A Novel Highly Invasive Cell-Related Gene Signature for Predicting the Prognosis and Treatment of Osteosarcoma

A Novel Highly Invasive Cell-Related Gene Signature for Predicting the Prognosis and Treatment of Osteosarcoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel Highly Invasive Cell-Related Gene Signature for Predicting the Prognosis and Treatment of Osteosarcoma Zijun Li, Mengting Wang, Yunlong Wang, Chengfeng Yi, Jun Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4495593/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) is a highly prevalent bone tumor derived from primitive mesenchymal cells that occurs mostly in adolescents and children. OS has a notable propensity for aggressive behavior and resistance to treatment. Additionally, accurately evaluating and predicting the prognosis of OS remains challenging. For this investigation, we utilized scRNA-seq data to identify seven subtypes of OS cells. Survival analysis of each OS cell subtype revealed that highly invasive OS (HIS-OS) had a poorer prognosis. Through differential expression analysis, an entire set of seven genes linked to HIS-OS was identified. Subsequently, these seven genes were employed to construct a predictive model using the LASSO approach. Based on the median risk score, the OS samples in the training set were categorized into high-risk and low-risk groups, and the high-risk group exhibited a significantly shorter survival time. The analysis of immunotherapy and anticancer treatment responsiveness indicated a negative correlation between HIS-OS-related gene signatures and immune checkpoints as well as chemotherapy sensitivity. In addition, functional analysis demonstrated high enrichment of these gene sets throughout the process of tumor invasion. Finally, SERPINE2 was identified as a therapeutically critical gene. Therefore, we subsequently selected an inhibitor, IITZ-01, that targets SERPINE2, and we performed molecular docking simulations. Furthermore, we validated the inhibitory effect of IITZ-01 on OS at the cellular level. The results suggest that HIS-OS-related genes are important for prognostic stratification and therapeutic strategies for OS. Osteosarcoma scRNA-seq Immune Therapeutic target Gene signature Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Osteosarcoma (OS) is a highly prevalent bone tumor derived from primitive mesenchymal cells; OS most commonly occurs among children and adolescents, accounting for more than 60% of all malignancies among children (Belayneh, Fourman, Bhogal, & Weiss, 2021 ; Kansara, Teng, Smyth, & Thomas, 2014 ; Mutsaers & Walkley, 2014 ). OS occurs primarily in fast-growing bones – most often in children and young adults – near the end of the leg or arm, such as the femur, tibia, and humerus, the most common locations (Isakoff, Bielack, Meltzer, & Gorlick, 2015 ). Local invasion and rapid metastasis to the lungs are the main features of OS, and metastatic OS often recurs with poor prognosis (Dean, Shen, Hornicek, & Duan, 2018 ). Although the combination of extensive surgical resection and multiagent chemotherapy slightly improves OS compared with surgical resection alone, there currently seems to be no substantial increase in survival for patients without metastasis, and there has been little improvement in survival for patients with metastasis in the past two decades (Pan et al., 2022 ). In patients with localized OS, the 5-year survival rate is 80%, and in patients with metastatic OS, the 5-year survival rate does not exceed 30% (Gaspar et al., 2018 ). The development of molecular biology approaches has considerably enhanced our understanding of the genetic etiology of OS (Gill & Gorlick, 2021 ). Despite advancements in medical research, the outlook for patients diagnosed with OS is still not favorable. As a consequence, finding novel molecular markers is essential for correctly predicting the prognosis of OS and improving treatment outcomes. Tumor heterogeneity involves multiple aspects of tumor prognosis, including drug resistance, recurrence, and metastasis (Schiavone, Garnier, Heymann, & Heymann, 2019 ). Like many other malignancies, OS has been shown to be characterized by extensive tumor heterogeneity, which has a significant impact on treatment and prognosis (Mohseny et al., 2009 ; Zhou et al., 2020 ). Conventional transcriptome analysis of mixed cell populations has shown that the resolution of detecting specific cell types is insufficient to accurately assess the complexity of tumor heterogeneity in OS. Recently, single-cell RNA sequencing (scRNA-seq) has become a valuable tool in tumor research. By using optimized next-generation sequencing technology to define the overall gene expression profile of a single cell, it is easy to analyze the heterogeneity hidden before the cell population; identify cell lineages, new cell subsets, and regulatory networks between genes and cell-specific biological characteristics; and provide new insights for complex biological systems (Dong et al., 2020 ; Peng et al., 2019 ). In particular, scRNA-seq studies have revealed a high degree of intratumor heterogeneity, differential expression of multiple genes in multiple cell types, and cellular cross-talk with the tumor microenvironment (TME), which cannot be determined by traditional bulk sequencing datasets (K. Sun et al., 2022 ). However, there have been few reports on the heterogeneity of OS tumors through scRNA-seq. Herein, we conducted a dimensionality reduction clustering analysis of scRNA-seq data from the GEO database, identified seven OS cell subtypes, and identified marker genes for each subtype. Through functional analysis, we found a close connection between highly invasive OS cells and OS patient prognosis. Next, we obtained OS clinical case samples and HIS-OS-related gene expression data from the database to further explore the associations of HIS-OS-related genes with OS patient survival. Based on our risk profile, we identified seven HIS-OS-related genes that independently predicted OS patient outcomes. Next, we assessed immune-infiltrating cells to determine whether they can predict immunological checkpoint activity and susceptibility to immunotherapy. In addition, we investigated the pathways and biological processes that are regulated by genes associated with HIS-OS. Finally, we investigated the potential therapeutic target SERPINE2 by analyzing the expression levels and