GBP1 inhibits osteosarcoma progression by regulating DDX17 protein stability via HSPA8

GBP1 inhibits osteosarcoma progression by regulating DDX17 protein stability via HSPA8 | 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 Article GBP1 inhibits osteosarcoma progression by regulating DDX17 protein stability via HSPA8 Shengyu Cui, Shuo Yang, Wen Huang, Xu Li, Shujiang Ye, Yixuan Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7033508/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Osteosarcoma (OS) stands as the preeminent primary malignant bone tumor, with its 5-year survival rate persistently lingering at 60%-70%. This therapeutic standstill is predominantly due to the shortage of sensitive early diagnostic markers and precise targeted treatment strategies. Guanylate-binding protein 1 (GBP1), a GTPase induced by interferons, serves as a tumor suppressor in multiple cancer types, yet its function and molecular mechanism in OS have remained uninvestigated. Our research reveals that GBP1 hinders OS tumor formation by promoting HSPA8-dependent ubiquitin-proteasome degradation of the DEAD-box RNA helicase 17 (DDX17) protein. Functional assays demonstrate that overexpression of GBP1 powerfully suppresses the proliferation, migration, and invasion of OS cells in vitro, and inhibits tumor growth in nude mouse xenograft models. Mechanistically, GBP1 forms a ternary complex with HSPA8 and DDX17, thereby enhancing the ubiquitination and proteasomal degradation of DDX17. Notably, overexpression of DDX17 counteracts the growth-inhibiting effects of GBP1, confirming its role as a crucial downstream effector. Clinical analyses show that GBP1 expression is markedly reduced in OS tissues, with a strong inverse correlation found between GBP1 levels and poor patient prognosis. These findings afford fresh perspectives on the biological processes driving OS progression and identify GBP1 as a potential therapeutic target for OS. Biological sciences/Cancer/Oncogenes Biological sciences/Cancer/Bone cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Osteosarcoma (OS) is the most prevalent primary malignant bone tumor, predominantly affecting the metaphyseal regions of long bones (e.g., the peri-knee area) in children and adolescents 1 . Despite current multimodal therapies (surgical resection, neoadjuvant/adjuvant chemotherapy), the 5-year survival rate has remained stagnant at 52.1% in China 2 . The lack of effective early diagnostic markers and precise therapeutic targets underscores the urgent need to identify novel molecular targets for improved OS treatment strategies. Guanylate-binding protein 1 (GBP1), a member of the interferon-responsive GTPase family, has been implicated as a tumor suppressor in diverse malignancies 3 . Preclinical evidence has shown that GBP1 overexpression exerts antiproliferative, antimigratory, and anti-invasive effects in colorectal cancer cells 4 . Conversely, in hepatocellular carcinoma, reduced GBP1 expression is associated with adverse clinical outcomes 5 . Notably, the role and mechanism of GBP1 in OS remain entirely uncharacterized, presenting a critical research gap. DEAD-box RNA helicase 17 (DDX17) regulates RNA metabolism (transcription, splicing, transport) and exhibits oncogenic functions in multiple cancers 6 , 7 . In breast cancer, DDX17 promotes proliferation via estrogen receptor α (ERα) transcriptional activation 8 , while in colorectal cancer, high DDX17 expression associates with metastasis and poor prognosis 9 . In liver cancer, DDX17 regulates alternative splicing of lncRNA PXN-AS1 to enhance metastasis 10 . However, its role in OS—particularly mechanisms governing protein stability—remains unexplored. Heat shock protein A8 (HSPA8), a HSP70 family chaperone, maintains protein quality control by facilitating folding, refolding, and degradation 11 . It promotes lysosomal degradation via -KFERQ motif recognition 12 and ubiquitin-mediated degradation through E3 ligase STUB1 binding 13 . Whether HSPA8 regulates DDX17 stability, and whether GBP1 modulates DDX17 degradation by influencing HSPA8-DDX17 interaction, are entirely unreported. This study hypothesizes that GBP1 suppresses OS progression by promoting HSPA8-mediated ubiquitination and degradation of DDX17. By experimentally validating this novel mechanism, we aim to uncover new insights into OS pathogenesis and provide a theoretical basis for developing GBP1/DDX17-axis-targeted therapies. Materials and Methods Antibodies and chemicals The following antibody was used in this study: GBP1 (Proteintech, catalog number: 15303-1-AP, 1:1000 for WB, 1:300 for IHC, 2ug for IP), DDX17 (Proteintech, catalog number: 19910-1-AP, 1:5000 for WB, 2ug for IP), HSPA8 (Proteintech, catalog number: 10654-1-AP, 3ug for IP), ubiquitin (Proteintech, catalog number: 10201–2-AP, 1:500 for WB), HA (Proteintech, catalog number: 51064-2-AP, 1:5000 for WB), GAPDH (Proteintech, catalog number: 60004-1-Ig, 1:10000 for WB). The following chemicals were used in this study: Cycloheximide (MCE, catalog number: HY-12320), Chloroquine (MCE, catalog number: HY-17589A), MG132 (MCE, catalog number: HY-13259). Collection of Specimens In this study, a total of 30 cases of OS tissues were procured through surgical resection, along with corresponding adjacent normal tissues from the same patients. Importantly, none of the patients had undergone any form of radiotherapy, chemotherapy, or other antitumor treatment prior to the surgical intervention. Once collected, these specimens were rapidly frozen using liquid nitrogen to preserve their integrity and subsequently stored at -80°C for future experimental use. The process of obtaining donor OS tissues and adjacent normal tissues, along with the ensuing experimental methodologies, received ethical approval from the Ethics Committee of Zhongshan Hospital. Cell Culture Human OS cell lines, specifically 143B and U2OS, in addition to normal human osteoblasts known as hFOB1.19, were sourced from the American Type Culture Collection (ATCC, USA). The hFOB1.19 osteoblasts were cultured in Dulbecco’s modified Eagle’s medium(Procell, catalog number: PM150210), which was enriched with 10% fetal bovine serum (FBS) to support optimal growth. Meanwhile, the 143B cell line was maintained in Minimum Essential Medium(Procell, catalog number: PM150410), also supplemented with 10% FBS, ensuring adequate nutrient availability. The U2OS cells were grown in McCoy’s 5A Medium(Procell, catalog number: PM150710), again with the addition of 10% FBS. All the cell lines mentioned above were incubated in a controlled environment set to 37°C with 5% CO2 to maintain physiological conditions conducive to cell growth and proliferation. Lentiviral Transduction To facilitate the study, lentiviral vectors encoding HA-tagged GBP1 were generated by Genechem (Shanghai), following the specified guidelines by the supplier. For the transduction process, both 143B and U2OS cells were plated in 12-well plates at a density of 1×10⁵ cells per well. Once the cells attained approximately 60% confluence, they were treated with lentiviral particles in the presence of polybrene for a duration of 12 hours. Following the transduction phase, the cells underwent selection with puromycin(Beyotime, catalog number:ST551) at a concentration of 4 µg/ml for a period of two weeks, leading to the establishment of stable cell lines. The resistant colonies that emerged were then isolated and expanded for subsequent downstream experiments, allowing for the investigation of the effects of the transduced elements within the cellular systems. SiRNA transfection In this study, 143B and U2OS cells underwent transfection with small interfering RNAs (siRNAs) utilizing Lipofectamine 2000, sourced from Invitrogen (catalog number: 2097561), at a final concentration of 50 nM. The siRNA transfection was conducted in serum-free Minimum Essential Medium or McCoy’s 5A Medium. Initially, both 143B and U2OS cells were distributed in 12-well plates at a density of 1 × 10⁵ cells per well. According to the manufacturer's protocol, complexes of siRNA and Lipofectamine 2000 were formulated and then introduced to the cells when they reached 60–70% confluency. After allowing the cells to incubate under conditions of 37°C for a duration of 4 to 6 hours, the transfection medium was replaced with complete medium supplemented with 10% FBS to facilitate recovery of the cells. The efficacy of silencing was evaluated either by qPCR or Western blot following a 48-hour period post-transfection. The DDX17 siRNA(5′-GGAAAGAGGUGUUGAGAUC-3′), USP10 siRNA(5′-GAGGAAAUGUUGAACCUAA-3′), HSPA8 siRNA(5′-ACGGAAAAGUCGAGAUAAU-3′), and non-targeting control siRNA(5′-UUCUCCGAACGUGUCACGU-3′) were synthesized by Tsingke Biotechnology. Cell Proliferation Assay and Colony Formation Assay The present study used a CCK-8 kit (Dojindo, catalog number: CK04) to evaluate cell proliferation ability. For this purpose, 3 × 10 3 cells were seeded into 96-well plates containing 100 µl of culture medium and cultured for 24, 48, and 72 hours. Subsequently, 10 µl of CCK-8 reagent was added to each well, and the plates were incubated for 2 hours. The absorbance at 450 nm was then measured using a microplate reader. For the colony formation assay, 1× 10 3 cells were seeded into 12-well plates and cultured at 37 ℃ for 12–14 days. The cells were then fixed with methanol, stained with crystal violet, and subsequently imaged. Cell Apoptosis Assay The process of measuring cell apoptosis was conducted with the aid of a Cell Apoptosis Kit from Beyotime (Catalog number: C1062S), following the manufacturer's detailed instructions. After allowing the cells to adhere for 24 hours, they were detached using EDTA-free trypsin(Beyotime, catalog number: C0205), washed twice with pre-cooled phosphate-buffered saline (PBS), and subsequently resuspended in 195 µL of Annexin V-FITC binding buffer to prepare for analysis. The cell suspension was gently mixed, after which 5 µl of Annexin V-FITC staining solution was added. To further enhance the analysis of apoptosis, 10 µl of propidium iodide (PI) staining solution was incorporated, and the cells were incubated for 10 to 20 minutes at room temperature, protected from light. A minimum of 1 × 10⁴ cells were then collected for analysis via flow cytometry, allowing for quantification and characterization of apoptotic cells. Cell Scratch Assay In a controlled experiment, 143B and U2OS cells were initially seeded into 12-well plates and allowed to grow until they reached full confluence. To assess cell migration, a scratch was introduced into the cell layer using the tip of a 200 µl pipette. Following this procedure, the cells were thoroughly washed twice with phosphate-buffered saline (PBS) to eliminate any floating cells that might interfere with the results. After washing, a medium containing 1% FBS was added to the wells, and the cells were cultured further. Images of the cell layer were captured at various time intervals, specifically at 0, 24 or 48 hours, using an optical microscope (×20 magnification), allowing for a comparison of the rate of cell migration over time. Cell Migration and Invasion For the evaluation of the migration and invasion capabilities of OS cells, transwell chamber assays were performed. The experimental setup involved utilizing chambers with an 8 µm pore size for migration assessments (Corning, catalog number: CLS3428) and Matrigel-precoated chambers (Corning, catalog number: CLS354234) for invasion evaluations. In this setup, a total of 5×10 4 143B and U2OS cells were carefully seeded into the upper chambers of 24-well plates. To promote cell migration via chemotaxis, a medium enriched with 10% FBS was provided in the lower chambers. Following a specified incubation period − 12 or 24 hours for migration assays and 24 or 36 hours for invasion assays — the cells that had migrated to the lower chambers were fixed using a 4% paraformaldehyde solution. To visualize the migrated cells, they were stained with crystal violet for a duration of 10 minutes and subsequently imaged using an optical microscope(×40 magnification). For data analysis, three random fields of view from each chamber were selected for cell counting, thereby ensuring an accurate assessment of the cell migration and invasion capabilities. Cycloheximide (CHX) Chase Assay In the cycloheximide chase assay, a total of 5×10 5 143B or U2OS cells were cultured in 6-well plates. Following a 24-hour incubation period, the cells were treated with a solution of CHX at a concentration of 50 µg/ml. Proteins from the cells were extracted at various time intervals, specifically at 0, 2, 4, or 8 hours after treatment. For the analysis of DDX17 levels, 20 µg of the extracted protein was subjected to Western blot, a reliable technique for protein detection and quantification. DDX17 Ubiquitination Assay To further explore the molecular dynamics involved in these processes, cells were cultured in 6-well plates and subjected to treatment with RIPA lysis buffer (Beyotime, catalog number: P2185S) containing a cocktail of protease inhibitors (Beyotime, catalog number: P2185S), which aids in the preservation of protein integrity during extraction. Prior to the harvesting of the cells, they were treated with 20 µM MG-132 for a period of 6 hours to inhibit proteasomal degradation. The resulting cell lysates were then clarified through centrifugation at 12,000 xg for 10 minutes at 4°C. Following clarification, equal amounts of the lysate were incubated with 2 µg of the DDX17 primary antibody on a shaking platform at 4°C for 8 hours to allow for specific binding. Subsequently, 10 µl of resuspended protein A + G beads were added to the mixture, which was then incubated overnight at the same temperature to facilitate immunoprecipitation. The immunoprecipitates were enriched through a centrifugation step, washed twice with 1 ml of RIPA buffer for 10 minutes each, and the supernatant was carefully aspirated and discarded to remove unbound materials. The remaining pellet was then resuspended in 20 µl of 1X sample buffer and subjected to heat at 95°C for 5 minutes to denature the proteins. Finally, the precipitated proteins were analyzed using western blot techniques to quantify the ubiquitination of DDX17, contributing to a deeper understanding of the cellular mechanisms under investigation. Hematoxylin-eosin and immunohistochemical Staining Paraffin-embedded tissue samples were meticulously sectioned into slices of 3 micrometers in thickness for the purposes of immunohistochemistry (IHC) and hematoxylin and eosin (H&E) staining. The IHC procedure began with the dewaxing of sections, followed by antigen retrieval by boiling in sodium citrate buffer (Proteintech, catalog number: PR30001) for a duration of 2 minutes. To inhibit the activity of endogenous peroxidase, the sections were treated with 3% hydrogen peroxide for 10 minutes. Subsequently, the sections were incubated with a primary antibody overnight at a temperature of 4°C. The following day, the sections were washed three times with phosphate-buffered saline-Tween (PBST) and then incubated with a horseradish peroxidase (HRP)-conjugated secondary antibody (MXB, catalog number: KIT-5010) for 30 minutes. The immunological reactions were developed using a DAB chromogenic kit (Proteintech, catalog number: PR30010), and finally, the sections were counterstained with hematoxylin to visualize the nuclei. For H&E staining, the process involved using hematoxylin for nuclear staining and eosin for the staining of cytoplasmic components. Western Blot Analysis The Western blot analysis was performed following established protocols with minor modifications. The cells were lysed on ice for 30 minutes using a RIPA lysis buffer (Beyotime, catalog number: P0013B) that was supplemented with a cocktail of protease inhibitors (Beyotime, catalog number:P1005). After lysis, the samples were centrifuged at 12,000 × g for 10 minutes at 4°C, allowing the clear supernatants to be collected