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Single-Cell Spatial–Temporal Analysis of ZNF451 in Mediating Drug Resistance and CD8+ T Cell Dysfunction
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Ning Tang1, 2, †, Woding Deng3, †, Yupeng Wu4, Zhixuan Deng5, Xin Wu6, *, Jianbin Xiong2, *, Qiangqiang Zhao7, 8, *
Research. Vol 7 Article ID 0530
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Research. Vol 7 Article ID 0530
• Research Article •
Single-Cell Spatial–Temporal Analysis of ZNF451 in Mediating Drug Resistance and CD8+ T Cell Dysfunction
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Ning Tang1, 2, †, Woding Deng3, †, Yupeng Wu4, Zhixuan Deng5, Xin Wu6, *, Jianbin Xiong2, *, Qiangqiang Zhao7, 8, *
Affiliations
  • 1Department of Orthopaedics, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • 2Department of Orthopaedics , Liuzhou Municipal Liutie Central Hospital, Liuzhou, Guangxi, China.
  • 3Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
  • 4Department of Spine Surgery, First Affiliated Hospital of University of South China, Hengyang, Hunan, China.
  • 5Institute of Cell Biology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
  • 6Department of Spine Surgery, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • 7Department of Hematology, Liuzhou People's Hospital affiliated to Guangxi Medical University, Liuzhou, Guangxi, China.
  • 8Department of Hematology, The Qinghai Provincial People's Hospital, Xining, Qinghai, China.
Published: 2024-11-12 doi: 10.34133/research.0530
Outline
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Cisplatin is widely used to treat osteosarcoma, but recurrent cases often develop resistance, allowing the disease to progress and complicating clinical management. This study aimed to elucidate the immune microenvironment of osteosarcoma, providing insights into the mechanisms of recurrence and identifying potential therapeutic strategies. By analyzing multiple single-cell and bulk RNA-sequencing datasets, we discovered that the SUMOylation-related gene ZNF451 promotes osteosarcoma recurrence and alters its immune microenvironment. ZNF451 was found to importantly enhance the growth, migration, and invasion of resistant cells while also reducing their sensitivity to cisplatin and lowering their apoptosis rate. Moreover, our data indicated that ZNF451 plays a crucial role in bone resorption and epithelial–mesenchymal transition. ZNF451 also regulates CD8+ T cell function, leading to their exhaustion and transition to the CD8T.EXH state. Additionally, β-cryptoxanthin has been identified as a potential therapeutic agent that inhibits osteosarcoma progression by targeting ZNF451. In summary, these findings highlight the critical role of ZNF451 in promoting osteosarcoma progression and underscore its potential as a therapeutic target and biomarker for osteosarcoma.

Ning Tang, Woding Deng, Yupeng Wu, Zhixuan Deng, Xin Wu, Jianbin Xiong, Qiangqiang Zhao. Single-Cell Spatial–Temporal Analysis of ZNF451 in Mediating Drug Resistance and CD8+ T Cell Dysfunction[J]. Research, 2024 , 7 (11) : 0530 . DOI: 10.34133/research.0530
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Introduction
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Osteosarcoma, most prevalent among children and young adults, is the most frequent primary bone cancer. However, its rarity affects the development of advanced treatments [1,2]. Since the 1980s, conventional treatment strategies, such as a combination of surgery and chemotherapy, have only modestly improved survival rates. This limited progress is largely due to the biological diversity and complexity of osteosarcoma [3,4]. Recent advances in molecular pathology have highlighted new avenues for understanding the molecular mechanism underlying osteosarcoma and identifying personalized treatment options [5]. Nonetheless, with the challenges of recurrence and metastasis, there is an imperative need for innovative therapeutic targets and strategies to enhance treatment efficacy and bolster long-term prognoses [6,7].
Addressing recurrent osteosarcoma highlights the need for an in-depth understanding of its pathogenesis and the identification of novel therapeutic targets. Although existing treatments struggle to address the disease's heterogeneity and complexity, they offer opportunities for the development of personalized therapies tailored to specific molecular profiles across diverse patient populations [2,8]. The immune microenvironment of osteosarcoma, composed of immune cells, cytokines, and signaling molecules, offers an intriguing avenue for research [6,9,10]. Given the pivotal roles of various immune cells in tumor growth, metastasis, and response to treatment, comprehensive research on their interactions in osteosarcoma could reveal new drugs that modulate immune response [11–14]. Enhancing current treatment protocols, including surgery and chemotherapy, and innovating new strategies that manipulate specific immune pathways could greatly benefit patients with osteosarcoma.
In recent years, the rapid advancement of single-cell sequencing technology has provided an unprecedented detailed perspective for uncovering tumor cell heterogeneity and microenvironmental interactions [10,15,16]. In osteosarcoma research, several research teams have successfully constructed single-cell resolution maps of osteosarcoma cells, substantially enhancing our understanding of the disease's complex biology [15,17,18]. Additionally, studies focusing on cellular communications within the tumor microenvironment and the identification of key genes have appreciably deepened our understanding of the role of cell signaling in osteosarcoma development [10,19,20]. Although these studies offer valuable insights into the biology and treatment challenges of osteosarcoma, research into recurrent osteosarcoma remains insufficient. Our study integrates advanced molecular and cellular biology techniques, including single-cell sequencing and bulk RNA sequencing (RNA-seq), to construct a single-cell resolution map of osteosarcoma. We focused on analyzing intercellular communication within the tumor microenvironment. By comparing the molecular characteristics of tumor tissues from patients with recurrent osteosarcoma with those of primary tumors, we aimed to further elucidate the mechanisms of intra- and intercellular communication in recurrent osteosarcoma and uncover potential therapeutic targets. Furthermore, this study identified SUMOylation-related genes closely associated with osteosarcoma, and through screening active ingredients in traditional Chinese medicine, innovative identification of potential therapeutic candidates with clinical translational value provides new scientific evidence for the development of personalized treatment and precision medicine strategies for osteosarcoma.
Materials and Methods
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Data collection and enhancement
This study leveraged the Gene Expression Omnibus (GEO) database to select single-cell RNA (scRNA) data from 2 osteosarcoma databases (GSE152048 [15] and GSE162454 [17]), an osteosarcoma-related lymphocyte dataset (GSE198896 [18]), and 2 control bone samples (GSE169396 [21] and GSE217792 [15]). Patient information and sequencing specifics were sourced from the supplementary material of related publications. We enriched the analysis by incorporating RNA-seq data and clinical records from the TARGET-OS project, a part of the TARGET initiative. The GSE21257 dataset [22], rich in survival data, served as a validation set for model reliability. Samples without comprehensive survival or clinical information were omitted. We processed the scRNA-seq data extensively using the Seurat R package (version 4.3.0) [23]. DoubletFinder was employed to remove potential doublet cells, and genes like hemoglobin and mitochondrial genes were selectively filtered out. All gene data were normalized and subjected to principal components analysis (PCA) for dimensionality reduction. To mitigate batch variations, we utilized the “Harmony” package. The FindVariableFeatures function was applied to the refined data to pinpoint highly variable genes (HVGs). Following cell categorization, advanced reduction techniques, uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE), were used for further analysis of the sample data. Marker genes were identified using the CellMarker database and relevant scholarly work. Ultimately, the pseudotemporal developmental trajectories of cells were visualized via Monocle 2 and Monocle 3.
Intercellular signaling analysis
In exploring the intricacies of intercellular communication across various cell clusters from scRNA-seq data, the CellChat toolkit (v1.6.1) [24] in R was utilized. We began by crafting a CellChat object with the “createCellChat” function, amalgamating RNA expression matrices and pertinent cell data. This was followed by incorporating interaction databases encompassing “Secreted Signaling”, “ECM-Receptor”, and “Cell-Cell Contact” to advance our signal analysis. The communication probabilities among cells were calculated using the “computeCommunProb” function. In the “selectK” procedure, our emphasis was on discerning global communication patterns, setting the nPatterns parameter to 2 to gauge both incoming and outgoing information flows.
SUMOylation-related gene identification and survival analysis
We initially identified a subset of candidate genes at the intersection of small ubiquitin-like modifier (SUMO)-related genes and differentially expressed genes (DEGs). Utilizing the GSE21257 dataset, we conducted univariate Cox proportional hazards regression analysis on these genes to pinpoint those significantly associated with prognosis. Individual gene survival analysis was performed using R's “survival” package. Samples were stratified into low- and high-expression groups based on predetermined expression cutoffs for each gene. Kaplan–Meier plots depicted the survival outcomes for these groups. The log-rank test was applied to evaluate the statistical significance of the differences observed between the survival curves.
Enrichment analysis
The FindMarkers function was utilized to identify DEGs with defined thresholds of Padj < 0.05 and logFC > 0.25. For Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, the clusterProfiler package was employed. Additionally, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were executed using their dedicated packages.
Evaluation of immune cell subtype distribution
The study utilized CibersortX and the average expression levels of signature genes for an in-depth analysis of cell subtype infiltration within the TARGET clinical cohort. Using Spearman correlation analysis, the study uncovered the relationships between ZNF451 and diverse cell populations.
Chemotherapy drug sensitivity analysis
This study delved into the influence of ZNF451 gene expression on chemotherapy efficacy. IC50 (median inhibitory concentration) values of 198 drugs, obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE), were analyzed using the oncoPredict package in R. Spearman's correlation was used to assess the association between the drugs' IC50 values and risk scores, thereby pinpointing pertinent drugs. For drugs with a correlation absolute value above 0.2, a comparative analysis of IC50 values between high- and low-expression groups was performed. Data visualization was achieved using the ggplot2 package in R.
Molecular docking investigation
The 3-dimensional (3D) structure of the ZNF451 protein and data on 4 drugs were acquired from the AlphaFold Protein Structure Database [25] and PubChem. Preprocessing steps, including the removal of water molecules and the addition of nonpolar hydrogen atoms, were conducted on the protein and drugs using PyMOL and AutoDock4. Molecular docking was performed with AutoDock4, following the establishment of suitable docking parameters and grid box dimensions. Docking outcomes were visualized via PyMOL, and binding energies below −2.0 kcal/mol were deemed indicative of successful docking.