survival rates of the prognostic model genes and further verified the inhibitory effect of IITZ-01, an inhibitor of SERPINE2, on the migration and invasion of OS cells via virtual screening and cell function experiments. These findings will aid in predicting the prognosis of OS and facilitate the development of targeted therapy for OS. Method Data collection scRNA-seq data of human OS samples were obtained from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). The testing cohort consisted of 53 clinical case samples from the GSE21257 dataset in the GEO database. The clinical case samples from 85 OS patients in the training cohort were obtained from the TARGET database ( https://www.cancer.gov/programs/target/data-matrix ). GTEx ( https://gtexportal.org/ ) was used to analyze normal tissue samples (n = 396). Subtypes of OS cells were annotated The tumor tissue scRNA-seq data were categorized into different cell populations, after which the OS cells and data were extracted for additional bioinformatics studies. Based on the characteristics of the cells, including intercellular heterogeneity and genetic similarity, seven cell subtypes were identified from the single-cell sequencing data of OS cells. These seven cell subtypes were subsequently annotated using the R programming language ( https://cran.R-project.org/ ), Gene Ontology (GO) enrichment analysis, and collection of gene signatures from published literature. Determination of OS cell subtypes to be studied and screening for genes The overall survival of patients in the different risk categories within each cell subtype was analyzed using R. The outcomes were evaluated using Kaplan‒Meier (K‒M) survival curves. The cell subtype that exhibited both the highest overall survival and the greatest variability in survival time (HIS-OS) was chosen. The next step was to apply screening criteria to search for genes with log2FC > 1 and p value < 0.01 among all highly invasive OS cells; ultimately, 25 genes were identified as significantly expressed genes. Construction of the risk prediction model To determine the clinical importance of the HIS-OS genes in predicting patient prognosis, differential expression analysis and univariate Cox regression analysis were carried out on 25 marker genes to compare them between clinical patient samples. Then, to further narrow the number of marker genes, a p value < 0.05 was applied as a selection standard, and seven genes significantly associated with survival were identified. The expression of seven HIS-OS-related genes composed the final signature, which significantly affected the prognostic model. Multivariate regression analysis was performed to assess the relative importance of the prognostic model's candidate genes in estimating OS patient risk scores. OS patient risk scores were computed as follows: $$Riskscore={\sum }_{x=1}^{n}coef\left(x\right)\times exp\left(x\right)$$ Coef(x) and exp(x) denote gene coefficients and gene x expression levels, respectively. The median risk score was utilized as the cutoff to categorize OS patients into high-risk subgroups and low-risk subgroups. Survival analysis The survival times of the patients in the several groups were studied (using the "survival" package, R software) and are shown using K‒M curves. The prognostic accuracy of the model was assessed using ROC analysis, and the outcomes were represented as ROC curves. Functional analysis The statistics were computed by executing GSEA software (4.1.0), which was used to identify signaling pathways in both risk groups. An investigation was conducted using GO and KEGG analyses to examine the cellular functions linked to seven genes that are considered risk factors. Tumor immune microenvironment and immune checkpoint molecules related to OS in the two risk groups We used the ESTIMATE algorithm to assess the immunological status of two risk subgroups in the TME, which has three dimensions: immune, stromal, and ESTIMATE scores. These three results were used to determine tumor purity. Subsequently, the same findings were verified in the test set, with comparable outcomes. The single sample gene set enrichment analysis (ssGSEA) algorithm was used to assess the different infiltration levels of 24 immune cells in the two risk groups. The ggpubr R package was used to analyze immune checkpoint molecules, and the findings are shown utilizing a boxplot. Assessment of the effectiveness of immunotherapy The tumor immune dysfunction and exclusion (TIDE) algorithm and subclass mapping in the TARGET dataset were applied to evaluate the clinical checkpoint effects on CTLA4 and PD-1 immune checkpoints in the high- and low-risk subgroups. In addition, we obtained the IPS of OS patients from the Cancer-immune Group Atlas (TCIA) ( https://tcia.athome ). The degree of immunogenicity was measured using immunophenotype scores, which included four separate immunophenotype scores: effector cells, immunosuppressive cells, major histocompatibility complex (MHC) molecules, and immune checkpoints. IPS scores that are significantly elevated are associated with heightened immunogenicity during this phase, which was assessed by evaluating gene expression in relevant cell types. Data analysis The statistical analysis and graph production operations were performed with GraphPad Prism 8 software. RStudio software was used to conduct both univariate and multivariate Cox regression analyses. The reliability of the risk model was evaluated using K‒M curves and ROC curves. The chi-square test was used to evaluate differences in clinical features among samples with varying median risk ratings. To assess correlations, we utilized Pearson's correlation analysis and Student's t test. Statistical significance was defined as a two-tailed p-value less than 0.05. Virtual screening and molecular docking The UniProtKB database ( https://www.uniprot.org/ ) provided the crystal structure of SERPINE2 with the PDB ID 4DY0. The correctness of the docking results was ensured by using AutoDockTools-1.5.7. The binding site for the 4DY0 receptor protein was found by reviewing the literature, and the docking box center was subsequently determined by AutoDockTools-1.5.7. There are 3764 compounds in the databases, including small molecule targets, FDA, Chinese drug library, etc. The 3D structures of the compounds were downloaded from the PubChem website ( https://pubchem.ncbi.nlm.nih.aov ). Structure-based