Application of a DCUN1D5 expression inhibitor in the preparation of cisplatin-resistant osteosarcoma drugs

Abstract

The present invention relates to the field of bioengineering technology, and specifically to the use of a DCUN1D5 expression inhibitor in the preparation of a drug for cisplatin-resistant osteosarcoma. The present invention provides the use of a DCUN1D5 expression inhibitor in the preparation of a drug for cisplatin-resistant osteosarcoma, comprising a DCUN1D5 expression inhibitor. This invention establishes for the first time the key role of DCUN1D5 in osteosarcoma drug resistance and discovers the traditional Chinese medicine active ingredient, physalisin A, that selectively inhibits DCUN1D5. This ingredient not only synergizes with cisplatin to reverse cisplatin resistance in osteosarcoma but also provides a new target site and molecular marker for clinically predicting and improving patient treatment outcomes. This innovative strategy offers new hope for osteosarcoma treatment, particularly by providing a more effective solution to the problem of cisplatin resistance.

Description

Technical Field

[0001] The present invention relates to the technical field of bioengineering, and in particular to application of a DCUN1D5 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug. Background Art

[0002] Osteosarcoma is a common bone tumor that shows significant heterogeneity in patients' clinical characteristics and treatment outcomes. This heterogeneity complicates treatment decisions and has a profound impact on patients' survival and quality of life. Notably, recurrence is often the main determinant of osteosarcoma prognosis and remains a serious problem. An emerging challenge in osteosarcoma management is drug resistance, which has attracted increasing attention in medical research. Numerous studies have confirmed the close connection between osteoclast overactivation, drug resistance and recurrence. Osteoclasts play a key role in the tumor immune microenvironment, and any increase in their activity can significantly alter the tumor's response to treatment.

[0003] Understanding the mechanisms of tumor development and progression is crucial. Ubiquitination is closely linked to apoptosis, proliferation, stem cell identity, and remodeling of the tumor microenvironment. E3 ubiquitin ligases (E3s) play a key role in this process, and their dysfunction can directly contribute to tumor development and progression. Therefore, E3s represent a promising therapeutic target and hold significant significance.

[0004] Standard treatment for osteosarcoma typically includes a multimodal treatment strategy, primarily involving surgical resection of the tumor, conventional chemotherapy, and sometimes radiotherapy. Among these numerous treatment options, cisplatin continues to play a key role in osteosarcoma treatment. Cisplatin is a widely used chemotherapy drug for the treatment of various cancers. However, cisplatin resistance has been a long-standing challenge in osteosarcoma.

[0005] Natural products from traditional Chinese medicine (TCM) are considered a promising source of novel small molecule therapeutics due to their broad bioactivity, low toxicity, and multi-target properties. Many natural compounds from herbal plants exhibit anticancer effects, including inhibiting proliferation, inducing apoptosis, preventing metastasis, and inhibiting angiogenesis. Furthermore, these compounds can regulate autophagy, overcome multidrug resistance, modulate immune balance, and enhance the efficacy of chemotherapy in vitro and in vivo. Physalis A is an active ingredient in the traditional Chinese medicine Lycium barbarum. Studies have shown that physalis A exhibits diverse biological activities, including anti-tumor, anti-inflammatory, immunomodulatory, and antiviral effects. Summary of the Invention

[0006] To address the above technical issues, the present invention provides a method for using a DCUN1D5 expression inhibitor in the preparation of a drug for cisplatin-resistant osteosarcoma. This invention establishes for the first time the key role of DCUN1D5 in osteosarcoma drug resistance. By inhibiting DCUN1D5, cisplatin resistance in osteosarcoma can be reversed. This provides a new target site and molecular marker for clinically predicting and improving treatment outcomes for patients with cisplatin-resistant osteosarcoma.

[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 DCUN1D5 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug, wherein the cisplatin-resistant osteosarcoma drug comprises the DCUN1D5 expression inhibitor.

[0009] Preferably, the DCUN1D5 expression inhibitor is physalisin A.

[0010] Preferably, in the osteosarcoma inhibitor, the concentration of cisplatin is 16-64 μM, and the concentration of physalisin A is 10-80 μM. Furthermore, in the osteosarcoma inhibitor, the concentration of cisplatin is 32 μM, and the concentration of physalisin A is 20 μM.

[0011] Preferably, excipients are also included. The excipients are selected from one or more of fillers, disintegrants, lubricants, suspending agents, binders or sweeteners. The filler is selected from one or more of starch, lactose, microcrystalline cellulose or sucrose; and / or the disintegrant is selected from one or more of starch, sodium carboxymethyl starch, pregelatinized starch, low-substituted hydroxypropyl cellulose or cross-linked sodium carboxymethyl cellulose; and / or the lubricant is selected from one or more of magnesium stearate, silicon dioxide or sodium lauryl sulfate; and / or the suspending agent is selected from one or more of polyvinyl pyrrolidone, sucrose or hydroxypropyl methylcellulose; and / or the binder is selected from one or more of hydroxypropyl methylcellulose, starch slurry or polyvinyl pyrrolidone; and / or the sweetener is selected from one or more of saccharin sodium, glycyrrhetinic acid, sucrose, aspartame or sodium cyclamate. The excipients can also be selected from water for injection or sodium chloride solution for injection.

[0012] Preferably, the cisplatin-resistant osteosarcoma drug is an oral preparation or an injectable preparation, including capsules, tablets or granules.

[0013] Compared with the prior art, the present invention has one of the following beneficial effects:

[0014] 1. This invention establishes for the first time the key role of DCUN1D5 in osteosarcoma resistance. By inhibiting DCUN1D5, cisplatin resistance in osteosarcoma can be reversed, which provides new target sites and molecular markers for clinically predicting and improving the treatment effect of cisplatin-resistant osteosarcoma patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is the single cell transcriptome analysis of osteosarcoma in Example 1 of the present invention, wherein: Figure 1 A represents the tree diagram displayed using the clustree tool. Figure 1 B represents the t-SNE and UMAP plots of 17 osteosarcoma samples and 6 osteosarcoma tumor-infiltrating lymphocyte (TIL) samples. Figure 1 C represents t-SNE and UMAP visualization of the ten major cell types identified in osteosarcoma samples. Figure 1 D represents the distribution of cell cluster proportions in 17 osteosarcoma samples and 6 osteosarcoma-TIL samples. Figure 1 E represents the expression of 23 characteristic genes in the ten identified cell clusters;

