Application of ZBED1 expression inhibitor in preparation of drug for cytarabine-resistant leukemia

Abstract

The invention relates to the technical field of bioengineering, in particular to application of a ZBED1 expression inhibitor in preparation of a medicine for treating cytarabine-resistant leukemia. The invention provides an application of a ZBED1 expression inhibitor in preparation of a drug for treating cytarabine-resistant leukemia. The drug for treating cytarabine-resistant leukemia comprises the ZBED1 expression inhibitor. The key effect of ZBED1 in leukemia drug resistance is determined for the first time, and a traditional Chinese medicine active component diosgenin with a function of selectively inhibiting ZBED1 is found. The component not only can have a synergistic effect with cytarabine to reverse the cytarabine resistance of leukemia, but also provides a new target site and a molecular marker for clinically predicting and improving the treatment effect of a patient. The innovative strategy brings a new hope for treating leukemia, and particularly provides a more effective solution for solving the problem of drug resistance of cytarabine.

Description

Technical Field

[0001] The invention relates to the technical field of bioengineering, and in particular to application of a ZBED1 expression inhibitor in the preparation of a cytarabine-resistant leukemia drug. Background Art

[0002] Acute myeloid leukemia (AML) is a hematological malignancy characterized by clonal proliferation of myeloid primitive cells and is highly heterogeneous. Its incidence has been increasing over the past few decades. Among all acute leukemia patients, AML patients have the shortest survival, with a five-year survival rate of less than 30%. Acute myeloid leukemia is a highly heterogeneous disease with a complex pathogenesis and limited therapeutic effects. Therefore, it is crucial to find and discover new AML molecular targets, which is of great significance for improving the clinical prognosis of patients and increasing their survival rate.

[0003] The tumor immune microenvironment of AML plays an important role in tumor progression. Recent studies have shown that the immune balance of the bone marrow microenvironment plays a key role in the treatment of AML. Tumor cells can hijack and reshape the bone marrow microenvironment to make it an environment that supports tumor growth, thereby helping AML bypass the immune system's attack and resistance to treatment. In this altered bone marrow microenvironment, AML's stem cells and initiating cells can maintain their regenerative capacity, and tiny residual lesions can be effectively incubated, ultimately leading to the recurrence of AML. Studies in recent years have shown that improving the understanding of the immune microenvironment of the bone marrow microenvironment will inspire the development of new treatment strategies to more effectively control the development of AML.

[0004] Relapse of AML is a major challenge in the treatment process, often leading to disease progression and worsening patient prognosis. Relapse is often closely associated with drug resistance. Cytarabine is currently widely used in AML as an effective monotherapy and as a mainstay of combination chemotherapy regimens. The formation of cytarabine resistance reduces the effectiveness of traditional treatments, limits treatment options, and increases the difficulty of treatment. Nowadays, more and more studies have shown that traditional herbal medicines (THMs) cover a variety of natural products. Due to their diverse biological activities, they are considered to be an important new source of candidate small molecule drugs, and they play an important role in reversing cancer chemotherapy resistance. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an application of a ZBED1 expression inhibitor in the preparation of a drug for cytarabine-resistant leukemia. The present invention finds that diosgenin, a traditional Chinese medicine active ingredient with the function of selectively inhibiting ZBED1, can not only synergize with cytarabine to reverse the cytarabine resistance of leukemia, but also provide new target sites and molecular markers for clinically predicting and improving the treatment effect of patients.

[0006] To this end, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides, in an optional embodiment, use of a ZBED1 expression inhibitor in the preparation of a cytarabine-resistant leukemia drug, wherein the cytarabine-resistant leukemia drug comprises the ZBED1 expression inhibitor.

[0008] Furthermore, the ZBED1 expression inhibitor is diosgenin.

[0009] In a second aspect, the present invention provides, in an optional embodiment, a pharmaceutical composition for inhibiting leukemia, comprising the above-mentioned ZBED1 expression inhibitor and cytarabine.

[0010] Furthermore, the pharmaceutical composition for inhibiting leukemia includes diosgenin and cytarabine.

[0011] Preferably, in the pharmaceutical composition for inhibiting leukemia, the concentration of cytarabine is 0.01-2 μM, and the concentration of diosgenin is 2-64 μM. Furthermore, in the pharmaceutical composition for inhibiting leukemia, the concentration of cytarabine is 0.1 μM, and the concentration of diosgenin is 16 μM.

[0012] Preferably, the leukemia inhibitor further comprises auxiliary materials.

[0013] Furthermore, the leukemia inhibitor is an oral preparation or an injectable preparation, and the oral preparation includes capsules, tablets or granules.

[0014] In the present invention, the leukemia inhibitor can be any conventional oral preparation or injectable preparation that is pharmaceutically acceptable and prepared by conventional preparation technology, such as capsules, tablets, granules or liquids. In order to make the above oral preparations possible, pharmaceutically acceptable excipients need to be added during the preparation process, and the pharmaceutically acceptable excipients are fillers, disintegrants, lubricants, suspending agents, binders or sweeteners, etc.

[0015] The filler includes at least one of starch, lactose, microcrystalline cellulose or sucrose; the disintegrant includes at least one of starch, sodium carboxymethyl starch, pregelatinized starch, low-substituted hydroxypropyl cellulose or cross-linked sodium carboxymethyl cellulose; the lubricant includes at least one of magnesium stearate or silicon dioxide, sodium lauryl sulfate; the suspending agent includes at least one of polyvinyl pyrrolidone, sucrose or hydroxypropyl methylcellulose; the binder includes at least one of hydroxypropyl methylcellulose, starch slurry or polyvinyl pyrrolidone; the sweetener is at least one of saccharin sodium, glycyrrhetinic acid, sucrose, aspartame or cyclamate.

[0016] In order to realize the above-mentioned injection preparation, pharmaceutically acceptable excipients need to be added during the preparation process, including water for injection and sodium chloride solution for injection.

