Use of a trim44 expression inhibitor in the preparation of a drug for treating cytarabine-resistant leukemia

Abstract

The application provides an application of a TRIM44 expression inhibitor in preparation of a cytarabine-resistant leukemia drug, and belongs to the technical field of bioengineering.The application provides the application of the TRIM44 expression inhibitor in preparation of the cytarabine-resistant leukemia drug.In the application, a key gene TRIM44 affecting AML drug resistance is identified by analyzing a single cell transcriptome data set, the expression of the TRIM44 can be lowered by using sinomenine, the function of regulatory T cells (T-reg cells) is inhibited, the sensitivity of AML cells to a chemotherapy drug is enhanced, a good synergistic treatment effect with cytarabine is shown, the cytarabine resistance of leukemia is reversed, and a new target site and a molecular marker are provided for predicting and improving the treatment effect of patients in clinic.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of bioengineering technology, and particularly relates to application of a TRIM44 expression inhibitor in preparation of a cytarabine-resistant leukemia drug. BACKGROUND

[0002] Acute myeloid leukemia (AML) is the most common acute leukemia, originating from hematopoietic stem cells, characterized by abnormal differentiation and clonal expansion of myeloid blasts. It is prevalent especially among adolescents and young adults, with incidence increasing with age. Despite some progress in current immunotherapy regimens for AML, the five-year survival rate of patients remains low, showing the limitations of existing treatment methods. The complexity of the disease is rooted in its heterogeneity at the genetic and metabolic levels, leading to immune evasion, drug resistance, and relapse of tumors. The bone marrow immune microenvironment plays a crucial role in the complexity of AML treatment, with tumor cells remodeling this environment to promote survival and immune evasion. Therefore, in-depth study of the AML microenvironment is crucial for discovering new therapeutic targets and improving treatment outcomes.

[0003] The frontier progress of single-cell sequencing technology has greatly promoted our understanding of the microstructure of healthy tissues and malignant tumors, generating high-resolution cell maps. Through the application of single-cell analysis technology, researchers can explore the tumor microenvironment of AML patients under different pathological conditions (such as relapse and remission), identify cell populations and their heterogeneity related to disease progression, and reveal the important role of cell-cell interactions in the AML microenvironment in tumor drug resistance. For example, a study analyzed bone marrow samples from AML patients, revealing the cell differentiation trajectories of leukemia stem cells and non-stem cell populations and their association with specific oncogenic factors, further promoting the understanding of AML clonal evolution. Another study focused on stem cells in the bone marrow microenvironment of AML patients and their role in drug resistance and disease recurrence, highlighting the critical role of interactions between leukemia cells and the microenvironment in forming drug resistance mechanisms. These findings highlight that the bone marrow microenvironment (BMM) not only serves as the birthplace of AML stem cells but also provides a sanctuary for these malignant cells, supporting their clonal evolution, thus deepening the understanding of drug resistance and leukemia recurrence mechanisms. Various factors, such as adhesion of leukemia cells to the microenvironment, signaling, and gene expression regulation, collectively constitute a complex regulatory network, with key components potentially serving as new targets for AML treatment. Therefore, in-depth understanding of cell-cell interactions in the AML microenvironment and their impact on drug resistance is crucial for developing treatment strategies targeting this complex network.

[0004] Cytarabine is an important chemotherapeutic drug, mainly used for induction remission and maintenance treatment of acute nonlymphocytic leukemia in adults and children, and also has therapeutic effect on leukemia such as acute lymphocytic leukemia and chronic myelocytic leukemia. However, after cytarabine treatment, many patients cannot be remitted or relapse after remission. In addition to individual differences and clinical medication schemes of patients, drug resistance may also be a part of the reason.

[0005] Traditional Chinese medicine has unique advantages and a long history in disease treatment. Many Chinese medicines and their active ingredients have been shown to have significant effects on anti-tumor, immune regulation and reversal of drug resistance. The multi-component and multi-target characteristics of traditional Chinese medicine make it have potential advantages in the treatment of complex diseases. Combining traditional Chinese medicine with modern biomedical technology is expected to provide new strategies and ideas for the treatment of malignant tumors such as AML and drug resistance. SUMMARY

[0006] The application embodiment provides a use of a TRIM44 expression inhibitor in the preparation of a cytarabine-resistant leukemia drug. The application finds that the active ingredient of traditional Chinese medicine, sinomenine, which selectively inhibits the function of TRIM44, can not only synergize with cytarabine to reverse the cytarabine resistance of leukemia, but also provide a new target site and molecular marker for predicting and improving the treatment effect of patients. At least one of the problems existing in the related art is solved. To achieve this purpose, the application is implemented by the following technical solutions.

[0007] In the first aspect, the application provides a use of a TRIM44 expression inhibitor in the preparation of a cytarabine-resistant leukemia drug in an optional embodiment. The NCBI accession number of TRIM44 is Gene ID: 54765.

[0008] Further, the TRIM44 expression inhibitor includes at least one of sinomenine, alpha-palustrine, mediodrosone, dihydrocapsaicin and berberine. The application finds that sinomenine, alpha-palustrine, mediodrosone, dihydrocapsaicin and berberine have good molecular docking effect with TRIM44.

[0009] In the specific embodiment of the application, sinomenine is taken as an example to conduct in vitro cell experiments and in vivo mouse experiments.

[0010] Further, the leukemia includes acute leukemia and / or chronic leukemia.

[0011] Further, the acute leukemia includes acute myeloid leukemia and / or acute lymphocytic leukemia.

[0012] In the specific embodiment of the application, acute myeloid leukemia is taken as an example to conduct experimental verification.

[0013] In a second aspect, the present application provides a medicine for preventing and / or treating leukemia, which comprises a TRIM44 expression inhibitor and cytarabine.

