Comprehensive Analysis of Prognosis and Immune Function of CD70-CD27 Signaling Axis in Pan-Cancer

doi:10.21203/rs.3.rs-1949691/v1

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Comprehensive Analysis of Prognosis and Immune Function of CD70-CD27 Signaling Axis in Pan-Cancer

https://doi.org/10.21203/rs.3.rs-1949691/v1

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The immune checkpoint molecule CD70 and its receptor CD27 constitute the signal transduction axis, which is abnormally expressed in many solid tumors and is crucial for T cell co-stimulation and immune escape. Tumor cells regulate the expression of CD27 by expressing CD70 in tumor microenvironment and promote immune escape.The discovery of the immunosuppressive effect of the CD70-CD27 signaling axis on tumor cells introduces a new anti-tumor immunotherapy - CD70. Although current research evidence suggests a link between CD70 and tumors, no pan-cancer analysis is available. Using the Cancer Genome Atlas and Gene Expression Omnibus datasets, we first explored the potential carcinogenic role of the CD70-CD27 signaling axis in human malignancies. CD70 expression is up-regulated in most cancers and has an obvious correlation with the prognosis of tumor patients. The expression of CD70 and CD27 is associated with the level of regulatory T cells (Tregs) infiltration. In addition, T cell receptor signaling pathways, PI3K-Akt, NF-κB, and TNF signaling pathways are also involved in CD70-mediated immune escape. CD70 mainly regulates tumor immune escape by regulating T cell-mediated tumor killing, while Tregs may be its main T cell subset. Our first pan-cancer study provides a relatively comprehensive understanding of the carcinogenic role of the CD70-CD27 signaling axis in different tumors.

CD70

CD27

Pan-Cancer

Immune escape

Immunotherapy

Regulatory T cells

The mechanism of tumor genesis and development is extremely complex [1]. Thus, extensive target gene/oncogene analysis is important for studying the relationship between target gene/oncogene and clinical prognosis, the potential of targeted therapy, and the underlying molecular mechanism. Currently, analyzing gene transcriptome data sets from publicly accessible tumor databases such as Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) is an important method of performing single-gene or multi-gene extensive cancer analysis [2–4].

Over the past few decades, advances in tumor immunology knowledge have led to the identification of immune checkpoints and the reinvigoration of cancer immunotherapy [5, 6]. Immune checkpoint inhibitors (ICIs) with strong immunotherapeutic potential in solid tumors have been shown to kill tumor cells by targeting immune checkpoints such as anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), anti-programmed death-1 receptor (PD-1), and anti-programmed death ligand-1 (PDL-1) [7]. Although immune checkpoint inhibitors have shown promising results in targeted tumor therapy, drug resistance remains one of the major challenges that immune checkpoint inhibitors face [8, 9]. Because in the microenvironment of solid tumors, the high response of immunosuppressive cells, including myeloid-derived suppressor cells, tumor-associated macrophages, and regulatory T cells (Tregs), to treatment is not guaranteed [10]. Concurrently, immunosuppressive components of the tumor microenvironment (TME) may cause T cell failure and immune checkpoint protein dysfunction, leading to increased resistance to ICIs [11]. According to research, tumor resistance to ICIs is caused by failure of antigen presentation in TME, impaired immune microenvironment, changes in immune checkpoint molecules, and immunosuppressive cells [12].

Given this, developing new immunotherapy targets may be a more effective and promising therapeutic strategy. Thus, the CD70-CD27 axis, which belongs to the tumor necrosis factor (TNF) superfamily, has emerged as an appealing target for tumor development[13, 14]. CD70 and CD27 are TNF ligand and receptor family ligand-receptor pairs that are critical for T cell co-stimulation [15]. CD27 is a costimulatory molecule on T cells that induces intracellular signaling-mediated cell activation, proliferation, effector function, and cell survival by binding to the ligand CD70 [16]. CD70, the only ligand of CD27, is a tightly regulated transmembrane glycoprotein and a member of the TNF superfamily. CD70 is expressed in B lymphocytes, T lymphocytes, and antigen-presenting cells (APCs) but not in non-lymphocyte tissues [17]. CD70 is abnormally expressed in malignant tumor cells, promoting tumor progression and immune escape via TME [5, 13]. Varlilumab, a CD70-CD27-mediated monoclonal antibody, has recently undergone extensive phase I/II clinical trials in vivo and in vitro for a variety of blood and solid tumor types, including Hodgkin’s lymphoma, non-Hodgkin’s lymphoma, glioblastoma, melanoma, renal cell carcinoma, prostatic adenocarcinoma, colorectal cancer, and ovarian cancer [16, 18]. However, based on clinical big data, there is no evidence of a relationship between the CD70-CD27 axis and various tumor types.

