CCR7 affects the tumor microenvironment by regulating the activation of naïve CD8⁺ T cells to promote the proliferation of oral squamous cell carcinoma

Highlights

• •Exploration of target genes for a novel anticancer therapy.
• •New findings in the tumor microenvironment that promote cancer progression.
• •A novel anticancer approach for targeting and regulating the tumor microenvironment.

Abstract

Background

Head and neck cancer is the sixth most common malignancy worldwide, and oral squamous cell carcinoma (OSCC) is the most common head and neck cancer, being one of the leading causes of cancer morbidity and mortality worldwide. CC Chemokine receptor 7(CCR7) is a multifunctional G protein-coupled trans-membrane chemokine that affects immune cell chemotaxis, migration, and cancer progression through its interaction with its ligands C-C motif chemokine ligand 19(CCL19) and C-C motif chemokine ligand 21(CCL21). Numerous studies have demonstrated the involvement of CCR7 in the malignant progression of a variety of cancers, reflecting the pro-cancer properties of CCR7. The Cancer Genome Atlas data suggests CCR7 has elevated expression in oral cancer. Specifically, CCR7 expression in tumor microenvironment (TME) may regulate the ability of some immune cells to engage in anti-tumor immune responses. Since CD8⁺ T cells have become a key immunotherapeutic target, the role of CCR7 in antitumor immune response of naïve CD8⁺ T cells in TME has not been thoroughly investigated.

Methods

A CCR7 knockout mouse model was constructed, and the mechanism of ccr7 on the regulation of tumor microenvironment by naïve CD8⁺ T cells was verified under the guidance of single-cell RNA sequencing combined with in vivo animal experiments and in vitro cell experiments.

Results

CCR7 is knocked out with impaired tumor growth and altered CD8⁺ T cell profiles, revealing the importance of this protein in OSCC.

Conclusions

Inhibition of CCR7 enhances CD8⁺ T cell activation, proliferation, and anti-tumor function, suggesting its potential as a therapeutic target.

Introduction

OSCC, as a cancer that seriously jeopardizes human health, is characterized by high incidence, high mortality, and complex pathogenesis and causative factors [1]. The pathogenesis of OSCC has not been well explained so far. Numerous studies have shown that its pathogenesis may be related to genetic variants [2], environmental factors [3] and inflammation [4]. Due to the complexity of OSCC onset and progression, the search for new biomarkers involved in the development of the cancer is currently a major area of research.

Recent studies have found that tumorigenesis and progression may be associated with alterations in the tumor microenvironment (TME), suggesting that changes in TME in cancer may serve as a new predictive biomarker [[5], [6], [7], [8]]. The TME includes the surrounding microenvironment in which tumor cells exist, comprising surrounding blood vessels, immune cells, fibroblasts, various signaling molecules and extracellular matrix [9], [10], [11], [12]. Immune cells are one of critical importance in the TME, as they can affect tumor survival, such as NK cells [13], DC cells [14], tumor-associated macrophages (TAMs) [15], and tumor infiltrating lymphocytes (TILs) [16]. CD8⁺ T cells, one member of the T cell family, constitute an important branch of adaptive immunity contributing to clearance of intracellular pathogens and providing long-term protection [17]. CD8⁺ T cells are of great significance for cancer immunotherapy, such as anti-PD-L1 receptor to inhibit immune escape of tumor cells in order to exert anti-tumor immunity of CD8⁺ T cells is currently an important tool for cancer immunotherapy [18]. CD8⁺ T cells consist of three subpopulations: naïve (T_n), effector (T_e) and memory (T_m). Naïve CD8⁺ T cells can be transformed into effector T cells and memory T cells after activated, effector CD8⁺ T cells exerts anti-tumor immune function by releasing a series of tumor-killing molecules, memory CD8⁺ T cells serve as reserve cells to combat subsequent infections [19].

Chemokines are a family of small chemotactic cytokines that direct the homing of immune cells and control their homeostasis [20]. Chemokines are classified into four major groups: CXC, CC, C and CX3C. CC chemokine receptor 7 (CCR7) is a member of the CC group of chemokine receptors, CCR7 exhibits multifunctional roles in cell migration, survival, and signal transduction, underscoring its significance in various biological processes [21]. CCR7 has two ligands: C-C motif chemokine ligand 19 (CCL19) and C-C motif chemokine ligand 21 (CCL21) [22]. Extensive evidence supports the involvement of CCR7 in a variety of other tumors, and its association with prognosis highlights its potential as a prognostic marker in OSCC. For example, in breast cancer, CCR7/CCL21 is associated with lymph node metastasis in breast cancer and promotes tumor progression [23]. In oral cancer, expression of chemokine receptor CCR7 is associated with cervical lymph node metastasis and poor prognosis in oral squamous cell carcinoma [24]. In addition to its effects in promoting cancer progression, CCR7 and its ligands also have regulatory effects on immune cells. For instance, the interaction between CCR7 and its ligands promotes the migration, invasion and chemotaxis of T cells, B cells, NK cells, and DC cells [25]. Thus, it appears that the biological properties of CCR7 are complex and diverse.

