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

doi:10.1007/s11684-021-0868-z

Front. Med.

2022, Vol. 16

Issue (4) : 596-609 https://doi.org/10.1007/s11684-021-0868-z

RESEARCH ARTICLE

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

Chen Zhang^1,², Jiandong Zhang^3,⁴, Fan Liang^1,², Han Guo¹, Sanhui Gao^1,², Fuying Yang^1,², Hua Guo², Guizhen Wang², Wei Wang³(

), Guangbiao Zhou^1,²(

)

¹. State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing 100101, China
². State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
³. Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
⁴. Shanxi Bethune Hospital Affiliated with Shanxi Academy of Medical Sciences, Taiyuan 030032, China

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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

Corresponding Author(s): Wei Wang,Guangbiao Zhou

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 09 November 2021 Online First Date: 19 January 2022 Issue Date: 02 September 2022

Cite this article:

Chen Zhang,Jiandong Zhang,Fan Liang, et al. Innate immune checkpoint Siglec10 in cancers: mining of comprehensive omics data and validation in patient samples[J]. Front. Med., 2022, 16(4): 596-609.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-021-0868-z
https://academic.hep.com.cn/fmd/EN/Y2022/V16/I4/596

Fig.1 Expression of Siglec10 across different human cancer types. (A) Siglec10 expression levels in different cancer types from the TCGA database were determined by TIMER. *P<0.05, **P<0.01, ***P<0.001. (B) Siglec10 expression is significantly upregulated in GBM, KIRC, LAML, LGG, PAAD, and STAD. *P<0.05. T, tumor; N, normal. (C) Siglec10 is dramatically reduced in ACC and THYM. *P<0.05. The data of (B) and (C), including data from TCGA and GTEx, are from the GEPIA database. (D) Increased (red) and decreased (blue) Siglec10 expression in data sets with P value<0.05 compared with normal tissues in the Oncomine database. (E) The body map shows Siglec10 expression in tumor tissues (red) and normal tissues (green); darker color indicates higher Siglec10 expression.

Fig.2 Overall survival of patients with different types of cancers with high or low Siglec10 expression levels. (A) High Siglec10 expression in CSCC, EA, READ, and UCEC is related to better prognosis in KM Plotter data sets. (B) High Siglec10 expression in ESCC, KIRC, TGCT, and THYM is related to worse outcome in KM Plotter. (C) High Siglec10 expression in SKCM is related to longer survival in the GEPIA database. (D) High Siglec10 expression in KIRC, LGG, THYM, and UVM is related to worse clinical outcome in the GEPIA database.

Fig.3 Siglec10 expression is related to tumor progression. (A, B) Siglec10 expression in different grades of KIRC (A) and LGG (B) by analyzing TCGA data sets. (C, D) Correlations between Siglec10 expression and grades of KIRC in GSE53757 (C) and GSE22541 (D) in the GEO database. (E) The ccB subtype of KIRC has a higher Siglec10 expression than the ccA subtype. (F) Siglec10 expression in GBM and LGG. (G, H) GO analysis of the coexpressed genes of Siglec10 in KIRC (G) and LGG (H). (I) Expression levels of Siglec10, PDCD1, CTLA4, and LAG3 in different cancer types in the GEPIA database.

Fig.4 Correlations between Siglec10 expression and immune cell infiltration in KIRC and LGG. (A) Siglec10 expression is negatively related to tumor purity but positively related to infiltrating levels of B cells, CD4⁺ T cells, CD8⁺ T cells, macrophages, dendritic cells, and neutrophils in KIRC. (B) Siglec10 expression is negatively related to tumor purity but positively related to infiltrating levels of B cells, CD4⁺ T, macrophages, dendritic cells, and neutrophils in LGG. (C) Siglec10 expression is positively related to the marker genes of monocytes, TAM, M2 macrophages, T cells, B cells, dendritic cells, and exhausted T cells. (D, E) Scatterplots of correlations between Siglec10 and marker genes of monocytes, TAMs, and M1 and M2 macrophages in KIRC (D) and LGG (E).

