TMEM158-mediated TGF-β signaling pathway modulates the sensitivity of TP53-deficient osteosarcoma to USP14 inhibitors

TMEM158-mediated TGF-β signaling pathway modulates the sensitivity of TP53-deficient osteosarcoma to USP14 inhibitors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article TMEM158-mediated TGF-β signaling pathway modulates the sensitivity of TP53-deficient osteosarcoma to USP14 inhibitors Zi-Yu Chen, Miersalijiang Yasen, Song-Yao Jiang, Zi-Yi Ye, Qin-Xin Yang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7071793/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Feb, 2026 Read the published version in Molecular and Cellular Biochemistry → Version 1 posted 12 You are reading this latest preprint version Abstract Background Previous studies have demonstrated that the USP14 inhibitor IU1 and USP14/UCHL5 inhibitor b-AP15 can extend the survival period of TP53-deficient mice with spontaneous osteosarcoma (OS). However, the underlying molecular mechanisms remain to be fully elucidated. The transmembrane protein TMEM158 has been identified as a key regulator in the progression of various cancers. Nevertheless, its functional role in OS remains largely unknown. Methods In this study, we conducted comprehensive bioinformatics analyses—including cluster analysis, differential expression analysis, and functional enrichment analysis—on clinical OS databases to assess the correlation between TMEM158 expression and the proteasome-associated USP14 and UCHL5. Primary tumor cells (TP53-deficient OS cells) and U2OS cells were treated with IU1 or b-AP15, respectively. The expression levels of TMEM158 were quantified using qPCR. Subsequently, TMEM158 was knocked down in both cell lines, and changes in cell viability and TGF-β signaling activity were evaluated. Additionally, single-cell RNA sequencing data were analyzed to identify cell types with high TMEM158 expression and their potential roles in intercellular communication. Results Both IU1 and b-AP15 significantly prolonged the survival of TP53-deficient OS mice and exhibited enhanced cytotoxic effects on TP53-deficient OS cells. These compounds selectively suppressed TMEM158 expression in TP53-deficient OS cells. Bioinformatics analysis revealed that TMEM158 is positively correlated with USP14 and UCHL5 expression and serves as an independent prognostic marker for poor clinical outcomes in OS patients. Experimental validation showed that TMEM158 knockdown significantly reduced the viability of TP53-deficient OS cells and inhibited TGF-β pathway activation. Osteoblastic OS cells displayed concurrent suppression of the P53 pathway and activation of the TGF-β pathway, with a strong covariant relationship between TMEM158 and TGF-β activity. Meanwhile, osteoblastic OS cells with high expression levels of TMEM158 demonstrate enhanced intercellular TGF-β signaling communication with macrophages. Conclusion Our findings demonstrated that the TMEM158-TGF-β pathway plays a central role in mediating the heightened sensitivity of TP53-deficient OS to USP14 inhibition. Targeting this pathway may offer a promising therapeutic strategy for precision treatment of osteosarcoma. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Osteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents, and the incidence rate among children aged 0 to 9 years is approximately 1.9 cases per million[ 1 , 2 ]. It accounts for about 20% of all primary malignant bone tumors, with a peak incidence occurring at two age ranges: adolescence (around 18 years old) and late adulthood (approximately 60 years old)[ 3 ]. Although surgical intervention combined with neoadjuvant therapy has significantly improved the 5-year survival rate for patients with localized disease, approximately 30–50% of these patients still experience recurrence or metastasis[ 4 , 5 ]. TP53 is the most frequently mutated gene in human OS and plays a critical role in the development and metastasis of the disease[ 6 , 7 ]. In murine models, the absence of functional TP53 leads to the development of OS[ 8 ]. TP53 is frequently disrupted by structural variations, resulting in the loss of its classical tumor suppressive function. However, certain components of the TP53 signaling pathway that are essential for cancer cell survival and proliferation remain active[ 9 ]. Our previous research demonstrated that heterozygous TP53 +/− mice spontaneously develop OS. Furthermore, we found that the USP14 inhibitor IU1 can induce tumor cell apoptosis and inhibit tumor growth in TP53-deficient in vivo models[ 10 ]. Additionally, studies have shown that Emodin inhibited the activity of U2OS cells with normal P53 expression through TP53-mediated cell cycle regulation[ 11 ]. These findings collectively highlight the critical role of TP53 in the initiation and progression of OS and suggest its involvement in the mechanisms of action of anti-OS therapies. TMEM158, also known as RIS1 or p40BBp, is a transmembrane protein-coding gene located at chromosomal locus 3p21.31. It has been demonstrated to be highly expressed in various types of cancer, including lung cancer, breast cancer, and gastric cancer, among others, and is significantly correlated with unfavorable clinical outcomes[ 12 , 13 ]. TMEM158 primarily influences the proliferation, migration, and invasive capacity of cancer cells through modulation of the PI3K-AKT signaling pathway, thereby contributing to the progression of lung adenocarcinoma and gastric cancer[ 14 , 15 ]. However, its potential involvement in OS, particularly in relation to other regulatory pathways, such as TGF-β, remains to be elucidated. In this study, we integrated data from animal models, TP53-deficient primary cell detection, and clinical data analysis to systematically elucidate the central role of the TMEM158-TGF-β signaling pathway in modulating the sensitivity of TP53-deficient OS to USP14 inhibitors. These findings provide novel insights into targeted therapeutic strategies for OS. 2. Methods and Materials 2.1 Chemicals and reagents Rabbit anti-TMEM158 Polyclonal Antibody(C-Term) (abs105949) was acquired from Absin Bioscience Inc. (China), TGF beta Receptor I (TGFBR1) Rabbit pAb (A0708) and Phospho-Smad2-S250 Rabbit mAb (AP1007) were acquired from ABclonal Biotechnology Co., Ltd. (China). GAPDH (10494-1-AP) was purchased from Proteintech Group, Inc. (Rosemont, IL, USA). Lipofectamine 2000 (#11,668 − 019) was acquired from Thermo Fisher Scientific. All other reagents were of analytical grade. 2.2 Cell cultural The human OS cells (U2OS) was obtained from Procell Life ScienceCells, and was routinely cultured and maintained in McCoy’s 5A (Procell Life Science) supplemented with 10% FBS (Shanghai Yuli Bio&Tech Inc.) and 1% penicillin/streptomycin (Shanghai Yuli Bio&Tech Inc.) at 37°C in a humidified incubator containing 5% CO 2 . 2.3 Extraction of OS primary cells from TP53-deficient mouse TP53-deficient (TP53-KO) mice were constructed. After OS spontaneously formed in them, the tumors were completely removed, washed with PBS and digested with Tissue Digestion Solution (WM-D-02) for at least 30 minutes, and the undigested tissue blocks were screened out using a sieve. After centrifugation at 1000 rpm for 5 minutes, the supernatant was removed. An equal volume of matrix Gel (OM21, OmaStem) was added to the cell precipitate for organoid culture (OM14, OmaStem). OS organoids were digested using TrypLE Express (12604021, Thermo Fisher) and subcultured. After adherent culture of primary tumor cells, experiments could be conducted. 2.4 Small interfering RNA (siRNA) U2OS cell and TP53-KO primary cell were digested and seeded in 6-well, 12-well or 96-well plates (5×10 4 /mL) before transfection. Lipofectamine 2000 was used to transfect 5 µl siRNA into cells which were cultured with a serum-free medium. After 6 hours, the medium was replaced with a fresh medium which contained serum. After incubation for 48 h, cells were collected. siTMEM58-1: Sense 5'-3' GCCCUAGAUUCAUGGCAGA/dT//dT/, Antisense (5'-3') UCUGCCAUGAAUCUAGGGC/dT//dT/; siTMEM158-2: Sense 5'-3' GGGAAGGAUUUAACACCGA/dT//dT/, Antisense (5'-3') CGGUGUUAAAUCCUUCCC/dT//dT/. 2.5 Cell viability detection U2OS cells and TP53-KO primary tumor cells were cultured in vitro and evenly distributed in 96-well plates at a density of 5,000 cells per well. After 24 hours, the cells adhered and were treated with b-AP15, IU1 or siTMEM158, the total volume was 100 µL. After 48 hours of treatment, 10 µL of CCK-8 detection reagent (MA0218, Meilunbio) was added to each well for 2 hours of reaction, and the absorbance at 450nm was measured. 2.6 Apoptosis Detection U2OS cells and TP53-KO primary tumor cells were plated into 12-well plates at a density of 5 × 10⁴ cells/mL, with 1 mL per well. After 48 hours of treatment of USP14 inhibitor or siTMEM158, the cell supernatant was collected first. Trypsin was then added to digest the adherent cells, which were subsequently collected. The cells were washed twice with pre-cooled PBS, resuspended in 100 µL of 1× Binding Buffer, transferred to flow cytometry tubes, and analyzed using a flow cytometer according to the manufacturer's protocol (A211-01, Vazyme). 2.7 qRT-PCR (quantitative real-time PCR) detection After U2OS cells and TP53-KO primary tumor cells were transfected with siTMEM158 or treated with varying concentrations of b-AP15 or IU1 for 48 hours, total RNA was extracted and purified using SteadyPure Quick RNA Extraction Kit (AG21023, Accurate Biotechnology), followed by reverse transcription into cDNA in accordance with the manufacturer's instructions for HiScript Ⅲ All-in-one RT SuperMix Perfect for qPCR (R333-01, Vazyme). Acquired cDNAs were assayed by real-time PCR following the manufacturer’s protocol (#RR420A, TaKaRa Biotechnology). RNAs’ relative expression level was analyzed using the 2–ΔΔCT method. Primers used for qRT-PCR were summarized as Table S1 . 2.8 Western blot Approximately 20–30 µg of protein was extracted from the cell lysate and subsequently separated using SDS-PAGE gel electrophoresis, followed by transfer onto a PVDF membrane. To maximize antibody efficiency, the membrane was trimmed to retain only the region corresponding to the target protein prior to Western blotting. The primary antibody was then applied to selectively bind to the target protein, forming an immune complex that could be recognized by the enzyme-conjugated secondary antibody. Finally, the target protein was detected using super sensitive ECL luminescence reagent (MA0186, Meilunbio). 2.9 Evaluation of the Tumor immune microenvironment (TIME) and response to immunotherapy in OS To assess the potential association between TP53, USP14, UCHL5 within the TIME of OS, the "CIBERSORT" algorithm was employed to estimate the relative proportions of 22 distinct immune cell subsets within tumor samples. Differences in the extent of immune cell infiltration across different gene expression groups were analyzed using the Wilcoxon rank-sum test. Additionally, to predict the likely responses of patients to immune checkpoint inhibitors (including anti-PD-1 and anti-CTLA-4 therapies), the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm developed at Harvard Medical School ( http://tide.dfci.harvard.edu/ ) was utilized to evaluate the potential efficacy of immunotherapy. 2.10 Analysis of Bulk RNA Sequencing Data To further investigate the downstream regulatory pathways of TP53, this study utilized the online analysis platform GSCA (Gene Set Cancer Analysis, https://guolab.wchscu.cn/GSCA/ ) to perform differential expression analysis on TCGA SARC bulk RNA-seq samples. Patients were categorized based on TP53 expression levels, and differentially expressed genes were identified and visualized using heatmaps. Subsequently, genes exhibiting a strong positive correlation with USP14 and UCHL5 expression were selected. These findings were integrated with survival analysis results to identify upregulated genes significantly associated with poor prognosis. 2.11 Survival Analysis Survival analysis was conducted using the TCGA-SARC cohort, based on the expression levels of TP53, USP14, and UCHL5 to assess the impact of key genes on the prognosis of sarcoma patients. Patients were stratified into four groups according to the expression levels of each gene, and Kaplan-Meier survival curves were generated using the R packages 'survminer' and 'survival' to evaluate overall survival differences among the groups. 