recently, microorganisms have been increasingly closely associated with the occurrence, development and metastasis of tumors. There are many risk factors involved in CRC. Fusobacterium nucleatum, a gram-negative anaerobic bacillus that may induce colorectal cancer, has received increased attention [2] [3] . According to recent findings, F. nucleatum can induce a proinflammatory microenvironment in the bowel through antiphlogistic mechanisms and immune responses, thereby promoting a microecology propitious for tumorigenesis and CRC progression [4] [5] . However, the molecular biology and pathogenesis of CRC attributed to F. nucleatum infection needs to be further elucidated.

The etiology of colorectal cancer has always been considered to be complex due to the many genetic alterations in critical genes and pathways [6] . With related studies in the field of genomics, high-throughput technology for the analysis and quantification of gene expression, such as microarrays, is increasingly utilized [7] . Genomic analysis with microarray technology has shown potential in clinical cancer research and in fundamental oncology [8] . One of the most promising techniques is bioinformatics analysis with microarrays, which is an up-to-date, open, systematic, and multidimensional method and can comprehensively analyze the changes in genetic material, including mRNA expression, during the occurrence and development of CRC. Therefore, we could help elucidate the process of tumor occurrence and find novel prognostic biomarkers and cancer therapeutic targets.

The epithelial cell molecule mucin 1 (MUC1) is a transmembrane glycoprotein aberrantly overexpressed in various cancers, including CRC [9] . Accumulated experimental evidence has shown that the expression of MUC1 is increased in CRC. Moreover, MUC1 could be a novel therapeutic target in chronic diseases, such as inflammation, cancer and autoimmune diseases [10] . Previous studies have reported that MUC1 is a novel T cell regulator and is expressed by activated human T cells. MUC1 prevents the activation of the intrinsic apoptotic pathway, thus assisting cancer cells in evading cell death [11] . In hypoxia, elevated reactive oxygen species (ROS) levels can activate apoptotic pathways. However, MUC1 overexpression plays a key role in decreasing intracellular ROS levels. Furthermore, MUC1 impedes hypoxic cell death in colon cancer by facilitating decreases in intracellular ROS concentration [12] . It has been shown that MUC1 is related to enhanced tumor initiation and progression [13] . Recently, studies have shown that approximately 15% of malignancies are associated with chronic microbial infection worldwide [14] . Nevertheless, few studies have investigated the effect of aberrant MUC1 expression associated with intestinal anaerobes, especially F. nucleatum, in human CRC.

In this study, we analyzed microarray data by bioinformatics, screened the DEGs of colorectal cancer caused by F. nucleatum infection from multiple datasets, and carried out functional, pathway enrichment and PPI network analysis on the aforementioned DEGs. The expression of MUC1 and its closely interacting hub genes in F. nucleatum-infected CRC cell lines were identified by experiments. Our research was designed to screen oncological targets and demonstrate the potential carcinogenic process of colorectal cancer caused by F. nucleatum infection.

 2. Materials and Methods 2.1. Microarray Data

The gene expression profiles of GSE110223, GSE110224, GSE113513 and GSE122183 were downloaded from the GEO database. GSE110223 was based on the GPL96 platform and contained 26 samples: 13 CRC samples and 13 normal colon samples. GSE110224 was registered on the GPL570 platform. A total of 34 samples were divided into two groups and included 17 cancer tissue specimens and 17 normal controls. The GSE113513 platform was GPL15207, comprising 14 colorectal tumor samples and the same number of normal samples. The GSE122183 dataset was based on the GPL17586 platform, and it contained 25 samples (15 colorectal cancers, 10 normal controls).

 <xref ref-type="bibr" rid="scirp.137001-"></xref>2.2. Processing of Microarray Data

All of the sample information was divided into a tumor group and a normal group. Fold change (FC) values and adjusted P-values were calculated based on t-tests in the LIMMA package in RStudio software. Adjusted P-values < 0.01 and |logFC| > 2 were defined as the cut-off criteria and were adopted for DEGs. Subsequently, the heat map and volcano plot were created with the pheatmap, ggplot2, ggthemes and Cairo packages in RStudio.

