Application of ZNF451 expression inhibitor in preparation of cis-platinum drug-resistant osteosarcoma drug
Through the synergistic effect of ZNF451 expression inhibitor and β-cryptoflavin, the problem of cisplatin resistance in osteosarcoma was solved, the treatment effect was improved, and new treatment hope was provided for patients with osteosarcoma.
Patent Information
- Application Number
- CN202411378369.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-30
- Publication Date
- 2024-12-17
AI Technical Summary
Recurrence and metastasis of osteosarcoma make it difficult for existing treatments to cope with the heterogeneity and complexity of the disease, especially the problem of cisplatin resistance, and lack of effective targeted therapeutic strategies.
A ZNF451 expression inhibitor was developed, using the Chinese medicine component β-cryptoflavin and cisplatin to synergize it to reverse the cisplatin resistance of osteosarcoma and provide new target sites and molecular markers for the preparation of bone and meat. Tumor drugs.
It effectively reverses the cisplatin resistance of osteosarcoma and improves the therapeutic effect, providing new therapeutic hope for osteosarcoma patients, especially providing a more effective solution on the issue of cisplatin resistance.
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Figure CN119139276A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of bioengineering, and in particular to application of a ZNF451 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug. Background Art
[0002] Osteosarcoma occurs mainly in children and adolescents and is the most common primary bone cancer, but its rarity has hindered the rapid development of advanced treatments. Recent advances in molecular pathology have opened up new avenues for understanding the molecular basis of osteosarcoma and determining personalized treatment options. However, due to the challenges of recurrence and metastasis, innovative therapeutic targets and strategies are urgently needed to enhance treatment efficacy and improve long-term prognosis.
[0003] The challenges of treating recurrent osteosarcoma highlight the need for a deeper understanding of its pathogenesis and the identification of new therapeutic targets. Existing treatments struggle to cope with the heterogeneity and complexity of the disease, but lay the foundation for personalized treatment tailored to the molecular characteristics of different patient populations. The immune microenvironment of osteosarcoma, composed of a complex network of immune cells, cytokines, and signaling molecules, provides a compelling research direction. The key roles of various immune cells in tumor growth, metastasis, and treatment response urgently require in-depth research on their interactions in osteosarcoma. Such studies have the potential to reveal innovative drugs that can target and regulate immune responses. By enhancing existing treatment options (including surgery and chemotherapy) and innovatively utilizing specific immune pathways, it is expected that the treatment outcomes of osteosarcoma patients will be significantly improved.
[0004] The importance of SUMOylation (ubiquitination-related modification) cannot be ignored in the development of new cancer treatments. SUMOylation refers to the process of covalently attaching small ubiquitin-like modifiers (SUMO) to specific proteins, which can affect the function, distribution and stability of proteins. This modification plays an important role in key cellular functions such as gene expression regulation, DNA repair and cell cycle control. In cancer progression, abnormal SUMOylation patterns, including overactivation or dysfunction, are closely related to tumor occurrence, development, invasion, recurrence and metastasis. Therefore, targeted therapy against abnormal SUMOylation has become a key area in cancer research, providing new opportunities for therapeutic intervention.
[0005] Traditional herbal medicines are diverse in terms of biological activities and are therefore considered an important source of candidate small molecule drugs. Many natural products extracted from traditional herbal medicines have shown significant anti-cancer effects, including inhibiting tumor proliferation and angiogenesis, inducing apoptosis, regulating autophagy, reversing multidrug resistance, regulating immune balance, and enhancing the effect of chemotherapy. Summary of the invention
[0006] In order to solve the above technical problems, the present invention provides an application of a ZNF451 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug. The present invention establishes for the first time the key role of SUMO E3 ligase ZNF451 in osteosarcoma resistance, and discovers β-cryptoxanthin, a traditional Chinese medicine active ingredient with the function of selectively inhibiting ZNF451. This ingredient can not only synergize with cisplatin to reverse the cisplatin resistance of osteosarcoma, but also provide new target sites and molecular markers for clinically predicting and improving the treatment effect of patients.
[0007] To this end, the present invention provides the following technical solutions:
[0008] In a first aspect, the present invention provides, in an optional embodiment, use of a ZNF451 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug, wherein the cisplatin-resistant osteosarcoma drug comprises a ZNF451 expression inhibitor.
[0009] Furthermore, the ZNF451 expression inhibitor is β-cryptoxanthin.
[0010] In a second aspect, the present invention provides an osteosarcoma inhibitor in an optional embodiment, which comprises the above-mentioned ZNF451 expression inhibitor and cisplatin.
[0011] Furthermore, the osteosarcoma inhibitor includes β-cryptoxanthin and cisplatin.
[0012] Preferably, in the osteosarcoma inhibitor, the concentration of cisplatin is 16-128 μM, and the concentration of β-cryptoxanthin is 4-128 μM. Furthermore, the concentration of cisplatin is 32 μM, and the concentration of β-cryptoxanthin is 16 μM.
[0013] Preferably, the osteosarcoma inhibitor further comprises auxiliary materials.
[0014] Furthermore, the osteosarcoma inhibitor is an oral preparation or an injectable preparation, and the oral preparation includes capsules, tablets or granules.
[0015] In the present invention, the osteosarcoma 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-mentioned 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.
[0016] 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.
[0017] 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.