[0017] Compared with the prior art, the present invention has one of the following beneficial effects:

[0018] 1. The present invention established for the first time the key role of ZBED1 in leukemia drug resistance and discovered Diosgenin, a traditional Chinese medicine active ingredient that selectively inhibits ZBED1. This ingredient can not only synergize with cytarabine to reverse cytarabine resistance in leukemia, but also provide new target sites and molecular markers for clinical prediction and improvement of patient treatment effects. This innovative strategy brings new hope for leukemia treatment, especially for the problem of cytarabine resistance, providing a more effective solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The single cell analysis results in Example 1 of the present invention reveal the characteristics of the AML microenvironment, wherein: Figure 1 A shows the cell clustering results at different resolutions. Each node represents a cluster, and the lines between nodes represent the clustering relationship when the resolution changes. The resolution changes from low to high. Figure 1 B represents 11 different cell types identified using specific markers, including: AML progenitor, B cell, Dendritic cell, Erythroid cell, GMP (Granulocyte-Monocyte Progenitor), HSC (Hematopoietic Stem Cell), Mono / Mac (Monocyte / Macrophage), Neutrophil, NK cell (Natural Killer cell), Plasma and T cell, Figure 1 C shows the sources of different cells. Each dot represents a single cell, and different colors represent different source samples, reflecting the diversity of the AML microenvironment. Figure 1 D shows the expression level of specific marker genes for each cell type. Each column represents a cell type, and each row represents a specific marker gene. The color depth indicates the level of gene expression. Figure 1 E shows the distribution ratio of different cell types in each sample. Each bar represents a sample, and different colors indicate the ratio of different cell types, providing an overview of the cell composition in the AML microenvironment.

[0020] Figure 2This is a schematic diagram of the results of studying the heterogeneity of AML cells in Example 1 of the present invention, wherein: Figure 2 A is a characteristic map of marker genes of different types of acute myeloid leukemia precursor cells (AML progenitors), showing the expression density of multiple key genes (such as DEFA3, MALAT1, CST3, RPS 12, S100A8, H3F3A, LGALS1, RPL9) in UMAP space. Figure 2 B shows the identification of four major acute myeloid leukemia precursor cell (AML progenitors) subpopulations, namely AML.progenitor.1, AML.progenitor.2, AML.progenitor.3, and AML.progenitor.4. Figure 2 C is the expression heat map and GO analysis of DEGs in the four subgroups of acute myeloid leukemia precursor cells (AML progenitors). The heat map shows the specific gene expression of each subgroup, and the right side shows the GO functional annotations related to these genes. Figure 2 D shows the differentially expressed genes between AML progenitors that relapsed after chemotherapy and AML progenitors that were in remission. Figure 2 E is the distribution ratio of different cell types in each sample, further showing the ratio of various acute myeloid leukemia precursor cell (AMLprogenitors) subpopulations in different samples, and different colors represent different cell subpopulations;

[0021] Figure 3 The characteristics of the T lymphocyte subsets in the AML microenvironment in Example 1 of the present invention, wherein: Figure 3 A shows the clustering results of all cells at different resolutions. Figure 3 B shows t-SNE and UMAP images of different cell types and different clinical types. Figure 3 C shows the expression level of marker genes of each T cell subset. The color depth indicates the level of gene expression. Figure 3 D shows the distribution ratio of different cell types in each sample. Figure 3 E shows the differentially expressed genes between T cells that were remitted after chemotherapy and T cells that were not remitted after chemotherapy;

[0022] Figure 4 The communication network between cell subgroups in Example 1 of the present invention is explained in detail, wherein: Figure 4 A- Figure 4 B shows the frequency and degree of communication between different cell subgroups. Figure 4 A shows the frequency of interactions between various cell types. Figure 4B shows the intensity of interaction between cell types. Each node represents a cell type. The lines between nodes represent the communication relationship between cells. The thickness and color of the lines represent the frequency or intensity of communication. Figure 4 C is a graphical illustration of the main signal senders (source end) and receivers (target end), showing the signal sending and receiving capabilities of each cell type. The horizontal axis represents the strength of the sent signal, the vertical axis represents the strength of the received signal, and the size of the dot represents the number of cells. Figure 4 D reveals the most important trends in signal transmission and reception, showing the signal transmission and reception patterns of each cell type, with the left side showing the signal transmission pattern and the right side showing the signal reception pattern. The color of each matrix block indicates the relative signal strength, with darker colors indicating stronger signals;

[0023] Figure 5 This is a schematic diagram of a comprehensive inspection of a cell communication network in Example 1 of the present invention, wherein: Figure 5 A and Figure 5 E is the output determined using cophenetic and silhouette metrics ( Figure 5 A) or input ( Figure 5 E) the number of signal patterns, Figure 5 B and Figure 5 F is the identification of various cell subpopulations and their respective outputs ( Figure 5 B) or input ( Figure 5 F) Signal mode, Figure 5 C and Figure 5 G shows the output from the signal-sending cell ( Figure 5 C) or input ( Figure 5 G) Distribution of signal patterns. The left side of the figure shows the cell population, the middle is the identified signal pattern, and the right side is the specific signal pathway, which further shows the overall flow from cell population to signal pattern and then to signal pathway. Figure 5 D and Figure 5 H shows the output in the critical path ( Figure 5 D) or input ( Figure 5 H) Signal changes further demonstrate the output signal patterns of different cell types in each key signal pathway. Different colors represent different cell types, and the size of the dots represents the strength of the signal contribution.

[0024] Figure 6 This is a schematic diagram of the results of the differential analysis of intercellular communication between relapsed and remitted AML after chemotherapy in Example 1 of the present invention, wherein: Figure 6 A shows the intercellular communication network in relapsed and remitted AML tissues after chemotherapy. Figure 6B is a comparison of the changes in the number and intensity of cell-cell interactions in relapsed AML relative to remission AML after chemotherapy. In the figure, red indicates that the relative interaction of relapsed AML is enhanced, and blue indicates that the relative interaction of relapsed AML is weakened. Figure 6 C is to compare the signal intensity and quantity differences of different clinical types. Figure 6 D shows the difference in the frequency and intensity of inter-tissue interactions between relapsed AML and remission AML after chemotherapy. The color depth of each cell in the figure indicates the frequency and intensity of interactions between different cell types. Dark colors indicate high interaction intensity, and light colors indicate low interaction intensity. This provides a detailed comparison of intercellular communication patterns in relapse and remission states. Figure 6 E is the comparison of the dominant signal transmitters and receivers in AML tissues in patients with relapsed AML after chemotherapy and those in patients with remission. Figure 6 F is a comparison of the expression of key signaling molecules and pathways between relapsed AML and relative remission AML tissues after chemotherapy. The figure shows the difference in expression levels of important signaling molecules and pathways in relapse and remission states, and the relative expression of each molecule or pathway in the two states is shown by color and height. Figure 6 G shows the analysis of outgoing signals between different cell subpopulations, and further shows the patterns and changes of outgoing signals between different cell subpopulations in the relapse and remission states. Figure 6 H shows the analysis of the signals received between different cell subpopulations. Figure 6 I shows the global signaling analysis of different cell subpopulations;