for subsequent protein quantification. The protein samples were then separated via SDS-PAGE, after which they were transferred to polyvinylidene difluoride (PVDF) membranes that were blocked with 5% skim milk for 90 minutes. Following the blocking step, membranes were incubated with primary antibodies overnight at 4°C to ensure thorough binding. The next day, membranes underwent three washes with TBST before being incubated with secondary antibodies for a period of 60 minutes at room temperature. Finally, protein bands were visualized using an ECL chemiluminescence system (Biosharp, catalog number: BL520B), resulting in reliable detection of the target proteins. Q-PCR In the mRNA expression analysis, total RNA was meticulously extracted employing the Trizol reagent (Beyotime, catalog number: R0016), which is well-regarded for its efficacy in isolating high-quality RNA. To facilitate downstream applications, the RNA samples underwent reverse transcription to synthesize complementary DNA (cDNA) using a specialized reverse transcription kit provided by Abcam (Catalog number: G490). Subsequently, quantitative PCR (qPCR) was conducted utilizing the QuantStudio 5 instrument from Applied Biosystems. This instrument performed the crucial function of quantifying the levels of cDNA generated from the reverse transcription process. The specific primer sequences employed during the qPCR analysis are enumerated in Supplementary Table S1 , ensuring that readers can reference the exact sequences used. Immunoprecipitation and mass-spectrometry The extraction of cellular proteins was accomplished using the RIPA lysis buffer (Beyotime, catalog number:P2185S), which was complemented with a protease inhibitor cocktail to safeguard protein integrity. The lysis process was conducted on ice for a duration of five minutes to minimize protein degradation. Following this, the lysate underwent centrifugation at a high speed of 12,000 rpm for five minutes at a cold temperature of 4°C, resulting in the separation of the supernatant. This supernatant was then incubated overnight at 4°C on a rotating platform with primary antibodies or control IgG antibodies sourced from Beyotime (Catalog number: P2185S) to specifically capture target proteins. On the following day, the immunoprecipitated complexes were rigorously washed three times using the lysis buffer to eliminate any non-specific binding that could interfere with the accuracy of subsequent analyses. After completing the washing steps, 100 µl of SDS-PAGE loading buffer was introduced to the samples, which were subsequently heated at 95°C for five minutes. To analyze the protein content, Western blot was employed, with the primary antibody being visualized through incubation with a universal secondary antibody that targets both heavy and light chains (Abmart, catalog number: M21008, diluted to 1:5000). For the mass spectrometry analysis, the immunoprecipitated proteins were separated via SDS-PAGE, and the resultant gel bands were subjected to an in-gel trypsin digestion process. The ensuing peptides were then analyzed through liquid chromatography-tandem mass spectrometry (LC-MS/MS) at Shanghai Bioprofile Biotechnology. This analysis aimed to identify proteins that interact with GBP1, thereby providing insights into protein-protein interactions relevant to the research. Immunofluorescence Staining In this study, multiplex immunofluorescence staining was conducted utilizing the TSAPLus kit from Servicebio (Catalog number: G1236). The initial staining procedure involved a sequence of adding a primary antibody, followed by an HRP-conjugated secondary antibody and subsequent TSA amplification. To facilitate the analysis of multiple antigens, antibody stripping was carried out using a designated buffer provided with the kit, maintaining a temperature of 37°C for 30 minutes. This staining cycle was repeated for additional antigens, employing various fluorophore-conjugated TSA reagents, specifically iF488-Tyramide, iF555-Tyramide, and iF647-Tyramide, which allowed for the visualization of multiple colors, including DAPI for nuclear staining. The imaging of the stained samples was performed with a Nikon confocal microscope, and the obtained images were further analyzed using ImageJ software. Nude Mouse Xenograft Model The animal experiments conducted in this study received approval from the Institutional Animal Care and Use Committee at Zhongshan Hospital, Fudan University, with the approval number designated as NO. 2022-06-DJ-70. A nude mouse xenograft model was established to investigate the influence of GBP1 on OS growth in a live animal setting. To achieve this, stable 143B cells—either overexpressing GBP1 or containing a control vector-were injected subcutaneously at a concentration of 5×10⁶ cells per mouse into the right flank of four-week-old female BALB/c nude mice. Following a period of 28 days post-inoculation, the mice were euthanized, and the tumors that developed were harvested for further analysis. The dimensions of the tumors were measured using a vernier caliper, allowing for the calculation of tumor volume using the formula V = 0.5 × a × b², where ‘a’ represents the length and ‘b’ signifies the width. Furthermore, the tumors were weighed, photographed for documentation, and a portion of the tissue was preserved for subsequent HE staining and IHC analyses. Molecular Docking Three-dimensional structure files of target proteins were obtained from the PDB database ( https://www.rcsb.org/ ). The software PyMOL (v4.60) was used for structural visualization inspection and preprocessing, including removing water molecules, ligands, and other unrelated atoms from the structure, as well as adding hydrogen atoms and charges to meet the requirements for protein interaction preparation files. The processed protein structure files were uploaded to the ZDOCK online tool ( https://zdock.wenglab.org/ ) for protein-protein interaction prediction, and the rationality and possibility of the models were evaluated based on scoring functions. Using PyMOL, the selected optimal interaction model was displayed as a surface model. The two interacting proteins were colored light blue and yellow respectively to highlight their overall morphology and interaction interface. The interaction interface area was marked in red to intuitively present the spatial distribution and morphological characteristics of the contact region between the two proteins, facilitating the observation of potential binding sites and interaction areas. The secondary structures of the proteins were presented in white and light yellow, with key amino acid residues involved in the interaction highlighted in red, and their numbers and names labeled. Specific interaction types and strengths, such as hydrogen bonds and hydrophobic interactions formed by amino acids, were displayed through connecting lines and related distance labeling, deeply revealing the molecular basis of protein interactions. Statistical Analysis All experimental procedures were consistently replicated at least three times to ensure the reliability and reproducibility of the data collected. The statistical analyses were conducted using GraphPad Prism 9.0.0, a software suite renowned for its statistical capabilities in biological research. The presentation of data is as mean values accompanied by the standard error of the mean (Mean ± SEM), which provides a clear indication of variability within the data. To denote statistical significance across the findings, specific p-value thresholds were established, with significance indicated at *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001, thus allowing for a comprehensive understanding of the experimental results. Result Overexpression of GBP1 inhibits OS proliferation Previous studies have demonstrated the regulatory role of GBP1 in various types of tumors 4 , 5 , 14 , which has piqued our curiosity about whether GBP1 regulates the progression of OS.To further characterize the functional role of GBP1, stable GBP1-overexpressing cell lines were generated using a lentiviral vector system, with successful expression of HA-tagged GBP1 validated by immunoblotting(Fig. 1 A). Subsequent functional assays first evaluated GBP1's impact on cell proliferation. The CCK-8 assay demonstrated that forced GBP1 expression significantly attenuated cell viability compared to control groups both in 143B and U2OS(Fig. 1 B). Consistent results were observed in colony formation assays(Fig. 1 C). Flow cytometry analysis using Annexin V-FITC/PI double staining further revealed that GBP1 overexpression induced a significant increase in apoptotic cell populations(Fig. 1 D). Collectively, these findings indicate that GBP1 exerts tumor suppressive effects by inhibiting cell proliferation and promoting apoptosis in OS cells. To evaluate the in vivo biological function of GBP1, a xenograft model was established using female nude mice. A total of 6×10⁶ 143B cells stably transfected with either GBP1-Vector or GBP1-OE were orthotopically injected into the flanks of recipient mice. Tumor growth was monitored by caliper measurements until sacrifice at week 4. Mice injected with GBP1-OE cells developed significantly smaller tumors compared to GBP1-Vector controls, as determined by both volume and weight(Fig. 1 E). Immunohistochemical staining of tumor sections further revealed elevated Ki-67 proliferation marker expression in GBP1-overexpressing tumors(Fig. 1 F). Collectively, these in vivo data demonstrate that GBP1 overexpression potently suppresses OS growth in a xenograft model. Overexpression of GBP1 inhibits OS cells migration and invasion To elucidate the role of GBP1 in OS metastasis, we initially analyzed the transcriptional profile of GBP1 in OS with lung metastasis using the GEO dataset (GSE85537). Comparative analysis revealed a significant downregulation of GBP1 expression in lung - metastatic OS tissues relative to primary OS specimens, suggesting the potential involvement of GBP1 in the metastatic cascade of OS(Fig. 2 A). Subsequently, a series of in - vitro assays were performed to evaluate the functional impact of GBP1 on cancer cell migration and invasion. Wound - healing assays demonstrated that overexpression of GBP1 in 143B and HOS cell lines significantly attenuated the wound - closure rate(Fig. 2 B-E), indicating a suppression of cell migratory capacity. Additionally, transwell invasion assays, with the addition of Matrigel to mimic the in - vivo extracellular matrix, showed that GBP1 overexpression markedly impaired the invasive potential of 143B cells(Fig. 2 F-G). Consistent results were obtained in U2OS cells, ruling out cell - line - specific effects(Fig. 2 H-I).Collectively, our findings suggest that GBP1 functions as a negative regulator of OS cell migration and invasion. GBP1 inhibits the proliferation and metastasis of OS cells through DDX17 To dissect the inhibitory mechanism of GBP1 in OS progression, we hypothesized that GBP1 interacts with key proteins governing tumor malignancy. Immunoprecipitation using anti-HA antibodies was performed to enrich GBP1-interacting proteins in 143B cells, followed by mass spectrometry analysis(Fig. 3 A). A protein was validated as a candidate when its peptide count in GBP1-expressing cells was ≥ 1.5-fold higher than controls, with a minimum of 10 peptides. This screen identified 178 potential GBP1 interactors, among which DDX17 showed significantly elevated peptide counts in GBP1-stable cells(Fig. 3 B). Immunoprecipitation confirmed the GBP1-DDX17 interaction(Fig. 3 C-D, Figure S1 ), further validated by immunofluorescence co-localization(Fig. 3 E). Subsequently, we investigated whether GBP1-DDX17 interaction regulates protein levels. Ectopic expression of GBP1 led to a marked decrease in DDX17 protein abundance in both 143B and U2OS cell lines(Fig. 3 F), suggesting DDX17 as a GBP1 target. While DDX17 has been validated as an oncogenic driver in various cancer types, its functional role and regulatory mechanisms in OS remain largely uncharacterized. To investigate its role, we employed siRNA - mediated knockdown of DDX17 in 143B and U2OS cells. Efficient silencing was confirmed by qPCR and Western blot analyses(Figure S2 A-B). We first evaluated the effect of DDX17 on the proliferation and apoptosis of osteosarcoma cells. The CCK − 8 assay showed that, compared with the DDX17 - NC group, the cell viability of the DDX17 - siRNA group in 143B and U2OS cells was significantly reduced(Fig. 4 A, Figure S3 A). Concurrently, the apoptosis rate of the DDX17 - siRNA group was significantly higher than that of the DDX17 - NC group in both U2OS and 143B cells(Fig. 4 B, Figure S3 B). Next, we used a series of experiments to analyze the effect of DDX17 on the metastasis of OS cells. The wound - healing assay indicated that the relative migration rate of the DDX17 - siRNA group was significantly lower than that of the DDX17 - NC group, suggesting that silencing DDX17 inhibits the migration of 143B and U2OS cells(Fig. 4 C, Figure S3 C). Additionally, transwell migration and invasion assays showed that silencing DDX17 significantly impaired the migration and invasion abilities of 143B and U2OS cells(Fig. 4 D, Figure S3 D). Collectively, these data establish DDX17 as a critical driver of OS progression, as its depletion inhibits proliferation, induces apoptosis, and impairs metastatic potential, highlighting DDX17 as a potential therapeutic target in osteosarcoma. To evaluate whether DDX17 mediates the regulatory effect of GBP1 on OS, we overexpressed DDX17 in GBP1-Vector and GBP1-OE cells. Western blot confirmed DDX17 overexpression (Figure S2 C), while CCK8 (Fig. 4 E, Figure S3 E) and colony formation(Fig. 4 F, Figure S3 F) assays showed that DDX17 overexpression rescued proliferation in GBP1-OE cells. Wound-healing assays demonstrated enhanced migration in DDX17-overexpressing 143B and HOS cells(Fig. 4 G, Figure S3 G), and transwell assays confirmed increased invasion compared to GBP1-OE controls(Figure S4 A-B). Collectively, these data establish DDX17 as a critical GBP1 target, mechanistically linking GBP1 to the suppression of OS malignant progression. GBP1 promotes DDX17 degradation via the ubiquitin-proteasome system Given that DDX17 mediates GBP1-induced suppression of OS malignancy, we investigated the precise mechanism by which GBP1 regulates DDX17(Fig. 5 A). qPCR assays showed no significant change in DDX17 transcription following GBP1 overexpression, prompting the hypothesis that GBP1 modulates DDX17 protein stability. To validate this, cells were treated with cycloheximide (CHX, a protein synthesis inhibitor), and Western blotting quantified DDX17 levels. GBP1 overexpression significantly shortened DDX17 half-life(Fig. 5 B). To explore degradation pathways, cells were co-treated with CHX and MG-132 (10 µM, proteasome inhibitor) or chloroquine (CQ, 25 µM, lysosome inhibitor). MG-132 significantly attenuated DDX17 degradation, whereas CQ had no effect(Fig. 5 C), indicating a dominant role for the proteasomal pathway in DDX17 turnover. Subsequently, DDX17 polyubiquitination was measured. Cells were treated with MG-132 to enhance ubiquitination, followed by immunoprecipitation of endogenous DDX17 and Western blotting for ubiquitin. GBP1-overexpressing cells exhibited augmented ubiquitin signals, confirming that GBP1 accelerates DDX17 polyubiquitination in OS cells(Fig. 5 D). Collectively, these data demonstrate that GBP1 promotes DDX17 degradation via the ubiquitin-proteasome system, providing mechanistic insights into the GBP1-DDX17 axis in OS progression. GBP1 promotes the ubiquitination of DDX17 via HSPA8 Given that GBP1 lacks intrinsic ubiquitination activity, we aimed to identify specific ubiquitin ligases or deubiquitinases mediating GBP1-regulated DDX17 ubiquitination. Through in silico prediction of DDX17-interacting ubiquitin/deubiquitin enzymes using the UbiBrowser database, combined with Venn analysis of IP-MS results, USP10 and HSPA8 