Innate immune checkpoint Siglec10 in cancers: mining of comprehensive omics data and validation in patient samples

Abstract

Sialic acid binding Ig-like lectin 10 (Siglec10) is a member of innate immune checkpoints that inhibits the activation of immune cells through the interaction with its ligand CD24 on tumor cells. Here, by analyzing public databases containing 64 517 patients of 33 cancer types, we found that the expression of Siglec10 was altered in 18 types of cancers and was associated with the clinical outcomes of 11 cancer types. In particular, Siglec10 was upregulated in patients with kidney renal clear cell carcinoma (KIRC) and was inversely associated with the prognosis of the patients. In 131 KIRC patients of our settings, Siglec10 was elevated in the tumor tissues of 83 (63.4%) patients compared with that in their counterpart normal kidney tissues. Moreover, higher level of Siglec10 was associated with advanced disease (stages III and IV) and worse prognosis. Silencing of CD24 in KIRC cells significantly increased the number of Siglec10-expressing macrophages phagocytosing KIRC cells. In addition, luciferase activity assays suggested that Siglec10 was a potential target of the transcription factors c-FOS and GATA1, which were identified by data mining. These results demonstrate that Siglec10 may have important oncogenic functions in KIRC, and represents a novel target for the development of immunotherapies.

Keywords

innate immune checkpoint / Siglec10 / kidney renal clear cell carcinoma

1 Introduction

Cancer immunotherapies targeting the cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), programmed death 1 (PD-1) and its ligand 1 (PD-L1), significantly prolong the overall survival (OS) of patients with most subtypes of cancer by restoring tumor-induced immune deficiency in tumor microenvironment [1,2]. However, only a proportion (20%–30%) of patients achieves a durable response [3], and some initial responders eventually develop resistance to these therapies. Moreover, these therapies can cause adverse events involving the heart, skin, gastrointestinal tract, endocrine glands, liver, the central nervous system, and pulmonary and hematologic systems [4,5]. Therefore, novel targets are desired for the development of new immunotherapies.

Siglec10, a member of the sialic acid binding Ig-like lectin (Siglec) family, is an inhibitory receptor expressed by immune cells such as B cells, monocytes, dendritic cells, natural killer (NK) cells, and a small subset of activated T cells [6–9]. It has five extracellular Ig-like domains, a transmembrane region, and a cytoplasmic tail containing two immune receptor tyrosine inhibitory motifs [10]. Siglec10 can bind to vascular adhesion protein-1 (VAP-1) to mediate lymphocyte adhesion to endothelium and modify the inflammatory microenvironment via enzymatic end products [11]. The expression of Siglec10 is increased in a subset of CD4⁺ T cells, which release soluble CD52 to induce immune suppression by binding to Siglec10 [12,13]. Many tumors overexpress the anti-phagocytic signal factor CD24, whereas tumor-associated macrophages express high level of Siglec10. Siglec10 expression is linked to NK cell dysfunction and inversely associated with the prognosis of patients with hepatocellular carcinoma [14]. Interestingly, ablation of either CD24 or Siglec10, as well as blockade of the CD24–Siglec10 interaction, robustly augment the phagocytosis of CD24-expressing human tumors [7].

However, the expression level, prognostic value, and the regulation of Siglec10 remain obscure in most malignant neoplasms. To address these questions, we conducted a comprehensive analysis of Siglec10 in eight data sets containing 64 517 patient samples of 33 human caner types and validated them in patient samples of our settings. The results suggested that Siglec10 may play an important role in cancer progression and represents a potential immunotherapeutic target for drug development.

2 Methods

2.1 Expression of Siglec10 in cancers

The expression of Siglec10 in tumor and normal tissues was assessed in databases including The Cancer Genome Atlas (TCGA) data in cBioPortal [15], Oncomine database [16], GEO database [17], Genotype-Tissue Expression [18], and Gene Expression Profiling Interactive Analysis (GEPIA) database [19]. The coexpressed genes of Siglec10 in tumor tissues and the correlations between Siglec10 and marker genes of monocytes, M1 and M2 macrophages, were determined using these datasets. The association between Siglec10 expression level and OS of the patients was analyzed by the Kaplan–Meier method and log-rank test by using the Kaplan–Meier Plotter (KM Plotter) [20] and GEPIA databases.