Development of cisplatin-resistant osteosarcoma cell lines
Cisplatin-resistant cell lines, termed MG63/R and U-2OS/R, were established from the MG63 and U-2OS cell lines [26]. Initially, MG63 and U-2OS cells were seeded in separate culture dishes, then treated with 1 μM cisplatin, and cultivated for 48 h. After removing cisplatin, cells were cultured in a fresh medium until reaching optimal growth. The cisplatin concentration was incrementally increased, doubling in each of the 6 stages. The successful development of the cisplatin-resistant cell lines was confirmed once the cells demonstrated stable growth in cisplatin concentrations above 32 μM.
qRT-PCR quantitative analysis
Initially, total RNA was extracted from the samples using the TRIzol (Invitrogen) method, followed by reverse transcription of RNA using the PrimeScript RT reagent kit (TaKaRa, Japan). We prepared reaction mixtures according to the recommendations for the SYBR Green reagent by Takara and performed quantitative real-time polymerase chain reaction (qRT-PCR) analysis on the Roche LightCycler 480 II system. Each sample was set up with 3 replicates.
siRNA and shRNA design and transfection
We synthesized 3 unique small interfering RNAs (siRNAs) targeting ZNF451 and a singular short hairpin RNA (shRNA) sequence utilizing Genepharma's siRNA and shRNA design tool, based in Shanghai, China. For shRNA studies, these sequences were integrated into the pLKO.1 vector. The combination of the pLKO.1 shRNA plasmid, along with packaging and envelope protein plasmids, was cotransfected into human embryonic kidney (HEK) 293T cells, facilitating the production of lentiviral particles. Osteosarcoma cells in the logarithmic phase of growth were incubated in 6-well plates until they achieved 70% to 80% confluence, after which transfection was performed using the viral particles, supplemented with Polybrene to enhance efficiency. The cells were then maintained in a standard culture medium for 24 h after a 12-h transfection period before being harvested. The efficacy of transfection was confirmed using qRT-PCR. In the siRNA approach, cells at logarithmic growth were plated in 6-well plates and transfected with Lipofectamine 3000, following the manufacturer's instructions, when they reached 70% to 80% confluence. After a 6-h transfection period, the medium was switched to a regular culture medium, and the cells were cultured for an additional 24 h prior to collection for further analysis. (The sequences for sh-ZNF451 and si-ZNF451 are detailed in Table S1.)
5-Ethynyl-2′-deoxyuridine staining
In accordance with the supplier's guidelines, the 5-ethynyl-2′-deoxyuridine (EdU) labeling reagent from Apexbio was utilized for cell treatment. Following this, each well-received Click reaction solution containing Cy3 azide was incubated in darkness at room temperature. After phosphate-buffered saline (PBS) wash, Hoechst 33342 solution was applied, and the wells were incubated once more under the same conditions. After completing these steps, the samples were examined under a fluorescence microscope.
Evaluation of cell viability with CCK-8 assay
Cell viability was evaluated using Sigma-Aldrich's Cell Counting Kit-8 (CCK-8). In compliance with the manufacturer's protocol, cells were plated in a 96-well format and permitted to fully adhere prior to specific treatments. Subsequently, the CCK-8 reagent was introduced to each well and incubated under dark conditions at a designated temperature. Cell viability was ascertained by quantifying the absorbance at 450 nm.
Western blot analysis
Cell lysis was performed using Beyotime's radioimmunoprecipitation assay (RIPA) buffer, followed by protein concentration measurement with their Bicinchoninic Acid (BCA) Assay kit. Equal amounts of protein were subjected to 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred onto polyvinylidene difluoride (PVDF) membranes. Membranes were blocked using 5% skim milk and incubated with primary antibodies on a shaker at 4 °C. Subsequently, membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies from Proteintech at room temperature. After washing, signal detection was conducted using the UVP ChemStudio system (Ultraviolet Products, USA), and data analysis was performed with VisionWorks software (Analytik Jena, Germany). Antibodies used included anti-ZNF451 (1:1,000, Proteintech), anti-BCL2 (1:200, Abcam), anti-BAX (1:1,000, Abcam), anti-E-cadherin (1:5,000, Proteintech), anti-N-cadherin (1:2,000, Proteintech), anti-vimentin (1:1,000, Cell Signaling Technology), anti-SNAI1 (1:1,000, Cell Signaling Technology), and anti-β-tubulin (1:1,000, Santa Cruz Biotechnology).
Fluorescence immunostaining
In the process of fluorescence immunostaining, paraffin-embedded sections undergo dewaxing and rehydration. This is followed by antigen retrieval in a citrate buffer. To minimize nonspecific binding, the sections are then treated with an antigen-blocking solution at room temperature. Subsequently, they are incubated with a ZNF451 antibody at a low temperature. After washing, the sections are incubated in the dark at room temperature with an Alexa Fluor 555-conjugated immunoglobulin G (IgG) antibody. Finally, the sections are mounted using a medium containing 4′,6-diamidino-2-phenylindole (DAPI) and examined using fluorescence microscopy.
Detection of apoptotic cells
In this study, cells from both shRNA-ZNF451 and shRNA-NC groups were cultured in 6-well plates and subsequently harvested. After harvest, the cells underwent digestion, centrifugation, and concentration adjustment prior to staining. For the detection of apoptotic cells, we employed Annexin V-FITC (fluorescein isothiocyanate) and propidium iodide staining (BD, UK), followed by analysis through flow cytometry after dark incubation at room temperature. Moreover, as caspase-3 activity is an essential indicator of apoptosis, we utilized the Caspase 3 Activity Assay Kit to process the cells through lysis, centrifugation, and mixing, culminating in a subsequent incubation period. The detection after incubation was conducted using a microplate reader. To corroborate the occurrence of apoptosis, the TUNEL (terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end labeling) assay kit was employed for identifying apoptotic cells within tissue samples. These samples, extracted as tumor cryosections from experimental animals, were incubated, stained, washed, mounted, and finally examined under a fluorescence microscope.
Soft agar colony formation analysis
This study employed a soft agar colony formation assay to assess cellular clonal formation capabilities. Initially, a foundational agar layer was set in 6-well plates. Following the solidification of this layer, a top agar layer, infused with a predetermined quantity of logarithmically growing osteosarcoma cells (subjected to specific transfection protocols), was applied. This layering process facilitated cell culture in a stable temperature- and humidity-controlled incubator until colony formation was evident. To enhance colony visibility, crystal violet staining was utilized, enabling precise enumeration via microscopy. The quantification of these colonies provided a measure of the cells' clonal formation potential. Rigorous replication in the experimental design ensured the accuracy and reliability of the findings.
Flow cytometry for cell cycle
Cell cycle detection was performed using the Cell Cycle Staining Kit (MultiSciences, China). Cells were cultured to the logarithmic growth phase, washed with PBS buffer, and digested with 0.25% trypsin-EDTA solution. The cell suspension was collected and centrifuged, the supernatant was discarded, and the cells were resuspended in 1 ml of PBS. After discarding the supernatant, DNA Staining Solution and Permeabilization Solution were added, and the mixture was incubated in the dark for 30 min. Finally, the cells were analyzed using a flow cytometer (CytoFlex SRT, Beckman, USA).
Isolation of CD8+ T cells from osteosarcoma tissue
During osteosarcoma resection surgery, human osteosarcoma tissues were obtained directly from the operating room. After rinsing with PBS (Gibco), the tissues were minced into small pieces and digested in RPMI (Gibco) digestion buffer containing 25 μg/ml Liberase (Roche) and 50 μg/ml DNase (Sigma) at 37 °C for 30 min with shaking. Following mechanical disruption, a single-cell suspension was prepared by passing the sample through a 70-μm filter, and excess red blood cells were removed using a red blood cell lysis buffer. After 2 washes with PBS, the single cells were stained at 4 °C with APC-CY7 fixable viability dye (BD Pharmingen) for 15 min. The samples were then incubated with FcR blocker for 20 min, followed by the addition of cell surface markers. All samples were acquired on a cytoFLEX flow cytometer and analyzed using FlowJo [27]. The following cell surface markers were used: CD3-AF546, CD4-FITC, CD8-PerCP, and PD1-BV510 (Santa Cruz Biotechnology).
Isolation and culture of peripheral blood CD8+ T cells
Peripheral blood lymphocytes were first isolated from healthy donors using density gradient centrifugation with Lymphocyte Separation Medium (Corning). CD8+ T cells were then further isolated from the lymphocytes using the EasySep Direct Human CD8+ T Cell Isolation Kit (Stemcell Technologies). High CD8-expressing T cells were subsequently validated and sorted using flow cytometry. In all experiments, CD8+ T cells were cultured in X-VIVO complete medium supplemented with IL-2 (100 IU/ml, Miltenyi Biotec) and ImmunoCult Human CD3/CD28 T Cell Activator (10 μl/ml, InvivoGen) [27].
Colony formation assay
Osteosarcoma cells in the logarithmic growth phase were digested and prepared as a single-cell suspension, and 1,000 cells per well were seeded in a 12-well plate. Cells were cultured for 14 d. Then, cells were stained with an appropriate amount of crystal violet staining solution for 30 min.
Animal experiment
In this study, we utilized six 4-week-old female nonobese diabetic (NOD)–severe combined immunodeficient (SCID) IL2rγnull (NSG) mice, building upon our previous research [28]. These mice were randomly allocated into 2 groups: the sh-ZNF451 group and the sh-NC group, each comprising 3 mice. Each group received inoculations of U2OS/R cells, transfected with either sh-ZNF451 or sh-NC. Tumor sizes were periodically measured using calipers. Upon the tumors reaching a specified average volume, cisplatin (3 mg/kg, twice weekly) was administered intraperitoneally at a predetermined dosage and frequency [29]. The tumor volume was calculated using the following formula: length × width × width/2. Upon conclusion of the experiment, the mice were humanely euthanized, and their tumor tissues were harvested for additional analyses, which included hematoxylin and eosin (H&E) staining and assessments of ZNF451 and TUNEL expression.
Establishment of patient-derived xenograft model
All human-derived samples used in this study comply with the ethical requirements of the Ministry of Science and Technology of the People's Republic of China (ethical approval number: [2023]CJ0051). Tumor fragments from osteosarcoma patient biopsies were injected into the subcutaneous tissue of 6-week-old NOD-SCID mice. When the tumor volume reached 100 to 200 mm3, the mice were euthanized (P0), and the tumor tissue was minced and injected into another mouse, gradually establishing tumor passages from P1 to P6. Once the P6 generation tumors reached a certain size, tumor-bearing mice were randomly assigned to control and experimental groups [30,31]. The experimental group received a daily oral administration of β-cryptoxanthin at a dose of 10 mg/kg, dissolved in dimethyl sulfoxide (DMSO) [32–34]. β-Cryptoxanthin used in the experiment was purchased from Sigma (catalog number: C6368).
