Comprehensive analysis of CCR7 as a biomarker for the diagnosis, prognosis, and immunity in pan-cancer

doi:10.21203/rs.3.rs-2815191/v1

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Comprehensive analysis of CCR7 as a biomarker for the diagnosis, prognosis, and immunity in pan-cancer

https://doi.org/10.21203/rs.3.rs-2815191/v1

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Chemokine receptor 7 (CCR7), a member of the CCR family genes, encodes CCR to bind chemokines and exert multiple types of biological processes. It has been evident that CCR7 can play a crucial role in cancer development and progression. Our study has investigated the value of CCR7 expression in the diagnosis and prognosis and immunological signatures of CCR7. Through the utilization of The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), The Human Protein Atlas (HPA), TIMER, TISIDB, GSEA, GeneMANIA, The Cancer single-cell state Atlas (CancerSEA), UALCAN, Shiny Methylation Analysis Resource Tool (SMART), MethSurv, and cBioPortal database, we used bioinformatics methods to conduct pan-cancer analysis, including differential expression, clinical evaluation, tumor microenvironment (TME) and immune-related analysis, GSEA, functional association analysis, DNA mutation and methylation level, and microsatellite instability (MSI) and tumor mutation burden (TMB). Our results showed that the expression of CCR7 showed significant differentiation between tumor tissues and normal tissues. CCR7 had the potential to be a biomarker in the diagnosis and prognosis of patients. Moreover, the CCR7 expression was closely associated with immune cell infiltration, immune-related genes, TMB, MSI, and DNA mutation. Through GSEA, it was revealed that CCR7 had a strong correlation with multiple immunological functions. Overall, we testified that CCR7 could be a novel tool for the diagnosis and prognosis of cancer patients. Moreover, the correlation between CCR7 and TME and immunological processes elucidated the value of CCR7 as a target in cancer immunotherapy.

Biological sciences/Cancer/Cancer microenvironment

Biological sciences/Cancer

Biological sciences/Immunology

chemokine receptor 7

pan-cancer analysis

prognosis

immune infiltration

diagnosis

Cancer is a serious health issue worldwide and its negative impact on humans is tremendous (Siegel et al., 2022). Immunotherapy, as a milestone of cancer treatment, utilized natural immune defenses and has accomplished enhanced clinical response of anti-tumor therapy (Zhang and Zhang, 2020); it has been proved that chemotherapy and radiotherapy can improve the efficacy of immunotherapy and reduce the side effects (Yu et al., 2019). To further validate the effectiveness of immunotherapy, pan-cancer analysis is an important tool to achieve the early detection of cancer and provide new diagnostic and prognostic markers for immunotherapy by comparing the differences and similarities of multiple types of genes (Srivastava and Hanash, 2020).

Chemokine receptor (CCR), a family of G-protein coupled receptors, can exert multifaceted biological function by binding chemokines and further mediate immunological processes (Salem et al., 2021). CCR7 acts as the receptor for chemokine ligand 21 (CCL21) and chemokine ligand 19 (CCL19) and can regulate the crosstalk between dendritic cells (DC) and T cells, ultimately controlling inflammation, autoimmunity, and tolerance (Brandum et al., 2021). In particular, CCR7 plays an opposing role in cancer progression, which can not only induce the elimination of cancer cells by promoting immune response but also result in cancer cell migration and metastasis through the lymphatic system (Salem et al., 2021). In the past twenty years, several studies have demonstrated that the expression of CCR7 can contribute to metastasis of breast cancer (Kochetkova et al., 2009, Hayasaka et al., 2022, Pang et al., 2016), squamous cell carcinoma (Irino et al., 2014, Shi et al., 2015, Liu et al., 2014, Mburu et al., 2012, Ishida et al., 2009), non-small cell lung cancer (NSCLC) (Takanami, 2003), cervical cancer (Kodama et al., 2007), gastric cancer (Arigami et al., 2009), pancreatic ductal adenocarcinoma (PDAC) (Sperveslage et al., 2012), and bladder cancer (Xiong et al., 2017), especially by lymph nodes and lymph vessels, which can be mediated by signal transduction, such as p38 MAPK, MEK, and ERK1/2. Additionally, it has been reported that tumor necrosis factor-α enables the lymphatic metastasis of gallbladder and prostate cancer cells through TNF-α/ERK1/2 or JNK/AP-1/CCR7 and TNF-a/ERK/CCR7/p38 signaling pathways, respectively (Hong et al., 2016, Maolake et al., 2018). Indeed, the induction of lymphangiogenesis by CCR7 has been testified by researchers in which it may be the underlying mechanism of lymph node metastasis (Zhao et al., 2011, Tutunea-Fatan et al., 2015). Importantly, Roberts et al. in 2016 showed that DC-mediated CD8 + T cell activation via a CCR7-dependent manner can contribute to the high level of T cell infiltration in the immune microenvironment and therefore be related to better clinical outcomes (Roberts et al., 2016). Generally, CCR7 can be the potential therapeutic marker in cancer diagnosis to predict metastasis and it is valuable to be exploited as a novel strategy in cancer immunotherapy (Salem et al., 2021).

Given the importance and the lack of pan-cancer analysis of CCR7, we have retrieved extensive databases, such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), The Human Protein Atlas (HPA), TIMER, UALCAN, and cBioPortal, and then extracted target data. On the one hand, the analysis and comparison of the expression and DNA methylation level of CCR7 have been conducted; the diagnostic and prognostic value of CCR7 in all 33 cancers have been analyzed. Moreover, we evaluated the interaction of CCR7 in the tumor microenvironment (TME). Immune infiltration levels and co-expression analysis of immune-related genes with CCR7 have also been shown in 33 types of cancers. On the other hand, Gene Set Enrichment Analysis (GESA) and single-cell functional analysis have been used to evaluate the CCR7-related function; the microsatellite instability (MSI) and tumor mutation burden (TMB) and the mutation data from cBioPortal have been shown. It is evident that CCR7 has the potential to be a novel biomarker in generalized cancers, which can be utilized in cancer diagnosis and prediction of prognosis and immunotherapeutic responses in cancer patients.