[0025] Figure 7 Schematic diagram of the important regulatory role of ZBED1 in AML in Example 1 of the present invention, wherein: Figure 7 A shows the intersection genes of SUMO modification-related genes, AML relapse genes, and AML prognosis genes. Figure 7 B- Figure 7 C shows the expression of ZBED1 in TCGA survival data ( Figure 7 B) and GSE71014 survival data ( Figure 7 C) Kaplan-Meier survival curve, Figure 7 D is the UMAP diagram of ZBED1 expression, showing the expression level of ZBED1 in different cell clusters. Each point represents a single cell, and different colors indicate different expression levels. Figure 7 E is the volcano plot of differentially expressed genes relative to the expression level of ZBED1. Figure 7 F- Figure 7 H is a schematic diagram of the results of analyzing differentially expressed genes (DEGs) using GSEA, GO, and KEGG;

[0026] Figure 8This is a schematic diagram of the results of ZBED1 remodeling the tumor microenvironment (TME) of acute myeloid leukemia in Example 1 of the present invention, wherein: Figure 8 A shows the immune infiltration of each sample in the TCGA-AML database. Figure 8 B shows the distribution of various cell types in all AML samples. Figure 8 C shows the correlation analysis between ZBED1 and cells in the immune microenvironment, and further shows the correlation coefficient between ZBED 1 expression and the abundance of various cell types in the bone marrow microenvironment. Figure 8 D shows the Kaplan-Meier survival curve showing the prognostic analysis results of patients with high expression of CD8+T cells and low expression of CD8+T cells;

[0027] Fig. 9 Schematic diagram of the correlation between increased ZBED1 expression and enhanced AML drug resistance in Example 1 of the present invention, wherein Fig. 9 A and Fig. 9 B is the relationship between ZBED1 expression and the half-inhibitory concentration (IC50) of various drugs using the GSE71014 database (A) and TCGA-AML (B) database. 50 ), Fig. 9 C shows the interaction between ZBED1 protein and several commonly used AML therapeutic drugs (such as Cytarabine, Doxorubicin, Mitoxantrone and Fludarabine) studied by molecular docking technology. The molecular docking results show the possible binding sites of ZBED1;

[0028] Fig.10 This is a schematic diagram of ZBED1 promoting CD8+T cell exhaustion in Example 1 of the present invention, wherein: Fig.10 A- Fig.10 C shows the Celltype of CD8+T cells ( Fig.10 A), pseudo timing diagram ( Fig.10 B), ZBED1 expression ( Fig.10 C), Fig.10 D shows the distribution of marker genes of different types of T cells. Fig.10 E- Fig.10 F shows CD8+ T cell subsets ( Fig.10 E) Quasi-series analysis ( Fig.10 F), Fig.10 G- Fig.10 H shows the cell types ( Fig.10 G) and ZBED1 expression ( Fig.10 H) dynamic changes, Fig.10 I shows the Marker genes related to developmental time and cell subpopulations;

[0029] Fig.11 Schematic diagram of the results of ZBED1 having an important effect on drug-resistant AML cells in Examples 2 and 3 of the present invention, wherein Fig.11 A is the result of comparing the expression of ZBED1 in AML cell lines and normal control HS-5 cells using qRT-PCR method; Fig.11 B- Fig.11 D is qRT-PCR ( Fig.11 B) and Western blot ( Fig.11 C- Fig.11 D) Detection of the expression of ZBED1 in MOLM13, MV4-11, MOLM13 / R and MV4-11 / R cells, Fig.11 E is immunofluorescence showing the localization of ZBED1 in drug-resistant cells and corresponding parental cell lines. The cell nuclei are stained with DAPI. Scale bar: 20 μm. Fig.11 F is the result of qRT-PCR verification of ZBED1 knockdown efficiency in MOLM13 / R and MV4-11 / R cells. Fig.11 G- Fig.11 I is the use of CCK-8 ( Fig.11 G) and EDU( Fig.11 H- Fig.11 I) Detect the proliferation ability of cells after knocking out ZBED1, Fig.11 J- Fig.11 M is to use WB( Fig.11 J- Fig.11 K), flow cytometry ( Fig.11 L), Caspace3 activity assay ( Fig.11 M) Detect cell apoptosis. Fig.11 N- Fig.11 O is the result of detecting the cell cycle using flow cytometry, Fig.11 P is the tumor entity of the mouse after dissection. Fig.11 Q- Fig.11 R is the tumor volume and weight of mice in each group. Fig.11 Q represents the tumor growth kinetics of NSG mice in the sh-ZBED 1 group and shRNA-NC group, which are displayed by growth curves. Fig.11 R represents comparative analysis of tumor mass of nude mice between sh-ZBED1 group and shRNA-NC group;

[0030] Fig.12 This is a schematic diagram of the results of ZBED1 inducing osteolysis in AML in Example 1 of the present invention, wherein: Fig.12 A- Fig.12 E is the 3D reconstruction of the femur of the sh-NC group ( Fig.12 A), Coronal section of the whole femur ( Fig.12 B), cross-sectional view and three-dimensional reconstruction of the proximal metaphysis of the distal femur ( Fig.12 C), 3D reconstruction of the cortical bone of the middle femur ( Fig.12 D), Coronal view of the proximal tibia ( Fig.12 E), Fig.12 F- Fig.12 J is the 3D reconstruction of the femur of the sh-ZBED1 group ( Fig.12 F), Coronal section of the whole femur ( Fig.12 G), cross-sectional view and three-dimensional reconstruction of the proximal metaphysis of the distal femur ( Fig.12 H), 3D reconstruction of the cortical bone of the middle femur ( Fig.12 I), coronal view of the proximal tibia ( Fig.12 J), Fig.12 K- Fig.12 P represents the change of trabecular bone microarchitecture parameters, and BMD represents bone mineral density ( Fig.12 K); BS represents bone surface area ( Fig.12 L), BV / TV represents the trabecular bone volume fraction ( Fig.12 M), Tb.Th represents the trabecular thickness ( Fig.12 N), Tb.N represents the number of trabeculae ( Fig.12 O), Tb.Sp represents the trabecular spacing ( Fig.12 P), Fig.12 Q- Fig.12 T represents the change in cortical bone microstructural parameters, and Tt.Ar represents the total cross-sectional area within the periosteal envelope ( Fig.12 Q), Ct.Ar represents the cortical bone area ( Fig.12 R), Ct.Ar / Tt.Ar represents the cortical bone area fraction ( Fig.12 S), Ct.Th represents the average cortical bone thickness ( Fig.12 T), *P<0.05, **P<0.01, ***P<0.001;