Current studies on the biological relationship between CCR7 and CD8⁺ T cells have mainly focused on the effect of CCR7 on the homing [26] and localization [27] of CD8⁺ T cells in secondary lymphoid organs and on their release from secondary lymphoid organs [28]. Although CCR7 has been generally recognized to be associated with lymph node metastasis in several malignancies, little is known about the importance of CCR7 interaction with CD8⁺ T cells influencing antitumor immunity in the TME. Therefore the influence of the tumor microenvironment on immune surveillance and responses is a crucial aspect to consider when investigating the impact of CCR7 on OSCC in our study. In this study, we constructed a CCR7 deficient tumor microenvironment, and found that CCR7-deficient tumors grew slower than controls with significantly higher percentage of CD8⁺ T cell infiltration through flow cytometry and single-cell RNA sequencing. Since CD8⁺ T cells are critical for anti-tumor growth, based on this, it is hypothesized that CCR7 may have a substantial impact on OSCC by modulating the immune tumor interface, thereby affecting disease progression. Therefore, the main objective of this study is to utilize mouse models and in vitro techniques including gene deletion, flow cytometry, co-culture and other relevant methodologies in order to comprehensively assess the role of CCR7 in OSCC.

Material and methods

Bioinformatic analysis

The differential mRNA expression data of Ccr7 mRNA in different human cancers were compared with their matched normal tissues through TCGA and GTEx based databases in SangerBox 3.0 to outline the expression pattern of Ccr7. The full names of tumor abbreviations were supplied in Supplementary Table SI. We also selected 2 datasets (GSE139869 and GSE138206) containing OSCC samples and normal oral tissue from gene expression omnibus (GEO) database to further explore Ccr7 expression in OSCC. Differences in Ccr7 expression and distribution at the protein level were assessed using immunohistochemical staining data from two patients in the Human Protein Atlas (HPA) database.

Cell culturing and OSCC mouse models

This study used mouse models of specific strains, ages, and sample sizes. Tumor induction methods were implemented to ensure the manifestation of oral squamous cell carcinoma (OSCC) in the mice. The generation and validation of CCR7 knockout mice were executed with meticulous attention to detail. This involved comprehensive procedures to ensure the accuracy and reliability of the knockout model. The C57BL/6 mouse oral cancer (MOC2) cells were obtained and cultured as the author described [29]. The specific methods for cell culture and OSCC mouse modeling are described in the Data S1. The present study was conducted according to the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by the Ethics Committee of China Medical University (Ethics ID: K2022028).

Single-cell RNA sequencing (scRNA-seq)

The methods and specific parameters for Single-cell collection and cDNA amplification, Library construction and sequencing, Data quality and filtering, Cell cluster analyses are described in Data S2.

Isolation, activation and assay grouping of naïve CD8⁺ T cells

Tumor harvest and processing in this study were conducted using established protocols, ensuring the preservation of tumor integrity. Flow cytometric staining panels and gating strategies were employed to accurately identify and analyze specific cell populations. Cell sorting was performed using precise techniques, and appropriate culture conditions were implemented for in vitro assays. Specific reagents and doses were utilized to ensure optimal conditions for the experiments. The specific description of the experiments for isolation and activation of naïve CD8⁺ T cells and the grouping of the assays are described in Data S3.

Flow cytometry

Cells were harvested at 48 h after activated and were stained with following antibodies: anti-CD3 (BD, clone 557596, USA) conjugated to APC-Cy7; anti-CD8α (BD, clone 553035, USA) conjugated to APC; anti-CD44 (BD, clone 553134, USA) conjugated to PE; and anti-CD62L (BD, clone 560516, USA) conjugated to PE-Cy7. Fixable Viability Stain 620(BD, clone 564996, USA) were used to gate out dead cells. All flow data were collected using an BD FACSCanto™ II (BD, USA) and analyzed with FlowJo v10.