Ethical Statement

The authors confirm that all methods were carried out in accordance with relevant guidelines and regulations of the Animal Ethics Committee and the study was approved by the ethics committee of Nanchang University.The patient gave informed consent to the collection of specimens.

The mRNA and protein expression of CCR7 in normal tissues and single-cell types

The HPA database (https://www.proteinatlas.org/) was used to investigate the CCR7 protein and mRNA expression in normal human tissues and single-cell types. HPA is a comprehensive human proteome database that can supply information at multiple levels of different cells and tissues (Uhlén et al., 2015).

Data collection and differential expression analysis of CCR7

We downloaded the mRNA expression information and clinical data of 33 types of cancer and corresponding normal samples from TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga), which is involved in 11,007 samples. The full names and corresponding abbreviations of 33 types of cancer were shown in Table 1. Gene expression data of 31 tissues from GTEx (https://commonfund.nih.gov/GTEx) were downloaded. The genes that are expressed differentially in tumor tissues and normal tissues were analyzed by log2 transformation and t-tests in TCGA.

We analyzed the expression levels of CCR7 in 31 normal tissues and 33 tumor tissues and further compare the cancer samples with the paired normal samples in 33 cancers. The difference in CCR7 expression was identified by Log2 transformation and t-tests with a p-value < 0.05, which was evaluated by R software (Version 4.2.1, https://www.Rproject.org). The box diagrams were drawn by the “ggplot2” R package. We also used the TIMER database (http://timer.cistrome.org/) to analyze the differential gene expression between tumor tissues and paired normal tissues.

For the analysis of the differential expression of GPC2 at the protein level, we obtained the immunohistochemical images of 4 types of tumor tissues and paired normal tissues from HPA. Moreover, we also obtained immunofluorescence images related to the subcellular localization of CCR7 (Uhlén et al., 2015).

Diagnostic value analysis of CCR7

The information on the tumor stage that is supplied by TCGA was analyzed to identify the relationship with CCR7 by “ggplot2” R packages. We used the “pROC” package to analyze the diagnostic accuracy of CCR7, which is shown by the ROC curve. The area under the curve (AUC) ranges from 1.0 to 0.5.

Prognostic value analysis of CCR7

The survival data of the samples downloaded from TCGA were evaluated. Overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), and progression-free survival (PFS) were used to assess the prognostic value of CCR7 in patients. The Kaplan–Meier analysis and Cox regression analysis and log-rank test were used. We used R packages “survival”, “survminer”, and “forestplot” to show the survival curves and forest plot to reflect the association between CCR7 expression with the prognosis of patients.

TME correlation analysis

Gene expression data from TGCA were used to evaluate the infiltration of stromal and immune cells into tumors, which reflected the relationship between TME with cancers. We calculated stromal and immune scores for tumor samples by using R packages “estimate” and “limma” and further explore the association between CCR7 expression and both scores by R packages “ggplot2”, “ggpubr,” and “ggExtra”.

Immune cell infiltration and immune-related gene analysis

We analyzed the immune cell infiltration of TGCA by using R packages “e1071”, “preprocessCore”, and “limma”. Furthermore, we used R packages “ggplot2”, “ggpubr,” and “ggExtra” to assess the correlation between CCR7 expression and immune cell infiltration level. We also utilized TIMER to obtain the results of immune cell infiltration analysis (Li et al., 2017, Li et al., 2020).

Additionally, we evaluated the co-expression of CCR7 and immune-related genes from the TISIDB database (http://cis.hku.hk/TISIDB), including genes encoding immunostimulator, immunoinhibitor, major histocompatibility complex (MHC) molecule, chemokine, and CCR protein. TISIDB is a user-friendly web portal, which integrates multiple heterogeneous data types for the analysis of tumor and immune system interaction (Ru et al., 2019).

GSEA of CCR7

We conducted GSEA to investigate the biological functions of CCR7 in each cancer sample. KEGG gene sets were downloaded from the GSEA database (http://www.gsea-msigdb.org/gsea/index.jsp). Pathway analysis was performed by using the R-packages “colorspace”, “stringi”, “ggplot2”, “limma”, “org.Hs.eg.db”, “DOSE”, “clusterProfiler,” and “enrichplot.”

Gene interaction analysis of CCR7

To evaluate the gene-gene interaction of CCR7, we utilized the GeneMANIA website (http://www.genemania.org) to detect similar gene expression patterns and gene functions. GeneMANIA database can be used to find genes that are functionally similar to the target gene based on genomics and proteomics data (Franz et al., 2018).

Single-cell functional analysis of CCR7

The Cancer single-cell state Atlas (CancerSEA, http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp) was used to analyze the association between CCR7 and functional states in different cancers (Yuan et al., 2019).

DNA methylation analysis of CCR7

We investigated the promoter methylation level of CCR7 in cancers by UALCAN (http://ualcan.path.uab.edu/) which is a comprehensive and interactive website for the assessment of cancer OMICS data and can be used to analyze gene expression data from TCGA (Chandrashekar et al., 2017, Chandrashekar et al., 2022). Shiny Methylation Analysis Resource Tool (SMART, http://www.bioinfo-zs.com/smartapp/#tab-8013-2) was used to obtain the sites of methylation probes in chromosomes, which is an interactive website to analyze DNA methylation of TCGA (Li et al., 2019). Furthermore, we used MethSurv (https://biit.cs.ut.ee/MethSurv/), a tool to perform survival analysis by using DNA methylation information (Modhukur et al., 2018).

Gene mutation analysis of CCR7

cBioPortal (http://www.cbioportal.org/) was used to assess the type and frequency of CCR7 gene mutation in all cancers (Cerami et al., 2012, Gao et al., 2013). We selected “TCGA PanCancer Atlas” which contains 10953 samples (32 types of cancer) for analysis. cBioPortal is a tool that can achieve visualization and analysis between the target gene and extensive cancer samples. The genetic alteration information was obtained by “OncoPrint”, “CancerTypesSummary”, “Plots”, “Mutations”, and “Comparison/Survival”.

TMB and MSI correlation analysis

We analyzed the mutation of CCR7, which was shown by the radar chart of TMB and MSI. Gene expression and mutation data of CCR7 were downloaded from TCGA. We used the R package “fmsb” for the analysis of TMB and MSI.