[0031] Fig.13 The results of diosgenin inhibiting the malignant phenotype of cytarabine-resistant AML cells and increasing their sensitivity to cytarabine in Examples 1-3 of the present invention are shown in FIG. Fig.13 A is the results of WB detection of the effects of Mairin, Astilbin, Diosgenin, Obacunone, and Isoimperatorin on the expression of ZBED1. Fig.13 B is the molecular docking display of diosgenin and ZBED1. Fig.13 C- Fig.13 D is the effect of dioscin at different concentrations on cell apoptosis rate detected by flow cytometry. Fig.13 E is a photo of the tumor entity of the mouse after dissection. Fig.13 F shows the tumor growth kinetics growth curves of NSG mice in different groups. Fig.13 G shows the comparative analysis results of tumor mass in nude mice of different groups of NSG mice. Fig.13 H is the confidence interval diagram of the synergistic drug combination (dioscin and cytarabine), Fig.13 I- Fig.13 L is the ZIP of MOLM13 / R cells treated with cytarabine and dioscin ( Fig.13 I) Bliss Fig.13 J) HSA( Fig.13 K) and Loewe( Fig.13 L) Synergy diagram. The synergy score was calculated using the SynergyFinder R package. Positive or negative values ​​indicate synergy or antagonism, respectively. Fig.13 M is the synergistic effect measurement result of the dose combination of 0.1 μM cytarabine and 16 μM dioscin, Fig.13 N- Fig.13 O is the flow cytometry experimental result based on the recommended concentration of the metering indicator in Figure M;

[0032] Fig.14 The schematic diagram is a mechanism diagram of the synergistic treatment of leukemia by the Chinese medicine monomer (dioscin) for targeted inhibition of ZBED1 and cytarabine of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0035] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.

[0036] See the schematic diagram of the mechanism of the Chinese medicine monomer (dioscin) for the targeted inhibition of ZBED1 and cytarabine for the synergistic treatment of leukemia in the present invention. Fig.14 .

[0037] Example 1

[0038] 1. Data Source and Processing

[0039] Three datasets (GSE235063, GSE116256, and GSE130756) were screened from the GEO database. The RNA-seq data of TCGA-AML and GSE71014 and their corresponding clinical records were integrated. Samples without complete survival or clinical data were excluded. The scRNA-seq data were processed in detail using the Seurat R package (version 4.3.0). DoubletFinder was used to exclude potential double cells and filter cells with excessively high rRNA and mitochondrial ratios. All gene data were normalized and PCA dimensionally reduced. The "Harmony" toolkit was then used to adjust differences between batches. Highly variable genes were screened out using the FindVariableFeatures function. Further dimensionality reduction was performed on the corrected data using the UMAP and tSNE methods. Marker genes were identified using the Cell Marker database. Results can be found at Figure 1 ,in, Figure 1 A shows the cell clustering results at different resolutions. Each node represents a cluster, and the lines between nodes represent the clustering relationship when the resolution changes. The resolution changes from low to high. Figure 1 B represents 11 different cell types identified using specific markers, including: AML progenitor, B cell, Dendritic cell, Erythroid cell, GMP (Granulocyte-Monocyte Progenitor), HSC (Hemat opoietic Stem Cell), Mono / Mac (Monocyte / Macrophage), Neutrophil, NK cell (Natural Killer cell), Plasma and T cell, Figure 1 C shows the sources of different cells. Each dot represents a single cell, and different colors represent different source samples, reflecting the diversity of the AML microenvironment. Figure 1 D shows the expression level of specific marker genes for each cell type. Each column represents a cell type, and each row represents a specific marker gene. The color depth indicates the level of gene expression. Figure 1 E shows the distribution ratio of different cell types in each sample. Each bar represents a sample, and different colors indicate the proportion of different cell types, providing an overview of the cell composition in the AML microenvironment.

[0040] Then, Monocle2 and Monocle3 were used to visualize the pseudo-temporal development trajectory of cells, and the plotpseudotime heatmap function was used to display the heat map of the dynamic changes in gene expression. The results are shown in Figure 3 and Fig.10 ,in, Figure 3 A shows the clustering results of all cells at different resolutions. Figure 3 B shows t-SNE and UMAP images of different cell types and different clinical types. Figure 3 C shows the expression level of marker genes of each T cell subset. The color depth indicates the level of gene expression. Figure 3 D shows the distribution ratio of different cell types in each sample. Figure 3 E shows the differentially expressed genes between T cells that were remitted after chemotherapy and T cells that were not remitted after chemotherapy; Fig.10 A- Fig.10 C shows the Celltype of CD8+T cells ( Fig.10 A), pseudo timing diagram ( Fig.10 B), ZBED1 expression ( Fig.10 C), Fig.10 D shows the distribution of marker genes of different types of T cells. Fig.10 E- Fig.10 F shows CD8+ T cell subsets ( Fig.10 E) Quasi-series analysis ( Fig.10 F), Fig.10 G- Fig.10 H shows the cell types ( Fig.10 G) and ZBED1 expression ( Fig.10 H) dynamic changes, Fig.10 I shows marker genes related to developmental time and cell subsets.