were identified as potential DDX17 interactors(Fig. 6 A). To validate the key enzyme, siRNA screening was performed to assess ubiquitin ligases/deubiquitinases involved in GBP1-mediated DDX17 degradation. Notably, in GBP1-overexpressing cells, HSPA8 silencing abrogated DDX17 level discrepancies, whereas USP10 silencing had no effect(Fig. 6 B-C). Half-life assays showed HSPA8 overexpression shortened DDX17 half-life(Fig. 6 D-E), and ubiquitination assays confirmed enhanced DDX17 ubiquitination(Fig. 6 F). These findings were recapitulated in 143B and U2OS cell lines, establishing HSPA8 as a mediator of GBP1-regulated ubiquitin-dependent DDX17 degradation. To investigate direct protein interactions, immunoprecipitation confirmed HSPA8-DDX17 binding(Fig. 7 A), consistent with immunofluorescence showing partial co-localization(Fig. 7 B). It was Interesting that immunoprecipitation revealed GBP1-HSPA8 interaction(Fig. 7 C,Figure S5 A), further validated by immunofluorescence(Fig. 7 D, Figure S5 B). Given GBP1-DDX17 interaction previously identified, we hypothesized a ternary complex formation. Protein-protein docking using ZDOCK predicted the GBP1-HSPA8-DDX17 complex structure, with 3D models derived from PDB and visualized by PyMOL(Fig. 7 E). Immunofluorescence confirmed co-localization of GBP1, HSPA8, and DDX17, indicating distinct binding domains on GBP1 for HSPA8 and DDX17(Fig. 7 F). Immunoprecipitation showed enhanced HSPA8-DDX17 interaction in GBP1-overexpressing cells, suggesting GBP1 promotes their binding(Fig. 7 G). Collectively, these data demonstrate that GBP1 forms a stable complex with HSPA8 and DDX17, whereby GBP1 enhances HSPA8-DDX17 interaction to promote DDX17 ubiquitination and proteasomal degradation. Clinical Relevance of GBP1 and DDX17 in OS Given the tumor-suppressive function of GBP1 in OS progression, we explored the clinical relevance of our findings. GBP1 transcriptional profiles were analyzed using the GEO dataset (GSE197158), revealing significantly reduced GBP1 mRNA expression in OS cell lines versus the normal osteoblast line hFOB1.19(Fig. 8 A). Immunoblotting validated translational downregulation in OS cells(Fig. 8 B-C). Western blot analysis of 10 pairs of OS tissues and adjacent normals showed GBP1 underexpression and DDX17 overexpression in OS(Fig. 8 D-E), with a significant negative correlation between GBP1 and DDX17 levels(Fig. 8 G). Immunohistochemical staining of 30 OS tissue pairs confirmed this inverse correlation: low GBP1 expression was associated with high DDX17, and vice versa, consistent with WB results(Fig. 8 F-H). Representative IHC images of differential expression are provided. Clinicopathological analysis identified low GBP1 expression as a significant biomarker for poor overall survival in OS patients(Fig. 8 I). Data from TARGET further showed that low GBP1 expression correlated with reduced disease-free survival(Fig. 8 J). Collectively, these findings implicate coordinated roles for GBP1 and DDX17 in regulating OS progression and patient prognosis. Discussion This study systematically elucidates the tumor-suppressive role of GBP1 in OS progression, uncovering a novel mechanism whereby GBP1 promotes HSPA8-mediated ubiquitin-proteasome degradation of DDX17. Multidimensional experimental validation confirms that GBP1 inhibits OS cell proliferation, migration, and invasion by enhancing DDX17 ubiquitination through a ternary complex with HSPA8. These findings not only expand the understanding of OS pathogenesis but also establish the GBP1/DDX17 axis as a promising therapeutic target. GBP1 expression is significantly downregulated in OS tissues and cell lines, with low expression tightly associated with poor patient prognosis—a finding consistent with its tumor-suppressive roles in colorectal and liver cancer 4 , 5 . Notably, OS exhibits a negative correlation between GBP1 expression and tumor metastatic potential, underscoring its critical role in metastatic regulation. In contrast, studies in cervical cancer 14 , cutaneous squamous cell carcinoma 15 , lung cancer 16 and glioma 17 , 18 have identified GBP1 as an oncogene, highlighting its context-dependent functions across malignancies. Functional assays further demonstrate that GBP1 overexpression suppresses OS cell malignancy in vitro and inhibits tumor growth in nude mouse xenografts, positioning GBP1 as a potential therapeutic target. However, the regulatory mechanisms of GBP1 expression in OS remain uncharacterized, warranting exploration of upstream regulators (e.g., transcription factors or epigenetic modifiers) in future investigations. The study reveals that GBP1 exerts tumor suppression by promoting DDX17 degradation via the ubiquitin-proteasome system. Mechanistically, GBP1 directly interacts with DDX17 to recruit HSPA8, forming a ternary complex that enhances DDX17 ubiquitination. Although HSPA8 lacks intrinsic E3 ligase activity, its C-terminus binds STUB1 to facilitate substrate ubiquitination 19 . As a molecular chaperone, HSPA8 plays dual roles: recognizing DDX17 domains to promote ubiquitination and stabilizing the HSPA8-DDX17 complex upon GBP1 binding, thereby accelerating degradation. This discovery expands HSPA8 functions in tumors, demonstrating its involvement in oncoprotein degradation beyond classical folding regulation. Molecular docking assays validate the formation of the GBP1-HSPA8-DDX17 complex, providing a structural basis for subsequent mechanistic studies. Implications DDX17, a DEAD-box family member, promotes OS malignancy through multiple mechanisms: its overexpression reverses GBP1-induced growth inhibition and enhances OS cell migration/invasion. This aligns with DDX17’s established oncogenic roles in breast cancer 8 , HCC 6 , colorectal cancer 9 and lung adenocarcinoma 20 , suggesting conserved regulatory functions across cancers. DDX17 degradation likely disrupts its involvement in RNA metabolism and target gene transcription, thereby influencing OS malignant phenotypes 21 . Future studies should characterize DDX17’s specific RNA substrates and the transcriptomic effects of the GBP1-DDX17 axis to decipher OS molecular heterogeneity. As an interferon-induced protein, GBP1 expression may be regulated by the immune microenvironment. Public cohort analyses show GBP1 predicts immunotherapy response, with macrophage-specific expression linked to T-cell chemotaxis 22 . Similar correlations between GBP1 and immune infiltration exist in cervical cancer 23 . However, this study did not address GBP1-immune crosstalk in OS-critical avenue for future research. Targeting the GBP1/DDX17 axis may exert dual effects of direct tumor suppression and immune activation, leveraging GBP1’s immunomodulatory functions. This work has notable limitations: 1) Animal models only validated GBP1’s effect on tumor growth, not metastasis; 2) The specific domains and key amino acids of the GBP1-HSPA8-DDX17 complex remain undefined; 3) GBP1’s regulation via alternative pathways requires validation. Future research should: 1) Construct conditional GBP1 knockout OS mouse models to study metastatic roles. 2) Elucidate the complex’s three-dimensional structure via crystallography to identify interaction hotspots. 3) Integrate omics approaches to screen DDX17 downstream targets and refine signaling networks. 4) Develop small-molecule inhibitors targeting GBP1-DDX17 interactions for translational applications. In summary, this study establishes GBP1 as a tumor suppressor in OS, uncovering a mechanism whereby GBP1 promotes HSPA8-mediated DDX17 degradation. These findings advance the understanding of OS molecular pathology and provide a theoretical framework for developing GBP1/DDX17-targeted therapies. Future studies should refine this regulatory network and facilitate its clinical translation to improve OS treatment outcomes. Declarations Acknowledgments We acknowledge all the publicly available database employed in this study ,in particular, Gene Expression Omnibus, as well as TARGET projects. We gratefully acknowledge all the patients involved in the study. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This research was funded by Nantong Municipal Health Commission Scientific Research Project (Youth Program, Shuo Yang, QN2024072), Nantong Municipal Health Commission Scientific Research Project (General Program, Shengyu Cui, MS2024027) and Bethune Charitable Foundation(Rongkui Luo, G-X-2019-0101-12). Availability of data and materials All the data generated during the current study are available from the corresponding author on reasonable request. Informed consent statement The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Zhongshan Hospital (No. B2024-121). Authors’ contributions Shuo Yang: Writing - original draft, Conceptualization, Investigation, Methodology, Validation, Funding acquisition. Wen Huang: Investigation, Methodology, Formal analysis, Resources, Data curation. Xu Li: Formal analysis, Validation, Conceptualization. Shujiang Ye: Methodology, Investigation. Yixuan Li: Validation, Methodology. Hanrui Liu: Data curation, Methodology. Jiafeng He: Investigation, validation. Rongkui Luo: Writing - review & editing, Supervision, Conceptualization, Funding acquisition. Shengyu Cui: Writing - review & editing, Supervision, Funding acquisition, Conceptualization, Project administration. References Young, E.P., Marinoff, A.E., Lopez-Fuentes, E., and Sweet-Cordero, E.A. (2024). Osteosarcoma through the lens of bone development, signaling, and microenvironment. Cold Spring Harb. Perspect. Med. 14 , a41635. Yao, Z., Tan, Z., Yang, J., Yang, Y., Wang, C., Chen, J., Zhu, Y., Wang, T., Han, L., and Zhu, L., et al. (2021). Prognostic nomogram for predicting 5-year overall survival in chinese patients with high-grade osteosarcoma. Sci. Rep. 11 , 17728. Honkala, A.T., Tailor, D., and Malhotra, S.V. (2019). Guanylate-binding protein 1: an emerging target in inflammation and cancer. Front. Immunol. 10 , 3139. Britzen-Laurent, N., Lipnik, K., Ocker, M., Naschberger, E., Schellerer, V.S., Croner, R.S., Vieth, M., Waldner, M., Steinberg, P., and Hohenadl, C., et al. (2013). Gbp-1 acts as a tumor suppressor in colorectal cancer cells. Carcinogenesis 34 , 153–162. Ning, Y., Fang, S., Fang, J., Lin, K., Nie, H., Xiong, P., Qiu, P., Zhao, Q., Wang, H., and Wang, F. (2023). Guanylate-binding proteins signature predicts favorable prognosis, immune-hot microenvironment, and immunotherapy response in hepatocellular carcinoma. Cancer Med. 12 , 17504–17521. Zhou, H., Li, F., Cheng, S., Xu, Y., Deng, H., Gu, D., Wang, J., Chen, W., Zhou, Y., and Yang, M., et al. (2022). Ddx17-regulated alternative splicing that produced an oncogenic isoform of pxn-as1 to promote hcc metastasis. Hepatology. 75 , 847–865. Jin, C., Han-Hua, D., Qiu-Meng, L., Deng, N., Peng-Chen, D., Jie, M., Lei, X., Xue-Wu, Z., Hui-Fang, L., and Yan, C., et al. (2023). Mtdh-stabilized ddx17 promotes tumor initiation and progression through interacting with yb1 to induce egfr transcription in hepatocellular carcinoma. Oncogene 42 , 169–183. Wortham, N.C., Ahamed, E., Nicol, S.M., Thomas, R.S., Periyasamy, M., Jiang, J., Ochocka, A.M., Shousha, S., Huson, L., and Bray, S.E., et al. (2009). The dead-box protein p72 regulates eralpha-/oestrogen-dependent transcription and cell growth, and is associated with improved survival in eralpha-positive breast cancer. Oncogene 28 , 4053–4064. Zhao, G., Wang, Q., Zhang, Y., Gu, R., Liu, M., Li, Q., Zhang, J., Yuan, H., Feng, T., and Ou, D., et al. (2023). Ddx17 induces epithelial-mesenchymal transition and metastasis through the mir-149-3p/cybrd1 pathway in colorectal cancer. Cell Death Dis. 14 , 1. Zhou, H., Li, F., Cheng, S., Xu, Y., Deng, H., Gu, D., Wang, J., Chen, W., Zhou, Y., and Yang, M., et al. (2022). Ddx17-regulated alternative splicing that produced an oncogenic isoform of pxn-as1 to promote hcc metastasis. Hepatology. 75 , 847–865. Ruzza, A., Zaltron, E., Vianello, F., Celotti, I., Scavezzon, M., Severin, F., and Leanza, L. (2025). Hspa8 and hspa9: two prognostic and therapeutic targets in breast, colon, and kidney cancers? Biochim. Biophys. Acta-Mol. Basis Dis. 1871 , 167827. Desideri, E., Castelli, S., Dorard, C., Toifl, S., Grazi, G.L., Ciriolo, M.R., and Baccarini, M. (2023). Impaired degradation of yap1 and il6st by chaperone-mediated autophagy promotes proliferation and migration of normal and hepatocellular carcinoma cells. Autophagy 19 , 152–162. Chen, B., Chang, Y., Lin, S., and Yang, W.Y. (2020). Hsc70/stub1 promotes the removal of individual oxidatively stressed peroxisomes. Nat. Commun. 11 , 5267. Tailor, D., Garcia-Marques, F.J., Bermudez, A., Pitteri, S.J., and Malhotra, S.V. (2023). Guanylate-binding protein 1 modulates proteasomal machinery in ovarian cancer. iScience 26 , 108292. Yu, S., Li, Y., Feng, W., Zeng, J., Cui, X., Zhou, S., and Zhang, P. (2024). Gbp1 promotes cutaneous squamous cell carcinoma proliferation and invasion through activation ofstat3 bysp1. Exp. Dermatol. 33 , e15112. Meng, Y., Wang, W., Chen, M., Chen, K., Xia, X., Zhou, S., and Yang, H. (2021). Gbp1 facilitates indoleamine 2,3-dioxygenase extracellular secretion to promote the malignant progression of lung cancer. Front. Immunol. 11 , 622467. Li, M., Mukasa, A., Del-Mar Inda, M., Zhang, J., Chin, L., Cavenee, W., and Furnari, F. (2011). Guanylate binding protein 1 is a novel effector of egfr-driven invasion in glioblastoma. J. Exp. Med. 208 , 2657–2673. Kalb, R.C., Nyabuto, G.O., Morran, M.P., Maity, S., Justinger, J.S., Nestor-Kalinoski, A.L., and Vestal, D.J. (2024). The large gtpase guanylate-binding protein-1 (gbp-1) promotes mitochondrial fission in glioblastoma. Int. J. Mol. Sci. 25 , 11236. Seo, J.H., Park, J., Lee, E.J., Vo, T.T.L., Choi, H., Kim, J.Y., Jang, J.K., Wee, H., Lee, H.S., and Jang, S.H., et al. (2016). Ard1-mediated hsp70 acetylation balances stress-induced protein refolding and degradation. Nat. Commun. 7 , 12882. Liu, X., Li, L., Geng, C., Wen, S., Zhang, C., Deng, C., Gao, X., Zhang, G., He, Q., and Liu, L. (2022). Ddx17 promotes the growth and metastasis of lung adenocarcinoma. Cell Death Discov. 8 , 425. Liu, H., Gao, X., Zhang, W., Fu, X., Zhang, J., Yuan, Q., Jin, J., Du, X., Li, R., and Li, Y., et al. (2025). Ddx17-mediated upregulation of cxcl8 promotes hepatocellular carcinoma progression via co-activating β-catenin/nf-κb complex. Int. J. Biol. Sci. 21 , 1342–1360. Wang, L., Wei, Y., Jin, Z., Liu, F., Li, X., Zhang, X., Bai, X., Jia, Q., Zhu, B., and Chu, Q. (2024). Ifn-α/β/ifn-γ/il- 15 pathways identify gbp1-expressing tumors with an immune-responsive phenotype. Clin. Exper. Med. 24 , 102. Wang, S., Zhang, Y., Ma, X., and Feng, Y. (2024). Function and mechanism of gbp1 in the development and progression of cervical cancer. J. Transl. Med. 22 , 11. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryTableS1.xlsx Tables1 FigureS1.tif Figure S1. The interaction between GBP1 and DDX17 in 143B cells. (A) Co-immunoprecipitation assay verifying the interaction between GBP1 and DDX17 in 143B cells. IgG was used as a negative control, and HA antibody was used for immunoprecipitation. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, *p < 0.05. FigureS2.tif Figure S2. DDX17 mRNA and protein expression level in different treatments. (A) Quantification of DDX17 mRNA expression in DDX17-NC cells and DDX17-siRNA measured by qPCR. Quantification of n=3 experiments. Mean ± SEM, **p < 0.01, ****p < 0.0001. (B) Western blot analysis of DDX17 protein levels in 143B and U2OS cells with different treatments (DDX17-NC cells and DDX17-siRNA). Quantification of three experiments. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, **p < 0.01, ***p < 0.001. (C) Western blot analysis of DDX17 protein levels in 143B and U2OS cells with different treatments (Vector, GBP1, GBP1 + Vector, GBP1 + DDX17). Quantification of three experiments. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, **p < 0.01, ***p < 0.001. FigureS3.tif Figure S3. Silencing DDX17 inhibits OS progression and DDX17 rescues GBP1-mediated suppression of OS progression in U2OS cells. (A)CCK8 assay was employed to measure the cell viability of U2OS cells. Quantification of three experiments, mean ± SEM, *p<0.05. (B) Flow cytometry analysis using Annexin V-FITC/PI double staining was employed to measure the cell apoptosis of U2OS cells. Quantification of three experiments, mean ±SEM, **p<0.01. (C) Wound healing assay was performed to assess the migration ability of U2OS cells. Representative images were shown (magnification at × 20). Quantification of three experiments, mean ± SEM, **p<0.01. (D) Transwell migration and matrigel invasion assays were used to determine the migratory and invasive ability of U2OS cells. Representative images were shown (magnification at × 40). Quantification of three experiments, mean ± SEM,*p<0.05. (E) CCK8 assay was employed to measure the cell viability of U2OS cells. Quantification of three experiments, mean ± SEM, **p<0.01. (F) Colony formation assay was employed to measure the cell proliferative capacity of 143B cells. Quantification of three experiments, mean ±SEM, **p<0.01. (G) Wound healing assay was performed to assess the migration ability of 143B cells.Representative images were shown (magnification at × 20). Mean ± SEM, *p < 0.05. FigureS4.tif Figure S4. DDX17 rescues GBP1-mediated suppression of migration and invasion in 143B and U2OS cells. (A) Transwell migration and matrigel invasion assays were used to determine the migratory and invasive ability of 143B cells. Representative images were shown (magnification at × 40). Quantification of three experiments, mean ± SEM,*p<0.05, **p<0.01, ***p < 0.001. (B) Transwell migration and matrigel invasion assays were used to determine the migratory and invasive ability of U2OS cells. Representative images were shown (magnification at × 40). Quantification of three experiments, mean ± SEM,**p<0.01. FigureS5.tif Figure S5. Validation of HSPA8 and USP10 expression modulation in OS cells (A) Western blot analysis of HSPA8 protein levels in 143B and U2OS cells transfected with HSPA8 siRNA or siRNA NC. Right: Representative experiment; Left: Quantification of three experiments. Mean ± SEM, **p<0.01. (B) Western blot analysis of HSPA8 protein levels in 143B and U2OS cells transfected with HSPA8 overexpression vector or empty vector. GAPDH served as a loading control. Right: Representative experiment; Left: Quantification of three experiments. Mean ± SEM, *p < 0.05, **p<0.01. (C) Western blot analysis of USP10 protein levels in 143B and U2OS cells transfected with USP10 siRNA or siRNA NC. Right: Representative experiment; Left: Quantification of three experiments. Mean ± SEM, *p < 0.05, ****p < 0.0001. FigureS6.tif Figure S6. The interaction between GBP1 and HSPA8 in OS cells. (A) Co-immunoprecipitation assay verifying the interaction between GBP1 and HSPA8 in 143B and U2OS cells. IgG was used as a negative control, and HA antibody was used for immunoprecipitation. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM,*p < 0.05, **p < 0.01. (B) Confocal microscopy images showing the colocalization of GBP1 and HSPA8 in U2OS-GBP1 cells. DAPI (blue) stains the nucleus, GBP1 (red) and HSPA8 (green) were immunofluorescently labeled, and Merge shows the merged images. Scale bars: 10 μm. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7033508","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485946415,"identity":"9af9b4df-63cb-4b6b-a140-90c1ffb93e78","order_by":0,"name":"Shengyu Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYFAC5gYQKcfG3n6AWC2MYC3GfDxnEkjTkjhPwsGAOA3m7I2ND378OZzeJsGQwPCjYhthLZY9B5sNe9sO57ZJNx5g7Dlzm7AWgxuJbdKMDUAtMgcSmBnbiNXCAHQYm0SCASla2A4nEK8F6pd0wzZgIB8kyi/m7M0HgSFmLS/f3g5kVBDjMAjVDCYPEFaP0FJHlOJRMApGwSgYoQAAInI92byH5jwAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Shengyu","middleName":"","lastName":"Cui","suffix":""},{"id":485946416,"identity":"527b6bd7-79e4-497c-bc48-d4cd279b0c2f","order_by":1,"name":"Shuo Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Yang","suffix":""},{"id":485946417,"identity":"c0c5e4f8-eff3-4218-a4bc-3945ffea254d","order_by":2,"name":"Wen Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Huang","suffix":""},{"id":485946418,"identity":"12ca420b-6521-4726-a237-cb8ddcee59e4","order_by":3,"name":"Xu Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Li","suffix":""},{"id":485946419,"identity":"3da22472-bf7d-45bd-805b-3675f2ad6751","order_by":4,"name":"Shujiang Ye","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shujiang","middleName":"","lastName":"Ye","suffix":""},{"id":485946420,"identity":"8d67477b-956e-451a-94b5-51d805c2fa75","order_by":5,"name":"Yixuan Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"Li","suffix":""},{"id":485946421,"identity":"a986b966-e4c8-4791-ad6c-b265f0c10d28","order_by":6,"name":"Hanrui Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hanrui","middleName":"","lastName":"Liu","suffix":""},{"id":485946422,"identity":"31088536-d113-42dd-9b0b-d8f97af792cb","order_by":7,"name":"Jiafeng He","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiafeng","middleName":"","lastName":"He","suffix":""},{"id":485946423,"identity":"fc730244-6595-4487-ab12-45dbe83d3e3c","order_by":8,"name":"Rongkui Luo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rongkui","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-07-03 02:45:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7033508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7033508/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87360065,"identity":"cdb679fe-1959-451b-84c9-b6ad8ef8883c","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33255441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of GBP1 inhibits OS proliferation.\u003c/strong\u003e (A) The overexpression efficiency of GBP1 in 143B and U2OS cells was measured by Western blot. Quantification of three experiments, Mean ± SEM, **p\u0026lt;0.01. (B) CCK8 assay was employed to measure the cell viability of 143B and U2OS cells. Quantification of three experiments, mean ± SEM, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, and ****p\u0026lt;0.0001. (C) Colony formation assay was employed to measure the cell proliferative capacity of 143B and U2OS cells. Quantification of three experiments, mean ±SEM, ***p\u0026lt;0.001, and ****p\u0026lt;0.0001. (D) Flow cytometry analysis using Annexin V-FITC/PI double staining was employed to measure the cell apoptosis of 143B and U2OS cells. Quantification of three experiments, mean ±SEM, *p\u0026lt;0.05, **p\u0026lt;0.01. (E) Relative tumor volume and weight of nude mice xenografts. Tumor volume was calculated using the formula V = 1/2 × a × b² (a: long diameter, b: short diameter). Quantification of five independent experiments, mean ± SEM, *p\u0026lt;0.05, **p\u0026lt;0.01. (F) Representative images of GBP1 and Ki-67immunohistochemical staining in xenograft tumor tissues from nude mice injected with 143B-GBP1 or vector control cells. Scale bars: 200 μm. Quantification of five independent experiments, mean ± SEM, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/3771578a0e7478ed2e7d546a.png"},{"id":87360060,"identity":"bd9ed3c1-f1b1-43a9-90ea-343b4ad18523","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17099838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of GBP1 inhibits OS cells migration and invasion. \u003c/strong\u003e(A) GBP1 expression levels in primary OS and metastatic OS samples were analyzed using the GSE85573 dataset. Mean ± SEM, *p \u0026lt; 0.05. (B-E) Wound healing assay was performed to assess the migration ability of 143B and U2OS cells with GBP1 overexpression or vector control.Representative images were shown (magnification at × 20). Quantification of three experiments, mean ± SEM,*p\u0026lt;0.05, **p\u0026lt;0.01. (F-I) Transwell migration and matrigel invasion assays were used to determine the migratory and invasive ability of 143B and U2OS cells. Representative images were shown (magnification at × 40). Quantification of three experiments, mean ± SEM,*p\u0026lt;0.05, **p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/3f95e848f3fa03f74e1e09c4.png"},{"id":87360049,"identity":"d0c45baa-45de-44d6-bc75-827b00c664d6","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9539859,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDDX17 was identified as an interacting partner of GBP1.\u003c/strong\u003e(A) Schematic workflow of immunoprecipitation combined with mass spectrometry to screen GBP1 - interacting proteins. HA - tagged GBP1 or empty vector was expressed in cells, followed by anti - HA IP and elution, and then analyzed by MS. (B) Heatmap showing the top 10 potential interacting proteins of GBP1 identified by MS. The color intensity represents the relative peptides of proteins. (C) Co-immunoprecipitation assay verifying the interaction between GBP1 and DDX17 in 143B cells. IgG was used as a negative control, and HA antibody was used for immunoprecipitation. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, *p \u0026lt; 0.05. (D) Reciprocal co-immunoprecipitation assays confirming the interaction between GBP1 and DDX17 in 143B and U2OS cells. Immunoprecipitation was performed with GBP1 or DDX17 antibodies, respectively. (E) Confocal microscopy images showing the colocalization of GBP1 and DDX17 in U2OS - GBP1 cells. DAPI (blue) stains the nucleus, GBP1 (red) and DDX17 (yellow) were immunofluorescently labeled, and Merge shows the merged images. Scale bars: 10 μm. (F) Western blot analysis of DDX17 protein levels in 143B - GBP1, 143B - Vector, U2OS - GBP1, and U2OS - Vector cells. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/1fd34ef0aa9de3ff27803e7d.png"},{"id":87360069,"identity":"eb1f94b2-271c-4512-b27c-968dcb32b6bc","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25662629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSilencing DDX17 inhibits OS progression and DDX17 rescues GBP1-mediated suppression of OS progression in 143B cells. \u003c/strong\u003e(A)CCK8 assay was employed to measure the cell viability of 143B cells. Quantification of three experiments, mean ± SEM, *p\u0026lt;0.05. (B) Flow cytometry analysis using Annexin V-FITC/PI double staining was employed to measure the cell apoptosis of 143B cells. Quantification of three experiments, mean ±SEM, **p\u0026lt;0.01. (C) Wound healing assay was performed to assess the migration ability of 143B cells. Representative images were shown (magnification at × 20). Quantification of three experiments, mean ± SEM,*p\u0026lt;0.05. (D) Transwell migration and matrigel invasion assays were used to determine the migratory and invasive ability of 143B cells. Representative images were shown (magnification at × 40). Quantification of three experiments, mean ± SEM,*p\u0026lt;0.05. (E) CCK8 assay was employed to measure the cell viability of 143B cells. Quantification of three experiments, mean ± SEM, **p\u0026lt;0.01. (F) Colony formation assay was employed to measure the cell proliferative capacity of 143B cells. Quantification of three experiments, mean ±SEM, **p\u0026lt;0.01, and ***p\u0026lt;0.001. (G) Wound healing assay was performed to assess the migration ability of 143B cells.Representative images were shown (magnification at × 20). Mean ± SEM, *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/b92f8d2254335bb1c75309da.png"},{"id":87361214,"identity":"a5f53dec-5fc4-4deb-8cd3-66197765df73","added_by":"auto","created_at":"2025-07-23 05:52:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7005171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGBP1 promotes DDX17 degradation via the ubiquitin-proteasome system. (A) \u003c/strong\u003eQuantification of DDX17 mRNA expression in GBP1 and Vector cells measured by qPCR. Quantification of n=3 experiments. Mean ± SEM, “ns”: not significant. (B) Overexpression of GBP1 cells were treated with 50 μg/ml CHX for 0, 2, 4 or 8 h. DDX17 levels were analyzed by western blot. Quantification of three experiments, Mean ± SEM, “ns”: not significant, *p \u0026lt; 0.05,**p \u0026lt; 0.01. (C) Western blot analysis of DDX17 levels in GBP1 stably expressing cells treated with CHX (50 μg/ml) with or without MG132 (10 μM) and CQ (25 μM) for 0, 2, 4 or 8 h. Quantification of three experiments, Mean ± SEM, “ns”: not significant,*p \u0026lt; 0.05,**p \u0026lt; 0.01. (D) After treatment with MG132 (10 μM) for 8 h, 143B and U2OS lysates were subjected to immunoprecipitation with anti-DDX17 antibody. Ubiquitin, DDX17, GBP1 and GAPDH levels were analyzed by immunoblot. Quantification of three experiments. Mean ± SEM, *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/314172494ec9bd0f5f0bb4cb.png"},{"id":87360056,"identity":"4a3247ee-23c8-4a07-8e3e-4ca8ba88ef23","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7179434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGBP1 promotes DDX17 protein degradation by HSPA8 in OS cells. \u003c/strong\u003e(A) Venn diagram showed the intersection of proteins between IPMS and UbiBrowser(prediction of DDX17-interacting ubiquitin/deubiquitin enzymes), identifying HSPA8 and USP10 as potential regulators. (B-C) Western blot analysis of DDX17 protein levels in 143B and U2OS cells transfected with HSP A8 siRNA or USP10 siRNA, along with GBP1 or Vector. Quantification of three experiments. Mean ± SEM,“ns”: not significant,*p \u0026lt; 0.05,**p \u0026lt; 0.01. (D-E) Overexpression of HSPA8 cells were treated with 50 μg/ml CHX for 0, 2, 4 or 8 h. DDX17 levels were analyzed by western blot. Quantification of three experiments, Mean ± SEM, *p \u0026lt; 0.05,**p \u0026lt; 0.01,***p \u0026lt; 0.001. (F)After treatment with MG132 (10 μM) for 8 h, 143B and U2OS lysates were subjected to immunoprecipitation with anti-DDX17 antibody. Ubiquitin, DDX17, GAPDH levels were analyzed by immunoblot. Quantification of three experiments. Mean ± SEM, *p \u0026lt; 0.05,**p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/df0bd6e8cb51dff45893bb9f.png"},{"id":87361220,"identity":"317ae351-a424-4d62-ac94-93e78a3c2a3f","added_by":"auto","created_at":"2025-07-23 05:52:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":18597008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGBP1 enhances HSPA8-DDX17 interaction in OS cells.\u003c/strong\u003e (A) Reciprocal co-immunoprecipitation assays confirming the interaction between DDX17 and HSPA8 in 143B and U2OS cells. Immunoprecipitation was performed with DDX17 and HSPA8 antibodies, respectively. (B) Confocal microscopy images showing the colocalization of DDX17 and HSPA8 in U2OS-GBP1 cells. DAPI (blue) stains the nucleus, DDX17 (yellow) and HSPA8 (green) were immunofluorescently labeled, and Merge shows the merged images. Scale bars: 10 μm. (C) Reciprocal co-immunoprecipitation assays confirming the interaction between GBP1 and HSPA8 in 143B and U2OS cells. Immunoprecipitation was performed with GBP1 and HSPA8 antibodies, respectively. (D) Confocal microscopy images showing the colocalization of GBP1 and HSPA8 in 143B-GBP1 cells. DAPI (blue) stains the nucleus, GBP1 (red) and HSPA8 (green) were immunofluorescently labeled, and Merge shows the merged images. Scale bars: 10 μm. (E) Predicted protein-protein interaction models of GBP1, HSPA8, and DDX17. (F) Confocal microscopy images showing the colocalization of GBP1, HSPA8, and DDX17 in U2OS-GBP1 cells. GBP1 (red), HSPA8 (green), DDX17 (yellow) were immunofluorescently labeled, and Merge shows the merged images. Scale bars: 10 μm. (G) Co-immunoprecipitation assays and quantification to detect the interaction between DDX17 and HSPA8 in the GBP1 or Vector in 143B and U2OS cells. IgG was used as a negative control. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, *p \u0026lt; 0.05,**p \u0026lt; 0.01,***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/a3938e38fd62135287abbdd9.png"},{"id":87360063,"identity":"70522f03-cfcc-4684-9c27-b26e0bfb3a08","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":24230129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGBP1 as a potential therapeutic target in OS.