molecular docking using AutoDockvina resulted in 50 compounds with high docking scores. After testing the 50 compounds, llTZ-01 was found to have the best effect. Then, we used AutoDockvina for molecular docking and visualized the results using PyMOL. Western blotting The proteins were then separated via electrophoresis via the SDS‒PAGE method and deposited onto polyvinylidene fluoride (PVDF) membranes. The primary antibodies were treated with the membrane overnight at 4°C. After removing the primary antibodies, the blots were incubated for 1 hour at room temperature with the secondary antibodies. For protein detection, we employed an Odyssey fluorescence scanner (ChemiDoc XRS Bio-Rad USA). Cell Counting Kit-8 (CCK8) Assay A CCK8 kit was used to track the proliferation of OS cells. In a 96-well plate, 2×10 3 cells were incubated for a fixed period of time, after which 10 µl of CCK8 reagent was subsequently added. We measured the absorbance of each well at 450 nm after 2 hours of incubation. Finally, we estimated cell activity using the manufacturer's recommendations. Migration and Invasion Assays We decided to use 24-well plates that Corning had given for our experiment. We resuspended 2 × 10 4 cells in pure media and placed them in the upper compartment for migration studies. A total of 1 × 10 5 cells were added to the substrate-coated top layer (356234; BD Biocoat) for invasion studies. Subsequently, each culture well received 600 µl of 30% fetal bovine serum added as media. For both the 48-hour invasion experiment and the 24-hour migration experiment, cells were continuously grown. Next, we used 4% paraformaldehyde to fix the cells for 30 minutes. The next step involved staining sections with 0.5% crystal violet dye for 15 minutes. After the cells in the upper chamber were removed with a swab, images were obtained using an inverted microscope. Results Identification and annotation of OS cell subtypes A diagram (Fig. 1A) was constructed to depict the research flow. OS cells were separated into seven clusters in accordance with the METHODS protocol for further bioinformatics investigation. Following an analysis of previous research, we propose to define OS cell subtypes as follows: HIS-OS (highly invasive OS), HSM-OS (homeostatic OS), TPS-OS (tumor-promoting protein-forming OS), PA-OS (proangiogenic OS), IR-OS (immunoreactive OS), ST-OS (stressed OS), and ECM-OS (extracellular matrix OS). These definitions are based on the signature genes (Fig. 1B, C), enriched pathways, and predictive functions (Fig. 1D). K‒M curves were generated to display the overall survival rate of patients in various risk categories within each OS cell subtype (Fig. 2G). The clinical patients with mutations in HIS-OS-related genes exhibited significant differences in survival time. These findings prompted us to further investigate the HIS-OS. Developing and verifying the predictive model for genes associated with HIS-OS We screened genes from all genes expressed in HIS-OS using the average log2FC > 1 and p value < 0.01 as the cutoff. A comprehensive inventory of 25 significant genes present in HIS-OS was subsequently generated, and the adjusted p values of each gene were shown to be statistically significant. A heatmap (Fig. 2A) was subsequently created to visualize the differences in the expression of the genes and the amounts to which the 25 genes were expressed in the normal and OS samples. The screening criterion was set at p < 0.05, and 25 genes closely associated with survival were selected with statistically significant p values in the TARGET set. A bubble plot (Fig. 2B) further demonstrated the connections between 25 genes closely linked to survival. The univariate Cox regression analysis of 25 HIS-OS-related genes is shown in Fig. 2C. The best-performing marker gene composition model included one with a nonzero regression coefficient. In addition, we used the Least absolute shrinkage and selection operator (LASSO) algorithm to calculate the optimal number of genes to limit the complexity of the prognostic models while preventing overfitting (Fig. 2D, E). As a final signature, seven HIS-OS-associated genes that significantly impact the prognostic model were selected. Next, we computed patient risk scores using gene expression and correlation coefficients. Based on the median risk score, all samples were classified into high- and low-risk subgroups. The high-risk group had a considerably higher number of clinical characteristics associated with malignancy, such as tumor metastasis, immune score, primary tumor site, and survival (Fig. 2F). K‒M curves demonstrated that the overall survival rate was lower for individuals who were categorized as high risk. (Fig. 2E). In addition, the ROC curve was used to calculate the AUC for 1, 3, and 5 years to assess the prognostic model's predictive capacity. The AUC values were 0.828, 0.796, and 0.815 for 1, 3, and 5 years, respectively, indicating that the model has effective predictive ability (Fig. 2H). We also calculated survival curves and ROC curves for the test cohort (Fig. 2I, J), and the findings matched those of the training cohort. The heatmap shows the expression of seven genes associated with HIS-OS across the two risk subgroups (Fig. 3A). For both risk groupings, the risk score and survival time distribution are presented in Fig. 3B, C. Metastasis and the risk score were found to be high-risk variables for OS in univariate Cox regression analysis (Fig. 3D). Relatively comparable results were also observed in the multivariate Cox regression analysis (Fig. 3E). These findings suggest that the model we designed can better predict the prognosis of OS. Tumor immune microenvironment with risk signature According to news reports, the TME of OS is closely related to its development and prognosis. Therefore, we evaluated the immune status and TME characteristics of patients in two risk subgroups using the ESTIMATE algorithm. Box plots revealed lower immune, stromal, and ESTIMATE scores as well as higher tumor purity in the high-risk subgroup than in the low-risk subgroup (Fig. 4A-D). Two risk subgroups were created from the clinical case samples in the GEO dataset GSE21257 based on the prognostic model that was constructed with the training cohort. We found that the high-risk group had greater tumor purity and lower immune scores, stromal scores, and ESTIMATE scores than did the low-risk group. (Fig. 