[0041] 2. Analysis of Cell-cell Communication

[0042] Using RNA expression data and cell annotations, a CellChat object was initialized using the "create Cell Chat" function. Then, the interaction database containing "Secreted Signaling" was selected for signal network analysis. The communication probability between cells was estimated using the "compute Commun Prob" function. When using the "selectK" function, the global communication mode was used, and the nPatterns parameter was set to 2 for both the input and output of the information flow. The results can be found in Figure 4-Figure 6 ,in, Figure 4 A- Figure 4 B shows the frequency and degree of communication between different cell subgroups. Figure 4 A shows the frequency of interactions between various cell types. Figure 4B shows the intensity of interaction between cell types. Each node represents a cell type. The lines between nodes represent the communication relationship between cells. The thickness and color of the lines represent the frequency or intensity of communication. Figure 4 C is a graphical illustration of the main signal senders (source end) and receivers (target end), showing the signal sending and receiving capabilities of each cell type. The horizontal axis represents the strength of the sent signal, the vertical axis represents the strength of the received signal, and the size of the dot represents the number of cells. Figure 4 D reveals the most important trends in signal transmission and reception, showing the signal transmission and reception patterns of each cell type, with the left side showing the signal transmission pattern and the right side showing the signal reception pattern. The color of each matrix block indicates the relative signal strength, with darker colors indicating stronger signals; Figure 5 A and Figure 5 E is the output determined using cophenetic and silhouette metrics ( Figure 5 A) or input ( Figure 5 E) the number of signal patterns, Figure 5 B and Figure 5 F is the identification of various cell subpopulations and their respective outputs ( Figure 5 B) or input ( Figure 5 F) Signal mode, Figure 5 C and Figure 5 G shows the output from the signal-sending cell ( Figure 5 C) or input ( Figure 5 G) Distribution of signal patterns. The left side of the figure shows the cell population, the middle is the identified signal pattern, and the right side is the specific signal pathway, which further shows the overall flow from cell population to signal pattern and then to signal pathway. Figure 5 D and Figure 5 H shows the output in the critical path ( Figure 5 D) or input ( Figure 5 H) Signal changes further demonstrate the output signal patterns of different cell types in each key signal pathway. Different colors represent different cell types, and the size of the dots represents the strength of the signal contribution. Figure 6 A shows the intercellular communication network in relapsed and remitted AML tissues after chemotherapy. Figure 6 B is a comparison of the changes in the number and intensity of cell-cell interactions in relapsed AML relative to remission AML after chemotherapy. In the figure, red indicates that the relative interaction of relapsed AML is enhanced, and blue indicates that the relative interaction of relapsed AML is weakened. Figure 6 C is to compare the signal intensity and quantity differences of different clinical types. Figure 6D shows the difference in the frequency and intensity of inter-tissue interactions between relapsed AML and remission AML after chemotherapy. The color depth of each cell in the figure indicates the frequency and intensity of interactions between different cell types. Dark colors indicate high interaction intensity, and light colors indicate low interaction intensity. This provides a detailed comparison of intercellular communication patterns in relapse and remission states. Figure 6 E is the comparison of the dominant signal transmitters and receivers in AML tissues in patients with relapsed AML after chemotherapy and those in patients with remission. Figure 6 F is a comparison of the expression of key signaling molecules and pathways between relapsed AML and remission AML tissues after chemotherapy. The figure shows the difference in expression levels of important signaling molecules and signaling pathways in the relapse and remission states, and the relative expression of each molecule or pathway in the two states is shown by color and height. Figure 6 G shows the analysis of outgoing signals between different cell subpopulations, and further shows the patterns and changes of outgoing signals between different cell subpopulations in the relapse and remission states. Figure 6 H shows the analysis of the signals received between different cell subpopulations. Figure 6 I shows the global signaling analysis of different cell subpopulations.

[0043] 3. Identification and survival analysis of SUMOylation-related genes

[0044] Several candidate genes were selected from the intersection of SUMO-related genes and differentially expressed genes (DEGs). Survival analysis of individual genes was performed using the R package “survival” by analyzing the TCGA-AML and GSE71014 datasets. Patients were divided into high-expression and low-expression groups according to the cutoff value of each gene. The Log-Rank test determined the statistical significance of the survival curves, and curves with P < 0.05 were considered different. The results are shown in Figure 7 A- Figure 7 E, where Figure 7 A shows the intersection genes of SUMO modification-related genes, AML relapse genes, and AML prognosis genes. Figure 7 B- Figure 7 C shows the expression of ZBED1 in TCGA survival data ( Figure 7 B) and GSE71014 survival data ( Figure 7 C) Kaplan-Meier survival curve, Figure 7 D is the UMAP diagram of ZBED1 expression, showing the expression level of ZBED1 in different cell clusters. Each point represents a single cell, and different colors indicate different expression levels. Figure 7 E is the volcano plot of differentially expressed genes relative to the expression level of ZBED1.

[0045] 4. Enrichment Analysis

[0046] Differentially expressed genes (DEGs) were identified by the FindMarkers function, applying thresholds of Padj.P < 0.05 and log2FC > 0.25. GO and KEGG analyses were performed using the cluster Profiler package, while GSEA analysis was performed using the respective packages, and the results are shown in Figure 2 , Figure 7 F- Figure 7 H, where Figure 2 A is a characteristic map of marker genes of different types of acute myeloid leukemia precursor cells (AML progenitors), showing the expression density of multiple key genes (such as DEFA3, MALAT1, CST3, RPS12, S100A8, H3F3A, LGALS1, RPL9) in UMAP space. Figure 2 B shows the identification of four major acute myeloid leukemia precursor cell (AML progenitors) subpopulations, namely AML.progenitor.1, AML.progenitor.2, AML.progenitor.3, and AML.progenitor.4. Figure 2 C is the expression heat map and GO analysis of DEGs in the four subgroups of acute myeloid leukemia precursor cells (AML progenitors). The heat map shows the specific gene expression of each subgroup, and the right side shows the GO functional annotations related to these genes. Figure 2 D shows the differentially expressed genes between AML progenitors that relapsed after chemotherapy and AML progenitors that were in remission. Figure 2 E is the distribution ratio of different cell types in each sample, further showing the ratio of various acute myeloid leukemia precursor cell (AML progenitors) subpopulations in different samples, with different colors representing different cell subpopulations; Figure 7 F- Figure 7 H is a schematic diagram of the results of analyzing differentially expressed genes (DEGs) using GSEA, GO, and KEGG.

[0047] 5. Assessment of Immune Cell Subtype Distribution

[0048] CibersortX and the average expression values ​​of signature genes were used to analyze the infiltration of cell subtypes in the TCGA-AML clinical cohort. Spearman correlation analysis revealed the association between ZBED1 and different cell populations. The results are shown in Figure 8 ,in, Figure 8 A shows the immune infiltration of each sample in the TCGA-AML database. Figure 8 B shows the distribution of various cell types in all AML samples. Figure 8C shows the correlation analysis between ZBED1 and cells in the immune microenvironment, and further shows the correlation coefficient between ZBED1 expression and the abundance of various cell types in the bone marrow microenvironment. Figure 8 D shows the Kaplan-Meier survival curve showing the prognostic analysis results of patients with high expression of CD8+T cells and low expression of CD8+T cells.

