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10 October 2022: Database Analysis  

Activation of Tumor-Associated Macrophages via Promotes Recurrence of Oral Squamous Cell Carcinoma

Chen-xi Li123ABCDEF, Ying Su4ABCD, Zhong-cheng Gong123ACDG*, Hui Liu56ACD

DOI: 10.12659/MSM.937126

Med Sci Monit 2022; 28:e937126

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Abstract

BACKGROUND: Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy, characterized by high recurrence rate resulting in poor prognosis. Porphyromonas gingivalis, most closely correlated with chronic periodontitis, is increasingly thought to play a significant role in OSCC development via influencing tumor-associated macrophages. However, its specific function remains unclear. In this study, we attempted to explore the mechanism of action of P. gingivalis in the recurrence of OSCC by bioinformatics analysis, to lay a foundation for subsequent basic experiments.

MATERIAL AND METHODS: The P. gingivalis-infected macrophage microarray dataset (GSE24897) and the OSCC advanced relapse patient microarray dataset (GSE87593) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially-expressed genes (DEGs) were screened using R system, and the intersected DEGs were analyzed for functional enrichment, and protein–protein interaction (PPI) networks were constructed. The expression of significant DEG in GSE24897 microarray was assessed to determine its effect on macrophage immune infiltration in pan-cancer by applying the TIMER 2.0 repository. To detect the expression of DOK3 in OSCC specimens, immunohistochemical (ICH) assay was used.

RESULTS: A total of 106 co-expressed DEGs were upregulated and 131 were downregulation. The biological processes were mainly enriched in DNA-templated transcription terms, the cellular component enrichment was mainly enriched in the nucleus terms, and the molecular function enrichment was mainly enriched in protein-binding function terms. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis found that the DEGs were mainly enriched in the MAPK signaling pathway. Overall analysis of the PPI network showed a significant aggregation, with the top 10 hub co-expressed genes (CASP3, FYN, HNRNPA2B1, NR3C1, RELA, REL, POLR2F, RAN, RHOA, and STAT5B). DOK3 is significantly upregulated in P. gingivalis-infected macrophages, which is associated with macrophage infiltration and differentiation. There was more positive DOK3 staining in the group with P. gingivalis infection.

CONCLUSIONS: P. gingivalis can affect the recurrence of OSCC by increasing the expression of DOK3 in TAMs, which may be involved in activation of signaling pathways such as TNF and MAPK.

Keywords: DOK3 Protein, Human, Macrophage Activation, Neoplasm Recurrence, Local, Porphyromonas gingivalis, Squamous Cell Carcinoma of Head and Neck, tumor microenvironment

Background

Cancer of the oral cavity has a high recurrence and high staging at diagnosis, which often does not allow surgical treatment because the interventions are too destructive and have secondary complications; therefore, the treatment of choice is chemoradiotherapy [1]. Oral squamous cell carcinoma (OSCC) is the most common subgroup of head and neck malignancies, which can develop in all parts of the oral cavity, resulting directly from apparently normal mucosa or progressively from potentially malign disorders [2]. Annually, there are around 400 000 new cases diagnosed all across the globe, accounting for 2% of all cancers [3]. Patients with advanced OSCC have poor prognosis due to strong invasive ability, high recurrence rate, and lymph node metastasis [4]. Locoregional recurrence is frequent (approximately 32.7~44.9%) [5–7] and remains a critical issue in clinical management of advanced-stage OSCC [8].

Increased attention has focused on the interaction between oral microbiome and OSCC in the past few decades [9]. The accumulated evidence indicates that chronic periodontitis is one of the pathogenic factors resulting in OSCC [10–13], which was found to increase the incidence of OSCC by 5.23-fold [14]. Microbiota homeostasis plays important roles in health and disease. Dysbiosis of oral florae can disrupt the host immune defense, thus deeply affecting tumorigenesis [15,16]. Porphyromonas gingivalis is a gram-negative absolute anaerobic bacterium that is a crucial bacterial pathogen of OSCC characterized by invading the cellular circle and normal physiological metabolism of epithelial cells, interfering with the host immune system, and producing a toxic effect of inhibiting programmed cell death [17,18]. Many anatomical structures (eg, gingiva, buccal mucosa, dorsum of the tongue, lingual margin, hard and soft palate) are easily colonized by various types of microflora [19,20]; therefore, P. gingivalis can prolong the time of contact with tumor cells to promote OSCC progression [9]. Tuominen et al [21] has reported that P. gingivalis can inhibit apoptosis, activate cell proliferation, promote cell invasion, induce chronic inflammation, and directly produce carcinogens. P. gingivalis has been detected in abundance in the oral cavity of patients with OSCC [22], suggesting it is an independent bacterial factor that raises the risk of death in OSCC patients [23]. Abnormal aggregation of P. gingivalis can cause local microecological imbalance of the oral cavity and is a dominant species in periodontitis, and further impact is the formation of a tumor microenvironment (TME) that is closely associated with oro-digestive carcinogenesis [24]. TME refers to a restricted internal environment constituted by locally tumor-associated infiltrating mesenchymal cells (eg, macrophages, fibroblasts, neutrophils, epithelial/endothelial cells) and their secretory extracellular matrix (ECM) together with inner cancer cells [25,26].