4E-H). Moreover, we applied ssGSEA to investigate the immune cell infiltration and abundance relationships of the two risk subgroups. These diagrams (Fig. 4I, J) demonstrated that the infiltration of iDCs, aDCs, CD8 + T cells, cytotoxic T cells, neutrophils, macrophages, and T cells in the high-risk category was considerably lower than that in the low-risk category. To investigate the degree of immunogenicity between the two risk groupings, IPS analysis was also used. In the high-risk subgroup, MHC molecular and effector cells were lower, whereas immune checkpoint and immunosuppressive cell (ICC) scores were simultaneously increased, and there was no significant difference in immunophenoscore (IPS) (Fig. 4K-O). These findings imply that immune infiltration is directly related to the prognosis of OS and is an important factor in OS progression. Forecasting the Efficacy of Immunotherapy and Anticancer Medications The applicability of the risk prognosis model was determined by distinguishing individuals who had various responses to immune checkpoint blockade treatment (Fig. 5A). According to the results, the low-risk cohort might have a more positive response to anti-CTLA4 and anti-PD1 treatments (Fig. 5B, C). Currently, chemotherapeutic agents are commonly used to treat OS, so we aimed to investigate the responsiveness of these two risk subgroups to commonly used chemotherapeutic agents. We calculated IC50 values for each sample in the TARGET dataset and found that the high-risk group exhibited potentially greater responsiveness to commonly used chemotherapeutic medicines (BIRB.0796, OSI.906) (Fig. 5D-L). Functional analysis of prognostic model-related genes For the purpose of further exploring potential changes in the functional characterization of the seven genes associated with HIS-OS, KEGG, GO, and GSEA analyses were carried out for both risk subgroups. GSEA revealed a significant negative association between the high-risk group and the following tumor hallmarks, as opposed to the low-risk group: IL-6/JAK/STAT3 signaling, inflammatory response, interferon-gamma (IFN-γ) response, and coagulation (Fig. 6A-D). GO enrichment studies revealed that high-risk subgroups were associated with biological processes such as ECM organization, extracellular structure organization, external encapsulating structure organization, and ossification (Fig. 6E). In addition, the results of the KEGG analysis support these pathways, including the PI3K-Akt signaling pathway, ribosome, focal adhesion and others (Fig. 6F). All of these pathways are strongly related to malignant processes such as tumor development, proliferation, metastasis, immunosuppression, and drug resistance. These findings reveal the relationship between prognostic model-related genes and biological processes, thus shedding light on the reasons for the poorer prognosis of OS patients. Association of HIS-OS-related genes with patient prognosis and OS To gain a deeper understanding of the functions of these seven HIS-OS-related genes in OS, we investigated the expression of individual genes and survival rates in the normal (396 samples) and tumor (88 samples) groups. The findings demonstrated that the expression levels of IFI44L, IFITM, and SERPINE2 were considerably greater in the OS samples (Fig. 7A-C) than in the normal samples. Among them, the group with elevated SERPINE2 expression had a considerably decreased survival rate compared to the group with low expression (Fig. 7D-F). In conclusion, among the three highly expressed genes (IFI44L, IFITM3, and SERPINE2), SERPINE2 independently promoted malignant progression and led to a worse prognosis among OS patients. Expression of SERPINE2 in OS cells was significantly inhibited by IITZ-01. To explore inhibitors targeting SERPINE2, we first used Western blotting to investigate SERPINE2 expression in tumor cells and observed that SERPINE2 was highly expressed in MG63 and HOS cells. (Fig. 8A). Next, we obtained 50 compounds with high docking scores to the target gene SERPINE2 from an FDA library containing 3764 compounds (including small molecule targets). To further clarify the inhibitory effects of the 50 compounds on HOS cells, we confirmed that IITZ-01 had a significant inhibitory effect on HOS cells by high-throughput screening technology (Fig. 8B). Next, 3D plots of the interaction between SERPINE2 and IITZ-01 were constructed (Fig. 8C) were obtained by molecular docking using AutoDockvina and PyMOL. The blue molecule is the receptor 4DY0, the green molecule is the ligand IITZ-01, and the rose is the amino acid residue of 4DY0 docking with IITZ-01. Coordinate files of receptors and ligands uploaded to ProteinPlus ( https://proteins.plus/ ) yielded a 2D map of the interaction between SERPINE2 and IITZ-01 (Fig. 8D). Finally, the protein expression levels were determined using Western blotting. IITZ-01 was shown to inhibit the expression of the SERPINE2 protein in HOS and MG-63 cells (Fig. 8E-F). IITZ-01 inhibits the proliferation, migration, and invasion of OS cells To more fully investigate the role of IITZ-01 in OS, we analyzed cell viability by performing a CCK-8 assay and found that IITZ-01 decreased survival of HOS and MG-63 cells compared to negative control cells (Fig. 9A, B). Finally, cell migration experiments showed that IITZ-01 inhibited the migration of MG-63 and 143B cells (Fig. 9C-D). In addition, cell invasion experiments showed that IITZ-01 impeded the invasion of MG-63 and 143B cells (Fig. 9E-F). The above results showed that IITZ-01 inhibited the proliferation, migration, and invasion of OS cells. Discussion The most common primary malignant tumor in orthopedics is OS. It is characterized by a strong inclination toward local aggressiveness and early metastasis, which leads to a poor prognosis for patients with OS (Luetke, Meyers, Lewis, & Juergens, 2014 ). In recent years, predictive models based on multiple functional genomic methods for OS have become increasingly common in forecasting the prognosis of OS patients. Wang et al. analyzed the expression levels of cupping-associated long noncoding RNAs (lncRNAs) in OS and constructed a prognostic model for cupping-associated lncRNAs (X. Wang, Xie, & Lin, 2023 ). Yu et al. analyzed the gene expression levels of CD8 + lymphocytes in OS, identified six genes related to OS prognosis, and further constructed a prognostic model (Yu Chen et al., 2023 ). However, few