The mRNA and protein expression of CCR7 in normal tissues and single-cell types

Supplementary Fig. 1A showed that CCR7 mRNA was mainly expressed in the bone marrow and lymphoid tissues, and CCR7 protein was expressed in the respiratory system, skin, bone marrow, and lymphoid tissues. Supplementary Fig. 1B and Supplementary Fig. 1C further showed the specific localization in respective tissues and organs, including the lung, spleen, lymph node, tonsil, bone marrow, appendix, and thymus. Moreover, Supplementary Fig. 2A indicated that CCR7 was significantly expressed in Langerhans cells, syncytiotrophoblasts, B cells, cytotrophoblasts, and T cells. The expression of CCR7 in distinctive single-cell tissues was shown in Supplementary Fig. 2B, which revealed that CCR7 was mainly expressed in adipose tissues, bone marrow, breast, endometrium, liver, lung, lymph node, placenta, prostate, salivary gland, skeletal muscle, skin, spleen, stomach, testis, thymus, tongue, and vascular. Besides, in most of these tissues, CCR7 expression was involved in blood and immune cells, such as macrophages, T cells, B cells, and Langerhans cells. Specially, in the placenta and testis, the expression of CCR7 was mainly in trophoblast cells (syncytiotrophoblasts and cytotrophoblasts) and germ cells.

Differential expression of CCR7 between cancer and normal tissue samples

We utilized the GTEx database to analyze the expression level of CCR7 in normal tissues, which demonstrated that the expression of CCR7 in blood and spleen is higher than in other normal tissues and is highest in the blood (Fig. 1A). According to the data from TGCA, in Fig. 1B, it was shown that CCR7 can be expressed extensively in all 33 types of cancers, especially in lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) with the highest expression level of CCR7. In paired normal tissues, Fig. 1C indicated that skin cutaneous melanoma (SKCM) and thymoma (THYM) have higher levels of CCR7 than other tissues.

Moreover, the comparison between cancers and corresponding paired normal tissues on CCR7 expression was also conducted. According to the TCGA data, excluding those cancers that had no or very few normal tissue samples and cannot provide credible information, the expression level of CCR7 in 10 types of cancer was significantly different from that in normal tissue, including breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), rectum adenocarcinoma (READ), and uterine corpus endometrial carcinoma (UCEC) (Fig. 1D). Amongst the 10 types of cancers, the expression of CCR7 was upregulated in BRCA, HNSC, KIRC, KIRP, PCPG, and UCEC. By contrast, a lower level of CCR7 in cancers than in normal tissues was shown in COAD, KICH, LUSC, and READ. Conversely, there is no remarkable difference in CCR7 levels in bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), sarcoma (SARC), SKCM, stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and THYM. Besides, the TIMER database revealed that BRCA, COAD, HNSC, KICH, KIRC, KIRP, LUSC, PCPG, PRAD, READ, and THCA showed a significant difference between tumor tissues and paired normal tissues (Fig. 1E). The expression of CCR7 was upregulated in BRCA, HNSC, KIRC, PCPG, and was downregulated in other 6 types of cancer. These results revealed that the differential expression of CCR7 in the corresponding cancers can be a potential biomarker in cancer diagnosis.

Additionally, we used the HPA database to acquire the immunohistochemical images and evaluate the expression of CCR7 at the protein level. It can be demonstrated that the protein expression of CCR7 is significantly higher in lymphoma and head and neck cancer. In contrast, the expression levels of CCR7 in cervical cancer and melanoma are moderate relatively (Supplementary Fig. 3A). The subcellular localization of CCR7 was shown in A-549, Rh30, U2OS cells, which was involved in nucleus, microtubules, and ER (Supplementary Fig. 3B). According to HPA database, CCR7 was primarily located at mitochondria.

Diagnostic value analysis of CCR7

To assess the relevance of CCR7 in different tumor stages, we analyzed the expression levels of CCR7 in all 33 types of cancers and the differential expression level of CCR7 can be found in 8 types of cancers including BRCA, COAD, HNSC, LIHC, LUAD, SKCM, testicular germ cell tumors (TGCT), and THCA (Fig. 2A), which indicated that CCR7 can be as a valuable clinical marker in the diagnosis and staging of cancers. Moreover, we estimated the diagnostic accuracy by gene signature performance. Through ROC curves, the cutoff of AUC has been utilized to demonstrate low diagnostic accuracy (AUC: 0.5–0.7), relative diagnostic accuracy (AUC: 0.7–0.9), or high diagnostic accuracy (AUC: 0.9–1.0). In Fig. 2B, at 1 year, the AUC of the ROC curve has high diagnostic accuracy in KICH, relative diagnostic accuracy in TGCT, and low diagnostic accuracy in 9 types of cancers including adrenocortical carcinoma (ACC), DLBC, GBM, KIRP, acute myeloid leukemia (LAML), brain lower grade glioma (LGG), ovarian serous cystadenocarcinoma (OV), STAD, uterine carcinosarcoma (UCS), and uveal melanoma (UVM). At 3 years, the AUC of the ROC curve has relative diagnostic accuracy in TGCT and UVM, and low diagnostic accuracy in 8 types of cancers including CHOL, DLBC, ESCA, GBM, KIRP, LAML, LGG, and PRAD. At 5 years, the AUC of the ROC curve has relative diagnostic accuracy in TGCT and low diagnostic accuracy in 13 types of cancers including CHOL, DLBC, ESCA, GBM, KICH, KIRC, KIRP, LAML, LGG, LUSC, PAAD, PRAD, and UVM. Furthermore, it is noteworthy that AUC at 1 year, 3 years, and 5 years has been all higher than 0.5 in DLBC, GBM, KIRP, LAML, LGG, and TGCT.

