Identification of NK cell inhibitory ligand CLEC2D-associating immune infiltration landscape and prognostic value in kidney renal clear cell carcinoma

doi:10.21203/rs.3.rs-3218020/v1

Download PDF

Research Article

Identification of NK cell inhibitory ligand CLEC2D-associating immune infiltration landscape and prognostic value in kidney renal clear cell carcinoma

https://doi.org/10.21203/rs.3.rs-3218020/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Kidney renal clear cell carcinoma (KIRC), the most common pathological subtype of RCC, is a characteristics of heterogeneous diseases, and the outcome was dismay with immense diversity. KIRC is obviously resistant to radiotherapy and chemotherapy. In addition, increasing evidence has demonstrated that C-type lectin-like domain family 2 (CLEC2D) expression in cancer contributes to immune escape and thereby emerges as a target for natural killer (NK) cell-mediated immunotherapy. Therefore, we aimed to identify and validate the specific and detailed effect and mechanism of CLEC2D in KIRC.

Methods

CLEC2D expression in KIRC and normal tissues from TCGA were preliminatively screened, and further confirmed in a separate cohort of the GEO. Multivariate analysis and Kaplan-Meier (KM) curves were used to evaluate the relationship between CLEC2D expression and clinicopathologic parameters, and overall survival (OS). Subsequently, noncoding RNAs (ncRNAs) responsible for CLEC2D over-expression were identified by a combination of a series of expression, correlation, and survival analyses. Moreover, we further studied the relationship between CLEC2D expression and immune cell. The compounds were collected from the CellMiner database to examine the significance of CLEC2D in predicting chemotherapy sensitivity.

Results

Contrast to normal tissues, CLEC2D level was significantly higher in KIRC (p < 0.05). Elevated CLEC2D level was tightly correlated with higher grade malignancy and M, N, and tumor stage. Furthermore, univariate and multivariate analyses demonstrated that KIRC cases with high CLEC2D level had shorter OS time than patients with low CLEC2D level (p < 0.05). Subsequently, the LINC00894/-miR-30c-2-3p axis may be considered as the most latent upstream ncRNA-related pathway for CLEC2D in KIRC. Moreover, CLEC2D expression was markedly positively associated with tumor infiltrating immune cells (TIICs), markers of immune cells, and IC. In addition, our research indicated that several drugs IC50s closely related to CLEC2D expression were screened.

Conclusions

In conclusion, abundant expression of CLEC2D was correlated with disease progression and reduced prognosis, and there is sufficient TIICs in KIRC, suggesting that CLEC2D may serve as a promising prognostic biomarker and provide a novel target for tumor immunotherapy of KIRC.

C-type lectin-like domain family 2

Natural killer cell

Tumor infiltrating immune cells

Kidney renal clear cell carcinoma

Tumor Microenvironment

RCC, originating from renal tubular epithelial cells, is one of the eight most common malignancies worldwide, accounting for approximately 2–3% of malignant tumors (Siegel et al. 2021). Over the past 20 years, crucial advances in the molecular characterization and histopathology of RCC have led to significant revisions of its classification. RCC consists of KIRC, chromophobe RCC, papillary RCC and other very rare subtypes. Based on the WHO classification criteria (Hsieh et al. 2017), KIRC is the most common pathological subtype, as well as the major part of this dissertation (Rathmell et al. 2007). Approximately 20–30% of cases diagnosed with RCC present initially with synchronous metastases, 20% will recurrence after radical operation, and more than 50% of cases eventually require systemic treatment (Wiechno et al. 2018; Attalla et al. 2020). Unfortunately, RCC is generally not sensitive to conventional cytotoxic chemoradiotherapy. Targeted therapy and immunotherapy have made a revolutionary breakthrough in the therapy of KIRC, but the existing targeted medicines and immunotherapies display unsatisfactory effect and almost all patients will eventually deteriorate their disease (Shingarev et al. 2017; Calvo et al. 2019; Park et al. 2012; Ascierto et al. 2016). Although the 5-year survival rate of localized or locally advanced RCC is 20–95%, patients with metastatic disease have dismal 5-year survival rates from 0–10% (Linehan et al. 2019). Therefore, diagnosis at an early stage and timely radical operation are critical factors in the RCC therapy. There is an urgent need to develop efficient and sensitive therapeutic targets to formulate more reasonable personalized treatment approaches or seek more prospective prognostic markers for RCC.

The pivotal functions of the immune system in malignant tumor have been known for several decades, but immunotherapy, as a key approach to tumor treatment, has only been considered in recent years (Van et al. 2018; Abbott et al. 2019). Despite different immunotherapy strategies, tumor cells often escape the elimination by the immune system. The immune avoidance, a hallmark of tumor cells, is a major issue in the development of efficient immunotherapy to eradicate tumor (Zhang et al. 2020; Vinay et al. 2015). There is compelling evidence that tumor cells can upregulate physiological inhibitory immune pathways to escape immune responses, like PDCD1 and CTLA-4. Likewise, in glioma, oropharyngeal squamous cell carcinoma, prostate cancer(PC), and breast cancer(BC),it has been described that the over-expression of CLEC2D contributes to immunosuppression of tumor cells and features of immune system evasion (Buller et al. 2020; Del et al. 2020; Mathew et al. 2016; Sanchez-Canteli et al. 2020; Bialoszewska et al. 2018; Llibre et al. 2016).