Our study is the first to analyze the CD70-CD27 axis using TCGA, GEO, and other databases. We also included gene expression, survival status, immune infiltration, and related cellular pathways to explore the potential molecular mechanisms of the CD70-CD27 axis in the pathogenesis, clinical prognosis, and immune escape of different cancers.

2.1. Gene expression analysis:

The Human Protein Atlas (HPA) database (http://www.proteinatlas.org/humanproteome/pathology) was used to obtain the expression of CD70-CD27 in different cells and tissues under physiological conditions. The detailed information about the low specificity of CD70-CD27 was stated as “Normalized expression (NX) ≥ 1 in at least one tissue/region/cell type but not elevated in any tissue/region/cell type,” which can be found at http://proteinatlas.org//search/CD70 and http://proteinatlas.org//search/CD27.

UALCAN (http://ualcan.path.uab.edu/): UALCAN is a clinical database that analyzes gene expression among tumor subgroups, survival, and cancer genome-based mapping (TCGA) of level 3 RNA-sequencing data across 31 cancer types [19]. In this study, UALCAN was used to examine the distinct expression levels between tumor and healthy tissues. The dataset from the TCGA database were downloaded and used for analyzing the expression of CD70 in pan-cancer. P-values were calculated using the Student’s t-test, with a cut-off of 0.05.

GEPIA (http://gepia.cancer-pku.cn/): GEPIA is a web server for cancer and normal gene expression profiling and interaction analysis [20]. In this study, the ‘Multiple Gene Comparison’ module was used to compare the expression of CD70 and CD27. The relationship between CD70 and CD27 expression and tumour stages was analyzed using the ‘Single Gene Analysis’ and ‘Pathological Stage Plot’ module. we obtained violin plots of CD70 and CD27 expression in different pathological stages (stage I, stage II, stage III, and stage IV) of all TCGA tumors. The log2 (Transcripts per million (TPM) +1) transformed expression data were used for the box or violin plots. P-values were calculated using the Student’s t-test, with a cut-off of 0.05.

We entered CD70 and CD27 into the “Gene_DE” module of the tumor immune estimation resource, version 2 (TIMER2) web (http://timer.cistrome.org/), and observed the expression difference of CD70 and CD27 between tumor and adjacent normal tissues for the different tumors or specific tumor subtypes of the TCGA project.

2.2. Survival prognosis analysis:

The Kaplan–Meier (K-M) plotter can be used to assess the effects of approximately 54,000 genes on survival across 21 cancer types [21]. In this study, K-M curves were used to analyze the relationship between gene mutations in CD70 and overall survival (OS) in patients with pan-cancer. The log-rank test was used to evaluate significant differences in the survival curves. A P-value of < 0.05 was considered statistically significant. Patients were divided into the high- and low-expression groups based on the median values of mRNA expression levels, and the results were validated using K-M survival curves based on the hazard ratio (HR) with 95% confidence intervals (CIs) and log-rank P-values. Patients were divided into groups to determine the best cut-off values, and survival analysis was performed. A P-value of < 0.05 was considered statistically significant.

We used the “Survival Map” module of GEPIA2[22] to obtain the OS and DFS (disease-free survival) significance map data for CD70 and CD27 across all TCGA tumors. Cut-off-high (50%) and cut-off-low (50%) values were used as expression thresholds to split the high-expression and low-expression cohorts. The log-rank test was used in the hypothesis test, and the survival plots were obtained using the “Survival Analysis” module of GEPIA2.

2.3. Immune infiltration analysis:

We used the “Immune-Gene” module of the TIMER2 web server to investigate the association between CD70-CD27 expression and immune infiltrates in all TCGA tumors. Treg cells were selected as immune cells. Immune infiltration was estimated using the TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, and XCELL algorithms. The purity-adjusted Spearman’s rank correlation test was used to calculate the P-values and partial correlation (cor) values obtained via the purity-adjusted Spearman’s rank correlation test. A heatmap and a scatter plot represented the data.

2.4. CD70-related gene enrichment analysis:

We first searched the STRING website (https://string-db.org/) with a single protein name (“CD70”), and organism (“Homo sapiens”). Following that, we set the following key parameters: the minimum required interaction score [“Low confidence (0.150)”], the meaning of network edges (“evidence”), the maximum number of interactors to show (“no more than 50 interactors” in the first shell), and active interaction sources (“experiments”). Finally, the available experimentally determined CD70-binding proteins were obtained.