Tumor-associated macrophages indeed are among the most plentiful and important tumor-infiltrating immune cell types in TME. They have great plasticity in function, which can present diverse phenotypes to act within distinct microenvironments based on their biogenesis and biological properties [27]. According to different regulatory signals and states, macrophages can be categorized into 2 polarizations: classical activation type (M1 type) and alternative activation type (M2 type) [28]. More importantly, the pro-tumor effects of TAMs have strong links with their M2 polarized phenotype [29,30]. The downstream of kinase (DOK) is a new type of family of tyrosine residue phosphorylated proteins that participates in the regulation of tumor cell growth [31]. To date, there are 7 known human DOK genes (DOK1 to DOK7) that participate in regulation of various signaling pathways, such as cell growth, proliferation, migration, and apoptosis [32,33]. As one of these subtypes, the DOK3 gene, located at 5q35.3, exon 8 [34], is an immune-specific possessing specific pattern of expression in immune cells [36,36]. Macrophages studies showed it can downregulate the macrophage response stimulated by granulocyte macrophage colony-stimulating factor (GM-CSF) [37], and can be strongly positively associated with marker genes in M2 TAMs [35].

Herein, we hypothesized that P. gingivalis might affect the macrophages infiltrated in TME to give rise to OSCC recurrence through modulation of DOK3, so the possible molecular mechanism was explored by performing bioinformatics analysis to provide research directions for future experimental studies.

Material and Methods

ETHICS:

Our study was reviewed and approved by the Ethics Committee at the First Affiliated Hospital of Xinjiang Medical University, Urumqi, PR China (approval number: K202107-06). Procedures in this research were completed in keeping with the standards of the Helsinki Declaration and laboratory guidelines of research in China. Written informed consent was not applicable.

DATA PREPARATION:

To screen the datasets related to P. gingivalis infection of macrophages, as well as late recurrence of OSCCs, we downloaded 2 expression profile sequencing datasets (GSE24897 and GSE87593, respectively) from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo) database using GSE24897 by GPL570 Platform (Affymetrix Human Genome U133 Plus 2.0 Array) and GSE87593 by GPL14550 Platform (Agilent-028004 SurePrint G3 Human GE 8x60K Microarray). Their probes were converted to the corresponding gene symbols based on annotation information on the platform. GSE24897 dataset contains 3 sets of uninfected macrophages and 3 sets of P. gingivalis-infected macrophage samples, and the GSE87593 dataset contains 8 sets of OSCC late non-recurrent tissue samples and 8 sets of OSCC late recurrent tissue samples.

DIFFERENTIALLY-EXPRESSED GENE ANALYSIS:

The 2 datasets were separately analyzed using the Limma package in R software version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria). The probes without corresponding gene symbols or with multiple gene symbols were removed or taken as intersection. The differentially-expressed genes (DEGs) were screened and heat-mapped using the R heatmap package. Accordingly, differences were considered statistically significant with logFC (fold change) >1 and adj. P value <0.01.

FUNCTIONAL ANNOTATIONS ANALYSIS:

We employed DAVID (Database for Annotation, Visualization and Integrated Discovery) (https://david.ncifcrf.gov/) resources to obtain information for gene ontology (GO), including biological processes, cellular component, and molecular function. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to annotate the potential functions. A significance level of P<0.05 was set as the cutoff criteria and the plots were constructed by the gplots package in R software.

