Application of Siglec-10 / G or fusion protein containing Siglec-10 / G protein structure in preparation of glioma treatment medicine

Abstract

The invention discloses an application of Siglec-10 / G or a fusion protein containing a Siglec-10 / G protein structure in preparation of a medicine for treating glioma. The application finds that Siglec-10 / G is a new key myeloid immune checkpoint molecule in a glioma immunosuppression microenvironment, and reveals a specific molecular mechanism that Siglec-10 / G and related action signals (such as MUC1, CKB and GOT1) thereof influence glioma generation. The Rebinant Mouse Siglec-10 (C-Fc) provided by the invention can be combined with a ligand on the surface of a tumor cell, so that endogenous Siglec-10 / G signal transduction on MDM is competitively blocked, the anti-tumor immunity of a tumor-bearing mouse is improved, the growth of glioma is inhibited, the survival time of the tumor-bearing mouse is prolonged, and the Rebinant Mouse Siglec-10 (C-Fc) can be used for treating glioma.

Description

Technical Field

[0001] The present invention relates to the application of Siglec-10 / G or a fusion protein containing a Siglec-10 / G protein structure in the preparation of a drug for treating glioma, and belongs to the field of pharmaceuticals. Background Art

[0002] Gliomas are the most common primary tumors of the central nervous system, with a global incidence of approximately 5 per 100,000 patients and a high mortality rate. Gliomas are classified into grades I to IV based on their tumor cell characteristics and degree of malignancy, with grade IV, or glioblastoma, being the most malignant. Traditional treatments such as surgery and chemotherapy have a low cure rate for gliomas, leading to high recurrence rates and a poor prognosis. While current tumor immunotherapies, such as CAR-T and PD-1 / PD-L1 blockers, are making continuous progress and breakthroughs, immunotherapy for "cold tumors" like gliomas has been less effective, potentially due to the unique immunosuppressive microenvironment of gliomas.

[0003] The glioma microenvironment harbors a variety of immunosuppressive cells, including TAMs (tumor-associated macrophages), MDSCs (myeloid-derived suppressor cells), and Treg cells. TAMs comprise the largest proportion of the glioma immune microenvironment, along with MDMs (monocyte-derived macrophages) and microglia. Numerous immune checkpoint molecules, such as SIRPA, PD-1, and LILRB1, have been identified in glioma MDMs. These molecules bind to ligands on the tumor cell surface, transmitting "don't eat me" signals, inhibiting macrophage phagocytosis and promoting tumor cell immune escape. Based on the role of these immune checkpoints, researchers have developed a variety of immune checkpoint blockers targeting glioma MDMs. However, these drugs have not yet achieved the desired results in clinical translational therapy. In particular, blockers of SIRPα or its ligand CD47 have been ineffective in glioma treatment. CD47 blockade alone does not significantly improve patient survival, and due to off-target effects, it can damage red blood cells, leading to serious side effects. These safety and efficacy issues further limit its clinical application. Furthermore, the use of PD-1 antibodies in glioma patients presents numerous challenges, particularly the failure to demonstrate an effective immune response in many patients, resulting in suboptimal clinical outcomes and an inability to significantly improve patient survival. Therefore, there is an urgent need to identify additional key MDM immune checkpoint molecules to provide new options for targeted treatment of glioma MDM.

[0004] Siglecs (Sialic acid-binding immunoglobulin-like lectins) are a family of receptors that bind to sialic acid-containing glycans. Currently, 16 human and 9 mouse Siglec molecules have been discovered. Depending on the intracellular signaling domain, Siglec receptors can also be divided into inhibitory and activating types. Inhibitory Siglec receptors contain intracellular domains containing immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and immunoreceptor tyrosine based switch motifs (ITSMs), which can participate in regulating intracellular signals through SHP1 and SHP2 phosphatases. Most Siglecs contain ITIMs in their intracellular segments, thereby exerting immunosuppressive functions. The Siglec family of molecules are single-pass transmembrane proteins with highly typical and conserved structural features. Their transmembrane region consists of 2-17 extracellular Ig domains, with the N-terminus comprised of a sialic acid-binding V-set Ig domain and a number of C2-set Ig domains. Recent studies have found that myeloid-derived Siglec-15 suppresses antigen-specific T cell responses and promotes tumor growth in various cancer tissues, including liver, lung, and thyroid cancers. Genetic knockout of Siglec-15 or antibody blockade of Siglec-15 effectively restores T cell activation. Knockout of Siglec-E on macrophages inhibits glioma growth, shifts the immunosuppressive phenotype of macrophages to one with more anti-tumor activity, and significantly enhances T cell activation and proliferation. Siglec-10 in breast and ovarian cancer TAMs inhibits macrophage phagocytosis and anti-tumor immunity by binding to its ligand CD24 on the tumor cell surface. In addition, analysis of the TCGA and CGGA databases found that Siglec-10 is the most highly expressed molecule in the Siglec family of proteins in gliomas, suggesting that it may play a key role in the occurrence of gliomas. Summary of the Invention

[0005] Objectives of the invention: The first objective of the present invention is to provide a method for preparing a drug for treating gliomas using a Siglec-10 / G or a fusion protein containing a Siglec-10 / G protein structure. The second objective of the present invention is to provide a pharmaceutical composition for treating gliomas.

[0006] Technical solution: Use of the Siglec-10 / G or a fusion protein containing the Siglec-10 / G protein structure described in the present invention in the preparation of drugs for treating gliomas.

[0007] This study found that Siglec-10 / G binds to and activates the tumor cell surface ligand MUC1 in glioma MDMs, recruiting the downstream creatine kinase CKB to promote creatine production. Creatine inhibits JAK1-STAT1 signaling, thereby inhibiting STAT1-mediated antigen presentation and GOT1 regulation, leading to αKG accumulation and promoting MDM immunosuppression, thereby promoting glioma growth. This study establishes the key role of the MUC1-Siglec-10 / G-CKB-STAT1-GOT1 axis in the immunosuppressive microenvironment of gliomas.

[0008] Furthermore, the Siglec-10 / G protein structure is the extracellular domain of the Siglec-10 / G protein.

[0009] Furthermore, the fusion protein comprising the Siglec-10 / G protein structure is Recombinant Mouse Siglec-10 (C-Fc). The Recombinant Mouse Siglec-10 (C-Fc) is composed of a fusion of the Human IgG1 Fc domain and Siglec-G.

[0010] Furthermore, the drug competitively blocks endogenous Siglec-10 / G signaling on MDM, thereby interfering with the accumulation of αKG and inhibiting glioma growth.