We used the “Similar Gene Detection” module of GEPIA2 to identify the top 100 CD70-correlated targeting genes using datasets from all TCGA tumors and normal tissues. We also used the “correlation analysis” module of GEPIA2 to conduct a pairwise gene Pearson correlation analysis of CD70 and selected genes. For the dot plot, the log2 TPM was used. The P-value and the correlation coefficient (R) were shown. Moreover, we used the TIMER2 “Gene_Corr” module to provide the heatmap data for the selected genes, which included, the cor, and P-value in the purity-adjusted Spearman’s rank correlation test.

We used jvenn, an interactive Venn diagram viewer [23], to conduct an intersection analysis to compare the CD70-binding and interacted genes. We also combined the two sets of data to perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. In brief, we uploaded the gene lists to the Database for Annotation Visualization and Integrated Discovery (DAVID) with the selected identifier (“OFFICIAL_GENE_SYMBOL”) and species (“Homo sapiens”) settings and obtained the functional annotation chart data. Finally, the enriched pathways were visualized using the R packages “tidyr” (https://cran.r-project.org/web/packages/tidyr/index.html) and “ggplot2” (https://cran.r-project.org/web/packages/ggplot2/index.html). Furthermore, we used the R package “clusterProfiler” (http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html) R package to conduct Gene Ontology (GO) enrichment analysis. The data for biological process (BP), cellular component (CC), and molecular function (MF) were visualized as cnetplots, using the cnetplot function (circular = F, colorEdge = T, and node_label = T). The R language software [R-3.6.3, 64-bit] (https://www.r-project.org/) was used in this analysis. Two-tailed P-value < .05 was considered statistically significant.

2.5. qRT-PCR assay:

RNA extraction and qRT-PCR: The tumor and para-cancerous tissues were treated with the TRIzol reagent (Invitrogen, USA) for 10 mins before centrifugation at 12,000 g at 4 °C for 15 min. Following that, the suppressed RNA was collected and mixed with isopropanol for isolation. After obtaining RNA, its purity and concentration were determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher, USA). A high-capacity cDNA reverse transcription kit (Life Tec, USA) was used to synthesize the cDNA. On a LightCycler 96 system (Roche, USA), qRT-PCR was performed using a 2X Universal SYBR Green Fast qPCR mix (ABclonal, China). All experiments were performed in triplicate.

2.6. Western blot:

The proteins separated on gels after electrophoresis were transferred to poly (vinylidene fluoride) membranes and subsequently incubated with primary and secondary antibodies. The following primary antibodies were incubated for 12 h at 4 °C: GAPDH (1:10000 dilution, ABclonal), CD70 (1:1000 dilution, ABclonal, China, Catalog No.: A2032). The secondary antibody (1:5000 dilution, ABclonal) was incubated for 2 h at room temperature. Immunoreactive bands were developed using an enhanced chemiluminescence detection kit (Genview Scientific Inc., USA). All experiments were performed in triplicate.

2.7. Immunohistochemistry:

Paracancer and tumor tissue samples were treated with formalin embedding. Each tissue was cut into 4 μm thick slices and laid flat on a slide. After decolorization, the slices were incubated with 3% hydrogen peroxide for 15 min. Heat-induced epitopes were searched for in a sodium citrate buffer (10 mM sodium citrate, 0.05% Tink 20, pH 6.0) at 96 °C for 30 min. After immersing in phosphate-buffered saline (PBS) for 3 times (5 min each), rabbit anti-human CD70 antibody (1:100 dilution, ABclonal, China, Catalog No.: A2032) were incubated for 2 h. Sections were incubated with a few drops of A solution (ChemMateTMEnVision+/HRP) for 30 minutes, followed by diamino-benzidine staining and hematoxylin restaining. The slices were then dehydrated, soaked in xylene, and fixed with a neutral resin.

2.8. Cell line and culture:

The HCC-LM3 cell line (Institute of Biochemistry and Cell Biology, CAS) was cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (Invitrogen Co., Carlsbad, CA, USA). Cells were maintained at 37 °C in a water-saturated atmosphere with 5% CO₂/95% air.

2.9. Cell transfection assay:

HCC-LM3 cells were seeded into 6-well plates (3 × 10⁵ cells/well) and 96-well plates (1 × 10⁴ cells/well). The plasmid transfection was performed using 2.5 μg/well of CD70 and the control group (Genechem, Shanghai, China) in Lipofectamine 3000™ (Invitrogen, Thermo Fisher Scientific Inc., UK) and Opti-MEMMedium (Gibco, Thermo Fisher Scientific Inc., 31985-062, UK) following the manufacturer’s protocol for 48 h.