2.2 Siglec10 and immune cell infiltration

The association between Siglec10 expression and immune cell infiltration was determined using TIMER, a comprehensive resource that contains 10 897 samples across 32 cancer types from TCGA and a powerful tool for systematic analysis of immune infiltrates across diverse cancer types to estimate the abundance of immune infiltrates via correlation modules [21].

2.3 Regulation of Siglec10

The transcription factors that may regulate Siglec10 expression were predicted using the GCBI online software. The transcription factors that can directly bind to Siglec10 were identified through Cistrome data browser.

2.4 Patient samples of our settings

The study was approved by the research ethics committees of Chinese Academy of Medical Sciences Cancer Hospital and Beijing Chaoyang Hospital Affiliated to Capital Medical University. The diagnosis of kidney renal clear cell carcinoma (KIRC) and brain lower grade glioma (LGG) was confirmed by at least two pathologists. The patients’ characteristics are listed in Tables S1 and S2. All cancer samples were collected with informed consent. Tissue samples were taken at the time of surgery and quickly frozen in liquid nitrogen. The counterpart normal controls were normal tissues adjacent to the tumors that were taken with the tumors and were free of tumor cells as confirmed by pathological examination. Total RNA was isolated using TRIZOL reagent (Invitrogen, Frederick, MD, USA), and proteins were extracted using lysates in RIPA buffer.

2.5 Immunohistochemistry assay

Immunohistochemistry (IHC) assay was performed to test Siglec10 expression in KIRC patient samples harvested from Beijing Chaoyang Hospital Affiliated to Capital Medical University (Table S2). A tissue microarray containing 90 pairs of KIRC tumor tissues and corresponding nontumor tissues (Table S2) was purchased from Shanghai Outdo Biotech (Shanghai, China; catalog number: HKid-CRC180Sur-01) to show further the expression of Siglec10 in KIRCs. Formalin-fixed and paraffin-embedded tissue specimens were deparaffinized and subjected to a heat-induced epitope retrieval step in citrate buffer solution. The sections were then blocked with 5% bovine serum albumin for 30 min and incubated with an anti-Siglec10 antibody (Clone 5G6, Biolegend; 1:50) at 4 °C overnight, followed by incubation with secondary antibody for 90 min at 37 °C. Detection was achieved with 3,3′-diaminobenzidine (Zhongshan Golden Bridge Biotechnology Co., Ltd., Beijing, China) and counterstained with hematoxylin, dehydrated, cleared and mounted as in routine processing. Immunoreactivity score (IRS) was calculated as IRS (0–12) = RP (0–4) × SI (0–3), where RP is the percentage of staining-positive cells, and SI is staining intensity.

2.6 Cell culture

The renal cell cancer (RCC) cell lines 786-O, A498, and ACHN; the human acute monocytic leukemia cell line THP-1; and the human T lymphocyte line Jurkat were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS). The RCC line Caki-2 was cultured in McCoy’s 5A medium containing 10% FBS. The erythroid leukemia line K562, the human B cell line KCB200546M, and the human embryonic kidney cell line 293T were cultured in Dulbecco’s modified Eagle medium (DMEM) containing 10% FBS (Gibco/BRL, Grand Island, NY, USA), 100 U/mL penicillin, and 100 mg/mL streptomycin.

2.7 Human peripheral blood mononuclear cells and macrophages

Human peripheral blood was collected from healthy volunteer donors, and peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll-Hypaque density gradient centrifugation. The PBMCs were seeded on plastic culture flasks for 2 h, and monocytes were isolated by plastic adherence. The monocytes were treated with macrophage-colony stimulating factor (M-CSF) to derive macrophages, which were further stimulated with 50 ng/mL recombinant human transforming growth factor-β1 (TGFβ1) (R&D Systems, Inc., Minneapolis, MN, USA) and 50 ng/mL recombinant human interleukin-10 (IL-10) (R&D Systems) to obtain M2 type macrophages [7].