[0014] Further, the TRIM44 expression inhibitor comprises at least one of sinomenine, α-boswellic acid, mederivatin, dihydrocapsaicin and berberine red.

[0015] Further, the present application takes sinomenine as an example, and finds that it has a synergistic effect when combined with cytarabine.

[0016] Further, the concentration of the cytarabine is 0.01-2 μM, and the concentration of the sinomenine is 1-120 μM.

[0017] Preferably, the concentration of the cytarabine is 0.1 μM, and the concentration of the sinomenine is 20 μM.

[0018] Further, the medicine further comprises a pharmaceutically acceptable excipient.

[0019] Further, the medicine is an oral preparation or an injection preparation. The oral preparation comprises a capsule, a tablet or a granule.

[0020] In the present application, the medicine can be any conventional oral preparation or injection preparation prepared by a conventional preparation process, and the oral preparation is a capsule, a tablet, a granule or a liquid. In order to realize the above oral preparation, a pharmaceutically acceptable excipient needs to be added in the preparation process, and the pharmaceutically acceptable excipient is a filler, a disintegrant, a lubricant, a suspending agent, a binder or a sweetening agent.

[0021] The filler comprises at least one of starch, lactose, microcrystalline cellulose or sucrose; the disintegrant comprises at least one of starch, sodium carboxymethyl starch, pregelatinized starch, low-substituted hydroxypropyl cellulose or cross-linked sodium carboxymethyl cellulose; the lubricant comprises at least one of magnesium stearate or silicon dioxide, sodium dodecyl sulfate; the suspending agent comprises at least one of polyvinylpyrrolidone, sucrose or hydroxypropyl methyl cellulose; the binder comprises at least one of hydroxypropyl methyl cellulose, starch paste or polyvinylpyrrolidone; and the sweetening agent is at least one of sodium saccharin, glycyrrhetinic acid, sucrose, aspartame or cyclamate.

[0022] In order to realize the above injection preparation, a pharmaceutically acceptable excipient needs to be added in the preparation process, which comprises water for injection and / or sodium chloride solution for injection.

[0023] In a third aspect, the present application provides use of the medicine in the preparation of a medicine for preventing and / or treating leukemia.

[0024] Further, the leukemia includes acute leukemia and / or chronic leukemia.

[0025] Still further, the acute leukemia includes acute myeloid leukemia and / or acute lymphoblastic leukemia.

[0026] In the specific embodiments of the present application, the acute myeloid leukemia is taken as an example for experimental verification.

[0027] The embodiments of the present application have the following beneficial effects:

[0028] In the present application, by analyzing single-cell transcriptome datasets (GSE235063, GSE239721 and GSE235923), the changes in the composition of immune cells in the bone marrow microenvironment of AML patients and their influence on disease progression are deeply explored. The immune cells in the AML microenvironment exhibit significant adaptability, reflecting the inherent heterogeneity of AML itself. By deeply analyzing the correlation between different cell phenotypes and AML drug resistance, focusing on key deubiquitinating enzyme (DUB) genes, the key gene TRIM44 affecting AML drug resistance is identified, and its influence on the occurrence, development and prognosis of AML is verified.

[0029] In addition, the regulatory effect of the active ingredient of traditional Chinese medicine Sinomenine on TRIM44 is studied. The results show that Sinomenine can down-regulate the expression of TRIM44, inhibit the function of regulatory T cells (T-reg cells), enhance the sensitivity of AML cells to chemotherapeutic drugs, and show good synergistic therapeutic effect with cytarabine. The present application further enriches the understanding of the AML immune microenvironment and its influence on chemotherapy resistance, provides a new perspective for AML treatment, emphasizes the importance of considering microenvironment factors and the application of traditional Chinese medicine in disease management, and lays a foundation for developing more effective treatment strategies.

[0030] The present application establishes the key role of TRIM44 in leukemia drug resistance and discovers the active ingredient of traditional Chinese medicine Sinomenine that selectively inhibits the function of TRIM44. It not only can synergize with cytarabine to reverse the cytarabine resistance of leukemia, but also provides a new target site and molecular marker for predicting and improving the treatment effect of patients in clinic. This innovative strategy brings new hope for leukemia treatment, especially in the problem of cytarabine resistance, and provides a more effective solution. BRIEF DESCRIPTION OF DRAWINGS

[0031] The accompanying drawings, which form a part of the present application, are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and serve to explain the principles of the present application. The present application should not be limited by these embodiments.

[0032] Figure 1Mechanism diagram for the synergistic treatment of leukemia by the Chinese medicine monomer (sinomenine) targeting the inhibition of TRIM44 and cytarabine of the present application.

[0033] Figure 2 Heterogeneity of acute myeloid leukemia (AML) tumor microenvironment is shown. Among them, A is to show the key genes highly expressed in 13 cell clusters by scatter plot, the size of the point represents the proportion of cells with specific marker gene expression, and the color depth represents the average expression intensity of the marker gene; B is to show the marker genes of different AML progenitor cell (AML progenitors) subgroups; C is to show the 13 main cell types identified in the AML microenvironment on the t-SNE and UMAP maps; D is four major AML progenitor cell subgroups determined by UMAP and t-SNE analysis; E is a volcano plot of differentially expressed genes in AML progenitor cells in remission and relapse patients after treatment, red points represent genes up-regulated in relapse; F is the expression heat map of DEGs in the four AML progenitor cell subgroups and the results of gene ontology (GO) analysis thereof; G is to show the key genes highly expressed in 7 T cell clusters by scatter plot. The size of the point represents the proportion of cells with specific marker gene expression, and the color depth represents the average expression intensity of the marker gene; H is 7 T cell subgroups determined by UMAP and t-SNE analysis; I is the t-SNE and UMAP distribution of T cells according to different clinical stages.