studies have directly targeted OS tumor cells, and the cell type composition, dynamics, and characteristics of OS tumor foci are largely unknown. Consequently, we speculated that the detection of OS tumor cell-related genes is important for the prognosis of OS patients. Furthermore, these discoveries might help to identify prognostic biomarkers for OS tumors and develop more accurate therapeutic regimens and potential targeted drugs. Therefore, we constructed a risk model using HIS-OS-related genes in OS cell subsets to predict the prognosis of OS patients. Within this research investigation, we established a bioinformatics prognostic model and validated its accuracy using HIS-OS genes. Seven HIS-OS-related genes were incorporated into a risk map to determine whether they could accurately predict OS prognosis. First, an alternative prognostic model comprising seven HIS-OS-associated genes was constructed by analyzing single-cell sequencing data of OS as well as differential gene expression analysis. These results proved that the risk profile is capable of accurately predicting an OS patient's prognosis. As a result, in the future clinical treatment of OS patients, the risk score can be calculated from our developed risk model, and the prognosis can be inferred from the calculated risk score. Furthermore, we examined the connection between the risk profile and the TME. Moreover, we investigated possible therapeutic targets and corresponding treatment drugs for this target. In conclusion, our HIS-OS-related gene-based prognostic model provides a valuable reference for evaluating the prognosis and treatment of OS. Tumor purity is the proportion of tumor cells in a mixture and is closely related to the prognosis of tumor patients (Y. Mao et al., 2018 ). OS patients with increased tumor purity as well as decreased stromal scores, ESTIMATE scores, and immune scores tend to present a higher degree of malignancy, which often results in a negative prognosis (Yoshihara et al., 2013 ). Consistent with our study, the high-risk group had high tumor purity and lower ESTIMATE, immune, and stromal scores. Tumor-infiltrating immune cells (TIICs) in OS are considered to have a substantial influence on tumor advancement and prognosis, among other factors (Ying Chen, Zhao, & Wang, 2020 ; C. Zhang et al., 2020 ; Z. Zhang et al., 2022 ). To further understand the impact of TIIC on the prognosis of OS, we compared the abundance of 24 TIICs in the two risk groups. The immature dendritic cells (iDCs) and activated dendritic cells (aDCs) are the immature and activated dendritic cell (DC) subsets, respectively. Moreover, iDCs strongly phagocytose tumor antigens and differentiate into aDCs when they ingest antigens or are stimulated by certain factors (Gardner & Ruffell, 2016 ). aDCs recognize and process immune signals, transport tumor antigens, and present them to T cells, which activate the antitumor function of T cells (Wculek et al., 2019 ). CD8 + T cells and cytotoxic cells are considered to have important antitumor effects. They are considered to be the main T cells that exert antitumor effects; these cells can be activated by tumor antigens presented by aDCs and macrophages and thus recognize and kill tumor cells, exerting antitumor effects and thus significantly prolonging patient survival (Y. Sun et al., 2021 ; Thommen & Schumacher, 2018 ). Our findings were consistent with those of previous studies showing that aDCs, iDCs, CD8 + T cells, cytotoxic cells, T cells, and macrophages were significantly downregulated in the high-risk group compared with the low-risk group. However, macrophages are M2-type macrophages in most malignant tumors and promote tumor metastasis due to their proangiogenic and other effects (Huang et al., 2021 ). However, macrophages in highly differentiated OSs are composed of a mixture of both M1-type and M2-type cells. The greater the number of M1-type patients was compared to that of M2-type patients, the lower the risk of metastasis and the longer the lifespan expectancy of OS patients (Zhao, Zhang, Zhang, Ma, & Feng, 2021 ). Therefore, we suggest that infiltrating macrophages in the low-risk group were likely dominated by M1-type macrophages. The IPS is an excellent biomarker for identifying responders to immunotherapy and consists of four main factors: immunosuppressive cells, effector cells, MHC molecules, and immune checkpoints (Charoentong et al., 2017 ). Increased IPS scores are associated with increased immunogenicity (Hajiran et al., 2021 ). In our study, IPS was found to be higher in both risk groups, without any notable difference between them, suggesting that OS is a highly immunogenic tumor that could benefit from immunotherapy. Immune checkpoint molecules are often found at higher levels in the TME of a variety of malignant tumors (Arum et al., 2010 ; Toor, Sasidharan Nair, Decock, & Elkord, 2020), and immune checkpoint inhibitor treatment has demonstrated encouraging clinical outcomes (Meftahpour, Aghebati-Maleki, Fotouhi, Safarzadeh, & Aghebati-Maleki, 2022 ; Tang et al., 2022 ). However, little information about this therapeutic strategy has been published for OS. Therefore, we compared the expression levels of 38 immune checkpoint molecules in the two risk groups. CD80 and CD86 are mostly found on the plasma membrane of antigen-presenting cells, namely, DCs and monocytes/macrophages, and their important function is to bind to and costimulate signals with CD28 receptor proteins on the surface of T cells, which in turn activate the proliferation and differentiation of T cells and contribute to antitumor immunity (Bolandi et al., 2021 ; Esensten, Helou, Chopra, Weiss, & Bluestone, 2016 ; Fleischer et al., 1996 ). CTLA4 is expressed at higher levels on activated T cells and interacts with the ligands CD80 and CD86 to provide coinhibitory signals that suppress T-cell activation and multiplication (Engelhardt, Sullivan, & Allison, 2006 ; Krummel & Allison, 1995 ). Multiple studies in the field of cancer have demonstrated that elevated levels of CD80, CD86, and CD28 are associated with the suppression of OS cell proliferation and metastasis (Li et al., 2022 ). Consistent with these findings, the expression levels of CD80, CD86, and CD28 were notably elevated in our study compared with those in the low-risk group. However, the low-risk group exhibited significant overexpression of CTLA4, which we suggested was associated with secondary upregulation of CTLA4 on the T-cell surface due to T-cell activation. TIDE is highly regarded for its usefulness in identifying the effectiveness of immune checkpoint inhibitors, and numerous reports have suggested that CTLA-4 and PD-1 inhibitors are essential for treating many intermediate malignancies. Additionally, combining medications that target CTLA-4 and PD-1 receptors may have additive advantages when used with anticancer immunotherapies (Rotte, 2019 ). New research has indicated that CTLA-4 and PD-1 could have functions in the treatment of OS, but it is still uncertain how individuals with varying prognostic risks respond to this kind of immunotherapy (S.