2.8 Gene expression and luciferase assay

The 293T cells were transfected with plasmids containing Siglec10 promoter-driven luciferase, constructs containing c-FOS, GATA1, and SPIB coding sequences, or small interfering RNAs (siRNAs) (Table S3). The total RNA of the cells was extracted with TRIZOL reagent (Invitrogen) according to the manufacturer’s instruction. The expression of the interested genes was tested by quantitative reverse transcription-polymerase chain reaction (qPCR) by using the primers listed in Table S3. Luciferase activity was measured using the dual luciferase reporter assay system (Promega, Madison, WI, USA).

2.9 Flow cytometry

The expression of CD24 on RCC cell lines was determined by flow cytometry by using APC anti-human CD24 antibody (Clone ML5; Biolegend, San Diego, CA). The expression levels of CD206 and CD209 on M0 and M2 macrophages and Siglecc10 on M2 macrophages were analyzed by flow cytometry and Brilliant Violet 421^TM anti-human CD206 (Clone 15-2, Biolegend), APC anti-human CD209 (Clone 9E9A8, Biolegend), and PE anti-human Siglec-10 (Clone 5G6, Biolegend) antibodies, respectively.

2.10 Assays of phagocytosis of macrophages

The effect of the Siglec10–CD24 axis on the phagocytic activity of the M2 macrophages in RCC cells was tested by transfecting 786-O cells with siRNA for negative control (siNC) or siCD24. Forty-eight hours later, the 786-O cells (1×10⁵) were labeled with CellTrace^TM CFSE Cell Proliferation Kit (Invitrogen) and then co-incubated with the M2 macrophages (5×10⁴) in 96-well U-bottom plates for 3 h. The cells were dissociated with TrypLE Express (Invitrogen), collected, stained with PE anti-human CD14 antibody (Clone M5E2, Biolegend), and analyzed by flow cytometry on an LRSFortessa Analyzer (BD Biosciences). Phagocytosis was calculated as the percentage of CD14⁺CFSE⁺ cells among CD14⁺ cells.

2.11 Western blot

For Western blot, proteins were subjected to 8%–15% sodium dodecyl sulfate-polyacrylamide gel, electrophoresed, and transferred onto a nitrocellulose membrane. After blocking with 5% nonfat milk in Tris-buffered saline, the membrane was washed and incubated with the indicated primary and secondary antibodies and detected by Luminescent Image Analyzer LSA 4000 (GE, Fairfield, CO, USA). The antibodies used included mouse anti-β-Actin (#A5441, Sigma, St. Louis, MO, USA; 1:5000) and rabbit anti-Siglec10 (#NBP1-82759, Novus Biologicals, LLC, USA; 1:250).

2.12 Statistical analysis

All statistical analyses were conducted using the GraphPad Prism 8 (GraphPad Software, La Jolla, CA, USA) software. Statistically significant differences were determined by Student’s t-test of unpaired data, Fisher’s exact test, or one-way ANOVA. The survival curve for each group was estimated by the Kaplan–Meier method and log-rank test. P values less than 0.05 were considered statistically significant. All statistical tests were two-sided.

3 Results

3.1 Siglec10 is widely expressed in human cancers

The expression of Siglec10 in human cancers was analyzed in TCGA, GTEx, and Oncomine (Table S4). In the RNA-seq data of TCGA data sets, Siglec10 was expressed in most human cancers (Fig. 1A). In patients with breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA), kidney chromophobe (KICH), KIRC, kidney renal papillary cell carcinoma (KIRP), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC), Siglec10 expression was significantly higher in tumor tissues than in counterpart normal tissues. In patients with colon adenocarcinoma (COAD), liver hepatocellular carcinoma (LIHC), prostate adenocarcinoma (PRAD), and rectum adenocarcinoma (READ), Siglec10 expression was lower in tumor tissues than in counterpart normal controls. In patients with bladder urothelial carcinoma (BLCA), cholangiocarcinoma (CHOL), head and neck cancer (HNSC), lung adenocarcinoma (LUAD), or lung squamous cell carcinoma (LUSC), Siglec10 expression in tumor tissues was approximately equal to that in counterpart normal controls (Fig. 1A). Siglec10 expression was higher in skin cutaneous melanoma (SKCM)-metastasis compared with that in SKCM (Fig. 1A).