RNA extraction and quantitative real-time PCR

We collected naïve CD8⁺ T cells and activated, then divided them into 3 groups: WT group, WT+CCL19/21 group and KO+CCL19/21 group. Quantitative real-time PCR (qRT-PCR) was carried out to evaluate the mRNA expression levels of CD8⁺ T cells activation related genes: TNF-α,IFN-γ,IL-2. Total RNA was extracted from cultured cells and reversely transcribed into cDNA using Trizol Reagent (TaKaRa, Japan) and PrimeScript RT reagent Kit (TaKaRa, Japan) according to the protocols recommended by the manufacturer. Real‐time PCR analysis was performed using SYBR Green Master Mix kit (Takara) via an ABI QuantStudio3 Real-Time PCR System (Applied Biosystems, Foster City, CA) with 40 reps at 95 °C for 5 s, 60 °C for 34 s, 95 °C for 15 s, 60 °C for 1 min, 95 °C for 15 s. The relative gene expression was calculated using the 2^−ΔΔCT method. Housekeeping gene GAPDH was used as internal standard control. The primer sequences designed by Sangon Biotech (Shanghai, China), the primers are detailed in Table SII.

CFSE for cell proliferation assay

We used CFSE-labeled sorted activated naïve CD8⁺ T cells to check their proliferation status, We labeled naïve CD8⁺ T cells with 10 μM Cell proliferation dye CFSE-FITC (BD, clone565082, USA), activated in vitro for 2 days. Dilution of Cell proliferation dye was then evaluated by flow cytometry. The initial state of non-proliferating cells (initial state) was checked by flow cytometry on day 0 as a reference, and then the condition of each group was checked after 48 h of activation, and the cell division and proliferation condition calculated using the relative average fluorescence intensity.

Cell counting kit-8 (CCK-8) assay

The MOC2 cells and the activated naïve CD8⁺ T cells were plated into 96-well plates at a density of 10,000 cells per well in 100 μL medium, both CCR7 KO and WT groups added 100 ng/ml of CCL19 and CCL21. After co-culturing for 48 h, the supernatant was replaced with 100 μL fresh medium containing 10 μL CCK-8 solution (Beyotime, China). The cells were incubated at 37 °C in the dark for four additional hours. Subsequently, the optical density (OD) value of each well at wavelength 450 nm was measured by a microplate reader (Tecan infinite M200). Cell viability was calculated according to the following formula:

$$\text{Cell}\text{viability}(\%)=\left[\text{Treat}-\text{Blank}\right]/\left[\text{Control}-\text{Blank}\right]\times 100\%.$$

Each group had five wells, and the experiment was repeated independent three times.

AnnexinV/PI apoptosis assay by flow cytometry

The mitomycin C-treated MOC2 cells were cultured for 24 h and then co-cultured with activated naïve CD8⁺ T cells for 48 h. After co-cultivation, discard the supernatant, and the MOC2 cells were stained with AnnexinV and PI and apoptotic cells were acquired by flow cytometry. The population of apoptotic cells were presented in dot plots evaluated by flow cytometry.

Immunohistochemical staining and evaluation

The MOC2 tumors were fixed with 4 % paraformaldehyde for 24 h. The tumors were embedded in paraffin and sectioned into 5-μm-thick sections. Then dewaxing, rehydration, antigen retrieval and inactivation of endogenous peroxidase activity with 3 % H₂O₂. Then an antibody directed against mouse (Anti-CD8α Abcam, clone EPR21769, 1:500, England). The next day, the sections were incubated with polymer helper and polyperoxidase-anti-rabbit IgG (Boster, China) for 30 min at room temperature. After rinsed, the sections were colored with DAB (Gene Tech, China) and counterstained with hematoxylin. Then the sections were observed under a microscope (E200, Japan), magnification, ×20.

Immunofluorescence staining

Fresh tissue was quick-frozen embedded with OCT embedding medium then sectioned into 6-μm-thick sections. Rewarm at room temperature, soaked in PBS to remove excess OCT. Then blocked with 5 % goat serum and 0.3 % Triton X-100 1xPBS. Then an antibody directed against mouse (Anti-CD8α Abcam, clone EPR21769, 1:500, England). The next day, after rinsed with PBS, then stained with an secondary antibody (Goat Anti-Rabbit IgG,1:100, Affinity, clone S0018, USA). Cell nuclei were stained by DAPI for 5 min. Representative fields of cells were photographed by fluorescence microscopy, magnification, ×20.

Statistical analysis

The statistical tests employed in this study were carefully chosen to provide robust analyses. Significance thresholds were applied to determine the statistical significance of the results obtained. All statistical analyses were conducted using GraphPad Prism 8.0 (GraphPad Software, Inc, CA, USA). All experiments were repeated at least three times independently, and data are presented as the mean ± standard deviation (SD). The significant difference between the two groups was determined by Student's t-test. A two-tailed P < 0.05 was considered statistically significant.