[0034] Figure 3 Global view of intercellular communication analysis in AML. Among them, A is the number and intensity of communication between different cell subgroups; B is the identification of important outgoing and incoming signal patterns; C is the dominant signal sending source and receiving target in two-dimensional space; D is the number of output patterns inferred by Cophenetic and Silhouette values; E is the number of input patterns inferred by Cophenetic and Silhouette values; F is to identify the signals corresponding to the signal subgroups and signal pathways of the output patterns and inputs, showing 5 output patterns; G is to identify the signals corresponding to the signal subgroups and signal pathways of the output patterns and inputs, showing 3 input patterns; H is to visualize the outgoing signal patterns of secretory cells by river plot; I is to visualize the incoming signal patterns of secretory cells by river plot; J is the communication pattern of different cell subgroups as signal output sources; K is the communication pattern of different cell subgroups as signal receiving sources.

[0035] Figure 4Figure 1 shows the different cell communication situations between CR and R. A: the number of cell interactions between CR and R patients with acute myeloid leukemia; B: the differential expression of the number or intensity of interactions (red lines represent high expression, blue represents low expression) in R; C: the inferred number and intensity of interactions between CR and R patients with acute myeloid leukemia; D: comparison of the main sources and destinations of CR and R patients with acute myeloid leukemia in 2D space; E: heat map showing the number and intensity of interactions between CR and R patients with acute myeloid leukemia; F: comparison of the outgoing signal flow associated with each cell population; G: comparison of the incoming signal flow associated with each cell population; H: comparison of the overall signal flow associated with each cell population; I: comparison of the differential signaling pathways between CR and R.

[0036] Figure 5 Figure 2 shows the important regulatory role of TRIM44 in acute myeloid leukemia. A: the overlap and unique parts between the Relapsed gene, unexplored gene, survival significantly affected gene set, and DUB; B: t-SNE and UMAP plots showing the effect of TRIM44 on cell distribution; C: expression density of TRIM44 on the UMAP distribution plot; D: cells are divided into TRIM44 high expression and low expression groups according to the expression level of TRIM44 on the UMAP and t-SNE distribution plots; E: volcano plot showing the differentially expressed genes between TRIM44 high and low expression; F: results of gene ontology (GO) analysis, reflecting the enrichment of genes in biological processes (BP), cellular components (CC), and molecular functions (MF); G: results of KEGG pathway analysis; H: results of GSVA pathway analysis.

[0037] Figure 6 Figure 3 shows that TRIM44 remodels the TME of acute myeloid leukemia. A: immune infiltration of each sample; B: cell composition ratio of all samples; C: survival difference analysis of patients with high expression of AML group cells and low expression of AML group cells; D: survival difference analysis of patients with high expression of Treg and low expression of Treg; E: correlation analysis of TRIM44 and cells in the bone marrow microenvironment.

[0038] Figure 7Figure 2. The role of TRIM44 in regulating the evolution of T-reg cells. A Monocle2 shows the distribution of different cell subpopulations; B Monocle2 shows the pseudotemporal differentiation time of different cells; C Monocle2 shows the TRIM44 expression of different cells; D Monocle3 shows the 3D distribution of different cell subtypes; E Monocle3 shows the distribution of different cell subpopulations; F Monocle3 shows the nodes of different cell subpopulations; G Monocle3 shows the pseudotemporal differentiation time of different cells; H Monocle3 shows the TRIMM of different cells in 3D space; I Heatmap hierarchical clustering of developmental time series and whole subgroup-specific marker genes; J Differential expression between subgroups at time point 1 of the developmental time series.

[0039] Figure 8 Figure 3. TRIM44 and its relationship with multidrug resistance. A Spearman correlation analysis between TRIM44 expression and drug IC50 values (TCGA database). B. Spearman correlation analysis between TRIM44 expression and drug IC50 values (GSE database).

[0040] Figure 9 Figure 4. Molecular docking analysis of TRIM44 and main leukemia treatment drugs. A Cytarabine; B Doxorubicin; C Mitoxantrone; D Fludarabine; E Sinomenine; F a-Boswellic acid; G Medifrutal; H Dihydrocapsaicin; I Berberine red base; J Molecular structure of sinomenine.

[0041] Figure 10 Figure 5. Knockdown of TRIM44 and its expression analysis in AML drug-resistant cell lines. A Effect of knocking down TRIM44 in MOLM13 / R cell lines with different siRNAs; B Effect of knocking down TRIM44 in MV4-11 / R cell lines with different siRNAs; C Western blot analysis showing the protein expression of TRIM44 in knockdown cell lines; D Western blot analysis showing the quantification of protein expression of TRIM44 in knockdown cell lines; E Immunofluorescence and TUNEL staining results showing the intracellular localization and expression of TRIM44 and its knockdown-induced apoptosis in MOLM13 / R cells; F Immunofluorescence and TUNEL staining results showing the intracellular localization and expression of TRIM44 and its knockdown-induced apoptosis in M4-11 / R cells. Cell nuclei were stained with DAPI (scale bar: 20 pm).

[0042] Figure 11Figure 4: Effects of TRIM44 knockdown on proliferation and apoptosis of acute myeloid leukemia cells. Figure A shows the statistical results of CCK-8 assays. Data are expressed as mean ± standard deviation (n = 3). Significant differences are marked as: *p < 0.05, **p < 0.01, ***p < 0.001. Figure B shows flow cytometry analysis of cell apoptosis. Figure 11 C is the cell cycle diagram of flow cytometry results analysis; Figure 11 D is the cell cycle quantification diagram of flow cytometry analysis results.