 2.3. Functional and Pathway Enrichment Analyses

For functional annotations of the DEGs, we then submitted the differentially expressed gene symbols to DAVID to conduct the gene ontology (GO) analyses. KOBAS 3.0 was used to conduct the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A P-value < 0.05 was set as the statistically significant threshold.

 2.4. Construction of the PPI Network and Hub Gene Identification

The STRING database can predict interactions of proteins encoded by differentially expressed genes [15] . In the STRING online database, we screened genes with interaction scores ≥0.4 and established the PPI network. The visualized results of the PPI network were output with Cytoscape software (version 3.6.1), and the CytoHubba plug-in ranked the DEGs according to the ranking rules of degree and then screened the hub gene. Finally, the top 6 genes were identified in CytoHubba.

 2.5. Bacterial Strains and Cell Lines

F. nucleatum subsp. (ATCC 25586) was purchased from ATCC [2] . F. nucleatum was cultured overnight at 37˚C in an anaerobic environment (5% CO2, 25% H2N2, and 70% N2) using a Hypoxia Physiological Cell Culture Workstation (Ruskinn Invivo 2 400, Baker, UK). The human normal colorectal epithelial cell line NCW460 and colorectal carcinoma cell lines (HCT116, SW620 and SW480) were supplied by the cell bank of the Chinese Academy of Sciences (CAS). It was cultured in DMEM (Gibco, USA) supplemented with 10% fetal bovine serum (FBS, Gibco, USA) at 36.5˚C in a humidified atmosphere with 5% CO2. The cells were seeded in 6-well plates and then infected with F. nucleatum (MOI 1:100) for 4, 12 or 24 h [16] .

 2.6. Quantitative Real-Time PCR Analysis

RT-qPCR was carried out to confirm the results of the microarray assay. In summary, total RNA was extracted by TRIzol reagent (Invitrogen, USA) and reverse transcribed to cDNA by a PrimeScriptTM RT reagent kit (TaKaRa, Tokyo, Japan). TB Green (TaKaRa, China) amplified and quantified complementary DNA (cDNA) in the real-time reaction system, and the reaction conditions were as follows: 30 s at 95˚C and 40 cycles of 5 s at 95˚C and 30 s at 60˚C. The relative expression of total mRNA was calculated by the 2−ΔΔCt method, and GAPDH was used as the experimental internal control. We screened the mRNA of 6 hub genes on the basis of their PPI networks for validation, and the relevant primer sequences.

 2.7. Western Blot Analysis

We extracted total cellular protein from F. nucleatum-treated cell lines by protein extraction solution (Beyotime, China). Equivalent amounts of protein were denatured in 5× SDS loading buffer, separated by 12% SDS-PAGE, and then transferred to a polyvinylidene difluoride (PVDF) membrane by constant voltage or current. The membranes were blocked in TBST with 5% BSA for 2 h at room temperature. After incubation with the primary antibody and secondary antibodies overnight at 4˚C and 2 h at room temperature, positive bands were developed by the BeyoECL Plus kit (Beyotime). The antibodies against MUC1 (ab45167) and GAPDH (ab8245) were obtained from Abcam Biotechnology.

 2.8. Statistical Analysis

All experimental data in this study are presented as the mean ± standard deviation (SD). Data were analyzed in Student’s t-test or post hoc tests for one-way ANOVA (GraphPad Prism 6.01, USA). A P-value < 0.05 was considered statistically significant.

 3. Results <xref ref-type="bibr" rid="scirp.137001-"></xref>3.1. Identification of the Aberrant DEGs in CRC Associated with F. nucleatum

After we standardized all the original gene expression data, the significant DEGs related to CRC (GSE110223 contains 82, GSE110224 contains 132 and GSE113513 contains 199) were screened. In addition, for the expression microarray GSE122183, a total of 139 DEGs in F. nucleatum-associated CRC were obtained. Given the small sample size of the microarray, the 4 datasets were overlapped to remove the DEGs in CRC associated with non-F. nucleatum. As a result, the nonoverlapping area of the GSE122183 dataset is considered to represent the DEGs of F. nucleatum-associated CRC, and 118 target genes in the nonoverlapping area were screened and visualized by a Venn diagram ( Figure 1A ), which consisted of 20 upregulated and 98 downregulated genes, as presented in Figure 1B . As shown in Figure 1C and Figure 1D , 118 DEGs were further identified by a heat map and volcano plot.