Siglec10 expression was further analyzed using the GEPIA data sets, which contain both TCGA and GTEx data. Compared with that in counterpart normal controls, Siglec10 expression was remarkably upregulated in glioblastoma multiforme (GBM), KIRC, acute myeloid leukemia, LGG, pancreatic adenocarcinoma (PAAD), and STAD (Fig. 1B). In patients with adrenocortical carcinoma (ACC) and thymoma (THYM), Siglec10 expression was significantly lower in tumor tissues than in counterpart normal tissues (Fig. 1C). In the Oncomine data sets, Siglec10 expression was upregulated in brain cancer, breast cancer, and kidney cancer but was downregulated in colorectal and gastric cancers, leukemia, and multiple myeloma (Fig. 1D, Table S5). The distribution of Siglec10 expression in human tissues is shown in a body map (Fig. 1E).

3.2 Association between Siglec10 expression and prognosis of the patients

Using KM Plotter, the potential association between Siglec10 expression and the OS of the patients was analyzed. Results showed that patients with cervical squamous cell carcinoma (CSCC), esophageal adenocarcinoma, READ, and UCEC, with higher Siglec10 expression, had longer OS than those with lower Siglec10 expression (Fig. 2A). By contrast, high Siglec10 expression was associated with worse prognosis in patients with esophageal squamous cell carcinoma (ESCC), KIRC, testicular germ cell tumor (TGCT), and THYM (Fig. 2B). In the GEPIA database, Siglec10 overexpression was associated with favorable prognosis in patients with SKCM (Fig. 2C), whereas high Siglec10 expression was associated with poor outcome in patients with KIRC, LGG, THYM, and uveal melanoma (UVM) (Fig. 2D).

3.3 Siglec10 and tumor progression of KIRC and LGG

In the TCGA data sets, higher grades of KIRC and LGG had a higher Siglec10 expression (Fig. 3A and 3B). In the GEO data set, higher Siglec10 expression was also seen in higher grades of KIRC (Fig. 3C and 3D). On the basis of the results of gene expression profiling, KIRC was divided into two subtypes, ccA and ccB, which are helpful in discriminating the clinical outcome of KIRC, and patients with the ccA subtype expression profile have a better prognosis than patients with the ccB subtype expression profile [22,23]. Siglec10 was upregulated in ccB subtype (Fig. 3E), and patients with a higher Siglec10 expression had a shorter survival time (Fig. 2B). GBM is the highest grade (grade IV), whereas LGG is a lower grade (grade II/III) glioma [24]. GBM exhibited higher Siglec10 expression than LGG (Fig. 3F), suggesting that Siglec10 may have a role in brain tumor progression.

3.4 Siglec10 is associated with immune cell infiltration

Gene ontology analysis showed that in KIRC and LGG, the coexpressed genes of Siglec10 were mainly enriched in the immune response, inflammatory response, adaptive immune response, and innate immune response (Fig. 3G and 3H). Compared with the other immune checkpoint receptors, e.g., PDCD1, CTLA4, and LAG3, Siglec10 exhibited a higher expression level in the tumor tissues of KIRC and LGG (Fig. 3I).

Tumor-infiltrating lymphocytes are considered an independent predicator of tumor progression, treatment response, and clinical outcome [25–27]. The clinical significance of this molecule was revealed using TIMER to explore the correlation between immune cell infiltration and Siglec10 expression. Siglec10 expression was positively correlated with the infiltration of B cells, CD4⁺ T cells, macrophages, dendritic cells, and neutrophils in KIRC (Fig. 4A) and LGG (Fig. 4B).

The relationship between Siglec10 and the expression of marker genes of cells in tumor microenvironment, e.g., T and B cells, dendritic cells (DC), NK cells, neutrophils, monocytes, M1 and M2 macrophages, tumor-associated macrophages (TAM), type 1 T helper (Th1) and type 2 T helper (Th2) cells, T follicular helper cells, Th17 cells, regulatory T cells, and markers of T cell exhaustion, was analyzed in KIRC and LGG by using TIMER data sets. In these data sets, Siglec10 had a strong correlation with the marker genes of monocytes, TAMs, and M2 macrophages (Fig. 4C–4E). Similar results were also found in the GEPIA database (Table S6). These findings may unveil the role of Siglec10 in the regulation of macrophage polarization in KIRC and LGG. In addition, Siglec10 expression was positively associated with the marker genes of T, B, DC cells, and exhausted T cells in KIRC and LGG (Fig. 4C and Table S7).