[0043] Figure 12 The effects of drugs on TRIM44 expression and cells. A shows the effects of sinomenine, α-spinasterol, medipterostilbene, dihydrocapsaicin, and berberine on TRIM44 expression as determined by Western blot; B shows the effects of sinomenine at different concentrations on cell apoptosis as determined by flow cytometry; and C shows the quantitative effects of sinomenine at different concentrations on cell apoptosis as determined by flow cytometry.

[0044] Figure 13 Figure 3 is the effect of sinomenine on inhibiting cytarabine-resistant AML cells. A is the confidence interval diagram of the combined use of cytarabine and sinomenine; B is the synergistic effect diagram of the combined treatment of U2OS / R cells by cytarabine and sinomenine using the ZIP (I), Bliss (J), HSA (K) and Loewe (L) evaluation models. Positive or negative synergistic scores indicate synergistic and antagonistic effects, respectively; C is a synergistic effect measurement diagram showing the effect of the combined use of 0.1 μM cytarabine and 20 μM sinomenine; D is based on Figure 13 The recommended concentrations in C are used to detect the effect of combined drug therapy on cell apoptosis by flow cytometry (n=5); E is a quantitative graph of the effect of combined drug therapy on cell apoptosis by flow cytometry.

[0045] Figure 14 This is an in vivo experiment of sinomenine inhibiting cytarabine-resistant mice. A is a representative image of subcutaneous tumor formation in NSG mice in the sh-TRIM44 and shRNA-NC groups; B is a growth curve showing the tumor growth kinetics in NSG mice in the sh-TRIM44 and shRNA-NC groups; C is a representative image of subcutaneous tumor formation in NSG mice in the control and sinomenine groups; D is a growth curve showing the tumor growth kinetics in NSG mice in the control and sinomenine groups; E is a comparative analysis of tumor mass in NSG mice in the control and sinomenine groups. DETAILED DESCRIPTION

[0046] For the purposes of the embodiments of the present application, the technical solutions and advantages will be clearer, the various embodiments of the present application will be described in detail below with reference to the drawings. However, those skilled in the art can understand that in the various embodiments of the present application, many technical details are presented in order to enable the reader to better understand the present application. However, even without these technical details and based on various changes and modifications of the following embodiments, the technical solutions claimed by the present application can be implemented. The division of the following various embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the various embodiments can be combined and referred to each other without contradiction.

[0047] The mechanism diagram of the synergistic treatment of leukemia by the traditional Chinese medicine monomer (sinomenine) of the present application targeting the ternary motif-44 (TRIM44) and cytarabine is shown in Figure 1 The NCBI accession number of TRIM44 is Gene ID: 54765.

[0048] Example 1

[0049] 1. Data source and processing

[0050] The single-cell sequencing (scRNA-seq) data sets related to acute myeloid leukemia (AML) were retrieved from the Gene Expression Omnibus (GEO) database. According to the clinical information of the patients from which the samples were derived, three scRNA-seq data sets were selected as the core objects for the study, namely GSE235063, GSE239721 and GSE235923.

[0051] At the same time, in order to further analyze the gene expression patterns of clinical samples, the bulk RNA-seq data sets of AML populations containing survival time information were also collected from the GEO database. After careful screening, two data sets, GSE71014 and GDC TCGAAcute Myeloid Leukemia (LAML), were determined to meet the requirements.

[0052] The acute myeloid leukemia (AML)-related single-cell RNA sequencing (scRNA-seq) data was analyzed in depth using the Seurat package (version 4.4.3), including quality control, dimensionality reduction, and clustering of cell types. To select high-quality cells, the criteria of less than 1000 or more than 9000 number of gene expression, and more than 10% of mitochondrial gene expression ratio were set as the screening conditions for low-quality cells. After global scaling by the "lognormalize" method, the filtered cell data was standardized, and then the high-variation genes were selected by the FindVariableFeatures function as the basis for subsequent principal component analysis (PCA). The analysis process was as follows: first, dimensionality reduction was performed based on the first 10 significant principal components, and then the cell type clustering was performed by the FindClusters function of Seurat with a resolution of 1.0. Subsequently, the UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) techniques were used for further dimensionality reduction and visualization analysis. By the FindAllMarkers function, the specific marker genes of each cell cluster were identified. The visualization of the clustree revealed the relationship between cell clusters at different resolutions. The annotation results of cell types were displayed in the form of heat maps and enrichment analysis of differentially expressed genes (DEGs) between cell subpopulations by the ClusterGVis tool. In addition, the Nebulosa package and scCustomize package, as well as the Ridgeplot function were used for the visualization of marker genes.

[0053] As shown in Figure 2 , Figure 2 A shows 11 different cell types identified by specific marker genes. These cell types include: hematopoietic stem cells (HSC), erythroid cells, acute myeloid leukemia progenitor cells (AML progenitor), granulocyte-monocyte progenitor cells (GMP), B cells, dendritic cells, monocyte-macrophage cells (Mono.Mac), plasma cells, CD4+ T cells (CD4.T), early basophil cells, natural killer cells (NK cell), and CD8+ T cells (CD8.T).

[0054] Figure 2 B shows the marker genes of different AML progenitor cell (AML progenitor) subpopulations.

[0055] Figure 2 C Different types of cells are shown by t-SNE and UMAP analysis plots. Each dot represents a single cell, and different colors represent different cell types.

[0056] Figure 2 D Cell subpopulations of four major AML progenitor cells determined by UMAP and t-SNE analysis plots.

[0057] Figure 2 E Volcano plot of cell differential expression genes of AML progenitor cells in remission and relapse patients after treatment, with red dots representing genes upregulated in relapse.

[0058] Figure 2 F Expression heatmap of differential expression genes in four AML progenitor cell subpopulations and their gene ontology (GO) analysis results.