 <xref ref-type="bibr" rid="scirp.137001-"></xref>3.2. GO and KEGG Enrichment Analysis for DEGs in CRC Associated with F. nucleatum

To analyze the key functional classification of DEGs, we performed GO analysis by DAVID. GO annotated and classified gene function primarily through BP (biological process), CC (cellular component) and MF (molecular function). GO enrichment analysis identified the BPs of the DEGs with significant enrichment in chemical synaptic transmission and regulation of cell proliferation, etc. ( Table 1 ). The enriched DEGs in CC were mainly involved in integral components of the membrane, extracellular space and plasma membrane ( Table 1 ). The most significantly enriched MFs were protein homodimerization activity and receptor binding ( Table 1 ). The comprehensive results of the gene ontology enrichment analysis of the above differentially expressed genes are presented in Figure 2 . At present, for genomic data analysis, high throughput data mining and many other omics research, KEGG has been proven to be a useful research tool and is widely used [17] . DEGs were analyzed in KEGG by KOBAS 3.0. These DEGs were mainly enriched in the following pathways: axon guidance (hsa04360), phospholipase D signaling pathway (hsa04072), calcium signaling pathway (hsa04020), histidine metabolism (hsa00340) and serotonergic synapse (hsa04726). These results are presented in Table 2 , and KEGG enrichment analyses are visualized in Figure 3 .

 3.3. Target Gene Screening by Construction of the PPI Network

To predict the associations of protein functions of the identified DEGs, we submitted 118 target genes in STRING. Subsequently, PPI network analysis was performed using Cytoscape 3.8.0 software. The screening of the PPI network module in Cytoscape depends on the cytoHubba plug-in. Furthermore, the construction of a PPI network based on hub genes with the highest connective degree was performed ( Figure 4A ). The hub genes in the network were ranked by the degree method. Finally, Muc1, which is overexpressed in CRC, was screened from the network, while 5 genes interact with it: SLC7A11, AGR2, KRT18, CARTPT and TSPYL5 ( Figure 4B ). There were 4 upregulated genes (MUC1, SLC7A11, AGR2, and KRT18) and 2 downregulated genes (CARTPT and TSPYL5) in this module. They were identified by the heat map shown in Figure 4C .

Figure 1. Identification and characterization of DEGs from the GSE110223, GSE110224, GSE113513 and GSE122183 datasets. (A) Venn diagram analysis of DEGs in CRC induced by F. nucleatum were obtained by overlapping 4 datasets. (B) The distribution of significant DEGs by F. nucleatum-induced CRC. (B) Heat map of the 118 DEGs; red indicates upregulation; conversely, green indicates downregulation, and gene expression with no difference is represented by white. (D) Volcano plot of DEGs by F. nucleatum-associated CRC. Red dots, significantly upregulated DEGs; green dots, DEGs; and yellow dots represent significantly upregulated, significantly downregulated and not significantly different DEGs. Significance was defined as adjusted P-value < 0.01 and |log FC| > 2. <xref ref-type="bibr" rid="scirp.137001-"></xref>Table 1. GO.