Prognostic value analysis of CCR7

We further investigated the prognostic significance of CCR7 expression level in different cancers by conducting the survival association analysis. The results of the Kaplan-Meier OS analysis demonstrated that the expression level of CCR7 is associated with OS in BRCA (p=0.001), CESC (p<0.001), GBM (p=0.003), HNSC (p<0.001), LGG (p=0.002), LIHC (p=0.018), LUAD (p=0.039), OV (p=0.008), SARC (p=0.009), and SKCM (p=0.008) (Fig. 3). Moreover, CCR7 can be a low-risk (protective) factor for patients with BRCA, CESC, HNSC, LIHC, LUAD, OV, SARC, and SKCM, which indicated that high expression of CCR7 is associated with better OS in patients (Fig. 3A, B, D, F-J). However, CCR7 can be a high-risk factor for patients with GBM and LGG, which indicated that high expression of CCR7 can decrease the survival time of patients (Fig. 3C, E).

Kaplan-Meier DSS analysis revealed that CCR7 expression is associated with DSS in CESC (p<0.001), GBM (p=0.002), HNSC (p<0.001), LGG (p=0.003), LIHC (p=0.035), LUAD (p=0.010), OV (p=0.010), and UCEC (p=0.026) (Fig. 4). Thereinto, high expression of CCR7 can be a low-risk (protective) factor for patients with CESC, HNSC, LIHC, LUAD, OV, SKCM, and UCEC (Fig. 4A, C, E-I). While it can also act as a high-risk factor for patients with GBM and LGG, indicating that high expression of CCR7 results in poor prognosis (Fig. 4B, D).

Furthermore, Kaplan-Meier PFS analysis presented the expression level of CCR7 is associated with ACC (p=0.033), BRCA (p=0.003), CESC (p<0.001), GBM (p=0.021), HNSC (p=0.001), LIHC (p=0.023), and UCEC ((p=0.045) (Fig. 5A-G). High expression of CCR7 acts as a low-risk (protective) factor for patients with ACC, BRCA, CESC, HNSC, LIHC, and UCEC, which is beneficial for better PFS (Fig. 5A-C, E-G). However, low expression of CCR7 has an association with good PFS in patients with GBM (Fig. 5D). Among the patients with CHOL (p=0.043) and LIHC (p=0.028), the association between CCR7 expression and DFS showed that high expression of CCR7 contributes to the better prognosis of patients (Fig. 5H, I).

According to the results of Cox regression analyses, the results of OS indicated that CCR7 can be a low-risk (protective) factor for patients with CESC, HNSC, LIHC, LUAD, OV, SARC, SKCM, and UCEC, while it can be a high-risk factor for patients with GBM, LGG, and UVM (Fig. 6A). The DSS results revealed that CCR7 can be a low-risk (protective) factor for patients with CESC, HNSC, LIHC, LUAD, OV, SARC, and SKCM, while it can be a high-risk factor for patients with LGG (Fig. 6B). The DFS analysis showed that CCR7 acts as a low-risk (protective) factor for patients with CESC, LIHC, and OV, while it can be a high-risk factor for patients with KIRP (Fig. 6C). Ultimately, the PFS analysis demonstrated that CCR7 acts as a low-risk (protective) factor for patients with BRCA, CESC, CHOL, HNSC, OV, and UCEC, while it can be a high-risk factor for patients with LGG and STAD (Fig. 6D).

Association between CCR7 expression level and TME

The TME has a vital role in cancer initiation and progression. In our pan-cancer study, we calculated the stromal and immune cell scores in 33 types of cancer and then analyzed the association between CCR7 expression and these two scores. The TME correlation analysis indicated that CCR7 expression was positively associated with immune scores in ACC, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, mesothelioma (MESO), OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS, and UVM (Fig. 7A). Additionally, it was revealed that CCR7 expression was positively associated with stromal scores in ACC, CHOL, COAD, GBM, KICH, KIRC, KIRP, LIHC, LUSC, PAAD, PCPG, PRAD, SKCM, STAD, THCA, UCEC, UVM (Fig. 7B). Through the TME correlation analysis, most types of cancer have a significant correlation between the expression of CCR7 and immune cells in the TME, especially in CHOL (R=0.81, p<1.6e⁻⁷), KICH (R=0.8, p<2.2e⁻¹⁶), PRAD (R=0.83, p<2.2e⁻¹⁶), SKCM (R=0.84, p<2.2e⁻¹⁶), THCA (R=0.86, p<2.2e⁻¹⁶), UVM (R=0.81, p<2.2e⁻¹⁶).

Association between CCR7 expression level and immune cell infiltration

In Fig. 8, it was shown that CCR7 expression level had a positive association with M1 macrophages in ACC, CD8+ T cells in ACC, naïve B cells in BLCA, M1 macrophages in LGG, naïve B cells in MESO, naïve B cells in PAAD, regulatory T (Treg) cells in PCPG, and naïve B cells in THYM. Moreover, the CCR7 expression level is negatively related to activated dendritic cells in ACC, eosinophils in ACC, monocytes in ACC, activated NK cells in CHOL, activated NK cells in KICH, M2 macrophages in SKCM, M2 macrophages in THCA, and M0 macrophages in UCS. Among them, the CCR7 expression level is significantly associated with activated dendritic cells in ACC (R =-0.87, p=2e⁻¹¹) and activated NK cells in CHOL (R=-0.8, p=1.7e⁻⁸), and ACC showed a strong correlation with immune cells. Moreover, through the TIMER database, we obtained several immune cells that were significantly associated with CCR7 expression in Supplementary Fig. 4, including CD8+ T cells, CD4+ T cells, Treg cells, follicular helper T cells (Tfh), B cells, DCs, macrophages, monocytes, myeloid derived suppressor cells (MDSC), whichshowed that the expression of CCR7 was positively correlated with immune cells in most types of cancer, especially for CD8+ T cells; however, CCR7 expression was negatively correlated with MDSC in most types of cancer.