[0011] A pharmaceutical composition for treating glioma, comprising Siglec-10 / G or a fusion protein comprising a Siglec-10 / G protein structure.

[0012] Furthermore, the fusion protein comprising the Siglec-10 / G protein structure is Recombinant Mouse Siglec-10 (C-Fc).

[0013] Furthermore, the medicine also contains a pharmaceutically acceptable carrier or excipient.

[0014] Furthermore, the pharmaceutically acceptable carrier is selected from one or more of a filler, a wetting agent, a binder, a disintegrant, and a lubricant.

[0015] Furthermore, the dosage form of the drug includes tablets, aqueous injections or capsules.

[0016] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: This application discovered that Siglec-10 / G is a new key myeloid immune checkpoint molecule in the immunosuppressive microenvironment of gliomas, and revealed the specific molecular mechanism by which Siglec-10 / G and its related action signals (such as MUC1, CKB, GOT1) affect the occurrence of gliomas. In addition to being a good theoretical discovery innovation, this discovery also provides a theoretical basis for the development of glioma MDM targeted drugs and has potential clinical transformation value. The Recombinant Mouse Siglec-10 (C-Fc) provided by the present invention can bind to the ligand on the surface of tumor cells, thereby competitively blocking the endogenous Siglec-10 / G signal transduction on MDM, improving the anti-tumor immunity of tumor-bearing mice, inhibiting glioma growth, and prolonging the survival time of tumor-bearing mice, and can be used for the treatment of gliomas. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The results of the analysis were as follows: the expression of SIGLEC10 in tumors, its relationship with patient prognosis, and its correlation with the expression of multiple immune checkpoint molecules. Figure 1 A is the expression of SIGLEC10 in various tumor tissues analyzed by TCGA database; Figure 1 B is the relationship between SIGLEC10 expression and prognosis of glioma patients analyzed using TCGA and CGGA databases; Figure 1 CD is the correlation between SIGLEC10 and the expression of immune checkpoint molecules such as PD-1, CTLA-4, HAVCR2, TIGIT, and CD274 in gliomas analyzed using the TCGA and CGGA databases; Figure 1 E is the expression of SIGLEC10 in gliomas of different grades analyzed using the CGGA database; Figure 1 F is the expression of SIGLEC family member molecules in gliomas analyzed using the TCGA and CGGA databases.

[0018] Figure 2 Siglec-10 / G is specifically and highly expressed in the glioma immune microenvironment MDM, Figure 2 A is the TISCH2 database analysis of Siglec-10 expression in various cells in the glioma microenvironment. Figure 2 BC is an orthotopic glioma mouse model to analyze the expression of SIGLEC10 in different cell types in the glioma microenvironment.

[0019] Figure 3 Results related to the inhibition of glioma growth by knockout of Siglec-G in MDM, including Figure 3 A is a flowchart for establishing a glioma mouse model; Figure 3BC is BLI detection of Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Tumor luminescence intensity of tumor-bearing mice on days 7 and 14; Figure 3 D is MRI detection of Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Tumor volume of tumor-bearing mice on days 7 and 14; Figure 3 E is HE staining to detect Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Tumor volume of tumor-bearing mice; Figure 3 F is the statistics of Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Survival rate of tumor-bearing mice.

[0020] Figure 4 The results showed that Siglec-G knockout in MDM promoted anti-tumor immune response, Figure 4 AB is flow cytometry analysis of Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Tumor-infiltrating CD8 + The proportion and number of T cells; Figure 4 CD is the CD8 cell that secretes the effector cytokine TNF-α infiltrating the tumor in tumor-bearing mice + The proportion and number of T cells; Figure 4 EF is a tumor-infiltrating CD8 cell that secretes effector cytokine IFN-γ in tumor-bearing mice. + The proportion and number of T cells; Figure 4 GH is the proportion and number of tumor-infiltrating B cells in tumor-bearing mice; Figure 4 IJ is the tumor-infiltrating CD4 + the number of T cells; Figure 4 KL is the number of tumor-infiltrating DC cells in tumor-bearing mice; Figure 4 MN is the MHCII infiltrating tumor in tumor-bearing mice + the proportion of macrophages; Figure 4 OP is the proportion and number of tumor-infiltrating MDSCs in tumor-bearing mice; Figure 4 QR is PD-1 infiltrating tumors in tumor-bearing mice + TIM-3 + CD8 + The proportion of T cells; Figure 4 ST is the tumor-infiltrating CD8 + Expression of TIGIT in T cells.

[0021] Figure 5 This is the preliminary analysis result of single-cell sequencing data, Figure 5 A is the sorting of Cx3cr1-Cre and Cx3cr1-CreSiglec-G fl / fl Tumor-infiltrating CD45 in tumor-bearing mice + Experimental flow chart for scRNA-seq of immune cells; Figure 5 B is a gene heat map showing the expression of top marker genes in different immune cells in each cell population; Figure 5 C is Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Tumor-infiltrating CD45 in tumor-bearing mice + UMAP cluster analysis results of immune cells; Figure 5 D is Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Changes in the proportions of different immune cells infiltrating tumors in tumor-bearing mice;

[0022] Figure 6 The scRNA-seq analysis shows the pathway enrichment analysis results after Siglec-G knockout in MDM; Figure 6 A is the result of further UMAP cluster analysis of myeloid cells with significant changes; Figure 6 B is Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Changes in the proportion of tumor-infiltrating myeloid cells in tumor-bearing mice; Figure 6 C is pathway enrichment analysis of differentially expressed genes between the two groups of MDM using annotation databases such as GO, KEGG, BioCarta, and Reactome; Figure 6 D is the expression of JAK-STAT pathway gene sets in two groups of MDM analyzed by ssGSEA, which is displayed using UAMP graphs. The redder the color, the higher the expression level. Figure 6 E: ChEA3 transcription factor prediction analysis of differentially expressed genes in the two groups of MDM.

[0023] Figure 7 RNA-seq analysis shows the pathway enrichment analysis results after Siglec-G knockout in MDM, where Figure 7 A is the gating strategy for flow cytometry sorting of tumor microenvironment MDM; Figure 7 B is the principal component analysis diagram of Bulk RNA-seq; Figure 7 C is the pathway enrichment analysis results of differentially expressed genes in MDM using the GO and KEGG annotation databases; Figure 7 D is GSEA analysis comparing Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Expression of JAK-STAT and antigen presentation gene sets in MDM of tumor-bearing mice; Figure 7 E is the expression of Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl ChEA3 transcription factor prediction analysis was performed on differentially expressed genes in MDM of tumor-bearing mice; Figure 7 F is a gene heat map showing Cx3cr1-Cre and Cx3cr1-Cre Siglec-G fl / fl Expression of interferon, antigen presentation, glycolysis, and mitochondria-related genes in MDM of tumor-bearing mice.