2.12 Functional enrichment and interaction analysis To systematically investigate the potential biological functions of TMEM158 in OS, this study utilized the GSE232592 dataset and applied Spearman correlation analysis to assess the relationship between TMEM158 expression and whole-genome transcriptomic profiles. Genes that showed a statistically significant association with TMEM158 ( P 1, P < 0.05), resulting in a set of functionally relevant differentially expressed genes. Subsequently, Gene Set Enrichment Analysis (GSEA) was performed using the R package "clusterProfiler" to identify the key signaling pathways and biological processes associated with this gene set. 2.13 Single-cell transcriptome data processing and analysis Single-cell RNA sequencing data from six OS patients were retrieved from the GEO database (accession number: GSE162454). Data preprocessing was conducted using the Seurat package (version 5.3.0) in R. All samples were merged, and low-quality cells were filtered out based on predefined criteria: cells with fewer than 300 detected genes or those exhibiting a mitochondrial gene content exceeding 10% were excluded. To address batch effects across samples, the Harmony package (version 1.2.3) was employed. Initial clustering was performed using the FindClusters function with a resolution parameter of 0.5, while dimensionality reduction and visualization were achieved through the t-SNE algorithm. For detailed analysis of myeloid cell subpopulations, specific subsets were extracted using the SubsetData function and subjected to re-clustering at a finer resolution (resolution = 0.2). 2.14 Copy number variation (CNV) analysis and identification of malignant cells To evaluate the genomic CNV patterns across different cell subpopulations in OS samples and to identify potential malignant cell populations, this study employed the R package inferCNV (v1.22.0). Endothelial cells, plasma cells, and osteoblasts were selected as reference normal cell types. The relative CNV levels for each cell were inferred based on single-cell transcriptomic data. Key parameters used during the analysis included a truncation value of 0.1, with cluster_by_groups set to TRUE, denoise enabled as TRUE, and HMM modeling activated (HMM = TRUE). All other parameters followed default settings. Ultimately, suspected malignant clones were identified by detecting cells exhibiting significantly elevated CNV scores. 2.15 Pathway activity score To assess the activation status of key signaling pathways within distinct cell populations, the run_mlm function from the decoupleR package (v2.12.0) was utilized to compute pathway activity scores for cellular groups, including those involved in the TP53 and TGF-β pathways. The results were visualized using heatmaps, providing an overview of the dynamic variations in pathway activation across different cell populations. 2.16 Analysis of intercellular communication Based on the median expression level of TMEM158, malignant OS cells were categorized into high-expression and low-expression groups. Intercellular communication analysis was performed using the R package "CellChat" (v1.6.1), aiming to systematically evaluate how TMEM158 expression influences receptor-ligand interaction networks among cell types. Particular emphasis was placed on analyzing specific intercellular communication patterns involving TMEM158 high- and low-expression groups and other annotated cell types within key signaling pathways. Results were visualized through network diagrams and heatmaps. 3. Results 3.1 USP14 inhibitors exhibit more pronounced inhibitory effects on OS with reduced TP53 expression. In previous studies, our research team observed that mice with reduced TP53 expression spontaneously developed various tumors, including lymphoma, OS, and sarcoma. Among these, lymphoma exhibited the highest incidence rate, while OS and sarcoma also demonstrated considerable prevalence (Fig. 1 A). Pharmacodynamic analyses revealed that administration of the USP14 inhibitor IU1 or the dual inhibitor b-AP15, which targets both USP15 and UCHL5, significantly prolonged the median survival time of OS mice with reduced TP53 expression. This finding indicates the notable anti-tumor efficacy of these compounds against OS (Fig. 1 B). Moreover, histopathological examination of bone marrow tissues via hematoxylin and eosin (HE) staining showed that tumor regions in untreated OS mice were highly dense. However, following treatment with IU1 or b-AP15, tumor tissue density decreased markedly, with the structure becoming sparse and loosely organized (Fig. 1 C), further supporting the therapeutic potential of these inhibitors. Based on these observations, we hypothesized that the effects of IU1 and b-AP15 might vary depending on the TP53 expression status in OS cells. To test this hypothesis, we isolated primary OS cells from TP53-deficient mice and compared them with U2OS cells, which express normal levels of TP53. Our results demonstrated that both cell types exhibited reduced viability after exposure to equivalent concentrations of IU1 or b-AP15 for the same duration. However, a significant difference in drug sensitivity was observed between the two cell lines (Fig. 1 D). Furthermore, apoptosis assays corroborated the differential anti-cancer effects of USP14 and UCHL5 inhibitors in cells with varying TP53 expression levels (Fig. 1 E). 3.2 The enhanced drug sensitivity of TP53-deficient OS may not be directly associated with immune cell infiltration. To investigate the underlying mechanism responsible for variations in drug sensitivity among cells with differing TP53 expression levels, we first analyzed OS data from a large-scale database. The dataset was stratified into two groups based on TP53 expression levels: high and low (Fig. 2 A, 2 B). Our analysis revealed no significant differences in immune cell infiltration within the tumor microenvironment (TME) between these two groups. Subsequently, we further categorized the patient data into four subgroups according to the combined expression levels of TP53 and USP14: TP53-High / USP14-High, TP53-High / USP14-Low, TP53-Low / USP14-High, and TP53-Low / USP14-Low. Survival curve analysis demonstrated that patients exhibiting TP53-High / USP14-High had the shortest survival duration, followed by those with TP53-Low / USP14-High (Fig. 2 C). The hazard ratio analysis corroborated this trend, showing that patients with elevated USP14 expression were at a higher risk (Fig. S1 A). We also examined the relationship between UCHL5 and P53 expression. Following a similar four-group classification, no significant differences in survival outcomes or risk profiles were observed among the subgroups (Fig. 2 D, S1B). In terms of immune cell infiltration, aside from M0-type macrophages and monocytes, no notable differences were detected across the other immune cell types among the groups (Fig. 2 E, 2 F). These findings suggest that USP14 inhibitors may hold therapeutic potential for OS patients and that their effects might primarily involve interactions with M0-type macrophages or monocytes. Collectively, these results indicate that the increased sensitivity of TP53-deficient OS to USP14 inhibitors may not be directly linked to changes in immune cell infiltration. 3.3 TP53-deficient OS was more sensitive to immunotherapy To predict the response of OS patients to immune checkpoint inhibitors, we conducted TIDE calculations. A positive TIDE value indicates a high score for T-cell dysfunction and clearance, suggesting no response to immune checkpoint inhibitors (ICBs), while a negative tide value indicates a response to ICBs. The results of Fig. 3 A and 3 B showed that under the condition of the same expression level of USP14 or UCHL5, compared with patients with high expression of TP53, patients with low expression of TP53 show higher sensitivity to immunotherapy. The same phenomenon is also reflected in the Dysfunction score results. Patients with low expression of USP14 or UCHL5 have a greater potential for immune escape, and the efficacy of immune checkpoint suppression therapy (ICI) may be poorer (Fig. 3 C, 3 D). 3.4 TMEM158 may serve as a critical determinant contributing to variations in drug efficacy. To elucidate the underlying mechanism of these differences in therapeutic response, we stratified OS data according to TP53 expression levels and identified differentially expressed genes. Our analysis revealed that, in tumors with low TP53 expression, genes located in the lower half exhibited significantly elevated expression levels (Fig. 4 A). Subsequently, correlation analyses between these genes and the expression of USP14 or UCHL5 were conducted, identifying four genes positively correlated with USP14 and five genes positively correlated with UCHL5 (Fig. 4 B). Notably, TMEM158 was the sole gene showing a positive correlation with both USP14 and UCHL5. Moreover, survival analysis indicated that patients with high TMEM158 expression had significantly shorter overall survival compared to those with low TMEM158 expression (Fig. 4 C), suggesting that TMEM158 may play a pivotal role in OS progression. Additionally, pathway enrichment analysis revealed that the TGF-β signaling pathway is significantly activated in OS with low TP53 expression and shows a positive correlation with TMEM158 expression changes. Key components of this pathway include TGFBR1 and SMAD2 (Fig. 4 D, 4 E). 3.5 The TMEM158 gene may contribute to variations in drug efficacy through its influence on the TGF-β signaling pathway. Following treatment of two cell types with IU1 and b-AP15, the expression levels of the TMEM158 gene were assessed. The results indicated that in U2OS cells, TMEM158 expression remained largely unchanged after exposure to either drug. In contrast, both compounds significantly reduced TMEM158 expression in TP53-KO primary cells (Fig. 5 A). To further investigate the functional role of TMEM158, siRNA was employed to knock down the gene in both cell types. As shown in Fig. 5 B, cell viability assays revealed a significant decrease in viability in TP53-KO primary cells following TMEM158 knockdown, whereas no notable changes were observed in U2OS cells. Moreover, TMEM158-depleted TP53-KO primary cells exhibited an increased proportion of late apoptotic and dead cells compared to those in the siNC control group (Fig. 5 C). Given the potential involvement of the TGF-β signaling pathway, the expression levels of TGFBR1 and SMAD2 were evaluated in both cell types after TMEM158 knockdown. It was found that in TP53-KO primary cells, both mRNA and protein levels of TGFBR1 and SMAD2 showed a positive correlation with TMEM158 expression. However, there were no significant alterations in the TGF-β signaling components in U2OS cells (Fig. 5 D, 5 E). Therefore, there is sufficient evidence to suggest that the TGF-β signal transduction pathway mediated by the TMEM158 gene may potentially account for the observed differences in drug efficacy. 3.6 Reduced TP53 activity and high expression of TMEM158 represent key characteristics of OS cells To further investigate the role of TMEM158 in the OS TME characterized by reduced TP53 expression, this study initially obtained relevant single-cell sequencing data and identified cell-specific markers based on existing literature (Fig. 6 A). Subsequently, cell populations were classified according to their specific markers, including B cells, Endothelial cells, Fibroblasts cells, Myeloid cells, Osteoblastic OS cells, Osteoclasts, Plasma cells, and T/NK cells. Data analysis revealed that Myeloid cells were the most abundant population, followed by Osteoblastic OS cells and T/NK cells (Fig. 6 B, 6 C). Further analysis of gene mutation frequencies across cell populations indicated that Osteoblastic OS cells exhibited the highest CNV, suggesting a higher degree of malignancy and confirming their identity as tumor cells within the TME of OS (Fig. 6 D). Additionally, this study explored dynamic changes in signaling pathways among different cell types. As shown in Fig. 6 E, compared with other cell populations, the P53 signaling pathway was markedly downregulated, while the TGF-β signaling pathway was significantly upregulated in the most malignant Osteoblastic OS cells. Moreover, TMEM158 demonstrated notably high expression levels in these cells and showed a positive correlation with TGF-β signaling activity (Fig. 6 F), which aligns with above findings. 