2.10. T-cell–mediated tumor cell killing assay:

To verify the effect of primary T cells on cancer cells, we co-cultured primary T cells with cancer cells. Human peripheral blood mononuclear cells (PBMCs) were purchased from TPCS (Milestone Biotechnologies, Shanghai, China). To activate T cells, a total of 3×10⁶/mL T cells were treated with 1 µg/mL of CD3 monoclonal antibody (OKT3) (Cat# 16-0037-81, Thermo, Shanghai, China) and 50 ng/mL of IL-2 (Cat# PHC0026, Thermo, Shanghai, China). After 48 h, activated T cells were maintained in the previously described culture medium at a density of 1 million cells per milliliter of culture medium and changed the fresh medium every 2–3 days. HCC-LM3 cells were co-cultured with activated CD45+CD3+ T cells isolated from PBMCs. The effector: target ratio was 1:1. After 4 days of co-culture of tumor cells and T cells on 6-well plates or 96-well plates, T cells were washed twice with PBS to remove T cells, and the survival tumor cells were fixed and stained with crystal violet solution or added with cell counting kit-8 (CCK-8) reagent to measure tumor cell viability.

2.11. CCK-8 assay:

After the T-cell mediated tumor cell killing experiment. Cells were plated in 96-well plates with 3 replicates per experimental group. Cells were cultured for 24 h at 37 °C in a humidified incubator with 5% CO₂. A microplate spectrophotometer was used to determine the optical density values at 450 nm using a CCK-8 kit (GeneView, USA) with (Thermo Fisher, USA). All data were normalized to control wells that contained no cells and are presented as the mean ± SD.

2.12. Clone formation assay:

Following the T-cell mediated tumor cell killing experiment. The cells were washed with PBS, fixed with 4% paraformaldehyde, and stained with crystal violet staining solution (Beyotime, China) before being used in 6-well plate experiment. A digital camera was used to photograph the colonies.

2.13. Ethics approval and consent to participate:

This study was approved by the Medical Ethics Committee of Zhongnan Hospital of Wuhan University. All subjects provided informed consent and all experiments were conducted following the study protocol.

2.14. Statistical analysis:

Statistical analyses included one-way ANOVA calculations and the unpaired t-test. SPSS 24.0 software (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis. A P-value of < 0.05 was considered statistically significant.

1. CD70-CD27 signal axis gene expression analysis data

In this study, we aimed to investigate the role and mechanism of human CD70 (mRNA: NM_001252.5, protein: NP_001243.1) in carcinogenesis and immune escape. Phylogenetic tree data (Supplementary Figure 1) show the evolutionary relationship of CD70 protein in humans.

We first analyzed the expression patterns of CD70 and CD27 in different cells and non-tumor tissues. Using data from the HPA, GTEx, and functional annotation of mammalian genome 5 (FANTOM5)datasets, we found that CD70 was most expressed in the appendix, spleen, and tonsil, followed by the lymph node and thymus (Figure 1A). Meanwhile, CD27 expression was highest in the appendix, spleen, tonsil, lymph node, and thymus (Supplementary Figure 2A). CD70 and CD27 were expressed in all tissues studied, indicating low RNA tissue specificity. When we analyzed the expression of CD70 and CD27 in different blood cells from the HPA/Monaco/Schmiedel dataset, we discovered immune cell type specificity and enhanced expression of CD70 and CD27 in Treg immune cells (Figure 1B). This suggests that the target cells of the CD70-CD27 axis could be Tregs.

TIMER2 was used to analyze the expression of CD70 and CD27 in different TCGA tumors. As shown in Figure 1C–D, CD70 was associated with breast cancer (BRCA), cholangiocarcinoma (CHOL), colon cancer (COAD), esophageal cancer (ESCA), multiform glioblastoma, head and neck squamous cell carcinoma (HNSC), kidney chromophobe cell carcinoma (KICH), and kidney renal clear cell carcinoma (KIRC). CD70 expression was up-regulated in kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), stomach adenocarcinomas (STAD), thyroid cancer (THCA), and endometrial carcinoma (UCEC). Similarly, CD27 was up-regulated in tumor tissue (Supplementary Figure 2B–C).

We also observed the correlation between the expression of CD70 and CD27 and the pathological staging of cancer using the “pathological staging map” module of HEPIA2. As shown in Figure 2, the pathological staging expression of CD70 was statistically different in testicular germ cell tumor (TGCT), KIRC, KIRP, KICH, LUAD, and THCA. Concurrently, the pathological staging of CD27 was significantly different in LUAD, KIRC, STAD, and skin cutaneous melanoma (SKCM). In addition, we further analyzed the expression of CD70 in different tumor types and pathological stages using the TCGA database. As shown in Figure 3 and Supplementary Figure 3, CD70 expression was up-regulated to varying degrees in the diffuse tissues, and the expression varied across clinical stages.