[0024] Figure 8 RNA-seq analysis shows the STAT1 binding peaks after Siglec-G knockout in MDM. Figure 8 A is the CUT&Tag analysis of STAT1 binding peaks in WT and CKO MDM in the in vitro co-culture system; Figure 8 A is the peak intensity of STAT1 binding at the TSS region in WT and CKO MDM in the in vitro co-culture system analyzed by CUT&Tag; Figure 8 C is the distribution of STAT1 binding peaks in WT and CKOMDM in the in vitro co-culture system; Figure 8 D is IGV showing the binding of STAT1 to Irf9, Irf1, Tap1, and B2m in WT and CKO MDM in the in vitro co-culture system; Figure 8 E is the GSEA analysis of the enrichment of STAT1 differentially regulated target genes JAK-STAT and antigen presentation gene sets in WT and CKO MDM in the in vitro co-culture system.

[0025] Figure 9 Siglec-G knockout promotes anti-tumor immune response in glioma by regulating the STAT1-GOT1 axis. Figure 9 A is a schematic diagram of the combined analysis of differentially expressed genes predicted by scRNA-seq Compass metabolism, differentially expressed genes by bulk RNA-seq, and differentially regulated target genes by STAT1CUT&Tag, combined with the TCGA tumorigenesis association score; Figure 9 B is the TCGA database analysis of GOT1 expression in glioma, its relationship with the prognosis of glioma patients, and the correlation analysis between GOT1 and the expression of multiple immune checkpoint molecules; Figure 9 C is IGV showing the binding of STAT1 to GOT1 in WT and CKO MDM in the in vitro co-culture system; Figure 9 D is the assay for GOT1 enzyme activity in WT and CKO MDMs in the in vitro co-culture system (n=5); Figure 9E is qPCR analysis of the expression of antigen presentation, M2 and other related genes in the in vitro co-culture systems of WT+DMSO, CKO+DMSO, WT+iGOT1 and CKO+iGOT1 MDM (n=4); Figure 9 F is flow cytometric analysis of CD206 expression in in vitro co-culture systems WT+DMSO, CKO+DMSO, WT+iGOT1, and CKO+iGOT1 MDM (representative graph on the left, statistical graph on the right, n=4); Figure 9 G is flow cytometric analysis of CD86 expression in in vitro co-culture systems WT+DMSO, CKO+DMSO, WT+iGOT1, and CKO+iGOT1 MDM (representative graph on the left, statistical graph on the right, n=4); Figure 9 H is HE staining to detect the tumor volume of WT+NT, CKO+NT, WT+iGOT1 and CKO+iGOT1 tumor-bearing mice (NT represents untreated), scale bar: 10×: 500 μm; Figure 9 I is the statistical survival rate of WT+NT, CKO+NT, WT+iGOT1 and CKO+iGOT1 tumor-bearing mice (n=7).

[0026] Figure 10 Siglec-G knockout in MDM promotes anti-tumor immune response by regulating the STAT1-GOT1-aKG axis. Figure 10 A is a metabolic flow chart regulated by GOT1 in cells; Figure 10 B: αKG content in WT and CKO MDMs of the in vitro co-culture system was detected by a kit (n=3); Figure 10 C is qPCR analysis of the expression of antigen presentation, M2 and other related genes in the in vitro co-culture systems WT+DMSO, CKO+DMSO, WT+DM-αKG and CKO+DM-αKG MDM (n=4); Figure 10 D is flow cytometry analysis of CD206 expression in in vitro co-culture systems WT+DMSO, CKO+DMSO, WT+DM-αKG, and CKO+DM-αKG MDM (representative graph on the left, statistical graph on the right, n=4); Figure 10 E is flow cytometry analysis of CD86 expression in in vitro co-culture systems WT+DMSO, CKO+DMSO, WT+DM-αKG and CKO+DM-αKG MDM (representative graph on the left, statistical graph on the right, n=4).

[0027] Figure 11 APEX2 neighbor protein labeling combined with mass spectrometry analysis revealed that Siglec-G interacts with Ckb, Figure 11 A is the experimental flow chart of Siglec-G-APEX2 neighboring protein labeling and mass spectrometry analysis; Figure 11 B: After iBMDM cells stably expressed Siglec-G-APEX2 fusion protein, biotin phenol (BP) and H2O2 were added for biotinylation reaction, and the biotinylation levels of each group in the cells were detected by western blot (the left side is the input sample, and the right side is the IP sample); Figure 11 C: Biotinylation of Siglec-G-APEX2 iBMDM cells co-cultured with or without GL261 cells. Protein enrichment was then performed using streptavidin magnetic beads. The enriched proteins were then sent for mass spectrometry analysis. The mass spectrometry results were combined with TCGA Score to identify key proteins. Figure 11 D: HEK 293T cells were transfected with FLAG-Siglec-G and MYC-CKB, and then CKB protein was immunoprecipitated with MYC-beads, and the expression of Siglec-G was detected by western blot.

[0028] Figure 12 Siglec-G promotes the immunosuppressive function of MDM by regulating the CKB-creatine-JAK1 / STAT1 axis. Figure 12 A: CKB enzyme activity in WT and CKO MDMs in the in vitro co-culture system detected by the kit (n=4); Figure 12 BC metabolomics was used to detect the changes in creatine content and creatine / phosphocreatine ratio in WT and CKO MDMs in the in vitro co-culture system (n=5); Figure 12 D is the analysis of creatine content in tumor tissues and PBMC MDM cells of glioma patients with public data (n=5); Figure 12 E is Western blot analysis of the protein expression levels of JAK1, p-JAK1, STAT1, and p-STAT1 in WT+NT, CKO+NT, WT+Cr, and CKO+Cr MDMs in the in vitro co-culture system; Figure 12 F is qPCR analysis of the expression of antigen presentation and M2-related genes in the in vitro co-culture system WT+NT, CKO+NT, WT+Cr, and CKO+CrMDM (n=4); Figure 12 G is flow cytometric analysis of CD206 expression in WT+NT, CKO+NT, WT+Cr, and CKO+Cr MDMs in the in vitro co-culture system (representative graph on the left, statistical graph on the right, n=4); Figure 12 H is flow cytometric analysis of CD86 expression in WT+NT, CKO+NT, WT+Cr, and CKO+Cr MDMs in the in vitro co-culture system (representative graph on the left, statistical graph on the right, n=4); Figure 12I is the kit for detecting the creatine content in the in vitro co-culture system WT+ShCtrl, CKO+ShCtrl, WT+ShCkb and CKO+ShCkb MDM (ShCtrl is the control without knockdown of Ckb); Figure 12 J is flow cytometric analysis of CD206 expression in in vitro co-culture systems WT+ShCtrl, CKO+ShCtrl, WT+ShCkb, and CKO+ShCkbMDM (representative graph on the left, statistical graph on the right, n=4); Figure 12 K Flow cytometry analysis of CD86 expression in in vitro co-culture systems WT+ShCtrl, CKO+ShCtrl, WT+ShCkb and CKO+ShCkb MDM (representative graph on the left, statistical graph on the right, n=4).