CLEC2D, also name lectin-like transcript 1(LLT1) and osteoclast inhibitory lectin(OCIL), was first regarded as a member of the C-type lectin-like domain family in 1999 (Boles et al. 1999). CLEC2D is a NK cell inhibitory ligand that is mainly present in activated lymphocytes, antigen presenting cells, and monocytes (Germain et al. 2011). CLEC2D expressed on target cells is a ligand of NKRP1a (or CD161) inhibitory receptor on NK cells, which inhibits the cytotoxicity and cytokine production of NK cells (Rosen et al. 2005). Studies have demonstrated that CLEC2D may have synergistic stimulatory activity on activated B cells (Mathew et al. 2016; Llibre et al. 2016). Surprisingly, recent conclusive evidence suggests that CLEC2D can bind to CD161 expressing T cells, and downregulate the CLEC2D-CD161 pathway may enforce T cell-mediated immunity against such gliomas and hematological tumor (Mathewson et al. 2021).

The expression of CLEC2D may be an immune escape mechanism, so it is of great significance to research the abnormal expression of CLEC2D in cancer. The role of CLEC2D in KIRC has not been determined. In the present research, we first determined CLEC2D expression in KIRC, and then assessed the detailed association between CLEC2D expression and the clinicopathological characteristics, and further analyzed the possible impact CLEC2D on the outcome of patients. Furthermore, the ncRNA-related regulation of CLEC2D, including lncRNAs and miRNAs, was also analyzed in KIRC. We evaluated the association of CLEC2D expression with TIICs, markers of immune cells, or immune checkpoints (IC) in KIRC. Finally, we also determined the potential of CLEC2D to forecast the drug sensitivity of approved or assessed drugs.

Data Download

The RNA-seq data and clinical parameters of the KIRC dataset were extracted from the UCSC (https://xenabrowser.net) and enrolled 71 normal and 530 KIRC samples (Goldman et al. 2020). The keywords “KIRC” were used to query the GEO (https://www.ncbi.nlm.nih.gov/gds/), and CLEC2D expression profiles from 4 datasets were obtained (Clough et al. 2016). The GSE36895 dataset contained 23 normal and 53 KIRC cases, the GSE53757 dataset contained 72 normal and 72 KIRC cases, the GSE40435 dataset contained 101 normal and 101 KIRC cases, the GSE66271 dataset contained 13 normal and 13 KIRC cases with KIRC.

Survival Analysis

To gain an in-depth understanding of the mechanism of KIRC, TCGA -KIRC cases were separated into low- and high- groups according to the median value of CLEC2D expression level, and KM analysis was performed to inspect OS in the two groups using “survminer” package. In order to further confirm whether CLEC2D can be used as an independent predictor, univariate and multivariate proportional hazards models were conducted to explore the predictive significance of CLEC2D, gender, age, grade, laterality, T, N, M, and stage. Only significant factors (p < 0.05) in the univariate regression method were included in the multivariate analysis. Nomograms have been used to predict tumor patient prognosis. This statistical methodology scores each contribution parameter by points. All of these points added up to generate a total point, and the bigger the point, the more likely the event is to happen. We constructed a prognostic nomogram in KIRC cases to anticipate the individual survival probability by weighing CLEC2D, gender, age, stage, pathologic T, pathologic N, pathologic M, and laterality (using the “rms” package of R software).

MiRNA prediction

A relatively comprehensive miRNA prediction method was used in this study. Upstream potentially binding miRNAs of CLC2D were predicted via four target gene prediction databases, containing microT (http://www.microrna.gr/webServer) (Paraskevopoulou et al. 2013), miRDB(http://mirdb.org/) (Chen et al. 2020), miRmap (https://mirmap.ezlab.org/) (Vejnar et al. 2013), and TargetScan (www.targetscan.org/) (Agarwal et al. 2015). Only the predictive miRNAs frequently appeared in more than two predicting programs as mentioned above were selected for following analysis. These predictive miRNAs were considered as potential miRNAs for CLEC2D.

StarBase database

StarBase (http://starbase.sysu.edu.cn/) is a comprehensive utilization of database for researching the ncRNA interactions (Li et al. 2014). starBase was utilized to investigate expression relationship of miRNA-CLEC2D in KIRC. The expression of hsa-miR-30c-2-3p in KIRC and normal samples and survival analysis were analyzed via starBase.

LncBase v.2 web server

Candidate lncRNAs of hsa-miR-30c-2-3p were predicted by LncBase Predicted V2.0 (www.microrna.gr/LncBase) (Paraskevopoulou et al. 2013). The score threshold was ༞0.9.

Scores for Tumor Microenvironment (TME) Using ESTIMATE Package

ESTIMATE is a newly developed algorithm that estimates stromal and immune scores according to the transcriptional expression profiles of tumor to infer infiltration of immune and stromal cells in cancer (Yoshihara et al. 2013). We used 3 different three kinds of scores (Immune, Stromal, and Estimate) to stand for the proportions of the infiltration of immune cells, stromal cells, and the sum of the former two, respectively. The higher the score, the greater the proportion of the corresponding partial in the TME. Furthermore, the estimate score can infer tumor purity, and the lower the estimate score, the higher the percentage of tumor cells, the higher the purity of the cancer.

Profiling TIICs with CIBERSORT

CIBERSORT is an analytical tool to provide an estimate of the abundance composition of 22 immune cells in a complex cell population (Chen et al. 2018). We analyzed the mRNA transcriptome data via CIBERSORT R script obtained from the CIBERSORT official web site(https://cibersort.stanford.edu/). Afterward, the samples with P < 0.05 were retained and association analysis was performed to study the contents of the 22 immune cells.