PROTEIN–PROTEIN INTERACTION ANALYSIS:

The DEGs were enrolled in a protein–protein interaction (PPI) network through the STRING database (https://string-db.org/). The filtering condition was medium confidence (0.400), and isolated nodes were excluded to generate the PPI network. The hub genes were calculated using Hubba plug-in of Cytoscape software version 3.8.0 (Free Software, Boston, MA, USA).

MACROPHAGE INFILTRATION:

We used the TIMER 2.0 online tool (http://timer.comp-genomics.org/ or http://timer.cistrome.org/) to explore macrophage infiltrates across diverse cancers using the immune module.

COLLECTION OF CLINICAL SAMPLES:

Fifty patients with recurrent OSCC admitted to our center from January 2020 to June 2022 were selected for the study, of whom 27 were males and 23 were females, aged 55~75 years. All patients were tested for P. gingivalis before enrollment (the detection method for P. gingivalis is shown in Supplementary Figure 1), of which 25 were P. gingivalis-positive and 25 were P. gingivalis-negative. All patients in this study voluntarily participated and signed the informed consent form. There was no significant difference between the 2 groups in terms of general information such as sex and age (P>0.05). The use of human samples in this study was approved by our center’s Ethics Committee and was performed according to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement.

HISTOPATHOLOGICAL EVALUATION:

Fresh OSCC tissues excised during clinical procedures were placed in 4% formaldehyde solution for fixation, and then were dehydrated, paraffin-embedded, and serially sectioned to 4-μm thickness. The sections were dewaxed in xylene; hydrated in ethanol with gradients of 100%, 95%, 90%, and 85% for 5 min each. Antigen repair was performed with 10 mmol/L sodium citrate buffer for 15 min, washed 3 times with PBS after natural cooling, and reacted with peroxidase blocker for 15 min to block endogenous peroxidase, then washed 3 times with PBS and incubated with goat serum. Sections were incubated with DOK3 monoclonal antibody (Abcam, ab236609, 1: 500) and incubated overnight at 4°C in a refrigerator. After rewarming for 2~5 min, goat anti-rabbit IgG was added after washing with PBS and incubated at room temperature for 25 min, then washed 3 times with PBS, and horseradish peroxidase-labeled streptavidin working solution was added for 15 min. After washing 3 times with PBS, the DAB kit (Beyotime, Shanghai, China) was used for 3~5 min; hematoxylin was used for restaining, 1% hydrochloric acid alcohol fractionation for 1~2 s, and water was used to return the blue. After natural drying, 10% neutral gum was used to seal the film. Double-blind scoring was performed by 2 professional pathologists. Eight fields of view per section were randomly selected for observation at 200× and 400×.

STATISTICAL ANALYSIS:

All data-mining work was performed using the packages Limma (Version 3.46.0) [38], MethylMix (Version 2.20.0) [39], clusterProfiler (Version 4.3.1) [40], survminer (Version 0.4.9), survival (Version 3.2.13), glmnet (Version 4.1.2) [41], pheatmap (Version 1.0.12), corrplot (Version 0.90), timeROC (Version 0.4) [42], WGCNA (Version 1.70.3) [43], maftools (Version 2.6.5) [44], and pRRophetic (Version 0.5) implemented in R software, version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria). When measurement data were normally distributed, data were expressed by mean±standard deviation (SD), and an independent samples t test was used for comparison of 2 groups, while the chi-square test was used for comparison of count data. For all analyses, two-tailed P<0.05 was set as the threshold for determining statistical significance. Differences were considered significant if * P<0.05; ** P<0.01; *** P<0.001, **** P<0.0001.

Results

IDENTIFICATION OF DEGS:

After normalizing the expression profile of sequencing data, the distributions of differential gene expression between 2 samples were visualized using volcano plots (Figure 1A, 1B). A total of 747 differentially-expressed genes were identified in the GSE24897 dataset, including 496 upregulated genes and 251 downregulated genes (Figure 1C), while 747 differentially-expressed genes were identified in the GSE87593 dataset, including 226 upregulated genes and 521 downregulated genes (Figure 1D). Posterior to taking the intersection of the 2 collections, there were 106 genes in common among all upregulated genes (Figure 2A) and 131 genes in common among all downregulated genes (Figure 2B).