Results

Overexpression of CCR7 in OSCC patients

We used RNAseq data of TCGA and GTEx from SangerBox 3.0 database to have a broad view of CCR7 expression pattern in pan-cancer scale. The results showed that CCR7 expression was significantly upregulated in various types of tumors, including HNSCC (red box) (Fig. 1(A)). Two GEO datasets were applied to further estimate CCR7 expression level in OSCC. We all found that CCR7 is overexpressed in OSCC (Fig. 1(B)). This is in general accordance with the immunohistochemical results from the HPA database, which is showed that CCR7 was barely expressed in normal oral mucosa and showed intense staining in OSCC (Fig. 1(C)). The above results indicated that CCR7 was significantly upregulated in OSCC patients, suggesting that CCR7 may be an oncogene in OSCC.

Fig. 1.

Overexpression of CCR7 in OSCC patients. (A) CCR7 expression patterns across different tumor types in SangerBox 3.0 based on TCGA and GTEx databases. The red box represents HNSCC tissues and adjacent normal tissues. (B) CCR7 expression levels in OSCC tissues and normal oral tissues in the GSE139869 and GSE138206 datasets in the GEO database. (C) CCR7 protein expression levels in OSCC tissues and normal oral epithelial tissues, determined by immunohistochemistry staining from HPA website. magnification, ×4. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001).

Single-cell sequencing reveals heterogeneity in CD8⁺ T cells distribution

We diligently recorded and analyzed the tumor growth curves and endpoints, and captured images of excised tumors to provide visual evidence of tumor development. As shown in (Fig. 2(A)), it can be found that the tumor growth volume of CCR7-deficient mice was significantly reduced in the same period compared with that of WT control mice. To investigate the effect of CCR7 on the complexity of different cellular infiltrations in the tumor microenvironment, we subsequently performed single-cell sequencing of tumor tissue cells isolated from three CCR7 KO mice and three WT mice (Fig. S1(A)). Here is the basic workflow for 10x Genomics single cell RNA sequencing (Fig. S1(B), (C)). Based on the raw files of the 10x Genomics, we used R package ‘Seurat’ to filter poor quality cells for quality control, as shown in Supplementary Fig. S2. Umapplot was used to visualize the clusters in a reduced 2D space, found the heterogeneity of the cell distribution, 29 clusters are presented after dimensionality reduction clustering (Fig. 2(B)), then after CD45(+) sorting (inflammatory cell marker), the descending clusters presented 21 clusters (Fig. 2(C)). Single-cell RNA-seq differences were represented using heatmaps, enabling a comprehensive analysis of the transcriptomic changes associated with CCR7 modulation. The top5 highly expressed genes of each cluster were then visualized in Fig. 2(D) using Heatmap plots. We used the marker (CD8a) to define the cluster 9 is CD8⁺ T cells (Fig. 2(E)). Fig. 2(E) also visualized the top 20 highly expressed genes in CD8⁺ T cell population by UMAPplot. Fig. 2(F) showed the top 10 up-regulated genes between CCR7 KO and WT CD8⁺ T cells. It is interesting to find Dusp1, Icos and Cd28, which were related to activation of T cells, were all up-regulated in CCR7 KO CD8⁺ T cells (Fig. 2(G), (H)).

Fig. 2.

Tumorigenic assay and single-cell sequencing analysis. (A) Subcutaneous MOC2 tumor growth is reduced in CCR7 deficient mice. (B) Visualizing cell clusters through UMP dimensionality reduction. (C) Visualizing cell clusters through UMP dimensionality reduction after CD45(+) sorting. (D) Heatmap of TOP5 highly expressed gene in each cluster after CD45(+) sorting. (E) Using marker “CD8a” defined CD8⁺ T cells, visualizing TOP20 highly expressed gene in CD8⁺ T cells through Heatmap and Umapplot. (F) Heatmap of top10 expressed genes in CD8⁺ T cells of CCR7 KO and WT sample. (G) Heatmap of “Dusp1”, “Icos”, “Cd28” gene expression in CD8⁺ T cells of CCR7 KO and WT sample. (H) Violin map of “Dusp1”, “Icos”, “Cd28” gene expression in CD8⁺ T cells of CCR7 KO and WT sample. (Student's t-test, WT: n = 4, KO: n = 4, The results are presented as the mean ± SD, *P < 0.05, **P < 0.01, ***P < 0.001).