3.5 Detection of Siglec10 in patients’ samples of our settings

The findings of bioinformatics analysis were validated. Patients’ samples were harvested and tested for Siglec10 expression. In six of the seven patients with LGG (Table S1), Siglec10 expression was higher in tumor tissues than in normal controls at both mRNA (Fig. 5A) and protein (Fig. 5B) levels. Siglec10 expression in 131 patients with KIRCs was carefully evaluated via three methods. Results showed that this molecule was overexpressed in 83 patients (63.4%), and its expression was associated with advanced disease stage (stages III and IV) (Table 1). qPCR analyses of 22 KIRC samples revealed that Siglec10 expression was higher in tumor tissues than in counterpart normal controls (Siglec10_tumor/Siglec10_normal>1) in 15 patients (68.2%) (Fig. 5C). Western blot assays of 14 samples of the 22 patients revealed that Siglec10 expression was higher in tumor tissues than in normal controls in 10 patients (71.4%) (Fig. 5D). IHC assays were conducted in additional 19 patients. Siglec10⁺ cells were frequently seen in tumor tissues rather than in counterpart normal controls in 11 patients (57.9%) (Fig. 6A). The IRS of tumor tissues was substantially higher than that of normal controls (Fig. 6B). IHC analysis of tissue microarray revealed that in tumor tissues, Siglec10 expression was upregulated and IRS increased in 57 of 90 (63.3%) patients with KIRCs (Fig. 6C and 6D). Moreover, high Siglec10 expression was associated with worse prognosis of the patients (Fig. 6E).

Flow cytometry analysis revealed that CD24 was relatively high in ACHN, Caki-2, and 786-O cell lines (Fig. S1A), whereas the M2 type macrophages derived from PBMCs expressed high levels of Siglec10 (Fig. 6G), CD206, and CD209 (Fig. S1B and S1C). Silencing of CD24 by siRNA (Fig. S1D) remarkably increased the percentage of macrophages phagocytosing 786-O cells (Fig. 6H), suggesting that Siglec10 has an important role in promoting immune evasion of KIRC cells.

3.6 Potential regulators of Siglec10

The potential transcription factors that may have a role in regulating Siglec10 expression were investigated. From the online GCBI software, 37 transcription factors were found, including AP1, E2F, GATA1, GATA3, HOXA13, SIRT6, SOX4, and others, that may control Siglec10 expression (Fig. 7A). Using Cistrome data browser, which is a comprehensive database that includes human and mouse ChiP-seq data, 77 transcription factors were found to be able to bind Siglec10. The intersection of the two prediction sets was taken, and three transcription factors, c-FOS, GATA1, and SPIB (Fig. 7B), were identified that may have important roles in regulating Siglec10.

Whether these three transcription factors can regulate Siglec10 expression or not was tested by luciferase assay in 293T cells, which showed a relatively low Siglec10 level (Fig. 7C). Results showed that ectopic expression of c-FOS and GATA1 increased the luciferase activity driven by the −2000 bp to 0 bp region of Siglec10 promoter (Fig. 7D). However, exogenous SP1B expression was unable to activate Siglec10 promoter-driven luciferase activity (Fig. 7D). By contrast, silencing of c-FOS and GATA1 by siRNAs remarkably inhibited Siglec10 expression in Jurkat cells (Fig. 7E).