Category

Term/gene function

Gene Count

%

P-value

Gene symbol

BP

GO:0002027~regulation of heart rate

4

3.70

0.000633

ANK2, POPDC2, SEMA3A, DMD

BP

GO:0050919~negative chemotaxis

4

3.70

0.000692

SEMA3E, SEMA3D, SEMA3A, EPHA7

BP

GO:0048843~negative regulation of axon extension involved in axon guidance

3

2.77

0.007779

SEMA3E, EMA3D, SEMA3A

BP

GO:0071526~semaphorin-plexin signaling pathway

3

2.77

0.012349

SEMA3E, SEMA3D, SEMA3A

BP

GO:0042127~regulation of cell proliferation

5

4.62

0.015032

AGTR1, GRPR, KIT, NOS2, SOX9

BP

GO:0006936~muscle contraction

4

3.70

0.017568

GNAO1, SCN7A, MYOM1

BP

GO:0034613~cellular protein localization

3

2.77

0.020436

TMOD1

BP

GO:0001755~neural crest cell migration

3

2.77

0.022258

ANK2, DMD, LRRK2

BP

GO:0048812~neuron projection morphogenesis

3

2.77

0.025112

SEMA3E, SEMA3D, SEMA3A

BP

GO:0007268~chemical synaptic transmission

5

4.62

0.034794

DMD, LRRK2, NEFL, SYT1, CARTPT, NRXN1

BP

GO:0042297~vocal learning

2

1.85

0.03531

LRFN5, NOVA1

BP

GO:2000020~positive regulation of male gonad development

2

1.85

0.040253

NRXN1, FOXP2, SEMA3A, SOX9

BP

GO:0007411~axon guidance

4

3.70

0.048226

SEMA3A, NRXN1, LGI1, CHL1

CC

GO:0005886~plasma membrane

35

32.40

0.00158

SYT1, PTCHD1, CADM2, FHL1, NBEA, KIT, FRRS1L, AGTR1, DGKB, ANK2, DMD, GRPR, SV2B, SCN7A, LRFN5, DPP6, NEGR1, SLC28A3, FCER1A, TRPC1, CACNA2D1, GNAO1, F8, LIFR, NRXN1, SLC7A11, EPHA7, EPHA6, CHRM2, CDH19, CNTN1, GFRA1, SGCE, LRRK2, CHL1

CC

GO:0030425~dendrite

8

7.40

0.001633

EPHA7, GNAO1, CHRM2, SGCE, SEMA3A, TTLL7, LRRK2, CHL1

CC

GO:0005887~integral component of plasma membrane

16

14.81

0.005177

MUC1, FCER1A, TRPC1, PTPRZ1, CADM2, NRXN1, SLC7A11, AGTR1, EPHA7, EPHA6, CHRM2, GRPR, SGCE, DCLK1, SLC28A3

CC

GO:0036464~cytoplasmic ribonucleoprotein granule

3

2.77

0.007161

DYNC1I1, INA, DDX3Y

CC

GO:0005615~extracellular space

15

13.88

0.008195

INA, MUC1, SOGA3, F8, KIT, CCBE1, SEMA3E, SEMA3D, CARTPT, SEMA3A, LGI1, LRRK2, AGR2, DMBT1, THBS4

CC

GO:0030054~cell junction

8

7.40

0.009071

ANKS1B, SYT1, CHRM2, SV2B, NRXN1, LRRK2, LGI1, FRRS1L

CC

GO:0042383~sarcolemma

4

3.70

0.009398

ANK2, DMD, POPDC2, SGCE

CC

GO:0045202~synapse

5

4.62

0.013853

CHRM2, CADM2, DMD, LGI1, FRRS1L

CC

GO:0016021~integral component of mem-brane

37

34.25

0.019459

SYT1, ABCA9, PTCHD1, CADM2, UTY, C3ORF80, KIT, FRRS1L, ABCA6, AGTR1, SEMA3D, GRPR, SV2B, POPDC2, LRFN5, DPP6, FCER1A, MUC1, TRPC1, KIAA1324L, FAXC, SOGA3, PTPRZ1, MAOB, F8, LIFR, KIAA1644, NRXN1, SLC7A11, EPHA6, SLITRK3, RNF150, CDH19, SGCE, ASB5, SLC27A2, CHL1