FUNCTIONAL ANALYSIS OF DEGS:

To understand the biological meaning behind lists of DEGs, enrichment analysis of signaling pathways and functions was performed using DAVID (Table 1). Biological processes (BP) were mainly enriched in DNA-templated transcription terms (Figure 3A), the cellular component (CC) enrichment was mainly enriched in the nucleus terms (Figure 3B), and molecular function (MF) enrichment was mainly enriched in protein-binding function terms (Figure 3C). KEGG enrichment analysis found that the genes were predominantly enriched in the MAPK signaling pathway (Figure 3D).

THE PPI NETWORK OF THE CO-EXPRESSION OF DEGS:

The co-expression of DEGs was imported into the online STRING Data Resources to derive the PPI network, which showed a total of 225 nodes and 232 edges. After hiding the isolated nodes in the network, 198 expected nodes remained, with a PPI enrichment P value of 0.0108. The overall analysis of the network showed a significant aggregation (Figure 4). Using cyto-Hubba software package, all the proteins in PPI network were calculated by applying maximum correntropy criterion (MCC) algorithm, which displaying that the top 10 ranked genes were CASP3, FYN, HNRNPA2B1, NR3C1, RELA, REL, POLR2F, RAN, RHOA, and STAT5B (Figure 5).

THE RELATIONSHIP BETWEEN DOK3 EXPRESSION AND MACROPHAGE INFILTRATION:

To investigate the effect of DOK3 expression on macrophage infiltration, we first analyzed the expression of DOK3 in P. gingivalis infection of macrophages microarray. The analysis revealed that the expression of DOK3 significantly increased after macrophages were treated with P. gingivalis (Figure 6A, P<0.0001). The effect of DOK3 on macrophage infiltration was further analyzed, showing that expression of DOK3 affected the infiltration of M1 and M2 TAMs in oral cancer (Figure 6B, P<0.05).

:

To confirm the expression of DOK3 in patients with different P. gingivalis infections, we collected oral cancerous tissues from patients with recurrent OSCC through clinical biopsy and divided the patients into P. gingivalis-infected and non-infected groups by P. gingivalis detection. The expression of DOK3 in 2 groups was detected by immunohistochemistry (ICH), and the results showed that the DOK3 staining was stronger in the group with P. gingivalis infection than in the group without P. gingivalis infection (Figure 7).

Discussion

LIMITATIONS:

The present study is a bioinformatics analysis based on data provided by a public database (GEO), and although we found high expression of DOK3 in tumor tissues of patients with recurrent OSCC and P. gingivalis infection by ICH detection using clinical specimens, the generalizability of our final conclusions remains limited. In the future, we will confirm through experimental in vitro cellular studies and in vivo animal studies that P. gingivalis affects TAMs infiltration and promotes tumor malignant progression in recurrent OSCC TME by DOK3 regulation.

Conclusions

Our study shows the participation of Porphyromonas gingivalis in the TME, as well as how P. gingivalis possibly modulates TAMs in OSCC. Via the role of DOK3 in TAMs, high expression of DOK3 is regulated in TNF/MAPK signaling pathways. The contribution of the TME has been important in identifying the role of components other than tumor cells that cause the development of oral cancer, thus changing the outlook. As the key molecule, DOK3 might be the keystone modulator of M2 macrophage polarized from infiltrated TAMs, in order to further mediate immune surveillance/escape to promote cancerous progression.

Figures

Figure 1. Distributions of differentially-expressed genes.(A, B) The volcano plot of GSE24897 and GSE85793. Green dots represent significantly downregulated genes, red dots represent significantly upregulated genes, and black dots represent insignificantly changed genes. (C, D) The heatmaps of DEGs. Blue color represents low expression genes, red color represents high expression genes, and different color brightness represents gene expression. R software 4.1.3 version was used for visualization. Figure 2. Venn plots of differentially-expressed genes (DEGs).Venn plots of DEGs showing upregulated (A) and downregulated genes (B). R software 4.1.3 version was used for visualization. Figure 3. The GO and KEGG analysis.The significant enrichment terms in (A) BP, (B) CC, and (C) MF were shown using a scatter plot. The significant pathways involved in (D) KEGG enrichment was shown using a scatter plot. Colors represent the P values and sizes of the spots represent the counts of genes. R software 4.1.3 version was used for visualization. Figure 4. The PPI network.A network of protein–protein interactions (PPI) was built using STRING. Different nodes represent different proteins and the edges represent the degree of connection between proteins. String version 11.5 was used for visualization. Figure 5. Hub genes computed by MCC algorithm.The top 10 hub genes were obtained by the MCC algorithm. Cytoscape version 3.9.0 was used for visualization. Figure 6. The relationship between DOK3 expression and macrophage infiltration. (A) The DOK3 expression in P. gingivalis infection of macrophages microarray; (B) macrophages infiltration affected by DOK3. GraphPad Prism 8 was used for visualization in Figure 5A, and the TIMER 2.0 online tool was used for visualization in Figure 5B. Figure 7. The expression of DOK3 in recurrent OSCC patients with differentP. gingivalis infections (B and D, 200×) compared with non-immune IgG staining as a control (A and C, 200×).The yellow-stained granules represent DOK3 staining-positive cells, and the larger yellow staining area represents higher DOK3 expression.