Micro-CT analysis
Following dissection, femurs and tibias were preserved in 4% paraformaldehyde and subsequently stored at 4 °C in PBS. The specimens were scanned using a high-resolution micro-CT (SkyScan 1276, SkyScan, Aartselaar, Belgium), operating at a resolution of 20.376 μm per pixel, 100-kV voltage, and 200-μA current. The region of interest (ROI) was selected starting 0.45 mm below the distal growth plate and extending proximally for 0.45 mm for trabecular parameter analysis [35], including bone mineral density (BMD), bone surface (BS), bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular number (Tb.N), and trabecular separation (Tb.Sp). Additionally, cortical bone parameters, such as total cross-sectional area inside the periosteal envelope (Tt.Ar), cortical bone area (Ct.Ar), cortical area fraction (Ct.Ar/Tt.Ar), and cortical thickness (Ct.Th), were assessed in a 0.2-mm midshaft section of the femur [36].
Results
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Single-cell analysis of the osteosarcoma microenvironment
In this study, we analyzed cell classification and gene distribution using data from 2 osteosarcoma databases (GSE152048 and GSE162454), a lymphocyte dataset associated with osteosarcoma (GSE198896), and 2 control bone samples (GSE169396 and GSE217792). To improve data accuracy, cells with elevated mitochondrial gene expression were omitted during the initial processing phase. We employed HVGs and PCA for dimensionality reduction and cell categorization (Fig. S1A and B). Furthermore, the influence of cell cycle-related genes on cell classification was investigated (Fig. S1C), along with an analysis of the correlations between mitochondrial genes, nfeature, erythrocyte-associated genes, and ncount (Fig. S1D to F). Comprehensive visualizations of the nfeature, ncount, and cell cycle-related scores for individual patients are provided (Fig. S1G to J).
Following meticulous quality control and batch-effect adjustments, we successfully integrated the data from 191,220 cells. Using the Clustree tool, we depicted the hierarchical structures of cell classifications at different resolutions (Fig. 1A). The t-SNE and UMAP plots, adjusted for batch effects, showed a uniform cell distribution, confirming sample uniformity (Fig. 1B). Using the marker genes cited in a previous study [15], we identified 11 unique cell subpopulations (Fig. 1C). An analysis of cell subgroup proportions across various samples highlighted the intratumoral diversity and shared characteristics of the lesions (Fig. 1D). Furthermore, the genes identified for each cell subgroup were categorized and anchored to their specific highly expressed genes (Fig. 1E).
Osteoblastic cell heterogeneity in osteosarcoma
This study focused on the heterogeneity of osteoblasts, a key cell subtype of osteosarcoma. We employed the cluster method to generate a multiresolution classification map of osteoblasts (Fig. 2A). Advanced visualization techniques such as t-SNE and UMAP were used to display the clinical information of the identified cells (Fig. 2B), their tissue origins (Fig. 2C), and 6 distinct osteoblastic subgroups (Osteoblastic1 to Osteoblastic6) (Fig. 2D). Each subgroup showed unique gene expression profiles and pathway enrichment patterns (Fig. 2E), suggesting diverse functional roles in vivo. GO enrichment analysis revealed several distinct characteristics of these subgroups. Osteoblastic1 osteosarcoma cells exhibited high levels of protein synthesis and ribosome biogenesis activity, as well as significant RNA processing and translation regulation capabilities, along with p53-mediated signaling regulation and molecular chaperone-mediated protein folding functions. These characteristics may confer high proliferative potential, anti-apoptotic ability, and adaptability to drug treatment. Osteoblastic2 cells displayed functions related to extracellular matrix remodeling, extracellular structure organization, collagen fiber formation, and cell-matrix adhesion, suggesting a potential role in tumor invasion, metastasis, and microenvironment regulation. Osteoblastic3 osteosarcoma cells are active in antigen processing and presentation, and leukocyte-mediated immune functions, indicating a potential role in modulating tumor immune responses, which may influence immune evasion mechanisms in the tumor. Osteoblastic4 osteosarcoma cells were enriched in cell-matrix adhesion, osteoblast differentiation, and ossification, implying a key role in bone tissue formation and tumor cell invasion. Osteoblastic5 osteosarcoma cells exhibit significant functions in antiviral responses, symbiont defense, and inhibition of viral replication, Osteoblastic6 cells showed significant activity in regulating T cell activation and differentiation, potentially affecting the immune microenvironment, tumor progression, and response to therapy (Fig. 2E). Notably, ZNF451 is highly expressed in Osteoblastic1 and Osteoblastic6, and these subgroups are closely associated with high proliferative potential, anti-apoptotic ability, drug treatment adaptability, and T cell regulatory functions (Fig. 2F). Notably, osteoblastic cells in recurrent osteosarcoma presented substantial variance in gene expression compared to those in primary osteosarcoma (Fig. 2G). Additionally, the proportion of each osteoblast subpopulation varied significantly across different disease types, with the ZNF451-high Osteoblastic1 subpopulation being scarce in normal tissues but significantly increased in osteosarcoma tissues. Notably, the proportion was much higher in recurrent osteosarcoma compared than in primary osteosarcoma. These findings support the crucial role of ZNF451 in the initiation, progression, and development of osteosarcoma (Fig. 2H).
Myeloid cell dynamics in osteosarcoma's immune microenvironment
Within the immune microenvironment of osteosarcoma, myeloid cells exhibit a dichotomous role: They contribute to both tumor growth and invasion, but also significantly influence immune response modulation and treatment outcomes. Through meticulous multiresolution analysis, we identified the ideal resolution for effectively differentiating various cell subpopulations (Fig. S2A). Using t-SNE technology, we mapped the spatial distribution of myeloid cells and observed no direct correlation with individual samples, which validated the removal of potential batch variances. We identified 6 distinct cell subpopulations (Fig. S2B) and used a dot plot to thoroughly examine the signature genes of these cells (Fig. S2C). GO enrichment analysis revealed the unique biological functions inherent to each myeloid cell type (Fig. S2D). Furthermore, a comparative analysis revealed notable transcriptional discrepancies between myeloid cells in primary and recurrent osteosarcoma (Fig. S2E), as well as significant variability distribution of myeloid cell distribution across osteosarcoma samples (Fig. S2E).
Analysis of tumor-infiltrating lymphocyte subgroups in osteosarcoma
Tumor-infiltrating lymphocytes (TILs) play a central role in the tumor microenvironment, significantly influencing immune regulation and the efficacy of therapeutic interventions. Extensive research have emphasized the critical role of TILs in the success of immunotherapy. Utilizing optimal resolution (Fig. S3A), our UMAP-based analysis clearly identified TIL marker genes, which are instrumental in differentiating various cell clusters (Fig. S3B to E and G). Additionally, a comparative study revealed pronounced transcriptional variance between TILs in recurrent and primary osteosarcoma tissues (Fig. S3F).
Investigating intercellular communication in the tumor microenvironment
Our study revealed extensive intercellular communication among various cell subgroups (Fig. 3A and B and Figs. S4 and S5). Within this network, osteoblastic cells were identified as the primary signal transmitters, whereas CD8+ T cells predominantly acted as signal receivers. Notably, mesenchymal stem cells (MSCs) uniquely acted as both significant senders and receivers of signals (Fig. 3C). Moreover, osteoblasts demonstrated the ability to interact with diverse cell subgroups via multiple signaling pathways (Fig. S6). To further elucidate these interactions, we identified key communication signals, including GALECTIN and MIF (Fig. 3D).
To explore intercellular interaction patterns more deeply, we integrated the CellChat approach with advanced pattern recognition techniques. Using the Silhouette method, we precisely identified and categorized various interaction patterns (Fig. S7A, B, E, and F). Sankey diagrams effectively demonstrated the connections among different cell subgroups, their communication networks, and associated signaling pathways (Fig. S7C and G). In addition, we conducted a comprehensive analysis of the evolving trends in key signaling pathways using pattern analysis techniques (Fig. S7D and H). Notably, cells including CD8+ T, CD4+ T, and regulatory T cells were identified to play pivotal roles in this communication network.
Differential intercellular communication in recurrent versus primary osteosarcoma
Our study further investigated the role of intercellular communication in osteosarcoma progression by comparing the communication networks in recurrent and primary osteosarcoma tissues. Although both forms exhibited intricate communication networks (Fig. 4A), significant differences were observed in the frequency and intensity of interactions between recurrent and primary forms (Fig. 4B). Analysis of communication among cell subgroups identified osteoblastic cells as the primary signal transmitters (Fig. 4C and D). Further investigation revealed elevated expression of key signaling molecules and pathways, such as epidermal growth factor (EGF) and interleukin-6 (IL-6), in recurrent osteosarcomas compared to their primary counterparts (Fig. 4E). These up-regulated signals showed significant associations with SUMOylation modifications, implying a potential role for SUMOylation in osteosarcoma recurrence [37–40]. Comparative analyses of signaling pathways, ligands, and receptor interactions further highlighted significant differences in signal dynamics and overall communication patterns relative to the control bone tissue (Fig. 4F to H and Fig. S8).
Role of ZNF451 in osteosarcoma
To explore the connection between SUMOylation and osteoblastic osteosarcoma, we examined 42 genes linked to SUMOylation [41,42]. By comparing these genes with 772 genes uniquely overexpressed in recurrent osteoblastic osteosarcoma, we identified 7 key genes, termed OS-SUMOs (Fig. 5A). The prognostic significance of OS-SUMOs was using the GSE21257 osteosarcoma dataset, where ZNF451, a SUMOylation-related gene, emerged as a key predictor of patient survival (Fig. S9). The expression dynamics of ZNF451 in osteosarcoma cells were extensively studied (Fig. 5B), stratifying cells into high and low ZNF451 expression categories based on their median expression levels, and disparities in gene expression are visually represented in a volcano plot (Fig. 5C).
GO enrichment analysis suggested that ZNF451 is involved in various cellular functions (Fig. 5D), impacting processes such as intracellular protein synthesis, enhancement of epithelial–mesenchymal transition (EMT), apoptotic signaling pathways (both promotion and inhibition of apoptosis), cellular activation, viral response, epithelial cell proliferation, and adaptation to hypoxia. ZNF451 was linked to components of the collagen-rich extracellular matrix, ribosomes and their subunits, focal adhesion sites, cell-matrix connections, and intracellular vesicle formation. In terms of molecular functions, ZNF451 may contribute to ribosomal structure, interact with DNA-binding transcription factors, bind to ubiquitin–protein ligases and integrins, and regulate several enzymatic processes. These findings suggest that ZNF451 plays a crucial role in osteosarcoma by influencing cellular proliferation, differentiation, EMT, and migration. Specifically, ZNF451 may enhance cell proliferation via intracellular protein synthesis, and affect tumor cell mobility and invasiveness by regulating EMT. Its effect on apoptotic signaling pathways may be crucial for determining cell fate, impacting osteosarcoma development and progression. Additionally, ZNF451's response to viral attacks and low-oxygen environments may also reflect its adaptability to the tumor microenvironment. Collectively, ZNF451 is hypothesized to be a critical regulator of the biological behavior of osteosarcoma, influencing tumor growth, cellular movement, invasion, and environmental stress responses.