[0059] Figure 2 G Key genes highly expressed in 7 T cell clusters are shown by dot plots. The size of the dot represents the proportion of cells with specific marker gene expression, while the color depth represents the average expression intensity of the marker gene.

[0060] Figure 2 H 7 T cell subpopulations determined by UMAP and t-SNE analysis.

[0061] Figure 2 I t-SNE and UMAP distribution of T cells at different clinical stages.

[0062] 2. Analysis of cell-to-cell communication

[0063] To explore the signal exchange between different cell populations revealed by single-cell RNA sequencing (scRNA-seq) data, the CellChat package (version 1.6.1) in R software was used for analysis. First, a CellChat object was initialized by the "createCellChat" function according to the database and corresponding analysis results determined in 1. Then, the signal network analysis was performed using the interaction database containing "SecretedSignaling". The communication probability between cells was estimated by the "computeCommunProb" function. In the "selectK" function, the global communication pattern was focused on, and the nPatterns parameters for input and output of information flow were set to 5 and 3, respectively.

[0064] Figure 3 The analysis of cell-to-cell communication in the AML tumor microenvironment is shown. Figure 3 A The number and intensity of communication between different cell subpopulations are shown.

[0065] Figure 3 B shows the incoming and outgoing signal analysis, showing that major histocompatibility complex class I molecules (MHC-I), CD99 antigen (CD99), and macrophage migration inhibitory factor (MIF) are the main signal pathways for cell-cell communication, highlighting their key role in response to the AML tumor microenvironment.

[0066] Figure 3 C shows the two-dimensional illustration of cell communication roles, with hematopoietic stem cells (HSCs), AML progenitor cells, and granulomonocytic precursors (GMPs) as the main signal senders, and cytotoxic T cells (CD8+ T cells), helper T cells (CD4+ T cells), and natural killer cells (NK cells) as the main receivers.

[0067] Figure 3 D and Figure 3 E show the communication mode quantification results using CellChat, showing the signal output ( Figure 3 D) and input ( Figure 3 E) patterns of different cell subpopulations.

[0068] Figure 3 F and Figure 3 G show the signal communication mode classification, showing 5 output and 3 input patterns.

[0069] Figure 3 H and Figure 3 I show the river plot revealing the relationship between signal output patterns and cell populations, showing the flow of specific signal pathways.

[0070] Figure 3 J and Figure 4 K represent the communication patterns of different cell subpopulations as signal output sources and receiving sources.

[0071] Figure 4 Differences in cell communication between patients in remission after chemotherapy and patients with relapse after chemotherapy are shown. Figure 4 A shows the number of cell interactions in patients with acute myeloid leukemia in remission after chemotherapy and patients with relapse after chemotherapy.

[0072] Figure 4 B shows the differential expression of interaction number or intensity in patients with relapse after chemotherapy (red lines represent high expression, blue represents low expression).

[0073] Figure 4 C shows the inferred number and intensity of interactions in patients with acute myeloid leukemia in remission after chemotherapy and patients with relapse after chemotherapy.

[0074] Figure 4 D shows the main sources and destinations of patients with acute myeloid leukemia in remission after chemotherapy and patients with relapse after chemotherapy in 2D space.

[0075] Figure 4 E Heatmap showing the number and strength of interactions between patients with remission after chemotherapy and relapse after chemotherapy in acute myeloid leukemia.

[0076] Figure 4 F, Figure 4 G and Figure 4 H Comparing the outflow (F), the inflow (G) and the overall signal flow (H) associated with each cell population by heatmaps. Figure 4 F), the inflow (G) and the overall signal flow (H) associated with each cell population by heatmaps. Figure 5 G) and the overall signal flow (H) associated with each cell population by heatmaps. Figure 5 H) and the overall signal flow (H) associated with each cell population by heatmaps.

[0077] Figure 5 I Comparing the differential signaling pathways between patients with remission after chemotherapy and relapse after chemotherapy in acute myeloid leukemia by bar charts.

[0078] 3. Identification of deubiquitination-related genes and survival analysis

[0079] Several candidate genes were selected from the intersection of deubiquitination and related genes DEGs. Through the analysis of LAML and GSE71014 datasets, survival analysis was performed on individual genes using the R package "survival". According to the cutoff value of each gene, the patients were divided into high expression group and low expression group. Log-Rank test determined the statistical significance of the survival curve, and the curves with P<0.05 were considered different.

[0080] Figure 5 A represents the screening process of the ternary motif-44 (TRIM44) gene (the key gene TRIM44 is obtained by taking the intersection of the relapse gene set (Rreiated_gene), the unexplored gene set (Unexplored), the gene set with significant influence on survival (Survival) and the ubiquitin-specific protease (DUB)).

[0081] Figure 5 B shows the expression density of the TRIM44 gene on the t-SNE and UMAP maps.

[0082] Figure 5 C shows the expression amount of the TRIM44 gene by UMAP map;

[0083] Figure 5 D is to divide the cells into high expression and low expression groups of the TRIM44 gene according to the expression level of the TRIM44 gene on the UMAP and t-SNE distribution maps.

[0084] Figure 5 E is a volcano plot showing the differentially expressed genes associated with high and low expression of TRIM44. AML progenitor cells are divided into high expression and low expression of TRIM44, and then the two types of cells are subjected to differential analysis.

[0085] 4. Enrichment analysis

[0086] Differentially expressed genes were identified by the FindMarkers function with a threshold of Padj.P < 0.05 and log2FC > 0.25. GO and KEGG analysis were performed with the cluster Profiler package, while GSEA analysis was performed with the respective package. Figure 6 F, Figure 6 G and Figure 6 H represent the analysis of differentially expressed genes using GO, KEGG and GSVA, respectively.