Fig.5 Detection of Siglec10 in patients’ samples by qPCR and Western blot. (A, B) Siglec10 expression in seven patients with LGG was detected by qPCR (A) and Western blot (B). (C, D) Siglec10 expression in 22 patients with KIRC was detected by qPCR (C) and Western blot (D).

Tab.1 Baseline demographic characteristics of the 131 patients with KIRC

Fig.6 Detection of Siglec10 in patients’ samples by IHC. (A, B) IHC assays were performed using samples of patients with KIRC and an anti-Siglec10 antibody (A). The corresponding immunoreactivity score of Siglec10 in 19 patients was calculated (B). (C, D) IHC assays were performed using KIRC samples on a tissue microarray and an anti-Siglec10 antibody (C). The corresponding immunoreactivity score of Siglec10 in 90 patients was calculated (D). (E) Overall survival of 90 patients with KIRC with a high or a low Siglec10 expression. (F) Immunoreactivity score of Siglec10 in a total of 131 patients of our settings. (G) Siglec10 expression (blue) in PBMC-derived M2 macrophages. Red, isotype control. (H) Percentage of macrophages phagocytosing 786-O cancer cells.

Fig.7 Potential regulators of Siglec10. (A) A total of 37 transcription factors are predicted to be able to regulate Siglec10 from the GCBI online database. (B) Cross-referencing of the transcription factors in the GCBI database and Cistrome data browser. (C) Expression of Siglec10 in the indicated cell lines was detected by qPCR. (D) 293T cells were transfected with Siglec10 promoter–luciferase reporter construct and c-FOS, GATA1, or SPIB genes and assessed by luciferase assays. (E) Jurkat cells were transfected with the indicated siRNAs, lysed 48 h later, and RNA was extracted and used to test the expression of Siglec10 by qPCR.