3.7 OS cells exhibiting high expression of TMEM158 were predominantly involved in intercellular communication with Macrophages. Although initial findings indicated that TMEM158 expression is most prominent in Osteoblastic OS cells, considering the heterogeneity of tumor cells, we categorized the cell population into two groups based on TMEM158 expression levels: high-expression and low-expression groups. Subsequently, Myeloid cells were further clustered (Fig. 7 A– 7 C). Additionally, we conducted an in-depth investigation into the differences in cellular communication between these two subpopulations and other cell types. Our analysis revealed that, compared to the low-expression group, the high-expression TMEM158 cell population exhibited more extensive signaling interactions (Fig. S1 E). An analysis of TGF-β signaling pathways demonstrated that TGFB1 was the primary ligand secreted in the OS TME, with TGFBR1 being the most frequently engaged receptor (Fig. 7 D). Further examination of TGF-β signaling across different cell populations showed that both the intensity of signal emission and reception were higher in the high-expression TMEM158 group than in the low-expression group (Fig. 7 E). The TGF-β signaling network diagram presented in Fig. 7 F visually illustrated the strong signaling inputs received by the high-expression TMEM158 cell population. Finally, additional intercellular communication analysis indicated that the high-expression TMEM158 cell population primarily received TGF-β signals from macrophages—particularly M1-type macrophages—via ACVR1 and TGFBR1 (Fig. 7 G). It can be inferred that in the TME of OS, macrophage-derived TGFB1 binds to ACVR1 and TGFBR1 on the surface of OS cells that highly express TMEM158, thereby activating the TGF-β signaling pathway and promoting tumor malignancy progression. 4. Discussion Previous studies conducted in our laboratory have demonstrated that P53 +/− mice can spontaneously develop OS. Furthermore, the USP14 inhibitor IU1 and the dual-target inhibitor b-AP15 of USP14/UCHL5 have been shown to effectively suppress the progression of OS and significantly extend the survival time of TP53-deficient mice with spontaneous OS. During the investigation of their mechanisms of action, this study revealed that at the cellular level, IU1 and b-AP15 exhibited significantly stronger cytotoxic effects on TP53-KO primary OS cells compared to U2OS cells with wild-type TP53. This indicated that the absence of functional TP53 is a critical prerequisite for the efficacy of USP14 inhibitors. Recent studies have also highlighted the diverse roles of TP53 in OS. The mutation rate of p53 in patients with OS reaches up to 80%[ 16 ], among which the p53R172H and p53N239S mutations have been confirmed to play significant roles in the metastatic process of the disease[ 17 , 18 ]. Cisplatin and doxorubicin are clinically approved chemotherapeutic agents used in the treatment of OS. Earlier investigations have shown that the combination of Ad5- Δ 24RGD with either cisplatin or doxorubicin exerts a more pronounced inhibitory effect on TP53-deficient OS cells[ 19 ]. Moreover, knockdown of PRRX1 has been found to enhance the sensitivity of TP53-deficient 143B cells to both doxorubicin and cisplatin[ 20 ]. Collectively, these findings suggest that TP53 expression status significantly influences the drug sensitivity of OS cells. To further elucidate the mechanism underlying differential drug sensitivity in OS cells with varying P53 expression levels, this study conducted a differential gene association analysis and identified TMEM158 as the only gene that is positively correlated with both USP14 and UCHL5 expression. High expression of TMEM158 was found to be significantly associated with poor prognosis in patients. Mechanistic investigations revealed that IU1 and b-AP15 specifically down-regulate TMEM158 expression in TP53-KO cells but have no obvious effect on U2OS cells. Furthermore, TMEM158 knockout reduced the viability of TP53-KO cells by 40%, while showing minimal impact on U2OS cells. These findings indicate that TMEM158 serves as a critical mediator through which USP14 inhibitors exert selective anti-tumor effects in the context of TP53 deletion. TMEM158, also known as RIS1, is upregulated during RAS-induced cellular senescence. Previous studies have demonstrated that TMEM158 overexpression enhances the invasive potential of pancreatic cancer cells by promoting cell cycle progression and epithelial-mesenchymal transition (EMT)[ 21 ]. Moreover, elevated TMEM158 levels facilitate the proliferation of lung adenocarcinoma cells via direct interaction with TWIST1[ 22 ]. These findings underscore the pivotal role of TMEM158 in cancer progression and suggest its potential as a promising therapeutic target for the treatment of related malignancies. Meanwhile, in TP53-KO cells, the expression of TMEM158 was strongly and positively correlated with key components of the TGF-β signaling pathway, specifically TGFBR1 and SMAD2. Notably, treatment with USP14 inhibitors effectively suppressed the activation of this pathway by downregulating TMEM158. Single-cell analysis revealed that the cell population exhibiting high TMEM158 expression corresponded to osteoblastic OS cells, which displayed the highest CNV and exhibited a synergistic pattern characterized by attenuated P53 signaling and enhanced TGF-β signaling. TGF-β serves as the prototype member of the transforming growth factor superfamily and is ubiquitously expressed in various tissues, with particularly high levels observed in bone, lung, kidney, and placental tissues[ 23 ]. TGF-β plays a critical role in regulating the differentiation and functional activities of osteoblasts and osteoclasts[ 24 ]. In recent years, accumulating evidence has highlighted the pivotal involvement of TGF-β signaling in the progression of OS. Several genes, including INHBA, SQLE, ACSL4, and PNO1, have been identified to promote osteosarcoma development through modulation of the TGF-β signaling pathway[ 25 – 28 ]. Furthermore, TGF-β antagonists have demonstrated inhibitory effects on osteosarcoma both in vitro and in vivo [ 29 – 31 ]. Findings from multiple phase 1, 2, and 3 clinical trials also indicate that TGF-β antagonists are generally safe and well tolerated[ 32 ]. Numerous previous studies have investigated the association between TMEM158 and the TGF-β signaling pathway. Overexpression of TMEM158 has also been implicated in EMT via TGF-β pathway activation, thereby facilitating the migration, invasion, and metastasis of triple-negative breast cancer[ 14 ]. During the progression of pancreatic cancer, TMEM158 promoted tumor invasiveness through the activation of both TGFβ1 and PI3K/AKT signaling pathways[ 21 ]. Additionally, in glioblastoma progression, TMEM158 overexpression may stimulate EMT through STAT3 activation[ 33 ]. Taken together, these findings indicate that TP53-deleted OS cells exhibit malignant characteristics, with upregulated expression of TMEM158, USP14 and UCHL5 contributing to sustained activation of the TGF-β pathway and rendering these cells sensitive to USP14 inhibition. This study built upon previous findings and proposes a precise therapeutic strategy utilizing USP14 inhibitors, guided by the TP53 status. Currently, gene therapy represented the most advanced treatment for OS, functioning by targeting specific genetic and molecular drivers of tumor growth and metastasis[ 34 ]. The findings of this study indicate that, in comparison to other patient groups, OS patients exhibiting high expression levels of TMEM158 and low expression levels of P53 are most likely to derive therapeutic benefits from USP14 inhibitors. Furthermore, the combination of TMEM158 inhibitors or TGFBR1 antagonists (e.g., Galunisertib) with USP14 inhibitors may enhance anti-tumor efficacy. This innovative combinatorial therapeutic strategy aims to disrupt the tumor-promoting microenvironment by targeting the TMEM158-TGF-β pathway in malignant OS cells, thereby influencing OS progression. Although this study is the first to identify the critical role of TMEM158 in OS, further investigation is required to clarify the specific molecular mechanisms through which TMEM158 regulates USP14, including ubiquitination modification sites. The therapeutic efficacy should also be validated using patient-derived xenograft (PDX) models and prospective clinical trials. Additionally, the involvement of downstream TGF-β effector molecules, such as EMT-related genes, in drug resistance remains to be fully elucidated. 5. Conclusion This study identified TMEM158 as a key mediator linking USP14 and TGF-β signaling pathways. The activation of this pathway underlies the heightened sensitivity of TP53-deficient OS to USP14 inhibitors. Targeting the TMEM158-TGF-β pathway hold promise as a novel strategy for improving the prognosis of patients with TP53-deficient OS. Declarations Acknowledgments The authors are grateful for the Experimental research platform and technical support from the laboratory of Prof. Da Fu. Ethical approval The procedures for care and use of animals were approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiaotong University School of Medicine (RJ2023038). All applicable institutional and governmental regulations concerning the ethical use of animals were followed. Data availability statement The original data shown in this paper will be provided by the authors without inappropriate reservation to any qualified researcher. Funding information This study was sponsored by Fujian Provincial Health Technology Project (2022J011427), Key Project Foundation of Taizhou School of Clinical Medicine, Nanjing Medical University (TZKY20240302), Taizhou Social Development Project Foundation, Jiangsu, China (TS202309). Author contribution Study concept and design: ZYC, DF, YSM, YH and XFW. Acquisition of data: ZYC, MY, SYJ, ZYY, QXY, CNL, YS, XYL, TTD and XMT. Analysis and interpretation of data: ZYC, ZYY, CNL and YS. Statistical analysis: ZYC, ZYY and TTD. Drafting of the manuscript: ZYC, MY, SYJ, ZYY. Critical revision and final approval of the manuscript: ZYC, DF, YSM, YH and XFW. Obtained funding: HY and XFW. ZYC, MY, SYJ and ZYY contributed equally to this work. All authors contributed to the article and approved the submitted version. Conflict of interest disclosure The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Cole S, Gianferante DM, Zhu B, Mirabello L. Osteosarcoma: A Surveillance, Epidemiology, and End Results program-based analysis from 1975 to 2017. Cancer. 2022;128(11):2107-2118. Ogura K, Kawano H, Shimose S, Hiraoka K, Matsumine A, Kawai A. Osteosarcoma in Japan: report from the bone and soft tissue tumor registry 2006-2022. Jpn J Clin Oncol. Published online June 2, 2025. Teixeira LEM, Guedes A, Nakagawa SA, Fonseca KC, Lima ER. Update on Conventional Osteosarcoma. Rev Bras Ortop (Sao Paulo). 2024;59(6):e815-e820. Zhao Y, Han Y, Wang B, Wang T. Sirtuins in Osteosarcoma: Cracking the Code and Opening Up New Treatment Options. Curr Pharm Biotechnol. Published online April 25, 2025. Krishnan CK, Karagiri MR, Radhakrishnan V, Raja A. Clinical Progression on Neoadjuvant Chemotherapy: An Indicator of Poor Outcomes in Extremity Osteosarcoma. Indian J Surg Oncol. 2025;16(2):567-575. Li G, Wu N, Ghabrial J, Stinnett V, Klausner M, Morsberger L, Long P, Baraban E, Gross JM, Zou YS. Chromoanagenesis in Osteosarcoma. Biomolecules. 2025;15(6):833. Tlemsani C, Bougeard G, Gauthier-Villars M, Denizeau P, Winter S, Michot C, Baujat G, Bressac B, Adam de Beaumais T, Rouchaud A, Mihoubi-Bouvier F, Bourdeaut F, Brugières L, Leblanc T, Kasper E, Corradini N; GroupOs SFCE oncogenetic's groups. Bone sarcomas and cancer predisposition syndromes. Bull Cancer. 2025 Jun;112(6):664-680. Horisawa Y, Matsumoto T, Takeda J, Tashiro Y, Nomura R, Takeuchi S, Kawai Y, Kazuma Y, Konishi Y, Yamazaki H, Matsui H, Shirakawa K, Takaori-Kondo A. APOBEC3B Does Not Promote Tumor Progression in Tp53 Hemizygous Mice. Cancer Rep (Hoboken). 2025 Apr;8(4):e70189. Saba KH, Difilippo V, Kovac M, Cornmark L, Magnusson L, Nilsson J, van den Bos H, Spierings DC, Bidgoli M, Jonson T, Sumathi VP, Brosjö O, Staaf J, Foijer F, Styring E, Nathrath M, Baumhoer D, Nord KH. Disruption of the TP53 locus in osteosarcoma leads to TP53 promoter gene fusions and restoration of parts of the TP53 signalling pathway. J Pathol. 2024 Feb;262(2):147-160. Ma YS, Wang XF, Zhang YJ, Luo P, Long HD, Li L, Yang HQ, Xie RT, Jia CY, Lu GX, Chang ZY, Zhang JJ, Xue SB, Lv ZW, Yu F, Xia Q, Fu D. Inhibition of USP14 Deubiquitinating Activity as a Potential Therapy for Tumors with p53 Deficiency. Mol Ther Oncolytics. 2020 Jan 11;16:147-157. Zhang Q, Hao S, Wei G, Liu X, Miao Y. The p53-mediated cell cycle regulation is a potential mechanism for emodin-suppressing osteosarcoma cells. Heliyon. 2024 Feb 23;10(5):e26850. Zhu X, Liu T, Yin X. TMEM158, as plasma cfRNA marker, promotes proliferation and doxorubicin resistance in ovarian cancer. Pharmacogenomics J. 2024 Nov 15;24(6):34. Jeon M, Yoo S, Park S, Choi Y, An J, Noh YR, Kim I. Over-expression of Transmembrane Protein 158 Predicts Aggressive Tumor Behavior and Poor Prognosis in Lung Cancer. Anticancer Res. 2024 Nov;44(11):4885-4893. Tong J, Li H, Hu Y, Zhao Z, Li M. TMEM158 Regulates the Canonical and Non-Canonical Pathways of TGF-β to Mediate EMT in Triple-Negative Breast Cancer. J Cancer. 