\u003c/strong\u003e (A) The GBP1 mRNA levels in OS cells compared with hFOB1.19. Data were obtained from the GEO database (GSE197158). Mean ± SEM, ****p \u0026lt; 0.0001. (B-C) GBP1 protein levels in hFOB1.19 and OS cells (143B, U2OS) were measured by Western blot. Quantification of three experiments, Mean ± SEM, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001. (D-E) GBP1 and DDX17 protein expression in paired tumor (T) and normal (N) tissues from OS patients, detected by Western blot. Quantification of ten experiments. Mean ± SEM, **p \u0026lt; 0.01. (F) Representative images of HE, GBP1, and DDX17 immunohistochemical staining in OS tissues with different GBP1 expression levels (Negative, Low, High). Scale bar: 100 μm. (G) Correlation analysis of GBP1 and DDX17 expression in OS samples (Speaman correlation, R = - 0.77, P = 0.014). (H) Correlation analysis of GBP1 and DDX17 IHC scores in OS tissues (Speaman correlation, R = - 0.344, P = 0.063). (I) Kaplan -Meier curves show the overall survival of patients with OS in terms of GBP1 expression. Data were obtained from the TARGET database, p \u0026lt; 0.001. (J) Kaplan -Meier curves show the disease free survival of patients with OS in terms of GBP1 expression. Data were obtained from the TARGET database, p = 0.007.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/963e645144b245d022365151.png"},{"id":89937487,"identity":"ee739119-d6fd-4e60-89ed-c8d6010b79d1","added_by":"auto","created_at":"2025-08-26 15:24:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":129467388,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/38ed388e-a476-42c2-b01f-a4ad40ff7a94.pdf"},{"id":87360047,"identity":"a0074cbe-a1b8-4e22-9706-e6b6070e5f55","added_by":"auto","created_at":"2025-07-23 05:44:01","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11230,"visible":true,"origin":"","legend":"Tables1","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/2e1ad4d7f49ba9fa88c37859.xlsx"},{"id":87360051,"identity":"54e98318-28cb-43b6-b92d-9235b80e407b","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1452656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. The interaction between GBP1 and DDX17 in 143B cells.\u003c/strong\u003e (A) Co-immunoprecipitation assay verifying the interaction between GBP1 and DDX17 in 143B cells. IgG was used as a negative control, and HA antibody was used for immunoprecipitation. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/573349b30264dc78ccf5fba1.tif"},{"id":87360052,"identity":"e868ee3e-b62e-4f6a-ab36-baf5321b25cf","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2867004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. DDX17 mRNA and protein expression level in different treatments. \u003c/strong\u003e(A) Quantification of DDX17 mRNA expression in DDX17-NC cells and DDX17-siRNA measured by qPCR. Quantification of n=3 experiments. Mean ± SEM, **p \u0026lt; 0.01, ****p \u0026lt; 0.0001. (B) Western blot analysis of DDX17 protein levels in 143B and U2OS cells with different treatments (DDX17-NC cells and DDX17-siRNA). Quantification of three experiments. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. (C) Western blot analysis of DDX17 protein levels in 143B and U2OS cells with different treatments (Vector, GBP1, GBP1 + Vector, GBP1 + DDX17). Quantification of three experiments. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/474f2c5f8679f9351076bec6.tif"},{"id":87360059,"identity":"67ed33ae-7b59-4739-bcc7-56aabd25010e","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19148496,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3. Silencing DDX17 inhibits OS progression and DDX17 rescues GBP1-mediated suppression of OS progression in U2OS cells. \u003c/strong\u003e(A)CCK8 assay was employed to measure the cell viability of U2OS cells. Quantification of three experiments, mean ± SEM, *p\u0026lt;0.05. (B) Flow cytometry analysis using Annexin V-FITC/PI double staining was employed to measure the cell apoptosis of U2OS cells. Quantification of three experiments, mean ±SEM, **p\u0026lt;0.01. (C) Wound healing assay was performed to assess the migration ability of U2OS cells. Representative images were shown (magnification at × 20). Quantification of three experiments, mean ± SEM, **p\u0026lt;0.01. (D) Transwell migration and matrigel invasion assays were used to determine the migratory and invasive ability of U2OS cells. Representative images were shown (magnification at × 40). Quantification of three experiments, mean ± SEM,*p\u0026lt;0.05. (E) CCK8 assay was employed to measure the cell viability of U2OS cells. Quantification of three experiments, mean ± SEM, **p\u0026lt;0.01. (F) Colony formation assay was employed to measure the cell proliferative capacity of 143B cells. Quantification of three experiments, mean ±SEM, **p\u0026lt;0.01. (G) Wound healing assay was performed to assess the migration ability of 143B cells.Representative images were shown (magnification at × 20). Mean ± SEM, *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/fb73bee172ef4cdab900d505.tif"},{"id":87361217,"identity":"40c2e2b4-83a2-45fc-a6f7-a26a07dc219f","added_by":"auto","created_at":"2025-07-23 05:52:02","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":18547792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4. DDX17 rescues GBP1-mediated suppression of migration and invasion in 143B and U2OS cells. \u003c/strong\u003e(A) Transwell migration and matrigel invasion assays were used to determine the migratory and invasive ability of 143B cells. Representative images were shown (magnification at × 40). Quantification of three experiments, mean ± SEM,*p\u0026lt;0.05, **p\u0026lt;0.01, ***p \u0026lt; 0.001. (B) Transwell migration and matrigel invasion assays were used to determine the migratory and invasive ability of U2OS cells. Representative images were shown (magnification at × 40). Quantification of three experiments, mean ± SEM,**p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/cd07c0e7c146ffd9cb5ed988.tif"},{"id":87360067,"identity":"9862c942-131e-4a8c-942e-fde6d86d72d1","added_by":"auto","created_at":"2025-07-23 05:44:02","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":3703364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5. Validation of HSPA8 and USP10 expression modulation in OS cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Western blot analysis of HSPA8 protein levels in 143B and U2OS cells transfected with HSPA8 siRNA or siRNA NC. Right: Representative experiment; Left: Quantification of three experiments. Mean ± SEM, **p\u0026lt;0.01. (B) Western blot analysis of HSPA8 protein levels in 143B and U2OS cells transfected with HSPA8 overexpression vector or empty vector. GAPDH served as a loading control. Right: Representative experiment; Left: Quantification of three experiments. Mean ± SEM, *p \u0026lt; 0.05, **p\u0026lt;0.01. (C) Western blot analysis of USP10 protein levels in 143B and U2OS cells transfected with USP10 siRNA or siRNA NC. Right: Representative experiment; Left: Quantification of three experiments. Mean ± SEM, *p \u0026lt; 0.05, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/0ceef0e7197a5267f9d50ce0.tif"},{"id":87361218,"identity":"39ca9f1d-f6fe-4a9a-9136-fa99768d8f92","added_by":"auto","created_at":"2025-07-23 05:52:02","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":10741196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S6. The interaction between GBP1 and HSPA8 in OS cells.\u003c/strong\u003e (A) Co-immunoprecipitation assay verifying the interaction between GBP1 and HSPA8 in 143B and U2OS cells. IgG was used as a negative control, and HA antibody was used for immunoprecipitation. Left: Representative experiment; Right: Quantification of three experiments. Mean ± SEM,*p \u0026lt; 0.05, **p \u0026lt; 0.01. (B) Confocal microscopy images showing the colocalization of GBP1 and HSPA8 in U2OS-GBP1 cells. DAPI (blue) stains the nucleus, GBP1 (red) and HSPA8 (green) were immunofluorescently labeled, and Merge shows the merged images. Scale bars: 10 μm.\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-7033508/v1/218f7dbf2e3ad09f87b81de8.tif"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"GBP1 inhibits osteosarcoma progression by regulating DDX17 protein stability via HSPA8","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteosarcoma (OS) is the most prevalent primary malignant bone tumor, predominantly affecting the metaphyseal regions of long bones (e.g., the peri-knee area) in children and adolescents\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite current multimodal therapies (surgical resection, neoadjuvant/adjuvant chemotherapy), the 5-year survival rate has remained stagnant at 52.1% in China\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The lack of effective early diagnostic markers and precise therapeutic targets underscores the urgent need to identify novel molecular targets for improved OS treatment strategies.\u003c/p\u003e\u003cp\u003eGuanylate-binding protein 1 (GBP1), a member of the interferon-responsive GTPase family, has been implicated as a tumor suppressor in diverse malignancies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Preclinical evidence has shown that GBP1 overexpression exerts antiproliferative, antimigratory, and anti-invasive effects in colorectal cancer cells\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Conversely, in hepatocellular carcinoma, reduced GBP1 expression is associated with adverse clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Notably, the role and mechanism of GBP1 in OS remain entirely uncharacterized, presenting a critical research gap.\u003c/p\u003e\u003cp\u003eDEAD-box RNA helicase 17 (DDX17) regulates RNA metabolism (transcription, splicing, transport) and exhibits oncogenic functions in multiple cancers\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In breast cancer, DDX17 promotes proliferation via estrogen receptor α (ERα) transcriptional activation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, while in colorectal cancer, high DDX17 expression associates with metastasis and poor prognosis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In liver cancer, DDX17 regulates alternative splicing of lncRNA PXN-AS1 to enhance metastasis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, its role in OS\u0026mdash;particularly mechanisms governing protein stability\u0026mdash;remains unexplored.\u003c/p\u003e\u003cp\u003eHeat shock protein A8 (HSPA8), a HSP70 family chaperone, maintains protein quality control by facilitating folding, refolding, and degradation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. It promotes lysosomal degradation via -KFERQ motif recognition\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and ubiquitin-mediated degradation through E3 ligase STUB1 binding\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Whether HSPA8 regulates DDX17 stability, and whether GBP1 modulates DDX17 degradation by influencing HSPA8-DDX17 interaction, are entirely unreported.\u003c/p\u003e\u003cp\u003eThis study hypothesizes that GBP1 suppresses OS progression by promoting HSPA8-mediated ubiquitination and degradation of DDX17. By experimentally validating this novel mechanism, we aim to uncover new insights into OS pathogenesis and provide a theoretical basis for developing GBP1/DDX17-axis-targeted therapies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eAntibodies and chemicals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe following antibody was used in this study: GBP1 (Proteintech, catalog number: 15303-1-AP, 1:1000 for WB, 1:300 for IHC, 2ug for IP), DDX17 (Proteintech, catalog number: 19910-1-AP, 1:5000 for WB, 2ug for IP), HSPA8 (Proteintech, catalog number: 10654-1-AP, 3ug for IP), ubiquitin (Proteintech, catalog number: 10201–2-AP, 1:500 for WB), HA (Proteintech, catalog number: 51064-2-AP, 1:5000 for WB), GAPDH (Proteintech, catalog number: 60004-1-Ig, 1:10000 for WB). The following chemicals were used in this study: Cycloheximide (MCE, catalog number: HY-12320), Chloroquine (MCE, catalog number: HY-17589A), MG132 (MCE, catalog number: HY-13259).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollection of Specimens\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, a total of 30 cases of OS tissues were procured through surgical resection, along with corresponding adjacent normal tissues from the same patients. Importantly, none of the patients had undergone any form of radiotherapy, chemotherapy, or other antitumor treatment prior to the surgical intervention. Once collected, these specimens were rapidly frozen using liquid nitrogen to preserve their integrity and subsequently stored at -80°C for future experimental use. The process of obtaining donor OS tissues and adjacent normal tissues, along with the ensuing experimental methodologies, received ethical approval from the Ethics Committee of Zhongshan Hospital.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Culture\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHuman OS cell lines, specifically 143B and U2OS, in addition to normal human osteoblasts known as hFOB1.19, were sourced from the American Type Culture Collection (ATCC, USA). The hFOB1.19 osteoblasts were cultured in Dulbecco’s modified Eagle’s medium(Procell, catalog number: PM150210), which was enriched with 10% fetal bovine serum (FBS) to support optimal growth. Meanwhile, the 143B cell line was maintained in Minimum Essential Medium(Procell, catalog number: PM150410), also supplemented with 10% FBS, ensuring adequate nutrient availability. The U2OS cells were grown in McCoy’s 5A Medium(Procell, catalog number: PM150710), again with the addition of 10% FBS. All the cell lines mentioned above were incubated in a controlled environment set to 37°C with 5% CO2 to maintain physiological conditions conducive to cell growth and proliferation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLentiviral Transduction\u003c/b\u003e\u003c/p\u003e\u003cp\u003e To facilitate the study, lentiviral vectors encoding HA-tagged GBP1 were generated by Genechem (Shanghai), following the specified guidelines by the supplier. For the transduction process, both 143B and U2OS cells were plated in 12-well plates at a density of 1×10⁵ cells per well. Once the cells attained approximately 60% confluence, they were treated with lentiviral particles in the presence of polybrene for a duration of 12 hours. Following the transduction phase, the cells underwent selection with puromycin(Beyotime, catalog number:ST551) at a concentration of 4 µg/ml for a period of two weeks, leading to the establishment of stable cell lines. The resistant colonies that emerged were then isolated and expanded for subsequent downstream experiments, allowing for the investigation of the effects of the transduced elements within the cellular systems.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSiRNA transfection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, 143B and U2OS cells underwent transfection with small interfering RNAs (siRNAs) utilizing Lipofectamine 2000, sourced from Invitrogen (catalog number: 2097561), at a final concentration of 50 nM. The siRNA transfection was conducted in serum-free Minimum Essential Medium or McCoy’s 5A Medium. Initially, both 143B and U2OS cells were distributed in 12-well plates at a density of 1 × 10⁵ cells per well. According to the manufacturer's protocol, complexes of siRNA and Lipofectamine 2000 were formulated and then introduced to the cells when they reached 60–70% confluency. After allowing the cells to incubate under conditions of 37°C for a duration of 4 to 6 hours, the transfection medium was replaced with complete medium supplemented with 10% FBS to facilitate recovery of the cells. The efficacy of silencing was evaluated either by qPCR or Western blot following a 48-hour period post-transfection. The DDX17 siRNA(5′-GGAAAGAGGUGUUGAGAUC-3′), USP10 siRNA(5′-GAGGAAAUGUUGAACCUAA-3′), HSPA8 siRNA(5′-ACGGAAAAGUCGAGAUAAU-3′), and non-targeting control siRNA(5′-UUCUCCGAACGUGUCACGU-3′) were synthesized by Tsingke Biotechnology.