1	MF Sanmamed, L Chen. A paradigm shift in cancer immunotherapy: from enhancement to normalization. Cell 2018; 175(2): 313–326 https://doi.org/10.1016/j.cell.2018.09.035 pmid: 30290139
2	M Zhang, J Yang, W Hua, Z Li, Z Xu, Q Qian. Monitoring checkpoint inhibitors: predictive biomarkers in immunotherapy. Front Med 2019; 13(1): 32–44 https://doi.org/10.1007/s11684-018-0678-0 pmid: 30680606
3	L Tu, R Guan, H Yang, Y Zhou, W Hong, L Ma, G Zhao, M. Yu. 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 https://doi.org/10.1002/ijc.32785 pmid: 31721169
4	MA Postow, R Sidlow, MD Hellmann. Immune-related adverse events associated with immune checkpoint blockade. N Engl J Med 2018; 378(2): 158–168 https://doi.org/10.1056/NEJMra1703481 pmid: 29320654
5	B Li, HL Chan, P Chen. Immune checkpoint inhibitors: basics and challenges. Curr Med Chem 2019; 26(17): 3009–3025 https://doi.org/10.2174/0929867324666170804143706 pmid: 28782469
6	E Bandala-Sanchez, Y Zhang, S Reinwald, JA Dromey, BH Lee, J Qian, RM Böhmer, LC Harrison. T cell regulation mediated by interaction of soluble CD52 with the inhibitory receptor Siglec-10. Nat Immunol 2013; 14(7): 741–748 https://doi.org/10.1038/ni.2610 pmid: 23685786
7	AA Barkal, RE Brewer, M Markovic, M Kowarsky, SA Barkal, BW Zaro, V Krishnan, J Hatakeyama, O Dorigo, LJ Barkal, IL Weissman. CD24 signalling through macrophage Siglec-10 is a target for cancer immunotherapy. Nature 2019; 572(7769): 392–396 https://doi.org/10.1038/s41586-019-1456-0 pmid: 31367043
8	E Bandala-Sanchez, N G Bediaga, ED Goddard-Borger, K Ngui, G Naselli, NL Stone, AM Neale, LA Pearce, A Wardak, P Czabotar, T Haselhorst, A Maggioni, LA Hartley-Tassell, TE Adams, LC Harrison. 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 https://doi.org/10.1073/pnas.1722056115 pmid: 29997173
9	GY Chen, J Tang, P Zheng, Y Liu. CD24 and Siglec-10 selectively repress tissue damage-induced immune responses. Science 2009; 323(5922): 1722–1725 https://doi.org/10.1126/science.1168988 pmid: 19264983
10	G Whitney, S Wang, H Chang, KY Cheng, P Lu, XD Zhou, WP Yang, M McKinnon, M Longphre. 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 https://doi.org/10.1046/j.0014-2956.2001.02543.x pmid: 11733002
11	E Kivi, K Elima, K Aalto, Y Nymalm, K Auvinen, E Koivunen, DM Otto, PR Crocker, TA Salminen, M Salmi, S Jalkanen. Human Siglec-10 can bind to vascular adhesion protein-1 and serves as its substrate. Blood 2009; 114(26): 5385–5392 https://doi.org/10.1182/blood-2009-04-219253 pmid: 19861682
12	AM Shathili, E Bandala-Sanchez, A John, ED Goddard-Borger, M Thaysen-Andersen, AV Everest-Dass, TE Adams, LC Harrison, NH Packer. Specific sialoforms required for the immune suppressive activity of human soluble CD52. Front Immunol 2019; 10: 1967 https://doi.org/10.3389/fimmu.2019.01967 pmid: 31507595
13	E Bandala-Sanchez, NG Bediaga, G Naselli, AM Neale, LC Harrison. Siglec-10 expression is up-regulated in activated human CD4+ T cells. Hum Immunol 2020; 81(2–3): 101–104 https://doi.org/10.1016/j.humimm.2020.01.009 pmid: 32046870
14	P Zhang, X Lu, K Tao, L Shi, W Li, G Wang, K Wu. Siglec-10 is associated with survival and natural killer cell dysfunction in hepatocellular carcinoma. J Surg Res 2015; 194(1): 107–113 https://doi.org/10.1016/j.jss.2014.09.035 pmid: 25450598
15	J Gao, BA Aksoy, U Dogrusoz, G Dresdner, B Gross, SO Sumer, Y Sun, A Jacobsen, R Sinha, E Larsson, E Cerami, C Sander, N Schultz. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013; 6(269): pl1 https://doi.org/10.1126/scisignal.2004088 pmid: 23550210
16	DR Rhodes, S Kalyana-Sundaram, V Mahavisno, R Varambally, J Yu, BB Briggs, TR Barrette, MJ Anstet, C Kincead-Beal, P Kulkarni, S Varambally, D Ghosh, AM Chinnaiyan. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 2007; 9(2): 166–180 https://doi.org/10.1593/neo.07112 pmid: 17356713
17	T Barrett, SE Wilhite, P Ledoux, C Evangelista, IF Kim, M Tomashevsky, KA Marshall, KH Phillippy, PM Sherman, M Holko, A Yefanov, H Lee, N Zhang, CL Robertson, N Serova, S Davis, A Soboleva. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 2013; 41(Database issue): D991–D995 pmid: 23193258
18	GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020; 369(6509): 1318–1330 https://doi.org/10.1126/science.aaz1776 pmid: 32913098