2022 May 21;13(8):2694-2704. Cui X, Lu J, Zhao C, Duan Y. Oncogenic transmembrane protein 158 drives the PI3K/Akt signaling pathway to accelerate gastric cancer cell growth. Braz J Med Biol Res. 2023 Nov 13;56:e12943. Olayode Babatunde, Ryan Quin Notti, and William D. Tap. Characterizing TP53 mutations in bone and soft tissue sarcoma.. Journal of Clinical Oncology. 2024(42), e23521-e23521. Zhang Y, Hu Q, Li G, et al. ONZIN Upregulation by Mutant p53 Contributes to Osteosarcoma Metastasis Through the CXCL5-MAPK Signaling Pathway. Cell Physiol Biochem. 2018;48(3):1099-1111. Lulin W, Jiawei L, Shuojie Z, et al. The P53N236S Mutation Plays a Regulatory Role in Osteosarcoma Metastasis Via the Cholesterol-Hedgehog Pathway. Cell Physiol Biochem. 2025;59(3):375-388. Graat HC, Witlox MA, Schagen FH, et al. Different susceptibility of osteosarcoma cell lines and primary cells to treatment with oncolytic adenovirus and doxorubicin or cisplatin. Br J Cancer. 2006;94(12):1837-1844. Joko R, Yamada D, Nakamura M, et al. PRRX1 promotes malignant properties in human osteosarcoma. Transl Oncol. 2021;14(1):100960. Fu Y, Yao N, Ding D, et al. TMEM158 promotes pancreatic cancer aggressiveness by activation of TGFβ1 and PI3K/AKT signaling pathway. J Cell Physiol. 2020;235(3):2761-2775. Xu T, Yin F, Shi K. TMEM158 functions as an oncogene and promotes lung adenocarcinoma progression through the PI3K/AKT pathway via interaction with TWIST1. Exp Cell Res. 2024;437(1):114010. Derynck R, Budi EH. Specificity, versatility, and control of TGF-β family signaling. Sci Signal. 2019;12(570):eaav5183. Trivedi T, Pagnotti GM, Guise TA, Mohammad KS. The Role of TGF-β in Bone Metastases. Biomolecules. 2021;11(11):1643. Zhang H, Huang Y, Wen Q, Li Y, Guo L, Ge N. INHBA gene silencing inhibits proliferation, migration, and invasion of osteosarcoma cells by repressing TGF-β signaling pathway activation [published correction appears in J Orthop Surg Res. 2025 Apr 9;20(1):355. Song Q, He L, Feng J. SQLE promotes osteosarcoma progression via activating TGFβ/SMAD signaling pathway. Mol Cell Probes. 2024;78:101993. Li X, Chen Q, Zhao D, et al. ACSL4 accelerates osteosarcoma progression via modulating TGF-β/Smad2 signaling pathway. Mol Cell Biochem. 2025;480(1):549-562. Fang L, Wang B, Yang Z, Zhao T, Hao W. PNO1 promotes the progression of osteosarcoma via TGF-β and YAP/TAZ pathway. Sci Rep. 2023;13(1):21827. Zhang H, Wu H, Zheng J, et al. Transforming growth factor β1 signal is crucial for dedifferentiation of cancer cells to cancer stem cells in osteosarcoma. Stem Cells. 2013;31(3):433-446. He D, Gao J, Zheng L, et al. TGF‑β inhibitor RepSox suppresses osteosarcoma via the JNK/Smad3 signaling pathway. Int J Oncol. 2021;59(5):84. Choi SH, Myers JT, Tomchuck SL, et al. Oral transforming growth factor-beta receptor 1 inhibitor vactosertib promotes osteosarcoma regression by targeting tumor proliferation and enhancing anti-tumor immunity. Cancer Commun (Lond). 2024;44(8):884-888. Ge R, Huang GM. Targeting transforming growth factor beta signaling in metastatic osteosarcoma. J Bone Oncol. 2023;43:100513. Li J, Wang X, Chen L, et al. TMEM158 promotes the proliferation and migration of glioma cells via STAT3 signaling in glioblastomas. Cancer Gene Ther. 2024 Mar;31(3):495-496. Brar GS, Schmidt AA, Willams LR, Wakefield MR, Fang Y. Osteosarcoma: current insights and advances. Explor Target Antitumor Ther. 2025;6:1002324. Additional Declarations No competing interests reported. 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07:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7071793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7071793/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11010-026-05487-0","type":"published","date":"2026-02-05T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89573983,"identity":"38169ee8-5275-4149-b2d7-15b272d14a52","added_by":"auto","created_at":"2025-08-21 12:50:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166901,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUSP14 inhibitors exhibit inhibitory effects on TP53-deficient, spontaneously developed OS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e TP53-deficient mice spontaneously develop various tumors, including lymphoma, osteosarcoma, and sarcoma. Tumor types are color-coded: red represents lymphoma, yellow indicates osteosarcoma, green denotes sarcoma, purple signifies other tumor types, and gray indicates no tumor formation. \u003cstrong\u003e(B)\u003c/strong\u003e Survival analysis of TP53-deficient mice: the control group is represented in blue, the IU1-treated group (USP14 inhibitor) in green, and the b-AP15-treated group (dual inhibitor of USP14 and UCHL5) in red. \u003cstrong\u003e(C)\u003c/strong\u003e HE staining of bone marrow tissue sections from mice across different experimental groups. \u003cstrong\u003e(D)\u003c/strong\u003e Comparative analysis of cell viability in U2OS and TP53-KO primary cells following treatment with equal concentrations of IU1 or b-AP15 for the same duration. \u003cstrong\u003e(E)\u003c/strong\u003e Comparative assessment of apoptosis induction under the respective treatment conditions. ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/3ab5b684d19e267389dc19d2.jpeg"},{"id":89573984,"identity":"739e92b0-c541-4100-8497-4213f1b44662","added_by":"auto","created_at":"2025-08-21 12:50:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":303131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the sensitivity of TP53-deficient OS to USP14 inhibitors and immune cell infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe dataset was stratified into high-expression and low-expression groups based on TP53 levels, and differences in immune cell infiltration within the TME between these two groups were analyzed. \u003cstrong\u003e(B)\u003c/strong\u003eInteractions among various immune cell types within the TME of OS. \u003cstrong\u003e(C)\u003c/strong\u003ePatient data were further categorized into four subgroups according to the expression levels of TP53 and USP14: TP53-high/USP14-high, TP53-high/USP14-low, TP53-low/USP14-high, and TP53-low/USP14-low, followed by survival curve analysis. \u003cstrong\u003e(D)\u003c/strong\u003e Similarly, patient data were classified into four subgroups based on TP53 and UCHL5 expression levels: TP53-high/UCHL5-high, TP53-high/UCHL5-low, TP53-low/UCHL5-high, and TP53-low/UCHL5-low, with subsequent survival curve analysis performed. \u003cstrong\u003e(E)\u003c/strong\u003e Variations in immune cell infiltration within the tumor microenvironment under different TP53 and USP14 expression conditions. \u003cstrong\u003e(F)\u003c/strong\u003e Variations in immune cell infiltration within the tumor microenvironment under different TP53 and UCHL5 expression conditions.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/6c2dc66cab7b5f9052c20ab9.jpeg"},{"id":89573986,"identity":"074124f4-191d-44bf-b290-758800ff51af","added_by":"auto","created_at":"2025-08-21 12:50:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction of TP53-deficient OS response to immunotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A, B)\u003c/strong\u003e The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was employed to assess and predict the sensitivity of osteosarcoma patients with different genotypes to immunotherapy. A positive TIDE score reflects a higher degree of T cell dysfunction and exclusion, indicating resistance to immune checkpoint inhibitors (ICBs). Conversely, a negative TIDE score suggests potential responsiveness to ICB treatment. \u003cstrong\u003e(A)\u003c/strong\u003eImmunotherapy sensitivity in patients with varying expression levels of TP53 and USP14 or \u003cstrong\u003e(B) \u003c/strong\u003eTP53 and UCHL5. \u003cstrong\u003e(C, D) \u003c/strong\u003eThe Dysfunction Score was used to evaluate the extent of T cell dysfunction within the tumor microenvironment. Higher scores indicate a greater likelihood of immune evasion and reduced efficacy of immune checkpoint inhibitor (ICI) therapy. \u003cstrong\u003e(C)\u003c/strong\u003eImmune Dysfunction Scores in patients with different expression levels of TP53 and USP14 or \u003cstrong\u003e(D) \u003c/strong\u003eTP53 and UCHL5.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/57f2c31cf804d05bd63215e7.jpeg"},{"id":89573994,"identity":"ab2a4131-6bc6-4197-9d7c-bae14b6421f4","added_by":"auto","created_at":"2025-08-21 12:50:12","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":262852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential genes and signaling pathways in OS with Varying TP53 expression levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eDifferentially expressed genes in OS with different levels of TP53 expression: High TP53 expression is represented in the right red section, while low TP53 expression is shown in the left blue section. Positive correlations are indicated in red, and negative correlations are indicated in blue. \u003cstrong\u003e(B) \u003c/strong\u003eGenes highly expressed in OS with low TP53 expression. Among these, the expression levels of four genes—ARNTL2, TMEM158, TNC, and PRUNE2—are positively correlated with USP14. Additionally, the expression levels of five genes—TMEM158, ACTA1, CA12, HSPB3, and JPH1—are positively correlated with UCHL5. \u003cstrong\u003e(C)\u003c/strong\u003e The impact of variations in the expression of eight selected genes on the survival curves of OS patients. \u003cstrong\u003e(D)\u003c/strong\u003eThe relationship between TMEM158 and genome-wide gene expression in OS. \u003cstrong\u003e(E)\u003c/strong\u003eThe correlation between key genes in the TMEM158 and TGF-β signaling pathways.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/a57c20c0088b156d8be1f6bd.jpeg"},{"id":89574003,"identity":"96818ba7-64f1-4549-9b82-900532d0c583","added_by":"auto","created_at":"2025-08-21 12:50:12","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":221954,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTMEM158 gene contributes to the differential effects of USP14 inhibitors through modulation of the TGF-β signaling pathway.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Effects of IU1 and b-AP15 on TMEM158 gene expression in U2OS cells and TP53-KO primary cells. \u003cstrong\u003e(B)\u003c/strong\u003e Impact of TMEM158 gene knockdown on the viability of U2OS cells and TP53-KO primary cells. \u003cstrong\u003e(C)\u003c/strong\u003e Influence of TMEM158 gene knockdown on apoptosis in U2OS cells and TP53-KO primary cells. \u003cstrong\u003e(D)\u003c/strong\u003e Effect of TMEM158 gene knockdown on the expression levels of key proteins TGFBR1 and p-SMAD2 in the TGF-β signaling pathway in U2OS cells and TP53-KO primary cells. \u003cstrong\u003e(E)\u003c/strong\u003e Effect of TMEM158 gene knockdown on the expression of TMEM158, TGFBR1, and SMAD2 in U2OS cells and TP53-KO primary cells. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/177c5e9da47d5bc5bb2db667.jpeg"},{"id":89575595,"identity":"db67f18e-d8f0-48c9-bb30-da771103d714","added_by":"auto","created_at":"2025-08-21 12:58:12","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":190707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey characteristics of tumor cells in the OS microenvironment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Cell clusters identified within the OS microenvironment, including B cells, Endothelial cells, Fibroblasts, Myeloid cells, Osteogenic OS cells, Osteoclasts, Plasma cells, and T/NK cells. \u003cstrong\u003e(B)\u003c/strong\u003eSpecific molecular markers associated with each distinct cell population. \u003cstrong\u003e(C)\u003c/strong\u003eRelative proportions of each cell population across six analyzed samples. \u003cstrong\u003e(D)\u003c/strong\u003eGene mutation frequencies observed within each cell population. \u003cstrong\u003e(E) \u003c/strong\u003eActivation statuses of key signaling pathways across different cell populations. \u003cstrong\u003e(F)\u003c/strong\u003eExpression levels of TMEM158 among various cell populations.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/3f905f8f9e27560567dc1aee.jpeg"},{"id":89573990,"identity":"f8b7989f-5c67-4ff4-b876-7c4d58382af4","added_by":"auto","created_at":"2025-08-21 12:50:12","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":276130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of intercellular communication in the OS microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Subclassification of myeloid cell subsets. \u003cstrong\u003e(B)\u003c/strong\u003e Subclassification of macrophage subsets. \u003cstrong\u003e(C) \u003c/strong\u003eSubclassification of cellular subsets within the OS microenvironment.