CellMiner database v2.6

CellMiner (https://discover.nci.nih.gov/cellminer/home.do) is an online website and search tool to facilitate integration and research of molecular and pharmacological information on the NCI-60, a group of 60 different tumor cell lines, including 8 renal cell lines, and used to screen over 100,000 compounds and natural products (Reinhold et al. 2012).

Elevated expression of CLEC2D in KIRC

To evaluate the possible roles of CLEC2D in carcinogenesis, we first calculated the difference in the expression of CLEC2D mRNA between KIRC and normal cases. In the TCGA-KIRC group, the CLEC2D expression was evidently higher than that in normal samples, and a box plot was drawn to demonstrate the difference (p = 3.88e-99) (Fig. 1a). Correspondingly, this analysis result was also validated in GSE36895(p = 7.544e-09), GSE40435(p = 8.472e-25), GSE53757(p = 1.57e-26), and GSE66271 databsets (p = 0.011) (Fig. 1b-e).

The clinicopathological characteristics associated with the CLEC2D expression levels

To further analysis the potential association between gene signature and clinicopathologic parameters, we compared the clinicopathological features of high-CLEC2D and low-CLEC2D group with the median level as cut-off values, and found that CLEC2D expression was not obviously associated with gender, age, N stage and laterality (Fig. 1j-n). Furthermore, CLEC2D expression level was significantly elevated at high M, N, stage, and grade (Fig. 1f-i).

The prognostic value of CLEC2D

Generally, CLEC2D expression was prominently correlated with OS (P = 0.034). KM plots manifested that KIRC patients with a high CLEC2D expression might have dismay survival and prognosis (Fig. 2a). Then, univariate analysis demonstrated that CLEC2D expression, age, T, N, M, as well as stage were confirmed negative predictors for OS in KIRC cases, while gender and laterality showed no statistical significance(Fig. 2c). Subsequently, in the multivariate Cox model, CLEC2D expression, gender, and grade were independent prognostic indicators correlated with OS in KIRC patients (Fig. 2d). Finally, in view of the prognostic significance of CLEC2D in KIRC, we attempted to construct the nomogram for calculating 1, 2, and 3-year survival rates. The result demonstrated that pathologic stage contributed the most to OS, followed by CLEC2D expression level group (Fig. 2b).

Prediction of upstream miRNAs of CLEC2D in KIRC

It has been widely acknowledged that miRNAs are mainly involved in the negative regulation of gene expression, and thus playing a vital role in the tumor initiation and development. To determine whether CLEC2D was regulated by some ncRNAs, we first predicted miRNAs that may bind to CLEC2D through the above four prediction databases, and finally 45 upstream miRNAs were found to possibly regulate CLEC2D (Fig. 3a). For better visualization, miRNA-CLEC2D regulatory network were constructed via cytoscape software, as shown in Fig. 3b. Based on the mechanism of miRNA regulating target genes, CLEC2D was negatively correlated with miRNA, and CLEC2D was found markedly negatively correlated with hsa-miR-30c-2-3p (Fig. 3d). On the bright side, hsa-miR-30c-2-3p and Let-7c-5p were substantially downregulated in KIRC compared with normal cases, and its high level indicated a favorable prognosis (Fig. 3c, e).

Prediction and analysis of upstream potential lncRNAs of hsa-miR-30c-2-3p

Therefore, LncBase v.2 was utilized to forecast the possible upstream lncRNAs of hsa-miR-30c-2-3p. A total of 296 lncRNAs were obtained. Among the 296 lncRNAs, only 44 lncRNAs were in the TCGA dataset. The relationship between the target lncRNAs and CLEC2D in KIRC was determined. Similarly, a lncRNA-CLEC2D regulatory network was provided by Cytoscape (Fig. 4b) (Fig. 5c, f). As listed in Fig. 4a, 21 lncRNAs were finally defined as the candidate lncRNA (cor༞0.1, p༜0.001) and choosed for succeeding analysis. As presented in Fig. 4c-d, the prognostic values of the 21 candidate lncRNAs were assessed in TCGA-KIRC. Subsequently, the high expression of LINC00839, LINC00894, and LINC01422 possessed terrible OS of patients with KIRC(p༜0.001)(Fig. 4c,d). Notably, only two lncRNAs (LINC00894 and LINC01422) were markedly upregulated in KIRC compared with normal cases (p༜0.001)(Fig. 5a, d). Finally, we further assessed the survival impacts of LINC00894 and LINC01422 on OS by KM plots (Fig. 5b, e). Together, LINC00894 and LINC01422 may be the two most potential upstream lncRNAs of miR-30c-2-3p /CLEC2D axis in KIRC based on the above analysis.

Calculation of Immune/Stromal Scores and Association of CLEC2D expression with scores

TIICs and stromal cells, the essential components of TME, play a substantial part in tumor biological characterizations. ESTIMATES algorithm was used to calculate the ratio of immune and stromal cells in TCGA-KIRC. The scores of immune and stromal distributed between − 677.76 to 3312.13 and − 1405.12 to 1964.65, respectively. The CLEC2D was positively correlated with the immune(cor = 0.54, p༜2.2e-16), stromal(cor = 0.17, p = 9.2e-5), estimate scores(cor = 0.44, p༜2.2e-16), while negatively associated with tumor purity(cor = -0.44, p ༜ 2.2e-16)(Fig. 6a-d).