[0029] Figure 13 Affinity mass spectrometry analysis revealed new ligands for the glioma cell membrane protein MUC1, Figure 13 A is the search for potential ligands of Siglec-G on GL261 cells by affinity mass spectrometry and the analysis flow chart; Figure 13 B: Western blot detection of the binding of Siglec-G-Fc to MUC1; Figure 13 C is the flow cytometry detection of the binding of Siglec-G-Fc to MUC1; Figure 13 D Immunofluorescence detection of Siglec-G-Fc and MUC1 expression and localization in GL261 cells, scale bar: 20×: 2.5 μm; Figure 13 E is the TISCH2 database analysis of MUC1 expression in various cells in the glioma microenvironment; Figure 13 F is the expression of MUC family protein molecules in glioma cells analyzed using CCLE database; Figure 13 G is the expression of MUC1 in GL261 and CT-2A cell lines detected by Western blot; Figure 13 H is the CGGA database analysis of MUC1 expression in different grades of gliomas.

[0030] Figure 14 MUC1 in tumor cells is involved in regulating anti-tumor immunity in gliomas, Figure 14 A is the expression of MUC1 in Sh-Ctrl and Sh-Muc1 GL261 cells detected by Western blot; Figure 14 B is the statistics of the proliferation number of Sh-Ctrl and Sh-Muc1GL261 cells; Figure 14 CD are BLI images of tumor luminescence intensity in Sh-Ctrl and Sh-Muc1 GL261 tumor-bearing mice on days 7 and 14 (C is a representative graph, D is a statistical graph, n = 5); Figure 14E is the statistical survival rate of Sh-Ctrl and Sh-Muc1 GL261 tumor-bearing mice (n=8); Figure 14 FG is flow cytometric analysis of the CD8 cytokines secreting the effector cytokine TNF-α in the tumor-infiltrating cells of Sh-Ctrl and Sh-Muc1 GL261 tumor-bearing mice. + The proportion of T cells (F is a representative figure, G is a statistical figure of the proportion and number, n = 5); Figure 14 HI flow cytometric analysis of tumor-infiltrating CD8 cells secreting effector cytokines IFN-γ in Sh-Ctrl and Sh-Muc1 GL261 tumor-bearing mice + The proportion of T cells (H is a representative figure, I is a statistical figure of the proportion and number, n=5).

[0031] Figure 15 Results of Siglec-G-Fc treatment of tumor-bearing mice, Figure 15 A is the flowchart of mouse modeling and BLI experiments with IgG1-Fc and Siglec-G-Fc intervention; Figure 15 BC is the BLI detection of tumor luminescence intensity on days 7 and 14 in IgG1-Fc and Siglec-G-Fc tumor-bearing mice; Figure 15 D is the statistical survival rate of tumor-bearing mice intervened by IgG1-Fc and Siglec-G-Fc. DETAILED DESCRIPTION

[0032] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0033] Recombinant Mouse Siglec-10 (C-Fc) (abbreviated as Siglec-G-Fc in the examples) was purchased from Nearshore Protein Company, Cat. No.: C05K.

[0034] Example 1. SIGLEC10 is highly expressed in gliomas and is highly correlated with poor patient prognosis and immune checkpoint molecule expression

[0035] GEPIA2 (http: / / gepia2.cancer-pku.cn / ) was used to analyze the expression of SIGLEC10 in 31 tumor types, the relationship between SIGLEC10 expression and the prognosis of glioma patients, and the correlation between SIGLEC10 expression and the expression of multiple immune checkpoint molecules. CGGA (https: / / www.cgga.org.cn / ) was used to analyze the relationship between SIGLEC10 expression and the prognosis of glioma patients, the correlation between SIGLEC10 expression and the expression of multiple immune checkpoint molecules, the expression of SIGLEC10 in patients with different grades of glioma, and the expression of Siglec family molecules in glioma.

[0036] The results are as follows Figure 1 As shown, TCGA database analysis found that compared with other tumors, the expression difference of SIGLEC10 in GBM (Glioblastoma multiforme, glioblastoma) and LGG (Lower-grade glioma, low-grade glioma) was the most significant ( Figure 1 A); Analysis of the TCGA and CGGA (Chinese glioma Genome Atlas) databases showed that high expression of SIGLEC10 was associated with poor prognosis of patients ( Figure 1 B), and SIGLEC10 expression is highly positively correlated with the expression of multiple immune checkpoint molecules such as PD-1, CTLA-4, HAVCR2, TIGIT, and CD274 ( Figure 1 CD). In addition, as the grade of glioma increases, the expression level of SIGLEC10 also gradually increases ( Figure 1 E). SIGLEC10 belongs to the sialic acid-binding immunoglobulin-like lectin family, which includes 16 members. It is worth noting that our analysis of the TCGA and CGGA databases found that among all family members, SIGLEC10 (the mouse homolog is Siglec-G) is the most highly expressed in gliomas ( Figure 1 F).

[0037] Example 2. Siglec-10 / G is specifically and highly expressed in the glioma immune microenvironment MDM

[0038] TISCH2 (http: / / tisch.comp-genomics.org / search-gene / ) and an orthotopic glioma mouse model were used to analyze the expression of SIGLEC10 in different cell types of the glioma microenvironment.

[0039] The results are as follows Figure 2 As shown, we use TISCH2( http: / / tisch.comp-genomics.org / search- gene / ) database to further analyze the expression of SIGLEC10 in various cells in the glioma microenvironment. The results showed that SIGLEC10 was specifically highly expressed in the glioma immune microenvironment MDM ( Figure 2 A). At the same time, we established an orthotopic glioma mouse model, and flow cytometry results also showed that Siglec-G was specifically highly expressed in MDM compared to other immune cells ( Figure 2 BC).