CC

GO:0045211~postsynaptic membrane

5

4.62

0.022927

ANKS1B, EPHA7, ANK2, CHRM2, DMD

CC

GO:0030672~synaptic vesicle membrane

3

2.77

0.032117

SYT1, SV2B, LRRK2

CC

GO:0005883~neurofilament

2

1.85

0.040111

INA, NEFL

CC

GO:0005856~cytoskeleton

6

5.55

0.041366

ANK2, PLEK2, DMD, SGCE, NEXN, SLC7A11

CC

GO:0043209~myelin sheath

4

3.70

0.042846

INA, GNAO1, CNTN1, NEFL

MF

GO:0045499~chemorepellent activity

4

3.70

0.000237

EPHA7, SEMA3E, SEMA3D, SEMA3A

MF

GO:0038191~neuropilin binding

3

2.77

0.002022

SEMA3E, SEMA3D, SEMA3A

MF

GO:0030215~semaphorin receptor binding

3

2.77

0.00476

SEMA3E, SEMA3D, SEMA3A

MF

GO:0005200~structural constituent of cytoskeleton

4

3.70

0.013413

INA, ANK2, DMD, NEFL

MF

GO:0044325~ion channel binding

4

3.70

0.014412

TRPC1, ANK2, FHL1, LRRK2

MF

GO:0008307~structural constituent of muscle

3

2.77

0.015331

DMD, MYOM1, NEXN

MF

GO:0005102~receptor binding

6

5.55

0.021439

CADM2, GFRA1, NRXN1, NOS2, LGI1, SLC27A2

MF

GO:0048306~calcium-dependent protein binding

3

2.77

0.028108

SYT1, NRXN1, DMBT1

MF

GO:0002162~dystroglycan binding

2

1.85

0.039806

DMD, AGR2

MF

GO:0042803~protein homodimerization activity

8

7.40

0.045792

CADM2, MAOB, KIT, MYOM1, NOS2, LRRK2, AGR2, FOXP2
<xref ref-type="bibr" rid="scirp.137001-"></xref>Table 2. KEGG.

Pathway ID

Pathway term

Gene count

P-value

Gene symbol

hsa04360

Axon guidance

6

0.00000242

SEMA3A, SEMA3E, SEMA3D, EPHA7 EPHA6, TRPC1

hsa04072

Phospholipase D signaling pathway

4

0.000268

DGKB, AGTR1, FCER1A, KIT

hsa04020

Calcium signaling pathway

4

0.000711

AGTR1, GRPR, NOS2, CHRM2

hsa00340

Histidine metabolism

2

0.001176

MAOB, ASPA

hsa04726

Serotonergic synapse

3

0.001808

MAOB, TRPC1, GNAO1

hsa04926

Relaxin signaling pathway

3

0.00254

FIGF, GNAO1, NOS2

hsa04514

Cell adhesion molecules (CAMs)

3

0.003498

NEGR1, CNTN1, NRXN1

hsa04261

Adrenergic signaling in cardiomyocytes

3

0.003699

SCN7A, AGTR1, CACNA2D1

hsa02010

ABC transporters

2

0.004118

ABCA6, ABCA9

hsa00330

Arginine and proline metabolism

2

0.005018

MAOB, NOS2

hsa04151

PI3K-Akt signaling pathway

4

0.006142

FIGF, THBS4, CHRM2, KIT

hsa05034

Alcoholism

3

0.006191

MAOB, PKIA, GNAO1

hsa04015

Rap1 signaling pathway

3

0.009368

FIGF, GNAO1, KIT

hsa05412

Arrhythmogenic right ventricular cardiomyopathy

2

0.011254

CACNA2D1, DMD

hsa04146

Peroxisome

2

0.012939

NOS2, SLC27A2

hsa05132

Salmonella infection

2

0.012939

DYNC1I1, NOS2

hsa04512

ECM-receptor interaction

2

0.01382

THBS4, SV2B

hsa05410

Hypertrophic cardiomyopathy (HCM)

2

0.015033

CACNA2D1, DMD

hsa05414

Dilated cardiomyopathy (DCM)

2

0.016936

CACNA2D1, DMD

hsa04933

AGE-RAGE signaling pathway in diabetic complications

2

0.018259

FIGF, AGTR1

hsa04916

Melanogenesis

2

0.018596

GNAO1, KIT

hsa05142

Chagas disease (American trypanosomiasis)