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Proliferation Assay and Colony Formation Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe present study used a CCK-8 kit (Dojindo, catalog number: CK04) to evaluate cell proliferation ability. For this purpose, 3 × 10\u003csup\u003e3\u003c/sup\u003e cells were seeded into 96-well plates containing 100 µl of culture medium and cultured for 24, 48, and 72 hours. Subsequently, 10 µl of CCK-8 reagent was added to each well, and the plates were incubated for 2 hours. The absorbance at 450 nm was then measured using a microplate reader.\u003c/p\u003e\u003cp\u003eFor the colony formation assay, 1× 10\u003csup\u003e3\u003c/sup\u003e cells were seeded into 12-well plates and cultured at 37 ℃ for 12–14 days. The cells were then fixed with methanol, stained with crystal violet, and subsequently imaged.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Apoptosis Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe process of measuring cell apoptosis was conducted with the aid of a Cell Apoptosis Kit from Beyotime (Catalog number: C1062S), following the manufacturer's detailed instructions. After allowing the cells to adhere for 24 hours, they were detached using EDTA-free trypsin(Beyotime, catalog number: C0205), washed twice with pre-cooled phosphate-buffered saline (PBS), and subsequently resuspended in 195 µL of Annexin V-FITC binding buffer to prepare for analysis. The cell suspension was gently mixed, after which 5 µl of Annexin V-FITC staining solution was added. To further enhance the analysis of apoptosis, 10 µl of propidium iodide (PI) staining solution was incorporated, and the cells were incubated for 10 to 20 minutes at room temperature, protected from light. A minimum of 1 × 10⁴ cells were then collected for analysis via flow cytometry, allowing for quantification and characterization of apoptotic cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Scratch Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn a controlled experiment, 143B and U2OS cells were initially seeded into 12-well plates and allowed to grow until they reached full confluence. To assess cell migration, a scratch was introduced into the cell layer using the tip of a 200 µl pipette. Following this procedure, the cells were thoroughly washed twice with phosphate-buffered saline (PBS) to eliminate any floating cells that might interfere with the results. After washing, a medium containing 1% FBS was added to the wells, and the cells were cultured further. Images of the cell layer were captured at various time intervals, specifically at 0, 24 or 48 hours, using an optical microscope (×20 magnification), allowing for a comparison of the rate of cell migration over time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Migration and Invasion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor the evaluation of the migration and invasion capabilities of OS cells, transwell chamber assays were performed. The experimental setup involved utilizing chambers with an 8 µm pore size for migration assessments (Corning, catalog number: CLS3428) and Matrigel-precoated chambers (Corning, catalog number: CLS354234) for invasion evaluations. In this setup, a total of 5×10\u003csup\u003e4\u003c/sup\u003e 143B and U2OS cells were carefully seeded into the upper chambers of 24-well plates. To promote cell migration via chemotaxis, a medium enriched with 10% FBS was provided in the lower chambers. Following a specified incubation period − 12 or 24 hours for migration assays and 24 or 36 hours for invasion assays — the cells that had migrated to the lower chambers were fixed using a 4% paraformaldehyde solution. To visualize the migrated cells, they were stained with crystal violet for a duration of 10 minutes and subsequently imaged using an optical microscope(×40 magnification). For data analysis, three random fields of view from each chamber were selected for cell counting, thereby ensuring an accurate assessment of the cell migration and invasion capabilities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCycloheximide (CHX) Chase Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the cycloheximide chase assay, a total of 5×10\u003csup\u003e5\u003c/sup\u003e 143B or U2OS cells were cultured in 6-well plates. Following a 24-hour incubation period, the cells were treated with a solution of CHX at a concentration of 50 µg/ml. Proteins from the cells were extracted at various time intervals, specifically at 0, 2, 4, or 8 hours after treatment. For the analysis of DDX17 levels, 20 µg of the extracted protein was subjected to Western blot, a reliable technique for protein detection and quantification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDDX17 Ubiquitination Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further explore the molecular dynamics involved in these processes, cells were cultured in 6-well plates and subjected to treatment with RIPA lysis buffer (Beyotime, catalog number: P2185S) containing a cocktail of protease inhibitors (Beyotime, catalog number: P2185S), which aids in the preservation of protein integrity during extraction. Prior to the harvesting of the cells, they were treated with 20 µM MG-132 for a period of 6 hours to inhibit proteasomal degradation. The resulting cell lysates were then clarified through centrifugation at 12,000 xg for 10 minutes at 4°C. Following clarification, equal amounts of the lysate were incubated with 2 µg of the DDX17 primary antibody on a shaking platform at 4°C for 8 hours to allow for specific binding. Subsequently, 10 µl of resuspended protein A + G beads were added to the mixture, which was then incubated overnight at the same temperature to facilitate immunoprecipitation. The immunoprecipitates were enriched through a centrifugation step, washed twice with 1 ml of RIPA buffer for 10 minutes each, and the supernatant was carefully aspirated and discarded to remove unbound materials. The remaining pellet was then resuspended in 20 µl of 1X sample buffer and subjected to heat at 95°C for 5 minutes to denature the proteins. Finally, the precipitated proteins were analyzed using western blot techniques to quantify the ubiquitination of DDX17, contributing to a deeper understanding of the cellular mechanisms under investigation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHematoxylin-eosin and immunohistochemical Staining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParaffin-embedded tissue samples were meticulously sectioned into slices of 3 micrometers in thickness for the purposes of immunohistochemistry (IHC) and hematoxylin and eosin (H\u0026amp;E) staining. The IHC procedure began with the dewaxing of sections, followed by antigen retrieval by boiling in sodium citrate buffer (Proteintech, catalog number: PR30001) for a duration of 2 minutes. To inhibit the activity of endogenous peroxidase, the sections were treated with 3% hydrogen peroxide for 10 minutes. Subsequently, the sections were incubated with a primary antibody overnight at a temperature of 4°C. The following day, the sections were washed three times with phosphate-buffered saline-Tween (PBST) and then incubated with a horseradish peroxidase (HRP)-conjugated secondary antibody (MXB, catalog number: KIT-5010) for 30 minutes. The immunological reactions were developed using a DAB chromogenic kit (Proteintech, catalog number: PR30010), and finally, the sections were counterstained with hematoxylin to visualize the nuclei.\u003c/p\u003e\u003cp\u003eFor H\u0026amp;E staining, the process involved using hematoxylin for nuclear staining and eosin for the staining of cytoplasmic components.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern Blot Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Western blot analysis was performed following established protocols with minor modifications. The cells were lysed on ice for 30 minutes using a RIPA lysis buffer (Beyotime, catalog number: P0013B) that was supplemented with a cocktail of protease inhibitors (Beyotime, catalog number:P1005). After lysis, the samples were centrifuged at 12,000 × g for 10 minutes at 4°C, allowing the clear supernatants to be collected for subsequent protein quantification. The protein samples were then separated via SDS-PAGE, after which they were transferred to polyvinylidene difluoride (PVDF) membranes that were blocked with 5% skim milk for 90 minutes. Following the blocking step, membranes were incubated with primary antibodies overnight at 4°C to ensure thorough binding. The next day, membranes underwent three washes with TBST before being incubated with secondary antibodies for a period of 60 minutes at room temperature. Finally, protein bands were visualized using an ECL chemiluminescence system (Biosharp, catalog number: BL520B), resulting in reliable detection of the target proteins.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQ-PCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the mRNA expression analysis, total RNA was meticulously extracted employing the Trizol reagent (Beyotime, catalog number: R0016), which is well-regarded for its efficacy in isolating high-quality RNA. To facilitate downstream applications, the RNA samples underwent reverse transcription to synthesize complementary DNA (cDNA) using a specialized reverse transcription kit provided by Abcam (Catalog number: G490). Subsequently, quantitative PCR (qPCR) was conducted utilizing the QuantStudio 5 instrument from Applied Biosystems. This instrument performed the crucial function of quantifying the levels of cDNA generated from the reverse transcription process. The specific primer sequences employed during the qPCR analysis are enumerated in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, ensuring that readers can reference the exact sequences used.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmunoprecipitation and mass-spectrometry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe extraction of cellular proteins was accomplished using the RIPA lysis buffer (Beyotime, catalog number:P2185S), which was complemented with a protease inhibitor cocktail to safeguard protein integrity. The lysis process was conducted on ice for a duration of five minutes to minimize protein degradation. Following this, the lysate underwent centrifugation at a high speed of 12,000 rpm for five minutes at a cold temperature of 4°C, resulting in the separation of the supernatant. This supernatant was then incubated overnight at 4°C on a rotating platform with primary antibodies or control IgG antibodies sourced from Beyotime (Catalog number: P2185S) to specifically capture target proteins. On the following day, the immunoprecipitated complexes were rigorously washed three times using the lysis buffer to eliminate any non-specific binding that could interfere with the accuracy of subsequent analyses. After completing the washing steps, 100 µl of SDS-PAGE loading buffer was introduced to the samples, which were subsequently heated at 95°C for five minutes. To analyze the protein content, Western blot was employed, with the primary antibody being visualized through incubation with a universal secondary antibody that targets both heavy and light chains (Abmart, catalog number: M21008, diluted to 1:5000).\u003c/p\u003e\u003cp\u003eFor the mass spectrometry analysis, the immunoprecipitated proteins were separated via SDS-PAGE, and the resultant gel bands were subjected to an in-gel trypsin digestion process. The ensuing peptides were then analyzed through liquid chromatography-tandem mass spectrometry (LC-MS/MS) at Shanghai Bioprofile Biotechnology. This analysis aimed to identify proteins that interact with GBP1, thereby providing insights into protein-protein interactions relevant to the research.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmunofluorescence Staining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, multiplex immunofluorescence staining was conducted utilizing the TSAPLus kit from Servicebio (Catalog number: G1236). The initial staining procedure involved a sequence of adding a primary antibody, followed by an HRP-conjugated secondary antibody and subsequent TSA amplification. To facilitate the analysis of multiple antigens, antibody stripping was carried out using a designated buffer provided with the kit, maintaining a temperature of 37°C for 30 minutes. This staining cycle was repeated for additional antigens, employing various fluorophore-conjugated TSA reagents, specifically iF488-Tyramide, iF555-Tyramide, and iF647-Tyramide, which allowed for the visualization of multiple colors, including DAPI for nuclear staining. The imaging of the stained samples was performed with a Nikon confocal microscope, and the obtained images were further analyzed using ImageJ software.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNude Mouse Xenograft Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003e The animal experiments conducted in this study received approval from the Institutional Animal Care and Use Committee at Zhongshan Hospital, Fudan University, with the approval number designated as NO. 2022-06-DJ-70. A nude mouse xenograft model was established to investigate the influence of GBP1 on OS growth in a live animal setting. To achieve this, stable 143B cells—either overexpressing GBP1 or containing a control vector-were injected subcutaneously at a concentration of 5×10⁶ cells per mouse into the right flank of four-week-old female BALB/c nude mice. Following a period of 28 days post-inoculation, the mice were euthanized, and the tumors that developed were harvested for further analysis. The dimensions of the tumors were measured using a vernier caliper, allowing for the calculation of tumor volume using the formula V = 0.5 × a × b², where ‘a’ represents the length and ‘b’ signifies the width. Furthermore, the tumors were weighed, photographed for documentation, and a portion of the tissue was preserved for subsequent HE staining and IHC analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular Docking\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThree-dimensional structure files of target proteins were obtained from the PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The software PyMOL (v4.60) was used for structural visualization inspection and preprocessing, including removing water molecules, ligands, and other unrelated atoms from the structure, as well as adding hydrogen atoms and charges to meet the requirements for protein interaction preparation files. The processed protein structure files were uploaded to the ZDOCK online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zdock.wenglab.org/\u003c/span\u003e\u003cspan address=\"https://zdock.wenglab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for protein-protein interaction prediction, and the rationality and possibility of the models were evaluated based on scoring functions. Using PyMOL, the selected optimal interaction model was displayed as a surface model. The two interacting proteins were colored light blue and yellow respectively to highlight their overall morphology and interaction interface. The interaction interface area was marked in red to intuitively present the spatial distribution and morphological characteristics of the contact region between the two proteins, facilitating the observation of potential binding sites and interaction areas. The secondary structures of the proteins were presented in white and light yellow, with key amino acid residues involved in the interaction highlighted in red, and their numbers and names labeled. Specific interaction types and strengths, such as hydrogen bonds and hydrophobic interactions formed by amino acids, were displayed through connecting lines and related distance labeling, deeply revealing the molecular basis of protein interactions.