\u003cstrong\u003e (D)\u003c/strong\u003e Ranking of key ligand-receptor contributions in the intercellular TGF-β signaling pathway. \u003cstrong\u003e(E)\u003c/strong\u003eInvolvement of the TGF-β signaling pathway across different cell populations. \u003cstrong\u003e(F)\u003c/strong\u003eTGF-β signal transduction network map in the OS microenvironment. \u003cstrong\u003e(G) \u003c/strong\u003eDistribution of key receptors in the TGF-β signaling pathway across distinct TMEM158-expressing cell populations.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/8b0a361c7b152c9d02da270f.jpeg"},{"id":102234840,"identity":"d2c8d0e8-bb10-4c03-80c4-63c84f7a03d5","added_by":"auto","created_at":"2026-02-09 16:13:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2959151,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/1e966b7b-8e78-41de-8119-6897aa1e39e2.pdf"},{"id":89575594,"identity":"d69c5c81-eafb-48ed-9912-6b42be6a7d4c","added_by":"auto","created_at":"2025-08-21 12:58:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":206071,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7071793/v1/ea1425b0ecec1927a0ccd243.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"TMEM158-mediated TGF-β signaling pathway modulates the sensitivity of TP53-deficient osteosarcoma to USP14 inhibitors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOsteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents, and the incidence rate among children aged 0 to 9 years is approximately 1.9 cases per million[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It accounts for about 20% of all primary malignant bone tumors, with a peak incidence occurring at two age ranges: adolescence (around 18 years old) and late adulthood (approximately 60 years old)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although surgical intervention combined with neoadjuvant therapy has significantly improved the 5-year survival rate for patients with localized disease, approximately 30\u0026ndash;50% of these patients still experience recurrence or metastasis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTP53 is the most frequently mutated gene in human OS and plays a critical role in the development and metastasis of the disease[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In murine models, the absence of functional TP53 leads to the development of OS[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. TP53 is frequently disrupted by structural variations, resulting in the loss of its classical tumor suppressive function. However, certain components of the TP53 signaling pathway that are essential for cancer cell survival and proliferation remain active[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Our previous research demonstrated that heterozygous TP53\u003csup\u003e+/\u0026minus;\u003c/sup\u003e mice spontaneously develop OS. Furthermore, we found that the USP14 inhibitor IU1 can induce tumor cell apoptosis and inhibit tumor growth in TP53-deficient \u003cem\u003ein vivo\u003c/em\u003e models[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, studies have shown that Emodin inhibited the activity of U2OS cells with normal P53 expression through TP53-mediated cell cycle regulation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings collectively highlight the critical role of TP53 in the initiation and progression of OS and suggest its involvement in the mechanisms of action of anti-OS therapies.\u003c/p\u003e\u003cp\u003eTMEM158, also known as RIS1 or p40BBp, is a transmembrane protein-coding gene located at chromosomal locus 3p21.31. It has been demonstrated to be highly expressed in various types of cancer, including lung cancer, breast cancer, and gastric cancer, among others, and is significantly correlated with unfavorable clinical outcomes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. TMEM158 primarily influences the proliferation, migration, and invasive capacity of cancer cells through modulation of the PI3K-AKT signaling pathway, thereby contributing to the progression of lung adenocarcinoma and gastric cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, its potential involvement in OS, particularly in relation to other regulatory pathways, such as TGF-β, remains to be elucidated.\u003c/p\u003e\u003cp\u003eIn this study, we integrated data from animal models, TP53-deficient primary cell detection, and clinical data analysis to systematically elucidate the central role of the TMEM158-TGF-β signaling pathway in modulating the sensitivity of TP53-deficient OS to USP14 inhibitors. These findings provide novel insights into targeted therapeutic strategies for OS.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Chemicals and reagents\u003c/h2\u003e\u003cp\u003eRabbit anti-TMEM158 Polyclonal Antibody(C-Term) (abs105949) was acquired from Absin Bioscience Inc. (China), TGF beta Receptor I (TGFBR1) Rabbit pAb (A0708) and Phospho-Smad2-S250 Rabbit mAb (AP1007) were acquired from ABclonal Biotechnology Co., Ltd. (China). GAPDH (10494-1-AP) was purchased from Proteintech Group, Inc. (Rosemont, IL, USA). Lipofectamine 2000 (#11,668\u0026thinsp;\u0026minus;\u0026thinsp;019) was acquired from Thermo Fisher Scientific. All other reagents were of analytical grade.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Cell cultural\u003c/h2\u003e\u003cp\u003eThe human OS cells (U2OS) was obtained from Procell Life ScienceCells, and was routinely cultured and maintained in McCoy\u0026rsquo;s 5A (Procell Life Science) supplemented with 10% FBS (Shanghai Yuli Bio\u0026amp;Tech Inc.) and 1% penicillin/streptomycin (Shanghai Yuli Bio\u0026amp;Tech Inc.) at 37\u0026deg;C in a humidified incubator containing 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Extraction of OS primary cells from TP53-deficient mouse\u003c/h2\u003e\u003cp\u003eTP53-deficient (TP53-KO) mice were constructed. After OS spontaneously formed in them, the tumors were completely removed, washed with PBS and digested with Tissue Digestion Solution (WM-D-02) for at least 30 minutes, and the undigested tissue blocks were screened out using a sieve. After centrifugation at 1000 rpm for 5 minutes, the supernatant was removed. An equal volume of matrix Gel (OM21, OmaStem) was added to the cell precipitate for organoid culture (OM14, OmaStem). OS organoids were digested using TrypLE Express (12604021, Thermo Fisher) and subcultured. After adherent culture of primary tumor cells, experiments could be conducted.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Small interfering RNA (siRNA)\u003c/h2\u003e\u003cp\u003eU2OS cell and TP53-KO primary cell were digested and seeded in 6-well, 12-well or 96-well plates (5\u0026times;10\u003csup\u003e4\u003c/sup\u003e/mL) before transfection. Lipofectamine 2000 was used to transfect 5 \u0026micro;l siRNA into cells which were cultured with a serum-free medium. After 6 hours, the medium was replaced with a fresh medium which contained serum. After incubation for 48 h, cells were collected. siTMEM58-1: Sense 5'-3' GCCCUAGAUUCAUGGCAGA/dT//dT/, Antisense (5'-3') UCUGCCAUGAAUCUAGGGC/dT//dT/;\u003c/p\u003e\u003cp\u003esiTMEM158-2: Sense 5'-3' GGGAAGGAUUUAACACCGA/dT//dT/, Antisense (5'-3') CGGUGUUAAAUCCUUCCC/dT//dT/.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Cell viability detection\u003c/h2\u003e\u003cp\u003eU2OS cells and TP53-KO primary tumor cells were cultured \u003cem\u003ein vitro\u003c/em\u003e and evenly distributed in 96-well plates at a density of 5,000 cells per well. After 24 hours, the cells adhered and were treated with b-AP15, IU1 or siTMEM158, the total volume was 100 \u0026micro;L. After 48 hours of treatment, 10 \u0026micro;L of CCK-8 detection reagent (MA0218, Meilunbio) was added to each well for 2 hours of reaction, and the absorbance at 450nm was measured.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Apoptosis Detection\u003c/h2\u003e\u003cp\u003eU2OS cells and TP53-KO primary tumor cells were plated into 12-well plates at a density of 5 \u0026times; 10⁴ cells/mL, with 1 mL per well. After 48 hours of treatment of USP14 inhibitor or siTMEM158, the cell supernatant was collected first. Trypsin was then added to digest the adherent cells, which were subsequently collected. The cells were washed twice with pre-cooled PBS, resuspended in 100 \u0026micro;L of 1\u0026times; Binding Buffer, transferred to flow cytometry tubes, and analyzed using a flow cytometer according to the manufacturer's protocol (A211-01, Vazyme).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 qRT-PCR (quantitative real-time PCR) detection\u003c/h2\u003e\u003cp\u003eAfter U2OS cells and TP53-KO primary tumor cells were transfected with siTMEM158 or treated with varying concentrations of b-AP15 or IU1 for 48 hours, total RNA was extracted and purified using SteadyPure Quick RNA Extraction Kit (AG21023, Accurate Biotechnology), followed by reverse transcription into cDNA in accordance with the manufacturer's instructions for HiScript Ⅲ All-in-one RT SuperMix Perfect for qPCR (R333-01, Vazyme). Acquired cDNAs were assayed by real-time PCR following the manufacturer\u0026rsquo;s protocol (#RR420A, TaKaRa Biotechnology). RNAs\u0026rsquo; relative expression level was analyzed using the 2\u0026ndash;ΔΔCT method. Primers used for qRT-PCR were summarized as Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Western blot\u003c/h2\u003e\u003cp\u003eApproximately 20\u0026ndash;30 \u0026micro;g of protein was extracted from the cell lysate and subsequently separated using SDS-PAGE gel electrophoresis, followed by transfer onto a PVDF membrane. To maximize antibody efficiency, the membrane was trimmed to retain only the region corresponding to the target protein prior to Western blotting. The primary antibody was then applied to selectively bind to the target protein, forming an immune complex that could be recognized by the enzyme-conjugated secondary antibody. Finally, the target protein was detected using super sensitive ECL luminescence reagent (MA0186, Meilunbio).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Evaluation of the Tumor immune microenvironment (TIME) and response to immunotherapy in OS\u003c/h2\u003e\u003cp\u003eTo assess the potential association between TP53, USP14, UCHL5 within the TIME of OS, the \"CIBERSORT\" algorithm was employed to estimate the relative proportions of 22 distinct immune cell subsets within tumor samples. Differences in the extent of immune cell infiltration across different gene expression groups were analyzed using the Wilcoxon rank-sum test. Additionally, to predict the likely responses of patients to immune checkpoint inhibitors (including anti-PD-1 and anti-CTLA-4 therapies), the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm developed at Harvard Medical School (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to evaluate the potential efficacy of immunotherapy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Analysis of Bulk RNA Sequencing Data\u003c/h2\u003e\u003cp\u003eTo further investigate the downstream regulatory pathways of TP53, this study utilized the online analysis platform GSCA (Gene Set Cancer Analysis, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guolab.wchscu.cn/GSCA/\u003c/span\u003e\u003cspan address=\"https://guolab.wchscu.cn/GSCA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to perform differential expression analysis on TCGA SARC bulk RNA-seq samples. Patients were categorized based on TP53 expression levels, and differentially expressed genes were identified and visualized using heatmaps. Subsequently, genes exhibiting a strong positive correlation with USP14 and UCHL5 expression were selected. These findings were integrated with survival analysis results to identify upregulated genes significantly associated with poor prognosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Survival Analysis\u003c/h2\u003e\u003cp\u003eSurvival analysis was conducted using the TCGA-SARC cohort, based on the expression levels of TP53, USP14, and UCHL5 to assess the impact of key genes on the prognosis of sarcoma patients. Patients were stratified into four groups according to the expression levels of each gene, and Kaplan-Meier survival curves were generated using the R packages 'survminer' and 'survival' to evaluate overall survival differences among the groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Functional enrichment and interaction analysis\u003c/h2\u003e\u003cp\u003eTo systematically investigate the potential biological functions of TMEM158 in OS, this study utilized the GSE232592 dataset and applied Spearman correlation analysis to assess the relationship between TMEM158 expression and whole-genome transcriptomic profiles. Genes that showed a statistically significant association with TMEM158 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were intersected with differentially expressed genes (logFC\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), resulting in a set of functionally relevant differentially expressed genes. Subsequently, Gene Set Enrichment Analysis (GSEA) was performed using the R package \"clusterProfiler\" to identify the key signaling pathways and biological processes associated with this gene set.