19	Z Tang, C Li, B Kang, G Gao, C Li, Z Zhang. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017; 45(W1): W98–W102 https://doi.org/10.1093/nar/gkx247 pmid: 28407145
20	Á Nagy, A Lánczky, O Menyhárt, B Győrffy. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep 2018; 8(1): 9227 https://doi.org/10.1038/s41598-018-27521-y pmid: 29907753
21	T Li, J Fan, B Wang, N Traugh, Q Chen, JS Liu, B Li, XS Liu. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 2017; 77(21): e108–e110 https://doi.org/10.1158/0008-5472.CAN-17-0307 pmid: 29092952
22	AR Brannon, A Reddy, M Seiler, A Arreola, DT Moore, RS Pruthi, EM Wallen, ME Nielsen, H Liu, KL Nathanson, B Ljungberg, H Zhao, JD Brooks, S Ganesan, G Bhanot, WK Rathmell. Molecular stratification of clear cell renal cell carcinoma by consensus clustering reveals distinct subtypes and survival patterns. Genes Cancer 2010; 1(2): 152–163 https://doi.org/10.1177/1947601909359929 pmid: 20871783
23	Y Huang, J Wang, P Jia, X Li, G Pei, C Wang, X Fang, Z Zhao, Z Cai, X Yi, S Wu, B Zhang. Clonal architectures predict clinical outcome in clear cell renal cell carcinoma. Nat Commun 2019; 10(1): 1245 https://doi.org/10.1038/s41467-019-09241-7 pmid: 30886153
24	P Wesseling, D Capper. WHO 2016 Classification of gliomas. Neuropathol Appl Neurobiol 2018; 44(2): 139–150 https://doi.org/10.1111/nan.12432 pmid: 28815663
25	L Cassetta, S Fragkogianni, AH Sims, A Swierczak, LM Forrester, H Zhang, DYH Soong, T Cotechini, P Anur, EY Lin, A Fidanza, M Lopez-Yrigoyen, MR Millar, A Urman, Z Ai, PT Spellman, ES Hwang, JM Dixon, L Wiechmann, LM Coussens, HO Smith, JW Pollard. Human tumor-associated macrophage and monocyte transcriptional landscapes reveal cancer-specific reprogramming, biomarkers, and therapeutic targets. Cancer Cell 2019; 35(4): 588–602.e10 https://doi.org/10.1016/j.ccell.2019.02.009 pmid: 30930117
26	M Kudo. Targeted therapy for liver cancer: updated review in 2012. Curr Cancer Drug Targets 2012; 12(9): 1062–1072 pmid: 22920326
27	S Kumar Vodnala, NP Restifo. Identifying the source of tumour-infiltrating T cells. Nature 2019; 576(7787): 385–386 https://doi.org/10.1038/d41586-019-03670-6 pmid: 31844254
28	H Läubli, A Varki. 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 https://doi.org/10.1007/s00018-019-03288-x pmid: 31485715
29	J Munday, S Kerr, J Ni, AL Cornish, JQ Zhang, G Nicoll, H Floyd, MG Mattei, P Moore, D Liu, PR Crocker. Identification, characterization and leucocyte expression of Siglec-10, a novel human sialic acid-binding receptor. Biochem J 2001; 355(2): 489–497 https://doi.org/10.1042/bj3550489 pmid: 11284738
30	K Miyazaki, K Sakuma, YI Kawamura, M Izawa, K Ohmori, M Mitsuki, T Yamaji, Y Hashimoto, A Suzuki, Y Saito, T Dohi, R Kannagi. Colonic epithelial cells express specific ligands for mucosal macrophage immunosuppressive receptors siglec-7 and-9. J Immunol 2012; 188(9): 4690–4700 https://doi.org/10.4049/jimmunol.1100605 pmid: 22467657
31	Q Haas, KF Boligan, C Jandus, C Schneider, C Simillion, MA Stanczak, M Haubitz, SM Seyed Jafari, A Zippelius, GM Baerlocher, H Läubli, RE Hunger, P Romero, HU Simon, S von Gunten. 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 https://doi.org/10.1158/2326-6066.CIR-18-0505 pmid: 30988027
32	J Wang, J Sun, LN Liu, DB Flies, X Nie, M Toki, J Zhang, C Song, M Zarr, X Zhou, X Han, KA Archer, T O’Neill, RS Herbst, AN Boto, MF Sanmamed, S Langermann, DL Rimm, L Chen. Siglec-15 as an immune suppressor and potential target for normalization cancer immunotherapy. Nat Med 2019; 25(4): 656–666 https://doi.org/10.1038/s41591-019-0374-x pmid: 30833750
33	B Weenink, K Draaisma, HZ Ooi, JM Kros, PAE Sillevis Smitt, R Debets, PJ French. 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 https://doi.org/10.1038/s41598-019-51063-6 pmid: 31601888
34	L Gutiérrez, N Caballero, L Fernández-Calleja, E Karkoulia, J Strouboulis. Regulation of GATA1 levels in erythropoiesis. IUBMB Life 2020; 72(1): 89–105 https://doi.org/10.1002/iub.2192 pmid: 31769197
35	K Milde-Langosch. The Fos family of transcription factors and their role in tumourigenesis. Eur J Cancer 2005; 41(16): 2449–2461 https://doi.org/10.1016/j.ejca.2005.08.008 pmid: 16199154

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