2. Survival analysis data of CD70-CD27 signal axis

Cancer cases were divided into high and low expression groups based on CD70 and CD27 expression levels, and the correlation between CD70 and CD27 expression and prognosis of different tumor patients was studied using the TCGA and GEO datasets, respectively. As shown in Figure 4, in TCGA, up-regulation of CD70 expression was associated with poor overall survival (OS) outcomes in low-grade glioma (LGG) (P = 3 e-05), mesothelioma (P = 0.0076), and uveal melanoma (UM) (P = 0.013). It was associated with poor prognosis of disease-free survival (DFS) in COAD (P = 0.036) and LGG (P = 0.0076). In addition, up-regulated CD27 expression was associated with poor prognosis of OS for LGG (P = 0.012) and UVM (P = 0.0067), and with poor prognosis of DFS for LGG (P = 0.035). In addition, we used Kaplan-Meier analysis to analyze CD70 survival data and found that high CD70 expression was associated with poor OS in LUAD (P = 0.0082), lung squamous cell carcinoma (LUSC) (P = 0.0088), STAD (P = 0.023), and THYM (P = 0.0071) (Supplementary Figure 4). Low CD70 expression in UCEC was associated with poor OS (P = 0.00012) (Supplementary Figure 4).

3. Immune infiltration analysis data of CD70-CD27 signal axis

Tumor-infiltrating immune cells are crucial in TME and are linked to tumor immune escape [24]. Tumor tissue and its microenvironment change the host immune system in many ways, promoting tumor growth. One approach is to increase the number of tumor-infiltrating Tregs to achieve immune escape. Tregs are a potential therapeutic barrier and a cause of immunotherapy failure due to their persistence in the TME and immunosuppression [25]. Using the CIBERSORT, CIBERSOR-ABS, QUANTISEQ, and XCELL algorithms, we investigated the potential relationship between Treg invasion level and CD70-CD27 expression in different tumor tissues. The results showed that Treg infiltration was positively correlated with CD70 gene expression in BRCA, LIHC, LUAD, LUSC, OV, PRAD, and THCA (Figure 5). Treg infiltration levels were also positively correlated with CD27 gene expression, in BLCA, BRCA, HNSC, LUSC, and OV(Supplementary Figure 5). These findings suggest that CD70 may activate Tregs and enhance their immunosuppressive function through co-stimulation with CD27 in Tregs, thus promoting immune escape, tumor occurrence, and development.

4. Interactions and Correlations of Predicted Proteins with CD70-CD27 signal axis

We attempted to screen out genes targeting CD70-binding proteins and related CD70 expression for a series of pathway enrichment analyses to further study the molecular mechanism of the CD70 gene in tumorigenesis. A total of 50 CD70-binding proteins were obtained using the STRING tool and supported by experimental evidence (Supplementary Table 1). Figure 6A shows the network of interactions between these proteins. In addition, we used GEPIA2 to combine all of the TCGA expression data to identify the top 100 genes related to CD70 expression (Supplementary Table 1). Through the intersection analysis of the protein data of the above two groups, we found a common protein, tumor necrosis factor superfamily member 9 (TNFSF9) (Figure 6B). TNFRSF9 was first reported as a T cell inducer in 1989 [26]. TNFRSF9 is cross-linked to activated T cell receptors and can deliver costimulatory signals to T cells, resulting in T cell proliferation, survival, memory formation, enhanced cytotoxicity, and cytokine production [27]. Recent studies have found that activating TNFSF9 reverse signaling mediates tumor immune escape by forming a tumor suppressor microenvironment. TNFSF9 reverse signaling inhibition may have an antitumor effect by remodeling the TME [28]. Thus, this preferential proliferation of T cells makes TNFRSF9 a particularly attractive target for anticancer therapy. Currently, antibodies targeting TNFRSF9 are being used in preclinical studies, including urelumab (BMS-663513), which blocks TNFRSF9 interaction with TNFSF9 [29]. Therefore, CD70 and TNFSF9 are expected to become new immunotherapy targets.

In addition, when combined with the above data set analysis, CD70 expression level and IL-2 (R = 0.053), TNFSF9 (R = 0.38), CD86 (R = 0.38), CCR4 (R = 0.05), CD27 (R = 0.12), forkhead box protein P3 (FoxP3) (R = 0.041), and GAL3ST1 expression (R = 0.56) were positively correlated (Figure 6C). In the most detailed cancer types, the corresponding heatmap data also revealed a positive correlation between CD70 and the seven genes mentioned above (Figure 6D). These findings support the notion that CD70 promotes tumor immune escape via Tregs.

