Application of a SLC25A5 expression inhibitor in the preparation of cisplatin-resistant osteosarcoma drugs

Abstract

The present invention relates to the field of bioengineering technology, and specifically to an application of an SLC25A5 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug. The present invention provides an application of an SLC25A5 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug, wherein the cisplatin-resistant osteosarcoma drug comprises an SLC25A5 expression inhibitor. The present invention establishes for the first time the key role of SLC25A5 in osteosarcoma resistance, and discovers the traditional Chinese medicine active ingredient phyllospermum officinale that has the function of selectively inhibiting SLC25A5. 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. This innovative strategy brings new hope for the treatment of osteosarcoma, especially 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 SLC25A5 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug. Background Art

[0002] Osteosarcoma is the most common malignant tumor among bone tumors, with significant malignant and heterogeneous characteristics. This tumor mainly affects adolescents and children. Although advances in treatment have improved the five-year survival rate, further improvement is still needed. Although current treatments such as high-dose chemotherapy, osteosarcoma resection and radiotherapy have prolonged survival and increased remission rates, the complex etiology and heterogeneity of the tumor have limited these treatments. Therefore, it is of great significance to identify new molecular targets, deeply explore the pathogenesis of osteosarcoma, and innovate targeted therapies.

[0003] The immune microenvironment plays a key role in the pathogenesis and progression of osteosarcoma. Studies have shown that factors such as extracellular matrix remodeling, altered intercellular communication, increased immunosuppressive cells, and abnormal release of inflammatory mediators can drive the proliferation and invasion of osteosarcoma cells and may increase their resistance to chemotherapy. Immunotherapy has achieved remarkable success in the treatment of various cancers and has become an emerging therapeutic approach. In the context of osteosarcoma, maintaining the balance of the immune microenvironment is essential for the efficacy of immunotherapy and other treatments. Therefore, in-depth research on the subtle differences in the immune microenvironment may produce more effective treatment strategies.

[0004] Drug resistance is an ongoing challenge in osteosarcoma treatment. Recent studies have highlighted the critical role of the osteosarcoma immune microenvironment in mediating this resistance. This microenvironment consists of a complex network of multiple cell types and their secreted cytokines and chemokines, coordinating the dynamic process of tumor proliferation, drug resistance generation, and immune-mediated tumor clearance. Elucidating the mechanisms that activate or inhibit immune responses in this network is crucial to combat immune resistance to drugs. The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized this research field, revealing cellular heterogeneity, discovering new cell subsets, clarifying their molecular basis, and delineating cell-cell interactions in osteosarcoma progression.

[0005] Natural products derived from traditional Chinese medicine are considered a promising source of novel small-molecule therapeutics due to their wide range of bioactivities, low toxicity, and multi-targets. Many natural compounds derived from herbs exhibit anticancer properties, including antiproliferative, pro-apoptotic, anti-metastatic, and anti-angiogenic effects. In addition, they can modulate autophagy, reverse multidrug resistance, balance immunity, and enhance the effects of chemotherapy in vitro and in vivo. Phyllanthin is a widely distributed, naturally occurring phenolic compound from the genus Phyllanthus. It has long been used to treat kidney, gallbladder, and liver-related diseases. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides an application of an SLC25A5 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug. The present invention finds that the traditional Chinese medicine active ingredient phylloxetine with the function of selectively inhibiting SLC25A5 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 patient treatment effects.

[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 SLC25A5 expression inhibitor in the preparation of a cisplatin-resistant osteosarcoma drug, wherein the cisplatin-resistant osteosarcoma drug comprises the SLC25A5 expression inhibitor.

[0009] Furthermore, the SLC25A5 expression inhibitor is phylloxeratin.

[0010] In a second aspect, the present invention provides an osteosarcoma inhibitor in an optional embodiment, which comprises the above-mentioned SLC25A5 expression inhibitor and cisplatin.

[0011] Furthermore, the osteosarcoma inhibitor includes phylloxeratin and cisplatin.

[0012] Preferably, in the osteosarcoma inhibitor, the concentration of cisplatin is 16-128 μM, and the concentration of globin is 25-100 μM. Furthermore, in the osteosarcoma inhibitor, the concentration of cisplatin is 32 μM, and the concentration of globin is 50 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. The present invention established for the first time the key role of SLC25A5 in osteosarcoma drug resistance and discovered a traditional Chinese medicine active ingredient, phyllanthin, that selectively inhibits SLC25A5. This ingredient can not only synergize with cisplatin to reverse cisplatin resistance in osteosarcoma, but also provide new target sites and molecular markers for clinically predicting and improving patient treatment outcomes. This innovative strategy brings new hope to osteosarcoma treatment, especially in the problem of cisplatin resistance, providing a more effective solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The single cell transcriptome profiles of osteosarcoma and control bone tissue in Example 1 of the present invention, wherein: Figure 1 A represents the hierarchical clustering of cells at multiple resolutions. Figure 1 B represents the cell distribution of 17 osteosarcoma samples and 9 control samples after dimensionality reduction by t-SNE and UMAP. Figure 1 C represents the 12 main cell types identified by t-SNE and UMAP dimensionality reduction. Figure 1 D represents the proportion of cell clusters in each sample. Figure 1 E represents the gene expression characteristics of each cell cluster. The size of the dots indicates the prevalence of expression of specific marker genes, and the color depth indicates the average expression level.