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Single-cell transcriptome data processing and analysis\u003c/h2\u003e\u003cp\u003eSingle-cell RNA sequencing data from six OS patients were retrieved from the GEO database (accession number: GSE162454). Data preprocessing was conducted using the Seurat package (version 5.3.0) in R. All samples were merged, and low-quality cells were filtered out based on predefined criteria: cells with fewer than 300 detected genes or those exhibiting a mitochondrial gene content exceeding 10% were excluded. To address batch effects across samples, the Harmony package (version 1.2.3) was employed. Initial clustering was performed using the FindClusters function with a resolution parameter of 0.5, while dimensionality reduction and visualization were achieved through the t-SNE algorithm. For detailed analysis of myeloid cell subpopulations, specific subsets were extracted using the SubsetData function and subjected to re-clustering at a finer resolution (resolution\u0026thinsp;=\u0026thinsp;0.2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Copy number variation (CNV) analysis and identification of malignant cells\u003c/h2\u003e\u003cp\u003eTo evaluate the genomic CNV patterns across different cell subpopulations in OS samples and to identify potential malignant cell populations, this study employed the R package inferCNV (v1.22.0). Endothelial cells, plasma cells, and osteoblasts were selected as reference normal cell types. The relative CNV levels for each cell were inferred based on single-cell transcriptomic data. Key parameters used during the analysis included a truncation value of 0.1, with cluster_by_groups set to TRUE, denoise enabled as TRUE, and HMM modeling activated (HMM\u0026thinsp;=\u0026thinsp;TRUE). All other parameters followed default settings. Ultimately, suspected malignant clones were identified by detecting cells exhibiting significantly elevated CNV scores.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Pathway activity score\u003c/h2\u003e\u003cp\u003eTo assess the activation status of key signaling pathways within distinct cell populations, the run_mlm function from the decoupleR package (v2.12.0) was utilized to compute pathway activity scores for cellular groups, including those involved in the TP53 and TGF-β pathways. The results were visualized using heatmaps, providing an overview of the dynamic variations in pathway activation across different cell populations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.16 Analysis of intercellular communication\u003c/h2\u003e\u003cp\u003eBased on the median expression level of TMEM158, malignant OS cells were categorized into high-expression and low-expression groups. Intercellular communication analysis was performed using the R package \"CellChat\" (v1.6.1), aiming to systematically evaluate how TMEM158 expression influences receptor-ligand interaction networks among cell types. Particular emphasis was placed on analyzing specific intercellular communication patterns involving TMEM158 high- and low-expression groups and other annotated cell types within key signaling pathways. Results were visualized through network diagrams and heatmaps.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.1 USP14 inhibitors exhibit more pronounced inhibitory effects on OS with reduced TP53 expression.\u003c/h2\u003e\u003cp\u003eIn previous studies, our research team observed that mice with reduced TP53 expression spontaneously developed various tumors, including lymphoma, OS, and sarcoma. Among these, lymphoma exhibited the highest incidence rate, while OS and sarcoma also demonstrated considerable prevalence (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Pharmacodynamic analyses revealed that administration of the USP14 inhibitor IU1 or the dual inhibitor b-AP15, which targets both USP15 and UCHL5, significantly prolonged the median survival time of OS mice with reduced TP53 expression. This finding indicates the notable anti-tumor efficacy of these compounds against OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Moreover, histopathological examination of bone marrow tissues via hematoxylin and eosin (HE) staining showed that tumor regions in untreated OS mice were highly dense. However, following treatment with IU1 or b-AP15, tumor tissue density decreased markedly, with the structure becoming sparse and loosely organized (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), further supporting the therapeutic potential of these inhibitors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on these observations, we hypothesized that the effects of IU1 and b-AP15 might vary depending on the TP53 expression status in OS cells. To test this hypothesis, we isolated primary OS cells from TP53-deficient mice and compared them with U2OS cells, which express normal levels of TP53. Our results demonstrated that both cell types exhibited reduced viability after exposure to equivalent concentrations of IU1 or b-AP15 for the same duration. However, a significant difference in drug sensitivity was observed between the two cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Furthermore, apoptosis assays corroborated the differential anti-cancer effects of USP14 and UCHL5 inhibitors in cells with varying TP53 expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2 The enhanced drug sensitivity of TP53-deficient OS may not be directly associated with immune cell infiltration.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the underlying mechanism responsible for variations in drug sensitivity among cells with differing TP53 expression levels, we first analyzed OS data from a large-scale database. The dataset was stratified into two groups based on TP53 expression levels: high and low (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Our analysis revealed no significant differences in immune cell infiltration within the tumor microenvironment (TME) between these two groups. Subsequently, we further categorized the patient data into four subgroups according to the combined expression levels of TP53 and USP14: TP53-High / USP14-High, TP53-High / USP14-Low, TP53-Low / USP14-High, and TP53-Low / USP14-Low. Survival curve analysis demonstrated that patients exhibiting TP53-High / USP14-High had the shortest survival duration, followed by those with TP53-Low / USP14-High (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The hazard ratio analysis corroborated this trend, showing that patients with elevated USP14 expression were at a higher risk (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also examined the relationship between UCHL5 and P53 expression. Following a similar four-group classification, no significant differences in survival outcomes or risk profiles were observed among the subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, S1B). In terms of immune cell infiltration, aside from M0-type macrophages and monocytes, no notable differences were detected across the other immune cell types among the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). These findings suggest that USP14 inhibitors may hold therapeutic potential for OS patients and that their effects might primarily involve interactions with M0-type macrophages or monocytes. Collectively, these results indicate that the increased sensitivity of TP53-deficient OS to USP14 inhibitors may not be directly linked to changes in immune cell infiltration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3 TP53-deficient OS was more sensitive to immunotherapy\u003c/h2\u003e\u003cp\u003eTo predict the response of OS patients to immune checkpoint inhibitors, we conducted TIDE calculations. A positive TIDE value indicates a high score for T-cell dysfunction and clearance, suggesting no response to immune checkpoint inhibitors (ICBs), while a negative tide value indicates a response to ICBs. The results of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB showed that under the condition of the same expression level of USP14 or UCHL5, compared with patients with high expression of TP53, patients with low expression of TP53 show higher sensitivity to immunotherapy. The same phenomenon is also reflected in the Dysfunction score results. Patients with low expression of USP14 or UCHL5 have a greater potential for immune escape, and the efficacy of immune checkpoint suppression therapy (ICI) may be poorer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.4 TMEM158 may serve as a critical determinant contributing to variations in drug efficacy.\u003c/h2\u003e\u003cp\u003eTo elucidate the underlying mechanism of these differences in therapeutic response, we stratified OS data according to TP53 expression levels and identified differentially expressed genes. Our analysis revealed that, in tumors with low TP53 expression, genes located in the lower half exhibited significantly elevated expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Subsequently, correlation analyses between these genes and the expression of USP14 or UCHL5 were conducted, identifying four genes positively correlated with USP14 and five genes positively correlated with UCHL5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Notably, TMEM158 was the sole gene showing a positive correlation with both USP14 and UCHL5. Moreover, survival analysis indicated that patients with high TMEM158 expression had significantly shorter overall survival compared to those with low TMEM158 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), suggesting that TMEM158 may play a pivotal role in OS progression. Additionally, pathway enrichment analysis revealed that the TGF-β signaling pathway is significantly activated in OS with low TP53 expression and shows a positive correlation with TMEM158 expression changes. Key components of this pathway include TGFBR1 and SMAD2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.5 The TMEM158 gene may contribute to variations in drug efficacy through its influence on the TGF-β signaling pathway.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing treatment of two cell types with IU1 and b-AP15, the expression levels of the TMEM158 gene were assessed. The results indicated that in U2OS cells, TMEM158 expression remained largely unchanged after exposure to either drug. In contrast, both compounds significantly reduced TMEM158 expression in TP53-KO primary cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). To further investigate the functional role of TMEM158, siRNA was employed to knock down the gene in both cell types. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, cell viability assays revealed a significant decrease in viability in TP53-KO primary cells following TMEM158 knockdown, whereas no notable changes were observed in U2OS cells. Moreover, TMEM158-depleted TP53-KO primary cells exhibited an increased proportion of late apoptotic and dead cells compared to those in the siNC control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Given the potential involvement of the TGF-β signaling pathway, the expression levels of TGFBR1 and SMAD2 were evaluated in both cell types after TMEM158 knockdown. It was found that in TP53-KO primary cells, both mRNA and protein levels of TGFBR1 and SMAD2 showed a positive correlation with TMEM158 expression. However, there were no significant alterations in the TGF-β signaling components in U2OS cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Therefore, there is sufficient evidence to suggest that the TGF-β signal transduction pathway mediated by the TMEM158 gene may potentially account for the observed differences in drug efficacy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Reduced TP53 activity and high expression of TMEM158 represent key characteristics of OS cells\u003c/h2\u003e\u003cp\u003eTo further investigate the role of TMEM158 in the OS TME characterized by reduced TP53 expression, this study initially obtained relevant single-cell sequencing data and identified cell-specific markers based on existing literature (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Subsequently, cell populations were classified according to their specific markers, including B cells, Endothelial cells, Fibroblasts cells, Myeloid cells, Osteoblastic OS cells, Osteoclasts, Plasma cells, and T/NK cells. Data analysis revealed that Myeloid cells were the most abundant population, followed by Osteoblastic OS cells and T/NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Further analysis of gene mutation frequencies across cell populations indicated that Osteoblastic OS cells exhibited the highest CNV, suggesting a higher degree of malignancy and confirming their identity as tumor cells within the TME of OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Additionally, this study explored dynamic changes in signaling pathways among different cell types. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, compared with other cell populations, the P53 signaling pathway was markedly downregulated, while the TGF-β signaling pathway was significantly upregulated in the most malignant Osteoblastic OS cells. Moreover, TMEM158 demonstrated notably high expression levels in these cells and showed a positive correlation with TGF-β signaling activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), which aligns with above findings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.7 OS cells exhibiting high expression of TMEM158 were predominantly involved in intercellular communication with Macrophages.\u003c/h2\u003e\u003cp\u003eAlthough initial findings indicated that TMEM158 expression is most prominent in Osteoblastic OS cells, considering the heterogeneity of tumor cells, we categorized the cell population into two groups based on TMEM158 expression levels: high-expression and low-expression groups. Subsequently, Myeloid cells were further clustered (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Additionally, we conducted an in-depth investigation into the differences in cellular communication between these two subpopulations and other cell types. Our analysis revealed that, compared to the low-expression group, the high-expression TMEM158 cell population exhibited more extensive signaling interactions (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE). An analysis of TGF-β signaling pathways demonstrated that TGFB1 was the primary ligand secreted in the OS TME, with TGFBR1 being the most frequently engaged receptor (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Further examination of TGF-β signaling across different cell populations showed that both the intensity of signal emission and reception were higher in the high-expression TMEM158 group than in the low-expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). The TGF-β signaling network diagram presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF visually illustrated the strong signaling inputs received by the high-expression TMEM158 cell population. Finally, additional intercellular communication analysis indicated that the high-expression TMEM158 cell population primarily received TGF-β signals from macrophages\u0026mdash;particularly M1-type macrophages\u0026mdash;via ACVR1 and TGFBR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). It can be inferred that in the TME of OS, macrophage-derived TGFB1 binds to ACVR1 and TGFBR1 on the surface of OS cells that highly express TMEM158, thereby activating the TGF-β signaling pathway and promoting tumor malignancy progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePrevious studies conducted in our laboratory have demonstrated that P53\u003csup\u003e+/\u0026minus;\u003c/sup\u003e mice can spontaneously develop OS. Furthermore, the USP14 inhibitor IU1 and the dual-target inhibitor b-AP15 of USP14/UCHL5 have been shown to effectively suppress the progression of OS and significantly extend the survival time of TP53-deficient mice with spontaneous OS. During the investigation of their mechanisms of action, this study revealed that at the cellular level, IU1 and b-AP15 exhibited significantly stronger cytotoxic effects on TP53-KO primary OS cells compared to U2OS cells with wild-type TP53. This indicated that the absence of functional TP53 is a critical prerequisite for the efficacy of USP14 inhibitors. Recent studies have also highlighted the diverse roles of TP53 in OS. The mutation rate of p53 in patients with OS reaches up to 80%[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], among which the p53R172H and p53N239S mutations have been confirmed to play significant roles in the metastatic process of the disease[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Cisplatin and doxorubicin are clinically approved chemotherapeutic agents used in the treatment of OS. Earlier investigations have shown that the combination of Ad5-\u003csup\u003eΔ\u003c/sup\u003e24RGD with either cisplatin or doxorubicin exerts a more pronounced inhibitory effect on TP53-deficient OS cells[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, knockdown of PRRX1 has been found to enhance the sensitivity of TP53-deficient 143B cells to both doxorubicin and cisplatin[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Collectively, these findings suggest that TP53 expression status significantly influences the drug sensitivity of OS cells.\u003c/p\u003e\u003cp\u003eTo further elucidate the mechanism underlying differential drug sensitivity in OS cells with varying P53 expression levels, this study conducted a differential gene association analysis and identified TMEM158 as the only gene that is positively correlated with both USP14 and UCHL5 expression. High expression of TMEM158 was found to be significantly associated with poor prognosis in patients. Mechanistic investigations revealed that IU1 and b-AP15 specifically down-regulate TMEM158 expression in TP53-KO cells but have no obvious effect on U2OS cells. Furthermore, TMEM158 knockout reduced the viability of TP53-KO cells by 40%, while showing minimal impact on U2OS cells. These findings indicate that TMEM158 serves as a critical mediator through which USP14 inhibitors exert selective anti-tumor effects in the context of TP53 deletion. TMEM158, also known as RIS1, is upregulated during RAS-induced cellular senescence. Previous studies have demonstrated that TMEM158 overexpression enhances the invasive potential of pancreatic cancer cells by promoting cell cycle progression and epithelial-mesenchymal transition (EMT)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, elevated TMEM158 levels facilitate the proliferation of lung adenocarcinoma cells via direct interaction with TWIST1[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings underscore the pivotal role of TMEM158 in cancer progression and suggest its potential as a promising therapeutic target for the treatment of related malignancies.\u003c/p\u003e\u003cp\u003eMeanwhile, in TP53-KO cells, the expression of TMEM158 was strongly and positively correlated with key components of the TGF-β signaling pathway, specifically TGFBR1 and SMAD2. Notably, treatment with USP14 inhibitors effectively suppressed the activation of this pathway by downregulating TMEM158. Single-cell analysis revealed that the cell population exhibiting high TMEM158 expression corresponded to osteoblastic OS cells, which displayed the highest CNV and exhibited a synergistic pattern characterized by attenuated P53 signaling and enhanced TGF-β signaling. TGF-β serves as the prototype member of the transforming growth factor superfamily and is ubiquitously expressed in various tissues, with particularly high levels observed in bone, lung, kidney, and placental tissues[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. TGF-β plays a critical role in regulating the differentiation and functional activities of osteoblasts and osteoclasts[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In recent years, accumulating evidence has highlighted the pivotal involvement of TGF-β signaling in the progression of OS. Several genes, including INHBA, SQLE, ACSL4, and PNO1, have been identified to promote osteosarcoma development through modulation of the TGF-β signaling pathway[\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, TGF-β antagonists have demonstrated inhibitory effects on osteosarcoma both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Findings from multiple phase 1, 2, and 3 clinical trials also indicate that TGF-β antagonists are generally safe and well tolerated[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Numerous previous studies have investigated the association between TMEM158 and the TGF-β signaling pathway. Overexpression of TMEM158 has also been implicated in EMT via TGF-β pathway activation, thereby facilitating the migration, invasion, and metastasis of triple-negative breast cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. During the progression of pancreatic cancer, TMEM158 promoted tumor invasiveness through the activation of both TGFβ1 and PI3K/AKT signaling pathways[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, in glioblastoma progression, TMEM158 overexpression may stimulate EMT through STAT3 activation[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Taken together, these findings indicate that TP53-deleted OS cells exhibit malignant characteristics, with upregulated expression of TMEM158, USP14 and UCHL5 contributing to sustained activation of the TGF-β pathway and rendering these cells sensitive to USP14 inhibition.\u003c/p\u003e\u003cp\u003eThis study built upon previous findings and proposes a precise therapeutic strategy utilizing USP14 inhibitors, guided by the TP53 status. Currently, gene therapy represented the most advanced treatment for OS, functioning by targeting specific genetic and molecular drivers of tumor growth and metastasis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The findings of this study indicate that, in comparison to other patient groups, OS patients exhibiting high expression levels of TMEM158 and low expression levels of P53 are most likely to derive therapeutic benefits from USP14 inhibitors. Furthermore, the combination of TMEM158 inhibitors or TGFBR1 antagonists (e.g., Galunisertib) with USP14 inhibitors may enhance anti-tumor efficacy. This innovative combinatorial therapeutic strategy aims to disrupt the tumor-promoting microenvironment by targeting the TMEM158-TGF-β pathway in malignant OS cells, thereby influencing OS progression. Although this study is the first to identify the critical role of TMEM158 in OS, further investigation is required to clarify the specific molecular mechanisms through which TMEM158 regulates USP14, including ubiquitination modification sites. The therapeutic efficacy should also be validated using patient-derived xenograft (PDX) models and prospective clinical trials. Additionally, the involvement of downstream TGF-β effector molecules, such as EMT-related genes, in drug resistance remains to be fully elucidated.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study identified TMEM158 as a key mediator linking USP14 and TGF-β signaling pathways. The activation of this pathway underlies the heightened sensitivity of TP53-deficient OS to USP14 inhibitors. Targeting the TMEM158-TGF-β pathway hold promise as a novel strategy for improving the prognosis of patients with TP53-deficient OS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful for the Experimental research platform and technical support from the laboratory of Prof. Da Fu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe procedures for care and use of animals were approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiaotong University School of Medicine (RJ2023038). All applicable institutional and governmental regulations concerning the ethical use of animals were followed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data shown in this paper will be provided by the authors without inappropriate reservation to any qualified researcher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was sponsored by Fujian Provincial Health Technology Project (2022J011427), Key Project Foundation of Taizhou School of Clinical Medicine, Nanjing Medical University (TZKY20240302), Taizhou Social Development Project Foundation, Jiangsu, China (TS202309).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: ZYC, DF, YSM, YH and XFW. Acquisition of data: ZYC, MY, SYJ, ZYY, QXY, CNL, YS, XYL, TTD and XMT. Analysis and interpretation of data: ZYC, ZYY, CNL and YS. Statistical analysis: ZYC, ZYY and TTD. Drafting of the manuscript: ZYC, MY, SYJ, ZYY. Critical revision and final approval of the manuscript: ZYC, DF, YSM, YH and XFW. Obtained funding: HY and XFW. ZYC, MY, SYJ and ZYY contributed equally to this work. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCole S, Gianferante DM, Zhu B, Mirabello L. Osteosarcoma: A Surveillance, Epidemiology, and End Results program-based analysis from 1975 to 2017. Cancer. 