[0040] Example 3: Siglec-G knockout in MDM inhibits glioma growth

[0041] Step 1: Monitoring tumor volume and survival in glioma-bearing mice

[0042] 1.1 Establishment of glioma mouse model

[0043] (1) Siglec-G fl / fl Mice (purchased from Saiye Bio) were crossed with Cx3cr1-cre mice (donated by Professor Zhou Jiawei from the Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences) to obtain Siglec-G conditional knockout mice in MDM (Cx3cr1-Cre Siglec-G fl / fl ).

[0044] (2) The experimental mice were anesthetized by intraperitoneal injection of 1% sodium pentobarbital solution. After the mice were quiet, the hair on their heads was removed with a razor and the mice were fixed in a stereotaxic apparatus. The experimental mice were Cx3cr1-cre (control group) and Cx3cr1-Cre Siglec-G fl / fl (Experimental group), experimental mice were 6-8 weeks old, weighed 20-25 g, and there were 10 mice in each group.

[0045] (2) Expose the bregma: Use scissors to make an incision starting from the top of the ear and extending approximately 1 cm upward along the midline of the brain to the top of the ear. Wipe 3% H2O2 solution at the incision site to remove the fascia on the surface of the skull and expose the bregma.

[0046] (3) Locating the target point: Use a microinjection needle to draw GL261-Luc glioma cell suspension with a concentration of 6×10 4 / μl, 5μl per mouse, ensuring there are no bubbles. Fix the microinjection needle on the brain stereotaxic instrument, set the bregma as the coordinate origin, and move 1mm to the right and then 1mm backward from the coordinate origin to find the target point.

[0047] (4) Remove the cranial drill and place it above the target point to drill a hole, being careful not to drill into the brain to prevent cortical damage.

[0048] (5) Slowly lower the microinjection needle by 3 mm, raise it by 0.5 mm, and inject at a rate of 1 μl / min at a depth of 2.5 mm.

[0049] (6) After the injection is completed, stop the injection for 5 minutes to ensure that the residual cells are completely absorbed.

[0050] (7) When the needle is stopped, slowly lift the injection needle.

[0051] (8) Use a disposable suture needle to suture, wipe with iodine, place on a heating pad, wait for the mouse to wake up, and put it back into the cage.

[0052] 1.2 Visible Light Imaging

[0053] On days 7 and 14 after orthotopic tumor implantation, visible light imaging was used to monitor intracranial tumor growth in mice. After gas anesthesia, D-luciferin potassium salt (15 mg / ml) was injected intraperitoneally into each nude mouse according to body weight, and then imaging was performed 10 minutes later. Statistical analysis was performed using GraphPad Prism 7.0, and data are presented as mean ± SEM. Multiple t-tests were used for visible light imaging data, and log-rank analysis was used for mouse survival. *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, P ≥ 0.05.

[0054] 1.3 Magnetic resonance imaging

[0055] Small animal MRI images were taken on the 7th and 14th days after the mice were orthotopically tumor-bearing to detect the growth of intracranial tumors in the mice. After the nude mice were anesthetized by gas, images were taken using MRI.

[0056] 1.4 HE staining

[0057] (1) Perfusion: Mice were anesthetized with 1% pentobarbital. The abdominal and thoracic cavities were then opened to expose the heart and liver. A 20 ml syringe filled with PBS was used to cut the right atrial appendage of the mouse heart (blood was observed to flow out). Then, a scalp needle was inserted into the left ventricle of the mouse. The needle should not be inserted too deep; 0.5 cm is sufficient. 20 ml of PBS was used to perfuse the liver until it turned white.

[0058] (2) After the brain tissue is peeled off, it is fixed with paraformaldehyde for at least 24 hours and then sent to the company for paraffin embedding and sectioning.

[0059] (3) Dewax the paraffin sections by soaking them in xylene for 30 minutes until the sections are completely transparent.

[0060] (4) The sections were sequentially immersed in 100% ethanol for 10 minutes, 95% ethanol for 5 minutes, 85% ethanol for 5 minutes, and 70% ethanol for 5 minutes, and finally rinsed with running water for 10 minutes.

[0061] (5) Soak the sections in hematoxylin for 3 minutes, then wash with water for 1-2 minutes until clear.

[0062] (6) Differentiate with 1% hydrochloric acid ethanol for 1 minute to make the nucleus and cytoplasm stain more obvious, and then rinse with running water for 10 minutes to remove the differentiation.

[0063] (7) Stain with eosin for 15 minutes and wash with water for 1-2 minutes until clear.

[0064] (8) The sections were sequentially immersed in 70% ethanol for 2 minutes, 90% ethanol for 2 minutes, 100% ethanol for 5 minutes, and xylene for 10 minutes to complete dehydration.

[0065] (9) Place two drops of resin on the tissue, tilt the coverslip about 45 degrees, and gently place the coverslip from below, taking care to avoid bubbles.

[0066] (10) After drying, images were collected using a Nikon inverted microscope.

[0067] The results showed that compared with the control group (Cx3cr1-cre), Siglec-G knockout in MDM significantly inhibited tumor growth ( Figure 3 BE), prolonged the survival time of tumor-bearing mice ( Figure 3 F).

[0068] Step 2: Flow cytometry detection of tumor-infiltrating immune cells

[0069] 2.1 Surface molecular detection

[0070] (1) On the 16th day after the mice were loaded with tumors, the tumor tissues were removed.

[0071] (2) Cut the tumor tissue into small pieces in a 50 ml centrifuge tube cap, transfer it to a centrifuge tube, and resuspend it in 8 ml of digestion solution (the digestion solution is 2% fetal bovine serum, 0.5 mg / mL collagenase IV and 100 U / mL DNase I dissolved in RPMI 1640 culture medium). Place the centrifuge tube in a shaker at 37°C and digest at 200 rpm for 45 min.

[0072] (3) After digestion, filter using a 70 μm cell sieve to obtain a single cell suspension, which was then centrifuged at 4°C, 1500 rpm, for 5 min.

[0073] (4) Discard the supernatant, resuspend with PBS, add PBS to 50 ml, centrifuge again at 4°C, 1500 rpm, 5 min, and repeat washing twice with PBS.

[0074] (5) After washing, discard the supernatant and resuspend the sample in 7 ml of 30% Percoll. Slowly add the resuspended solution to the surface of 3 ml of 70% Percoll, taking care not to disrupt the surface. Centrifuge at room temperature and 800 g for 30 min.