[0018] The third invention, in an optional embodiment, the present invention provides a use of the above-mentioned osteosarcoma inhibitor in the preparation of an anti-osteosarcoma drug.
[0019] Compared with the prior art, the present invention has one of the following beneficial effects:
[0020] 1. This invention established for the first time the key role of SUMO E3 ligase ZNF451 in osteosarcoma drug resistance, and discovered β-cryptoxanthin, a traditional Chinese medicine active ingredient that selectively inhibits ZNF451. This ingredient can not only synergize with cisplatin to reverse cisplatin resistance in osteosarcoma, 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 osteosarcoma treatment, especially for the problem of cisplatin resistance, providing a more effective solution. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 This is the comprehensive single-cell transcriptome analysis in Example 1 of the present invention, wherein: Figure 1 A represents the clustering of cell populations at various resolutions. Figure 1 B represents the spatial distribution of cells from different samples in t-SNE and UMAP visualization. Figure 1 C represents the classification of the main cell types in the t-SNE and UMAP maps. Figure 1 D represents the relative distribution of cell populations in each sample. Figure 1 E represents the gene expression pattern in different cell populations, the size of the dot indicates the percentage of cells expressing a specific marker gene, and the color intensity indicates the average expression level of the gene;
[0022] Figure 2 This is the osteoblast diversity analysis in Example 1 of the present invention, wherein: Figure 2 A represents the clustering of cell populations at different resolutions. Figure 2 B- Figure 2 D represents the UMAP and t-SNE visualization showing osteoblasts in different clinical types ( Figure 2 B) Sample source ( Figure 2 C) and major subgroups ( Figure 2 D) distribution, Figure 2 E represents the gene expression heat map and gene ontology (GO) analysis results of osteoblast subpopulations. Figure 2 F represents the expression of ZNF451 in six subgroups. Figure 2 G represents the differentially expressed genes (DEGs) of osteoblasts in primary and recurrent osteosarcoma. The red color highlights the genes that are highly expressed in recurrent osteosarcoma. Figure 2 H represents the distribution ratio of different osteoblast subsets in different clinical samples;
[0023] Figure 3 This is a comprehensive analysis of the communication network between each cell subpopulation in Example 1 of the present invention, wherein: Figure 3 A- Figure 3 B represents the schematic diagram of the frequency and intensity of interactions between cell subpopulations. Figure 3 C represents the visualization of key signal senders (sources) and receivers (targets), Figure 3 D represents the identification of the main trend of signal transmission and reception;
[0024] Figure 4 The comparative analysis of intercellular communication in recurrent and primary osteosarcoma in Example 1 of the present invention, wherein: Figure 4 A represents the communication network in recurrent and primary osteosarcoma tissues. Figure 4 B represents the changes in the frequency and intensity of cell-cell interactions in recurrent osteosarcoma, with red indicating enhancement and blue indicating weakening. Figure 4 C represents a heat map showing the differences in the frequency and intensity of interactions between recurrent and primary osteosarcoma tissues. Figure 4 D represents a comparative assessment of the major signal transmitters and receivers in recurrent and primary osteosarcoma. Figure 4 E represents the expression of key signaling molecules and pathways Figure 4 F represents the signal output analysis of different cell subsets. Figure 4 G represents the signal reception analysis of different cell subpopulations. Figure 4 H represents the comprehensive assessment of signal transduction between various cell subsets.
[0025] Figure 5 Schematic diagram of the functional role of ZNF451 in osteosarcoma in Example 1 of the present invention, wherein: Figure 5 A represents the overlap between SUMOylation-related genes and genes highly expressed in recurrent osteosarcoma. Figure 5B represents the expression changes of ZNF451 in osteoblasts. Figure 5 C represents the difference in gene expression between ZNF451 high-expressing cells and ZNF451 low-expressing cells. Figure 5 D represents the gene ontology (GO) analysis of differentially expressed genes. Figure 5 E represents the gene set enrichment analysis (GSEA) of differentially expressed genes. Figure 5 F represents the analysis of differentially expressed genes using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Figure 5 G represents gene set variation analysis (GSVA) of differentially expressed genes;
[0026] Figure 6 This is a study on the effect of ZNF451 on the tumor immune microenvironment (TIME) in Example 1 of the present invention, wherein: Figure 6 A represents the description of immune cell infiltration in different samples. Figure 6 B represents the distribution ratio of cell types in each specimen. Figure 6 C represents the evaluation of the correlation between ZNF451 and tumor microenvironment cells. Figure 6 D represents the survival analysis of patients with high and low CD8+ T cell expression.
[0027] Figure 7 is the correlation between high expression of ZNF451 and enhanced drug resistance of osteosarcoma in Example 1 of the present invention, wherein, Figure 7 A- Figure 7 B represents the analysis of ZNF451 expression and its relationship with the half-inhibitory concentration (IC) of several drugs using the GSE21257 database. 50 relationship, Figure 7 C represents the molecular docking study of ZNF451 and common first-line osteosarcoma drugs.