2022;128(11):2107-2118. \u003c/li\u003e\n\u003cli\u003eOgura K, Kawano H, Shimose S, Hiraoka K, Matsumine A, Kawai A. Osteosarcoma in Japan: report from the bone and soft tissue tumor registry 2006-2022. Jpn J Clin Oncol. Published online June 2, 2025.\u003c/li\u003e\n\u003cli\u003eTeixeira LEM, Guedes A, Nakagawa SA, Fonseca KC, Lima ER. Update on Conventional Osteosarcoma. Rev Bras Ortop (Sao Paulo). 2024;59(6):e815-e820. \u003c/li\u003e\n\u003cli\u003eZhao Y, Han Y, Wang B, Wang T. Sirtuins in Osteosarcoma: Cracking the Code and Opening Up New Treatment Options. Curr Pharm Biotechnol. Published online April 25, 2025. \u003c/li\u003e\n\u003cli\u003eKrishnan CK, Karagiri MR, Radhakrishnan V, Raja A. Clinical Progression on Neoadjuvant Chemotherapy: An Indicator of Poor Outcomes in Extremity Osteosarcoma. Indian J Surg Oncol. 2025;16(2):567-575.\u003c/li\u003e\n\u003cli\u003eLi G, Wu N, Ghabrial J, Stinnett V, Klausner M, Morsberger L, Long P, Baraban E, Gross JM, Zou YS. Chromoanagenesis in Osteosarcoma. Biomolecules. 2025;15(6):833.\u003c/li\u003e\n\u003cli\u003eTlemsani C, Bougeard G, Gauthier-Villars M, Denizeau P, Winter S, Michot C, Baujat G, Bressac B, Adam de Beaumais T, Rouchaud A, Mihoubi-Bouvier F, Bourdeaut F, Brugi\u0026egrave;res L, Leblanc T, Kasper E, Corradini N; GroupOs SFCE oncogenetic\u0026apos;s groups. Bone sarcomas and cancer predisposition syndromes. Bull Cancer. 2025 Jun;112(6):664-680. \u003c/li\u003e\n\u003cli\u003eHorisawa Y, Matsumoto T, Takeda J, Tashiro Y, Nomura R, Takeuchi S, Kawai Y, Kazuma Y, Konishi Y, Yamazaki H, Matsui H, Shirakawa K, Takaori-Kondo A. APOBEC3B Does Not Promote Tumor Progression in Tp53 Hemizygous Mice. Cancer Rep (Hoboken). 2025 Apr;8(4):e70189.\u003c/li\u003e\n\u003cli\u003eSaba KH, Difilippo V, Kovac M, Cornmark L, Magnusson L, Nilsson J, van den Bos H, Spierings DC, Bidgoli M, Jonson T, Sumathi VP, Brosj\u0026ouml; O, Staaf J, Foijer F, Styring E, Nathrath M, Baumhoer D, Nord KH. Disruption of the TP53 locus in osteosarcoma leads to TP53 promoter gene fusions and restoration of parts of the TP53 signalling pathway. J Pathol. 2024 Feb;262(2):147-160.\u003c/li\u003e\n\u003cli\u003eMa YS, Wang XF, Zhang YJ, Luo P, Long HD, Li L, Yang HQ, Xie RT, Jia CY, Lu GX, Chang ZY, Zhang JJ, Xue SB, Lv ZW, Yu F, Xia Q, Fu D. Inhibition of USP14 Deubiquitinating Activity as a Potential Therapy for Tumors with p53 Deficiency. Mol Ther Oncolytics. 2020 Jan 11;16:147-157. \u003c/li\u003e\n\u003cli\u003eZhang Q, Hao S, Wei G, Liu X, Miao Y. The p53-mediated cell cycle regulation is a potential mechanism for emodin-suppressing osteosarcoma cells. Heliyon. 2024 Feb 23;10(5):e26850.\u003c/li\u003e\n\u003cli\u003eZhu X, Liu T, Yin X. TMEM158, as plasma cfRNA marker, promotes proliferation and doxorubicin resistance in ovarian cancer. Pharmacogenomics J. 2024 Nov 15;24(6):34. \u003c/li\u003e\n\u003cli\u003eJeon M, Yoo S, Park S, Choi Y, An J, Noh YR, Kim I. Over-expression of Transmembrane Protein 158 Predicts Aggressive Tumor Behavior and Poor Prognosis in Lung Cancer. Anticancer Res. 2024 Nov;44(11):4885-4893.\u003c/li\u003e\n\u003cli\u003eTong J, Li H, Hu Y, Zhao Z, Li M. TMEM158 Regulates the Canonical and Non-Canonical Pathways of TGF-\u0026beta; to Mediate EMT in Triple-Negative Breast Cancer. J Cancer. 2022 May 21;13(8):2694-2704.\u003c/li\u003e\n\u003cli\u003eCui X, Lu J, Zhao C, Duan Y. Oncogenic transmembrane protein 158 drives the PI3K/Akt signaling pathway to accelerate gastric cancer cell growth. Braz J Med Biol Res. 2023 Nov 13;56:e12943.\u003c/li\u003e\n\u003cli\u003eOlayode Babatunde, Ryan Quin Notti, and William D. Tap. Characterizing TP53 mutations in bone and soft tissue sarcoma.. Journal of Clinical Oncology. 2024(42), e23521-e23521.\u003c/li\u003e\n\u003cli\u003eZhang Y, Hu Q, Li G, et al. ONZIN Upregulation by Mutant p53 Contributes to Osteosarcoma Metastasis Through the CXCL5-MAPK Signaling Pathway. Cell Physiol Biochem. 2018;48(3):1099-1111.\u003c/li\u003e\n\u003cli\u003eLulin W, Jiawei L, Shuojie Z, et al. The P53N236S Mutation Plays a Regulatory Role in Osteosarcoma Metastasis Via the Cholesterol-Hedgehog Pathway. Cell Physiol Biochem. 2025;59(3):375-388.\u003c/li\u003e\n\u003cli\u003eGraat HC, Witlox MA, Schagen FH, et al. Different susceptibility of osteosarcoma cell lines and primary cells to treatment with oncolytic adenovirus and doxorubicin or cisplatin. Br J Cancer. 2006;94(12):1837-1844.\u003c/li\u003e\n\u003cli\u003eJoko R, Yamada D, Nakamura M, et al. PRRX1 promotes malignant properties in human osteosarcoma. Transl Oncol. 2021;14(1):100960.\u003c/li\u003e\n\u003cli\u003eFu Y, Yao N, Ding D, et al. TMEM158 promotes pancreatic cancer aggressiveness by activation of TGF\u0026beta;1 and PI3K/AKT signaling pathway. J Cell Physiol. 2020;235(3):2761-2775.\u003c/li\u003e\n\u003cli\u003eXu T, Yin F, Shi K. TMEM158 functions as an oncogene and promotes lung adenocarcinoma progression through the PI3K/AKT pathway via interaction with TWIST1. Exp Cell Res. 2024;437(1):114010.\u003c/li\u003e\n\u003cli\u003eDerynck R, Budi EH. Specificity, versatility, and control of TGF-\u0026beta; family signaling. Sci Signal. 2019;12(570):eaav5183.\u003c/li\u003e\n\u003cli\u003eTrivedi T, Pagnotti GM, Guise TA, Mohammad KS. The Role of TGF-\u0026beta; in Bone Metastases. Biomolecules. 2021;11(11):1643.\u003c/li\u003e\n\u003cli\u003eZhang H, Huang Y, Wen Q, Li Y, Guo L, Ge N. INHBA gene silencing inhibits proliferation, migration, and invasion of osteosarcoma cells by repressing TGF-\u0026beta; signaling pathway activation [published correction appears in J Orthop Surg Res. 2025 Apr 9;20(1):355.\u003c/li\u003e\n\u003cli\u003eSong Q, He L, Feng J. SQLE promotes osteosarcoma progression via activating TGF\u0026beta;/SMAD signaling pathway. Mol Cell Probes. 2024;78:101993.\u003c/li\u003e\n\u003cli\u003eLi X, Chen Q, Zhao D, et al. ACSL4 accelerates osteosarcoma progression via modulating TGF-\u0026beta;/Smad2 signaling pathway. Mol Cell Biochem. 2025;480(1):549-562.\u003c/li\u003e\n\u003cli\u003eFang L, Wang B, Yang Z, Zhao T, Hao W. PNO1 promotes the progression of osteosarcoma via TGF-\u0026beta; and YAP/TAZ pathway. Sci Rep. 2023;13(1):21827. \u003c/li\u003e\n\u003cli\u003eZhang H, Wu H, Zheng J, et al. Transforming growth factor \u0026beta;1 signal is crucial for dedifferentiation of cancer cells to cancer stem cells in osteosarcoma. Stem Cells. 2013;31(3):433-446.\u003c/li\u003e\n\u003cli\u003eHe D, Gao J, Zheng L, et al. TGF‑\u0026beta; inhibitor RepSox suppresses osteosarcoma via the JNK/Smad3 signaling pathway. Int J Oncol. 2021;59(5):84.\u003c/li\u003e\n\u003cli\u003eChoi SH, Myers JT, Tomchuck SL, et al. Oral transforming growth factor-beta receptor 1 inhibitor vactosertib promotes osteosarcoma regression by targeting tumor proliferation and enhancing anti-tumor immunity. Cancer Commun (Lond). 2024;44(8):884-888.\u003c/li\u003e\n\u003cli\u003eGe R, Huang GM. Targeting transforming growth factor beta signaling in metastatic osteosarcoma. J Bone Oncol. 2023;43:100513.\u003c/li\u003e\n\u003cli\u003eLi J, Wang X, Chen L, et al. TMEM158 promotes the proliferation and migration of glioma cells via STAT3 signaling in glioblastomas. Cancer Gene Ther. 2024 Mar;31(3):495-496.\u003c/li\u003e\n\u003cli\u003eBrar GS, Schmidt AA, Willams LR, Wakefield MR, Fang Y. Osteosarcoma: current insights and advances. Explor Target Antitumor Ther. 2025;6:1002324.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-and-cellular-biochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcbi","sideBox":"Learn more about [Molecular and Cellular Biochemistry](https://www.springer.com/journal/11010)","snPcode":"11010","submissionUrl":"https://submission.nature.com/new-submission/11010/3","title":"Molecular and Cellular Biochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7071793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7071793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePrevious studies have demonstrated that the USP14 inhibitor IU1 and USP14/UCHL5 inhibitor b-AP15 can extend the survival period of TP53-deficient mice with spontaneous osteosarcoma (OS). However, the underlying molecular mechanisms remain to be fully elucidated. The transmembrane protein TMEM158 has been identified as a key regulator in the progression of various cancers. Nevertheless, its functional role in OS remains largely unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this study, we conducted comprehensive bioinformatics analyses\u0026mdash;including cluster analysis, differential expression analysis, and functional enrichment analysis\u0026mdash;on clinical OS databases to assess the correlation between TMEM158 expression and the proteasome-associated USP14 and UCHL5. Primary tumor cells (TP53-deficient OS cells) and U2OS cells were treated with IU1 or b-AP15, respectively. The expression levels of TMEM158 were quantified using qPCR. Subsequently, TMEM158 was knocked down in both cell lines, and changes in cell viability and TGF-β signaling activity were evaluated. Additionally, single-cell RNA sequencing data were analyzed to identify cell types with high TMEM158 expression and their potential roles in intercellular communication.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBoth IU1 and b-AP15 significantly prolonged the survival of TP53-deficient OS mice and exhibited enhanced cytotoxic effects on TP53-deficient OS cells. These compounds selectively suppressed TMEM158 expression in TP53-deficient OS cells. Bioinformatics analysis revealed that TMEM158 is positively correlated with USP14 and UCHL5 expression and serves as an independent prognostic marker for poor clinical outcomes in OS patients. Experimental validation showed that TMEM158 knockdown significantly reduced the viability of TP53-deficient OS cells and inhibited TGF-β pathway activation. Osteoblastic OS cells displayed concurrent suppression of the P53 pathway and activation of the TGF-β pathway, with a strong covariant relationship between TMEM158 and TGF-β activity. Meanwhile, osteoblastic OS cells with high expression levels of TMEM158 demonstrate enhanced intercellular TGF-β signaling communication with macrophages.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings demonstrated that the TMEM158-TGF-β pathway plays a central role in mediating the heightened sensitivity of TP53-deficient OS to USP14 inhibition. Targeting this pathway may offer a promising therapeutic strategy for precision treatment of osteosarcoma.\u003c/p\u003e","manuscriptTitle":"TMEM158-mediated TGF-β signaling pathway modulates the sensitivity of TP53-deficient osteosarcoma to USP14 inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 12:50:07","doi":"10.21203/rs.3.rs-7071793/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T23:21:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T22:18:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T19:10:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T14:56:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184267234911028894772893909653952398087","date":"2025-10-27T12:15:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49932735301058700284923461240055059425","date":"2025-10-27T07:31:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194228858267098232821266408186820971038","date":"2025-10-24T22:21:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224219475050260380636736162508544475226","date":"2025-09-06T15:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T01:44:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-27T17:15:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T06:21:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular and Cellular Biochemistry","date":"2025-07-08T07:20:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-and-cellular-biochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcbi","sideBox":"Learn more about [Molecular and Cellular Biochemistry](https://www.springer.com/journal/11010)","snPcode":"11010","submissionUrl":"https://submission.nature.com/new-submission/11010/3","title":"Molecular and Cellular Biochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6bb38587-42ae-4152-bae7-b0828da95ca2","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:08:43+00:00","versionOfRecord":{"articleIdentity":"rs-7071793","link":"https://doi.org/10.1007/s11010-026-05487-0","journal":{"identity":"molecular-and-cellular-biochemistry","isVorOnly":false,"title":"Molecular and Cellular Biochemistry"},"publishedOn":"2026-02-05 15:57:42","publishedOnDateReadable":"February 5th, 2026"},"versionCreatedAt":"2025-08-21 12:50:07","video":"","vorDoi":"10.1007/s11010-026-05487-0","vorDoiUrl":"https://doi.org/10.1007/s11010-026-05487-0","workflowStages":[]},"version":"v1","identity":"rs-7071793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7071793","identity":"rs-7071793","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}