TIICs landscape Related to CLEC2D in KIRC

Under different copy numbers of CLEC2D, KIRC had no significant change in immune cell infiltration except T cells. We next assessed the proportions of 22 types of TIICs in KIRC and elaborated a graphical of the resultant profiles through Barplot and Heatmap using CIBERSORT (Fig. 7a-c). Subsequently, relevance analysis of CLEC2D expression and TIICs was conducted. Furthermore, we also analyzed the difference of immune cells between 2 groups (high-CLEC2D and low-CLEC2D expression group) ( Fig. 7d). The outcomes of relevance and difference analysis revealed that 8 types of TIICs were positively interrelated with the expression of CLEC2D, including T cells CD4 memory activated, T cells CD8, B cells memory, Macrophages M1, NK cells activated, Plasma cells, T cells follicular helper (TFH), as well as T cells regulatory (Tregs) (Fig. 7e-l). Additionally, 7 types of TIICs (B cells naive, T cells CD4 memory resting, Macrophages M2, NK cells resting, Mast cells resting, Neutrophils, and Dendritic cells (DC) activated) were negatively related to CLEC2D expression (Fig. 7m-t).

Association analysis between CLEC2D and biomarker sets of immune infiltrates in KIRC

In addition, in view of the relationship between CLEC2D expression and various TIICs levels, we next sought to assess whether CLEC2D level was associated with typical biomarkers of essential immune cells in KIRC. Surprisingly, the results indicated that CLEC2D level was strongly positively associated with B cell markers (CD19, CD79A), CD4 + T cell marker (CD4), CD8 + T cell markers (CD8A, CD8B), DC markers (CD1C, ITGAX, HLA-DPA1, HLA-DPB1, HLA-DQB1, HLA-DRA), M1 macrophage markers (IRF5), M2 macrophage markers (CD163, MS4A4A, VSIG4), and neutrophil markers (CCR7, CD44, CEACAM8, ITGAM), instead, they were significantly negatively associated with M1 macrophage markers (NOS2) (Table 1). These evidences indicated that CLEC2D genes may modulate the activation and infiltration of TIICs in KIRC.

CLEC2D was positively related with IC

PD1(PDCD1), PD-L1(CD274), and CTLA-4 are crucial IC responsible for tumour immune escape. Considering the potential carcinogenic effect of CLEC2D in KIRC, we further investigated the correlation between CLEC2D expression and IC to evaluate the importance of CLEC2D. By coincidence, as listed in Fig a-c, we discovered that CLEC2D level was mostly associated with PD1 (cor = 0.623 p = 9.89E-59), PD-L1 (cor = 0.244 p = 1.16E-08), and CTLA-4 (cor = 0.702 p = 1.57E-80) in KIRC(Fig. 8a-c). As expected, these results also unravelled that CLEC2D was significantly positively correlated with PD1 (cor = 0.357 p = 0.00025), PD-L1 (cor = 0.344 p = 0.00043), and CTLA-4 (cor = 0.354 p = 0.00028) in GEO cohort(Fig. 8d-f). Collectively, our analysis suggested that CLEC2D may be involved in tumor immune regulation.

Co-expression genes correlated with CLEC2D in KIRC

To ascertain the latent biological significance of CLEC2D in KIRC, we examined the CLEC2D-correlated mode. The results demonstrated that 1959 genes were positively correlated with CLEC2D, whereas 138 genes showed prominent negative correlations(cor༜0.5 p༜0.001). The 40 significant gene sets with markedly negatively and positively correlation with CLEC2D were then illustrated in the heat map (Fig. 9a-b). This result indicated that CLEC2D had a widespread effect on the transcriptome. The expression of CLEC2D was strongly associated with PARP15 level (cor = 0.857 p = 4.69E-176), and ZAP70(cor = 0.842 p = 1.83E-164), which reflect changes of negative transcription regulation of RNA polymerase II, NF-kappa B signaling pathway, transcription corepressor activity, T cell activation primary immunodeficiency, and NAD + ADP-ribosyltransferase activity.

Compounds strongly related to CLEC2D expression

To further explore the high reliability of CLEC2D, we researched the drug susceptibility of compounds selected on the basis of CLEC2D expression. In order to better link our CLEC2D with clinical application, we only included 202 compounds that had been approved by the FDA or were in clinical trials. For each drug, we analyzed the association between CLEC2D and IC50, and then screened several drugs (Everolimus, Dasatinib, Oxaliplatin, Vandetanib, Parthenolide, Rapamycin, Hydroxyurea, Cordycepin, Chelerythrine, Amonafide, Cytarabine, Dexrazoxane, Erlotinib, Lapachone, Fulvestrant, Benzimate, Palbociclib, Imexon, Methotrexate, Ifosfamide) whose IC50s was closely related to CLEC2D expression (Fig. 9c-v). This implyed that we can screen out drugs according to the CLEC2D expression levels, which may facilitate more accurate administration.

RCC is a heterogeneous and complex cancer associated with a variety of genetic mutations, epigenetic changes, chromosome abnormality and so on (Hsieh et al. 2017). RCC has multiple subtypes, each with distinctive histological, genetic, molecular characteristics and biological behavior. KIRC, often accompanied with high metastasis rate and mortality, and is significantly resistant to chemoradiotherapy (Padala et al. 2021). There is growing evidence that CLEC2D plays a vital part in the initiation and development of multiple tumors. However, the knowledge of CLEC2D in KIRC is still unclear, which deserves further research and exploration.