Co-expression and correlation analysis of immune-related gene

We utilized the TISIDB database to evaluate the association between CCR7 and immune-related genes, including genes encoding immunostimulator, immunoinhibitor, MHC molecule, chemokine, and CCR protein (Fig. 9). Fig. 9A indicated the correlation between CCR7 expression and immunostimulator. Most of the genes encoding immunostimulator showed a positive correlation with CCR7 expression, especially for CD27, CD28, CD40LG, CD48, CD80, CD86, and ICOS; CD276, PVR, and ULBP1 showed associations with CCR7 expression negatively. Particularly, Supplementary Fig. 5A revealed that the expression of CD48 in CHOL had a positive association with CCR7 expression significantly (R=0.902, p<2.2e⁻¹⁶). Fig. 9B revealed that except KLR2DL1/3, most of the genes encoding immunoinhibitor had a strong correlation with CCR7 expression positively, especially for BTLA, CD96, CSF1R, CTLA4, PDCD1, and TIGIT. Among them, the expression of BTLA in CHOL (R=0.901, p<2.2e⁻¹⁶) and PAAD (R=0.919, p<2.2e⁻¹⁶) showed a significant association with CCR7 expression (Supplementary Fig. 5B, C). In Fig. 9C, it was indicated that for most of the genes encoding MHC molecules, the association between CCR7 expression and the expression of MHC molecules was positive in different types of cancers, except in BLCA.

Fig. 9D showed the correlation between CCR7 expression and chemokine. Except for CCL1/16/24/25/26/27, most of the genes encoding chemokine were associated with CCR7 expression. Additionally, in Fig. 9E, genes encoding CCR protein had a significant relationship with CCR7 expression, especially for the expression of CXCR5 in SKCM (R=0.901, p< 2.2e⁻¹⁶) Supplementary Fig. 5D). In particular, within all types of CCR protein, only CXCR4 expression in ACC showed a negative correlation with CCR7 expression Supplementary Fig. 5E).

GSEA of CCR7

We investigated the pathways which are related to CCR7 by using KEGG gene sets from the GSEA database. In Supplementary Fig. 6, it was shown that except in DLBC, MESO, THYM, and UCEC, the activity degree of CCR7-mediated signaling pathways was significantly correlated to the high expression of CCR7. In addition, for most types of cancer, CCR7 had an association with the pathways that are involved in immune function, especially in the chemokine signaling pathway, cytokine-cytokine receptor interaction, and T cell receptor signaling pathway. Among them, CCR7 was related to the T cell receptor signaling pathway in ACC, BRCA, CESC, CHOL, HNSC, KIRP, LAML, LGG, LIHC, LUAD, OV, PAAD, PCPG, SARC, SKCM, THCA, UCEC, UCS, and UVM; associated with chemokine signaling pathway in BRCA, CHOL, GBM, KICH, KIRC, KIRP, LGG, LIHC, LUSC, MESO, OV, PAAD, PCPG, PRAD, READ, SKCM, THCA, UCEC, and UCS; and related to cytokine-cytokine receptor interaction in BRCA, CHOL, GBM, HNSC, KICH, KIRC, KIRP, LAML, LGG, LIHC, LUAD, OV, PCPG, PRAD, SKCM, STAD, THCA, UCEC, UCS, and UVM.

Gene interaction analysis of CCR7

GeneMANIA is a user-friendly platform for us to evaluate the gene-gene interaction for CCR7 and the correlated genes. In Fig. 10, the results indicated that 20 genes were closely related to CCR7, and among them, 10 genes, including CCL19, CCL21, CCR10, CXCR5, CCR2, CCR1, CCL18, CCL26, CCR6, and CCL20, exert the biological functions that are related to the chemokine family, especially for CCL19, which showed the most significant association with CCR7. Additionally, it was revealed that the functions of CCR7 and its related genes had a close correlation to the regulation of cytosolic calcium ion concentration and cellular calcium ion homeostasis; besides, CCR7 and the members of chemokine family and correlated receptors showed associations with many immune functions, including lymphocyte migration, leukocyte chemotaxis, cell chemotaxis, and leukocyte migration.

Single-cell functional analysis of CCR7

Through CancerSEA, we investigated the function of CCR7 at the single-cell level in different cancers. It was revealed that CCR7 was negatively associated with DNA repair and invasion in GBM. In ALL, CCR7 was positive with differentiation and inflammation, and negative with proliferation. In BRCA, CCR7 was positive with differentiation and inflammation. CCR7 was negative with apoptosis, DNA damage, DNA repair, invasion, metastasis, and quiescence (Fig. 11).

DNA methylation analysis of CCR7

We utilized the UALCAN database to explore promoter methylation levels of CCR7 among two groups: “Normal” and “Patients with primary tumor”. The Y axis “beta value” reveals the promoter methylation level ranging from 0 (unmethylated) to 1 (fully methylated) and the beta value cutoffs can demonstrate hypomethylation (beta-value: 0.3–0.25) and hypermethylation (beta-value: 0.7–0.5). It was indicated that the promoter methylation level of CCR7 was higher in CESC, CHOL, ESCA, KIRP, PCPG, PRAD, STAD, THCA, THYM, and UCEC than those in normal groups (Fig. 12A). In Fig. 12B, CCR7 had 11 methylation probes, including cg13504059, cg07388018, cg17067993, cg07248223, cg16047279, cg26960939, cg04571513, cg11729107, cg07479709, cg22994634, cg23663547. Furthermore, we used the MethSurv web tool to obtain the association between methylation probes and patients’ prognosis (Table 2).

Gene mutation analysis of CCR7

Thanks to the cBioPortal website, we assessed the mutation of the CCR7 gene through “TCGA PanCancer Atlas” which contains 10953 samples. Fig. 13A showed that amplification is the most common in all types of mutation, especially in esophageal adenocarcinoma, STAD, BRCA, and UCS, which have high occurrence rates of 9.89%, 7.73%, 3.87%, and 3.51%, respectively. Fig. 13B showed that Amplification, missense mutation, and deep deletion are the main types of genetic alteration of the CCR7 gene. Furthermore, the mutation types, numbers, and sites of the CCR7 gene were shown in Supplementary Fig. 7A,which indicated that missense mutation is the most common type of genetic alteration. Supplementary Fig. 7B revealedthat diploid and gain function were the most frequent putative copy-number alteration of the CCR7 gene. In addition, Supplementary Fig. 7C showedthat genetic alteration of ALOX12P1, TOP2A, TNS4, IGFBP4, RAR4, ERBB2, CDC6, WIPF2, IKZF3, and SMARCE1 had higher frequency in the altered group than in the unaltered group.