2

0.019279

GNAO1, NOS2

hsa04725

Cholinergic synapse

2

0.022478

GNAO1, CHRM2

hsa04010

MAPK signaling pathway

3

0.022786

FIGF, CACNA2D1, KIT

hsa05145

Toxoplasmosis

2

0.022846

GNAO1, NOS2

hsa04724

Glutamatergic synapse

2

0.023217

GNAO1, TRPC1

hsa05200

Pathways in cancer

4

0.02339

FIGF, NOS2, AGTR1, KIT

hsa04728

Dopaminergic synapse

2

0.029887

MAOB, GNAO1

hsa04080

Neuroactive ligand-receptor interaction

3

0.032141

GRPR, AGTR1, CHRM2

hsa04371

Apelin signaling pathway

2

0.032402

NOS2, AGTR1

hsa04915

Estrogen signaling pathway

2

0.032829

GNAO1, KRT18

hsa00360

Phenylalanine metabolism

1

0.035737

MAOB

hsa04145

Phagosome

2

0.039036

DYNC1I1, THBS4

hsa04921

Oxytocin signaling pathway

2

0.039495

GNAO1, CACNA2D1

hsa00220

Arginine biosynthesis

1

0.043506

NOS2

hsa04630

Jak-STAT signaling pathway

2

0.04372

FHL1, LIFR

hsa04614

Renin-angiotensin system

1

0.047366

AGTR1
Figure 2. Gene Ontology (GO) annotation for differentially expressed genes in F. nucleatum-associated CRC. The X-axis represents gene function; the Y-axis represents the gene count. Three different colors of columns represent BP, CC, and MF. Figure 3. KEGG pathway enrichment for DEGS in F. nucleatum-associated CRC. The X-axis shows the rich factor; rich factor indicates the number of genes enriched in the target pathway and the proportion of all genes in the pathway; the Y-axis shows the significantly enriched pathways of DEGs. The size of the dot indicates the number of target genes in the pathway, and the color of the dot reflects the different P-value range (P < 0.05). Figure 4. Screening of oncogenes in F. nucleatum-associated colorectal cancer through the construction of a PPI network. (A) Construction of the PPI network of differentially expressed genes in F. nucleatum-associated colorectal cancer. (B) PPI network of MUC1 and five other interacting hub genes. (C) Heat map of MUC1 and 5 hub genes interacting with it. 3.4. F. nucleatum Infection Was Significantly Associated with the Expression of Screened Hub Genes in Colorectal Normal/Cancer Cell Lines

To confirm the global effect of F. nucleatum infection in vitro, we cultured normal and tumor epithelial colorectal cell lines (NCW460, HCT116, SW620 and SW480) for 24 h with or without F. nucleatum. Then, the relative expression of MUC1, AGR2, SLC7A11, KRT18, CARTPT and TSPYL5 mRNA was measured by RT-qPCR. The measurements of the relative expression of mRNA were consistent with the bioinformatics analysis. MUC1, SLC7A11, AGR2, and KRT18 were significantly upregulated in the CRC cell lines (HCT116, SW480 and SW620) infected with F. nucleatum. Moreover, CARTPT and TSPYL5 were significantly downregulated, similar to the results of the microarray assay, as shown in Figure 5A . Furthermore, we investigated the effect of F. nucleatum infection on the protein and mRNA expression of MUC1 in colorectal tumor or normal cell lines (NCW460, SW620, SW480 and HCT116). Real-time PCR analysis showed that F. nucleatum infection at different time intervals (4 h, 12 h, and 24 h) could increase the expression of MUC1 ( Figure 6A ). F. nucleatum infection caused differential expression of MUC1 protein at different time intervals in distinct cell lines, which were tested by western blots. Through the above experiments, we can conclude that the protein levels of MUC1 were significantly associated with F. nucleatum ( Figure 6B ).

Figure 5. qRT-PCR analysis for F. nucleatum infection significantly induced the expression of screened hub genes. (A)-(D) Relative MUC1, SLC7A11, AGR2, and KRT18 mRNA expression in the CRC cell lines was significantly upregulated by F. nucleatum treatment. In addition, there was no significant change in the NCW460 cell model. (E)-(F) CARTPT and TSPYL5 are significantly downregulated by F. nucleatum infection in the treatment groups. Similarly, there was no significant change in the control groups. (All results were standardized by GAPDH. ***: P < 0.001, **: P < 0.01, *: P < 0.05.). <xref ref-type="bibr" rid="scirp.137001-"></xref>Figure 6. (A) Relative MUC1 mRNA expression was determined in the CRC cell lines with or without F. nucleatum for 4 h, 12 h and 24 h. (B) Representative western blots for the MUC1 and GAPDH proteins were extracted from the CRC cells infected with F. nucleatum for 0 h, 4 h, 12 h and 24 h. 4. Discussion