[0016] Figure 2 The heterogeneity of malignant osteosarcoma cells in Example 1 of the present invention, wherein, Figure 2 A represents the expression of specific markers of osteoblasts (COL1A1, RUNX2, COL3A1) and chondrocytes (ACAN, COL2A1, SOX9) in osteosarcoma malignant cells. Figure 2 B represents five different subtypes of malignant osteosarcoma cells. Figure 2 C represents the heat map of differentially expressed genes and gene ontology analysis of the five identified malignant osteosarcoma cell subpopulations. Figure 2 D represents the comparison of DEG distribution in recurrent and primary osteosarcoma cells. Figure 2 E represents the proportional distribution of cell clusters identified in 17 osteosarcoma samples;

[0017] Figure 3 This is the study of myeloid cells in the immune microenvironment of osteosarcoma in Example 1 of the present invention, wherein: Figure 3 A represents cell clustering at multiple resolutions, Figure 3 B represents the distribution of myeloid cells in different samples, cell types, clusters, and clinical categories. Figure 3 C represents the ridge plot of myeloid cell-related markers, Figure 3 D represents the distribution of myeloid cell subtypes in 11 osteosarcoma specimens. Figure 3 E represents a volcano plot comparing differential gene expression between recurrent myeloid cells and primary myeloid cells. Figure 3 F represents the expression of myeloid cell identification marker genes;

[0018] Figure 4 This is an in-depth analysis of tumor-infiltrating lymphocytes in the immune environment of osteosarcoma in Example 1 of the present invention, wherein: Figure 4 A represents the spatial distribution of eight TIL subsets in the immune landscape of osteosarcoma. Figure 4 B represents the density plot of key immune markers, Figure 4 C represents the dot plot of traditional TIL-related markers, Figure 4 D represents the differential gene expression heatmap and GO evaluation of TIL clusters. Figure 4 E represents the proportional distribution of TIL subsets in 17 osteosarcoma specimens;

[0019] Figure 5 This is the extensive interaction between cell subpopulations in the osteosarcoma TME in Example 1 of the present invention, wherein, Figure 5 A represents the quantity and intensity of inter-subpopulation communication, Figure 5 B represents the two-dimensional space representation of the main transmitter (sender) and receiver (target), Figure 5 C represents the key outward communication mode, Figure 5 D represents the main inward communication mode showing the important signals received by each cell subpopulation;

[0020] Figure 6 This is a holistic analysis of the cell communication pattern in the osteosarcoma tumor microenvironment in Example 1 of the present invention, wherein: Figure 6 A represents the number of output patterns estimated using Cophenetic and Silhouette indicators, Figure 6 B represents the determination of cell subtypes and signaling pathways associated with output patterns in the output configuration. Figure 6 C represents the outward communication mode of secretory cells, and the relative contribution of different cell types to the overall signal output is shown through a stacking diagram. Figure 6 D represents the detailed dot plot of signal changes in key pathways. Figure 6 E represents the number of input patterns estimated using Cophenetic and Silhouette indicators, Figure 6 F represents the classification of cell subtypes and signal trajectories associated with the input pattern in the input configuration, Figure 6 G represents a visualization of the signaling pattern received by secretory cells, and 6H represents a dot plot of signals received in key pathways;

[0021] Figure 7 The differential cell communication between primary and recurrent patients in Example 1 of the present invention, wherein: Figure 7 A represents the quantification of cellular interactions in primary and recurrent settings, Figure 7 B represents the difference in interaction count or strength during recurrence, Figure 7C represents the comparison of the frequency and strength of the interaction between recurrent and primary osteosarcoma. Figure 7 D represents a heatmap of interaction counts or intensity differences, Figure 7 E represents the two-dimensional spatial juxtaposition of the main signal sources and targets between primary and recurrent osteosarcoma patients, Figure 7 F represents the comprehensive flow analysis of each signal path, Figure 7 G represents the comparison of outward signals associated with each cell population, Figure 7 H represents the analysis of inward signals associated with each cell population, Figure 7 I represents the overall view of the relevant signals for each cell population;

[0022] Figure 8 The regulatory effect of DCUN1D5 in osteosarcoma in Example 1 of the present invention, wherein, Figure 8 A represents the overlap between E3 ubiquitin ligases and overexpressed genes in osteosarcoma. Figure 8 B represents the UMAP representation of DCUN1D5 expression pattern, Figure 8 C represents the volcano plot of differential gene expression based on DCUN1D5 expression levels. Figure 8 D represents the Gene Ontology (GO) analysis of differentially expressed genes. Figure 8 E represents gene set variation analysis (GSVA) ​​of differentially expressed genes, Figure 8 F represents the KEGG analysis of differentially expressed genes, Figure 8 G represents gene set enrichment analysis (GSEA) of differentially expressed genes;

[0023] Figure 9 The effect of DCUN1D5 in the tumor microenvironment of osteosarcoma in Example 1 of the present invention, wherein: Figure 9 A represents the immune infiltration analysis in osteosarcoma samples. Figure 9 B represents the proportion of different cell types in the osteosarcoma tumor microenvironment. Figure 9 C represents the analysis of the correlation between DCUN1D5 expression and microenvironmental cells in osteosarcoma;

[0024] Figure 10 Schematic diagram of the effect of DCUN1D5 on osteoclast maturation and activation in osteosarcoma in Example 1 of the present invention, wherein: Figure 10 A represents the Monocle 2 trajectory analysis showing the distribution and clustering of osteoclast subpopulations. Figure 10 B represents the detailed classification of subpopulations within the osteoclast lineage, Figure 10 C represents the time course of osteoclast differentiation, Figure 10 D represents the spatial representation of DCUN1D5 expression levels in different osteoclast subclusters. Figure 10E stands for Monocle 3 trajectory visualization provides a comprehensive view of osteoclast subtype dynamics from precursors to mature and non-functional states. Figure 10 F represents the expression pattern of DCUN1D5 during osteoclast maturation. Figure 10 G represents a heatmap with hierarchical clustering, Figure 10 H represents the differential clustering heatmap highlighting the different developmental pathways derived from branch point 4. Figure 10 I represents a schematic diagram of the key role of DCUN1D5 in directing the differentiation of precursor osteoclasts toward their active and functional mature form;

[0025] Figure 11 The increased drug resistance of osteosarcoma caused by upregulation of DCUN1D5 in Example 1 of the present invention, wherein, Figure 11 A represents the expression of DCUN1D5 in osteosarcoma tissue samples and the half inhibitory concentration (IC 50 ) value, Figure 11 B represents the expression of DCUN1D5 in osteosarcoma tissue samples and the drug IC in the GSE21257 database 50 The relevance of the values, Figure 11 C represents the molecular interaction analysis between DCUN1D5 and major osteosarcoma therapeutic drugs (including methotrexate, doxorubicin, cisplatin, and cyclophosphamide);