[0049] 6. Chemotherapy drug sensitivity analysis

[0050] IC values ​​of 198 drugs were obtained from the Cancer Drug Sensitivity Gene Set (GDSC; https: / / www.cancerrxgene.org / ) and the Cancer Cell Line Encyclopedia (CCLE, https: / / sites.broadinstitute.org / ccle / ) using the R package oncoPredict. 50 The drug IC was explored by Spearman analysis. 50 The correlation between the values ​​and the risk score was used to identify relevant drugs. The IC 50 The results were visualized using the R language gplot2 to plot correlation analysis dot plots and lollipop plots. Fig. 9 A- Fig. 9 B.

[0051] 7. Molecular Docking Analysis

[0052] The three-dimensional structures of ZBED1 protein and various drug molecules were obtained from PubChem database. Preparation of protein and drug structures for docking analysis included removal of water molecules and addition of nonpolar hydrogen atoms using PyMOL and Auto Dock4. Subsequently, appropriate docking parameters and grid box sizes were established. Molecular docking simulations were performed using AutoDock4, and the results were analyzed and visualized using PyMOL. The results are shown in Fig. 9 C and 13B, the docking results showed that the binding energy was lower than -2.0 kcal / mol indicating significant molecular interactions.

[0053] 8. Screening of the best active ingredients of traditional Chinese medicine

[0054] The gene-related information was collected using the Coremine Medical database, which is an open retrieval platform that collects comprehensive medical information such as herbal medicine, gene ontology, protein expression, and anatomy. The selected key genes were mapped to Coremine Medical (http: / / www.coremine.com / ) and the Chinese medicines associated with the key genes were screened out. P < 0.05 was considered statistically significant. TCMs significantly associated with the key genes were identified, including Evodia rutaecarpa, Jujube, Rhubarb, and Smilax glabra (p < 0.05). The traditional Chinese medicine system pharmacology database and analysis platform (TCMSP) was used to screen the main active ingredients of high-frequency TCMs, requiring oral bioavailability (OB) ≥ 30% and drug similarity (DL) ≥ 0.18. Molecular docking was then performed to verify the binding energy of all TCM monomers with ZBED1, and 20 different docking simulations were performed for each monomer. The five monomers with the lowest binding energy were identified as Mairin, Astilbin, Diosgenin, Obacunone, and Isoimper atorin. 50 At the same concentration, whether the above Chinese medicine treatment of MV4-11 / R and MOLM13 / R cells for 48 hours has any effect on ZBED1. Fig.13 A- Fig.13 B. The results showed that among the above five drugs, only Astilbin, Diosgenin, Diosgenin and Isoimperat orin had an inhibitory effect on ZBED1, among which Diosgenin had the strongest inhibitory effect (see Fig.13 A). The results of molecular docking showed that Diosgenin may be able to bind directly to ZBED1 and form a hydrogen bond with the R33 site on the ZBED1 protein chain (see Fig.13 B).

[0055] Example 2

[0056] In vitro experiments

[0057] 9. Quantitative Real-time PCR Analysis

[0058] Total RNA was isolated using TRIzol reagent (Invitrogen) and subsequently reverse transcribed using Revert Aid FirstStrand cDNA Synthesis Kit (Thermo Fisher Scientific, USA). SYBR-Green assay mixtures were prepared and quantitative PCR analysis was performed using a Roche Light Cycler 480I I instrument. To ensure the reproducibility of the experiment, the number of replicate wells for each sample was three. By comparing 2 -ΔΔCt The gene expression levels were determined by the method and the results are shown in Fig.11 A, Fig.11 B and Fig.11 F.

[0059] 10. Small interfering RNA (siRNA), short hairpin RNA (shRNA) and plasmid transfection

[0060] Three siRNAs and one shRNA sequence targeting ZBED1 were designed using the online siRNA and shRNA design tools on the Genepharma website. In the shRNA experiment, the designed shRNA sequence was cloned into the expression vector pLKO.1. Subsequently, the pLKO.1 shRNA plasmid, packaging plasmid, and envelope protein plasmid were co-transfected into HEK293T cells to produce and collect lentiviral particles. Leukemia cell lines in the logarithmic growth phase were seeded into 6-well plates until they reached 70%-80% confluence, at which time the collected viral particles were used for cell transfection, and Polybrene was added to increase transfection efficiency. After 12 hours of transfection, the cells were maintained in regular culture medium for 24 hours and then harvested. The successful transfection was verified by qRT-PCR. For the siRNA experiment, when the cell density reached 70%-80%, the cells in the logarithmic growth phase were seeded into 6-well plates and transfected using Lipofectamine 3000 transfection reagent. After 6 hours of transfection, the culture medium was replaced with normal culture medium and cultured for another 24 hours before collection for subsequent experiments. Cells were transfected with the indicated plasmids using Lipofectamine 2000 (Invitrogen, Life Technologies) according to the manufacturer's protocol. The transfection efficiency was optimized by adjusting the ratio of plasmid to Lipofectamine and the incubation time. The results are shown in Fig.11 F.

[0061] 11. EdU staining assay

[0062] The cells were incubated with 10 μM EdU staining solution (provided by Apexbio). After incubation, the cells were treated with 500 μl of Click reaction mixture including Cy3 azide for 30 minutes and kept at room temperature in a dark environment for reaction. After the reaction, the cells were washed with PBS and then stained with 1 ml of Hoechst 33342 dye with a final concentration of 5 μg / ml and incubated again at room temperature in a dark environment for 30 minutes. The stained specimens were then observed using a fluorescence microscope. See Fig.11 H- Fig.11 I.

[0063] 12.CCK-8 assay

[0064] Cell viability was assessed using the Cell Counting Kit-8 (CCK-8, Sigma-Aldrich, St. Louis, MO). Cell lines were seeded into 96-well plates following the instructions of the kit. Once cells were attached, they were exposed to the indicated drugs for the indicated time. 10 μl of CCK-8 solution was then added to each well and incubated for one hour in the dark. Cell viability was assessed by reading the optical density at a wavelength of 450 nm. Results are shown in Fig.11 G.