[0075] (6) After centrifugation, aspirate 2 ml of the middle layer immune cell suspension into a 15 ml centrifuge tube, add PBS to 14 ml, and centrifuge at 4°C, 1500 rpm, and 10 min.

[0076] (7) Discard the supernatant and stain with the live / dead dye FVD, dilute it with PBS (1:1000), and incubate on ice in the dark for 25 min.

[0077] (8) After staining, centrifuge at 4°C, 1500 rpm, for 10 min.

[0078] (9) Discard the supernatant and resuspend the cells in staining buffer (containing PBS, 2% FBS and 5 mM EDTA) containing the relevant antibodies, and incubate on ice in the dark for 25 min.

[0079] (10) Centrifuge at 1500 rpm for 10 min.

[0080] (11) Discard the supernatant, resuspend the cells in 400 μl staining buffer, and analyze using a flow cytometer.

[0081] 2.2 Detection of intracellular cytokines

[0082] Single cell suspensions were transferred to 96-well plates, and RPMI medium supplemented with PMA, ionomycin, and Brefeldin A was added, with 200 μl added to each well. The cells were incubated at 37°C for 4 h. After incubation, the cells were stained with the live / dead dye FVD and the cell surface markers CD45 and CD8. The cells were then fixed and permeabilized using the eBioscience fixation and permeabilization kit. The cells were then centrifuged at 4°C, 1500 rpm, for 10 min. The supernatant was discarded, and IFN-γ, TNF-α, and other antibody dilutions were prepared in 1× permeabilization buffer. The cells were incubated on ice in the dark for 25 min. After staining, unbound antibodies were washed away with 1× permeabilization buffer, and the cells were centrifuged at 4°C, 1500 rpm, for 10 min. The supernatant was discarded, and the cells were resuspended in 400 μl of 1× permeabilization buffer. The cells were analyzed using an Attune NxT flow cytometer, and the data were further analyzed using Flow Jo 7.6.1 software. The experimental data were statistically analyzed using GraphPad Prism 7.0 and expressed as mean ± SEM. The test method was two-tailed unpaired student's t-test, where *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, P ≥ 0.05.

[0083] The results are as follows Figure 4 The results showed that Siglec-G knockout in MDM significantly increased tumor-infiltrating CD8 + T, CD8 + IFN-γ + T, CD8 + TNF-α+ T, CD19 + The proportion and number of B cells ( Figure 4 AH). At the same time, CD4 + T cells and DCs (CD11c + MHCII + ) has also increased ( Figure 4 IL). In addition, MHCII + The proportion of (with antigen presenting function) also increased significantly ( Figure 4 MN), while the proportion and number of MDSCs, CD8 + PD1 in T cells + TIM3 + The ratio and expression of TIGIT were significantly down-regulated ( Figure 4 OT).

[0084] In the following examples, we compared Cx3cr1-Cre (WT) tumor-bearing mice and Cx3cr1-Cre Siglec-G fl / fl The tumor microenvironment of (CKO) tumor-bearing mice was further analyzed, and the MUC1-Siglec-10 / G-CKB-STAT1-GOT1 axis was finally established.

[0085] Example 4: scRNA-seq analysis shows that Siglec-G knockout in MDM promotes the formation of an anti-tumor immune microenvironment

[0086] Next, to explore the potential mechanism by which Siglec-10 / G regulates the immunosuppressive microenvironment of glioma, we sorted CD45 + scRNA-seq (Xingeyuan Bio) Figure 5 A), the detected cell types were divided into 22 groups using the bias-free clustering method, and visualized using the unified manifold approximation and projection (UMAP), and cell subpopulations were annotated according to the most significant cell markers ( Figure 5 BC). The results showed that the proportion of myeloid cells (monocytes and macrophages) changed most significantly after Siglec-G knockout in MDM ( Figure 5 D). We conducted a more in-depth analysis of the myeloid cells with significant changes and found that the proportion of two groups of macrophages (MDMs) - Mac-1 and Mac-2 - was high and the proportion changed significantly ( Figure 6 AB), so we mainly focused on MDM for subsequent analysis. MDM differentially expressed gene pathway enrichment analysis showed that Siglec-G knockout significantly upregulated JAK-STAT, NF-κB, antigen presentation and other signaling pathways, while significantly downregulated metabolic pathways such as oxidative phosphorylation and fatty acid oxidation ( Figure 6C), and the expression of JAK-STAT signaling pathway gene set was significantly upregulated after Siglec-G knockout ( Figure 6 D). ChEA3 (Chromatin immunoprecipitation-X Enrichment Analysis Version3) differential gene transcription factor prediction analysis showed that STAT1 was the most significantly enriched ( Figure 6 E). The above results indicate that the JAK-STAT pathway is significantly enriched and upregulated after Siglec-G knockout in MDM.

[0087] Example 5: RNA-seq analysis showed that the JAK-STAT pathway was significantly enriched and upregulated after Siglec-G knockout in MDM

[0088] In addition, we also sorted MDM in the tumor microenvironment for bulk RNA-seq ( Figure 7 A). Principal component analysis showed that the gene expression profiles of Siglec-G-deficient MDMs were significantly different from those of control cells ( Figure 7 B). The results of differentially expressed gene pathway enrichment analysis were similar to those of single-cell sequencing. Siglec-G knockout significantly upregulated signaling pathways such as JAK-STAT, NF-κB, and antigen presentation, while metabolic pathways such as oxidative phosphorylation and fatty acid oxidation were significantly downregulated ( Figure 7 C); GSEA enrichment analysis also found that compared with the control group, JAK-STAT and antigen presentation pathways were significantly upregulated after Siglec-G knockout ( Figure 7 D). ChEA3 differential gene transcription factor prediction analysis found that STAT1 was also the most significantly enriched ( Figure 7 E). In addition, gene heat map analysis showed that the expression of interferon, antigen presentation, and glycolysis-related genes was significantly upregulated after Siglec-G knockout, while the expression of mitochondrial-related genes was significantly downregulated ( Figure 7 F). The above results show that the JAK-STAT pathway is significantly enriched and upregulated after Siglec-G knockout in MDM.

[0089] Example 6: Siglec-G knockout in MDM significantly increases STAT1 binding peak

[0090] Next, we performed STAT1 CUT&Tag on MDM in an in vitro co-culture system, and the results showed that Siglec-G knockout could significantly increase the binding peak of STAT1 ( Figure 8 A), especially the TSS region ( Figure 8 B), although the peaks distribution of the two combinations is similar ( Figure 8C). In addition, the binding of STAT1 target genes such as those regulating interferon and inflammation (Irf9, Irf1) and antigen presentation (Tap1, B2m) was enhanced after Siglec-G knockout ( Figure 8 D). GSEA enrichment analysis also found that compared with the control group, JAK-STAT, antigen presentation and other gene sets were significantly enriched and upregulated after Siglec-G knockout ( Figure 8 E). These results indicate that Siglec-G knockout in MDM significantly increases STAT1 binding peaks, thereby promoting anti-tumor immune responses.