The proportion of CD8⁺ T cells is higher in CCR7 KO tumors

Based on the single cell sequencing data, we counted the number of CD8⁺ T cells and the number of inflammatory cells in 6 samples (3 CCR7 KO, 3 WT) for statistical analysis, the results showed that CD8⁺ T cells constituted a larger proportion in inflammatory cells and total tumor tissue cells in CCR7 KO samples than in WT samples. As shown in the Fig. 3(A) below.

Fig. 3.

The Proportion of CD8⁺ T Cells is higher in CCR7 KO Tumors. (A) Statistics on the amount of CD8⁺ T cells in single cell sequencing data. (B)These panels uses the marker CD3, CD8, CD44, CD62L gating strategy to define CD8⁺ T cell (CD3⁺,CD8⁺) populations and CD8⁺ T cell subpopulations T_e (CD44⁺,CD62L⁻), T_m (CD44⁺,CD62L⁺), and T_n (CD44⁻,CD62L⁺) in tumor tissues of mice, the statistical analysis examines the proportion of cells within a specific gating. (C) Immunohistochemical staining of tumor tissues in CCR7 KO and WT mouse using Cd8a antibody, magnification, × 20. (D) Immunofluorescence staining of tumor tissues in CCR7 KO and WT mouse using Cd8a antibody, magnification, × 20. The results are presented as the mean ± SD, *P < 0.05, **P < 0.01, ***P < 0.001.

We then used CD45 microbeads to sort the tumor tissue single cell suspension to obtain inflammatory cells, then used flow cytometry to detect the proportion of CD8⁺ T cells as well as their subpopulations. Flow cytometry data on CD8⁺ T cell profiles were presented as figures accompanied by statistical analyses. This allowed for a comprehensive understanding of the impact of CCR7 on CD8⁺ T cell populations. As shown in Fig. 3(B), the proportion of CD8⁺ T cells in the CCR7 KO tumor tissue was more than that of WT, and the proportion of naïve CD8⁺ T cells (T_n) in KO was smaller than in WT. Interestingly, in both WT and CCR7 KO tumors, activated naïve CD8⁺ T cells were mainly in the form of memory cells (T_m), and effector cells (T_e) were almost absent. In Fig. 3(C), (D), we then performed immunohistochemical staining and immunofluorescence staining of tumor tissues and found that the proportion of CD8⁺ T cell infiltration in CCR7 KO tumor tissues was higher than that in WT tissues.

Heterogeneity of CD8⁺ T cell ratios in spleen and tumor

Since T cells are mainly derived from the spleen, we hypothesized that the role of CCR7 in the heterogeneous distribution of CD8⁺ T cells within the tumor is associated with the spleen. Therefore, we extracted the spleen from CCR7 KO and WT mouse to detect the proportion of CD8⁺ T cells and their subpopulations naïve CD8⁺ T cells (T_n), effector CD8⁺ T cells (T_e) and memory CD8⁺ T cells (T_m). As shown in Fig. 4(A), CCR7 KO spleen had fewer CD8⁺ T cells and more naïve CD8⁺ T cells than WT, which is different with the tumor tissue results. We hypothesized that perhaps this was due to a lack of tumor stimulation. Therefore, we detected the spleens of tumor-bearing mouse and found that CCR7 KO had more CD8⁺ T cells and fewer naïve CD8⁺ T cells than WT in Fig. 4(B). This result is consistent with the tumor microenvironment.

Fig. 4.

Heterogeneity of CD8⁺ T cell ratios in spleen and tumor. These panels uses the marker CD3,CD8,CD44,CD62L gating strategy to define CD8⁺ T cell (CD3⁺,CD8⁺) populations and CD8⁺ T cell subpopulations T_e (CD44⁺,CD62L⁻), T_m (CD44⁺,CD62L⁺), and T_n (CD44⁻,CD62L⁺) in spleens of tumor-free mice(A), spleens of tumor-bearing mice(B). The statistical analysis examines the proportion of cells within a specific gating. The results are presented as the mean ± SD, *P < 0.05, **P < 0.01, ***P < 0.001.

CCR7 inhibits the activation of naïve CD8⁺ T cells and proliferating

To verify in vitro whether CCR7 can affect the activation of naïve CD8⁺ T cells, we extracted naïve CD8⁺ T cells from CCR7 KO and WT spleens by magnetic bead sorting. As shown in Fig. 5(A), after sorting of 1 × 10⁸ cells using the Naïve CD8⁺ T cell kit, (4 × 10⁶±2.75 × 10⁵) naïve CD8⁺ T cells were obtained from CCR7 KO mice and (5 × 10⁶ ± 2.52 × 10⁵) naïve CD8⁺ T cells were obtained from WT mice, the difference is significant, which was in substantial concordance with the previous flow cytometry results.