[0065] 13. Western Blotting

[0066] Cell lysis was performed using radioimmunoprecipitation buffer (RIPA, Beyotime), and protein levels were quantified using the BeyotimeBCA protein assay kit (Beyotime). Equal amounts of cell extracts were separated by 10% SDS-PAGE and subsequently transferred to PVDF membranes. To block nonspecific binding, the membrane was incubated in 5% skim milk, followed by incubation with primary antibodies overnight on a shaker at 4°C. Subsequently, the membrane was incubated with HRP-labeled secondary antibodies (1:2000, Proteintech) for one hour at room temperature. After thorough washing, the fluorescence signal was detected using the UVP ChemStudio system (Ultraviolet Products, USA), and semi-quantitative analysis was performed using VisionWorks software (Analytik Jena, Germany), and the results are shown in Fig.11 C. Fig.11 D. Fig.11 K. Fig.11 J and Fig.13 A.

[0067] The primary antibodies used included: anti-ZBED1 (1:1000, Proteintech), anti-BCL2 (1:200, Abcam), anti-BAX (1:1000, Abcam), and anti-Beta-Tubulin (1:1000, Santa Cruz Biotechnology).

[0068] 14. Immunofluorescence

[0069] For immunofluorescence staining of paraffin-embedded sections, sections were dewaxed and rehydrated, followed by antigen retrieval in citrate buffer at pH 6.0. Antibody staining was then performed. First, sections were treated with antigen blocking solution for 20 min at room temperature to prevent nonspecific binding. They were then incubated in a 1:1000 dilution of ZBED1 antibody solution at 4°C overnight. After thorough washing with PBST, sections were incubated with Alexa Fluor 555-labeled IgG antibody at a dilution of 1:600 ​​for 20 min at room temperature in the dark. Finally, nuclear staining was performed with a medium containing DAPI, and images were taken using a fluorescence microscope. The results are shown in Figure 2. Fig.11 E.

[0070] 15 Flow cytometry

[0071] Flow cytometry apoptosis: FITC Annexin V Apoptosis Detection Kit (BD, UK) was used to detect apoptosis. The cells were washed with PBS and centrifuged. The cells were resuspended in buffer, 1×10^5 cells were counted and collected into a 1.5 ml EP tube, and 5 μl PI and 5 μl FITC Annexin V were added and incubated for 20 minutes. The results are shown in Fig.11 L, Fig.13 C, Fig.13 D, Fig.13 N and Fig.13 O.

[0072] Flow cytometry: Cell Cycle Staining Kit (MultiSciences, China) was used to detect the cell cycle. After washing with PBS and centrifuging the cells, the upper PBS layer was discarded. 10 μl of Permeabilization solution and 1 ml of DNA Staining solution were added and incubated for 25 minutes. The results can be found in Fig.11 N and Fig.11 O.

[0073] The cells after the above treatments were detected on a flow cytometer (CytoFlex SRT, Beckman, USA), and the collected data were imported into FlowJo software (version 10.8.1) for analysis.

[0074] 16Caspase 3 activity detection

[0075] Caspase 3 Activity Assay Kit (Beyotime, China) was used to detect Caspase 3 activity. Cells were first inoculated into 6-well plates. After specific treatment, the cells were collected by centrifugation with PBS, and 150ul of lysis buffer was added. After sufficient lysis, the supernatant was collected by centrifugation. According to the instructions, an appropriate amount of A c-DEVD-pNA (2mM) was added to prepare the reaction system. After incubation at 37°C for 70 minutes, the cells were detected using an ELISA reader. The results are shown in Fig.11 M.

[0076] 17. Drug synergy visualization

[0077] The synergy of anti-AML drugs was evaluated using the synergyfinder R package. In order to obtain the most reliable results, four different models were used: ZIP, LOEWE, BLISS and HSA. BL ISS model: Assuming that the drugs act independently, the effect is predicted by calculating the probability that each drug in the combination acts independently. HSA model: If the response of the combination is greater than the response of any single drug, it is considered a synergistic effect. LOEWE model: Considering the dose-response relationship of a single drug, the expected additive response is calculated, and a synergistic effect is considered if it is higher than expected. ZIP model: Assuming that each drug does not affect the efficacy of another drug, the combination effect is calculated. Each model analyzes the effect of drug combinations based on different assumptions and methods, thereby providing a comprehensive understanding of drug interactions. Mairin, Astilbin, Diosgenin, Diosgenin, and Isoimperatorin were all purchased from Sigma-Aldrich.

[0078] For detailed evaluation results, see Fig.13 H- Fig.13 M. Among them, Fig.13 H is the confidence interval diagram of the synergistic drug combination (dioscin and cytarabine), Fig.13 I- Fig.13 L is the ZIP of MOLM13 / R cells treated with cytarabine and dioscin ( Fig.13 I) Bliss Fig.13 J) HSA( Fig.13 K) and Loewe( Fig.13L) Synergy diagram. The synergy score was calculated using the SynergyFinder R package. Positive or negative values ​​indicate synergy or antagonism, respectively. Fig.13 M is the synergistic effect measurement result at the dose combination of 0.1 μM cytarabine and 16 μM dioscin.

[0079] Example 3

[0080] In vivo experiments

[0081] 18. Animal Experimentation

[0082] In vivo animal experiments were conducted using female humanized NSG mice constructed previously. Six 4-week-old humanized NSG mice were randomly divided into sh-NC group (n=3) and sh-ZBED1 group (n=3). The mice were inoculated with MOLM13 / R cells (1×10^7, 0.1mL PBS) transfected with sh-ZBED1 or sh-NC, respectively. Each group of mice was intraperitoneally injected with cytarabine (at a dose of 250 mg / kg, Sigma-Aldrich, USA) for 7 consecutive days, and the tumor volume was measured every 3 days thereafter (the formula for calculating the tumor volume is: length×width×width / 2). After 21 days, the ex vivo tumor tissues of the mice were collected and weighed. See Fig.11 P- Fig.11 R, where Fig.11 P is the tumor entity of the mouse after dissection. Fig.11 Q- Fig.11 R is the tumor volume and weight of mice in each group. Fig.11 Q represents the tumor growth kinetics of NSG mice in the sh-ZBED1 group and shRNA-NC group, which are displayed by growth curves. Fig.11 R represents the comparative analysis of tumor mass between the sh-ZBED1 group and the shRNA-NC group in nude mice.