In our research, we first analyzed the expression of CLEC2D via TCGA data, and then further verified the expression of CLEC2D using 4 GEO cohorts. Several studies have showed that CLEC2D overexpression has been observed in PC, glioblastoma, B cell lymphomas. Consistent with these studies, our study found similar results in KIRC. Survival analysis of CLEC2D in KIRC showed that cases with low CLEC2D expression had an ideal outcome. Furthermore, multivariate analysis demonstrated that CLEC2D expression could be an independent biomaker of dismal prognosis in KIRC patients. Moreover, the higher CLEC2D expression was contributed to the risk of nodal and distant metastasis, as well as higher grade. Consequently, the CLEC2D has a stronger ability to discriminate patients with KIRC from healthy individuals, implying that CLEC2D may be a prospective and indicator and valid therapeutic target for KIRC.

Sufficient literatures have demonstrated that ncRNAs can participate gene expression regulation by interaction with each other through the ceRNA mechanism (Qi et al. 2015; Smillie et al. 2018). MiRNAs can bind to partially or completely complementary mRNA targets and guide to gene silencing through mRNA degradation or translation inhibition. Previous researchs displayed that miR-30c-2-3p was a latent cancer inhibitor gene in GC, PDAC, and BC, and low expression of which was strongly correlated with inferior prognosis (Tanaka et al. 2020; Tang et al. 2018; Shukla et al. 2015). Shukla et al. have elucidated that miR-30c-2-3p negatively regulated NF-κB signaling and cell cycle progression in BC (Shukla et al. 2015). Mathew and colleagues have reported that miR-30c-2-3p was down regulated in von Hippel-Lindau (VHL)-deficient KIRC (Mathew et al. 2014). The miR-30c-2-3p can specifically bind to and inhibit HIF2α transcripts which functions as cancer suppressors. Consistent with the previous studies, we unravelled that miR-30c-2-3p played an inhibitory role in the regulation of KIRC. After correlation, expression, and survival analysis, miR-30c-2-3p was selected as the most principal upstream inhibitor miRNA of CLEC2D.

LncRNAs were previously identified as "transcriptional noise" without any biological function (Kung et al. 2013). However, existing researchs have proved that LncRNAs played vital parts in numerous physiological and pathophysiological processes, especially tumorigenesis. Through expression, survival and correlation analysis, LINC00894 and LINC01422 might be the potential regulatory lncRNAs of the miR-30c-2-3p/CLEC2D axis in KIRC. LINC00894 is reported to be an oncogene in various tumours, including KIRP, LUAD, and BC (Meng et al. 2021; Zhou et al. 2021). LINC00894 competitively binds to miR-429 to regulate the transcriptional factor Zinc finger E-box binding homeobox 1 expression, leading to BC progression (Meng et al. 2021). Searching for existing articles, we did not retrieve the articles related to LINC01422. Therefore, LINC00894/-miR-30c-2-3p/CLEC2D axis may be considered as latent regulatory pathways of KIRC.

KIRC has long been recognized as an immunogenic tumour. In recent years, the approval of immunotherapeutic drug strategy has changed the outcome of patients with metastatic RCC. PD-1 (Nivolumab, pembrolizumab), PD-L1 (avelumab), and CTLA-4 (ipilimumab) antibody are also approved to treat advanced RCC and are likely to make dramatic changes in the therapeutic landscape. Despite proven benefits, a substantial proportion of patients remain unresponsive to therapy, indicating that there are still unreported mechanisms of immunosuppressive in the tumor and its microenvironment.

Our study indicated that the CLEC2D was negatively associated with the tumor purity, implying that high expression of CLEC2D was correlated with high TIICs. There were differences in the composition of some immune cells between the two CLEC2D subgroups. M1 Macrophages, NK cells, Plasma cells, CD4 + T cells, CD8 + T cells, TFH cells, and Tregs were more enriched in the high-CLEC2D subgroup, while DC, M2 Macrophages, and Neutrophils were more enriched in the low-CLEC2D subgroup. Further work indicated that M1 macrophage markers IRF5 was positively correlated with CLEC2D expression, while markers of M2 macrophage (CD163, VSIG4, MS4A4A) were moderately associated with CLEC2D level, indicating that CLEC2D may play a regulatory role in TAM polarization. M2 macrophage, a predominant subtype of macrophages, has been proved to be conducive to tumour growth and progression of invasive phenotype, and is related to terrible prognosis in RCC, BC, bladder cancer, ovarian cancer, gastric cancer and PC. Intriguingly, Treg cells are the main manipulators of the immunosuppressive TME. Treg cell infiltration in tumor is related to higher pathological stage and terrible outcome of KIRC. DC are necessary for T cell-mediated cancer immunity. Mayoux et al discovered that RCC patients treated with PD-L1 blockade, DC gene signature were closely related to improved OS (Mayoux et al. 2020). Previous researchs have demonstrated that TFH related cells were positively correlated with long term survival of patients with melanoma tumor, breast tumor, and colorectal tumor. Xu et al found that neutrophils inhibit anti-tumor immunity by acting on local angiogenesis, lymphangiogenesis and inflammation, thereby promoting tumor progression (Xu et al. 2020). To summarise, CLEC2D may be an excellent immune index for evaluating the prognosis of KIRC.

There is compelling evidence that CD8 + T and NK cells played an indispensable part in the clearance of immunogenic tumor cells. Our study unraveled that CLEC2D level was prominently associated with CD8 + T cells infiltration and its corresponding biomarkers CD8A and CD8B in KIRC. CD8 + T cells are critical responder cells for tumor immunity treatment. The target cell expression of CLEC2D can inhibit the function of NK cells. Mathewson and Marrufo et al. have demonstrated that blocking CLEC2D-CD161 interaction could enhance T cell-mediated immunity against hematologic malignancies and NK cell mediated killing of triple negative BC cells (Mathewson et al. 2021; Marrufo et al. 2018). Furthermore, as manifested by Roth and Mathew et al. unravelled that glioblastoma and PC cells evade NK cell mediated killing by over-expressing CLEC2D, further confirming the pivotal role of CLEC2D in immunotherapy (Mathew et al. 2016; Roth et al. 2007).