-D. Wang et al., 2016 ). Therefore, the TIDE score was chosen for this study to evaluate the effectiveness of PD-1 and CTLA-4 immunotherapy in both the high-risk and low-risk patients. The findings of our study indicate that individuals with a low risk of death exhibit positive responses to immune checkpoint therapy and may benefit from immunotherapy. The high-risk group had modest levels of immunosuppressive checkpoint expression, which remained unresponsive to immune checkpoint therapy. This finding is consistent with our conclusions and speculations above. According to our functional analysis, GSEA revealed that genes associated with biological processes such as the inflammatory response and IFN-γ response were enriched predominantly in patients at low risk for OS. The inflammatory response has dual functions in tumor progression and can either promote or inhibit tumor progression (Greten & Grivennikov, 2019 ; Philip, Rowley, & Schreiber, 2004 ). Current research suggests that the induction of acute inflammation promotes immune cell maturation and antigen presentation, leading to an antitumor immune response (H. Zhao et al., 2021 ). IFN-γ has numerous antitumor effects, including promoting inflammatory responses, modulating antigen presentation, inhibiting angiogenesis, and promoting tumor dormancy and apoptosis to inhibit tumor growth (Burke & Young, 2019 ; Mauldin et al., 2016 ). In addition, it has been shown that enhanced IFN-γ secretion could reduce the development of tumorigenic M2 macrophages, thereby inhibiting the growth of OS (Kang et al., 2017 ). GO enrichment analysis revealed that these genes were involved mostly in biological processes such as ECM organization, external encapsulating structure organization, and ossification. Several investigations have indicated that the tumor-associated ECM is involved in promoting tumor cell growth, invasion, metastasis, and angiogenesis and that it resists cell death and drug diffusion (Najafi, Farhood, & Mortezaee, 2018 ; Theocharis, Skandalis, Gialeli, & Karamanos, 2016 ). The collective influence of these EMC-associated biological processes may serve as a significant catalyst for OS cell migration. In addition, KEGG analysis revealed that these genes were enriched in pathways including the PI3k-Akt signaling pathway, The ribosome pathway, and the focal adhesion pathway. The PI3K-Akt signaling pathway is an intracellular signaling pathway that responds to extracellular signals to promote metabolism, proliferation, cell survival, growth, and angiogenesis (Yang et al., 2019 ). Ribosome synthesis is increased in cancer cells as a reaction to increased protein synthesis and maintenance of unrestricted growth, and several articles have revealed that the ribosome pathway is related to the development of tumors and a negative prognosis (El Khoury & Nasr, 2021 ; Figueiredo & McCarthy, 2021 ). High expression of the focal adhesion pathway has been shown to be closely associated with tumor metastasis, and inhibition of related genes improves the survival of tumor patients (Legerstee & Houtsmuller, 2021 ; Lu, Linares, Xu, & Rui, 2021 ). The above evidence demonstrated that pathways enriched for HIS-OS-related genes included those that promote tumor proliferation, migration, and invasion, ultimately resulting in a worse prognosis among patients with OS. We examined the gene expression levels in both the tumor and normal groups and the association between gene expression and survival prognosis to further determine the role of the above prognosis-related genes in OS. Analysis of the findings revealed a substantial increase in SERPINE2 expression in the tumor group, and these patients had a poorer prognosis. SERPINE2 encodes a serpin protein that belongs to a family of proteins that inhibit serine proteases and are effective against trypsin, thrombin, and plasma proteases (Buchholz et al., 2003 ). In other malignancies, the role of SERPINE2 in inhibiting serine proteases and promoting tumor progression has been widely reported, especially in cases of tumor metastasis, such as breast, gastric, and lung adenocarcinomas (Dokuni et al., 2020 ; Fayard et al., 2009 ; K. Wang et al., 2014 ). Experimental investigations have demonstrated that SERPINE2 is significantly upregulated in OS tissues, particularly in stage II-III patients with metastases and tumor nodal metastases (M. Mao & Wang, 2016 ). High expression of SERPINE2 in OS stimulates tumor cell proliferation, promotes drug resistance, and leads to poor survival through regulation of CDK4 and cell cycle protein D. The aforementioned information indicates that SERPINE2 might serve as a promising target for therapeutic intervention in patients with OS. The PDB structure of SERPINE2 was shown to dock well with that of IITZ-01 in virtual screening and molecular docking simulations. We further conducted CCK-8 and migration invasion tests to confirm the inhibitory impact of IITZ-01 on OS cells. Specifically, we observed significant inhibition of OS cell proliferation, migration, and invasion. However, the exact underlying mechanism requires further investigation. Furthermore, future research will need animal trials to confirm the inhibitory impact of IITZ-01 on OS in living organisms. In conclusion, our findings suggest that SERPINE2 might serve as a promising therapeutic target for OS and that IITZ-01, an inhibitor of SERPINE2, shows promise as a therapeutic drug for OS. Conclusion In the present research, we constructed a new model that enables us to predict the prognosis of OS patients based on seven HIS-OS prognosis-related genes screened, and the results verified that the model has good predictive capability. Moreover, the immune characteristics