[0091] Example 7: Siglec-G knockout promotes glioma anti-tumor immune response by regulating the STAT1-GOT1 axis

[0092] Both scRNA-seq and bulk RNA-seq differential gene pathway enrichment analysis found that Siglec-G knockout in MDM significantly affected metabolic responses. Therefore, we conducted a joint analysis of differentially expressed genes predicted by single-cell Compass metabolic responses, differentially expressed genes by bulk RNA-seq, and target genes differentially regulated by STAT1 CUT&Tag, and found that five candidate metabolic genes (GOT1, APRT, TALDO1, MGAT1, LDHA) were significantly enriched. The TCGA tumorigenesis association score showed that GOT1 (glutamic oxaloacetic transaminase) was the most significantly negatively correlated with glioma occurrence ( Figure 9 A). Further analysis revealed that GOT1 is lowly expressed in both high-grade and low-grade gliomas, and that high GOT1 expression is beneficial to the prognosis of glioma patients. In addition, GOT1 is negatively correlated with the expression of multiple immune checkpoints ( Figure 9 B). STAT1CUT&Tag analysis results also showed that Siglec-G knockout could enhance the binding of STAT1 to the Got1 promoter region ( Figure 9 C).

[0093] Next, we detected the GOT1 enzyme activity and found that Siglec-G knockout in MDM promoted GOT1 enzyme activity ( Figure 9 D). Therefore, we added a GOT1 inhibitor (iGOT1, working concentration 100 μM) to the in vitro co-culture system. After 24 hours, the results showed that the addition of the GOT1 inhibitor significantly inhibited the expression of antigen-presenting genes such as H2-Aa and H2-Eb1, while immunosuppressive M2-related genes such as Arg1 and Il10 were significantly upregulated. In addition, the gene expression in the Siglec-G knockout group returned to the same level as the control group ( Figure 9 E). Flow cytometry analysis also showed that CD206 expression was upregulated after the addition of GOT1 inhibitor, while CD86 expression was downregulated. Similarly, the phenotype of the Siglec-G knockout group returned to the same level as the control group ( Figure 9 Finally, tumor-bearing mice were intraperitoneally injected with a GOT1 inhibitor (working concentration 20 mg / kg). 14 days later, the results showed that inhibiting GOT1 significantly promoted tumor growth and shortened the survival time of tumor-bearing mice. In addition, the anti-tumor phenotype of the Siglec-G knockout group mice, which had been improved, returned to the same level as the control group ( Figure 9 These results indicate that Siglec-G knockout promotes anti-tumor immune response in glioma by regulating the STAT1-GOT1 axis.

[0094] Example 8: Siglec-G knockout in MDM promotes anti-tumor immune response by regulating the STAT1-GOT1-αKG axis

[0095] GOT1 can catalyze the conversion of αKG and aspartate (Asp) to glutamate (Glu) and oxaloacetate (OAA) in the cytoplasm ( Figure 10 A), where αKG has been previously reported to promote glioma cell growth and M2 polarization of macrophages. We detected the intracellular αKG content and found that the αKG content was significantly decreased after Siglec-G knockout in MDM ( Figure 10 B). Next, we supplemented the in vitro co-culture system with DM-αKG (working concentration 2mM). After 24 hours, the results showed that DM-αKG significantly inhibited the expression of antigen-presenting genes such as H2-Aa and H2-Eb1, promoted the expression of M2-related genes such as Arg1 and Il10, and the gene expression in the Siglec-G knockout group returned to the same level as the control group ( Figure 10 C). Flow cytometry analysis also showed that CD206 expression was upregulated after the addition of DM-αKG, while CD86 expression was downregulated. Similarly, the phenotype of the Siglec-G knockout group returned to the same level as the control group ( Figure 10 DE). These results indicate that Siglec-G knockout in MDM promotes anti-tumor immune response in glioma by regulating the STAT1-GOT1-αKG axis.

[0096] Example 9: APEX2 neighbor protein labeling combined with mass spectrometry analysis revealed that Siglec-G interacts with CKB

[0097] To further investigate the specific molecular signaling mechanism of Siglec-G in regulating MDM immunosuppression, we used APEX2 neighbor protein labeling combined with mass spectrometry analysis for high-throughput screening. First, we validated the biotinylation reaction system in iBMDM. Then, iBMDM stably expressing Siglec-G-APEX2 were co-cultured with GL261 cells in vitro. The co-cultured iBMDMs were then sorted for biotinylation reaction. Finally, the biotinylated reaction proteins were identified by mass spectrometry ( Figure 11AB). Mass spectrometry results showed that protein molecules such as DPYSL2, CKB and MCCC1 ranked at the top, and further TCGA tumorigenesis association score showed that CKB was most significantly negatively correlated with glioma occurrence ( Figure 11 C). Subsequently, we performed a CO-IP experiment in 293T cells, and the results showed that Siglec-G interacted with CKB ( Figure 11 D).

[0098] Example 10: Siglec-G promotes the immunosuppressive function of MDM by regulating the CKB-creatine-JAK1 / STAT1 axis

[0099] As a metabolic enzyme, CKB reversibly catalyzes the transfer reaction between creatine phosphate / ATP and creatine phosphate / ADP. Studies have found that in macrophages, creatine is a metabolite that inhibits the interaction between IFN-γ receptor 2 (IFN-γR2) and JAK2 to hinder JAK-STAT signaling, thereby inhibiting the expression of immune inflammatory genes downstream of interferon signaling. Therefore, we first detected the intracellular CKB enzyme activity and found that Siglec-G knockout in MDM promoted CKB enzyme activity ( Figure 12 A), Metabolomics analysis showed that creatine content in MDM was significantly reduced after Siglec-G knockout ( Figure 12 B), and the creatine / phosphocreatine ratio was also downregulated after Siglec-G knockout ( Figure 12 C). In addition, we analyzed the public data of glioma patients and found that the creatine content in the patient's tumor tissue was significantly higher than that in PBMC ( Figure 12 D), suggesting that Siglec-G may promote creatine production by inhibiting CKB enzyme activity.