[0083] Six 4-week-old humanized NSG mice were randomly divided into a control group (n=3) and a dioscin group (n=3). All mice were inoculated with MOLM13 / R cells (1×10^7, 0.1mL PBS), and each group of mice was intraperitoneally injected with cytarabine (at a dose of 250mg / kg, Sigma-Aldrich, USA) for 7 consecutive days. Mice in the dioscin group were orally administered with dioscin at a concentration of 40mg / kg / day. The control group was orally administered with the same volume of normal saline. The tumor volume was measured once a week (the formula for calculating the tumor volume is: length×width×width / 2). After 4 weeks, the ex vivo tumor tissues of the mice were collected and weighed. See Fig.13 E- Fig.13 G, Fig.13 E is a photo of the tumor entity of the mouse after dissection. Fig.13 F shows the tumor growth kinetics growth curves of NSG mice in different groups. Fig.13 G shows the comparative analysis results of nude mouse tumor mass in different groups of NSG mice.

[0084] 19. Micro-CT analysis

[0085] In the construction of the orthotopic model, 3-week-old female humanized NSG mice were randomly divided into two groups: sh-ZBED1 group and shRNA-NC group. Approximately 2×10 6 When the average tumor volume reached approximately 100 mm 3 At 24 hours, mice in each group were intraperitoneally injected with cytarabine (at a dose of 250 mg / kg, Sigma-Aldrich, USA) for 7 consecutive days. The dissected femur and tibia samples were subjected to micro CT scanning and analysis. The samples were first fixed with 4% paraformaldehyde and then stored in PBS solution at 4°C. Skyscan 1276 (from Skycan, Aartselaar, Belgium) was used for high-resolution μCT scanning, with the scanning resolution set to 20.376 μm per pixel, the voltage was 100 kV, and the current was 200 μA. When selecting the region of interest (ROI), starting from 0.45 mm below the distal growth plate and extending 0.45 mm proximally, the relevant parameters of trabeculae were analyzed, such as bone mineral density (BMD), bone surface (BS), bone volume ratio (BV / TV), trabecular thickness (Tb.Th), trabecular number (Tb.N), and trabecular spacing (Tb.Sp). In addition, cortical bone parameters within 0.2 mm of the mid-femur were analyzed, such as total cross-sectional area (Tt.Ar), cortical bone area (Ct.Ar), cortical area ratio (Ct.Ar / Tt.Ar), and cortical thickness (Ct.Th). Fig.12 ,in, Fig.12 A- Fig.12 E is the 3D reconstruction of the femur of the sh-NC group ( Fig.12 A), Coronal section of the whole femur ( Fig.12 B), cross-sectional view and three-dimensional reconstruction of the proximal metaphysis of the distal femur ( Fig.12 C), 3D reconstruction of the cortical bone of the middle femur ( Fig.12 D), Coronal view of the proximal tibia ( Fig.12 E), Fig.12 F- Fig.12 J is the 3D reconstruction of the femur of the sh-ZBE D1 group ( Fig.12 F), Coronal section of the whole femur ( Fig.12 G), cross-sectional view and three-dimensional reconstruction of the proximal metaphysis of the distal femur ( Fig.12 H), 3D reconstruction of the cortical bone of the middle femur ( Fig.12 I), coronal view of the proximal tibia ( Fig.12 J), Fig.12 K- Fig.12 P represents the change of trabecular bone microarchitecture parameters, and BMD represents bone mineral density ( Fig.12 K); BS represents bone surface area ( Fig.12 L), BV / TV represents the trabecular bone volume fraction ( Fig.12 M), Tb.Th represents the trabecular thickness ( Fig.12 N), Tb.N represents the number of trabeculae ( Fig.12 O), Tb.Sp represents the trabecular spacing ( Fig.12 P), Fig.12 Q- Fig.12 T represents the change in cortical bone microstructural parameters, and Tt.Ar represents the total cross-sectional area within the periosteal envelope ( Fig.12 Q), Ct.Ar represents the cortical bone area ( Fig.12 R), Ct.Ar / Tt.Ar represents the cortical bone area fraction ( Fig.12 S), Ct.Th represents the average cortical bone thickness ( Fig.12 T), *P<0.05, **P<0.01, ***P<0.001.

[0086] Although the principles of the present invention are described in detail above in conjunction with the preferred embodiments of the present invention, those skilled in the art should understand that the above embodiments are merely explanations of the exemplary implementations of the present invention, and are not intended to limit the scope of the present invention. The details in the embodiments do not constitute limitations on the scope of the present invention, and any obvious changes such as equivalent transformations, simple replacements, etc. based on the technical solution of the present invention, without departing from the spirit and scope of the present invention, fall within the protection scope of the present invention.

Claims

1. Use of a ZBED1 expression inhibitor in the preparation of a cytarabine-resistant leukemia drug, characterized in that: The cytarabine-resistant leukemia drug includes a ZBED1 expression inhibitor.

2. The use of the ZBED1 expression inhibitor according to claim 1 in the preparation of a cytarabine-resistant leukemia drug, characterized in that: The ZBED1 expression inhibitor is diosgenin.

3. A pharmaceutical composition for inhibiting leukemia, characterized in that: The method comprises the ZBED1 expression inhibitor according to claim 1 or 2 and cytarabine.

4. The pharmaceutical composition for inhibiting leukemia according to claim 3, characterized in that: The pharmaceutical composition for inhibiting leukemia comprises diosgenin and cytarabine.

5. The leukemia inhibitor according to claim 4, characterized in that In the pharmaceutical composition for inhibiting leukemia, the concentration of cytarabine is 0.01-2 μM, and the concentration of diosgenin is 2-64 μM.

6. The leukemia inhibitor according to claim 4, characterized in that In the pharmaceutical composition for inhibiting leukemia, the concentration of cytarabine is 0.1 μM, and the concentration of diosgenin is 16 μM.

7. The pharmaceutical composition for inhibiting leukemia according to claim 3, characterized in that: Also includes auxiliary materials.

8. The pharmaceutical composition for inhibiting leukemia according to claim 7, characterized in that: The auxiliary materials include fillers, disintegrants, lubricants, suspending agents, binders or sweeteners.

9. The pharmaceutical composition for inhibiting leukemia according to any one of claims 3 to 8, characterized in that: The pharmaceutical composition for inhibiting leukemia is an oral preparation or an injection preparation.

10. The pharmaceutical composition for inhibiting leukemia according to claim 9, characterized in that: The oral preparations include capsules, tablets or granules.

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