of OS patients in the two groups exhibited significant differences. The functional analysis showed that the HIS-OS prognostic genes might be related to malignant processes such as OS migration and invasion. In addition, we identified a potential therapeutic target, SERPINE2, and a drug that inhibits SERPINE2, IITZ-01, which may inhibit tumor function by inhibiting the SERPINE2 protein. In conclusion, our study can provide guidance regarding the prediction of the prognosis of OS as well as the clinical treatment of this disease. Abbreviations ADCs Activated dendritic cells AUC Area under the curve CCK8 Cell Counting Kit‑8 DC Dendritic cell DEGs Differentially expressed genes ECM Extracellular matrix ECM-OS Extracellular matrix OS GO Gene Ontology HIS-OS Highly invasive OS HSM-OS Homeostatic OS ICC Immunosuppressive cell iDCs Immature dendritic cells IFN-γ Interferon-gamma IPS Immunophenoscore IR-OS Immunoreactive OS K‒M Kaplan‒Meier LASSO Least absolute shrinkage and selection operator LncRNAs Long noncoding RNAs MHC Major histocompatibility complex OS Osteosarcoma PA-OS Proangiogenic OS PVDF Polyvinylidene fluoride scRNA-seq Single-cell RNA sequencing ssGSEA Single sample gene set enrichment analysis ST-OS Stressed OS TCIA The Cancer-immune Group Atlas TIDE Tumor immune dysfunction and exclusion TIICs Tumor-infiltrating immune cells TME Tumor microenvironment TPS-OS Tumor-promoting protein-forming OS Declarations Data availability All data included in this study are available upon request by contact with the corresponding author. Competing interests The authors declare no competing interests. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Funding This research was funded by Clinical Research and Cultivation Program of the Second Hospital of Anhui Medical University (2020LCZD05); Translational Medicine Research Foundation of the Second Hospital of Anhui Medical University (2022ZHYJ13); Key Projects of Natural Science Research in Colleges and Universities in Anhui Province (2022AH040102); Research Foundation of Anhui Institute of Translational Medicine (No.2022zhyx-C49). Author Contribution EB, DT, ZL, and MW conceived the study. MW, CY, JL, HX, and YW performed the experiments. EB, ZL, MW, DT and CY analyzed and interpreted the results. EB, ZL, and MW wrote the manuscript. All authors read and approved the final manuscript Acknowledgments The author thanks TARGET network and GEO network for their contributions. 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J Zhejiang University-SCIENCE B 22(11):885–892. 10.1631/jzus.B2100029 Zhou Y, Yang D, Yang Q, Lv X, Huang W, Zhou Z, Hu H (2020) Single-cell rna landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma. Nat Commun 11(1). 10.1038/s41467-020-20059-6 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation1.pdf Supplementary Fig 1. Original western blotting. (A) The expression levels of SERPINE2 in HFOB, MG63, HOS, 143B, SAOS2, U2OS, and 1059D cells were determined via western blotting. (B) The expression level of SERPINE2 in HOS cells and (C) MG-63 cells treated without IITZ-01 was determined by western blotting. 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. 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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-4495593","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312223837,"identity":"995d51a9-a1bb-4153-8c4c-589c5da28be7","order_by":0,"name":"Zijun Li","email":"","orcid":"","institution":"School of Pharmacy, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zijun","middleName":"","lastName":"Li","suffix":""},{"id":312223838,"identity":"98c3df9e-ac4c-4228-bfae-03dd927d8ee0","order_by":1,"name":"Mengting Wang","email":"","orcid":"","institution":"School of Pharmacy, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengting","middleName":"","lastName":"Wang","suffix":""},{"id":312223839,"identity":"3dba6cb2-7afb-4198-a268-a1c866ab1524","order_by":2,"name":"Yunlong Wang","email":"","orcid":"","institution":"School of Pharmacy, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Wang","suffix":""},{"id":312223840,"identity":"f37e2a58-d9a2-4010-98ee-3feac4f7bb3d","order_by":3,"name":"Chengfeng Yi","email":"","orcid":"","institution":"Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengfeng","middleName":"","lastName":"Yi","suffix":""},{"id":312223841,"identity":"3c8f6bb3-9341-462a-a4b4-b964fb2914e4","order_by":4,"name":"Jun Liu","email":"","orcid":"","institution":"Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Liu","suffix":""},{"id":312223842,"identity":"efbd3668-69e3-46ec-953d-cc5ef206b905","order_by":5,"name":"Xie Han","email":"","orcid":"","institution":"Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xie","middleName":"","lastName":"Han","suffix":""},{"id":312223843,"identity":"1e6a20ca-427e-4061-89f2-3b9454bb597d","order_by":6,"name":"Erbao Bian","email":"","orcid":"","institution":"School of Pharmacy, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Erbao","middleName":"","lastName":"Bian","suffix":""},{"id":312223844,"identity":"654a33bc-95ae-46c3-8e09-09bd75eb100a","order_by":7,"name":"Dasheng Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACZiDmYWBIAFKMDxgbwGIGeHXwIGlhNiBOCwNCC5sEUVrs2ZmfPXhTU5fHL91+reLnjm2JDezN2yQYau7gcRibueGcY4eLJeecKbvZe+Z2YgPPsTIJhmPP8PnFTJqH7UDihhs5aTd424BaJHLMgC48jEcL+zdpnn91ifuBWgr/grTIvyGkhcdMmreNOXGDRPoxZogtPAS0HOYpk5zbdzhxxo0cZmnZttvGbTxpxRYJx3BrYe8/vk3izbe6xP4Z6Q8/vm27LdvPfnjjjQ81uLUgWwiJDjYQkUCMBqCFD4hTNwpGwSgYBSMOAAC5oVWZCnMi9AAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dasheng","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2024-05-29 08:44:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4495593/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4495593/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59122352,"identity":"6bf5923a-ae18-49a8-9076-36663c169a16","added_by":"auto","created_at":"2024-06-26 15:04:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":590655,"visible":true,"origin":"","legend":"\u003cp\u003eProcesses examined at the single-cell level. (A) Schematic representation of the sequential steps and processes involved in this research. (B) Bubble plots depicting the expression of marker genes in seven distinct cell subtypes of OS cells. (C) GO analysis of one cluster of OS cells. (D) A heatmap displaying the differentially expressed genes (DEGs) for each cell subtype.