[0087] 5. Evaluation of immune cell subtype distribution

[0088] The CibersortX and mean expression values of signature genes were used to analyze the infiltration of cell subtypes in the LAML clinical cohort. Spearman correlation analysis revealed the association between TRIM44 and different cell populations.

[0089] Monocle2 and Monocle3 were used to visualize the pseudotemporal development trajectories of cells, and the plotpseudotimeheatmap function was used to display the heatmap of gene expression dynamics.

[0090] Figure 6 A represents the immune cell infiltration analysis for each sample.

[0091] Figure 6 B represents the distribution of various cell types in the sample.

[0092] Figure 7 C represents the Kaplan-Meier survival curve showing the prognosis analysis of patients with high and low expression of AML progenitor cells.

[0093] Figure 7 D represents the Kaplan-Meier survival curve showing the prognosis analysis of patients with high and low expression of T-reg cells.

[0094] Figure 7 E represents the correlation analysis of TRIM44 expression with cells in the tumor microenvironment.

[0095] Monocle2 and Monocle3 show the subpopulation ( Figure 7 A, Figure 7 D and Figure 7 E), the analog time ( Figure 7 B, Figure 7 F and Figure 7 G) and the expression of TRIM44 ( Figure 7C and Figure 8 H) dynamic changes. Figure 8 I and Figure 9 J heatmap hierarchical clustering of developmental timing and subgroup-specific marker genes, Figure 9 I overall subgroups, Figure 9 J differential expression between subgroups at time point 1.

[0096] 6. Chemotherapy drug sensitivity analysis

[0097] IC values for 198 drugs were obtained from the Cancer Drug Sensitivity Genomics (GDSC; https: / / www.cancerRxgene.org / ) and Cancer Cell Line Encyclopedia (CCLE, https: / / sites.broadinstitute.org / ccle / ) using the R package oncoPredict. The correlation between drug IC values and risk scores was explored by Spearman analysis to identify relevant drugs. Subsequently, IC differences between TRIM44 high and low expression groups were compared, focusing on drugs with absolute correlation values exceeding 0.2. Correlation analysis dot plots were drawn using ggplot2 in R to visualize the results, which are shown in 50 A and 50 B. 50 Figure 10 Figure 10

[0098] 7. Optimal traditional Chinese medicine active ingredient screening

[0099] To identify potential traditional Chinese medicine (TCM) targets associated with TRIM44 gene, the Coremine Medical database was used to collect gene-related information, which is an open search platform that integrates medical information such as herbs, gene ontology, protein expression, and anatomy. The screened key genes were mapped to Coremine Medical (http: / / www.coremine.com / ) respectively, and the traditional Chinese medicines related to the key genes were screened, with P<0.05 considered statistically significant. The present application identified TCMs that were significantly associated with TRIM44 key genes, including sea squid, Yu brain stone, nitrate brain stone, and Yu brain stone, etc. (p<0.05). The main active ingredients of high-frequency TCMs were screened using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), with the requirements of oral bioavailability (OB) ≥30% and drug similarity (DL) ≥0.18. Subsequently, molecular docking was performed to verify the binding energy of all TCM monomers with TRIM44, with 20 different docking simulations performed for each monomer. Five drugs with the lowest binding energy were identified, which were sinomenine, α-phytosterol, medy santal, dihydrocapsaicin, and berberine red base.​​​

[0100] The three-dimensional structures of TRIM44 protein and various drug molecules were obtained from the PubChem database. The docking analysis of protein and drug structures was prepared by removing water molecules and adding non-polar hydrogen atoms using PyMOL and AutoDock4. Subsequently, appropriate docking parameters and grid box sizes were established. Molecular docking simulation was performed using AutoDock4, and the results were analyzed and visualized using PyMOL.

[0101] Results are shown in Figure 10 A to Figure 10 I, the docking results show that the binding energy below -5.0 kcal / mol indicates significant molecular interaction, and sinomenine, α-phytol, mediodrosin, dihydrocapsaicin and berberine red base 5 drugs have inhibitory effect on TRIM44, among which sinomenine has the strongest inhibitory effect, and the results of molecular docking show that sinomenine can be directly combined with TRIM44. Figure 10 J shows the molecular structure of sinomenine.

[0102] Example 2 in vitro experiment

[0103] Sinomenine, α-phytol, mediodrosin, dihydrocapsaicin and berberine red base were purchased from Aladdin Biochemical Technology Co., Ltd.

[0104] 1. siRNA and shRNA knockdown experiment of TRIM44 gene

[0105] The construction process of cytarabine-resistant human myelomonocytic leukemia cells MV4-11 / R and cytarabine-resistant human acute myelogenous leukemia cells MOLM13 / R refers to the previous research results of the research team of the present application (DOI: 10.1186 / s12967-023-04579-5). The logarithmically growing MV4-11 / R and MOLM13 / R cells were inoculated into a 6-well plate, and after the cells grew to 70%-80% density, Lip3000 transfection reagent was used for transfection. After 6h of transfection, fresh culture medium was replaced, and the cells were cultured for another 24h. The cells were collected, and the knockdown efficiency of siRNA was verified by Western blot and qPCR.

[0106] The siRNA sequence used is shown in Table 1.

[0107] Table 1

[0108]

[0109] In addition, in order to construct the MV4-11 / R and MOLM13 / R cells stably knocking down TRIM44, the shRNA sequence targeting TRIM44 was designed by BLOCK-iT RNAi Designer and cloned into pLKO.1 vector. Subsequently, the MOLM13 / R and MV4-11 / R cells were infected by viral particles and the transduction efficiency was improved under the action of 8 μg / mL polybrene. The cells stably knocking down TRIM44 were obtained by screening with 2-5 μg / mL puromycin for 48-72 h, and the knocking down efficiency of TRIM44 was further verified by qRT-PCR and Western blot.