[0028] Figure 8 This is a schematic diagram of the results of CD8+T cell depletion induced by ZNF451 in Example 1 of the present invention, wherein: Figure 8 A- Figure 8 E represents Monocle 2 trajectory analysis showing CD8+ T cell clustering ( Figure 8 A. Cell type identification ( Figure 8 B), pseudo-time trajectory ( Figure 8 C) ZNF451 expression level ( Figure 8 D) and the dynamics of ZNF451 expression in pseudotime ( Figure 8 E), Figure 8 F- Figure 8 G represents Monocle3 locus analysis showing CD8+ T cell subset differentiation ( Figure 8 F) and pseudo-time course ( Figure 8 G), Figure 8 H- Figure 8 I represents the 3D visualization of Monocle 3 showing the fluctuation of ZNF451 expression ( Figure 8 H) and cell type dynamics ( Figure 8 I), Figure 8 J represents the hierarchical clustering heat map showing the marker genes related to developmental time and cell subpopulations. Figure 8 K represents the heat map of marker gene clustering in different branches, indicating the differentiation of CD8T.EXH in branch 1 and CD8T.TOX in branch 4. Figure 8 L represents CD8T.EXH and CD8T.Non.EXH in osteosarcoma tumor-infiltrating lymphocytes sorted by flow cytometry. Figure 8 M- Figure 8 N represents the expression level of ZNF451 in CD8T.EXH and CD8T.Non.EXH detected by WB. Figure 8 O- Figure 8 P represents the proportion of CD8T.EXH detected by flow cytometry;
[0029] Fig. 9 The expression of ZNF451 in osteosarcoma cell line in Example 2 of the present invention, wherein: Fig. 9 A represents the expression difference of ZNF451 in osteosarcoma cell lines and normal hFOB cells analyzed by qRT-PCR (n=3). Fig. 9 B represents the quantitative detection of the growth rates of U2OS, MG63, MG63 / R and U2OS / R cell lines by CCK-8 method (n=5). Fig. 9 C represents the determination of ZNF451 expression in U2OS, MG63, MG63 / R and U2OS / R cell lines (n=3), Fig. 9 D represents Western blot analysis of ZNF451 expression in U2OS, MG63, MG63 / R and U2OS / R cell lines (n=3). Fig. 9 E represents the localization of ZNF451 in U2OS, MG63, MG63 / R and U2OS / R cells observed by confocal microscopy, scale bar, 50 μm. Significance is indicated as *P<0.05, **P<0.01, ***P<0.001.
[0030] Fig.10 This is a study of the effect of ZNF451 on osteosarcoma drug-resistant cell lines in vitro in Example 2 of the present invention, wherein: Fig.10 A represents the proliferation of sensitive and resistant osteosarcoma cell lines detected by EdU staining (n=3), the scale bar is 20 μm, Fig.10B represents the soft agar colony formation assay to evaluate the effect of ZNF451 knockdown on drug-resistant cells (n=3), scale bar is 200 μm, Fig.10 C represents the apoptosis rate of osteosarcoma cells detected by flow cytometry (n=3). Fig.10 D represents the analysis of Caspase-3 enzyme activity (n=5), Fig.10 E represents the quantitative detection of apoptosis-related proteins by Western blot (n=3). Fig.10 F represents the expression of cell adhesion-related proteins in osteosarcoma cell lines in the control group and ZNF451 knockdown group by Western blot analysis (n=3). Fig.10 G represents the Transwell assay to detect cell migration and invasion abilities (n=3), scale bar is 50 μm, Fig.10 H- Fig.10 I represents the cell cycle detected by flow cytometry (n=3), and the significance is indicated as *P<0.05, **P<0.01, ***P<0.001;
[0031] Fig.11 Schematic diagram of the in vivo tumor-promoting potential study results of ZNF451 in Example 3 of the present invention, wherein: Fig.11 A represents the in vitro tumor model display of each experimental group (n=3), Fig.11 B represents the growth dynamics analysis of in vitro tumors in each group (n=3). Fig.11 C represents the statistical analysis of in vitro tumor weight comparison (n=3), Fig.11 D represents the expression of ZNF451 in tumor tissues in vitro detected by immunofluorescence staining (n=3), the scale bar is 50 μm, Fig.11 E represents H&E extrachromosomal tumor tissue (n=3), scale bar is 50 μm. Fig.11 F represents the immunofluorescence detection of TUNEL positive expression in tumor tissues in vitro (n=3), the scale bar is 100 μm;
[0032] Fig.12 Schematic diagram of the micro-CT analysis results of ZNF451 in osteosarcoma-related bone resorption in Example 3 of the present invention, wherein: Fig.12 A- Fig.12 E represents the 3D reconstruction of the femur of the sh-NC group (n = 3), including the anterior and lateral views ( Fig.12 A) Coronal section of the entire femur ( Fig.12 B) Cross-section and 3D view of the distal femoral metaphysis ( Fig.12 C) 3D view of the cortical bone in the middle femur ( Fig.12 D) and coronal view of the proximal tibia ( Fig.12 E), Fig.12 F- Fig.12 J represents the 3D reconstruction of the femur of the sh-ZNF451 group (n=3), including the anterior and lateral views ( Fig.12 F) Coronal section of the entire femur ( Fig.12 G), cross-section and 3D view of the distal femoral metaphysis ( Fig.12 H) 3D view of the cortical bone in the middle part of the femur ( Fig.12 I) and coronal view of the proximal tibia ( Fig.12 J), Fig.12 K- Fig.12 P represents the quantification of trabecular microarchitecture parameters (n=3), including bone mineral density (BMD), bone surface area (BS), bone volume fraction (BV / TV), trabecular thickness (Tb.Th), number (Tb.N) and separation (Tb.Sp), Fig.12 Q- Fig.12 T represents the changes in cortical bone microarchitectural parameters, including total cortical cross-sectional area (Tt.Ar), cortical bone area (Ct.Ar), cortical area fraction (Ct.Ar / Tt.Ar), and cortical thickness (Ct.Th), *P<0.05, **P<0.01, ***P<0.001;