[0022] Figure 2 This is the osteoblast heterogeneity analysis in Example 1 of the present invention, wherein: Figure 2 A represents the cell clustering based on resolution, Figure 2 B- Figure 2 D UMAP projections distinguished clinical categories ( Figure 2 B) Sample variation ( Figure 2 C) and major osteoblast subsets ( Figure 2 D), Figure 2 E represents the gene ontology (GO) enrichment analysis of five osteoblast subpopulations. Figure 2 F represents the differentially expressed gene (DEG) volcano plot comparing osteoblasts in osteosarcoma and control bone tissues, with red highlighting genes that are highly expressed in osteosarcoma. Figure 2 G represents the comparison of the relative abundance of five osteoblast clusters in different samples;

[0023] Figure 3 The inter-subpopulation cell communication network in Example 1 of the present invention, wherein: Figure 3 A- Figure 3 B represents the quantification and intensity of inter-subpopulation cell communication, Figure 3 C represents a graphical representation of the main signal sources and targets. Figure 3 D represents the main signal transmission mode, Figure 3 E represents the main signal receiving mode;

[0024] Figure 4 The dynamic comparison of the communication between osteosarcoma and control bone tissue in Example 1 of the present invention, wherein: Figure 4 A represents an individual representation of cell-cell communication in two tissue types, Figure 4 B represents the change in the extent or strength of the interaction in osteosarcoma, with red lines indicating increased expression and blue lines indicating decreased expression. Figure 4 C represents the quantitative and qualitative difference of the interaction index, Figure 4 D represents the comparative analysis of the main emitters and receivers in a two-dimensional framework, Figure 4 E represents the comparative analysis of signals emitted in different cell subsets. Figure 4 F represents the comparative analysis of signaling in different cell subsets. Figure 4 G represents the comparative analysis of the signals received by different cell subsets. Figure 4 H represents a comprehensive analysis of signaling dynamics within each cell subset;

[0025] Figure 5 The regulatory effect of SLC25A5 in osteosarcoma in Example 1 of the present invention, wherein: Figure 5 A represents the screening process of five osteosarcoma-cell senescence genes. Figure 5 B represents the prognostic validation of SLC25A5 using the TARGET database. Figure 5 C represents UMAP analysis showing the differential expression of SLC25A5 in osteoblasts. Figure 5D represents the difference in gene expression related to SLC25A5. Figure 5 E represents GO enrichment analysis of differentially expressed genes. Figure 5 F represents the KEGG pathway analysis of differentially expressed genes. Figure 5 G represents the GSVA analysis of differentially expressed genes. Figure 5 H represents GSEA analysis of differentially expressed genes;

[0026] Figure 6 The effect of SLC25A5 on the tumor immune microenvironment of osteosarcoma in Example 1 of the present invention, wherein: Figure 6 A represents the immune cell infiltration analysis of each sample, Figure 6 B represents the distribution of various cell types in the sample, Figure 6 C represents the correlation analysis between SLC25A5 expression and cells in the tumor microenvironment;

[0027] Figure 7 Schematic diagram of the correlation between up-regulation of SLC25A5 and enhanced drug resistance in osteosarcoma in Example 1 of the present invention, wherein: Figure 7 A- Figure 7 B represents SLC25A5 and multiple drug IC 50 Schematic diagram of the relationship between values, Figure 7 C represents a schematic diagram of the molecular docking analysis results between SLC25A5 and major osteosarcoma drug treatments;

[0028] Figure 8 This is a schematic diagram of the atypical evolution results of SLC25A5 promoting CD8+T cells in Example 1 of the present invention, wherein: Figure 8 A- Figure 8 D represents Monocle 2 trajectory analysis showing the dynamic transformation of CD8+ T cell clusters ( Figure 8 A) Changes in cell types ( Figure 8 B), pseudo-time sorting ( Figure 8 C) and SLC25A5 expression levels ( Figure 8 D), Figure 8 E is a hierarchical clustering heat map showing marker genes associated with developmental time and subpopulation differentiation. Figure 8 F is a heat map of hierarchical clustering differences, which identifies marker genes associated with developmental timing and cell subpopulation differentiation in the branch-2 differentiation lineage. Figure 8 H- Figure 8 I represents the Monocle 3 trajectory analysis showing the dynamic changes in the distribution of CD8+ T cell subsets and SLC25A5 expression. Figure 8 J Schematic diagram illustrating the SLC25A5-mediated differentiation process from naive CD8+ T cells to CD8+TOX and EXH subtypes;

[0029] Fig. 9 The expression of SLC25A5 in osteosarcoma cell line in Example 2 of the present invention, wherein, Fig. 9 A represents qRT-PCR analysis of SLC25A5 expression in osteosarcoma cell lines and normal control hFOB cells, Fig. 9 B represents the relative growth of U2OS, MG63, MG63 / R and U2OS / R cells quantitatively analyzed using CCK-8 assay. Fig. 9 C represents qRT-PCR analysis of SLC25A5 expression in U2OS, MG63, MG63 / R and U2OS / R cells. Fig. 9 D represents Western blot analysis of SLC25A5 expression in U2OS, MG63, MG63 / R and U2OS / R cells. Fig. 9 E represents the expression of SLC25A5 in sensitive and resistant osteosarcoma cells observed using confocal microscopy. *P<0.05, **P<0.01, ***P<0.001.