TMB and MSI correlation analysis

We further investigated the correlation between CCR7 expression and TMB and MSI. In Supplementary Fig. 8A, CCR7 expression was negatively associated with TMB in DLBC, ESCA, HNSC, KIRC, KIRP, LIHC, LUSC, OV, SKCM, STAD, and TGCT. In addition, CCR7 expression was positively related to MSI in LGG and UCEC. For ACC, BLCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, MESO, PAAD, PRAD, READ, STAD, TGCT, THCA, and THYM, the expression of CCR7 was negatively correlated with MSI (Supplementary Fig. 8B).

Recently, pan-cancer analysis has been a significant method to find valuable biomarkers for cancer patients’ diagnosis, prognosis, and therapeutics. In our present study, we conducted differential expression analysis, clinical analysis, TME analysis, immune correlation analysis, and gene-related analysis to explore the application of CCR7, which indicated that CCR7 has a strong association with many types of cancer.

We first compared the differential expression between normal tissues and tumor tissues and found that the CCR7 expression level was higher in blood and spleen than in other normal tissues. In TGCA, the expression level of CCR7 was upregulated in DLBC and showed a significant difference between 10 types of cancer and normal tissue, including BRCA, COAD, HNSC, KICH, KIRC, KIRP, LUSC, PCPG, READ, and UCEC. Moreover, for BRCA, HNSC, KIRC, KIRP, PCPG, and UCEC, the CCR7 expression level was increased in these six tumors. In comparison, a lower level of CCR7 was observed in four types of cancer, including COAD, KICH, LUSC, and READ. Through the TIMER database, it was revealed that BRCA, COAD, HNSC, KICH, KIRC, KIRP, LUSC, PCPG, PRAD, READ, and THCA showed a significant difference between tumor tissues and paired normal tissues. Previous research has shown that the expression of CCR7 was higher in ESCA, HNSC, NSCLC, PDAC, gastric cancer, breast cancer, and diffuse large B-cell lymphoma (Ishida et al., 2009, Arigami et al., 2009, Takanami, 2003, Sperveslage et al., 2012, Mburu et al., 2012, Mohammed et al., 2021, Du et al., 2019). Overexpression of CCR7 can further lead to poor prognosis in patients (Du et al., 2019, Takanami, 2003, Mburu et al., 2012, Xiong et al., 2017). These studies were consistent with our research and confirmed that CCR7 can play an important role in tumorigenesis. However, due to the lack of paired normal tissues, we have not successfully analyzed the differential expression in ACC, DLBC, LAML, LGG, MESO, OV, SKCM, TGCT, UCS, and UVM. Moreover, the immunohistochemical and immunofluorescence images from the HPA database further supplied more information on CCR7 expression in tissues and subcellular localization, which revealed that it is useful to pay more attention to and explore the differentiation in these tumors.

We utilized the information on tumor staging to finish the differential expression analysis and indicated that BRCA, COAD, HNSC, LIHC, LUAD, SKCM, TGCT, and THCA showed a discrepancy in different stages. Besides, the AUC of the ROC curve was used to reveal the diagnostic value of CCR7. At 1 year, CCR7 showed high diagnostic accuracy (AUC: 0.9–1.0) in KICH and showed relative (AUC: 0.7–0.9) or low diagnostic accuracy (AUC: 0.5–0.7) in 10 types of cancers. At 3 years and 5 years, CCR7 revealed relative or low diagnostic accuracy in 10 and 14 types of cancers respectively. Especially for DLBC, GBM, KIRP, LAML, LGG, and TGCT, the AUC of the ROC curve all showed diagnostic values at 1, 3, and 5 years. We further confirmed the prognostic value of CCR7 in pan-cancer. The Kaplan–Meier analysis and Cox regression analysis were carried out to investigate OS, DSS, DFS, and PFS. In combination with these four indications, we found that CCR7 expression showed a good prognosis in ACC, BRCA, CESC, CHOL, HNSC, LIHC, LUAD, OV, SARC, SKCM, and UCEC, and a poor prognosis in GBM, KIRP, LGG, STAD, and UVM. Based on the abovementioned information, CCR7 can be a potential biomarker with diagnostic and prognostic value to be used for the early detection of cancer. However, Mohammed et al. found that the expression of CCR7 in breast cancer is higher in the metastatic group compared to the non-metastatic and control group (Mohammed et al., 2021), which may indicate that CCR7 acted as a risk factor in breast cancer patients. It is potential for us to further explore the real effect of CCR7 on patients’ prognosis. Additionally, it is inevitable that we cannot exclude death that was caused by non-cancer factors when we made use of OS to assess the survival condition; therefore, we might not analyze the impact of tumors and the response to treatment in patients accurately. In the future, it is more valuable to adopt DFS and PFS to investigate the association between a certain factor and patients’ condition.

Research has demonstrated that the importance of TME in cancer development has increased in recent years. Different components in TME can induce and promote cancer cell proliferation, growth, and metastasis, which can further influence the efficacy of cancer therapy (Jin and Jin, 2020). Quail et al. also indicated that stromal cells in TME can act as a target for the treatment of cancer to decrease the occurrence of drug resistance (Quail and Joyce, 2013). Significantly, our result of the TME correlation analysis showed that stromal cells and immune cells were positively associated with the expression of CCR7 in most types of cancers. Moreover, we further investigated the relationship between immune cell infiltration and CCR7 expression level in TCGA. It was revealed that the expression of CCR7 showed a positive correlation with M1 macrophages in ACC, CD8+ T cells in ACC, naïve B cells in BLCA, M1 macrophages in LGG, naïve B cells in MESO, naïve B cells in PAAD, regulatory T cells in PCPG, and naïve B cells in THYM, and a negative correlation with activated dendritic cells in ACC, eosinophils in ACC, monocytes in ACC, activated NK cells in CHOL, activated NK cells in KICH, M2 macrophages in SKCM, M2 macrophages in THCA, and M0 macrophages in UCS. Through the TIMER database, it was revealed that the expression of CCR7 was positively correlated with immune cells in most types of cancer. In addition, our studies also showed that CCR7 gene expression was positively correlated with most types of immune-related genes.