4 Discussion

Siglecs belong to the Ig superfamily and have a characteristic N-terminal V-set Ig-like domain [28]. Most members of the Siglec family have the ability to inhibit innate and adaptive immune response to maintain immune homeostasis [29]. Recent studies showed that Siglec7 and Siglec9 are expressed on macrophages, natural killer cells, T cells, and dendritic cells, and can promote immune suppression by interacting with sialic acids on target cells [30]. The Siglec9⁺CD8⁺ cytotoxic T cell subset with a high functional capacity and a clonal expansion activity can be inhibited by Siglec9 ligands [31]. Siglec15 can exert diverse functions in osteoclast development, bone resorption, and suppress T cell antitumor immunity through its expression in tumor cells [32]. Siglec10 is mainly expressed on innate immune cells and lymphocytes [12,13], but its role in carcinogenesis remains to be understood. In this study, we performed pan-cancer analysis of Siglec10 expression. Results showed that the expression of this gene remarkably varied among the 33 types of cancers analyzed herein, i.e., upregulated in some cancers and downregulated in others. Higher Siglec10 expression was associated with worse prognosis in patients with ESCC, KIRC, LGG, TGCT, THYM, and UVM, but was associated with better outcome in patients with CSCC, EA, READ, SKCM, and UCEC. These results indicated that Siglec10 may have different roles in different types of cancer, and its context-dependent functions should be carefully scrutinized.

Siglec10 may play a vital role in KIRC and LGG because it was overexpressed in these two cancers, and its expression levels were positively associated with disease grades but inversely associated with the clinical outcome of the patients. It was coexpressed with the genes involved in antitumor immunity and represented the most upregulated immune checkpoint among molecules including PDCD1, CTLA4, and LAG3 in KIRC and LGG. In KIRC, Siglec10 expression was positively associated with tumor-infiltrating immune cells, such as B cells, CD4⁺ T cells, CD8⁺ T cells, macrophages, dendritic cells, and neutrophils and was linked to macrophage polarization. By contrast, in LGG, Siglec10 was negatively associated with CD8⁺ T cell infiltration, possibly due to the decreased CD8⁺ T cells in LGG compared with that in other types of cancers [33]. Bioinformatics analysis revealed that Siglec10 was elevated in KIRC and LGG as confirmed by the detection of Siglec10 in patients’ samples at both mRNA and protein levels. In particular, Siglec10 was elevated in 83 out of 131 (63.4%) patients with KIRC, and knockdown of CD24 enhanced the phagocytic activity of macrophages in KIRC cells, indicating that the Siglec10–CD24 axis plays a role in KIRC pathogenesis by inhibiting innate and adaptive immune systems. Our results also demonstrated the rationale and significance of using online omics resources in investigating specific gene(s) in tumorigenesis.

Siglec10 on the surface of immune cells mediates cancer cells’ “don’t eat me” signal by interacting with CD24 [7]. In this study, the main molecules that can regulate Siglec10 were unveiled by systematically analyzing the transcription factors that could regulate this gene. Results suggested that c-FOS, GATA1, and SPIB might modulate Siglec10 expression because of the presence of transcription factor binding sites in the promoter region of Siglec10. Interestingly, the “wet” experiment showed that the ectopic expression of c-Fos and GATA1 but not of SPIB was able to upregulate Siglec10 promoter-driven luciferase activity in 293T cells. Silencing of c-Fos and GATA1 by siRNAs consistently inhibited Siglec10 expression in Jurkat cells. GATA1 is a transcription factor that plays an important role in erythroid development by regulating the switch of fetal hemoglobin to adult hemoglobin [34]. c-Fos represents a leucine zipper protein that can dimerize with proteins of the JUN family and thereby form the transcription factor complex AP-1 to regulate cell proliferation, differentiation, transformation, and apoptotic cell death [35]. The role of these two transcription factors in regulating Siglec10 and how they affect its downstream molecules to modulate immune system to promote carcinogenesis remain open questions.

In summary, by mining the comprehensive omics data of 64 517 patients, we found that the immune checkpoint Siglec10 had differential expression patterns in 33 cancer subtypes and was widely expressed in human cancers. In KIRC and LGG, Siglec10 was substantially upregulated, and its expression level was positively associated with tumor progression but inversely associated with the clinical outcome of the patients. Siglec10 expression was associated with exhausted T cells and TAMs, suggesting that inhibition of Siglec10 may boost anticancer immunity in these types of cancers.