[0030] Fig.10 The proliferation, anti-apoptosis, migration and invasion properties of SLC25A5 on U2OS / R cells in Example 2 of the present invention, wherein: Fig.10 A represents the proliferation of drug-resistant osteosarcoma cell lines analyzed by EdU incorporation. Fig.10 B represents the effect of SLC25A5 reduction in drug-resistant osteosarcoma cells observed in a 3D culture environment. Fig.10 C represents apoptosis assessed by flow cytometry 48 hours after transfection. Fig.10 D represents the analysis of caspase-3 enzyme activity. ***P<0.001, Fig.10 E represents the level of apoptotic proteins detected by Western blot. Fig.10 F represents Transwell migration and invasion assay. Fig.10 G represents the levels of EMT-related proteins detected by Western blot.

[0031] Fig.11 This is a graph showing the results of an in vivo experiment on the carcinogenic function of SLC25A5 in Example 2 of the present invention, wherein: Fig.11 A represents the representative images of subcutaneous tumor formation in nude mice in the sh-SLC25A5 group and the shRNA-NC group. Fig.11 B represents the tumor growth dynamics of nude mice in the sh-SLC25A5 group and shRNA-NC group, which are displayed by growth curves. Fig.11 C represents the comparative analysis of tumor mass between sh-SLC25A5 group and shRNA-NC group in nude mice. Fig.11D represents the characterization of SLC25A5 expression in resected tumor specimens by immunofluorescence. Fig.11 E represents histological evaluation of resected tumor specimens stained with hematoxylin and eosin (H&E), Fig.11 F represents TUNEL expression analysis in tumor sections by immunofluorescence to determine apoptotic activity;

[0032] Fig.12 This is a schematic diagram of the results of using micro-CT to examine the effect of SLC25A5 in osteosarcoma-related osteolysis in Example 1 of the present invention, wherein: Fig.12 A- Fig.12 E represents the 3D reconstruction of the femur in the sh-NC group, including the anteroposterior and coronal sections of the entire femur ( Fig.12 A), side view and vertical section ( Fig.12 B), cross-sectional and 3D view of the distal femoral shaft ( Fig.12 C), mid-femoral cortical bone and 3D view ( Fig.12 D), and coronal section of the proximal tibia ( Fig.12 E); Fig.12 F- Fig.12 J represents the 3D reconstruction of the femur in the sh-SLC25A5 group, including the anteroposterior and coronal sections of the entire femur ( Fig.12 F), side view and vertical section ( Fig.12 G), cross-sectional and 3D view of the distal femoral shaft ( Fig.12 H), mid-femoral cortical bone and 3D view ( Fig.12 I), and coronal section of the proximal tibia ( Fig.12 J), Fig.12 K- Fig.12 P represents the quantitative diagram of trabecular bone microstructure parameters. Fig.12 Q- Fig.12 T Party represents the schematic diagram of the changes in cortical bone microstructure parameters, *P < 0.05, **P < 0.01;

[0033] Fig.13 The present invention's embodiments 1 and 3 show that globin inhibits the malignant phenotype of cisplatin-resistant osteosarcoma cells in vitro and in vivo, wherein: Fig.13 A- Fig.13 B represents the docking results of five Chinese medicine monomers, showing the lowest binding energy. Fig.13 C- Fig.13 D represents the CCK8 experiment to detect the effects of different concentrations of Chinese medicine monomers on the viability of MG63 / R and U2OS / R cells. Fig.13 E- Fig.13 G represents the detection of SLA25A5 mRNA transcription in MG63 / R and U2OS / R cells at the half-inhibitory concentration of Chinese medicine monomer ( Fig.13 E- Fig.13 F) and SLA25A5 protein expression ( Fig.13 G); the concentration of jujube saponin At used was 128 μM, Fig.13 H represents the molecular structure diagram of glomerulone. Fig.13 I represents the plate clone formation experiment after treatment with different concentrations of Chinese medicine, Fig.13 J represents the cell cycle analysis of different treatment groups and control group by flow cytometry. Fig.13 K represents the analysis of cell apoptosis after treatment with different concentrations of traditional Chinese medicine using flow cytometry. Fig.13 L represents the images of subcutaneous tumors in nude mice in different treatment groups. Fig.13 M represents the growth curves of nude mouse tumors in different treatment groups. Fig.13 N represents the statistics of tumor weights of nude mice in different treatment groups. Fig.13 O represents the schematic diagram of tumor xenograft model construction. Fig.13 P represents the images of PDX tumors of NOD-SCID mice in different treatment groups. Fig.13 Q represents the growth curve of PDX tumors in NOD-SCID mice in different treatment groups. Fig.13 R represents the statistics of PDX tumor weights of NOD-SCID mice in different treatment groups;

[0034] Fig.14 This is a visualization diagram of the synergistic effect of globin combined with cisplatin in Example 2 of the present invention, wherein: Fig.14 A represents the confidence interval diagram of cisplatin and glomerulone. Fig.14 B- Fig.14 E represents the synergistic graph of cisplatin and phyllotoxin combined treatment of U2OS / R cells when evaluating synergy using BLISS, HSA, LOEWE and ZIP models, respectively. The synergistic score was calculated by SynergyFinder software. Positive synergistic scores or negative synergistic scores indicate synergistic and antagonistic effects, respectively. Fig.14 F represents the synergistic measurement of cisplatin and 32 μM glomerulone at a given dose combination, Fig.14 G stands for Fig.14 The bar graph in F shows the recommended concentrations for the plate cloning experiment. Fig.14 H represents the drug concentration and synergy score that inhibits the response by more than 90%;

[0035] Fig.15 This is a schematic diagram of the mechanism of the synergistic treatment of osteosarcoma by the traditional Chinese medicine monomer (phylloxanthin) for targeted inhibition of SLC25A5 and cisplatin of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0038] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.