For GSEA analysis, the results indicated that CCR7 showed a significant correlation with the immune-related pathways. Among them, the chemokine signaling pathway, cytokine-cytokine receptor interaction, and T cell receptor signaling pathway were the most common signaling pathway of CCR7 in pan-cancer. Through the GeneMANIA database, the biological function of 20 genes was associated with CCR7, which included the regulation of cytosolic calcium ion concentration, cellular calcium ion homeostasis, lymphocyte migration, leukocyte chemotaxis, cell chemotaxis, leukocyte migration, and response to tumor necrosis factor. Moreover, through single-cell functional analysis, we showed that CCR7 was negative with apoptosis, DNA damage, DNA repair, invasion, metastasis, and proliferation in some cancers.

DNA methylation, as one of the most extensive epigenetic modifications, can regulate gene expression and cancer progression. Research indicated that DNA methylation plays a significant role in the development of tumors, which involves the modulation of DNA repair and cell cycle and further affect cancer cell proliferation and apoptosis (Pfeifer, 2018). Therefore, we utilized the UALCAN database to find that promoter methylation level of CCR7 was increased in CESC, CHOL, ESCA, KIRP, PCPG, PRAD, STAD, THCA, THYM, and UCEC when comparing to the normal groups, and decreased in BLCA, BRCA, COAD, GBM, HNSC, KIRC, LIHC, LUAD, LUSC, PAAD, READ, and SARC. Moreover, we further analyzed the chromosomal distribution of methylation probes related to CCR7 and explore the relationship between CCR7 methylated CpG and survival, which showed the significance of the methylation level of CCR7 in patients’ prognosis.

Additionally, the result of the cBioPortal platform revealed that CCR7 mutation can occur in most types of tumors, and esophageal adenocarcinoma and STAD had the highest incidence, which suggested that we can further explore the association between CCR7 gene mutation with tumors in the digestive system. Amplification is the most common type among the different mutations; moreover, genetic alteration of the CCR7 gene can co-occur with ALOX12P1, TOP2A, TNS4, IGFBP4, RAR4, ERBB2, CDC6, WIPF2, IKZF3, and SMARCE1 with higher frequency. According to the results of gene mutation and GSEA analysis, we can pay more attention to the influence on the biological function of genetic alteration.

It has been reported that TMB and MSI as biomarkers can be used to predict the effect of cancer immunotherapy and play an impact on patients’ prognosis (Luchini et al., 2019, Schrock et al., 2019); and multiple studies have shown that high level of TMB and MSI can cause long term immunotherapy-related response and ultimately contribute to better prognosis of patients (Filipovic et al., 2020, Reck et al., 2019, Zhao et al., 2019). Our research indicated that the expression of CCR7 was negatively associated with TMB in DLBC, ESCA, HNSC, KIRC, KIRP, LIHC, LUSC, OV, SKCM, STAD, and TGCT, and negatively associated with MSI in ACC, BLCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, MESO, PAAD, PRAD, READ, STAD, TGCT, THCA, and THYM, which suggested that patients with the low expression level of CCR7 can obtain a better response of cancer immunotherapy. Besides, CCR7 expression was positively correlated to MSI in LGG and UCEC.

We have conducted many analyses on CCR7, including differential expression analysis, clinical analysis, TME correlation analysis, immune cell infiltration and immune-related gene analyses, GSEA analysis, DNA mutation and methylation analysis, and TMB and MSI correlation analysis. We can conclude that CCR7 expression was differential between tumor tissues and paired normal tissue, which further indicated the diagnostic and prognostic value. Moreover, TME and immune-related analyses showed that the expression of CCR7 was significantly associated with stromal and immune-related expression. Collectively, CCR7 may act as biomarkers to predict patients’ prognosis and therapeutic effects. Our research will be useful to better understand the potential of CCR7 and further guide more researchers to explore the mechanism of CCR7 in cancer progression.

In summary, CCR7 expression showed a significant association with clinicopathology, which was useful for the diagnosis and prognosis in patients. Moreover, the expression of CCR7 was strongly correlated with immune cells, immune-related genes, and immunological processes, indicating that CCR7 has the potential to act as a biomarker and target for cancer immunotherapy. Our study has laid a good foundation for further validation through clinical trials in vitro or in vivo to reveal the biological mechanisms in pan-cancer.

Data availability: The data that underpin the findings of this study can be obtained from the corresponding author upon request.

Acknowledgments: We express our gratitude to the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), The Human Protein Atlas (HPA), TIMER, TISIDB, GSEA, GeneMANIA, The Cancer single-cell state Atlas (CancerSEA), UALCAN, Shiny Methylation Analysis Resource Tool (SMART), MethSurv, and cBioPortal database for providing public data.

Funding: This work was supported by the Fund of the National Natural Science Foundation of China (No. 41966006).

Ethics declarations: The authors declare no competing interests.

Author contribution: Conceptualization: Jianrui Chen, Haiyue Huang, Zhenzhen Xie; Methodology: Jianrui Chen, Haiyue Huang, Zhenzhen Xie; Formal analysis and investigation: Jianrui Chen, Haiyue Huang; Writing—original draft: Jianrui Chen, Haiyue Huang, Qingfang Zhang, Yuxin Zhu; Writing—review and editing: Jianrui Chen, Haiyue Huang, Zhenzhen Xie; Figure editing: Jianrui Chen. All authors contributed to the article and approved the submitted version.

Conflict of interest: The authors declare that they have no conflicts of interest.