[0033] Fig.13 The β-cryptoxanthin in Example 3 of the present invention inhibits the malignant characteristics of cisplatin-resistant osteosarcoma cells in vivo and in vitro, wherein Fig.13 A represents the docking result of TCM monomer with the lowest binding energy, Fig.13 B- Fig.13 C represents the CCK8 experimental results showing the effects of different concentrations of Chinese medicine monomers on the viability of MG63 / R and U2OS / R cells (n=5). Fig.13 D- Fig.13 F represents the mRNA level of ZNF451 (n=3, DE) and the protein expression of ZNF451 (n=3, F) in MG63 / R and U2OS / R cells after treatment with half-inhibitory concentration of TCM monomer. Fig.13 G represents the clone formation experiment showing the effect of different concentrations of TCM treatment (n=3), Fig.13 H- Fig.13 I represents the cell cycle of different treatments and control group analyzed by flow cytometry (n=3), Fig.13 J- Fig.13 K represents the flow cytometry analysis of cell apoptosis after treatment with different concentrations of TCM (n=3). Fig.13 L represents the NOD-SCID mouse PDX tumor photos from different treatment groups (n=3), Fig.13 M represents the growth curve of PDX tumors in NOD-SCID mice in each treatment group (n=3). Fig.13N represents the weight measurement of PDX tumors in NOD-SCID mice in different treatment groups (n=3). Fig.13 O represents the clone formation assay used to detect the combined effect of drugs (n=3). *P<0.05, **P<0.01, ***P<0.001;
[0034] Fig.14 The key role of SUMO E3 ligase ZNF451 in osteosarcoma drug resistance of the present invention, and the mechanism diagram of the killing of osteosarcoma by the active ingredient β-cryptoxanthin of traditional Chinese medicine that selectively inhibits the function of ZNF451. DETAILED DESCRIPTION
[0035] 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.
[0036] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.
[0037] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.
[0038] Fig.14 The key role of the SUMO E3 ligase ZNF451 of the present invention in the drug resistance of osteosarcoma is exemplified, and a diagram of the mechanism by which the active ingredient β-cryptoxanthin of traditional Chinese medicine selectively inhibits the function of ZNF451 and kills osteosarcoma is shown.
[0039] Example 1
[0040] 1. Data collection and enhancement
[0041] Single-cell RNA sequencing (scRNA-seq) data from two osteosarcoma databases (GSE152048, GSE162454), one lymphocyte dataset associated with osteosarcoma (GSE198896), and two control bone samples (GSE169396, GSE217792) were selected from the GEO database. The analysis was further enriched by integrating RNA-seq data from the TARGET-OS project and clinical records (part of the TARGET project). The GSE21257 dataset containing rich survival data was used as a validation set for model reliability, and samples without comprehensive survival or clinical information were excluded. The scRNA-seq data were fully processed using the Seurat R package (version 4.3.0). Potential doublets were eliminated by using Doublet Finder, and hemoglobin and mitochondrial genes were selectively filtered. All gene data were normalized and PCA dimensionality reduction was performed. The batch effect between integrated samples was mitigated using the Harmony package, and highly variable genes were screened by the FindVariable Features function. After completing the cell classification, UMAP and tSNE were used for advanced dimensionality reduction analysis. The marker genes were determined through the CellMarker database and relevant academic literature. The results are shown in Figure 1 ,in, Figure 1 A represents the clustering of cell populations at various resolutions. Figure 1 B represents the spatial distribution of cells from different samples in t-SNE and UMAP visualization. Figure 1 C represents the classification of the main cell types in the t-SNE and UMAP maps. Figure 1 D represents the relative distribution of cell populations in each sample. Figure 1 E represents the gene expression pattern in different cell populations, the size of the dot indicates the percentage of cells expressing a specific marker gene, and the color intensity indicates the average expression level of the gene. Finally, Monocle2 and Monocle3 were used to visualize the pseudo-time developmental trajectory of cells. The results are shown in Figure 8 A- Figure 8 I, where Figure 8 A- Figure 8 E represents Monocle 2 trajectory analysis showing CD8+ T cell clustering ( Figure 8 A. Cell type identification ( Figure 8 B), pseudo-time trajectory ( Figure 8 C) ZNF451 expression level ( Figure 8 D) and the dynamics of ZNF451 expression in pseudotime ( Figure 8 E), Figure 8 F- Figure 8G represents Monocle 3 trajectory analysis showing CD8+ T cell subset differentiation ( Figure 8 F) and pseudo-time course ( Figure 8 G), Figure 8 H- Figure 8 I represents the 3D visualization of Monocle 3 showing the fluctuation of ZNF451 expression ( Figure 8 H) and cell type dynamics ( Figure 8 I).