Fig. 5.

CCR7 inhibits the Activation of Naïve CD8⁺ T cells and Proliferating. (A) Analysis of the number of isolated naïve CD8⁺ T cells from 1 × 10⁸ cells. (B) These panels uses the marker CD44,CD62L gating strategy to define CD8⁺ T cell subpopulations T_e (CD44⁺,CD62L⁻), T_m (CD44⁺,CD62L⁺), and T_n (CD44⁻,CD62L⁺) and assess the activation effect by checking for changes in their ratios, the statistical analysis examines the proportion of cells within a specific gating.(C) The relative gene expression of transformed effector CD8⁺ T cells (IFN-γ TNF-α) and memory CD8⁺ T cells (IL-2) after activation was evaluated to assess the activation effect (D) Analysis of flow cytometry data for assaying cell proliferation with CFSE, the statistical analysis examined the average fluorescence intensity of the gated population. Both groups added 100 ng/ml of CCL19 and 100 ng/ml of CCL21 and co-cultured for 48 h.The results are presented as the mean ± SD, *P < 0.05, **P < 0.01, ***P < 0.001.

We stimulated the activation of naïve CD8⁺ T cells with CD3/CD28, and the ratio of activated naïve CD8⁺ T cells (T_n), effector CD8⁺ T cells (T_e) and memory CD8⁺ T cells (T_m) was detected by flow cytometry. The effects of CCR7 modulation on T cell activation and proliferation were presented graphically with statistical analysis. This facilitated a clear visualization of the in vitro outcomes. As shown in Fig. 5(B), without the addition of activator there was almost no activation in the WT group, and with the addition of the activator, the proportion of T_n was significantly reduced and the proportion of T_e and T_m was significantly increased, which indicates a great activation effect. When co-incubation with CCL19 and CCL21, this CD3/CD28 induced activation trend was attenuated, the proportion of T_n was increased, and the proportion of T_e and T_m was reduced again. And the CCR7 KO group without the addition of activator was also almost no activation, consistent with the WT group, and the activation effect was also well after the addition of activator, with little proportion of T_n and high proportion of T_e and T_m. However, when co-incubation with the subsequent addition of CCL19 and CCL21 did not significantly change the activation effect, which is different with WT group. In addition, we found that the activation effect of CCR7 KO group was significantly stronger than that of WT group with the same addition of CCL19/21, with more T_e, T_m and less T_n. Thus, the results of flow cytometry suggest that the action of CCR7 may somewhat inhibit the activation of naive CD8⁺ T cells.

Then, the treatments of these three groups were kept the same as before, the real-time fluorescence quantitative PCR was performed to detect the relative gene expression of effector CD8⁺ cells and memory CD8⁺ T cells after activated. As shown in Fig. 5(C), when CCL19/21 was added to the WT group, the expression of effector CD8⁺ T cells marker (IFN-γ TNF-α) and memory CD8⁺ T cells marker (IL-2) were all reduced. While when CCL19/21 was added to the CCR7 KO group, the expression of these three target genes was found significantly increase than WT group. These results are generally consistent with the trend of the experimental results of flow cytometry.

We further detected the proliferation effect of naïve CD8⁺ T cells by CFSE methods. Proliferation was assessed by the relative mean fluorescence intensity (MFI). As shown in Fig. 5(D), when CCL19/21 was added to the WT group, the relative mean fluorescence intensity was elevated, and when CCL19/21 was added to the CCR7 KO group, the relative mean fluorescence intensity has no increase, which indicates that the CCR7 KO group cells have a stronger proliferative effect. These results suggest that the action of CCR7 may somewhat inhibit the proliferation of naive CD8⁺ T cells.

CD8⁺ T cells affects apoptosis and viability of tumor cells

We activated naïve CD8⁺ T cells by CD3/CD28, incubated with CCL19/21, and co-cultured with MOC2 tumor cells. As shown in Fig. 6(A), when co-cultured with WT+CCL19/21 group CD8⁺ T cells, the apoptosis rate of MOC2 cells is 23.43 ± 2.157 %, while when co-cultured with KO+CCL19/21 group CD8⁺ T cells, the apoptosis rate raised to 32.83 ± 2.676 %. The difference is significant. CCK-8 assays were performed to detect the MOC2 cell vitality. As shown in Fig. 6(B), when co-cultured with KO+CCL19/21 group CD8⁺ T cells, the MOC2 cell viability was decreased to 50.52 ± 5.172 % from 100 % of cultured by WT+CCL19/21 group CD8⁺ T cells. Taken together, we carefully measured and reported changes in apoptotic/viability endpoints resulting from co-culture experiments. These findings shed light on the influence of CCR7 on apoptotic and viability processes.