In addition, the efficacy of immunotherapy depends not only on the sufficient TIICs into the TME, but also on the adequate expression of IC. Therefore, we also evaluated the correlation between CLEC2D and IC, and the results unravelled that the CLEC2D expression was closely correlated to PD1, PD-L1 or CTLA-4 in KIRC, meaning that CLEC2D expression may be a latent novel predictor to guide immunotherapy stratification of patients with KIRC, which may be one of the major elements that a panel repeatedly prognosticate for the KIRC cases.

In summary, with the new advance in the understanding of therapeutic biology and potential tyrosine kinase inhibitors drug resistance, immunotherapy have become a major breakthrough in KIRC treatment and attracts an ever-growing number of attentions from researches. We identified that elevated CLEC2D expression was a novel diagnostic biomarker and an independent predictor of terrible OS in cases with KIRC. Furthermore, our current results also indicated that CLEC2D may play its carcinogenic role by increasing TIICs and IC expression. However, our findings need to be validated in future analyses.

Acknowledgements

We sincerely thank TCGA and GEO databases for providing public data resources for this research.

Authors contributions

Y. M. and L.H. designed the research. C.C. and X.S. modified the manuscript. Y.M. drafted the manuscript and performed the statistical analysis. L.H. contributed to the literature search. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding

No funding resources to be declared.

Data availability

Data are available in a public, open access repository. Data are available upon reasonable request. All data relevant to the study are included in the article.

Conflict of interest

The authors report no conflict of interest.

Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer Statistics. CA: a cancer journal for clinicians 71(1):7-33. https://doi.org/10.3322/caac.21654
Hsieh JJ, Purdue MP, Signoretti S, Swanton C, Albiges L, Schmidinger M, et al (2017) Renal cell carcinoma. Nature reviews Disease primers 3:17009. https://doi. org/10.1038/nrdp.2017.9
Rathmell WK, Martz CA, Rini BI (2007) Renal cell carcinoma. Current opinion in oncology 19(3):234-40. https://doi.org/10.1097/CCO.0b013e3280ad4388
Wiechno P, Kucharz J, Sadowska M, Michalski W, Sikora-Kupis B, Jonska-Gmyrek J, et al (2018) Contemporary treatment of metastatic renal cell carcinoma. Medical oncology (Northwood, London, England) 35(12):156. https://doi.org/10.1007/ s12032-018-1217-1
Attalla K, Weng S, Voss MH, Hakimi AA (2020) Epidemiology, Risk Assessment, and Biomarkers for Patients with Advanced Renal Cell Carcinoma. The Urologic clinics of North America 47(3):293-303. https://doi.org/10.1016/j.ucl.2020.04.002
Shingarev R, Jaimes EA (2017) Renal cell carcinoma: new insights and challenges for a clinician scientist. American journal of physiology Renal physiology 313(2):F145-f54. https://doi.org/10.1152/ajprenal.00480.2016
Calvo E, Porta C, Grünwald V, Escudier B (2019) The Current and Evolving Landscape of First-Line Treatments for Advanced Renal Cell Carcinoma. The oncologist 24(3):338-48. https://doi.org/10.1634/theoncologist.2018-0267
Park K, Lee JL, Park I, Park S, Ahn Y, Ahn JH, et al (2012) Comparative efficacy of vascular endothelial growth factor (VEGF) tyrosine kinase inhibitor (TKI) and mammalian target of rapamycin (mTOR) inhibitor as second-line therapy in patients with metastatic renal cell carcinoma after the failure of first-line VEGF TKI. Medical oncology (Northwood, London, England) 29(5):3291-7. https://doi.org/10.1007/s12032-012-0227-7
Ascierto ML, McMiller TL, Berger AE, Danilova L, Anders RA, Netto GJ, et al (2016) The Intratumoral Balance between Metabolic and Immunologic Gene Expression Is Associated with Anti-PD-1 Response in Patients with Renal Cell Carcinoma. Cancer immunology research 4(9):726-33. https://doi.org/ 10.1158/2326-6066.Cir-16-0072
Linehan WM, Schmidt LS, Crooks DR, Wei D, Srinivasan R, Lang M, et al (2019) The Metabolic Basis of Kidney Cancer. Cancer discovery 9(8):1006-21. https://doi.org/10.1158/2159-8290.Cd-18-1354 van den Bulk J, Verdegaal EM, de Miranda NF (2018) Cancer immunotherapy: broadening the scope of targetable tumours. Open biology 8(6). https://doi.org/10.1098/rsob.180037
Abbott M, Ustoyev Y (2019) Cancer and the Immune System: The History and Background of Immunotherapy. Seminars in oncology nursing 35(5):150923. https://doi.org/10.1016/j.soncn.2019.08.002
Zhang Y, Zheng J (2020) Functions of Immune Checkpoint Molecules Beyond Immune Evasion. Advances in experimental medicine and biology 1248:201-26. https://doi.org/10.1007/978-981-15-3266-5_9
Vinay DS, Ryan EP, Pawelec G, Talib WH, Stagg J, Elkord E, et al (2015) Immune evasion in cancer: Mechanistic basis and therapeutic strategies. Seminars in cancer biology 35 Suppl:S185-s98. https://doi.org/10.1016/j.semcancer.2015.03.004
Buller CW, Mathew PA, Mathew SO (2020) Roles of NK Cell Receptors 2B4 (CD244), CS1 (CD319), and LLT1 (CLEC2D) in Cancer. Cancers 12(7). https://doi.org/ 10.3390/cancers12071755
Del Fresno C, Sancho D (2020) Clec2d Joins the Cell Death Sensor Ranks. Immunity 52(1):6-8. https://doi.org/10.1016/j.immuni.2019.12.015
Mathew SO, Chaudhary P, Powers SB, Vishwanatha JK, Mathew PA (2016) Overexpression of LLT1 (OCIL, CLEC2D) on prostate cancer cells inhibits NK cell-mediated killing through LLT1-NKRP1A (CD161) interaction. Oncotarget 7(42):68650-61. https://doi.org/10.18632/oncotarget.11896
Sanchez-Canteli M, Hermida-Prado F, Sordo-Bahamonde C, Montoro-Jiménez I, Pozo-Agundo E, Allonca E, et al (2020) Lectin-Like Transcript 1 (LLT1) Checkpoint: A Novel Independent Prognostic Factor in HPV-Negative Oropharyngeal Squamous Cell Carcinoma. Biomedicines 8(12). https://doi.org/ 10.3390/biomedicines8120535
Bialoszewska A, Malejczyk J (2018) Biological and Clinical Significance of Human NKRP1A/LLT1 Receptor/Ligand Interactions. Critical reviews in immunology 38(6):479-89. https://doi.org/10.1615/CritRevImmunol.2019029559
Llibre A, Klenerman P, Willberg CB (2016) Multi-functional lectin-like transcript-1: A new player in human immune regulation. Immunology letters 177:62-9. https://doi.org/10.1016/j.imlet.2016.07.007
Boles KS, Barten R, Kumaresan PR, Trowsdale J, Mathew PA (1999) Cloning of a new lectin-like receptor expressed on human NK cells. Immunogenetics 50(1-2):1-7. https://doi.org/10.1007/s002510050679
Germain C, Meier A, Jensen T, Knapnougel P, Poupon G, Lazzari A, et al (2011) Induction of lectin-like transcript 1 (LLT1) protein cell surface expression by pathogens and interferon-γ contributes to modulate immune responses. The Journal of biological chemistry 286(44):37964-75. https://doi.org/ 10.1074/jbc.M111.285312
Rosen DB, Bettadapura J, Alsharifi M, Mathew PA, Warren HS, Lanier LL (2005) Cutting edge: lectin-like transcript-1 is a ligand for the inhibitory human NKR-P1A receptor. Journal of immunology (Baltimore, Md : 1950) 175(12):7796-9. https://doi.org/10.4049/jimmunol.175.12.7796
Llibre A, López-Macías C, Marafioti T, Mehta H, Partridge A, Kanzig C, et al (2016) LLT1 and CD161 Expression in Human Germinal Centers Promotes B Cell Activation and CXCR4 Downregulation. Journal of immunology (Baltimore, Md : 1950) 196(5):2085-94. https://doi.org/10.4049/jimmunol.1502462
Mathewson ND, Ashenberg O, Tirosh I, Gritsch S, Perez EM, Marx S, et al (2021) Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell 184(5):1281-98.e26. https://doi.org/10.1016/j.cell.2021.01.022
Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, et al (2020) Visualizing and interpreting cancer genomics data via the Xena platform. Nature biotechnology 38(6):675-8. https://doi.org/10.1038/s41587-020-0546-8
Clough E, Barrett T (2016) The Gene Expression Omnibus Database. Methods in molecular biology (Clifton, NJ) 1418:93-110. https://doi.org/10.1007/978-1-4939-3578-9_5
Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, et al (2013) DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic acids research 41(Web Server issue):W169-73. https://doi.org/10.1093/nar/gkt393
Chen Y, Wang X (2020) miRDB: an online database for prediction of functional microRNA targets. Nucleic acids research 48(D1):D127-d31. https://doi.org/10.1093/nar/gkz757
Vejnar CE, Blum M, Zdobnov EM (2013) miRmap web: Comprehensive microRNA target prediction online. Nucleic acids research 41(Web Server issue):W165-8. https://doi.org/10.1093/nar/gkt430
Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. eLife 4:e05005. https://doi.org/10.7554/eLife.05005
Li JH, Liu S, Zhou H, Qu LH, Yang JH (2014) starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic acids research 42(Database issue):D92-7. https://doi.org/ 10.1093/nar/gkt1248
Paraskevopoulou MD, Georgakilas G, Kostoulas N, Reczko M, Maragkakis M, Dalamagas TM, et al (2013) DIANA-LncBase: experimentally verified and computationally predicted microRNA targets on long non-coding RNAs. Nucleic acids research 41(Database issue):D239-45. https://doi.org/10.1093/nar/gks1246
Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al (2013) Inferring tumour purity and stromal and immune cell admixture from expression data. Nature communications 4:2612. https://doi.org/ 10.1038/ncomms3612
Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA (2018) Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods in molecular biology (Clifton, NJ) 1711:243-59. https://doi.org/10.1007/978-1-4939-7493-1_12
Reinhold WC, Sunshine M, Liu H, Varma S, Kohn KW, Morris J, et al (2012) CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. Cancer research 72(14):3499-511. https://doi.org/10.1158/0008-5472.Can-12-1370
Hsieh JJ, Purdue MP, Signoretti S, Swanton C, Albiges L, Schmidinger M, Heng DY, Larkin J, Ficarra V (2017) Renal cell carcinoma. Nat Rev Dis Primers 3:17009. https://doi.org/ 10.1038/nrdp.2017.9.
Qi X, Zhang DH, Wu N, Xiao JH, Wang X, Ma W (2015) ceRNA in cancer: possible functions and clinical implications. Journal of medical genetics 52(10):710-8. https://doi.org/10.1136/jmedgenet-2015-103334
Smillie CL, Sirey T, Ponting CP (2018) Complexities of post-transcriptional regulation and the modeling of ceRNA crosstalk. Critical reviews in biochemistry and molecular biology 53(3):231-45. https://doi.org/10.1080/10409238.2018.1447542
Tanaka T, Okada R, Hozaka Y, Wada M, Moriya S, Satake S, et a (2020) Molecular Pathogenesis of Pancreatic Ductal Adenocarcinoma: Impact of miR-30c-5p and miR-30c-2-3p Regulation on Oncogenic Genes. Cancers 12(10). https://doi.org/10.3390/cancers12102731
Tang CT, Liang Q, Yang L, Lin XL, Wu S, Chen Y, et al (2018) RAB31 Targeted by MiR-30c-2-3p Regulates the GLI1 Signaling Pathway, Affecting Gastric Cancer Cell Proliferation and Apoptosis. Frontiers in oncology 8:554. https://doi.org/10.3389/fonc.2018.00554
Shukla K, Sharma AK, Ward A, Will R, Hielscher T, Balwierz A, et al (2015) MicroRNA-30c-2-3p negatively regulates NF-κB signaling and cell cycle progression through downregulation of TRADD and CCNE1 in breast cancer. Molecular oncology 9(6):1106-19. https://doi.org/10.1016/j.molonc.2015.01.008
Mathew LK, Lee SS, Skuli N, Rao S, Keith B, Nathanson KL, et al (2014) Restricted expression of miR-30c-2-3p and miR-30a-3p in clear cell renal cell carcinomas enhances HIF2α activity. Cancer discovery 4(1):53-60. https://doi.org/ 10.1158/2159-8290.Cd-13-0291
Kung JT, Colognori D, Lee JT (2013) Long noncoding RNAs: past, present, and future. Genetics 193(3):651-69. https://doi.org/10.1534/genetics.112.146704
Meng DF, Shao H, Feng CB (2021) LINC00894 Enhances the Progression of Breast Cancer by Sponging miR-429 to Regulate ZEB1 Expression. OncoTargets and therapy 14:3395-407.https://doi.org/10.2147/ott.S277284
Zhou Q, Li D, Zheng H, He Z, Qian F, Wu X, et al (2021) A novel lncRNA-miRNA-mRNA competing endogenous RNA regulatory network in lung adenocarcinoma and kidney renal papillary cell carcinoma. Thoracic cancer 12(19):2526-36. https://doi.org/10.1111/1759-7714.14129
Mayoux M, Roller A, Pulko V, Sammicheli S, Chen S, Sum E, et al (2020) Dendritic cells dictate responses to PD-L1 blockade cancer immunotherapy. Science translational medicine 12(534). https://doi.org/10.1126/scitranslmed.aav7431
Xu W, Jiang X, Guan C, Gu M (2020) The prognostic and predictive value of tumor infiltrating Macrophage and Neutrophil in patient with clear cell renal cell carcinoma: Tumor infiltrating lymphocytes in renal cell carcinoma. Medicine 99(46):e23181. https://doi.org/10.1097/md.0000000000023181
Marrufo AM, Mathew SO, Chaudhary P, Malaer JD, Vishwanatha JK, Mathew PA (2018) Blocking LLT1 (CLEC2D, OCIL)-NKRP1A (CD161) interaction enhances natural killer cell-mediated lysis of triple-negative breast cancer cells. American journal of cancer research 8(6):1050-63. https://doi.org/eCollection 2018.
Roth P, Mittelbronn M, Wick W, Meyermann R, Tatagiba M, Weller M (2007) Malignant glioma cells counteract antitumor immune responses through expression of lectin-like transcript-1. Cancer research 67(8):3540-4. https://doi.org/10.1158/0008-5472.Can-06-4783