[0042] 2. Analysis of intercellular signaling
[0043] The Cell Chat object was constructed using the create Cell Chat function to combine the RNA expression matrix and relevant cell data. Next, databases such as secretion signals, ECM-receptors, and cell-cell contacts were integrated to facilitate signal analysis. The communication probability between cells was calculated using the compute Commun Prob function. In the selectK step, the global communication pattern was analyzed, and the nPatterns parameter was set to 2 to evaluate the input and output information flows. The results are shown in Figure 3-4 ,in, Figure 3 A- Figure 3 B represents the schematic diagram of the frequency and intensity of interactions between cell subpopulations. Figure 3 C represents the visualization of key signal senders (sources) and receivers (targets), Figure 3 D represents the main trend identification of signal transmission and reception, Figure 4 A represents the communication network in recurrent and primary osteosarcoma tissues. Figure 4 B represents the changes in the frequency and intensity of cell-cell interactions in recurrent osteosarcoma, with red indicating enhancement and blue indicating weakening. Figure 4 C represents a heat map showing the differences in the frequency and intensity of interactions between recurrent and primary osteosarcoma tissues. Figure 4 D represents a comparative assessment of the major signal transmitters and receivers in recurrent and primary osteosarcoma. Figure 4 E represents the expression of key signaling molecules and pathways Figure 4 F represents the signal output analysis of different cell subsets. Figure 4 G represents the signal reception analysis of different cell subpopulations. Figure 4 H represents the comprehensive assessment of signal transduction between various cell subsets.
[0044] 3. Identification and survival analysis of SUMOylation-related genes
[0045] First, the intersection of SUMO-related genes and differentially expressed genes (DEGs) was identified. Using the GSE21257 dataset, univariate Cox proportional hazards regression analysis was performed on these genes to identify genes significantly associated with prognosis. Survival analysis of individual genes was performed using the survival package in R language. According to the predetermined expression cutoff value of each gene, the samples were divided into low expression and high expression groups. Kaplan-Meier curves depict the survival outcomes of these groups. The statistical significance of the differences observed between the survival curves was evaluated by the Log-Rank test, and a P value below 0.05 indicated statistical significance. The results are shown in Figure 5 A- Figure 5 C, where Figure 5 A represents the overlap between SUMOylation-related genes and genes highly expressed in recurrent osteoblasts. Figure 5 B represents the expression changes of ZNF451 in osteoblasts. Figure 5 C represents the volcano plot showing the gene expression differences between ZNF451-overexpressing osteosarcoma cells and ZNF451-overexpressing osteosarcoma cells.
[0046] 4. Enrichment Analysis
[0047] The Find Markers function was used to identify differentially expressed genes (DEGs) at the thresholds of Padj.P < 0.05 and logFC > 0.25. In the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the clusterProfiler software package was used, and the analysis results are shown in Figure 5 D and Figure 5 F. In addition, GSEA and GSVA were performed using their dedicated software packages, and the analysis results are shown in Figure 2 E. Figure 5 E and Figure 5 G, where Figure 2 A represents the clustering of cell populations at different resolutions. Figure 2 B- Figure 2 D represents the UMAP and t-SNE visualization showing osteoblasts in different clinical types ( Figure 2 B) Sample source ( Figure 2 C) and major subgroups ( Figure 2 D) distribution, Figure 2 E represents the gene expression heat map and gene ontology (GO) analysis results of osteoblast subpopulations. Figure 2 F represents the expression of ZNF451 in six osteoblast subsets. Figure 2 G represents the differentially expressed genes (DEGs) of osteoblasts in primary and recurrent osteosarcoma. The red color highlights the genes that are highly expressed in recurrent osteosarcoma. Figure 2H represents the distribution ratio of different osteoblast subsets in different clinical samples.
[0048] 5. Assessment of immune cell subtype distribution
[0049] CibersortX and the average expression levels of marker genes were used to conduct in-depth analysis of cell subtype infiltration in the TARGET clinical cohort. Spearman correlation analysis revealed the relationship between ZNF451 and different cell populations. Figure 6 ,in, Figure 6 A represents the description of immune cell infiltration in different samples. Figure 6 B represents the distribution ratio of cell types in each specimen. Figure 6 C represents the evaluation of the correlation between ZNF451 and tumor microenvironment cells. Figure 6 D represents the survival analysis of patients with high expression of CD8+T cells and patients with low expression of CD8+T cells.
[0050] 6. Chemotherapy drug sensitivity analysis
[0051] The IC values of 198 drugs from the Genomic Collection of Drug Sensitivity to Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE) were analyzed using the oncoPredict package in R. 50 Drug IC was estimated by Spearman correlation 50 The correlation between the value and the risk score was analyzed to locate the relevant drugs. For drugs with an absolute correlation value greater than 0.2, the IC50 values between the high expression group and the low expression group were compared. The data visualization used the ggplot2 package in the R language. The results are shown in Figure 7 A- Figure 7 B.
[0052] 7. Molecular docking study
[0053] The three-dimensional structure of ZNF451 protein and the data of four drugs were obtained from AlphaFold protein structure database and PubChem, respectively. PyMOL and AutoDock4 tools were used to pre-process proteins and drugs, remove water molecules and add non-polar hydrogen atoms. Molecular docking was performed by AutoDock4 after setting appropriate docking parameters and grid frame dimensions. The results are shown in Figure 7 C and Fig.13 A, The docking results were visualized by PyMOL, and the binding energy below -2.0 kcal / mol was considered as an indicator of successful docking.