Fig. 6.

CD8⁺ T Cells Affects Apoptosis and Viability of Tumor. (A) Analysis of flow cytometry data for assaying cell apoptosis by AnnexinV/PI, the statistical analysis examines the proportion of cells within a specific gating. (B) CCK8 assay for MOC2 cell viability. Both groups added 100 ng/ml of CCL19 and 100 ng/ml of CCL21 and co-cultured for 48 h. The results are presented as the mean ± SD, *P < 0.05, **P < 0.01, ***P < 0.001.

Discussion

OSCC, a malignant tumor with high morbidity and mortality, accounts for approximately 400,000 newly diagnosed cases of oral cancer worldwide each year, with the majority of cases occurring in developing countries [30]. OSCC remains a major medical challenge, which highlights the importance of further understanding of the development of OSCC.

CCR7 is a multifunctional G protein-coupled transmembrane chemokine that is involved in many biological behaviors [31]. Numerous previous studies have shown that CCR7 is abnormally upregulated in many malignancies, such as breast cancer [23], bladder cancer [32], and gastric cancer [33], and CCR7 also correlates with tumor clinical stage and tumor size level [34]. Previous studies have shown that CCR7 mainly exerts pro-tumorigenic effects by promoting angiogenesis, lymphangiogenesis, immune escape of tumor cells, and enhancement of tumor cell stemness [35], [36], [37]. And in this study, CCR7 KO induced more CD8⁺ T cells to infiltrate oral tumors and arrested their growth process.

It has been shown that co-infusion of CCR7 with MSCs effectively naïve T cells in SLOs, thereby inhibiting the transport of T cells from SLOs to target organs for the purpose of suppressing anti-tumor immunity [38]. And our study, we found that CCR7 knockout resulted in increased expression of many genes in tumor-infiltrating CD8⁺ T cells, included Dusp1, Icos and Cd28. Dusp1 is an essential gene for the T cell activation process [39], and Icos,Cd28 are the markers of T-cell activation [40,41]. Further studies showed that tumor-infiltrating CD8⁺ T cells were mainly composed of T_m, with a minority of T_e, in both CCR7 KO and WT, which can be explained by the study of Jiang et al. [42] that excessive activation in TME leading to T_e depletion and then storage as T_m. In addition, the proportion of T_n was less in CCR7 KO than WT. T_n subpopulation may be the most appropriate cells for antitumor immunity due to their high replicative potential and their ability to convert into effector cells with low depletion and high efficiency functions [43]. Theresa Barberi et al. [44] demonstrated that deficiency of NF-κB p50 promoted activation of T_n and slowed tumor growth, so we inferred that CCR7 may have a similar role.

In the spleen of tumor-free CCR7 KO mouse, the proportion of CD8⁺ T cells was lower than WT mouse, and the proportion of T_n in the CD8⁺ T subpopulation was higher than in WT mouse. This may due to the lack of CCR7 result in a reduced ability of naïve T cells and DC cells to enter the spleen along the CCL19/CCL21 ligand, leading to a failure of the T cell receptor (TCR) to receive activation signals and thus reduced activation and proliferation of CD8⁺ T cells [45].

It has been demonstrated that some circulating tumor cells (CTC) are able to act on systemic organs via the bloodstream, bringing about related effects [46]. The spleen acts as an immune organ and when the body is exposed to foreign stimuli or infections, naïve CD8⁺ T cells (T_n) in the spleen can be activated into T_e and T_m, then enters from the spleen to the site of infection to perform immune effects [47,48]. This is consistent with our tumor-bearing spleens from both CCR7 KO and WT mice showing a T_n and CD8⁺ T cells reduction compared to tumor-free spleen.

Although our tumor-bearing spleens from both CCR7 KO and WT mice showed a reduced proportion of T_n and CD8⁺ T cells compared to tumor-free spleens, this reduction was not equivalent. The WT group had a higher reduction in the proportion of CD8⁺ T than the CCR7 KO group. At first, we thought that this might be due to more CD8⁺ T cells entering the tumor in the WT group. However, we were surprised that tumor-bearing CCR7 KO mice had increased CD8⁺ T cells. Thus, we believe that as a consequence of the induction of tumor-stimulating factors, more activated CD8⁺ T cells emerged in the CCR7 KO group. This accompanied by stronger proliferation of CD8⁺ T cells, thus supplying more CD8⁺ T cells entering the tumor. Indeed, spleens of tumor-bearing mice had an increased proportion of CD8⁺ T cells with increased proportion of T_e and T_m and decreased proportion of T_n in the CCR7 KO group. In vitro experiments also showed that the activation and proliferation of naïve CD8⁺ T cells were negatively regulated by CCR7, and then negatively regulated tumor cells survival. Taken together, this study demonstrated that under the influence of TME, CCR7 inhibits the activation of naïve CD8⁺ T cells, thereby affecting the proliferation and replenishment of antitumor immune CD8⁺ T cells and promoting tumor growth.