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Table 1. The full name and corresponding abbreviation of 33 types of cancer

Cancer type	Abbreviation
adrenocortical carcinoma	ACC
bladder urothelial carcinoma	BLCA
breast invasive carcinoma	BRCA
cervical squamous cell carcinoma and endocervical adenocarcinoma	CESC
cholangiocarcinoma	CHOL
colon adenocarcinoma	COAD
lymphoid neoplasm diffuse large B-cell lymphoma	DLBC
esophageal carcinoma	ESCA
glioblastoma multiforme	GBM
head and neck squamous cell carcinoma	HNSC
kidney chromophobe	KICH
kidney renal clear cell carcinoma	KIRC
kidney renal papillary cell carcinoma	KIRP
acute myeloid leukemia	LAML
brain lower grade glioma	LGG
liver hepatocellular carcinoma	LIHC
lung adenocarcinoma	LUAD
lung squamous cell carcinoma	LUSC
mesothelioma	MESO
ovarian serous cystadenocarcinoma	OV
pancreatic adenocarcinoma	PAAD
pheochromocytoma and paraganglioma	PCPG
prostate adenocarcinoma	PRAD
rectum adenocarcinoma	READ
sarcoma	SARC
skin cutaneous melanoma	SKCM
stomach adenocarcinoma	STAD
testicular germ cell tumors	TGCT
thyroid carcinoma	THCA
thymoma	THYM
uterine corpus endometrial carcinoma	UCEC
uterine carcinosarcoma	UCS
uveal melanoma	UVM

Table 2. Association between CCR7 methylated CpG and survival

Cancer type	Island region	Gene region	CpG site	HR	P value
ACC	Open sea	Body	cg07248223	2.933	0.0043
			cg04571513	3.259	0.0019
			cg11729107	0.372	0.04
			cg16047279	3.482	0.0011
			cg26960939	4.956	3.6e^-5
		TSS1500	cg13504059	2.554	0.013
BLCA	Open sea	Body	cg07479709	1.615	0.0028
			cg16047279	1.399	0.03
			cg26960939	1.721	0.0024
		TSS200	cg07388018	1.387	0.029
		TSS1500	cg13504059	1.488	0.012
		5'UTR; 1stExon	cg17067993	1.455	0.012
		3'UTR	cg23663547	0.668	0.028
BRCA	Open sea	Body	cg04571513	0.652	0.032
			cg07479709	1.671	0.031
		3'UTR	cg23663547	0.6	0.021
CESC	Open sea	Body	cg04571513	1.846	0.013
			cg11729107	0.525	0.0069
			cg22994634	0.359	8.3e^-5
		TSS1500	cg13504059	1.899	0.026
		3'UTR	cg23663547	1.852	0.012
COAD	Open sea	Body	cg16047279	2.098	0.015
ESCA	Open sea	Body	cg11729107	0.595	0.025
GBM	Open sea	Body	cg07248223	0.495	0.0047
			cg22994634	1.829	0.016
		5'UTR; 1stExon	cg17067993	0.607	0.02
		3'UTR	cg23663547	0.602	0.035
HNSC	Open sea	Body	cg07479709	1.32	0.046
		3'UTR	cg23663547	1.553	0.0033
KICH	Open sea	Body	cg07248223	0	0.015
			cg26960939	0	0.014
KIRC	Open sea	Body	cg07248223	1.861	0.011
			cg07479709	0.661	0.039
			cg11729107	0.54	0.0044
			cg22994634	2.088	0.0066
			cg26960939	2.188	0.0022
		TSS200	cg07388018	0.452	0.0015
		TSS1500	cg13504059	0.469	0.00042
KIRP	Open sea	TSS200	cg07388018	0.414	0.04
		TSS1500	cg13504059	0.311	0.0029
LAML	Open sea	Body	cg07248223	1.68	0.0063
			cg16047279	1.816	0.002
			cg26960939	1.849	0.002
		TSS1500	cg13504059	1.78	0.0066
		3'UTR	cg23663547	1.778	0.0069
LGG	Open sea	Body	cg04571513	0.593	0.0063
			cg07248223	0.471	0.00014
			cg07479709	0.556	0.0035
			cg11729107	1.497	0.032
			cg16047279	0.298	4.8e^-10
			cg26960939	0.33	3.2e^-9
		TSS200	cg07388018	0.23	3.2e^-14
		TSS1500	cg13504059	0.19	0
		5'UTR; 1stExon	cg17067993	0.398	3.6e^-6
		3'UTR	cg23663547	0.265	4.1e^-11
LIHC	Open sea	Body	cg04571513	1.729	0.004
			cg07479709	1.532	0.015
			cg11729107	1.499	0.022
			cg22994634	1.525	0.017
		3'UTR	cg23663547	1.451	0.037
LUAD	Open sea	Body	cg16047279	1.469	0.049
		TSS1500	cg13504059	1.375	0.046
		3'UTR	cg23663547	1.849	0.0014
LUSC	Open sea	Body	cg04571513	0.676	0.031
			cg16047279	0.707	0.034
			cg26960939	0.695	0.026
MESO	Open sea	TSS1500	cg13504059	0.599	0.034
		5'UTR; 1stExon	cg17067993	0.518	0.0069
PAAD	Open sea	Body	cg04571513	1.501	0.049
			cg07248223	0.57	0.016
			cg07479709	0.659	0.043
READ	Open sea	Body	cg04571513	0.286	0.017
		TSS200	cg07388018	3.696	0.043
		TSS1500	cg13504059	3.201	0.021
SARC	Open sea	Body	cg07479709	0.647	0.043
			cg16047279	0.599	0.025
		5'UTR; 1stExon	cg17067993	0.593	0.016
		3'UTR	cg23663547	1.529	0.039
SKCM	Open sea	Body	cg07248223	1.361	0.022
			cg07479709	1.981	0.00011
		TSS200	cg07388018	1.554	0.0017
		TSS1500	cg13504059	1.755	3.7e^-5
		5'UTR; 1stExon	cg17067993	1.645	0.0011
		3'UTR	cg23663547	0.737	0.046
STAD	Open sea	Body	cg11729107	0.65	0.028
UCEC	Open sea	Body	cg04571513	1.991	0.0046
			cg07479709	1.899	0.017
		3'UTR	cg23663547	1.913	0.021
UCS	Open sea	Body	cg11729107	2.603	0.014
			cg26960939	0.475	0.043
UVM	Open sea	Body	cg04571513	0.246	0.0026
			cg07248223	0.099	2.5e^-6
			cg16047279	0.071	7.2e^-6
			cg26960939	0.201	0.00051
		TSS200	cg07388018	0.219	0.00046
		TSS1500	cg13504059	0.102	0.0014
		5'UTR; 1stExon	cg17067993	0.206	0.00023

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Comprehensive analysis of CCR7 as a biomarker for the diagnosis, prognosis, and immunity in pan-cancer

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