[0054] Example 2
[0055] In vitro experiments
[0056] 8. Establishment of Cisplatin-resistant Osteosarcoma Cell Line
[0057] Cisplatin-resistant cell lines, named MG63 / R and U-2OS / R, were established from MG63 and U-2OS cell lines. MG63 and U-2OS cells were seeded in culture dishes, respectively, and then treated with 1 μM cisplatin and cultured for 48 hours. After the removal of cisplatin, the cells were cultured in fresh medium until the optimal growth state was achieved. The concentration of cisplatin was increased stepwise, doubling the concentration at each stage, for a total of six stages. After confirming that the cells grew stably at cisplatin concentrations above 32 μM, the cisplatin-resistant cell line was successfully established.
[0058] 9. qRT-PCR quantitative analysis
[0059] First, total RNA was extracted from the samples using the TRIzol (Invitrogen) method, and then reverse transcribed using the PrimeScript RT reagent kit (TaKaRa, Japan). The reaction mixture was prepared according to the recommendations of Takara's SYBR-Green reagent, and qRT-PCR analysis was performed on the Roche Light Cycler480 II system. Three replicates were set for each sample, see Fig.13 D- Fig.13 E.
[0060] 10. siRNA and shRNA design and transfection
[0061] Three specific siRNAs and one shRNA sequence against ZNF451 were synthesized using the siRNA and shRNA design tools from Genepharma (Shanghai, China). For shRNA studies, these sequences were incorporated into the pLKO.1 vector. Lentiviral particles were generated by co-transfecting the pLKO.1 shRNA plasmids with packaging and envelope protein plasmids into HEK293T cells. Viral particles were used for transfection after osteosarcoma cells in logarithmic growth phase were cultured to 70%-80% confluence in 6-well plates, supplemented with Polybrene to increase efficiency. Cells were maintained in standard culture medium for 24 hours and harvested 12 hours after transfection. Transfection efficiency was confirmed by RT-qPCR. For the siRNA approach, cells were cultured in 6-well plates in logarithmic growth phase and transfected with Lipofectamine 3000 according to the manufacturer's instructions when they reached 70%-80% confluence. After the 6-hour transfection period, the medium was replaced with regular medium, and cells were cultured for an additional 24 hours before being harvested for further analysis; the results are shown in Fig. 9 A and Fig. 9 C.
[0062] 11.EdU staining procedure
[0063] The cells were treated with Apexbio's EdU labeling reagent. Each well then received a Click reaction solution containing Cy3 azide and incubated at room temperature in the dark. After rinsing with PBS, Hoechst 33342 solution was added and the samples were incubated again under the same conditions. After completing these steps, the samples were observed using a fluorescence microscope. The results are shown in Fig.10 A.
[0064] 12.CCK-8 assay for cell viability assessment
[0065] Cell viability was assessed using the Cell Counting Kit-8 from Sigma-Aldrich. Following the manufacturer's protocol, cells were plated in 96-well plates and treated as needed after complete attachment. CCK-8 reagent was then added to each well and incubated at the specified temperature in the dark. Cell viability was determined by quantifying the absorbance at 450 nm. Results are shown in Fig. 9 B. Fig.13 B and Fig.13 C.
[0066] 13. Western Blot Analysis
[0067] Cell lysis was performed using Beyotime's RIPA buffer, and protein concentration was determined using a BCA kit. Equal amounts of protein samples were electrophoresed by 10% SDS-PAGE and subsequently transferred to a PVDF membrane. The membrane was blocked with 5% skim milk powder and incubated with primary antibodies on a shaker at 4°C. Proteintech's HRP-conjugated secondary antibodies were then incubated at room temperature. After washing, signal detection was performed using the UVPChemStudio system (Ultraviolet Products, USA), and data analysis was performed using VisionWorks software (Analytik Jena, Germany). The results are shown in Fig. 9 D. Fig.10 E- Fig.10 G and Fig.13 F.
[0068] The antibodies used included: anti-ZNF451 (1:1000, proteintech), anti-BCL2 (1:200, Abcam), anti-BAX (1:1000, Abcam), anti-E-cadherin (1:5000, proteintech), anti-N-cadherin (1:2000, proteintech), anti-vimentin (1:1000, Cell Signaling Technology), anti-SNAI1 (1:1000, CellSignaling Technology), and anti-β-tubulin (1:1000, Santa Cruz Biotechnology).
[0069] 14. Fluorescent Immunostaining
[0070] During fluorescent immunostaining, paraffin-embedded sections were first dewaxed and rehydrated. Next, antigen retrieval was performed in citrate buffer. To reduce nonspecific binding, sections were treated with antigen blocking solution at room temperature. Subsequently, sections were incubated with ZNF451 antibody at low temperature. After washing, sections were incubated with Alexa Fluor 555-conjugated IgG antibody at room temperature in the dark. Finally, sections were mounted with mounting medium containing DAPI and observed under a fluorescence microscope. The results are shown in Fig. 9 E.
[0071] 15. Apoptotic Cell Detection
[0072] Cells in the shRNA-ZNF451 and shRNA-NC groups were cultured in 6-well plates and subsequently harvested. After harvesting, cells were digested, centrifuged, and stained after concentration adjustment. To detect apoptotic cells, Annexin V-FITC and propidium iodide staining (BD, UK) were used and analyzed by flow cytometry after incubation at room temperature in the dark. The results are shown in Fig.10 C and Fig.13 J- Fig.13 K. In addition, since Caspase 3 activity is an important indicator of apoptosis, the Caspase 3 activity detection kit was used to treat the cells by cell lysis, centrifugation and mixing, and finally incubation. After incubation, the cells were detected by a microplate reader, and the results were shown in Fig.10 D. To confirm the occurrence of apoptosis, a TUNEL kit was used to detect apoptotic cells in tissue samples. These samples were frozen sections of tumors extracted from experimental animals. The samples were incubated, stained, washed, sealed, and finally observed under a fluorescence microscope.