This biological function of CCR7 may involve some signaling molecules and signaling pathways. Our previous work had demonstrated that CCR7 can promote head and neck squamous cell carcinoma growth by inducing phosphorylation of MAPK [49]. The MAPK pathway is one of the signaling pathways affecting T cell activation [50]. A study by Zhang et al. [39] confirmed Dusp1 can promote T cell activation and proliferation by inhibiting activation of the JNK MAPKs pathway. In this study, by single-cell RNA sequencing, we also found that CCR7 KO increased Dusp1 expression in tumor-infiltrating CD8⁺ T cells. This result has been doubly demonstrated in macrophages in vitro experiments (unpublished). Therefore, we hypothesized that CCR7 may downregulates Dusp1 expression, then promote the activation of the JNK MAPKs pathway, and thereby inhibit the activation of naïve CD8⁺ T cells.

In summary, our study reveals a novel pathway of CCR7 regulating the development of OSCC. Nevertheless, it is undeniable that several potential limitations should be taken into account when interpreting our results. First, we focused only on the gene function of CCR7 and lacked studies on its downstream molecular mechanisms. Second, our approach focused only on genomics, and future studies should consider examining precise mechanisms using multi-omics approaches, which would enable a more comprehensive analysis of the molecular landscape and regulatory networks associated with CCR7-mediated processes. And, due to time and other constraints, all of our study data were based on mouse models and cell lines, and we lacked data related to human samples to validate the clinical relevance, which will continue to be accomplished in our further studies.

Therefore, our future work should focus on the study and development of CCR7-targeted therapeutic strategies, including the study of combination therapeutic strategies targeting CCR7 with some immune checkpoints to improve therapeutic efficacy, and the use of CCR7 expression as a biomarker for prognostic and therapeutic monitoring, as well as the exploration of the interactions between CCR7 and the environment, which will provide personalized therapeutic approaches for targeting patients in clinical settings in order to improve prognosis. Needless to say, a thorough preclinical evaluation of the safety and efficacy of CCR7-targeted therapies is essential before applying them in the clinic. In addition, in the next five years, it is foreseeable that gene-targeted therapies will have a broad development prospect, and in the future, with the continuous progress of technology, gene-targeted therapies will play a role in the treatment of more diseases, realizing a more efficient and precise treatment of human diseases.

Conclusion

CCR7 deficiency inhibits OSCC tumor growth; CCR7 affects the heterogeneity of CD8⁺ T cell distribution in tumors and spleens; CCR7 promotes oral squamous cell carcinoma by affecting the tumor microenvironment through activation of naïve CD8⁺ T cells.

Ethics approval and consent to participate

All methods in this study were carried out in accordance with relevant guidelines and regulations. The animal experiments involved in this research is reported in accordance with ARRIVE guidelines (https://arriveguidelines.org). This study was approved by the Ethics Committee of China Medical University (Ethics ID: K2022028). Since this study does not involved human, so exempt from review.

Consent for publication

Not applicable.

Funding

This study was supported by National Natural Science Foundation of China (Grant no. 82203680), Liaoning Department of Education Research Foundation (Grant no. JC2019025), Liaoning Science and Technology Program (Grant no. 2019-ZD-0751), Natural Scientific Foundation of Liaoning Province (Grant no. 2021-MS-176) and Special Funds of “First-Class Universities and Disciplines of the World” Project (Grant no. 115-3110210730).

CRediT authorship contribution statement

Cong Yan: Writing – review & editing, Writing – original draft, Formal analysis, Data curation. Weidong Du: Formal analysis, Data curation. Keith L. Kirkwood: Validation, Supervision. Yao Wang: Validation. Wanhang Zhou: Validation. Zhenning Li: Validation. Yuan Tian: Validation. Shanfeng Lin: Validation. Li Zheng: Validation. Maged Ali Al-Aroomi: Validation. Jiaxing Gao: Validation. Sheng Jiang: Validation. Changfu Sun: Validation, Supervision. Fayu Liu: Validation, Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

• The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. All data generated or analyzed during this study are available in this published article [and its supplementary information file] and in public databases.