Table 1 is available in the Supplementary Files section.

No competing interests reported.

Table1.tif
Table 1Correlation analysis between CLEC2D and markers of immune cells in KIRC.

Download PDF

Version 1

posted

You are reading this latest preprint version

Identification of NK cell inhibitory ligand CLEC2D-associating immune infiltration landscape and prognostic value in kidney renal clear cell carcinoma

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Data Download

Survival Analysis

MiRNA prediction

StarBase database

LncBase v.2 web server

Scores for Tumor Microenvironment (TME) Using ESTIMATE Package

Profiling TIICs with CIBERSORT

CellMiner database v2.6

Results

Elevated expression of CLEC2D in KIRC

The clinicopathological characteristics associated with the CLEC2D expression levels

The prognostic value of CLEC2D

Prediction of upstream miRNAs of CLEC2D in KIRC

Prediction and analysis of upstream potential lncRNAs of hsa-miR-30c-2-3p

Calculation of Immune/Stromal Scores and Association of CLEC2D expression with scores

TIICs landscape Related to CLEC2D in KIRC

Association analysis between CLEC2D and biomarker sets of immune infiltrates in KIRC

CLEC2D was positively related with IC

Co-expression genes correlated with CLEC2D in KIRC

Compounds strongly related to CLEC2D expression

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1