The results from GSEA on “Pathways in Cancer” revealed that ZNF451 may play a substantial role in numerous cancer-related pathways (Fig. 5E). These findings suggest a strong association between ZNF451 expression and a range of cancer-related biological pathways that could significantly affect tumor development, progression, and response to treatment. ZNF451 likely participates in critical regulatory processes, including cell proliferation, apoptosis, cell cycle control, signal transduction, cell migration, and invasion, as well as in the dynamics of the tumor microenvironment.
The KEGG enrichment analysis (Fig. 5F) indicated that ZNF451 potentially affects ribosomal function, which may subsequently influence protein synthesis and tumor cell growth. ZNF451's involvement in antigen processing and presentation suggests a role in tumor immune evasion, altering the immune landscape. Its link to endocrine resistance pathways hints at a potential role in osteosarcoma's hormonal response and drug resistance strategies. Furthermore, its role in oxidative phosphorylation and the Leishmania pathway suggests that it influences energy metabolism. Collectively, these pathways suggest the critical involvement of ZNF451 in the evolution, progression, and therapeutic response in osteosarcoma.
GSVA (Fig. 5G) revealed a potential positive correlation between ZNF451 expression and several key biological pathways, including ubiquitin-mediated protein degradation and various cancer-associated pathways such as acute and chronic myeloid leukemia, endocrine cancers, non-small cell lung cancer, pancreatic cancer, prostate cancer, colorectal cancer, and glioma. Pathways such as WNT, insulin, mitogen-activated protein kinase (MAPK), and mechanistic target of rapamycin (mTOR) were also implicated, suggesting involvement in cellular proliferation, differentiation, metabolism, and signal transduction. Conversely, ZNF451 expression negatively correlated with various immune and metabolic pathways, including calcium signaling, antigen processing and presentation, and cytokine–cytokine receptor interactions, hinting at a regulatory role in immune responses and metabolic functions.
The integration of GO, KEGG, GSVA, and GSEA suggests that ZNF451 plays a complex and multifaceted role in osteosarcoma, influencing ribosomal function, protein synthesis, cell proliferation, apoptosis, EMT, and migration. Moreover, ZNF451 is implicated in a variety of cancer-related pathways such as ubiquitin-mediated protein degradation and the WNT and ERBB signaling pathways, highlighting its significance in cell growth, differentiation, and signal transduction, as well as immune evasion mechanisms in the tumor microenvironment.
ZNF451's role in osteosarcoma's immune microenvironment
To assess ZNF451's impact on the osteosarcoma immune microenvironment, we employed CibersortX, a technique that leverages marker-specific scRNA data, to analyze immune cell composition in TARGET-OS. The analysis, (Fig. 6A) revealed substantial heterogeneity in immune cell infiltration within osteosarcoma, with osteoblasts and chondroblasts being the primary constituents (Fig. 6B). ZNF451 was significantly associated with most of the infiltrating cells, suggesting its potential influence on osteosarcoma treatment outcomes by modulating the immune microenvironment, as shown in Fig. 6C. Consistent with previous studies, patients with high CD8+ T cell expression demonstrated a markedly improved prognosis (Fig. 6D).
Elevated ZNF451 expression and its potential role in augmenting chemotherapy resistance in osteosarcoma
Using the OncoPredict tool and the GSE21257 database, we assessed drug sensitivity in relation to ZNF451 expression, including IC50 values of several key chemotherapy agents, with correlations calculated via Spearman's method (Fig. 7A and B). These results suggested a potential link between increased ZNF451 expression and chemotherapy resistance in osteosarcoma. Molecular docking studies revealed a notable affinity between ZNF451 and primary osteosarcoma treatment drugs (Fig. 7C).
Role of ZNF451 in CD8+ T cell exhaustion dynamics
CD8+ T cells were isolated and categorized into 3 primary subtypes using precise resolution criteria: CD8.Navie.T (naïve CD8+ T cells), CD8T.TOX (cytotoxic CD8+ T cells), and CD8T.EXH (exhausted CD8+ T cells), as illustrated in Fig. S10A to C. Each subtype displayed unique gene expression profiles (Fig. S10D and E). Notably, ZNF451 was predominantly expressed in the CD8T.EXH cells (Fig. S10D). To analyze the spatiotemporal development of CD8+ T cells, we applied the Monocle 2 and Monocle 3 algorithms for a comprehensive pseudo-temporal analysis. Monocle 2 facilitated the organization of cells and visualization of their trajectory, as shown in Fig. 8A (cell clusters), Fig. 8B (cell classification), Fig. 8C (pseudo-temporal analysis results), Fig. 8D (ZNF451 expression levels), and Fig. 8E (dynamic changes in ZNF451 expression over time). ZNF451 expression initially increased, decreased, and then increased again during the pseudotemporal sequence (Fig. 8E and J). These single-cell trajectories elucidated the cell differentiation pathways based on varying gene expression profiles. Importantly, as depicted in Fig. 8B, at the critical branching point 1, naive CD8+ T cells (CD8.Navie.T) differentiated toward both cytotoxic CD8+ T cells (CD8T.TOX) and exhausted CD8+ T cells (CD8T.EXH), with the latter pathway showing a significant increase in ZNF451 expression (Fig. 8E and K). Moreover, the expression of ZNF451 during CD8+ T cell differentiation exhibited complex fluctuations, correlating with the incremental stages of CD8T.EXH cell proliferation (Fig. 8I and L). Monocle 3 analysis provided a more visual representation of the temporal and spatial progression from CD8.Navie.T to CD8T.TOX and CD8T.EXH, as well as the transition from CD8T.TOX to CD8T.EXH (Fig. 8F to I). Exhausted CD8+ T cells typically exhibit elevated PD1 expression; therefore, many previous studies have used this gene as a marker for identifying CD8+ T cells [43–45]. To validate the bioinformatic results, we used PD1 as a marker gene for exhausted T cells and sorted CD8T.EXH and CD8T.Non.EXH cells from patients with osteosarcoma samples using flow cytometry (Fig. 8L and Fig. S10F). Subsequently, we detected ZNF451 expression via Western blotting and found that CD8T.EXH cells exhibited significantly higher expression compared to CD8T.Non.EXH cells (Fig. 8M and N). To further explore whether ZNF451 induces CD8+ T cell exhaustion, we isolated CD8+ T cells from healthy individuals (Fig. S10G). Following ZNF451 overexpression, flow cytometry was used to assess the proportion of exhausted CD8+ T cells (Fig. S10H). The results showed that ZNF451 overexpression increased PD1 expression in CD8+ T cells, thereby inducing CD8+ T cell exhaustion (Fig. 8O and P). These findings indicated that ZNF451 regulates PD1 expression in CD8+ T cells, thereby modulating their activation state.
Evaluating ZNF451 expression across diverse osteosarcoma cell lines
In our study of ZNF451's role in osteosarcoma, a variety of osteosarcoma cell lines (U2OS, Saos-2, MG63, 143 B, MNNG, and SJSA-1) and normal osteoblasts (hFOB) were selected for mRNA expression analysis. qRT-PCR consistently showed elevated ZNF451 mRNA levels in all osteosarcoma cell lines compared to hFOB cells, with notably higher levels in U2OS and MG63 cells (n = 3; Fig. 9A).
We developed 2 cisplatin-resistant osteosarcoma cell variants, MG63/R and U2OS/R (n = 3; Fig. S11A), using a gradient adaptation approach. Initially, the IC50 values for MG63 and U2OS cells against cisplatin were 8.45 μM and 9.46 μM, respectively. After resistance, these values increased to 52.28 μM for MG63/R and 62.61 μM for U2OS/R. Remarkably, the proliferation rates of these drug-resistant cells significantly exceeded those of the original U2OS and MG63 cells at 72 and 96 h (n = 5; Fig. 9B). qRT-PCR and Western blot analyses showed that ZNF451 expression was markedly lower in MG63 and U2OS cells than in the resistant variants MG63/R and U2OS/R (n = 3; Fig. 9C and D and Fig. S11B). Immunofluorescence staining showed that ZNF451 was predominantly localized in the cytoplasm of osteosarcoma cells (Fig. 9E).
Impact of ZNF451 on osteosarcoma cells in vitro
To investigate ZNF451's function in osteosarcoma cells, we synthesized 3 targeted siRNAs against ZNF451, designated as si-ZNF451#1, si-ZNF451#2, and si-ZNF451#3, to examine ZNF451's function in osteosarcoma cells. Our data demonstrated that all 3 siRNAs successfully decreased ZNF451 expression, with si-ZNF451#1 being the most effective (n = 3; Fig. S11C). Consequently, si-ZNF451#1 was selected for further experiments involving ZNF451 silencing. Following cisplatin treatment, we noted a marked reduction in cell viability and IC50 values in the si-ZNF451-treated group compared to the si-NC control group (n = 5; Fig. S11D and E). Both EdU assays (n = 3; Fig. 10A) and cell colony formation assays (n = 3; Fig. 10B) indicated that ZNF451 suppression reduced the proliferation of MG63/R and U2OS/R cells. Furthermore, flow cytometry (n = 3), caspase-3 activity (n = 5), and Western blotting (n = 3) indicated an increase in apoptosis following ZNF451 knockdown (Fig. 10C to E). In vitro experiments also revealed that ZNF451 knockdown promoted EMT in drug-resistant osteosarcoma cell lines (n = 3; Fig. 10F). Additionally, transwell assays showed a significant reduction in the migratory and invasive abilities of MG63/R and U2OS/R cells after si-ZNF451 treatment (n = 3; Fig. 10G). Flow cytometry analysis indicated that ZNF451 knockdown significantly reduced the proportion of cells in the S phase and increased the number of cells in the G2-M phase. The knockdown did not significantly affect the proportion of cells in the G1-G0 phase in MG63/R cells, whereas in U2OS/R cells, there was a significant reduction in the number of cells in the G1-G0 phase (n = 3; Fig. 10H and I). Furthermore, we investigated whether ZNF451 overexpression enhanced cisplatin resistance in MG63 and U2OS osteosarcoma cells. As shown in Fig. S11F, the overexpression of ZNF451 in MG63 and U2OS cells enhanced their resistance to cisplatin. Following cisplatin treatment, both MG63 and U2OS cells overexpressing ZNF451 exhibited significantly increased proliferation and reduced apoptosis compared to the control group (Fig. S11G to I). These results further support the notion that ZNF451 enhances drug resistance in tumor cells.