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Figures

Figure 1. Distributions of differentially-expressed genes.(A, B) The volcano plot of GSE24897 and GSE85793. Green dots represent significantly downregulated genes, red dots represent significantly upregulated genes, and black dots represent insignificantly changed genes. (C, D) The heatmaps of DEGs. Blue color represents low expression genes, red color represents high expression genes, and different color brightness represents gene expression. R software 4.1.3 version was used for visualization.Figure 2. Venn plots of differentially-expressed genes (DEGs).Venn plots of DEGs showing upregulated (A) and downregulated genes (B). R software 4.1.3 version was used for visualization.Figure 3. The GO and KEGG analysis.The significant enrichment terms in (A) BP, (B) CC, and (C) MF were shown using a scatter plot. The significant pathways involved in (D) KEGG enrichment was shown using a scatter plot. Colors represent the P values and sizes of the spots represent the counts of genes. R software 4.1.3 version was used for visualization.Figure 4. The PPI network.A network of protein–protein interactions (PPI) was built using STRING. Different nodes represent different proteins and the edges represent the degree of connection between proteins. String version 11.5 was used for visualization.Figure 5. Hub genes computed by MCC algorithm.The top 10 hub genes were obtained by the MCC algorithm. Cytoscape version 3.9.0 was used for visualization.Figure 6. The relationship between DOK3 expression and macrophage infiltration. (A) The DOK3 expression in P. gingivalis infection of macrophages microarray; (B) macrophages infiltration affected by DOK3. GraphPad Prism 8 was used for visualization in Figure 5A, and the TIMER 2.0 online tool was used for visualization in Figure 5B.Figure 7. The expression of DOK3 in recurrent OSCC patients with differentP. gingivalis infections (B and D, 200×) compared with non-immune IgG staining as a control (A and C, 200×).The yellow-stained granules represent DOK3 staining-positive cells, and the larger yellow staining area represents higher DOK3 expression.

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10 December 2022 : Clinical Research  

Retrospective Study to Identify Risk Factors for Severe Disease and Mortality Using the Modified Early Warn...

Med Sci Monit 2022; 28:e938647

In Press

06 Feb 2023 : Clinical Research  

Double C-Arm Digital Subtraction Angiography Guidance During Transjugular Intrahepatic Portosystemic Shunt ...

Med Sci Monit In Press; DOI: 10.12659/MSM.938912  

06 Feb 2023 : Review article  

Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review

Med Sci Monit In Press; DOI: 10.12659/MSM.939366  

03 Feb 2023 : Clinical Research  

Treatment of Gingival Recession Defects with Pouch and Tunnel Technique Using Connective Tissue Graft and L...

Med Sci Monit In Press; DOI: 10.12659/MSM.938865  

02 Feb 2023 : Clinical Research  

Online Questionnaire-Based Study to Evaluate the Attitudes and Use of Rubber Dental Dams by Saudi Dental Pr...

Med Sci Monit In Press; DOI: 10.12659/MSM.938672  

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13 Nov 2021 : Clinical Research  

Acceptance of COVID-19 Vaccination and Its Associated Factors Among Cancer Patients Attending the Oncology ...

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30 Dec 2021 : Clinical Research  

Retrospective Study of Outcomes and Hospitalization Rates of Patients in Italy with a Confirmed Diagnosis o...

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08 Mar 2022 : Review article  

A Review of the Potential Roles of Antioxidant and Anti-Inflammatory Pharmacological Approaches for the Man...

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Med Sci Monit 2022; 28:e936292

01 Jan 2022 : Editorial  

Editorial: Current Status of Oral Antiviral Drug Treatments for SARS-CoV-2 Infection in Non-Hospitalized Pa...

DOI :10.12659/MSM.935952

Med Sci Monit 2022; 28:e935952

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750