We combined these two data sets for GO and KEGG enrichment analysis. Among them, GO enrichment analysis showed that the tumor cell BP was primarily immune response, T cell stimulation, and T cell activation pathways (Supplementary Table 2), suggesting that the CD70-CD27 axis was primarily a T cell-mediated immune response process (Figure 7A). CC is primarily made up of plasma membrane, integral membrane components, and extracellular exosomes (Figure 7B, Supplementary Table 3). Furthermore, cytokine activity, tumor necrosis factor receptor binding, and tumor necrosis factor-activated receptor activity comprise MF (Figure 7C, Supplementary Table 4). KEGG enrichment analysis showed that the pi3K-Akt signaling pathway, the NF-κB signaling pathway, and the TNF signaling pathway were activated. In addition, the cytokine receptor interaction pathway is also involved in activating the CD70-CD27 signaling axis (Figure 7D, Supplementary Table 5).

5. CD70 expression was up-regulated in tumor tissues

According to an analysis of UALCAN and GEPIA databases, CD70 expression was up-regulated in tumor tissues. To further validate the expression of CD70 in tumor tissues, we collected surgical specimens (paracomcinoma tissue and tumor tissue) from patients with LIHC, KIRP, KIRC, ESCA, and cervical cancer (CESC). The clinical characteristics of the patients are shown in Supplementary Table 6. Immunohistochemical results showed that CD70 expression was up-regulated in LIHC, KIRP, KIRC, CESC, and ESCA (Figure 8). This is consistent the findings of our analyses in the UALCAN and GEPIA databases. These findings support the abnormal expression of CD70 in tumor tissues, implying that CD70 is involved in the development and immune escape of middle tumors.

6. CD70-CD27 signaling axis promotes tumor immune escape through T-cells

According to the findings of the immune infiltration study, CD70-related partners, GO, and KEGG enrichment analysis, Tregs may be the primary T cell subgroup targeted by the CD70-CD27 signal axis, which primarily achieved immunological escape via the T cell pathway. To verify the results of this analysis, we constructed CD70 knockdown and overexpression plasmids and transfected them into the HCC-LM3 cell line using a cell transfection experiment. Figure 9A–B shows that CD70 expression was down-regulated after CD70 knockdown while CD70 expression was up-regulated after CD70 overexpression. Meanwhile, the T cell-mediated tumor killing experiment revealed that under CD70 knockdown, the tumor cell viability was significantly lower than that of the control group, whereas the cell viability of the overexpressed CD70 group was significantly higher than that of the control group (Figure 9C). Interestingly, in the absence of T cell co-culture, there was no significant difference in tumor cell viability in either the knockdown or overexpression CD70 groups (Figure 9C). The findings were further verified by cloning experiments (Figure 9D). Therefore, the findings suggest that the immune escape of tumor cells via the CD70-CD27 signaling axis is primarily mediated by T cells. This is also consistent with the above findings of the previous analysis.

The importance of CD70 in solid tumors has become increasingly clear in recent decades, and abnormal expression of CD70 on tumor cells has been reported in various solid tumors with different levels of expression [5, 6, 30]. Therefore, treatments targeting CD70 have great potential for treating both early and advanced cancers. However, some studies have shown that CD70 is not up-regulated or not significantly up-regulated in tumor cells from Kaposi's sarcoma, prostate cancer, Langerhans cell histiocytosis, and colorectal cancer [14, 31]. Although CD70 is prevalent in many cancers, the expression pattern of CD70 varies in terms of spatial distribution, expression intensity, and percentage of positive cells. In addition, recent studies have reported that CD70 is involved in developing malignant tumors, particularly in regulating tumor immune escape. However, whether the CD70-CD27 signaling axis can play a role in immune escape in different tumors via some common molecular mechanisms remains to be further studied. Through a literature search, we were unable to find any literature on a generalized analysis of the CD70-CD27 signaling axis from a global tumor perspective. Therefore, we performed a comprehensive analysis of the CD70-CD27 signaling axis in 33 different tumors using data from the TCGA, CPTAC, and GEO databases, as well as gene expression, survival prognosis, and immune infiltration analysis.