[0026] Figure 12 The tumorigenic potential of DCUN1D5 was verified in vitro in Example 2 of the present invention, wherein: Figure 12 A represents the quantitative analysis of the growth of sensitive and resistant osteosarcoma cell lines using CCK-8, *P<0.05, Figure 12 B represents the qRT-PCR evaluation of DCUN1D5 levels in osteosarcoma and cisplatin-resistant cell lines. Figure 12 C represents the Western blot analysis of protein expression. Figure 12 D represents confocal imaging of DCUN1D5 in sensitive and resistant osteosarcoma cells. Figure 12 E represents the observation of tumor sphere formation of drug-resistant cells after DCUN1D5 knockdown. Figure 12 F represents the 3D culture analysis of drug-resistant cells after DCUN1D5 knockdown. Figure 12 G represents the flow cytometric analysis of osteosarcoma cell apoptosis after 48 hours. Figure 12 H represents the quantitative evaluation of osteosarcoma cell apoptosis, ***P<0.001, Figure 12 I represents a Si-NC, b Si-DCUN1D5 co-cultured with U-2OS / CDDP cells for 24 hours, c Si-NC, d Si-DCUN1D5 co-cultured with MG63 / CDDP cells for 24 hours, Figure 12 J represents a Oe-NC, b Oe-DCUN1D5 co-cultured with U-2OS / CDDP cells for 24 h, c Oe-NC, d Oe-DCUN1D5 co-cultured with MG63 / CDDP cells for 24 h;

[0027] Figure 13 This is an in vivo verification of the role of DCUN1D5 in osteosarcoma tumorigenesis in Example 3 of the present invention, wherein, Figure 13 A represents representative images of subcutaneous tumors in NSG mice from different treatment groups. Figure 13 B represents the quantitative analysis of tumor volume in NSG mice over time. Figure 13 C represents the comparison of tumor weights of NSG mice in different groups. Figure 13 D represents the immunofluorescence staining of DCUN1D5 in the resected tumor tissue. Figure 13 E represents hematoxylin and eosin (H&E) staining of tumor sections. Figure 13 F represents TUNEL staining and Ki-67 immunofluorescence staining to evaluate apoptosis and proliferation in tumor sections;

[0028] Figure 14 The exosomes secreted by the drug-resistant osteosarcoma cell line in Example 3 of the present invention enhance osteoclast differentiation and bone resorption, wherein, Figure 14 A represents the qPCR analysis of DCUN1D5 expression in culture medium (CM) of osteosarcoma parental and drug-resistant cell lines. Figure 14 B represents the qPCR evaluation of DCUN1D5 expression in MG63 and MG63 / CDDP cell lines after treatment with control medium, RNaseA (2 mg / mL), or combined with Triton X-100 (0.1%) for 0.5 h. Figure 14 C represents the transmission microscopy phenotypic analysis of MG63 cell-derived exosomes. Figure 14 D represents the tracking of MG63 / CDDP nanoparticles using Nano Sight. Figure 14 E represents the Western blot detection of exosome biomarkers (TSG101, CD9, ALIX) in osteosarcoma cell lines. Figure 14 F and Figure 14 G represents qPCR evaluation of DCUN1D5 expression in osteosarcoma cell CM after exosome depletion, using GW4869 ( Figure 14 F) or ultracentrifugation ( Figure 14 G) Treatment, compared with MG63 and MG63 / CDDP CM, Figure 14 H and Figure 14I represents the comparison of DCUN1D5 expression in osteosarcoma-derived exosomes and exosomes from cisplatin-resistant cells using qRT-PCR (H) and Western blot (I). Figure 14 J represents the uptake of PKH26 (red) labeled exosomes into CFSE (green) labeled RAW264.7 cells using confocal microscopy. Figure 14 K and Figure 14 L represents the expression of MMP9 and CTSK analyzed by QRT-PCR and Western blot, *P<0.05, **P<0.01, ***P<0.001, Figure 14 M and Figure 14 P represents the three-dimensional images of the femurs in the exosome-treated and control groups. Figure 14 N and Figure 14 Q represents the images of different important sections of the femur in the exosome-treated and control groups. Figure 14 O and Figure 14 R represents different important sections and three-dimensional images of the tibia in the exosome-treated and control groups.

[0029] Figure 15 This is a visualization diagram of the synergistic effect of the combination of monomeric physalisin A and cisplatin in Examples 1-3 of the present invention, wherein: Figure 15 AB represents the molecular docking results with the lowest binding energy among the five TCM monomers. Figure 15 C represents the detection of DCUN1D5 protein expression in MG63 / R and U2OS / R cells after using the half-inhibitory concentration of Chinese medicine monomers; the concentration of Oxysanguinarine and Sennoside D_qt used was 80 μM. Figure 15 D represents the CCK8 assay to detect the effects of different concentrations of Chinese herbal medicine monomers on the activity of MG63 / R and U2OS / R cells. 15E represents the flow cytometry assay for cell apoptosis after cells were treated with different concentrations of physalisin A. Figure 15 F represents the dose-response matrix of cisplatin and physalisin A, Figure 15 G represents the synergistic graph of cisplatin and physalis A combined treatment of U2OS / R cells when synergy was evaluated using BLISS, HSA, LOEWE, and ZIP models. The synergistic score was calculated using Synergy Finder software. Positive or negative synergistic scores indicate synergistic and antagonistic effects, respectively. Figure 15 H represents the synergistic measure of cisplatin and physalisin A at a given dose combination, Figure 15 I represents the specific synergistic score at different dose combinations;

[0030] Figure 16 Schematic diagram of the mechanism of the synergistic treatment of osteosarcoma by the monomeric physalisin A targeting and inhibiting DCUN1D5 of the present invention and cisplatin. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions 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 intended to limit the present invention.

[0032] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0033] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.

[0034] The schematic diagram of the mechanism of the synergistic treatment of osteosarcoma by the monomeric physalisin A and cisplatin for the targeted inhibition of DCUN1D5 of the present invention is shown in FIG. Figure 16 .

[0035] Example 1

[0036] 1. Data Collection

[0037] Processed count matrices were extracted from GSE152048, GSE162454, and GSE198896. Furthermore, patient clinical data were extracted from the supplemental data set. Bulk RNA sequencing data for clinical samples labeled with TARGET-OS and GSE21257 were obtained from the TARGET database (https: / / ocg.cancer.gov / programs / target) and the GEO database (https: / / www.ncbi.nlm.nih.gov / ).