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll experimental procedures were consistently replicated at least three times to ensure the reliability and reproducibility of the data collected. The statistical analyses were conducted using GraphPad Prism 9.0.0, a software suite renowned for its statistical capabilities in biological research. The presentation of data is as mean values accompanied by the standard error of the mean (Mean ± SEM), which provides a clear indication of variability within the data. To denote statistical significance across the findings, specific p-value thresholds were established, with significance indicated at *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ****p \u0026lt; 0.0001, thus allowing for a comprehensive understanding of the experimental results.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cb\u003eOverexpression of GBP1 inhibits OS proliferation\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated the regulatory role of GBP1 in various types of tumors\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, which has piqued our curiosity about whether GBP1 regulates the progression of OS.To further characterize the functional role of GBP1, stable GBP1-overexpressing cell lines were generated using a lentiviral vector system, with successful expression of HA-tagged GBP1 validated by immunoblotting(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subsequent functional assays first evaluated GBP1's impact on cell proliferation. The CCK-8 assay demonstrated that forced GBP1 expression significantly attenuated cell viability compared to control groups both in 143B and U2OS(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Consistent results were observed in colony formation assays(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Flow cytometry analysis using Annexin V-FITC/PI double staining further revealed that GBP1 overexpression induced a significant increase in apoptotic cell populations(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Collectively, these findings indicate that GBP1 exerts tumor suppressive effects by inhibiting cell proliferation and promoting apoptosis in OS cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the in vivo biological function of GBP1, a xenograft model was established using female nude mice. A total of 6×10⁶ 143B cells stably transfected with either GBP1-Vector or GBP1-OE were orthotopically injected into the flanks of recipient mice. Tumor growth was monitored by caliper measurements until sacrifice at week 4. Mice injected with GBP1-OE cells developed significantly smaller tumors compared to GBP1-Vector controls, as determined by both volume and weight(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Immunohistochemical staining of tumor sections further revealed elevated Ki-67 proliferation marker expression in GBP1-overexpressing tumors(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Collectively, these in vivo data demonstrate that GBP1 overexpression potently suppresses OS growth in a xenograft model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverexpression of GBP1 inhibits OS cells migration and invasion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo elucidate the role of GBP1 in OS metastasis, we initially analyzed the transcriptional profile of GBP1 in OS with lung metastasis using the GEO dataset (GSE85537). Comparative analysis revealed a significant downregulation of GBP1 expression in lung - metastatic OS tissues relative to primary OS specimens, suggesting the potential involvement of GBP1 in the metastatic cascade of OS(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Subsequently, a series of in - vitro assays were performed to evaluate the functional impact of GBP1 on cancer cell migration and invasion. Wound - healing assays demonstrated that overexpression of GBP1 in 143B and HOS cell lines significantly attenuated the wound - closure rate(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-E), indicating a suppression of cell migratory capacity. Additionally, transwell invasion assays, with the addition of Matrigel to mimic the in - vivo extracellular matrix, showed that GBP1 overexpression markedly impaired the invasive potential of 143B cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-G). Consistent results were obtained in U2OS cells, ruling out cell - line - specific effects(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH-I).Collectively, our findings suggest that GBP1 functions as a negative regulator of OS cell migration and invasion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGBP1 inhibits the proliferation and metastasis of OS cells through DDX17\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo dissect the inhibitory mechanism of GBP1 in OS progression, we hypothesized that GBP1 interacts with key proteins governing tumor malignancy. Immunoprecipitation using anti-HA antibodies was performed to enrich GBP1-interacting proteins in 143B cells, followed by mass spectrometry analysis(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A protein was validated as a candidate when its peptide count in GBP1-expressing cells was ≥ 1.5-fold higher than controls, with a minimum of 10 peptides. This screen identified 178 potential GBP1 interactors, among which DDX17 showed significantly elevated peptide counts in GBP1-stable cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Immunoprecipitation confirmed the GBP1-DDX17 interaction(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), further validated by immunofluorescence co-localization(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Subsequently, we investigated whether GBP1-DDX17 interaction regulates protein levels. Ectopic expression of GBP1 led to a marked decrease in DDX17 protein abundance in both 143B and U2OS cell lines(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), suggesting DDX17 as a GBP1 target.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhile DDX17 has been validated as an oncogenic driver in various cancer types, its functional role and regulatory mechanisms in OS remain largely uncharacterized. To investigate its role, we employed siRNA - mediated knockdown of DDX17 in 143B and U2OS cells. Efficient silencing was confirmed by qPCR and Western blot analyses(Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-B). We first evaluated the effect of DDX17 on the proliferation and apoptosis of osteosarcoma cells. The CCK − 8 assay showed that, compared with the DDX17 - NC group, the cell viability of the DDX17 - siRNA group in 143B and U2OS cells was significantly reduced(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). Concurrently, the apoptosis rate of the DDX17 - siRNA group was significantly higher than that of the DDX17 - NC group in both U2OS and 143B cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB). Next, we used a series of experiments to analyze the effect of DDX17 on the metastasis of OS cells. The wound - healing assay indicated that the relative migration rate of the DDX17 - siRNA group was significantly lower than that of the DDX17 - NC group, suggesting that silencing DDX17 inhibits the migration of 143B and U2OS cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC). Additionally, transwell migration and invasion assays showed that silencing DDX17 significantly impaired the migration and invasion abilities of 143B and U2OS cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eD). Collectively, these data establish DDX17 as a critical driver of OS progression, as its depletion inhibits proliferation, induces apoptosis, and impairs metastatic potential, highlighting DDX17 as a potential therapeutic target in osteosarcoma.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate whether DDX17 mediates the regulatory effect of GBP1 on OS, we overexpressed DDX17 in GBP1-Vector and GBP1-OE cells. Western blot confirmed DDX17 overexpression (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC), while CCK8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eE) and colony formation(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eF) assays showed that DDX17 overexpression rescued proliferation in GBP1-OE cells. Wound-healing assays demonstrated enhanced migration in DDX17-overexpressing 143B and HOS cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eG), and transwell assays confirmed increased invasion compared to GBP1-OE controls(Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA-B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCollectively, these data establish DDX17 as a critical GBP1 target, mechanistically linking GBP1 to the suppression of OS malignant progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGBP1 promotes DDX17 degradation via the ubiquitin-proteasome system\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven that DDX17 mediates GBP1-induced suppression of OS malignancy, we investigated the precise mechanism by which GBP1 regulates DDX17(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). qPCR assays showed no significant change in DDX17 transcription following GBP1 overexpression, prompting the hypothesis that GBP1 modulates DDX17 protein stability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo validate this, cells were treated with cycloheximide (CHX, a protein synthesis inhibitor), and Western blotting quantified DDX17 levels. GBP1 overexpression significantly shortened DDX17 half-life(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). To explore degradation pathways, cells were co-treated with CHX and MG-132 (10 µM, proteasome inhibitor) or chloroquine (CQ, 25 µM, lysosome inhibitor). MG-132 significantly attenuated DDX17 degradation, whereas CQ had no effect(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), indicating a dominant role for the proteasomal pathway in DDX17 turnover.\u003c/p\u003e\u003cp\u003eSubsequently, DDX17 polyubiquitination was measured. Cells were treated with MG-132 to enhance ubiquitination, followed by immunoprecipitation of endogenous DDX17 and Western blotting for ubiquitin. GBP1-overexpressing cells exhibited augmented ubiquitin signals, confirming that GBP1 accelerates DDX17 polyubiquitination in OS cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eCollectively, these data demonstrate that GBP1 promotes DDX17 degradation via the ubiquitin-proteasome system, providing mechanistic insights into the GBP1-DDX17 axis in OS progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGBP1 promotes the ubiquitination of DDX17 via HSPA8\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven that GBP1 lacks intrinsic ubiquitination activity, we aimed to identify specific ubiquitin ligases or deubiquitinases mediating GBP1-regulated DDX17 ubiquitination. Through in silico prediction of DDX17-interacting ubiquitin/deubiquitin enzymes using the UbiBrowser database, combined with Venn analysis of IP-MS results, USP10 and HSPA8 were identified as potential DDX17 interactors(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). To validate the key enzyme, siRNA screening was performed to assess ubiquitin ligases/deubiquitinases involved in GBP1-mediated DDX17 degradation. Notably, in GBP1-overexpressing cells, HSPA8 silencing abrogated DDX17 level discrepancies, whereas USP10 silencing had no effect(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). Half-life assays showed HSPA8 overexpression shortened DDX17 half-life(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E), and ubiquitination assays confirmed enhanced DDX17 ubiquitination(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). These findings were recapitulated in 143B and U2OS cell lines, establishing HSPA8 as a mediator of GBP1-regulated ubiquitin-dependent DDX17 degradation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo investigate direct protein interactions, immunoprecipitation confirmed HSPA8-DDX17 binding(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), consistent with immunofluorescence showing partial co-localization(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). It was Interesting that immunoprecipitation revealed GBP1-HSPA8 interaction(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eC,Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA), further validated by immunofluorescence(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eB). Given GBP1-DDX17 interaction previously identified, we hypothesized a ternary complex formation. Protein-protein docking using ZDOCK predicted the GBP1-HSPA8-DDX17 complex structure, with 3D models derived from PDB and visualized by PyMOL(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Immunofluorescence confirmed co-localization of GBP1, HSPA8, and DDX17, indicating distinct binding domains on GBP1 for HSPA8 and DDX17(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). Immunoprecipitation showed enhanced HSPA8-DDX17 interaction in GBP1-overexpressing cells, suggesting GBP1 promotes their binding(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCollectively, these data demonstrate that GBP1 forms a stable complex with HSPA8 and DDX17, whereby GBP1 enhances HSPA8-DDX17 interaction to promote DDX17 ubiquitination and proteasomal degradation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Relevance of GBP1 and DDX17 in OS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the tumor-suppressive function of GBP1 in OS progression, we explored the clinical relevance of our findings. GBP1 transcriptional profiles were analyzed using the GEO dataset (GSE197158), revealing significantly reduced GBP1 mRNA expression in OS cell lines versus the normal osteoblast line hFOB1.19(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Immunoblotting validated translational downregulation in OS cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eB-C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWestern blot analysis of 10 pairs of OS tissues and adjacent normals showed GBP1 underexpression and DDX17 overexpression in OS(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-E), with a significant negative correlation between GBP1 and DDX17 levels(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Immunohistochemical staining of 30 OS tissue pairs confirmed this inverse correlation: low GBP1 expression was associated with high DDX17, and vice versa, consistent with WB results(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eF-H). Representative IHC images of differential expression are provided.\u003c/p\u003e\u003cp\u003eClinicopathological analysis identified low GBP1 expression as a significant biomarker for poor overall survival in OS patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eI). Data from TARGET further showed that low GBP1 expression correlated with reduced disease-free survival(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eJ). Collectively, these findings implicate coordinated roles for GBP1 and DDX17 in regulating OS progression and patient prognosis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically elucidates the tumor-suppressive role of GBP1 in OS progression, uncovering a novel mechanism whereby GBP1 promotes HSPA8-mediated ubiquitin-proteasome degradation of DDX17. Multidimensional experimental validation confirms that GBP1 inhibits OS cell proliferation, migration, and invasion by enhancing DDX17 ubiquitination through a ternary complex with HSPA8. These findings not only expand the understanding of OS pathogenesis but also establish the GBP1/DDX17 axis as a promising therapeutic target.