[0039] The schematic diagram of the mechanism of the synergistic treatment of osteosarcoma by the Chinese medicine monomer (phylloxanthin) for the targeted inhibition of SLC25A5 of the present invention and cisplatin is shown in Fig.15 .

[0040] Example 1

[0041] 1. Data Source and Processing

[0042] GSE152048 and GSE162454 datasets were selected from the GEO database to provide single-cell RNA sequencing (scRNA-seq) data for OS (osteosarcoma), and GSE217792, GSE169396, and GSE147390 datasets were selected to provide bone tissue data for control individuals. At the same time, the TARGET database containing comprehensive survival information was selected for prognostic analysis, and samples lacking complete survival and clinical data were excluded. The Seurat R package (v4.3.0) was used to analyze 26 single-cell RNA sequencing (scRNA-seq) data samples for OS (osteosarcoma). Potential doublets were eliminated by DoubletFinder, and cells with high hemoglobin genes and mitochondrial genes were selectively filtered out (screening conditions were: nFeature_RNA>300 and nFeature_RNA<9000 and percent.mt<20 and percent.HB<5 and nCount_RNA<100000). Subsequently, all genes were standardized using the ScaleData function and dimension reduction was performed using principal component analysis (PCA). The batch effect between integrated samples was mitigated using the Harmony package, and highly variable genes were screened using the FindVariable Features function. Cell clustering was performed using FindNeighbor and FindCluster. Dimensionality reduction was then performed using t-SNE and UMAP. Marker genes were selected using the FindAllMarker function, and different clusters were manually annotated based on the Cell Marker database and previous studies, see Figure 1 ,in, Figure 1 A represents the hierarchical clustering of cells at multiple resolutions. Figure 1 B represents the cell distribution of 17 osteosarcoma samples and 9 control samples after dimensionality reduction by t-SNE and UMAP. Figure 1 C represents the 12 main cell types identified after dimensionality reduction using t-SNE and UMA. Figure 1 D represents the proportion of cell clusters in each sample. Figure 1 E represents the gene expression characteristics of each cell cluster. The size of the dots indicates the prevalence of expression of specific marker genes, and the color depth indicates the average expression level. Monocle2 and Monocle3 were used to visualize the pseudo-time trajectory of cells, and the plot pseudotime heatmap function was used to display the heat map of dynamic changes in gene expression. The results are shown in Figure 8 ,in, Figure 8 A- Figure 8 D represents Monocle 2 trajectory analysis showing the dynamic transformation of CD8+ T cell clusters ( Figure 8 A) Changes in cell types ( Figure 8 B), pseudo-time sorting ( Figure 8 C) and SLC25A5 expression levels ( Figure 8 D), Figure 8 E is a hierarchical clustering heat map showing marker genes associated with developmental time and subpopulation differentiation. Figure 8 F is a heat map of hierarchical clustering differences, which identifies marker genes associated with developmental timing and cell subpopulation differentiation in the branch-2 differentiation lineage. Figure 8 H- Figure 8 I represents the Monocle 3 trajectory analysis showing the dynamic changes in the distribution of CD8+ T cell subsets and SLC25A5 expression. Figure 8 J Schematic diagram illustrating the SLC25A5-mediated differentiation process from naive CD8+ T cells to CD8+ TOX and EXH subsets.

[0043] 2. Analysis of Cell-cell Communication

[0044] Cell Chat (version 1.6.1) was used to parse and interpret the intercellular communication networks extracted from the single-cell data and to classify them into various cell populations. The first step was to create a Cell Chat object using the RNA expression matrix and the associated cell information using the create Cell Chat function. Next, a ligand-receptor interaction database including secreted signals and ECM-receptors was used for further analysis. Communication probabilities were calculated using compute Commun Prob. Global communication patterns were identified using selectK, with nPatterns set to 4 to analyze inbound and outbound communications, see Figure 3-Figure 4 ,in, Figure 3 A- Figure 3 B represents the quantification and intensity of inter-subpopulation cell communication, Figure 3 C represents a graphical representation of the main signal sources and targets. Figure 3 D represents the main signal transmission mode, Figure 3 E represents the main signal receiving mode, Figure 4 A represents an individual representation of cell-cell communication in two tissue types, Figure 4 B represents the change in the extent or strength of the interaction in osteosarcoma, with red lines indicating increased expression and blue lines indicating decreased expression. Figure 4 C represents the quantitative and qualitative difference of the interaction index, Figure 4 D represents the comparative analysis of the main emitters and receivers in a two-dimensional framework, Figure 4 E represents the comparative analysis of signals emitted in different cell subsets. Figure 4 F represents the comparative analysis of signaling in different cell subsets. Figure 4 G represents the comparative analysis of the signals received by different cell subsets. Figure 4 H represents a comprehensive analysis of signaling dynamics within each cell subset.

[0045] 3. Identification and survival analysis of cellular senescence (CS)-related genes

[0046] The candidate genes were the intersection of CS-associated genes and genes overexpressed in malignant osteosarcoma cells compared with control osteoblasts. Using the TARGET dataset, univariate Cox proportional hazards regression analysis was performed on these genes to identify signature genes associated with prognosis. For survival analysis of individual genes, the survival package in R was used. Samples were divided into low-expression and high-expression groups based on the determined cutoff value of each gene expression. The survival results of the stratified expression groups were presented by Kaplan-Meier plots. The significance of the differences observed between the curves was evaluated using the Log-Rank test, and a P value below 0.05 indicated statistical significance. The results are shown in Figure 5 A- Figure 5 D, where Figure 5 A represents the screening process of five osteosarcoma-cell senescence genes. Figure 5 B represents the prognostic validation of SLC25A5 using the TARGET database. Figure 5 C represents UMAP analysis showing the differential expression of SLC25A5 in osteoblasts. Figure 5 D represents the differential expression of genes related to SLC25A5.