[0038] 2. Clustering, annotation, and analysis of cell subtypes

[0039] Single-cell RNA sequencing data were processed using Seurat software (version 4.3.0). Low-quality single cells were filtered out based on UMI counts, nfeatures, and the presence of mitochondrial-derived genes. Patient data were integrated using the H ARMONY function to address batch effects between patients, and the top 3000 variable genes were considered to calculate integration anchors. The data matrix was then normalized using Seurat's NormalizeData function. The main cell subtypes were clustered unsupervised using Seurat's FindClusters function and represented by two-dimensional UMAP or t-distributed stochastic neighbor embedding (tSNE). Cell cluster markers were then identified and annotated using Seurat's FindAllMarkers function. The relationship between cell clusters at different resolutions was visualized using Clustree, and the results are shown in

[15] . Figure 1 , Figure 2 , Figure 3 and Figure 4 The visualization of marker genes used the Nebulosa package, scCustomiz e package, and Ridgeplot function.

[0040] in, Figure 1 A represents the dendrogram displayed using the clustree tool, which shows the cell clustering at different resolutions, emphasizing the stability of cluster formation and the impact of resolution selection on cell grouping. Figure 1 B represents the t-SNE and UMAP plots of 17 osteosarcoma samples and 6 osteosarcoma tumor-infiltrating lymphocyte (TIL) samples. These plots show the uniform distribution of cells among samples, indicating that the data normalization was successful. Figure 1 C represents t-SNE and UMAP visualization of the ten major cell types identified in osteosarcoma samples, demonstrating the cellular diversity within the tumor microenvironment and the unique transcriptional signatures of each cell type. Figure 1 D represents the distribution of cell cluster proportions in 17 osteosarcoma samples and 6 osteosarcoma-TIL samples, emphasizing the heterogeneity of cell populations in different lesions and the consistency of specific cell types in the tumor microenvironment. Figure 1 E represents the expression of 23 signature genes in ten identified cell clusters. The size of each dot corresponds to the proportion of cells within the cluster expressing a specific marker gene, and the color gradient represents the average expression level of the marker gene. This plot provides insights into the expression patterns of marker genes and their relative abundance within each cell cluster.

[0041] in, Figure 2 A represents the expression of specific markers of osteoblasts (COL1A1, RUNX2, C OL3A1) and chondrocytes (ACAN, COL2A1, SOX9) in osteosarcoma malignant cells. The color intensity indicates the gene expression level. Figure 2 B represents five different subtypes of malignant osteosarcoma cells, differentiated by gene expression profiles. Each subtype is color-coded, with four subtypes aligned to the osteoblast lineage and one to the chondrocyte lineage, demonstrating the cellular diversity within osteosarcoma. Figure 2 C represents a heat map of differentially expressed genes and gene ontology analysis of the five identified malignant osteosarcoma cell subpopulations. The heat map demonstrates the unique transcriptional signature of each subpopulation, while GO analysis highlights the enriched biological processes, revealing the potential functional roles of these subtypes in tumor biology. Figure 2 D represents the comparison of DEG distribution in recurrent and primary osteosarcoma cells, demonstrating transcriptional changes associated with tumor recurrence and highlighting the molecular complexity of osteosarcoma progression. Figure 2E represents the distribution of the proportions of cell clusters identified in 17 osteosarcoma samples, emphasizing the intertumor heterogeneity of osteosarcoma, with variations in the proportions of subtypes observed in different tumor samples;

[0042] Figure 3 A represents cell clustering at multiple resolutions. This panel shows the process of determining the optimal resolution for cell clustering. Figure 3 B represents the distribution of myeloid cells in different samples, cell types, clusters, and clinical categories. Figure 3 C represents the ridge plot of myeloid cell-related markers. This panel shows the expression of key marker genes for different myeloid cell subtypes, demonstrating the effectiveness of these markers in distinguishing subpopulations and highlighting their expression patterns in different samples. Figure 3 D represents the distribution of myeloid cell subtypes in 11 osteosarcoma specimens. This panel shows the relative abundance of different myeloid cell subtypes in 11 osteosarcoma samples, emphasizing the heterogeneity and variability of myeloid cell populations in the tumor microenvironment. Figure 3 E represents a volcano plot of differential gene expression comparing relapsed myeloid cells with primary myeloid cells, where red indicates genes upregulated in relapse and blue indicates genes downregulated. Figure 3 F represents the expression of myeloid cell identification marker genes, the color change of the dots corresponds to the expression level, and the size of the dots indicates the expression percentage;

[0043] Figure 4 A represents the spatial distribution of eight TIL subsets in the immune landscape of osteosarcoma, including CD4 / 8-T, CD8+T, CD4+T, proliferating T, NK, Treg, B cells and NKT cells. Figure 4 B represents the density map of key immune markers, showing the expression patterns of important immune markers in different TIL subsets, highlighting their unique immune characteristics. Figure 4 C represents a dot plot of traditional TIL-related markers, which shows in detail the expression levels of marker genes and their distribution in each TIL subpopulation, helping to identify different cell phenotypes. Figure 4 D represents the differential gene expression heatmap and GO evaluation of TIL clusters, depicting the differentially expressed genes and the related biological processes and pathways, emphasizing the functional diversity of each TIL subset and its potential role in the immune environment of osteosarcoma. Figure 4 E represents the proportional distribution of TIL subsets in 17 osteosarcoma specimens, demonstrating the variability of TIL subset composition in different patients and highlighting the heterogeneity of immune infiltration in the tumor microenvironment;