[0073] 16. Soft Agar Colony Formation Assay
[0074] The soft agar colony formation assay was used to assess the ability of cells to form colonies. First, a base agar layer was placed in a 6-well plate. After this layer solidified, an upper agar layer containing osteosarcoma cells in logarithmic growth phase with the specified transfection protocol was added. This layer allowed the cells to be cultured in a stable temperature and humidity controlled incubator until colonies formed. To enhance the visibility of the colonies, crystal violet staining was used and accurate counting was performed by microscopy. Quantification of colonies provides a measure of the cell's clonogenic potential. The rigorous experimental design and replication ensured the accuracy and reliability of the results, which can be found in the results. Fig.10 B.
[0075] 17. Flow cytometry cell cycle detection
[0076] Cell cycle detection was performed using a cell cycle staining kit (MultiSciences, China). After the cells were cultured to the logarithmic growth phase, they were washed with PBS buffer and digested with 0.25% trypsin-EDTA solution. The cell suspension was collected and centrifuged, and the supernatant was discarded and the cells were resuspended in 1 mL of PBS. After the supernatant was discarded again, DNA staining solution and permeabilization solution were added and incubated for 30 minutes under light-proof conditions. Finally, a flow cytometer (CytoFlex SRT, Beckman, USA) was used for cell analysis. The results are shown in Fig.10 H- Fig.10 I and Fig.13 H- Fig.13 I.
[0077] 18. Isolation of CD8+ T cells from osteosarcoma tissue
[0078] Human osteosarcoma tissue was obtained directly from the operating room during osteosarcoma resection surgery. After rinsing with PBS (Gibco), the tissue was cut into small pieces and digested with shaking in RPMI (Gibco) digestion buffer containing 25μg / ml Liberase TM (Roche) and 50μg / ml DNase (Sigma) at 37°C for 30 minutes. After mechanical disruption, single-cell suspension was prepared through a 70μm filter, and excess red blood cells were removed using red blood cell lysis buffer. After washing twice with PBS, it was incubated with APC-CY7 fixative viability dye (BD Pharmingen) for 15 minutes at 4°C. The samples were then incubated with FcR blocker for 20 minutes before the addition of cell surface markers. All samples were acquired on a cytoFLEX flow cytometer and analyzed using FlowJo. Cell surface markers: CD3-AF546, CD4-FITC, CD8-PerCP, and PD1-BV510 (Santa Cruz Biotechnology), results see Figure 8 L.
[0079] 19. Isolation and culture of peripheral blood CD8+T cells
[0080] Peripheral blood lymphocytes were first isolated from healthy donors by density gradient centrifugation using lymphocyte separation medium (Corning). CD8+ T cells were then further isolated from lymphocytes using the EasySep Direct Human CD8+ T Cell Isolation Kit (Stemcell Technologies). T cells with high CD8 expression were subsequently verified and sorted by flow cytometry. The results are shown in Figure 8 J- Figure 8 P, where Figure 8 J represents the hierarchical clustering heat map showing the marker genes related to developmental time and cell subpopulations. Figure 8 K represents the heat map of marker gene clustering in different branches, indicating the differentiation of CD8T.EXH in branch 1 and CD8T.TOX in branch 4. Figure 8 L represents CD8T.EXH and CD8T.Non.EXH in osteosarcoma tumor-infiltrating lymphocytes sorted by flow cytometry. Figure 8 M- Figure 8 N represents the expression level of ZNF451 in CD8T.EXH and CD8T.Non.EXH detected by WB. Figure 8 O- Figure 8 P represents the proportion of CD8 T.EXH detected by flow cytometry. In all experiments, CD8+ T cells were cultured in X-VIVO complete medium supplemented with IL-2 (100 IU / mL, Miltenyi Biotec) and ImmunoCult TM Human CD3 / CD28 T cell activator (10 μL / mL, InvivoGen).
[0081] 20. Clone formation assay
[0082] Osteosarcoma cells in the logarithmic growth phase were digested and prepared into single cell suspensions, and 1000 cells were inoculated in each well of a 12-well plate. The cells were cultured for 14 days. After being treated with different concentrations of drugs, they were stained with an appropriate amount of crystal violet staining solution for 30 minutes. For details, see Fig.13 G and Fig.13 O.
[0083] Example 3
[0084] In vivo experiments
[0085] 21. Animal Experimentation
[0086] Six 4-week-old female NOD-SCID IL2rγnull (NSG) mice were used. The mice were randomly divided into two groups: sh-ZNF451 group and sh-NC group, with 3 mice in each group. Each group was inoculated with U2OS / R cells, transfected with sh-ZNF451 or sh-NC, respectively. Tumor size was measured regularly using calipers. When the tumor reached a specific average volume, the designated drug was injected intraperitoneally at a predetermined dose and frequency. The tumor volume was calculated according to the formula: Length×Width×Width / 2. At the end of the experiment, the mice were humanely killed and their tumor tissues were collected for further analysis, including H&E staining and evaluation of ZNF451 and TUNEL expression. The results can be found in. Fig.11 and Fig.13 L- Fig.13 N, where Fig.11 A and Fig.13 L represents the in vitro tumor model display of each experimental group (n=3), Fig.11 B and Fig.13 M represents the growth dynamics analysis of in vitro tumors in each group (n=3). Fig.11 C and Fig.13 N represents the in vitro tumor weight comparison statistical analysis (n=3), Fig.11 D represents the expression of ZNF451 in tumor tissues in vitro detected by immunofluorescence staining (n=3), the scale bar is 50 μm, Fig.11 E represents H&E extrachromosomal tumor tissue (n=3), scale bar is 50 μm. Fig.11 F represents the immunofluorescence detection of TUNEL positive expression in tumor tissues in vitro (n=3), the scale bar is 100 μm.