[0100] Next, we supplemented creatine (Cr, working concentration 100 μM) in the in vitro co-culture system. After 24 hours, the results showed that creatine significantly inhibited the phosphorylation levels of JAK1 and STAT1, and the expression of Siglec-G knockout histones returned to the same level as the control group ( Figure 12 E). The expression of antigen-presenting genes such as H2-Aa and H2-Eb1 was significantly downregulated after the addition of creatine, while the expression of M2-related genes such as Arg1 and Il10 was significantly upregulated, and the gene expression in the Siglec-G knockout group returned to the same level as the control group ( Figure 12 F). Flow cytometry analysis also showed that CD206 expression was upregulated after the addition of creatine, while CD86 expression was downregulated. Similarly, the phenotype of the Siglec-G knockout group returned to the same level as the control group ( Figure 12 GH).

[0101] Subsequently, we used ShRNA to knock down the expression of Ckb in MDM and co-cultured with tumor cells in vitro for 24 hours. The results showed that the creatine content in MDM increased significantly after Ckb knockdown, and the creatine content in the Siglec-G knockout group returned to the same level as the control group ( Figure 12 I). Flow cytometry analysis showed that CD206 expression was upregulated after Ckb knockdown, while CD86 expression was downregulated. Similarly, the phenotype of the Siglec-G knockout group returned to the same level as the control group ( Figure 12 These results suggest that Siglec-G in MDMs inhibits STAT1 signaling by regulating the CKB-creatine axis, thereby promoting the immunosuppressive function of MDMs.

[0102] Example 11: Affinity mass spectrometry analysis revealed that glioma cell membrane protein MUC1 is a new ligand for Siglec-G

[0103] Although the ligand molecules of Siglec-10 / G have been reported before, its activating ligand in the glioma microenvironment remains unknown. Therefore, we co-incubated Siglec-G-Fc fusion protein (Siglec-G-Fc composed of Human IgG1 Fc domain and Siglec-G fusion) with GL261 glioma cell lysate and used affinity mass spectrometry analysis to search for potential ligands of Siglec-G on tumor cells. The mass spectrometry results showed that a total of 57 surface proteins were screened, and the TCGA tumorigenesis association score showed that MUC1 was the most significantly positively correlated with glioma occurrence ( Figure 13 A). CO-IP and flow cytometry also verified the interaction between Siglec-G-Fc and MUC1 ( Figure 13 BC), and immunofluorescence results also showed that Siglec-G-Fc and MUC1 were significantly co-localized ( Figure 13 D). Further analysis of the TISCH2 database revealed that MUC1 is specifically highly expressed on tumor cells ( Figure 13 E). MUC1 is a mucin composed of a peptide core and sugar chains. We analyzed the expression of MUC mucin family members on glioma cells using the CCLE database. The results showed that MUC1 had the highest expression among its family members ( Figure 13 F), Western blot analysis also showed that MUC1 was highly expressed in GL261 and CT-2A cell lines ( Figure 13 G), and MUC1 expression increased with the increasing grade of glioma ( Figure 13 H).

[0104] Example 12: Tumor cell MUC1 participates in regulating glioma anti-tumor immunity

[0105] To further investigate the role of MUC1 in gliomagenesis, we knocked down Muc1 in GL261 cells and performed efficiency verification ( Figure 14 A). The results of cell proliferation experiments showed that knocking down Muc1 did not affect the proliferation of GL261 cells ( Figure 14 B). However, the results of in vivo tumor-bearing experiments showed that knockdown of MUC1 significantly inhibited tumor growth ( Figure 14 CD), prolonging the survival time of tumor-bearing mice ( Figure 14 E) Flow cytometry results showed that knocking down Muc1 could significantly increase the expression of CD8 + TNF-α + T and CD8 + IFN-γ + The proportion and number of T cells ( Figure 14 FI). The above results further indicate that Siglec-G's ligand MUC1 on tumor cells regulates the formation of the immunosuppressive microenvironment in gliomas.

[0106] Through the above experiments, we finally established the key role of the MUC1-Siglec-10 / G-CKB-STAT1-GOT1 axis in the immunosuppressive microenvironment of glioma.

[0107] Example 13: Siglec-G-Fc significantly inhibits glioma growth and promotes survival of tumor-bearing mice

[0108] The method for establishing a glioma mouse model was the same as in Example 3. Siglec-G-Fc or IgG1-Fc (purchased from Nearshore Protein, Cat. No.: C30D) (50 μg, concentration of 2 μg / μl) was injected intravenously on days 4, 8, and 12 after wild-type mice were orthotopically tumor-bearing. The tumor volume was detected using a visible light instrument on days 7 and 14, and the survival rate of tumor-bearing mice was calculated. The experimental data were statistically analyzed using GraphPad Prism 7.0 and expressed as mean ± SEM; Multiple t-tests were used for visible light imaging data, and Log-rank was used for mouse survival statistics, where *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, P ≥ 0.05.

[0109] The results are as follows Figure 15 As shown, the Siglec-G-Fc group significantly inhibited tumor growth compared with the IgG1-Fc treatment group ( Figure 15 BC), prolonging the survival time of tumor-bearing mice ( Figure 15 D).

Claims

1. Use of Siglec-10 / G or a fusion protein containing a Siglec-10 / G protein structure in the preparation of drugs for treating gliomas.

2. The use according to claim 1, characterized in that The Siglec-10 / G protein structure is the extracellular domain of the Siglec-10 / G protein.

3. The use according to claim 1, characterized in that The fusion protein comprising Siglec-10 / G protein structure is Recombinant Mouse Siglec-10 (C-Fc).

4. The use according to claim 1, characterized in that The drug competitively blocks endogenous Siglec-10 / G signaling on MDMs, thereby interfering with the accumulation of αKG and inhibiting glioma growth.

5. A pharmaceutical composition for treating glioma, characterized in that: The drug includes Siglec-10 / G or a fusion protein containing a Siglec-10 / G protein structure.

6. The pharmaceutical composition according to claim 5, characterized in that The fusion protein comprising Siglec-10 / G protein structure is Recombinant Mouse Siglec-10 (C-Fc).

7. The pharmaceutical composition according to claim 5, characterized in that The medicine further contains a pharmaceutically acceptable carrier or excipient.

8. The pharmaceutical composition according to claim 7, characterized in that The pharmaceutically acceptable carrier is selected from one or more of a filler, a wetting agent, a binder, a disintegrant, and a lubricant.

9. The pharmaceutical composition according to claim 5, characterized in that The dosage form of the medicine includes tablets, water injections or capsules.

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