[0047] 4. Enrichment Analysis

[0048] Differentially expressed genes (DEGs) were identified by the FindMarkers function, with a threshold of Padj.P < 0.05 and logFC > 0.25. GO and KEGG analyses were performed using the clusterProfiler package, and the results are shown in Figure 5 E and Figure 5 F, GSVA and GSEA analyses were performed simultaneously using their respective packages, and the results are shown in Figure 2 , Figure 5 G and Figure 5 H, where Figure 2 A represents the cell clustering based on resolution, Figure 2 B- Figure 2 D UMAP projections distinguished clinical categories ( Figure 2 B) Sample variation ( Figure 2 C) and major osteoblast subsets ( Figure 2 D), Figure 2 E represents the gene ontology (GO) enrichment analysis of five osteoblast subpopulations. Figure 2 F represents the differentially expressed gene (DEG) volcano plot comparing osteoblasts in osteosarcoma and control bone tissues, with red highlighting genes that are highly expressed in osteosarcoma. Figure 2 G represents the comparison of the relative abundance of five osteoblast clusters in different samples.

[0049] 5. Assessment of Immune Cell Subtype Distribution

[0050] CibersortX and the average expression values ​​of the signature genes were used to analyze the infiltration of cell subtypes in the TARGET clinical cohort. Spearman correlation analysis revealed the association between SLC25A5 and different cell populations. Figure 6 ,in, Figure 6 A represents the immune cell infiltration analysis of each sample, Figure 6 B represents the distribution of various cell types in the sample, Figure 6 C represents the correlation analysis between SLC25A5 expression and cells in the tumor microenvironment.

[0051] 6. Chemotherapy drug sensitivity analysis

[0052] IC values ​​of 198 drugs were obtained from the Cancer Drug Sensitivity Gene Set (GDSC; https: / / www.cancerrxgene.org / ) and the Cancer Cell Line Encyclopedia (CCLE, https: / / sites.broadinstitute.org / ccle / ) using the R package oncoPredict. 50 The drug IC was explored by Spearman analysis. 50The correlation between the values ​​and the risk score was used to identify relevant drugs. The IC 50 The results were visualized using ggplot2 in R language to plot correlation analysis dot plots and lollipop plots. Figure 7 A- Figure 7 B.

[0053] 7. Molecular Docking Analysis

[0054] The three-dimensional structures of SLC25A5 protein and various drug molecules were obtained from the PubChem database. Preparation of protein and drug structures for docking analysis included removal of water molecules and addition of nonpolar hydrogen atoms using PyMOL and AutoDock4. Subsequently, appropriate docking parameters and grid box sizes were established. Molecular docking simulations were performed using AutoDock4, and the results were analyzed and visualized using PyMOL. The results are shown in Figure 7 C, Docking results show that the binding energy is lower than -2.0 kcal / mol indicating significant molecular interaction.

[0055] To identify potential traditional Chinese medicine (TCM) targets associated with SLC25A5, the Coremine Medical database was used to collect gene-related information, and TCMs significantly associated with this key gene were identified, including Eucommia ulmoides leaves, jujubes, and Eucommia ulmoides seeds (p<0.001). Using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the main active ingredients of high-frequency TCMs were screened, requiring oral bioavailability (OB) ≥ 30% and drug similarity (DL) ≥ 0.18. Molecular docking was then performed to verify the binding energy of all TCM monomers with SLC25A5, and 20 different docking simulations were performed for each monomer. The five monomers with the lowest binding energy were identified as jujuboside A_qt, L-stephanine, phyllotoxin, (-)-epigallocatechin-3-gallate, and α-amyrin, and the results are shown in Fig.13 A- Fig.13 B.

[0056] Furthermore, the inhibitory effects of these TCM monomers on the proliferation of MG63 / R and U2OS / R cell lines at different concentrations were evaluated, and the CCK8 method was used for 24 hours. The results showed that L-stephanine, phyllotoxin, (-)-epigallocatechin-3-gallate and α-amyrin all showed inhibitory effects on drug-resistant osteosarcoma cell lines when the drug concentration increased, while jujube saponin A_qt did not show a significant inhibitory effect. The results are shown in Fig.13 C- Fig.13 D. IC of phyllospermum indica 50The value is the lowest among these monomers. Given the significant binding interaction between these five TCM monomers and SLC25A5, we further studied whether they could affect the transcription and translation of SLC25A5 at half-maximal inhibitory concentration. The concentration of jujube saponin A_qt was 128 μM. These five monomers can reduce the transcription and translation of SLC25A5 to a certain extent. Among them, the inhibitory effect of linalool is the most significant. The results are shown in Fig.13 E- Fig.13 G.

[0057] HPLC grade phytosalisin (purity: 95.0%) was purchased from Sigma-Aldrich, and L-stepholidine, (-)-epigallocatechin-3-gallate, α-amyrin and jujuboside At were purchased from Master of Bioactive Molecules.

[0058] The molecular structure diagram of phylloxeratin can be found in Fig.13 H.