Investigating ZNF451's function in osteosarcoma through in vivo models
We developed an animal model to assess ZNF451's influence on tumor development. Tumor volume significantly increased in mice treated with sh-NC U2OS cells and decreased notably in mice treated with sh-ZNF451- U2OS (n = 3; Fig. 11A and B). As depicted in Fig. 11C (n = 3), the tumor mass was considerably reduced in the sh-ZNF451 group compared to the control group. Immunofluorescence analysis revealed a substantial reduction in ZNF451 protein expression in the sh-ZNF451-treated mice (n = 3; Fig. 11D). H&E staining provided evidence of a reduced tumor growth rate in the sh-ZNF451 group (n = 3; Fig. 11E). Furthermore, TUNEL staining showed increased apoptosis in the sh-ZNF451 group, especially in the heightened count of TUNEL-positive cells marked with red fluorescence (n = 3; Fig. 11F). Considering these findings, our results suggest that ZNF451 silencing accelerates apoptosis in osteosarcoma cells and restricts their proliferation.
Investigating ZNF451's influence on bone resorption
To determine ZNF451's effect on the trabecular bone at the distal femoral metaphysis and the cortical bone at the midshaft of the femur, we utilized in vivo micro-computed tomography (CT) for 3D reconstruction of the femoral structure within the animal model (n = 3; Fig. 12A and F). Quantitative analysis of both trabecular and cortical bones revealed that, compared to the sh-NC group, the sh-ZNF451 group exhibited a significant decrease in bone loss. Notably, bone mass increased in both trabecular and cortical regions in the sh-ZNF451 group (Fig. 12B to D and G to I). Relative to the sh-NC group, the sh-ZNF451 group exhibited marked increases in BMD, BS, BV/TV, Tb.Th, and Tb.N in the trabecular bone of the distal femoral metaphysis, along with a significant reduction in Tb.Sp (Fig. 12K to P). Furthermore, in the cortical bone at the midshaft of the femur, the sh-ZNF451 group showed a considerable increase in Tt.Ar, Ct.Ar, Ct.Ar/Tt.Ar, and Ct.Th (Fig. 12Q to T). Additionally, an increase in bone mass was noted in the trabecular bone of the proximal tibia in the sh-ZNF451 group compared to that in the sh-NC group (Fig. 12E and J).
β-Cryptoxanthin may inhibit malignant behavior in cisplatin-resistant osteosarcoma cells by targeting ZNF451
To identify potential traditional Chinese medicine (TCM) interactions with ZNF451, we used the Coremine Medical database (https://www.pubgene.com/coremine-medical/) to retrieve relevant information on this gene and identify significantly associated TCMs. We screened major active components of frequently identified TCMs (statistical frequency ≥2) using the TCM Systems Pharmacology Database and Analysis Platform (TCMSP), focusing on oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18. Molecular docking was performed to test the binding energies of all TCM monomers with ZNF451, with each monomer undergoing 3 independent docking experiments, and the results were visualized (Fig. S12). Finally, we identified the 5 TCM monomers with the lowest binding energies: β-cryptoxanthin, campesterol, cyanidin, loliolide, and δ-carotene (Fig. 13A). We treated MG63/R and U2OS/R cell lines with these monomers at different concentrations for 72 h and assessed the inhibition of cell proliferation using a CCK-8 assay. The results showed significant inhibitory effects on resistant osteosarcoma cells with increasing drug concentrations, with β-cryptoxanthin showing the most pronounced inhibition in both cell lines (n = 5; Fig. 13B and C). Subsequent experiments used the IC50 value of each monomer in subsequent experiments to explore their potential mechanisms of action in inhibiting tumor cell activity. Interestingly, qRT-PCR and Western blot analyses revealed that β-cryptoxanthin significantly inhibited ZNF451 transcription and translation in MG63/R and U2OS/R cell lines, while other drugs had minimal effects on this gene (n = 3; Fig. 13D to F).
The colony formation assay showed a gradual decrease in cell proliferation with increasing concentrations of β-cryptoxanthin (n = 3; Fig. 13G). Treatment with IC50 concentrations of β-cryptoxanthin significantly arrested the cell cycle, with a notable increase in G2-M phase cells and a decrease in S and G1 phase cells (n = 3; Fig. 13H and I). Additionally, β-cryptoxanthin promoted apoptosis in a concentration-dependent manner in resistant cell lines (n = 3; Fig. 13J and K). Next, we established a patient-derived xenograft (PDX) mouse model. Once tumors reached an appropriate size, PDX mice were treated with β-cryptoxanthin. After 2 weeks of continuous treatment, β-cryptoxanthin significantly inhibited tumor growth compared to the control group (n = 3; Fig. 13L). Moreover, both tumor volume and weight were significantly reduced in the treatment group compared to the control group (n = 3; Fig. 13M and N). Cotreatment of MG63/R and U2OS/R cells with IC50 concentrations of β-cryptoxanthin and cisplatin significantly reduced the proliferation rate (n = 3; Fig. 13O). These findings suggest that β-cryptoxanthin may enhance the sensitivity of cisplatin-resistant osteosarcoma cell lines to cisplatin by targeting ZNF451.
Discussion
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Addressing recurrent osteosarcoma remains a significant challenge in the clinical setting [46,47]. Although advancements have been made in the treatment of localized osteosarcoma, recurrence typically results in reduced treatment efficacy and a corresponding deterioration in patient prognosis [48]. Tumor recurrence is closely linked to cellular drug resistance, complicating treatment [49,50]. Cisplatin, a standard chemotherapeutic agent for osteosarcoma, is often rendered less effective by the development of drug resistance, presenting a major hurdle to improving patient survival rates [51,52]. Several studies have explored the causes of cisplatin resistance and potential therapeutic approaches. For example, Wang et al. [53] revealed that the up-regulation of miR-1293 enhances cisplatin-induced apoptosis in osteosarcoma cells by inhibiting TIMP1 and its downstream Notch1/Hes1 and TGFBR1/Smad2/3 pathways. Tang et al. [54] found that Sestrin2 enhances chemotherapy resistance in osteosarcoma by enhancing autophagy and inhibiting apoptosis. Additionally, ursolic acid enhances cisplatin-induced DNA damage in osteosarcoma cells by promoting ferritinophagy and ferroptosis, thereby synergistically reducing drug resistance and inhibiting tumor growth. He et al. [55] explored an innovative strategy using targeted arsenene nanosheets (Her2-ANs@CDDP) to overcome cisplatin resistance in osteosarcoma cells. However, the specific mechanisms underlying cisplatin resistance are not fully understood. This underscores the urgent need for innovative treatment approaches and the identification of novel therapeutic targets to enhance patient survival rates and quality of life.
In the pursuit of new cancer treatments, the role of SUMOylation, a pivotal posttranslational modification process, is not well understood [56]. SUMOylation involves the covalent addition of SUMOs to specific proteins, which in turn affects their function, distribution, and stability [41,42]. This process is instrumental in key cellular functions, such as the regulation of gene expression, DNA repair, and cell cycle governance [57]. During cancer progression, aberrant SUMOylation patterns, resulting from either overactivation or malfunction, are intricately linked to tumor initiation, growth, aggression, recurrence, and metastasis. Therefore, targeting abnormal SUMOylation has emerged as a crucial area of cancer research and presents novel opportunities for therapeutic intervention [58].
With the support of single-cell technologies, researchers can analyze tumor heterogeneity and complex intercellular interactions within the tumor microenvironment with unprecedented resolution [59]. This technology allows for the in-depth exploration of gene expression differences among cell populations, revealing the specific roles of different cell types in disease progression and identifying potential drug targets. For example, Liu et al. [60] revealed the synergistic effects of 11-keto-β-boswellic acid and Z-guggulsterone in treating ischemic stroke using single-cell transcriptomics, identifying Spp1 as a key target of KBA-Z-GS. By analyzing multiple single-cell and bulk sequencing datasets, we compared primary and recurrent osteosarcomas, revealing a significant up-regulation of divergent signaling pathways in recurrent osteosarcomas, particularly those associated with SUMOylation. Through an in-depth analysis of osteosarcoma samples using single-cell data, we identified dysregulation or functional abnormalities of the SUMOylation-related gene ZNF451, which correlated positively to the recurrence and prognosis of osteosarcoma. ZNF451 is a zinc-finger domain-containing protein belonging to the SUMO E3 ligase family [61]. It was initially discovered in the promyelocytic leukemia (PML) protein nuclear bodies [61–63]. Multiple studies have shown that ZNF451 regulates protein stability through SUMOylation, thereby promoting tumor progression [64,65]. For instance, ZNF451 interacts with SLUG, facilitating SLUG-mediated CCL5 transcription, thereby driving the development of triple-negative breast cancer [65]. By employing enrichment analysis methods such as GO, KEGG, GSVA, and GSEA, we found that the gene is enriched in multiple pathways related to cancer progression, suggesting that ZNF451 might play a crucial role in reshaping the tumor immune microenvironment and cancer development. These enrichment results are consistent with those of previous studies on ZNF451 [64–66].
In both in vivo and in vitro analyses, we explored the effects of ZNF451 on osteosarcoma development and progression. Our findings revealed that ZNF451 is overexpressed in osteosarcoma cell lines, with an even more pronounced increase in cisplatin-resistant variants. Previous studies have suggested that ZNF451-mediated TOP2cc repair pathway may help tumor cells adapt to treatment with TOP2 inhibitors during chemotherapy [66,67]. This adaptation can lead to tumor cell resistance to chemotherapeutic drugs and can indirectly promote cancer progression. Overexpression of ZNF451 in cisplatin-resistant osteosarcoma cell lines may be associated with enhanced tumor drug resistance.