A new understanding of the CD70/CD27 axis has recently revealed its unique characteristics in TME. We found that CD70 was up-regulated in tumor tissues by analyzing the abnormal expression of CD70 on tumor cells and the continuous signal transduction in TME. Furthermore, the CD70-CD27 axis mediates immune escape by regulating T cell apoptosis, T cell failure, and Treg survival. In addition, blocking this pathway can prevent the proliferation of malignant tumors. In our study of the immune infiltrating module, we found that CD70 and CD27 were positively correlated with Treg infiltration intensity. This implies that the CD70-CD27 axis is crucial in regulating Tregs. Treg is critical for immune tolerance and modulating immune responses to pathogens and tumors [32]. Tregs are an immunosuppressive subpopulation of CD4+ T cells characterized by the regulation of the transcription factor FoxP3, regulates Treg differentiation and development[33]. Tregs are vital in maintaining tolerance and immune homeostasis and promote tumor progression by impeding effective anti-tumor immunity [34]. However, it remains unknown how Tregs function in the TME. Thus, studying the molecular mechanism of Tregs’ function is conducive to developing new immunotherapy targets. Several studies have confirmed that the CD70-CD27 signaling axis mediates immune escape via its effect on Tregs. For example, in a mouse solid tumor model, CD70-CD27 signaling can directly reduce Treg apoptosis, whereas in vitro experiments revealed an increase in IL-2 secreted by non-Treg CD4+ T cells. Indirectly increased apoptosis of Tregs [35].

To study the potential molecular mechanism of the CD70-CD27 signaling axis in regulating Tregs, we screened for CD70-binding proteins and genes related to CD70 expression for a series of pathway enrichment analyses. Using the STRING tool, a total of 50 CD70-binding proteins were identified and supported by experimental evidence. In addition, the GEPIA2 tool was combined with all TCGA tumor expression data of to obtain the top 100 genes related to CD70 expression. Interestingly, we found that CD70 was closely related to the expression of several genes associated with Tregs function. For example, FoxP3, CCR4, CD86, and TGNFSF9. FoxP3, as a Treg-specific marker molecule is crucial , maintaining Treg function and increasing Treg numbers [36]. Tumor-infiltrating Tregs express higher chemokine receptor CCR4 than peripheral Tregs in BRCA patients according to studies. At the same time, FoxP3, as a CCR4 transcription activator , can increase CCR4 expression in Tregs and promote of BRCA immune escape [37]. In addition, Tregs can reduce CD80/CD86 expression of APCs via CTLA-4-dependent autophagy, thereby inhibiting the T-cell stimulating activity of APCs. Reduced CD80/CD86 expression on APCs can double-inhibit T cell immune responses by limiting the co-stimulation of naive T cells by CD80/CD86 and increasing the free PDL-1 expression on effector T cells used to inhibit PD-1 expression. Therefore, combined blocking of CTLA-4 and PD-1/PDL-1 may synergistically block Tregs-mediated immunosuppression, effectively enhancing immune responses, including tumor immunity [38]. In GO and KEGG enrichment analyses, we found that GO enrichment analysis showed that the CD70-CD27 pathway is important in immune response, T cell stimulation, and T cell activation pathways. KEGG enrichment analysis showed that the PI3K-AKT signaling pathway, the NF-κB signaling pathway, and the TNF signaling pathway were all activated. In addition, the cytokine receptor interaction pathway is also involved in the activation of the CD70-CD27 signaling axis. Many studies over the last decade have found that the PI3K/AKT pathway is vital in the development, function, and stability of Tregs [39]. The PI3K/AKT pathway, by integrating signals from a variety of cell surface receptors, is involved not only in the natural and induced differentiation of Tregs but also regulates mature Tregs function [40]. The NF-κB signaling pathway is essential for Treg differentiation and maturation, and despite significant progress in the last 10–15 years, many aspects of NF-κB function in Tregs remain unstudied [41]. Determine the target gene of NF-κB, distinguish the common and unique role of NF-κB in individuals, and elucidate the potential molecular mechanism of NF-κB protein regulation of Tregs specific genes and tumor immune escape are all important future research tasks [41]. To this end, we used a T cell mediated tumor destruction assay to confirm the immune escape of the CD70-CD27 signaling axis. Our findings showed that when tumor cells and T cells were co-cultured, the tumor cell viability after CD70 knockdown was significantly lower than that of the control group, whereas the cell viability of the overexpressed CD70 group was significantly higher than that of the control group. In addition, in the absence of T cells, there was no statistically significant difference in cell viability between the treated and control groups, whether CD 70 was knocked down or overexpressed. This means that the CD70 gene does not promote tumor cell proliferation, but rather facilitates immune escape by regulating T cells function in the TME via the CD70-CD27 signaling axis.