[0059] Example 2

[0060] In vitro experiments

[0061] 8. Establishment of Cisplatin-resistant Osteosarcoma Cell Line

[0062] Cisplatin-resistant osteosarcoma cell lines MG63 / CDDP and U-2OS / CDDP were developed from the MG63 and U-2OS parental cell lines. Parental cells were cultured in culture dishes and exposed to 1 μM cisplatin for 48 hours. After treatment, cells were allowed to resume growth in medium without cisplatin until robust growth was restored. The cisplatin dose was then increased stepwise, doubling the concentration each time, and repeated six times. Cell lines that were able to stably proliferate in the presence of cisplatin concentrations up to 32 μM were considered resistant, marking the successful establishment of drug-resistant strains.

[0063] 9. Quantitative Real-time PCR Analysis

[0064] Total RNA was isolated using TRIzol reagent (Invitrogen) and subsequently reverse transcribed using RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, USA). SYBR-Green assay mixtures were prepared and quantitative PCR analysis was performed using a Roche Light Cycler 480II instrument. To ensure the reproducibility of the experiment, the number of replicate wells for each sample was three. By comparing 2 -ΔΔCt The gene expression levels were determined by the method and the results are shown in Fig.13 E and Fig.13 F.

[0065] 10. Small interfering RNA (siRNA), short hairpin RNA (shRNA) and plasmid transfection

[0066] Three siRNAs and one shRNA sequence targeting SLC25A5 were carefully designed using the online siRNA and shRNA design tools on the Genepharma website. In the shRNA experiment, the designed shRNA sequences were cloned into the expression vector pLKO.1. Subsequently, the pLKO.1 shRNA plasmid, packaging plasmid, and envelope protein plasmid were co-transfected into HEK293T cells to produce and collect lentiviral particles. Osteosarcoma cell lines in the logarithmic growth phase were seeded into 6-well plates until they reached 70%-80% confluence, at which time the collected viral particles were used for cell transfection, and Polybrene was added to increase the transfection efficiency. After 12 hours of transfection, the cells were maintained in regular culture medium for 24 hours and then harvested. The successful transfection was verified by qRT-PCR. For the siRNA experiment, when the cell density reached 70%-80%, the cells in the logarithmic growth phase were seeded into 6-well plates and transfected using Lipofectamine 3000 transfection reagent. After 6 hours of transfection, the culture medium was replaced with normal culture medium and cultured for another 24 hours before collection for subsequent experiments. Cells were transfected with the indicated plasmids using Lipofectamine 2000 (Invitrogen, Life Technologies) according to the manufacturer's protocol. The transfection efficiency was optimized by adjusting the ratio of plasmid to Lipofectamine and the incubation time. The results are shown in Fig. 9 A and Fig. 9 C.

[0067] 11. EdU staining assay

[0068] The cells were incubated with 10 μM EdU staining solution (provided by Apexbio). After incubation, the cells were treated with 500 μl of Click reaction mixture including Cy3 azide for 30 minutes and kept at room temperature in a dark environment for reaction. After the reaction, the cells were washed with PBS and then stained with 1 ml of Hoechst 33342 dye with a final concentration of 5 μg / ml and incubated again at room temperature in a dark environment for 30 minutes. The stained specimens were then observed using a fluorescence microscope. See Fig.10 A.

[0069] 12.CCK-8 assay

[0070] Cell viability was assessed using the Cell Counting Kit-8 (CCK-8, Sigma-Aldrich, St. Louis, MO). Cell lines were seeded into 96-well plates following the instructions of the kit. Once cells were attached, they were exposed to the indicated drugs for the indicated time. 10 μl of CCK-8 solution was then added to each well and incubated for one hour in the dark. Cell viability was assessed by reading the optical density at a wavelength of 450 nm. Results are shown in Fig. 9 B, Fig.13 CD.

[0071] 13. Transwell assay and wound healing assay

[0072] For the Transwell assays, Corning inserts were used. For the invasion assays, these inserts were coated with Matrigel (Clontech, Madison, WI), whereas for the migration assays, uncoated inserts were used. For both assays, cells were placed in the upper chamber containing serum-free medium, whereas the lower chamber was filled with conditioned medium. After 12 h of incubation, cells that had migrated or invaded to the bottom were stained with crystal violet and quantified by light microscopy, see Fig.10 F.

[0073] In the wound healing assay, cells were cultured in 6-well plates until confluence. The cell monolayer was then manually scratched using a 20 μL pipette tip. Subsequently, the scratched monolayer was rinsed with PBS, and photographs were taken under a microscope at 0 h and 12 h after scratching, and the wound area was measured using a caliper.

[0074] 14. Western Blotting

[0075] Cell lysis was performed using radioimmunoprecipitation buffer (RIPA, Beyotime), and protein levels were quantified using the Beyotime BCA protein assay kit (Beyotime). Equal amounts of cell extracts were separated by 10% SDS-PAGE and subsequently transferred to PVDF membranes. To block nonspecific binding, the membranes were incubated in 5% skim milk, followed by incubation with primary antibodies overnight on a shaker at 4°C. Subsequently, the membranes were incubated with HRP-labeled secondary antibodies (1:2000, Proteintech) for one hour at room temperature. After thorough washing, the fluorescence signal was detected using the UVP ChemStudio system (Ultraviolet Products, USA), and semi-quantitative analysis was performed using VisionWorks software (Analytik Jena, Germany), see Fig. 9 D. Fig.10 E. Fig.10 G and Fig.13 G.

[0076] The primary antibodies used included: anti-SLC25A5 (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, Cell Signaling Technology), and anti-Beta-Tubulin (1:1000, Santa Cruz Biotechnology).