Apoptosis plays a crucial role in increasing chemosensitivity [53]. Previous studies have shown that ZNF451 inhibits apoptosis of human pancreatic ductal adenocarcinoma cells [64]. In this study, we found that ZNF451 reduced the sensitivity of parental osteosarcoma cell lines to cisplatin, thereby inhibiting apoptosis. In contrast, ZNF451 knockout in cisplatin-resistant osteosarcoma cell lines significantly increased apoptosis. Moreover, ZNF451 stabilizes TWIST2 via SUMOylation, which promotes EMT [68]. In this study, we found that ZNF451 knockout significantly reduced N-cadherin expression and increased E-cadherin expression in resistant osteosarcoma cell lines, leading to enhanced cell adhesion. Additionally, the ZNF451 knockout significantly weakened the migration and invasion abilities of these cells. Studies have indicated that N-cadherin acts as a tumor suppressor in osteosarcoma cells, with its reduced expression typically associated with decreased cell adhesion [53,69]. Lower cell adhesion improves drug permeability, thereby increasing sensitivity to chemotherapy [53,70]. Therefore, our findings support the mechanism by which ZNF451 regulates the expression of cell adhesion-related proteins, weakens cell adhesion, promotes cell migration and invasion, and reduces sensitivity to cisplatin. Collectively, these results reveal the key role of ZNF451 in regulating osteosarcoma cell sensitivity to chemotherapeutic drugs.
Osteosarcoma cells secrete factors that promote bone resorption, stimulating osteoclast differentiation and activity, which, in turn, drives bone dissolution and facilitates tumor growth, invasion, and distant metastasis [71]. These cells promote osteoclast differentiation and activation by secreting specific osteoclast-stimulating factors such as RANKL, further accelerating bone tissue breakdown [71–74]. According to the micro-CT analysis, silencing of ZNF451 demonstrated notable anti-resorptive effects. This was evidenced by significant increases in BMD, bone surface area, BV/TV, and Tb.Th, Tb.N, Tt.Ar, Ct.Ar, Ct.Ar/Tt.Ar, and Ct.Th, along with a marked reduction in Tb.Sp. These results underscore the crucial role of ZNF451 in sustaining osteosarcoma malignancy and its potential as a predictive biomarker for the biological behavior and progression of the disease.
TCM is considered a valuable source of candidate small-molecule drugs because of their diverse bioactivities [75]. Many natural products employed in TCM exhibit significant anticancer effects, including the inhibition of tumor proliferation and angiogenesis, induction of apoptosis, modulation of autophagy, reversal of multidrug resistance, regulation of immune balance, and enhancement of chemotherapy efficacy [76]. The antitumor effects of β-cryptoxanthin are primarily attributed to its antioxidant and antiproliferative properties [77,78]. Studies have shown that β-cryptoxanthin induces apoptosis and inhibits the proliferation and migration of cancer cells by modulating various cell signaling pathways [32]. Additionally, β-cryptoxanthin has demonstrated significant anticancer effects in animal models [34,79]. However, its role in osteosarcoma remains unclear. Therefore, we investigated its effects on cisplatin-resistant osteosarcoma cells.
Our study demonstrated that β-cryptoxanthin significantly reduced the expression level of ZNF451, thereby inhibiting the malignant phenotype of cisplatin-resistant osteosarcoma cells. Furthermore, β-cryptoxanthin treatment not only reduced cell proliferation and caused G2-M phase cell cycle arrest but also induced apoptosis in resistant osteosarcoma cell lines. These in vitro findings were validated in a PDX mouse model, where β-cryptoxanthin significantly inhibited tumor growth. Additionally, β-cryptoxanthin synergized with cisplatin, enhancing the sensitivity of resistant osteosarcoma cell lines to cisplatin. These results suggest that β-cryptoxanthin, by regulating ZNF451 expression, not only inhibits the malignant behavior of cisplatin-resistant osteosarcoma cells but also increases their sensitivity to cisplatin, highlighting its potential application in osteosarcoma treatment.
CD8+ T cells, which are crucial effectors in the immune response, specifically target and eliminate cells [80]. However, within the intricate immune microenvironment of tumors, tumor-directed CD8+ T cells often undergo functional exhaustion, which is characterized by diminished effector functionality and reduced proliferation [81,82]. This state is important in the progression of osteosarcoma and treatment resistance. In our study, we utilized pseudo-temporal analysis to investigate the dynamic shifts in CD8+ T cells and identified significant up-regulation of ZNF451 in CD8+ TEXH cells. This observation implies that ZNF451 may be involved in the transition of naive CD8+ T cells to CD8+ TEXH cells, thereby affecting the evolving immune microenvironment in osteosarcoma. To validate the results of the bioinformatics analysis, we employed experimental methods to demonstrate that ZNF451 is highly expressed in exhausted CD8+ T cells within tumor tissues. More importantly, our experiments demonstrated that ZNF451 not only exhibits high expression in these cells but also regulates PD1 expression in CD8+ T cells, thereby inducing their exhaustion and playing a key role in promoting this process. These findings further enhance our understanding of the role of ZNF451 in the tumor immune microenvironment, suggesting that ZNF451 may influence tumor immune evasion and the development of therapeutic resistance by regulating the exhaustion of CD8+ T cells.
ZNF451, a key molecule, shows great potential for clinical applications in osteosarcoma. First, high ZNF451 expression is closely related to chemotherapy resistance and could serve as an important biomarker for predicting treatment responses. These findings may assist clinicians in formulating personalized treatment plans based on ZNF451 expression levels, particularly for managing drug-resistant osteosarcoma. Second, ZNF451 holds potential as a therapeutic target for reversing tumor cell resistance and enhancing the efficacy of chemotherapy. Notably, β-cryptoxanthin has shown the potential to down-regulate ZNF451, making it a promising candidate for targeted interventions. Targeting ZNF451 with β-cryptoxanthin may offer a novel therapeutic approach to overcome cisplatin resistance, bringing hope to patients with cisplatin-resistant osteosarcoma. As further research continues to explore the function and mechanisms of ZNF451, this molecule is expected to play a significant role in the personalized treatment of osteosarcoma, offering new hope for improving patient outcomes.
Despite the significant findings of our study, several limitations remain. First, although advanced technologies such as single-cell sequencing and large-scale RNA-seq were employed, there is still room for improvement in the breadth and depth of the analysis. Second, while the study identified the critical role of ZNF451 in osteosarcoma progression and drug resistance, the precise mechanisms by which ZNF451 contributes to tumor resistance are not yet fully understood. Although we observed that ZNF451 inhibits apoptosis and promotes proliferation of osteosarcoma cells both in vitro and in vivo, the specific molecular pathways or signaling networks through which it induces resistance require further investigation. Future research should explore the interaction between ZNF451 and other known resistance, anti-apoptotic, and proliferative mechanisms to clarify its exact role in the development of tumor resistance and progression, which will provide a theoretical basis for new therapeutic strategies. Additionally, the mechanisms by which ZNF451 induces osteolysis remain unclear. While this study found that ZNF451 is associated with osteolysis, further investigation is needed to determine how it drives this process by regulating the balance between bone resorption and formation. Elucidating the role of ZNF451 in osteolysis will shed light on the biological basis of bone destruction in osteosarcoma patients and offer new therapeutic targets for clinical intervention. We also found that ZNF451 promotes CD8+ T cell exhaustion by inducing PDL1 expression, but the exact mechanism by which it drives the conversion of CD8+ T cells into an exhausted phenotype remains unclear and requires further investigation. Finally, considering the clinical translational potential of our findings, further drug optimization and experimental validation of β-cryptoxanthin are needed to ensure its safety and efficacy.
Conclusion
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This study elucidates the critical role of ZNF451 in osteosarcoma cells. ZNF451 significantly enhanced the growth, migration, and invasion abilities of resistant cells while reducing their sensitivity to cisplatin and apoptosis rates. Moreover, ZNF451 plays a crucial role in promoting osteolysis, contributing to tumor progression through bone resorption. Our research also indicated that ZNF451 regulates CD8+ T cell function, leading to their exhaustion and transition to the CD8T.EXH state, thereby disrupting the immune balance in the osteosarcoma microenvironment. Concurrently, β-cryptoxanthin may inhibit the malignant phenotype of cisplatin-resistant osteosarcoma cells by down-regulating ZNF451 expression. Drug resistance in osteosarcoma cells results from interactions between multiple molecular mechanisms and pathways. Further comprehensive research is required to fully understand the effect of ZNF451 on osteosarcoma resistance. Such studies are crucial for identifying new therapeutic targets and providing a solid scientific basis for the development of effective treatments against osteosarcoma resistance. Future research should aim to elucidate the complex interactions between ZNF451 and osteosarcoma cells, providing deeper insights that will enable optimization of clinical treatment strategies.
Funding
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  • National Natural Science Foundation of China (82260365)
  • National Natural Science Foundation of China (82372315)
  • Project of Kunlun Elite, High-End Innovation and Entrepreneurship Talents of Qinghai Province(2021 No. 13)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2023-ZJ-716)
  • Postgraduate Scientific Research Innovation Project of Hunan Province(CX20230296)
References
Less
1.
Meltzer PS, Helman LJ. New horizons in the treatment of osteosarcoma. N Engl J Med. 2021;385(22):2066–2076.
2.
Grünewald TGP, Alonso M, Avnet S, Banito A, Burdach S, Cidre-Aranaz F, Di Pompo G, Distel M, Dorado-Garcia H, Garcia-Castro J, et al. Sarcoma treatment in the era of molecular medicine. EMBO Mol Med. 2020;12(11):33.
3.
Gill J, Gorlick R. Advancing therapy for osteosarcoma. Nat Rev Clin Oncol. 2021;18(10):609–624.
4.
Wen Y, Tang F, Tu CQ, Hornicek F, Duan ZF, Min L. Immune checkpoints in osteosarcoma: Recent advances and therapeutic potential. Cancer Lett. 2022;547:14.
5.
Chen YQ, Liu RZ, Wang W, Wang C, Zhang N, Shao XJ, He QJ, Ying MD. Advances in targeted therapy for osteosarcoma based on molecular classification. Pharmacol Res. 2021;169:105684.
6.
Wu CW, Gong SM, Duan YJ, Deng C, Kallendrusch S, Berninghausen L, Osterhoff G, Schopow N. A tumor microenvironment-based prognostic index for osteosarcoma. J Biomed Sci. 2023;30(1):23.
7.
Whelan JS, Davis LE. Osteosarcoma, chondrosarcoma, and chordoma. J Clin Oncol. 2018;36(2):188–193.
8.
DePeaux K, Delgoffe GM. Metabolic barriers to cancer immunotherapy. Nat Rev Immunol. 2021;21(12):785–797.
9.
Tian H, Cao J, Li B, Nice EC, Mao H, Zhang Y, Huang C. Managing the immune microenvironment of osteosarcoma: The outlook for osteosarcoma treatment. Bone Res. 2023;11(1):11.