[0044] 3. Intercellular Communication

[0045] Cell-cell communication analysis was performed using CellChat-1.6.1 (R package). First, a CellChat object was created using the createCellChat function based on the RNA expression matrix and related cell information. Subsequent analysis was performed using ligand-receptor interaction databases such as SecretedSignaling, ECM-Receptor, and Cell-Cell Contact. The computeCommunProb function was used to evaluate communication probabilities. The selectK function was used to identify global communication patterns. nPatterns was set to 2 and 4 to analyze input and output communication patterns, respectively. The results are shown in Figure 5 、 Figure 6 and Figure 7 ,in, Figure 5 A represents the quantification and intensity of inter-subpopulation communication, showing the strong interactions in the osteosarcoma tumor microenvironment. The size and color intensity of each circle represent the frequency and intensity of communication between cell subpopulations. Figure 5 B represents the two-dimensional spatial representation of the main transmitters (senders) and receivers (targets), demonstrating the directionality of communication. Malignant osteosarcoma cells, fibroblasts, myocytes, and mesenchymal stem cells were identified as the main transmitters, while macrophages, neutrophils, dendritic cells, and osteoclasts were the main receivers. Figure 5 C represents the key outward communication mode, showing the main signals emitted by different cell types. The color depth in the heat map indicates the intensity of signal transmission, and different colors on the horizontal axis represent different cell types. Figure 5 D represents the main inward communication mode, showing the important signals received by each cell subpopulation. The color depth in the heat map represents the intensity of signal transmission. Different colors on the horizontal axis represent different cell types. Figure 6 A represents the use of Cophenetic and Silhouette metrics to estimate the number of output patterns to identify different communication patterns between cell subpopulations. This analysis helps understand the diversity and complexity of signal output in the tumor microenvironment. Figure 6 B represents the identification of cell subtypes and signaling pathways associated with output patterns in the output configuration, demonstrating the specific roles of different cell populations in regulating the communication landscape. Figure 6 C represents the outward communication pattern of secretory cells. The relative contributions of different cell types to the overall signaling output are shown in a stacked diagram, which emphasizes the dominant role of specific cell populations in shaping the communication dynamics. Figure 6 D represents a detailed dot plot of signaling changes in key pathways, providing insights into specific changes in signaling activity associated with different cellular interactions. Figure 6 E represents the number of estimated input patterns using Cophenetic and Silhouette metrics, emphasizing the diversity of signals received by different cell subpopulations. Figure 6 F represents the classification of cell subtypes and signal trajectories associated with the input pattern in the input configuration, showing the specific roles of different cell populations in responding to input signals. Figure 6 G represents the visualization of the signaling pattern received by secretory cells, showing the relative contribution of different cell types to the overall signaling input. Figure 6 H represents a dot plot of the signals received in key pathways, providing insights into signaling changes associated with different cellular interactions. Figure 7 A represents the quantification of cellular interactions in primary and recurrent settings, showing the total number of cellular communication events. Figure 7 B represents the difference in interaction count or intensity during the recurrence process, where red indicates increased expression and blue indicates decreased expression, showing the dynamic changes in communication patterns. Figure 7 C represents the comparison of the frequency and intensity of interactions between recurrent and primary osteosarcomas, emphasizing the increased communication in recurrent cases. Figure 7 D represents a heat map of interaction counts or intensity differences, providing an overview of the differences in cellular communication between primary and recurrent osteosarcoma. Figure 7 E represents the two-dimensional spatial juxtaposition of the main signal sources and targets between primary and recurrent osteosarcoma patients, showing the distribution and direction of cellular interactions. Figure 7 F represents the comprehensive flow analysis of each signaling pathway, demonstrating the complexity and diversity of signal transmission in cellular communication. Figure 7 G represents a comparison of the outward signals associated with each cell population, showing the variation in the signals emitted by different cell populations. Figure 7 H represents the analysis of the inward signals associated with each cell population, reflecting the differences in the signals received by different cell populations. Figure 7 I represents a holistic view of the relevant signals for each cell population, providing an overview of the signaling networks and their roles in cellular communication.

[0046] 4. Cell Trajectory Analysis

[0047] The transcriptional trajectory of osteoclasts was analyzed using Monocle2 and Monocle3, and the results are shown in Figure 10 ,in, Figure 10 A represents Monocle 2 trajectory analysis showing the distribution and clustering of osteoclast subsets, emphasizing different developmental stages. Figure 10 B represents a detailed classification of subpopulations within the osteoclast lineage, highlighting the heterogeneity and complexity of the cell types. Figure 10 C represents the time course of osteoclast differentiation, showing the dynamic changes of cell status over time. Figure 10 D represents the spatial representation of DCUN1D5 expression levels in different osteoclast subclusters, showing its dominant expression at specific developmental stages. Figure 10E stands for Monocle 3 trajectory visualization provides a comprehensive view of osteoclast subtype dynamics from precursors to mature and non-functional states. Figure 10 F represents the expression pattern of DCUN1D5 during osteoclast maturation, indicating its key role in cell stage transition. Figure 10 G represents a heat map with hierarchical clustering showing the developmental timeline of osteoclast subtypes and their associated marker genes, illustrating the molecular features that define each stage. Figure 10 H represents the differential clustering heatmap highlighting the different developmental pathways derived from branch point 4, focusing on the differentiation trajectories leading to immature, mature, and non-functional osteoclasts. Figure 10 Figure 1 represents a schematic representation of the critical role of DCUN1D5 in directing the differentiation of precursor osteoclasts toward their active and functional mature forms, thereby affecting osteosarcoma progression through aberrant osteoclast activation.

[0048] 5. Pathway Analysis

[0049] The FindMarkers function of Seurat was used to identify DEGs between cell clusters, and the thresholds of Padj.P value < 0.05 and log2FC > 0.25 were applied. Subsequently, these DEGs were subjected to GO and KEGG analysis using clusterProfiler. For GSEA and GSVA analysis, the GSEA and GSVAR packages were used, respectively. The results are shown in Figure 8 , Figure 8 A represents the overlap between E3 ubiquitin ligases and genes overexpressed in osteosarcoma. Twenty-one osteosarcoma-associated E3s were identified from 2142 E3 ubiquitin ligases and 727 differentially overexpressed genes in osteosarcoma osteoblasts. Figure 8 B represents the UMAP expression pattern of DCUN1D5. Osteosarcoma osteoblasts were divided into high expression group and low expression group according to the median expression of DCUN1D5. Figure 8 C represents a volcano plot of differential gene expression based on DCUN1D5 expression levels, showing genes that are significantly upregulated and downregulated in cells with high DCUN1D5 expression. Figure 8 D represents the Gene Ontology (GO) analysis of differentially expressed genes, which revealed functions related to the extracellular matrix, immune system regulation, protease activity, and cellular components (such as ribosomes and organelle lumen). Figure 8 E represents gene set variation analysis (GSVA) ​​of differentially expressed genes, showing that metabolic and functional pathways related to ubiquitin-mediated proteolysis, vesicle trafficking, and energy metabolism were upregulated in high DCUN 1D5-expressing cells. Figure 8 F represents the KEGG analysis of differentially expressed genes, indicating enrichment of ribosome pathway, cancer-related proteoglycans, and multiple carcinogenic processes. Figure 8G represents gene set enrichment analysis (GSEA) of differentially expressed genes, showing increases in metabolism, oxidative phosphorylation, and reactive oxygen species in high DCUN1D5-expressing cells.