In conclusion, our first pan-cancer analysis of the CD70-CD27 signaling axis revealed that CD70 expression was statistically correlated with clinical prognosis, immune cell infiltration, and tumor cell immune escape in multiple tumors, which aids in understanding the role of the CD70-CD27 signaling axis in tumorigenesis from the perspective of clinical tumor samples. Currently, antibody-drug conjugate compounds and chimeric antigen receptor T cell therapy targeting CD70 have been developed and are under clinical evaluation in blood and solid cancers [42-45]. Although this approach has been successful in hematological malignancies, challenges remain in solid tumors due to multiple barriers, such as metastasis to the tumor site, infiltration into the tumor, and survival maintenance [46]. Thus, the potential molecular mechanism of the CD70-CD27 signaling axis in regulating T cell-mediated immune escape requires further investigation.

Combinations of anti-CD70 targeted therapies have demonstrated promise in preclinical and clinical settings. To date, a number of promising CD70 treatments are being investigated, both in preclinical and clinical settings, with the aim of improving treatment outcomes for cancer patients. Therefore, blocking the CD70-CD27 pathway is a hotspot of clinical research and has great potential as single drug therapy. In addition, anti-CD70 therapy offers many opportunities for rational combination strategies with conventional therapy and immunotherapy. Therefore, strategies that inhibit the CD70-CD27 axis signaling pathway may be promising new therapeutic options in the future, in addition to existing methods of targeting CD70. Further preclinical studies and clinical evaluation of CD70 targeting strategies will provide new insights into the mechanisms and actions of CD70 and potentially pave the way for new treatment options in the oncology field.

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgements:

This study was supported by the National Natural Science Foundation of China, Grant No. 81970548；Medical Science Advancement Program (Clinical Medicine) of Wuhan University, Grant No. TFLC2018003 and Wuhan Science and Technology Project, Grant No. 2019020701011485.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Fanhua Kong, Yan Xiong and Qifa Ye. The first draft of the manuscript was written by Fanhua Kong and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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No competing interests reported.

SupplementaryFigure1.tiff
Supplementary Figure 1: A phylogenetic tree of CD70. We used a constraint-based multiple alignment tool of NCBI to obtain the phylogenetic tree of CD70 in Homo species.
SupplementaryFigure2.tif
Supplementary Figure 2: The expression level of the CD27 gene in different cells and tissues under normal physiological conditions and different tumor and pathological stages. (A) The expression of CD27 gene in different tissues was analyzed using the consensus dataset of HPA, GTEx, and FANTOM5; Analysis of CD27 gene expression in different cancers or specific cancer subtypes by TIMER2. * P < 0.05; ** P < 0.01; *** P < 0 .001. (C) Analysis of CD27 gene expression in different cancers or specific cancer subtypes using TCGA database in UALCAN (Pan-cancer view module).
SupplementaryFigure3.tif
Supplementary Figure 3: The expression of CD70 in different tumor types and different clinical stages (Normal, Stage 1, Stage 2, Stage 3, and Stage 4) was analyzed using the TCGA database. The ordinate is a transcript per million. * P < 0.05，** P < 0.01，*** P < 0.001.
SupplementaryFigure4.tif
Supplementary Figure 4: Correlation between CD70 gene expression and prognosis of cancers (OS) using the Kaplan-Meier plotter.
SupplementaryFigure5.tif
Supplementary Figure 5: The TIMER2 database was used to analyze the correlation between immune infiltration of regulatory T cells (Tregs) and CD27 expression. Different algorithms were used to analyze the potential correlation between CD27 gene expression level and Treg invasion level in all types of cancer in the TCGA database.
SupplementaryTable1Thetop100genesassociatedwithCD70andthetop50proteinsthathavebeenexperimentallyverifiedtobindtoCD70.docx
Supplementary Table 1: The top 100 genes associated with CD70 and the top 50 proteins that have been experimentally verified to bind to CD70.
SupplementaryTable2BPofGOenrichmentanalysis.xlsx
Supplementary Table 2: BP of GO enrichment analysis.
SupplementaryTable3CCofGOenrichmentanalysis.xlsx
Supplementary Table 3: CC of GO enrichment analysis.
SupplementaryTable4MFofGOenrichmentanalysis.xlsx
Supplementary Table 4: MF of GO enrichment analysis.
SupplementaryTable5KEGGenrichmentanalysis.xlsx
Supplementary Table 5: KEGG enrichment analysis.
SupplementaryTable6Clinicalfeaturesofpatients.docx
Supplementary Table 6: Clinical features of patients.

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Comprehensive Analysis of Prognosis and Immune Function of CD70-CD27 Signaling Axis in Pan-Cancer

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