[0077] 15. Immunofluorescence

[0078] For immunofluorescence staining of paraffin-embedded sections, sections were dewaxed and rehydrated, followed by antigen retrieval in citrate buffer, pH 6.0. Antibody staining was then performed. First, sections were treated with antigen blocking solution for 20 min at room temperature to prevent nonspecific binding. They were then incubated in a 1:1000 dilution of SLC25A5 antibody solution at 4°C overnight. After thorough washing with PBST, sections were incubated with AlexaFluor 555-labeled IgG antibody at a dilution of 1:600 ​​for 20 min at room temperature in the dark. Finally, nuclear staining was performed with a medium containing DAPI, and images were captured using a fluorescence microscope, see Fig. 9 E.

[0079] 16. Assessment of Cell Apoptosis

[0080] In the si-SLC25A5 and si-NC groups, cells were digested with EDTA-free trypsin, centrifuged, resuspended in PBS, and stained with Annexin V-FITC and propidium iodide. Apoptosis assessment was performed immediately by flow cytometry in a dark environment, see Fig.10 C. The enzymatic activity of Caspase 3 was assessed by using a specific assay kit provided by Beyotime, see Fig.10D. The treated cells were lysed with the provided lysis buffer and the supernatant was collected after centrifugation. Subsequently, the cell lysate was mixed with Ac-DEVD-pNA (2mmol / L) and incubated at 37°C for 2 hours. After incubation, the absorbance at 405nm was determined by a microplate reader. For tissue apoptosis assessment, a TUNEL assay kit (Beyotime) was used according to the manufacturer's instructions. The process involves extracting tumor tissue from mice and preparing frozen sections. 50μL of a mixture of TdT and dUTP was applied to the sections and incubated for 60 minutes. After rinsing with PBS, the sections were blocked and observed using a fluorescence microscope.

[0081] 17. Soft Agar Colony Formation Assay

[0082] Soft agar colony formation assays were first performed by preparing a 0.6% agar solution to form a base layer in a 6-well plate. Subsequently, a 0.3% top agar was mixed with osteosarcoma cells in logarithmic growth phase that had been previously transfected with si-NC or si-SLC25A5 and suspended in culture medium to achieve a concentration of 1000 cells per 0.5 ml. This cell-infused agar layer was layered on top of the solidified base layer and incubated at 37°C with 5% CO 2 Incubate for two weeks in an environment of 40 °C. To assess the clonogenic potential, cell colonies were stained with crystal violet for 20 min and colony counts were performed using an inverted microscope. This procedure was repeated three times independently and the results are shown in Fig.10 B.

[0083] 18. Plate colony formation experiment

[0084] Osteosarcoma cells in the logarithmic growth phase were digested by trypsin and dispersed into a single cell suspension. 1000 cells were seeded in a 12-well culture plate for each experimental group. The cells were cultured for 14 days, the culture medium was changed every three days and the cell status was observed. After the clones were formed, the cells were rinsed once with PBS and fixed with paraformaldehyde for 15 minutes. The clones were then stained with crystal violet solution for 30 minutes, gently rinsed with running water, air-dried, and photographed. The results are shown in Fig.13 I.

[0085] 19. Flow cytometry analysis experiment

[0086] The cells were harvested by trypsinization and centrifuged to collect 1×10 510,000 live cells were collected and the supernatant was discarded. The cells were washed once with PBS and the supernatant was removed by centrifugation. A mixture of 1 ml DNA staining solution and 10 μl permeabilization solution was added and vortexed for 5-10 seconds to mix thoroughly. Subsequently, the mixture was incubated for 30 minutes at room temperature in the dark. Analysis was performed on a CytoFlex SRT flow cytometer (Beckman, USA) at a low sample flow rate, recording 10,000 target cells. Cell cycle data were analyzed using FlowJo (version 10.8.1), and the results are shown in Fig.13 J. Apoptosis was detected using the FITC Annexin V apoptosis detection kit (BD, UK). Cells were washed twice with PBS and resuspended in 1× binding buffer after trypsin digestion. 1×105 cells were collected and incubated with 5μl FITC Annexin V and 5μl PI for 15 minutes in the dark at room temperature (25°C). Apoptosis data were analyzed using CytExpert SRT on a CytoFlexSRT flow cytometer (Beckman, USA). The results are shown in Fig.13 K.

[0087] 20. Evaluating synergy using SynergyFinderPlus software

[0088] A variety of models were used to evaluate synergy, mainly to analyze the synergistic effect of the combination of glomerulone 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 tested 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 built 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 combined 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 Fig.14 .in, Fig.14 A represents the confidence interval diagram of cisplatin and glomerulone. Fig.14 B- Fig.14 E represents the synergistic graph of cisplatin and phyllospermuminosin combined treatment of U2OS / R cells when the synergistic effect was evaluated using BLISS, HSA, LOEWE and ZIP models, respectively. The synergistic score was calculated by Synergy Finder software. A positive synergistic score or a negative synergistic score indicated a synergistic effect and an antagonistic effect, respectively. Fig.14 F represents the synergistic measurement of cisplatin and 32 μM glomerulone at a given dose combination, Fig.14 G stands for Fig.14 The bar graph in F shows the recommended concentrations for the plate cloning experiment. Fig.14 H represents the drug concentration that inhibits the response by more than 90% and the synergy score.