Colorectal carcinoma (CRC) is one of the most common cancers with a high morbidity/fatality rate worldwide [1] . CRC survival rates have been improved in recent decades, mainly due to screening. However, morbidity has increased among young patients [18] . Although this disease was previously attributed to an unhealthy diet, obesity, sedentary lifestyle and underlying genetic background, the cause of carcinogenesis is being reevaluated. The triggers for cancers are multifactorial, and accumulating evidence has shown that tissues continuously infected with microorganisms are more likely to become cancerous [19] [20] . Recent studies have revealed that F. nucleatum is closely associated with CRC and plays a key role in tumor initiation and progression through multiple biological mechanisms. F. nucleatum induced the cell surface protein Fad A to bind with E-cadherin in CRC epithelial cells to promote tumor cell growth by activating the β-catenin pathway and enhancing the expression of lymphoid enhancer factor (LEF)/T cytokines [21] [22] . In addition, E-cadherin activated the Wnt/β-catenin-Annexin A1 pathway by Fad A, thereby promoting bacterial invasion and tumor metastasis [23] . Human intestinal microbiota plays a vital role in various gastrointestinal diseases, especially in inflammatory bowel disease and CRC [24] [25] , so the molecular mechanism between F. nucleatum and colorectal cancer is a current research focus [2] - [4] .

Thousands of proteins and genes can be simultaneously examined by bioinformatics in a single experiment [26] , and this technique is also increasingly used in clinical oncology research. The occurrence and development of CRC is a complex biological process, and bioinformatics has been a guide for discovering tumor markers through high-throughput computing, novel hypotheses and logical analytical strategies. These methods have become precise and efficient tools for studying the molecular mechanisms of colorectal cancer and are particularly necessary in our research. In this study, we used bioinformatics to screen hub genes differentially expressed in colorectal cancer resulting from F. nucleatum infection. The effect of F. nucleatum on differentially expressed genes in colorectal cancer was confirmed in cell line experiments. Through the identification of a reliable biomarker, it may become possible to detect patients during the primary stage of disease, thus significantly reducing the mortality and enhancing the survival of CRC. In this paper, the GSE110223, GSE110224, GSE113513 and GSE122183 datasets were extracted from GEO. A total of 118 DEGs of F. nucleatum-associated CRC were screened. Gene ontology functional analysis suggested that these DEGs were densely enriched in diverse GO terms, particularly chemical synaptic transmission, regulation of cell proliferation, integral component of membrane, extracellular space, protein homodimerization activity and receptor binding. In addition, several KEGG signaling pathways of DEGs were closely associated with CRC [27] - [30] , including the PI3K−Akt, MAPK, Rap1 and JAK−STAT signaling pathways. Moreover, a PPI network with 6 hub genes was constructed (MUC1, AGR2, SLC7A11, KRT18, CARTPT and TSPYL5), and MUC1 was suggested as a key biotarget closely related to CRC. Finally, the results of cell lines by RT-qPCR and immunoblotting are consistent with the results of bioinformatics analysis.