[0050] 6. Correlation with bulk RNA-seq in clinical cohorts

[0051] The infiltration score of each cell subtype in the TARGET clinical cohort was interpreted using CibersortX and the average expression of the signature genes. The relationship between DCUN1D5 and cell clusters was determined by Spearman correlation. Figure 9 , Figure 9 Figure A represents immune infiltration analysis in osteosarcoma samples, showing the variability of immune cell infiltration in samples from different osteosarcoma patients, highlighting the heterogeneity of the tumor immune landscape. Figure 9 B represents the ratio of different cell types in the osteosarcoma tumor microenvironment, showing the relative abundance of different cell types including malignant osteoblasts, fibroblasts and osteoclasts. Figure 9 C represents the analysis of the correlation between DCUN1D5 expression and microenvironmental cells in osteosarcoma, showing the strength and direction of the correlation between DCUN1D5 expression levels and the presence of various cell types in the tumor microenvironment, suggesting the potential impact of DCUN1D5 on tumor cell composition.

[0052] 7. OncoPredict Drug Sensitivity Analysis

[0053] Tissue gene expression profiles were compared with drug IC using the OncoPredict R package 50 The gene expression profiles were matched to those in the Cancer Drug Sensitivity Database (GDSC; https: / / www.cancerrxgene.org / ). This match was consistent with the cancer cell lineage profiles in the Broad Institute Cancer Cell Line Encyclopedia (CCLE; https: / / portals.broadinstitute.org / ccle_legacy / home). A total of 198 drugs were calculated and their IC values ​​were examined by Spearman analysis. 50 The correlation between the gene and DCUN1D5 is shown in Figure 11 A- Figure 11 B, where Figure 11 A represents the expression of DCUN1D5 in osteosarcoma tissue samples and the half inhibitory concentration (IC 50 ) values, each point represents a different drug, the horizontal axis represents the DCUN1D5 expression level, and the vertical axis represents the corresponding IC 50 The positive correlation indicated that higher expression levels of DCUN1D5 were associated with increased drug tolerance. Figure 11B represents the expression of DCUN1D5 in osteosarcoma tissue samples and the drug IC in the GSE21257 database 50 Similar to panel A, this figure shows the relationship between DCUN1D5 expression and drug resistance, further supporting the hypothesis that DCUN1D5 plays a role in multidrug resistance in osteosarcoma.

[0054] 8. Molecular Docking Analysis and Molecular Dynamics

[0055] The crystal structures of key target proteins were obtained from the SWISS-MODEL Protein Data Bank (https: / / swissmodel.expasy.org / ) and deposited in PDB format. Drug structures were obtained in sdf format from PubChem (https: / / pubchem.ncbi.nlm.nih.gov / ) and converted to mol2 format using the Open Babel GUI software. Protein conformations were adjusted using PyMOL and AutoDock4, including removal of water molecules, addition of nonpolar hydrogen atoms, calculation of protein charges, identification of rotatable bonds in the ligand, and export in PDBQT format. Docking parameters were configured based on the dimensions of the receptor and ligand. Molecular docking was performed using AutoDock4, and the results were visualized using PyMOL. A threshold of less than -2.0 kcal / mol was selected for binding energy. The Desmond module of Schrodinger 2019 software was used for the simulation of water molecules. Appropriate amounts of chloride / sodium ions were added and randomly placed in the solution to neutralize the system charge. After constructing the dissolved system, energy minimization was performed using the default protocol integrated with the Desmond module (using the OPLS2005 force field parameters). Nose-Hoover temperature coupling and isotropic scaling were used to maintain the temperature and pressure at 300 K and 1 atm, respectively. NPT simulations were then run, with trajectories saved every 100 ps. The results are available in [1]. Figure 11 C, Figure 15 A and Figure 15 B, where Figure 11 C represents the molecular interaction analysis between DCUN1D5 and the main therapeutic drugs for osteosarcoma (including methotrexate, doxorubicin, cisplatin and cyclophosphamide), Figure 15 A represents the visualization of the molecular binding energy between DCUN1D5 and five Chinese medicine monomers. Figure 15 B represents the molecular docking visualization results of DCUN1D5 and five traditional Chinese medicine monomers.

[0056] Example 2

[0057] In vitro experiments

[0058] 9. Establishment of drug-resistant osteosarcoma cell lines

[0059] A drug-resistant cell line was successfully established using a method of gradually increasing drug concentrations. By inducing cisplatin in the osteosarcoma cell line for a certain period of time, its sensitivity to the drug was reduced, thereby establishing a drug-resistant cell line.

[0060] 10. Real-time Fluorescence Quantitative PCR

[0061] RNA was extracted using TRIzol (Invitrogen, USA) and reverse transcribed using the PrimeScript RT kit (TaKaRa, Japan). qPCR was performed on an Applied Biosystems 7300 system using SYBR Premix ExTaq (TaKaRa, Japan). The results are shown in Figure 12 B, Figure 14 B, Figure 14 F, Figure 14 G, Figure 14 H, and Figure 14 K.

[0062] 11. RNA interference

[0063] Invitrogen synthesized two pairs of siRNAs targeting human DCUN1D5, named Gene-SiRNA1 and Gene-SiRNA2. Osteosarcoma cells were plated in 512-well plates at a density of 10 × 10 cells per well (Rosen, 1985; Ritter and Bielack, 2010; Li et al., 2011; Goguet-Surmenian et al., 2013; Smelland et al., 2019). 5 When the cells reached 80% confluence, Lipofectamine TM siRNA transfection was performed at 3000 cells / mL, following the manufacturer's instructions. Total RNA was extracted using TRIzol and reverse-transcribed into cDNA, and mRNA levels were measured by RT-qPCR. These experiments were repeated three times to identify the most effective siRNA.

[0064] 12.CCK-8 assay

[0065] Cells were cultured in clear 96-well plates and treated as described in the figure legends. Cell proliferation was assessed using the CCK-8 assay kit (Sigma-Aldrich, St. Louis, MO) according to the manufacturer's instructions. Absorbance was measured at 450 nm and corrected by subtracting a reference wavelength. Data presented in the figures represent relative optical density (OD). Figure 12 A and Figure 15 D.