[0089] Example 3

[0090] In vivo experiments

[0091] Six-week-old BALB / c nude mice were randomly divided into two groups: one group was injected subcutaneously with sh-SLC25A5 / U2-OS / R cells in the flank (sh-SLC25A5 group), and the other group was injected with sh-NC / U2-OS / R cells (shRNA-NC group). Each mouse was injected with approximately 2×10 6 Tumor cells were collected. Tumor volume was measured every two weeks using a caliper. When the tumor volume reached an average of approximately 50 mm 3 At 4 pm, mice were intraperitoneally injected with cisplatin (3 mg / kg every other day). Tumor volume was quantified by the following calculation formula: Volume (mm 3 )=(length×width 2 ) / 2. On day 30 after inoculation, mice were humanely sacrificed, and tumor samples were extracted for hematoxylin-eosin (H&E) staining and evaluated for SLC25A5 expression and TUNEL positivity, see Fig.11 ,in, Fig.11 A represents the representative images of subcutaneous tumor formation in nude mice in the sh-SLC25A5 group and the shRNA-NC group. Fig.11 B represents the tumor growth dynamics of nude mice in the sh-SLC25A5 group and shRNA-NC group, which are displayed by growth curves. Fig.11 C represents the comparative analysis of tumor mass between sh-SLC25A5 group and shRNA-NC group in nude mice. Fig.11 D represents the characterization of SLC25A5 expression in resected tumor specimens by immunofluorescence. Fig.11 E represents histological evaluation of resected tumor specimens stained with hematoxylin and eosin (H&E), Fig.11 F represents TUNEL expression analysis in tumor sections by immunofluorescence to determine apoptotic activity.

[0092] In the construction of the orthotopic model, 3-week-old female BALB / c nude mice were randomly divided into two groups: sh-SLC25A5 group and shRNA-NC group. Approximately 2×10 6 When the average tumor volume reached approximately 100 mm 3Mice received intraperitoneal injections of cisplatin (3 mg / kg every other day) at 4 ℃. Femurs and tibiae were then fixed in 4% paraformaldehyde and stored in PBS at 4 °C. Specimens were scanned using high-resolution microCT (Skyscan 1276, Skycan, Atsera, Belgium) at a resolution of 20.376 μm per pixel, 100 kV voltage, and 200 μA current. The region starting from 0.45 mm below the distal growth plate and extending 0.45 mm proximally was selected as the region of interest (ROI) for trabecular parameter analysis, 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 parameters such as total cross-sectional area medial to the endosteal membrane (Tt.Ar), cortical bone area (Ct.Ar), cortical area fraction (Ct.Ar / Tt.Ar), and cortical thickness (Ct.Th) were evaluated in a 0.2-mm section of the midshaft of the femur, and the results are presented in Fig.12 ,in, Fig.12 A- Fig.12 E represents the 3D reconstruction of the femur in the sh-NC group, including the anteroposterior and coronal sections of the entire femur ( Fig.12 A), side view and vertical section ( Fig.12 B), cross-sectional and 3D view of the distal femoral shaft ( Fig.12 C), mid-femoral cortical bone and 3D view ( Fig.12 D), and coronal section of the proximal tibia ( Fig.12 E); Fig.12 F- Fig.12 J represents the 3D reconstruction of the femur in the sh-SLC25A5 group, including the anteroposterior and coronal sections of the entire femur ( Fig.12 F), side view and vertical section ( Fig.12 G), cross-sectional and 3D view of the distal femoral shaft ( Fig.12 H), mid-femoral cortical bone and 3D view ( Fig.12 I), and coronal section of the proximal tibia ( Fig.12 J), Fig.12 K- Fig.12 P represents the quantitative diagram of trabecular bone microstructure parameters. Fig.12 Q- Fig.12 T represents the schematic diagram of changes in cortical bone microstructure parameters, *P<0.05, **P<0.01.

[0093] All human samples in this invention meet ethical requirements (ethical approval number:

[2023] CJ0051). Initially, a patient-derived tumor xenograft (PDX) model was established using 5-6 week-old NOD-SCID mice, see Fig.13O. Biopsied tumor fragments from patients with osteosarcoma were injected into the midline region of the back of mice. When the tumor reached 100-200 mm 3 When the tumor reached 5mm×5mm×5mm, the mice were killed (P0 generation). The tumor tissue was then minced and injected into the next mouse, and the development of P1 to P6 tumors continued. When the P6 tumor reached approximately 5mm×5mm×5mm, the tumor-loaded mice were randomly divided into several groups, including a control group, a globin group (30 mg / kg, dissolved in normal saline, and administered orally), a cisplatin group (administered according to the previous nude mouse tumorigenesis experiment), and a cisplatin plus globin combination group (30 mg / kg of globin was administered orally one hour before cisplatin injection, and the same dose of cisplatin as before). For results, see Fig.13 L- Fig.13 N and Fig.13 P- Fig.13 R, where Fig.13 L- Fig.13 In N, control represents the control group, which was injected with saline, and Phyllatin represents the treatment group, which was injected with phyllalisin, and the cells used were U2OS / R cells; Fig.13 P- Fig.13 In R, control represents the control group, which was injected with normal saline, and Phyllatin represents the treatment group, which was injected with phyllalisin, and the cells used were patient-derived cells.

[0094] 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. Application of SLC25A5 expression inhibitor and cisplatin in the preparation of anti-osteosarcoma drugs; The SLC25A5 expression inhibitor is phyllotoxin; The concentration of cisplatin is 32 μM, and the concentration of glomerulone is 50 μM.

2. The use according to claim 1, characterized in that: The medicine also includes auxiliary materials.

3. The use according to any one of claims 1-2, characterized in that: The medicine is an oral preparation or an injection preparation.

4. The use according to claim 3, characterized in that: The oral preparations include capsules, tablets or granules.

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