Aberrantly glycosylated MUC1 as an oncogenic molecule has generated intense attention since it is overexpressed in most human epithelial cancers [31] . The interaction between some bacterial infections and MUC1 may be associated with the risk of developing cancer. MUC1 interacts directly with components of the NF-kB pathway during Helicobacter pylori infection, thus inhibiting tumor necrosis factor (TNF-α) inflammatory responses in gastric epithelial cells [32] . A previous study reported that highly invasive F. nucleatum evoked significantly greater MUC2 and tumor necrosis factor alpha (TNF-α) gene expression than minimally invasive strains in colonic epithelial cells [33] . In addition, since MUC1 is a proinflammatory factor following intestinal infection, it is an independent biomarker that can detect high expression in various stages of CRC, and it can be used in the early detection of CRC [34] . Therefore, we believe that MUC1, which is a valuable clinical tumor biomarker, is significantly overexpressed in CRC caused by F. nucleatum. The hub gene MUC1 was selected from the PPI network and verified in vitro using RT-qPCR and western blot experiments. In human CRC cell lines (SW480, SW620, HCT116), we found that the expression of MUC1 was significantly higher than that in the colonic epithelial cell line (NCW460). Similarly, MUC1 was significantly overexpressed in F. nucleatum-infected CRC and intestinal epithelial cells compared to that in the noninfected control group. Thus, MUC1 was overexpressed in F. nucleatum-associated colorectal cancer, which indicates that it may be a novel oncogene and promising therapeutic biomarker for evaluating the prognosis of CRC.

We also screened five hub genes that interact with MUC1. Current studies have confirmed that AGR2, KRT18, TSPYL5 and SLC7A11 play a role in the initiation, chemotherapy resistance, metastasis, invasion and survival of CRC. Anterior gradient 2 (AGR2) protein upregulates Wnt11 to activate JNK and CaMKII to promote the migration of CRC cells [35] . One result showed that metformin-dependent activation of the AMPK signaling pathway was interfered with by the presence of AGR2. In colorectal cancer cells whose expression of AGR2 was interrupted, metformin enhanced the antitumor activity of 5-FU and oxaliplatin [36] . Moreover, in CRC patients, keratin 18 (KRT18) overexpression acted as an independent predictor and has been found to be detrimental for overall survival [37] . Testis-specific protein Y-encoded-like 5 (TSPYL5) is a tumor suppressor gene. Huang demonstrated that miR-19-5p plays a crucial role in the development and progression of CRC by targeting TSPYL5 [38] . Solute carrier family 7 member 11 (SLC7A11) is a crucial antioxidant component and a glutaminolysis-related gene. Ju found that downregulated SLC7A11 could inhibit tumor cell growth due to regulation by increased lactate production, resulting in a decreased tumor microenvironmental pH under the induction of low-dose paclitaxel (PTX) [39] . Although CARTPT has rarely been shown to play a role as a biomarker in the biological process of CRC, CARTPT has strong associations with other kinds of cancer. For example, CARTPT was associated with the risk of breast cancer [40] . According to the results of bioinformatics analysis, it is not difficult to predict that the abnormal expression of CARTPT caused by F. nucleatum may affect the tumorigenesis and biological behavior of colorectal cancer. Therefore, more studies will be explored and verified the role of Muc1 and the interaction with the aforementioned 5 DEGs in colorectal tumorigenesis.

In this study, we analyzed the potential association between F. nucleatum infection and colorectal carcinogenesis by bioinformatics. Although CRC patients infected with F. nucleatum are common, evidence showing that F. nucleatum is has a causative function in CRC is lacking. The causative role of F. nucleatum in CRC remains controversial [41] . In particular, F. nucleatum interacts with oncogenic bacteria in the intestinal microecology, and strategies for intestinal carcinogenesis are extremely important. To assess these roles, Tjalsma presented a bacterial ‘driver–passenger’ model to clarify how intestinal bacteria can directly or indirectly mediate CRC development [42] . Moreover, fusobacteria, an important 'bridging organism', cluster microorganisms together for interaction [43] . Many pathways that were screened from GO and KEGG are correlated with the pathogenesis of interactions between microbial species and interactions between intestinal microorganisms and hosts. Previous studies have shown that P. gingivalis is favorable for the survival of F. nucleatum and prompts them to invade deeper periodontal tissues, which is due to infection inactivating the PI3K/AKT pathway [44] . The above conclusions indicate that the study of the correlation between F. nucleatum and CRC is important.

 5. Conclusion

In conclusion, MUC1 was confirmed to be a novel oncogene of F. nucleatum-associated CRC by bioinformatics and experimental analyses. However, the role and mechanism of MUC1 in CRC caused by F. nucleatum remain to be confirmed by further discussion.

 Acknowledgements

We thank the Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau), No. 2022MSXM002

 NOTES

*First author.

#Corresponding authors.

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