[0087] 22. Establishment of Patient-Derived Xenograft (PDX) Models
[0088] All human samples in this invention meet ethical requirements (ethical approval number:
[2023] CJ0051). Tumor tissue fragments from osteosarcoma patients were injected into the subcutaneous tissue of 6-week-old NOD-SCID mice. When the tumor volume reached 100 to 200 mm 3 When the tumor reached a certain size, the mice were randomly assigned to the control group and the experimental group. The experimental group was orally administered with 10 mg / kg of β-cryptoxanthin dissolved in DMSO. The β-cryptoxanthin used in the experiment was purchased from Sigma (Catalog No.: C6368).
[0089] 23. Micro-CT analysis
[0090] After dissection, the femurs and tibiae were preserved in 4% paraformaldehyde and subsequently stored in PBS at 4°C. The specimens were scanned using high-resolution microCT (Skyscan 1276, Skycan, Aartselaar, Belgium) operating at a resolution of 20.376 μm per pixel, a voltage of 100 kV, and a current of 200 μA. The region of interest (ROI) was selected starting from 0.45 mm below the distal growth plate and extending 0.45 mm proximally for trabecular bone parameter analysis
[16] , including bone mineral density (BMD), bone surface area (BS), bone volume fraction (BV / TV), trabecular thickness (Tb.Th), number (Tb.N), and separation (Tb.Sp). In addition, cortical bone microarchitectural parameters such as total cortical cross-sectional area, cortical bone area, cortical area fraction, and cortical thickness were evaluated in a 0.2 mm section of the mid-femur. Results are shown in Fig.12 ,in, Fig.12 A- Fig.12 E represents the 3D reconstruction of the femur of the sh-NC group (n = 3), including the anterior and lateral views ( Fig.12 A) Coronal section of the entire femur ( Fig.12 B) Cross-section and 3D view of the distal femoral metaphysis ( Fig.12 C) 3D view of the cortical bone in the middle femur ( Fig.12 D) and coronal view of the proximal tibia ( Fig.12 E), Fig.12 F- Fig.12 J represents the 3D reconstruction of the femur of the sh-ZNF451 group (n=3), including the anterior and lateral views ( Fig.12 F) Coronal section of the entire femur ( Fig.12 G), cross-section and 3D view of the distal femoral metaphysis ( Fig.12 H) 3D view of the cortical bone in the middle part of the femur ( Fig.12 I) and coronal view of the proximal tibia ( Fig.12 J), Fig.12 K- Fig.12 P represents the quantification of trabecular microarchitecture parameters (n=3), including bone mineral density (BMD), bone surface area (BS), bone volume fraction (BV / TV), trabecular thickness (Tb.Th), number (Tb.N) and separation (Tb.Sp), Fig.12 Q- Fig.12 T represents the changes in cortical bone microarchitectural parameters, including total cortical cross-sectional area (Tt.Ar), cortical bone area (Ct.Ar), cortical area fraction (Ct.Ar / Tt.Ar) and cortical thickness (Ct.Th), *P<0.05, **P<0.01, ***P<0.001.
[0091] 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 ZNF451 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug, characterized in that: The cisplatin-resistant osteosarcoma drug includes a ZNF451 expression inhibitor.
2. The use of the ZNF451 expression inhibitor according to claim 1 in the preparation of a cisplatin-resistant osteosarcoma drug, characterized in that: The ZNF451 expression inhibitor is β-cryptoxanthin.
3. An osteosarcoma inhibitor, characterized in that: The method comprises the ZNF451 expression inhibitor according to claim 1 or 2 and cisplatin.
4. The osteosarcoma inhibitor according to claim 3, characterized in that The osteosarcoma inhibitors include β-cryptoxanthin and cisplatin.
5. The osteosarcoma inhibitor according to claim 4, characterized in that In the osteosarcoma inhibitor, the concentration of cisplatin is 16-128 μM, and the concentration of β-cryptoxanthin is 4-128 μM.
6. The osteosarcoma inhibitor according to claim 4, characterized in that In the osteosarcoma inhibitor, the concentration of cisplatin is 32 μM, and the concentration of β-cryptoxanthin is 16 μM.
7. The osteosarcoma inhibitor according to claim 6, characterized in that Also includes auxiliary materials.
8. The osteosarcoma inhibitor according to any one of claims 3 to 7, characterized in that: The osteosarcoma inhibitor is an oral preparation or an injectable preparation.
9. The osteosarcoma inhibitor according to claim 8, characterized in that The oral preparations include capsules, tablets or granules.
10. Use of the osteosarcoma inhibitor according to any one of claims 3 to 9 in the preparation of an anti-osteosarcoma drug.