[0110] The shRNA sequence used is shown in Table 2.

[0111] Table 2

[0112]

[0113] 2. Quantitative real-time PCR analysis

[0114] The total RNA of the cells knocking down TRIM44 in step 1 was separated by TRIzol reagent (brand: Invitrogen; A33248), and reverse transcription was performed by RevertAid First Strand cDNA Synthesis Kit (brand: Thermo Fisher Scientific, USA; Catalog No: K1621) to obtain cDNA. The fluorescence quantitative PCR (SYBR-Green) assay mixture was prepared, and quantitative PCR analysis was performed by PCR instrument Roche Light Cycler 480II. In order to ensure the repeatability of the experiment, the number of repeated holes for each sample was 3. The expression level of the target gene was determined by comparing 2 -ΔΔCt The expression level of the gene was determined.

[0115] 3. Western blot

[0116] Cell lysis was performed with radioimmunoprecipitation assay buffer (RIPA, Beyotime) to obtain proteins, and the protein level was quantified using a BCA protein assay kit (Beyotime). The proteins were separated by 10% (w / v) SDS-PAGE and transferred to a PVDF membrane, and the PVDF membrane with the transferred proteins was obtained; to prevent non-specific binding, the PVDF membrane with the transferred proteins was incubated in 5% (w / v) skim milk, and the primary antibody was incubated at 4°C overnight on a shaker, and the PVDF membrane with the transferred proteins after incubation with the primary antibody was obtained; the PVDF membrane with the transferred proteins after incubation with the primary antibody was incubated with a HRP-labeled secondary antibody (dilution ratio 1:2000, Proteintech) at room temperature for 1 h, washed, and the fluorescent signal was detected using a UVP ChemStudio system (Ultraviolet Products, USA), and semi-quantitative analysis was performed using VisionWorks software (Analytik Jena, Germany). The primary antibodies used included: anti-TRIM44 (dilution ratio 1:1000, Proteintech) and anti-Beta-Tubulin (dilution ratio 1:1000, Santa Cruz Biotechnology). The bands of the WB were semi-quantitatively analyzed using ImageJ.

[0117] 4. TUNEL (TdT-mediated dUTP Nick-End Labeling, a method for detecting apoptosis) and immunofluorescence double staining

[0118] To detect apoptosis and the expression of TRIM44, cells were subjected to TUNEL and immunofluorescence double staining using an in situ fluorescent cell death detection kit and immunofluorescence staining.

[0119] (1) Cells were seeded on appropriate culture plates and grown to 70%-80% confluence. Then, the cells were fixed with 4% (w / v) paraformaldehyde for 15 min and washed with PBS three times for 5 min each time.

[0120] (2) The cells treated in step (1) were permeabilized with 0.1% (v / v) Triton X-100 for 5 min, and washed with PBS three times for 5 min each time.

[0121] (3) The cells treated in step (2) were blocked with 5% (v / v) normal goat serum for 1 h to reduce non-specific binding.

[0122] (4) Incubate the cells treated in step (3) with a mixture of terminal deoxynucleotidyl transferase (TdT) and deoxyuridine triphosphate (dUTP) for TUNEL staining at 37°C in a humidified dark environment for 60 min.

[0123] (5) Wash the cells treated in step (4) with PBS and incubate with diluted anti-TRIM44 primary antibody at 4°C overnight. The next day, wash the cells with PBS three times for 5 min each, and then incubate with Alexa Fluor 488-labeled secondary antibody at room temperature for 1 h (avoid light). TM 555dye-labeled secondary antibody at room temperature for 1 h (avoid light).

[0124] (6) Wash the cells treated in step (5) with PBS three times for 5 min each, and then mount with a mounting medium containing 4',6-diamidino-2-phenylindole (DAPI).

[0125] (7) Observe and photograph the apoptosis and TRIM44 expression of the cells treated in step (6) using a fluorescence microscope.

[0126] 5. CCK-8 assay

[0127] Cell viability after relevant drug treatment was evaluated using a cell counting kit-8 (CCK-8, Sigma-Aldrich, St. Louis, MO), and cell survival rate was evaluated by reading the optical density at a wavelength of 450 nm.

[0128] 6. Flow cytometry

[0129] Flow cytometry apoptosis: FITC Annexin V Apoptosis Detection Kit (BD, UK) was used to detect apoptosis.

[0130] Flow cytometry cycle: Cell Cycle Staining Kit (MultiSciences, China) was used to detect the cell cycle.

[0131] The cells treated above 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.

[0132] 7. Drug synergy visualization

[0133] Synergy finder R package was used to evaluate the synergistic effect of anti-AML drugs. To obtain as reliable results as possible, four different models were used: ZIP, LOEWE, BLISS, and HSA. BLISS model: assumes that drugs act independently, predicts the effect by calculating the probability of each drug acting independently in combination. HSA model: defines the response of a combination as synergistic if it is greater than the response of any single drug. LOEWE model: considers the dose-response relationship of individual drugs, calculates the expected additive response, and is synergistic if it is higher than expected. ZIP model: assumes that each drug does not affect the potency of the other, calculates the effect of the combination. Each model analyzes the effect of drug combinations based on different assumptions and methods, providing a comprehensive understanding of drug interactions.

[0134] As shown in Figure 10 A and Figure 11 B, the effect of knocking down TRIM44 in MOLM13 / R and MV4-11 / R cell lines with different siRNAs. The results show that si-TRIM44-2 has the best knockdown efficiency in MOLM13 / R cells, while si-TRIM44-4 has the most significant knockdown effect in MV4-11 / R cells. Based on these results, TRIM44 low-expression cell lines were constructed using the corresponding siRNAs. As shown in Figure 11 C and Figure 11 D, Western Blot (WB) analysis shows that the expression of TRIM44 protein is significantly reduced in the knockdown cell lines, confirming the effectiveness of siRNA. As shown in Figure 11 E and as shown in Figure 11 F, immunofluorescence further proves that the expression of TRIM44 in the knockdown cell lines is significantly reduced compared with the control group, which is consistent with the qPCR and WB results.