References

[1] Sanmamed MF, Chen L. A paradigm shift in cancer immunotherapy: from enhancement to normalization. Cell 2018; 175(2): 313–326

[2] Zhang M, Yang J, Hua W, Li Z, Xu Z, Qian Q. Monitoring checkpoint inhibitors: predictive biomarkers in immunotherapy. Front Med 2019; 13(1): 32–44

[3] Tu L, Guan R, Yang H, Zhou Y, Hong W, Ma L, Zhao G, Yu M.. Assessment of the expression of the immune checkpoint molecules PD-1, CTLA4, TIM-3 and LAG-3 across different cancers in relation to treatment response, tumor-infiltrating immune cells and survival. Int J Cancer 2020; 147(2): 423–439

[4] Postow MA, Sidlow R, Hellmann MD. Immune-related adverse events associated with immune checkpoint blockade. N Engl J Med 2018; 378(2): 158–168

[5] Li B, Chan HL, Chen P. Immune checkpoint inhibitors: basics and challenges. Curr Med Chem 2019; 26(17): 3009–3025

[6] Bandala-Sanchez E, Zhang Y, Reinwald S, Dromey JA, Lee BH, Qian J, Böhmer RM, Harrison LC. T cell regulation mediated by interaction of soluble CD52 with the inhibitory receptor Siglec-10. Nat Immunol 2013; 14(7): 741–748

[7] Barkal AA, Brewer RE, Markovic M, Kowarsky M, Barkal SA, Zaro BW, Krishnan V, Hatakeyama J, Dorigo O, Barkal LJ, Weissman IL. CD24 signalling through macrophage Siglec-10 is a target for cancer immunotherapy. Nature 2019; 572(7769): 392–396

[8] Bandala-Sanchez E, G Bediaga N, Goddard-Borger ED, Ngui K, Naselli G, Stone NL, Neale AM, Pearce LA, Wardak A, Czabotar P, Haselhorst T, Maggioni A, Hartley-Tassell LA, Adams TE, Harrison LC. CD52 glycan binds the proinflammatory B box of HMGB1 to engage the Siglec-10 receptor and suppress human T cell function. Proc Natl Acad Sci USA 2018; 115(30): 7783–7788

[9] Chen GY, Tang J, Zheng P, Liu Y. CD24 and Siglec-10 selectively repress tissue damage-induced immune responses. Science 2009; 323(5922): 1722–1725

[10] Whitney G, Wang S, Chang H, Cheng KY, Lu P, Zhou XD, Yang WP, McKinnon M, Longphre M. A new siglec family member, siglec-10, is expressed in cells of the immune system and has signaling properties similar to CD33. Eur J Biochem 2001; 268(23): 6083–6096

[11] Kivi E, Elima K, Aalto K, Nymalm Y, Auvinen K, Koivunen E, Otto DM, Crocker PR, Salminen TA, Salmi M, Jalkanen S. Human Siglec-10 can bind to vascular adhesion protein-1 and serves as its substrate. Blood 2009; 114(26): 5385–5392

[12] Shathili AM, Bandala-Sanchez E, John A, Goddard-Borger ED, Thaysen-Andersen M, Everest-Dass AV, Adams TE, Harrison LC, Packer NH. Specific sialoforms required for the immune suppressive activity of human soluble CD52. Front Immunol 2019; 10: 1967

[13] Bandala-Sanchez E, Bediaga NG, Naselli G, Neale AM, Harrison LC. Siglec-10 expression is up-regulated in activated human CD4⁺ T cells. Hum Immunol 2020; 81(2–3): 101–104

[14] Zhang P, Lu X, Tao K, Shi L, Li W, Wang G, Wu K. Siglec-10 is associated with survival and natural killer cell dysfunction in hepatocellular carcinoma. J Surg Res 2015; 194(1): 107–113

[15] Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013; 6(269): pl1

[16] Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, Varambally S, Ghosh D, Chinnaiyan AM. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 2007; 9(2): 166–180

[17] Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 2013; 41(Database issue): D991–D995

[18] GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020; 369(6509): 1318–1330

[19] Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017; 45(W1): W98–W102

[20] Nagy Á, Lánczky A, Menyhárt O, Győrffy B. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep 2018; 8(1): 9227

[21] Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 2017; 77(21): e108–e110