10.
Liu WJ, Hu HZ, Shao ZW, Lv X, Zhang ZC, Deng XT, Song QC, Han Y, Guo T, Xiong LM, et al. Characterizing the tumor microenvironment at the single-cell level reveals a novel immune evasion mechanism in osteosarcoma. Bone Res. 2023;11(1):4.
11.
Laumont CM, Banville AC, Gilardi M, Hollern DP, Nelson BH. Tumour-infiltrating b cells: Immunological mechanisms, clinical impact and therapeutic opportunities. Nat Rev Cancer. 2022;22(7):414–430.
12.
Tay C, Tanaka A, Sakaguchi S. Tumor-infiltrating regulatory t cells as targets of cancer immunotherapy. Cancer Cell. 2023;41(3):450–465.
13.
Christofides A, Strauss L, Yeo A, Cao C, Charest A, Boussiotis VA. The complex role of tumor-infiltrating macrophages. Nat Immunol. 2022;23(8):1148–1156.
14.
Cózar B, Greppi M, Carpentier S, Narni-Mancinelli E, Chiossone L, Vivier E. Tumor-infiltrating natural killer cells. Cancer Discov. 2021;11(1):34–44.
15.
Zhou Y, Yang D, Yang QB, Lv XB, Huang WT, Zhou ZH, Wang YL, Zhang ZC, Yuan T, Ding XM, et al. Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma. Nat Commun. 2020;11(1):2567.
16.
Zheng XJ, Liu X, Zhang XX, Zhao ZG, Wu WC, Yu SJ. A single-cell and spatially resolved atlas of human osteosarcomas. J Hematol Oncol. 2024;17(1):71.
17.
Liu Y, Feng WY, Dai Y, Bao MY, Yuan ZC, He MW, Qin ZJ, Liao SJ, He JL, Huang Q, et al. Single-cell transcriptomics reveals the complexity of the tumor microenvironment of treatment-naive osteosarcoma. Front Oncol. 2021;11:709210.
18.
Cillo AR, Mukherjee E, Bailey NG, Onkar S, Daley J, Salgado C, Liu DY, Ranganathan S, Burgess M, Sembrat J, et al. Ewing sarcoma and osteosarcoma have distinct immune signatures and intercellular communication networks. Clin Cancer Res. 2022;28(22):4968–4982.
19.
Li RQ, Yan L, Jiu J, Liu HF, Li DJ, Li XK, Zhang J, Li SY, Fan ZJ, Lv Z, et al. Psme2 offers value as a biomarker of m1 macrophage infiltration in pan-cancer and inhibits osteosarcoma malignant phenotypes. Int J Biol Sci. 2024;20(4):1452–1470.
20.
Gao WJ, Zhou J, Huang JT, Zhang ZG, Chen WQ, Zhang RH, Kang TB, Liao D, Zhong L. Up-regulation of RAN by MYBL2 maintains osteosarcoma cancer stem-like cells population during heterogeneous tumor generation. Cancer Lett. 2024;586:216708.
21.
Qiu X, Liu Y, Shen H, Wang Z, Gong Y, Yang JX, Li XH, Zhang HX, Chen Y, Zhou C, et al. Single-cell RNA sequencing of human femoral head in vivo. Aging. 2021;13(11):15595–15619.
22.
Buddingh EP, Kuijjer ML, Duim RAJ, Bürger H, Agelopoulos K, Myklebost O, Serra M, Mertens F, Hogendoorn PCW, Lankester AC, et al. Tumor-infiltrating macrophages are associated with metastasis suppression in high-grade osteosarcoma: A rationale for treatment with macrophage activating agents. Clin Cancer Res. 2011;17(8):2110–2119.
23.
Hao YH, Hao S, Andersen-Nissen E, Mauck WM III, Zheng SW, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573–3587.e29.
24.
Jin SQ, Guerrero-Juarez CF, Zhang LH, Chang I, Ramos R, Kuan CH, Myung P, Plikus MV, Nie Q. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088.
25.
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Zídek A, Potapenko A, et al. Highly accurate protein structure prediction with alphafold. Nature. 2021;596(7873):583–596.
26.
Xin W, Tang N, Zhao QQ, Xiong JB. Spatiotemporal evolutionary process of osteosarcoma immune microenvironment remodeling and C1QBP-driven drug resistance deciphered through single-cell multi-dimensional analysis. Bioeng Transl Med. 2024;9(5):e10654.
27.
Ning K, Peng YL, Jiang Y, Li Z, Luo X, Lin LD, Deng MH, Wu Y, Huang TX, Huang YX, et al. Sex differences in renal cell carcinoma: A single-cell analysis reveals exhausted CD8+ T-cells highly infiltrated in males. Biol Sex Differ. 2023;14(1):58.
28.
Wu X, Ban CM, Deng WD, Bao XW, Tang N, Wu YP, Deng ZX, Xiong JB, Zhao QQ. Unveiling the PDK4-centered rituximab-resistant mechanism in DLBCL: The potential of the “smart” exosome nanoparticle therapy. Mol Cancer. 2024;23(1):144.
29.
Niu JF, Yan TQ, Guo W, Wang W, Ren TT, Huang Y, Zhao ZQ, Yu YY, Chen CL, Huang QS, et al. The COPS3-FOXO3 positive feedback loop regulates autophagy to promote cisplatin resistance in osteosarcoma. Autophagy. 2023;19(6):1693–1710.
30.
Sayles LC, Breese MR, Koehne AL, Leung SG, Lee AG, Liu HY, Spillinger A, Shah AT, Tanasa B, Straessler K, et al. Genome-informed targeted therapy for osteosarcoma. Cancer Discov. 2019;9(1):46–63.
31.
Schott CR, Koehne AL, Sayles LC, Young EP, Luck C, Yu K, Lee AG, Breese MR, Leung SG, Xu H, et al. Osteosarcoma pdx-derived cell line models for preclinical drug evaluation demonstrate metastasis inhibition by dinaciclib through a genome-targeted approach. Clin Cancer Res. 2024;30(4):849–864.
32.
Gao ML, Dang F, Deng C. β-cryptoxanthin induced anti-proliferation and apoptosis by G0/G1 arrest and AMPK signal inactivation in gastric cancer. Eur J Pharmacol. 2019;859:172528.
33.
Iskandar AR, Miao BC, Li XL, Hu KQ, Liu C, Wang XD. β-cryptoxanthin reduced lung tumor multiplicity and inhibited lung cancer cell motility by downregulating nicotinic acetylcholine receptor α7 signaling. Cancer Prev Res. 2016;9(11):875–886.
34.
Lim JY, Liu C, Hu KQ, Smith DE, Wu DY, Lamon-Fava S, Ausman LM, Wang XD. Xanthophyll β-cryptoxanthin inhibits highly refined carbohydrate diet-promoted hepatocellular carcinoma progression in mice. Mol Nutr Food Res. 2020;64(3):e1900949.
35.
Hu Y, Zhang Y, Ni CY, Chen CY, Rao SS, Yin H, Huang J, Tan YJ, Wang ZX, Cao J, et al. Human umbilical cord mesenchymal stromal cells-derived extracellular vesicles exert potent bone protective effects by clec11a-mediated regulation of bone metabolism. Theranostics. 2020;10(5):2293–2308.
36.
Liu CJ, Xiong QC, Li QW, Lin WM, Jiang S, Zhang DT, Wang Y, Duan XB, Gong P, Kang N. CHD7 regulates bone-fat balance by suppressing ppar-γ signaling. Nat Commun. 2022;13(1):1989.
37.
Zhang ZK, Du JJ, Wang S, Shao L, Jin K, Li F, Wei BJ, Ding W, Fu PF, van Dam H, et al. OTUB2 promotes cancer metastasis via hippo-independent activation of YAP and TAZ. Mol Cell. 2019;73(1):7–21.e7.
38.
Sheng ZH, Cao X, Deng YN, Zhao XY, Liang SF. SUMOylation of AnxA6 facilitates EGFR-PKCα complex formation to suppress epithelial cancer growth. Cell Commun Signal. 2023;21(1):189.
39.
Barysch SV, Stankovic-Valentin N, Miedema T, Karaca S, Doppel J, Achour TN, Vasudeva A, Wolf L, Sticht C, Urlaub H, et al. Transient deSUMOylation of IRF2BP proteins controls early transcription in EGFR signaling. EMBO Rep. 2021;22(3):e49651.
40.
Lee YJ, Won TJ, Hyung KE, Jang YW, Kim SJ, Lee DI, Park SY, Hwang KW. IL-6 induced proliferation and cytotoxic activity of cd8+ t cells is elevated by SUMO2 overexpression. Arch Pharm Res. 2016;39(5):705–712.
41.
Vertegaal ACO. Signalling mechanisms and cellular functions of sumo. Nat Rev Mol Cell Biol. 2022;23(11):715–731.
42.
Cheng X, Yang W, Lin W, Mei F. Paradoxes of cellular sumoylation regulation: A role of biomolecular condensates? Pharmacol Rev. 2023;75(5):979–1006.
43.
Sakuishi K, Apetoh L, Sullivan JM, Blazar BR, Kuchroo VK, Anderson AC. Targeting Tim-3 and PD-1 pathways to reverse t cell exhaustion and restore anti-tumor immunity. J Exp Med. 2010;207(10):2187–2194.
44.
Muftuoglu M, Li L, Chen H, Mak D, Gokdemir E, Shaim H, Burks J, Basar R, Imahashi N, Mendt MC, et al. Mass cytometry reveals heterogeneity in the exhaustion profile of t-cells in chronic lymphocytic leukemia and the CD4:CD8 ratio may be a reliable predictor of CD8+ T cell compartment “Fitness”. Blood. 2016;128(22):353.
45.
Hoffmann M, Pantazis N, Martin GE, Hickling S, Hurst J, Meyerowitz J, Willberg CB, Robinson N, Brown H, Fisher M, et al. Exhaustion of activated cd8 t cells predicts disease progression in primary hiv-1 infection. PLOS Pathog. 2016;12(7):e1005661.
46.
Beird HC, Bielack SS, Flanagan AM, Gill J, Heymann D, Janeway KA, Livingston JA, Roberts RD, Strauss SJ, Gorlick R. Osteosarcoma. Nat Rev Dis Primers. 2022;8(1):77.
47.
Lu B, Zou CY, Yang ML, He YY, He JC, Zhang CX, Chen SY, Yu JM, Liu KY, Cao Q, et al. Pharmacological inhibition of core regulatory circuitry liquid-liquid phase separation suppresses metastasis and chemoresistance in osteosarcoma. Adv Sci. 2021;8(20):e2101895.