\u003c/p\u003e\u003cp\u003eGBP1 expression is significantly downregulated in OS tissues and cell lines, with low expression tightly associated with poor patient prognosis\u0026mdash;a finding consistent with its tumor-suppressive roles in colorectal and liver cancer\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Notably, OS exhibits a negative correlation between GBP1 expression and tumor metastatic potential, underscoring its critical role in metastatic regulation. In contrast, studies in cervical cancer\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, cutaneous squamous cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, lung cancer\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and glioma\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e have identified GBP1 as an oncogene, highlighting its context-dependent functions across malignancies. Functional assays further demonstrate that GBP1 overexpression suppresses OS cell malignancy in vitro and inhibits tumor growth in nude mouse xenografts, positioning GBP1 as a potential therapeutic target. However, the regulatory mechanisms of GBP1 expression in OS remain uncharacterized, warranting exploration of upstream regulators (e.g., transcription factors or epigenetic modifiers) in future investigations.\u003c/p\u003e\u003cp\u003eThe study reveals that GBP1 exerts tumor suppression by promoting DDX17 degradation via the ubiquitin-proteasome system. Mechanistically, GBP1 directly interacts with DDX17 to recruit HSPA8, forming a ternary complex that enhances DDX17 ubiquitination. Although HSPA8 lacks intrinsic E3 ligase activity, its C-terminus binds STUB1 to facilitate substrate ubiquitination\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. As a molecular chaperone, HSPA8 plays dual roles: recognizing DDX17 domains to promote ubiquitination and stabilizing the HSPA8-DDX17 complex upon GBP1 binding, thereby accelerating degradation. This discovery expands HSPA8 functions in tumors, demonstrating its involvement in oncoprotein degradation beyond classical folding regulation. Molecular docking assays validate the formation of the GBP1-HSPA8-DDX17 complex, providing a structural basis for subsequent mechanistic studies.\u003c/p\u003e\u003cp\u003eImplications DDX17, a DEAD-box family member, promotes OS malignancy through multiple mechanisms: its overexpression reverses GBP1-induced growth inhibition and enhances OS cell migration/invasion. This aligns with DDX17\u0026rsquo;s established oncogenic roles in breast cancer\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, HCC\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, colorectal cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and lung adenocarcinoma\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, suggesting conserved regulatory functions across cancers. DDX17 degradation likely disrupts its involvement in RNA metabolism and target gene transcription, thereby influencing OS malignant phenotypes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Future studies should characterize DDX17\u0026rsquo;s specific RNA substrates and the transcriptomic effects of the GBP1-DDX17 axis to decipher OS molecular heterogeneity.\u003c/p\u003e\u003cp\u003eAs an interferon-induced protein, GBP1 expression may be regulated by the immune microenvironment. Public cohort analyses show GBP1 predicts immunotherapy response, with macrophage-specific expression linked to T-cell chemotaxis\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Similar correlations between GBP1 and immune infiltration exist in cervical cancer\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, this study did not address GBP1-immune crosstalk in OS-critical avenue for future research. Targeting the GBP1/DDX17 axis may exert dual effects of direct tumor suppression and immune activation, leveraging GBP1\u0026rsquo;s immunomodulatory functions.\u003c/p\u003e\u003cp\u003eThis work has notable limitations: 1) Animal models only validated GBP1\u0026rsquo;s effect on tumor growth, not metastasis; 2) The specific domains and key amino acids of the GBP1-HSPA8-DDX17 complex remain undefined; 3) GBP1\u0026rsquo;s regulation via alternative pathways requires validation. Future research should: 1) Construct conditional GBP1 knockout OS mouse models to study metastatic roles. 2) Elucidate the complex\u0026rsquo;s three-dimensional structure via crystallography to identify interaction hotspots. 3) Integrate omics approaches to screen DDX17 downstream targets and refine signaling networks. 4) Develop small-molecule inhibitors targeting GBP1-DDX17 interactions for translational applications.\u003c/p\u003e\u003cp\u003eIn summary, this study establishes GBP1 as a tumor suppressor in OS, uncovering a mechanism whereby GBP1 promotes HSPA8-mediated DDX17 degradation. These findings advance the understanding of OS molecular pathology and provide a theoretical framework for developing GBP1/DDX17-targeted therapies. Future studies should refine this regulatory network and facilitate its clinical translation to improve OS treatment outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We acknowledge all the publicly available database employed in this study ,in particular, Gene Expression Omnibus, as well as TARGET projects. We gratefully acknowledge all the patients involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Nantong Municipal Health Commission Scientific Research Project (Youth Program, Shuo Yang, QN2024072), \u0026nbsp;Nantong Municipal Health Commission Scientific Research Project (General Program, Shengyu Cui, MS2024027) and Bethune Charitable Foundation(Rongkui Luo, G-X-2019-0101-12).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Zhongshan Hospital (No. B2024-121).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuo Yang: Writing - original draft, Conceptualization, Investigation, Methodology, Validation, Funding acquisition. Wen Huang: Investigation, Methodology, Formal analysis, Resources, Data curation. Xu Li: Formal analysis, Validation, Conceptualization. Shujiang Ye: Methodology, Investigation. Yixuan Li: Validation, Methodology. Hanrui Liu: Data curation, Methodology. Jiafeng He: Investigation, validation. Rongkui Luo: Writing - review \u0026amp; editing, Supervision, Conceptualization, Funding acquisition. Shengyu Cui: Writing - review \u0026amp; editing, Supervision, Funding acquisition, Conceptualization, Project administration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYoung, E.P., Marinoff, A.E., Lopez-Fuentes, E., and Sweet-Cordero, E.A. (2024). Osteosarcoma through the lens of bone development, signaling, and microenvironment. Cold Spring Harb. Perspect. Med. \u003cem\u003e14\u003c/em\u003e, a41635.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao, Z., Tan, Z., Yang, J., Yang, Y., Wang, C., Chen, J., Zhu, Y., Wang, T., Han, L., and Zhu, L., et al. (2021). Prognostic nomogram for predicting 5-year overall survival in chinese patients with high-grade osteosarcoma. Sci. Rep. \u003cem\u003e11\u003c/em\u003e, 17728.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHonkala, A.T., Tailor, D., and Malhotra, S.V. (2019). Guanylate-binding protein 1: an emerging target in inflammation and cancer. Front. Immunol. \u003cem\u003e10\u003c/em\u003e, 3139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBritzen-Laurent, N., Lipnik, K., Ocker, M., Naschberger, E., Schellerer, V.S., Croner, R.S., Vieth, M., Waldner, M., Steinberg, P., and Hohenadl, C., et al. (2013). Gbp-1 acts as a tumor suppressor in colorectal cancer cells. Carcinogenesis \u003cem\u003e34\u003c/em\u003e, 153\u0026ndash;162.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNing, Y., Fang, S., Fang, J., Lin, K., Nie, H., Xiong, P., Qiu, P., Zhao, Q., Wang, H., and Wang, F. (2023). Guanylate-binding proteins signature predicts favorable prognosis, immune-hot microenvironment, and immunotherapy response in hepatocellular carcinoma. Cancer Med. \u003cem\u003e12\u003c/em\u003e, 17504\u0026ndash;17521.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, H., Li, F., Cheng, S., Xu, Y., Deng, H., Gu, D., Wang, J., Chen, W., Zhou, Y., and Yang, M., et al. (2022). Ddx17-regulated alternative splicing that produced an oncogenic isoform of pxn-as1 to promote hcc metastasis. Hepatology. \u003cem\u003e75\u003c/em\u003e, 847\u0026ndash;865.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin, C., Han-Hua, D., Qiu-Meng, L., Deng, N., Peng-Chen, D., Jie, M., Lei, X., Xue-Wu, Z., Hui-Fang, L., and Yan, C., et al. (2023). Mtdh-stabilized ddx17 promotes tumor initiation and progression through interacting with yb1 to induce egfr transcription in hepatocellular carcinoma. Oncogene \u003cem\u003e42\u003c/em\u003e, 169\u0026ndash;183.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWortham, N.C., Ahamed, E., Nicol, S.M., Thomas, R.S., Periyasamy, M., Jiang, J., Ochocka, A.M., Shousha, S., Huson, L., and Bray, S.E., et al. (2009). The dead-box protein p72 regulates eralpha-/oestrogen-dependent transcription and cell growth, and is associated with improved survival in eralpha-positive breast cancer. Oncogene \u003cem\u003e28\u003c/em\u003e, 4053\u0026ndash;4064.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao, G., Wang, Q., Zhang, Y., Gu, R., Liu, M., Li, Q., Zhang, J., Yuan, H., Feng, T., and Ou, D., et al. (2023). Ddx17 induces epithelial-mesenchymal transition and metastasis through the mir-149-3p/cybrd1 pathway in colorectal cancer. Cell Death Dis. \u003cem\u003e14\u003c/em\u003e, 1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, H., Li, F., Cheng, S., Xu, Y., Deng, H., Gu, D., Wang, J., Chen, W., Zhou, Y., and Yang, M., et al. (2022). Ddx17-regulated alternative splicing that produced an oncogenic isoform of pxn-as1 to promote hcc metastasis. Hepatology. \u003cem\u003e75\u003c/em\u003e, 847\u0026ndash;865.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuzza, A., Zaltron, E., Vianello, F., Celotti, I., Scavezzon, M., Severin, F., and Leanza, L. (2025). Hspa8 and hspa9: two prognostic and therapeutic targets in breast, colon, and kidney cancers? Biochim. Biophys. Acta-Mol. Basis Dis. \u003cem\u003e1871\u003c/em\u003e, 167827.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesideri, E., Castelli, S., Dorard, C., Toifl, S., Grazi, G.L., Ciriolo, M.R., and Baccarini, M. (2023). Impaired degradation of yap1 and il6st by chaperone-mediated autophagy promotes proliferation and migration of normal and hepatocellular carcinoma cells. Autophagy \u003cem\u003e19\u003c/em\u003e, 152\u0026ndash;162.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, B., Chang, Y., Lin, S., and Yang, W.Y. (2020). Hsc70/stub1 promotes the removal of individual oxidatively stressed peroxisomes. Nat. Commun. \u003cem\u003e11\u003c/em\u003e, 5267.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTailor, D., Garcia-Marques, F.J., Bermudez, A., Pitteri, S.J., and Malhotra, S.V. (2023). Guanylate-binding protein 1 modulates proteasomal machinery in ovarian cancer. iScience \u003cem\u003e26\u003c/em\u003e, 108292.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu, S., Li, Y., Feng, W., Zeng, J., Cui, X., Zhou, S., and Zhang, P. (2024). Gbp1 promotes cutaneous squamous cell carcinoma proliferation and invasion through activation ofstat3 bysp1. Exp. Dermatol. \u003cem\u003e33\u003c/em\u003e, e15112.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng, Y., Wang, W., Chen, M., Chen, K., Xia, X., Zhou, S., and Yang, H. (2021). Gbp1 facilitates indoleamine 2,3-dioxygenase extracellular secretion to promote the malignant progression of lung cancer. Front. Immunol. \u003cem\u003e11\u003c/em\u003e, 622467.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, M., Mukasa, A., Del-Mar Inda, M., Zhang, J., Chin, L., Cavenee, W., and Furnari, F. (2011). Guanylate binding protein 1 is a novel effector of egfr-driven invasion in glioblastoma. J. Exp. Med. \u003cem\u003e208\u003c/em\u003e, 2657\u0026ndash;2673.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalb, R.C., Nyabuto, G.O., Morran, M.P., Maity, S., Justinger, J.S., Nestor-Kalinoski, A.L., and Vestal, D.J. (2024). The large gtpase guanylate-binding protein-1 (gbp-1) promotes mitochondrial fission in glioblastoma. Int. J. Mol. Sci. \u003cem\u003e25\u003c/em\u003e, 11236.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeo, J.H., Park, J., Lee, E.J., Vo, T.T.L., Choi, H., Kim, J.Y., Jang, J.K., Wee, H., Lee, H.S., and Jang, S.H., et al. (2016). Ard1-mediated hsp70 acetylation balances stress-induced protein refolding and degradation. Nat. Commun. \u003cem\u003e7\u003c/em\u003e, 12882.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, X., Li, L., Geng, C., Wen, S., Zhang, C., Deng, C., Gao, X., Zhang, G., He, Q., and Liu, L. (2022). Ddx17 promotes the growth and metastasis of lung adenocarcinoma. Cell Death Discov. \u003cem\u003e8\u003c/em\u003e, 425.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, H., Gao, X., Zhang, W., Fu, X., Zhang, J., Yuan, Q., Jin, J., Du, X., Li, R., and Li, Y., et al. (2025). Ddx17-mediated upregulation of cxcl8 promotes hepatocellular carcinoma progression\u003cem\u003evia\u003c/em\u003e co-activating β-catenin/nf-κb complex. Int. J. Biol. Sci. \u003cem\u003e21\u003c/em\u003e, 1342\u0026ndash;1360.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, L., Wei, Y., Jin, Z., Liu, F., Li, X., Zhang, X., Bai, X., Jia, Q., Zhu, B., and Chu, Q. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eIfn-α/β/ifn-γ/il-\u003c/span\u003e\u003cspan address=\"http://Ifn-α/β/ifn-γ/il-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e15 pathways identify gbp1-expressing tumors with an immune-responsive phenotype. Clin. Exper. Med. \u003cem\u003e24\u003c/em\u003e, 102.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, S., Zhang, Y., Ma, X., and Feng, Y. (2024). Function and mechanism of gbp1 in the development and progression of cervical cancer. J. Transl. Med. \u003cem\u003e22\u003c/em\u003e, 11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7033508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7033508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOsteosarcoma (OS) stands as the preeminent primary malignant bone tumor, with its 5-year survival rate persistently lingering at 60%-70%. This therapeutic standstill is predominantly due to the shortage of sensitive early diagnostic markers and precise targeted treatment strategies. Guanylate-binding protein 1 (GBP1), a GTPase induced by interferons, serves as a tumor suppressor in multiple cancer types, yet its function and molecular mechanism in OS have remained uninvestigated. Our research reveals that GBP1 hinders OS tumor formation by promoting HSPA8-dependent ubiquitin-proteasome degradation of the DEAD-box RNA helicase 17 (DDX17) protein. Functional assays demonstrate that overexpression of GBP1 powerfully suppresses the proliferation, migration, and invasion of OS cells in vitro, and inhibits tumor growth in nude mouse xenograft models. Mechanistically, GBP1 forms a ternary complex with HSPA8 and DDX17, thereby enhancing the ubiquitination and proteasomal degradation of DDX17. Notably, overexpression of DDX17 counteracts the growth-inhibiting effects of GBP1, confirming its role as a crucial downstream effector. Clinical analyses show that GBP1 expression is markedly reduced in OS tissues, with a strong inverse correlation found between GBP1 levels and poor patient prognosis. These findings afford fresh perspectives on the biological processes driving OS progression and identify GBP1 as a potential therapeutic target for OS.\u003c/p\u003e","manuscriptTitle":"GBP1 inhibits osteosarcoma progression by regulating DDX17 protein stability via HSPA8","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 05:43:57","doi":"10.21203/rs.3.rs-7033508/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80eb80ed-b4ad-466c-b974-e86a4a7fb688","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51589020,"name":"Biological sciences/Cancer/Oncogenes"},{"id":51589021,"name":"Biological sciences/Cancer/Bone cancer"}],"tags":[],"updatedAt":"2025-08-26T15:15:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 05:43:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7033508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7033508","identity":"rs-7033508","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}