[0135] As shown in Figure 12 , the effect of TRIM44 knockdown on acute myeloid leukemia cell proliferation and apoptosis. Figure 12 A is the statistical chart of CCK-8 experiment results, the results show that TRIM44 knockdown significantly reduces cell proliferation. Figure 13 B is the flow cytometry analysis of cell apoptosis, consistent with the TUNEL staining results, flow cytometry analysis shows that TRIM44 knockdown significantly increases cell apoptosis. Figure 13 C and Figure 13 D is the flow cytometry result analysis of cell cycle, cell cycle analysis shows that after TRIM44 knockdown, cell proliferation is reduced and cell cycle is arrested in G0 / G1 phase, further highlighting the key role of TRIM44 in cell proliferation.

[0136] As shown in Figure 13A shows the effect of sinomenine, a-boswellic acid, mediasantalin, dihydrocapsaicin and berberine on TRIM44 expression detected by Western blot. The results show that sinomenine, a-boswellic acid, mediasantalin, dihydrocapsaicin and berberine significantly inhibit the expression of TRIM44, among which sinomenine shows the most significant effect. As shown in Figure 14 B and Figure 14 C shows the effect of sinomenine on the apoptosis rate at different concentrations (10, 20 and 50 μM) detected by flow cytometry (n = 5), and the results show that sinomenine induces apoptosis of MV4-11 / R and MOLM13 / R cells in a dose-dependent manner.

[0137] As shown in Figure 14 A, the confidence interval chart of the combination of cytarabine and sinomenine. Cytarabine and sinomenine can kill AML.

[0138] As shown in Figure 14 B, the synergistic effect chart of the combination of cytarabine and sinomenine in treating U2OS / R cells is drawn by ZIP (I), Bliss (J), HSA (K) and Loewe (L) evaluation models, and positive synergistic score or negative synergistic score respectively represents synergistic effect and antagonistic effect. The average scores of ZIP, Bliss, HSA and Loewe are 18.24, 18.26, 22.55 and 22.25 Figure 14 B), respectively, indicating that there is a strong synergistic effect between sinomenine and cytarabine.

[0139] As shown in ​ C, the synergistic effect measurement chart shows the effect of 0.1 μM cytarabine combined with 20 μM sinomenine. The actual inhibitory response exceeds the predicted effect of HSA, Loewe, Bliss and ZIP models, further confirming the synergistic effect between the two drugs.

[0140] As shown in ​ D and E, based on the recommended concentration in ​ C, flow cytometry is used to detect the effect of combination on cell apoptosis (n = 5), and it is found that cytarabine and sinomenine have a strong synergistic effect.

[0141] Example 3 in vivo experiment

[0142] 1. Animal experiment

[0143] In vivo animal experiments were conducted using female humanized NSG mice. Six 4-week-old humanized NSG mice were randomly divided into the sh-NC group (n=3) and the sh-TRIM44 group. sh-TRIM44-2 (n=3) was selected as the group with the best TRIM44 knockdown effect. The mice were inoculated with MOLM13 / R cells (1×10 7 Each group of mice received an intraperitoneal injection of cytarabine (250 mg / kg, Sigma-Aldrich, USA) for 7 consecutive days. Tumor volume was measured every 3 days thereafter (the formula for calculating tumor volume is: length × width × width / 2). Tumor tissue was collected and weighed after 21 days.

[0144] In addition, six 4-week-old humanized NSG mice were randomly divided into a sinomenine group (n=3) and a control group (n=3). The mice were orally administered 40 mg / kg of sinomenine and 40 mg / kg of normal saline, respectively. Each group of mice received an intraperitoneal injection of cytarabine (250 mg / kg, Sigma-Aldrich, USA) for 7 consecutive days. Tumor volume was measured every 3 days thereafter (the formula for calculating tumor volume is: length × width × width / 2). After 21 days, the ex vivo tumor tissues of the mice were collected and weighed.

[0145] ​ A represents representative images of subcutaneous tumor formation in NSG mice in the sh-TRIM44 group and the sh-NC group. ​ B represents the growth kinetics of tumors in NSG mice in the sh-TRIM44 and sh-NC groups, as demonstrated by growth curves. Knockout of TRIM44 effectively inhibits AML cell proliferation and promotes apoptosis, suggesting that TRIM44 is a target for AML therapy. ​ C represents representative images of subcutaneous tumor formation in NSG mice in the Control group and the Sinomenine group. ​ D represents the tumor growth kinetics of NSG mice in the Control group and the Sinomenine group, as shown by growth curves. ​ E represents a comparative analysis of tumor mass in NSG mice between the control group and the sinomenine group. Sinomenine effectively suppressed the malignant phenotype of cytarabine-resistant AML cells and enhanced their sensitivity to cytarabine.

[0146] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A drug for inhibiting the expression of acute myeloid leukemia MV4-11 / R and MOLM13 / R cells, characterized in that: The drugs include a TRIM44 expression inhibitor and cytarabine; The TRIM44 expression inhibitor is sinomenine; The concentration of the cytarabine was 0.1 μM, and the concentration of the sinomenine was 20 μM.

2. The drug according to claim 1, characterized in that The drug further contains pharmaceutically acceptable excipients.

3. Use of the drug according to any one of claims 1 to 2 in the preparation of a drug for preventing and / or treating leukemia.

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