48.
Bao ZQ, Zhu RZ, Fan HG, Ye YC, Li T, Chai DM. Aberrant expression of SPAG6 and NM23 predicts poor prognosis of human osteosarcoma. Front Genet. 2022;13:1012548.
49.
Wang H, Zhou XW, Li CB, Yan SX, Feng CY, He JY, Li ZH, Tu C. The emerging role of pyroptosis in pediatric cancers: From mechanism to therapy. J Hematol Oncol. 2022;15(1):140.
50.
Brown HK, Tellez-Gabriel M, Heymann D. Cancer stem cells in osteosarcoma. Cancer Lett. 2017;386:189–195.
51.
Tang Z, Wei XH, Li T, Wang W, Wu H, Dong H, Liu YC, Wei FL, Shi L, Li XK, et al. Sestrin2-mediated autophagy contributes to drug resistance via endoplasmic reticulum stress in human osteosarcoma. Front Cell Dev Biol. 2021;9:722960.
52.
Xiao X, Wang W, Li YQ, Yang D, Li XK, Shen C, Liu Y, Ke XZ, Guo S, Guo Z. HSP90AA1-mediated autophagy promotes drug resistance in osteosarcoma. J Exp Clin Cancer Res. 2018;37:201.
53.
Wang TX, Huang JC, Chen G, Fu JH, Li T, Zou XN, Yi HL. Mir-1293 suppresses osteosarcoma progression by modulating drug sensitivity in response to cisplatin treatment. Int Immunopharmacol. 2024;130:111702.
54.
Tang Z, Dong H, Li T, Wang N, Wei XH, Wu H, Liu YC, Wang W, Guo Z, Xiao X. The synergistic reducing drug resistance effect of cisplatin and ursolic acid on osteosarcoma through a multistep mechanism involving ferritinophagy. Oxidative Med Cell Longev. 2021;2021:5192271.
55.
He P, Xu SL, Miao ZH, Que YK, Chen Y, Li S, Ma QM, Yang R, Wei W, Zha ZB, et al. Anti-Her2 affibody-decorated arsenene nanosheets induce ferroptosis through depleting intracellular GSH to overcome cisplatin resistance. J Nanobiotechnol. 2023;21(1):203.
56.
Seeler JS, Dejean A. SUMO and the robustness of cancer. Nat Rev Cancer. 2017;17(3):184–197.
57.
Chang HM, Yeh ETH. Sumo: From bench to bedside. Physiol Rev. 2020;100(4):1599–1619.
58.
Kroonen JS, Vertegaal ACO. Targeting SUMO signaling to wrestle cancer. Trends Cancer. 2021;7(6):496–510.
59.
Wu XL, Fan JC, Zhang XM, Li T, Song JC. Global trends of single cell sequence associated in cancer from 2011 to 2024: A bibliometric analysis. Heliyon. 2024;10(12):e32847.
60.
Liu TL, Bai M, Liu MN, Li T, Liao YC, Zhao C, Yao MN, Wang JW, Wen AD, Ding Y. Novel synergistic mechanism of 11-keto-β-boswellic acid and z-guggulsterone on ischemic stroke revealed by single-cell transcriptomics. Pharmacol Res. 2023;193:106803.
61.
Karvonen U, Jääskeläinen T, Rytinki M, Kaikkonen S, Palvimo JJ. ZNF451 is a novel PML body- and SUMO-associated transcriptional coregulator. J Mol Biol. 2008;382(3):585–600.
62.
Koidl S, Eisenhardt N, Fatouros C, Droescher M, Chaugule VK, Pichler A. The SUMO2/3 specific E3 ligase ZNF451-1 regulates PML stability. Int J Biochem Cell Biol. 2016;79:478–487.
63.
Cappadocia L, Pichler A, Lima CD. Structural basis for catalytic activation by the human ZNF451 SUMO E3 ligase. Nat Struct Mol Biol. 2015;22(12):968–975.
64.
Zhang ZP, Chen HX, Lu YB, Feng TC, Sun WJ. LncRNA BC032020 suppresses the survival of human pancreatic ductal adenocarcinoma cells by targeting ZNF451. Int J Oncol. 2018;52(4):1224–1234.
65.
Zhang Y, Wang WY, Min JL, Liu SS, Wang QR, Wang Y, Xiao Y, Li X, Zhou ZG, Liu SS. ZNF451 favors triple-negative breast cancer progression by enhancing SLUG-mediated CCL5 transcriptional expression. Cell Rep. 2023;42(6):112654.
66.
Schellenberg MJ, Lieberman JA, Herrero-Ruiz A, Butler LR, Williams JG, Muñoz-Cabello AM, Mueller GA, London RE, Cortés-Ledesma F, Williams RS. ZATT (ZNF451)-mediated resolution of topoisomerase 2 DNA-protein cross-links. Science. 2017;357(6358):1412–1416.
67.
Zagnoli-Vieira G, Caldecott KW. TDP2, TOP2, and SUMO: What is ZATT about? Cell Res. 2017;27(12):1405–1406.
68.
Zeng W, Gu SC, Yu Y, Feng YL, Xiao M, Feng XH. ZNF451 stabilizes TWIST2 through SUMOylation and promotes epithelial-mesenchymal transition. Am J Cancer Res. 2021;11(3):898–915.
69.
Kashima T, Nakamura K, Kawaguchi J, Takanashi M, Ishida T, Aburatani H, Kudo A, Fukayama M, Grigoriadis AE. Overexpression of cadherins suppresses pulmonary metastasis of osteosarcoma in vivo. Int J Cancer. 2003;104(2):147–154.
70.
Grantab R, Sivananthan S, Tannock IF. The penetration of anticancer drugs through tumor tissue as a function of cellular adhesion and packing density of tumor cells. Cancer Res. 2006;66(2):1033–1039.
71.
Lu KH, Lu EWH, Lin CW, Yang JS, Yang SF. New insights into molecular and cellular mechanisms of zoledronate in human osteosarcoma. Pharmacol Ther. 2020;214:107611.
72.
Wu X, Zhang XB, Feng WJ, Feng HM, Ding ZY, Zhao QQ, Li XS, Tang N, Zhang P, Li J, et al. A targeted erythrocyte membrane-encapsulated drug-delivery system with anti-osteosarcoma and anti-osteolytic effects. ACS Appl Mater Interfaces. 2021;13(24):27920–27933.
73.
Freeman FE, Dosta P, Shanley LC, Tamez NR, Javelly CR, Mahon OR, Kelly DJ, Artzi N. Localized nanoparticle-mediated delivery of mir-29b normalizes the dysregulation of bone homeostasis caused by osteosarcoma whilst simultaneously inhibiting tumor growth. Adv Mater. 2023;35(23):e2207877.
74.
Yin Q, Tang L, Cai KM, Tong R, Sternberg R, Yang XJ, Dobrucki LW, Borst LB, Kamstock D, Song ZY, et al. Pamidronate functionalized nanoconjugates for targeted therapy of focal skeletal malignant osteolysis. Proc Natl Acad Sci USA. 2016;113(32):E4601–E4609.
75.
Peng F, Liao MR, Qin R, Zhu SO, Peng C, Fu LL, Chen Y, Han B. Regulated cell death (RCD) in cancer: Key pathways and targeted therapies. Signal Transduct Target Ther. 2022;7(1):286.
76.
Luo H, Vong CT, Chen HB, Gao Y, Lyu P, Qiu L, Zhao MM, Liu Q, Cheng ZH, Zou J, et al. Naturally occurring anti-cancer compounds: Shining from chinese herbal medicine. Chin Med. 2019;14(1):48.
77.
Saini RK, Keum YS, Daglia M, Rengasamy KRR. Dietary carotenoids in cancer chemoprevention and chemotherapy: A review of emerging evidence. Pharmacol Res. 2020;157:104830.
78.
Lim JY, Wang XD. Mechanistic understanding of β-cryptoxanthin and lycopene in cancer prevention in animal models. Biochim Biophys Acta Mol Cell Biol Lipids. 2020;1865(11):158652.
79.
San Millán C, Soldevilla B, Martín P, Gil-Calderón B, Compte M, Perez-Sacristán B, Donoso E, Peña C, Romero J, Granado-Lorencio F, et al. β-Cryptoxanthin synergistically enhances the antitumoral activity of oxaliplatin through Δnp73 negative regulation in colon cancer. Clin Cancer Res. 2015;21(19):4398–4409.
80.
Belk JA, Daniel B, Satpathy AT. Epigenetic regulation of T cell exhaustion. Nat Immunol. 2022;23(6):848–860.
81.
Chow A, Perica K, Klebanoff CA, Wolchok JD. Clinical implications of T cell exhaustion for cancer immunotherapy. Nat Rev Clin Oncol. 2022;19(12):775–790.
82.
Belk JA, Yao WN, Ly N, Freitas KA, Chen YT, Shi QM, Valencia AM, Shifrut E, Kale N, Yost KE, et al. Genome-wide CRISPR screens of T cell exhaustion identify chromatin remodeling factors that limit T cell persistence. Cancer Cell. 2022;40(7):768–786.e7.
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Article Info
doi: 10.34133/research.0530
  • Receive Date：2024-06-21
  • Online Date：2025-07-24
  • Published：2024-11-12
Article Data
Affiliations
History
  • Received：2024-06-21
  • Revised：2024-10-02
  • Accepted：2024-10-19
Funding
National Natural Science Foundation of China (82260365)
National Natural Science Foundation of China (82372315)
Project of Kunlun Elite, High-End Innovation and Entrepreneurship Talents of Qinghai Province(2021 No. 13)
Basic and Applied Basic Research Foundation of Guangdong Province (2023-ZJ-716)
Postgraduate Scientific Research Innovation Project of Hunan Province(CX20230296)
Affiliations
    1Department of Orthopaedics, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
    2Department of Orthopaedics , Liuzhou Municipal Liutie Central Hospital, Liuzhou, Guangxi, China.
    3Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
    4Department of Spine Surgery, First Affiliated Hospital of University of South China, Hengyang, Hunan, China.
    5Institute of Cell Biology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
    6Department of Spine Surgery, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
    7Department of Hematology, Liuzhou People's Hospital affiliated to Guangxi Medical University, Liuzhou, Guangxi, China.
    8Department of Hematology, The Qinghai Provincial People's Hospital, Xining, Qinghai, China.

Corresponding:

* Address correspondence to: (X.W.); (J.X.); (Q.Z.)
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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