[22] Brannon AR, Reddy A, Seiler M, Arreola A, Moore DT, Pruthi RS, Wallen EM, Nielsen ME, Liu H, Nathanson KL, Ljungberg B, Zhao H, Brooks JD, Ganesan S, Bhanot G, Rathmell WK. Molecular stratification of clear cell renal cell carcinoma by consensus clustering reveals distinct subtypes and survival patterns. Genes Cancer 2010; 1(2): 152–163

[23] Huang Y, Wang J, Jia P, Li X, Pei G, Wang C, Fang X, Zhao Z, Cai Z, Yi X, Wu S, Zhang B. Clonal architectures predict clinical outcome in clear cell renal cell carcinoma. Nat Commun 2019; 10(1): 1245

[24] Wesseling P, Capper D. WHO 2016 Classification of gliomas. Neuropathol Appl Neurobiol 2018; 44(2): 139–150

[25] Cassetta L, Fragkogianni S, Sims AH, Swierczak A, Forrester LM, Zhang H, Soong DYH, Cotechini T, Anur P, Lin EY, Fidanza A, Lopez-Yrigoyen M, Millar MR, Urman A, Ai Z, Spellman PT, Hwang ES, Dixon JM, Wiechmann L, Coussens LM, Smith HO, Pollard JW. Human tumor-associated macrophage and monocyte transcriptional landscapes reveal cancer-specific reprogramming, biomarkers, and therapeutic targets. Cancer Cell 2019; 35(4): 588–602.e10

[26] Kudo M. Targeted therapy for liver cancer: updated review in 2012. Curr Cancer Drug Targets 2012; 12(9): 1062–1072

[27] Kumar Vodnala S, Restifo NP. Identifying the source of tumour-infiltrating T cells. Nature 2019; 576(7787): 385–386

[28] Läubli H, Varki A. Sialic acid-binding immunoglobulin-like lectins (Siglecs) detect self-associated molecular patterns to regulate immune responses. Cell Mol Life Sci 2020; 77(4): 593–605

[29] Munday J, Kerr S, Ni J, Cornish AL, Zhang JQ, Nicoll G, Floyd H, Mattei MG, Moore P, Liu D, Crocker PR. Identification, characterization and leucocyte expression of Siglec-10, a novel human sialic acid-binding receptor. Biochem J 2001; 355(2): 489–497

[30] Miyazaki K, Sakuma K, Kawamura YI, Izawa M, Ohmori K, Mitsuki M, Yamaji T, Hashimoto Y, Suzuki A, Saito Y, Dohi T, Kannagi R. Colonic epithelial cells express specific ligands for mucosal macrophage immunosuppressive receptors siglec-7 and-9. J Immunol 2012; 188(9): 4690–4700

[31] Haas Q, Boligan KF, Jandus C, Schneider C, Simillion C, Stanczak MA, Haubitz M, Seyed Jafari SM, Zippelius A, Baerlocher GM, Läubli H, Hunger RE, Romero P, Simon HU, von Gunten S. Siglec-9 regulates an effector memory CD8⁺ T-cell subset that congregates in the melanoma tumor microenvironment. Cancer Immunol Res 2019; 7(5): 707–718

[32] Wang J, Sun J, Liu LN, Flies DB, Nie X, Toki M, Zhang J, Song C, Zarr M, Zhou X, Han X, Archer KA, O’Neill T, Herbst RS, Boto AN, Sanmamed MF, Langermann S, Rimm DL, Chen L. Siglec-15 as an immune suppressor and potential target for normalization cancer immunotherapy. Nat Med 2019; 25(4): 656–666

[33] Weenink B, Draaisma K, Ooi HZ, Kros JM, Sillevis Smitt PAE, Debets R, French PJ. Low-grade glioma harbors few CD8 T cells, which is accompanied by decreased expression of chemo-attractants, not immunogenic antigens. Sci Rep 2019; 9(1): 14643

[34] Gutiérrez L, Caballero N, Fernández-Calleja L, Karkoulia E, Strouboulis J. Regulation of GATA1 levels in erythropoiesis. IUBMB Life 2020; 72(1): 89–105

[35] Milde-Langosch K. The Fos family of transcription factors and their role in tumourigenesis. Eur J Cancer 2005; 41(16): 2449–2461

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