[0066] 13. Protein Western Blotting

[0067] Proteins were extracted from cell lysates containing RIPA buffer (Beyotime, China) and quantified by BCA assay (Beyotime). Proteins were separated by 10% SDS-PAGE and transferred to polyvinylidene difluoride membranes (Sigma-Aldrich, USA). The membrane was incubated with the following primary antibodies: anti-DCUN1D5 (1:1000, proteintech), anti-MMP9 (1:500, proteintech), anti-CTSK (1:500, proteintech), anti-AKT (1:1000; CellSignaling Technology, USA), anti-PI3K (1:1000; Cell Signaling Technology), anti-GSK3β (1:1000; Cell Signaling Technology), anti-p-Akt (Ser-473; 1:1000; Cell Signaling Technology), anti-p-PI3K (Tyr458; 1:1000; Cell Signaling Technology), anti-p-GSK3β (Ser-9; 1:1000; Cell Signaling Technology) chnology),anti-GAPDH(1:1000,abcam),anti-CD9(1:1000,Cell Signaling Technology), anti-Calnexin (1:500, Cell Signaling Technology), anti-TS G101 (1:2000, Abcam), anti-81 (1:1000, Abcam), and then incubated with secondary antibodies (1:5000, Cell Signaling Technology). After washing, the signal was detected using a chemiluminescence system (Bio-Rad, USA) and analyzed using Image Lab software (Bio-Rad). The results are shown in Figure 12 C, Figure 12 I, Figure 12 J, Figure 14 E, Figure 14 I. Figure 14 L and Figure 15 C.

[0068] 14. Immunofluorescence

[0069] For immunofluorescence staining of paraffin-embedded sections, sections were dewaxed and rehydrated, followed by antigen retrieval in a pH 6.0 citrate buffer. Antibody staining was then performed. First, sections were treated with antigen blocking solution for 20 minutes at room temperature to prevent nonspecific binding. They were then incubated overnight at 4°C in a 1:1000 dilution of SLC25A5 antibody solution. After thorough washing with PBST, sections were incubated with Alexa Fluor 555-labeled IgG antibody at a dilution of 1:600 ​​for 20 minutes at room temperature in the dark. Finally, nuclear staining was performed with a medium containing DAPI, and images were captured using a fluorescence microscope, see [see Figure 2]. Figure 12 D.

[0070] 15. Tumor sphere formation assay

[0071] Cells (1000 cells per well) were seeded in ultra-low attachment 24-well plates (Corning, Corning, NY) and cultured in Dulbecco's Modified Eagle Medium (DME M) / F12 containing human epidermal growth factor (EGF, 10 ng / ml, PeproTech), human basic fibroblast growth factor (bFGF, 10 ng / ml, PeproTech), and N-2 supplement (1×, Gibco, Waltham, MA). Cells were cultured at 37°C in a 5% CO2 atmosphere for 14 days. 100 μl of fresh culture medium was added every three days. The diameter and number of spheroids were recorded using an inverted microscope, and the dryness of tumor cells was assessed by comparing the average diameter and number of spheroids under different conditions. The results are shown in Figure 2. Figure 12 E.

[0072] 16. Flow cytometry detection of cell apoptosis

[0073] After 48 hours of culture after transfection, the supernatant was collected. The cells were treated with EDTA-free trypsin to form a single-cell suspension. After resuspending in cold PBS and centrifuging, the cells were washed with binding buffer and stained with Annexin V-FITC and propidium iodide. Apoptosis analysis was immediately performed by flow cytometry. The results are shown in Figure 12 G. Figure 12 H and Figure 15 E.

[0074] 17. Soft agar colony formation assay

[0075] The soft agar colony formation experiment was performed as follows: 10% fetal bovine serum, 100 units / mL penicillin, and 100 μg / mL streptomycin were added to MEM medium, with 0.6% low melting point agarose as the base layer. Next, the top layer consisted of 0.3% agarose and 5000 designated cells. The culture plates were incubated in a humidity-controlled environment at 37°C and 5% CO2 for approximately two weeks. Cell colonies were stained with 0.5% crystal violet and 25% methanol for 20 minutes. The culture plates were then scanned and photographed, and colony counts were performed using Bio-Rad's QuantityOne v.4.0.3 software. The results are shown in the table. Figure 12 F.

[0076] 18. Exosome Isolation, Characterization, and Uptake in RAW264.7 Cells

[0077] RAW264.7 cells were cultured at 5×10 4 / mL density was seeded in a culture dish suitable for laser confocal microscopy and incubated overnight. According to the instructions of PKH26 dye, exosomes were labeled with PKH26, and 15ug / mL PKH26-exosomes were introduced into RAW264.7 cells. After incubation for 6 hours, RAW264.7 cells were collected and the supernatant was discarded. The cells were fixed with 4% paraformaldehyde for 10 minutes, washed three times with PBS, covered with CFSE dye for 5 minutes, and finally stained with DAPI for 3 minutes. The cellular uptake of exosomes was observed by confocal microscopy. For details, refer to Figure 14 C-14E, Figure 14 C represents the transmission microscopy phenotypic analysis of MG63 cell-derived exosomes. Figure 14 D represents the tracking of MG63 / CDDP nanoparticles using Nano Sight. Figure 14 E represents Western blot detection of exosome biomarkers (TSG101, CD9, ALIX) in osteosarcoma cell lines.

[0078] 19. Evaluating Synergy Using SynergyFinderPlus Software

[0079] Multiple models were used to evaluate synergy, mainly to analyze the synergistic effect of the combination of physalis A and cisplatin. Using models based on different null hypotheses to analyze the same experimental data may lead to different interpretations of the synergistic effects of the test drugs. In order to reliably evaluate the interactions between anti-osteosarcoma drugs, the Synergy FinderPlus calculator was used, which uses four main models to evaluate synergy: ZIP, LOEWE, BLISS, and HSA. These models are constructed based on specific biological or empirical assumptions. The BLISS model assumes that the effects of the drugs are independent and calculates the expected combination effect based on the probability of independent effects. The HSA model defines synergy as a combination response that is greater than the response of either drug alone. The LOEWE model considers the dose-response relationship of each drug and calculates the expected additive response; if the response is higher than expected, it is marked as synergistic. The ZIP model evaluates the effect of the two-drug combination under the assumption that each drug does not affect the efficacy of the other drug. For specific evaluation results, see Figure 15 FI. Among them, Figure 15 F represents the confidence interval diagram of cisplatin and physalisin A. Figure 15 G represents the synergistic graph of cisplatin and physalis A combined treatment of U2OS / R cells when synergy was evaluated using BLISS, HSA, LOEWE, and ZIP models. The synergistic score was calculated using Synergy Finder software. Positive or negative synergistic scores indicate synergistic and antagonistic effects, respectively. Figure 15 H represents the synergistic measure of cisplatin and physalisin A at a given dose combination, Figure 15 I represents the synergistic score at different dose combinations.

[0080] Example 3

[0081] In vivo experiments

[0082] 20. Animal Experimentation

[0083] NOD-scid IL2rγnull (NSG) mice were injected subcutaneously with 1×10 7 U2OS / R cells were used to construct a drug-resistant tumor model. The successfully modeled NSG mice were randomly divided into two groups: the SiRNA-NC group (subcutaneous injection of Si-NC / MG63 cells and intraperitoneal injection of cisplatin) and the SiRNA-DCUN1D5 group (subcutaneous injection of Si-DCUN1D5 / MG63 cells and intraperitoneal injection of cisplatin). Each group included three mice. Tumor growth was continuously observed and tumor volume was recorded. When the tumor volume reached 50 mm 3Cisplatin (diluted to 1 μg / g in saline) was administered intraperitoneally twice weekly according to group. Tumor volume and mouse body weight were assessed weekly. On the fourth week, mice were euthanized; tumors were excised, weighed, and subjected to T UNEL and Ki67 staining, DCUN1D5 expression analysis, and H&E staining. The results are shown in the table. Figure 13 ,in, Figure 13 A represents representative images of subcutaneous tumors in NSG mice from different treatment groups, showing the effect of DCUN1D5 silencing on tumor size. Figure 13 B represents the quantitative analysis of the tumor volume of NSG mice over time. The tumor volume (V) was calculated by the formula V = (a × b 2 ) / 2, a is the longest diameter of the tumor, b is the shortest diameter, Figure 13 C represents the comparison of tumor weights in different groups of NSG mice, highlighting the effect of DCUN1D5 knockdown on tumor growth. Figure 13 D represents the immunofluorescence staining of DCUN1D5 in the resected tumor tissue, showing the downregulation of DCUN1D5 expression in the Si-DCUN1D5 group compared with the Si-NC group. Figure 13 E represents hematoxylin and eosin (H&E) staining of tumor sections, revealing a decrease in the structural integrity of tumor cells in the Si-DCUN1D5 group. Figure 13 F represents TUNEL staining and Ki-67 immunofluorescence staining to evaluate apoptosis and proliferation in tumor sections. Compared with the SiRNA-NC group, the number of TUNEL-positive apoptotic cells in the SiRNA-DCUN1D5 group increased, and Ki-67 expression decreased, indicating that DCUN1D5 silencing has the effects of promoting apoptosis and inhibiting proliferation in vivo. Statistical significance is indicated by ***P<0.005.

[0084] To evaluate the effects of exosomes derived from drug-resistant cell lines on bone resorption, NSG mice were injected every three days with 100 μL of PBS containing either 10 μg of MG63 / CDDP cell-derived exosomes or plain PBS. Four weeks later, NSG mice were euthanized, and femurs and tibias were removed, fixed with 4% paraformaldehyde, and stored in PBS at 4°C. Specimens were scanned using high-resolution microCT (Skyscan 1276, Skycan, Aartselaar, Belgium) at a resolution of 20.376 μm per pixel, a voltage of 100 kV, and a current of 200 μA. Trabecular bone parameters, including bone mineral density (BMD), bone surface area (BS), bone volume fraction (BV / TV), trabecular thickness (Tb.Th), trabecular number (TbN), and trabecular spacing (Tb.Sp), were analyzed. In addition, cortical bone parameters of the mid-femoral shaft were evaluated, including total cross-sectional area (Tt.Ar), cortical area (Ct.Ar), cortical area ratio (C t.Ar / Tt.Ar), and cortical thickness (Ct.Th) with a range of 0.2 mm, and the results are shown in Figure 14 , Figure 14 A represents the qPCR analysis of DCUN1D5 expression in the culture medium (CM) of osteosarcoma parental and drug-resistant cell lines. Figure 14 B represents the qPCR evaluation of DCUN1D5 expression in MG63 and MG63 / CDDP cell lines after treatment with control medium, RNase A (2 mg / mL), or combined with Triton X-100 (0.1%) for 0.5 h. Figure 14 F and Figure 14 G represents qPCR evaluation of DCUN1D5 expression in osteosarcoma cell CM after exosome depletion, using GW4869 ( Figure 14 F) or ultracentrifugation ( Figure 14 G) Treatment, compared with MG63 and MG63 / CDDP CM, Figure 14 H and Figure 14 I represents the comparison of DCUN1D5 expression in osteosarcoma-derived exosomes and exosomes from cisplatin-resistant cells using qRT-PCR (H) and Western blot (I). Figure 14 J represents the uptake of PKH26 (red) labeled exosomes into CFSE (green) labeled RAW264.7 cells using confocal microscopy. Figure 14 K and Figure 14 L represents the expression of MMP9 and CTSK analyzed by QRT-PCR and Western blot, *P<0.05, **P<0.01, ***P<0.001, Figure 14 M and Figure 14 P represents the three-dimensional images of the femurs in the exosome-treated and control groups. Figure 14 N and Figure 14 Q represents the images of different important sections of the femur in the exosome-treated and control groups. Figure 14 O and Figure 14 R represents different important sections and three-dimensional images of the tibia in the exosome-treated and control groups.

[0085] Although the principles of the present invention have been 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 illustrative of the present invention and are not intended to limit the scope of the present invention. The details in the embodiments do not constitute a limitation on the scope of the present invention. Without departing from the spirit and scope of the present invention, any obvious changes such as equivalent transformations and simple substitutions based on the technical solution of the present invention fall within the scope of protection of the present invention.

Claims

1. A use of a DCUN1D5 expression inhibitor and cisplatin in the preparation of a cisplatin-resistant osteosarcoma drug, characterized in that: The DCUN1D5 expression inhibitor is physalisin A; The concentration of cisplatin was 32 μM, and the concentration of physalisin A was 20 μM.

2. The use according to claim 1, characterized in that The medicine also includes excipients.

3. The use according to claim 2, characterized in that The auxiliary material is selected from one or more of a filler, a disintegrant, a lubricant, a suspending agent, a binder or a sweetener.

4. The use according to claim 3, characterized in that The filler is selected from one or more of starch, lactose, microcrystalline cellulose or sucrose; and / or, The disintegrant is selected from one or more of starch, sodium carboxymethyl starch, pregelatinized starch, low-substituted hydroxypropyl cellulose or cross-linked sodium carboxymethyl cellulose; and / or, The lubricant is selected from one or more of magnesium stearate, silicon dioxide or sodium lauryl sulfate; and / or, The suspending agent is selected from one or more of polyvinyl pyrrolidone, sucrose or hydroxypropyl methylcellulose; and / or, The binder is selected from one or more of hydroxypropyl methylcellulose, starch slurry or polyvinyl pyrrolidone; and / or, The sweetener is selected from one or more of saccharin sodium, glycyrrhetinic acid, sucrose, aspartame or cyclamate.

5. The use according to any one of claims 1 to 4, characterized in that The medicine is an oral preparation or an injection preparation.

6. The use according to claim 5, characterized in that The oral preparations include capsules, tablets or granules.

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