\u003c/p\u003e","description":"","filename":"Figure135.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/f012c93fc04b166e878f85c9.png"},{"id":59121531,"identity":"d47fd0c4-b79b-472c-b9c6-9f8fee23814d","added_by":"auto","created_at":"2024-06-26 14:56:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188582,"visible":true,"origin":"","legend":"\u003cp\u003eWe obtained HIS-OS marker genes and establishedprognostic models. (A) Heatmap displaying the expression levels of 25 specific genes that were differentially expressed between the normal and tumor groups according to the HIS-OS. (B) The bubble chart shows the correlation between 25 marker genes. (C) The p value and hazard ratio of 25 marker genes in the HIS-OS cohort were evaluated via univariate Cox regression. (D) Model average cross-validation error rate. (E) The optimum penalized log-likelihood was maximized to obtain seven genes that impacted the model. (F) Heatmap illustrating the variations in the distribution of clinicopathological characteristics and the expression levels of seven HIS-OS genes in two distinct risk groupings. (G) The TARGET dataset was used to evaluate patient survival; two risk subgroups were compared (the high-risk subgroup is represented by the color red, while the low-risk subgroup is represented by the color blue.). (H) The area under the curve (AUC) for the prediction of survival at 1, 3, and 5 years was calculated using the gene signature associated with the HIS-OS to assess the ability of the model to predict patient prognosis. (I) Survival analysis of the GEO dataset. (J) AUC for 3- and 5-year survival according to theHIS-OS-related gene signature in the GEO dataset.\u003c/p\u003e","description":"","filename":"Figure230.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/b2493856d1937f42597a88ce.png"},{"id":59121532,"identity":"b31dd63f-bb31-420b-88f5-c7e97750e4c7","added_by":"auto","created_at":"2024-06-26 14:56:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31686,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic value of the seven HIS-OS genes. (A) Expression levels of the seven HIS-OS-associated genes. Survival time (B) and risk score (C) distribution for both risk groupings. Univariate (D) and multivariate (E) Cox regression analyses of clinical characteristics and the risk score of the seven genes for OS in the TARGET database.\u003c/p\u003e","description":"","filename":"Figure321.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/00ee359a87ceae46fdda2de1.png"},{"id":59120694,"identity":"75fef7b2-cddb-44a0-b7fd-7d34b16a5fd5","added_by":"auto","created_at":"2024-06-26 14:48:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98238,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between OS risk and immune infiltration in OS patients. Immune score, ESTIMATE score, stromal score, and tumor purity between the two risk subgroups in the training (A-D) and testing (E-H) sets. (I) The enrichment levels of 24 immune cells in the two risk groups. (J) Box plots showing the different proportions of tumor-infiltrating cells between the two risk groups. (K-O) Immunophenotype score analysis of the two risk groups.\u003c/p\u003e","description":"","filename":"Figure415.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/bb9556d4087e94183817e0e0.png"},{"id":59120692,"identity":"7241a809-5b85-4865-baf2-b4d6b66f2348","added_by":"auto","created_at":"2024-06-26 14:48:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92227,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of chemotherapy and immunotherapy efficacy (A) Response of OS to 38 immunological checkpoint agents in the high- and low-risk groups. (B) The TIDE value and response outcomes of immunotherapy in patients with OS. (C) The SubMap algorithm demonstrated that the low-risk group had greater responsiveness to anti-CTAL-4 and anti-PD-1 therapy. (D-L) Predicting the therapeutic effects of nine different chemotherapeutic drugs in the two risk groups.\u003c/p\u003e","description":"","filename":"Figure58.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/00e377eb359078e18ca43a9a.png"},{"id":59121535,"identity":"51c506d9-33b1-471c-8523-77ed77f950f3","added_by":"auto","created_at":"2024-06-26 14:56:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":669489,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional analysis of DEGs (A-D) GSEA revealed that the signature was enriched in four characteristic pathways. Differential gene-based GO (E) and KEGG (F) analyses.\u003c/p\u003e","description":"","filename":"Figure69.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/e7bc9791dfd9f97fc882f010.png"},{"id":59120686,"identity":"f995770c-5f7c-44e1-8dc5-b057b5855125","added_by":"auto","created_at":"2024-06-26 14:48:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41348,"visible":true,"origin":"","legend":"\u003cp\u003eIFI44L, IFITM3, and SERPINE2 were selected from among the seven HIS-OS genes. \u003cstrong\u003eA-C\u003c/strong\u003eDifferential expression of IFI44L (A), IFITM3 (B), and SERPINE2 (C) in the normal and tumor groups. \u003cstrong\u003eD-F\u003c/strong\u003eIFI44L (D), IFITM3 (E), and SERPINE2 (F) overall survival analysis.\u003c/p\u003e","description":"","filename":"Figure77.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/07238ad6fcbf10db6328dd6b.png"},{"id":59121534,"identity":"93136bbb-e23a-4fb0-814a-c79fae1da606","added_by":"auto","created_at":"2024-06-26 14:56:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":650654,"visible":true,"origin":"","legend":"\u003cp\u003eVirtual screening and molecular docking. (A) The expression levels of SERPINE2 in HFOB, MG63, HOS, 143B, SAOS2, U2OS, and 1059D cells were determined via western blotting. (B) Average inhibition of the drug in HOS cells. (C) A 3D diagram of the interaction between SERPINE2 and IITZ-01. The blue molecule is the receptor 4 DY0, the green molecule is the ligand IITZ-01, and the rose is the 2D interaction between the amino acid residues.(D) A 2D diagram of the interaction between SERPINE2 and IITZ-01. (E) The expression level of SERPINE2 in HOS cells and (F) MG-63 cells treated without IITZ-01 was determined by western blotting. (A), (E) and (F) have been cropped.\u003c/p\u003e","description":"","filename":"Figure85.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/b40ee71ee5c67d49764ebd42.png"},{"id":59120695,"identity":"24b5908e-1f16-48bd-ab84-1940493ef9f8","added_by":"auto","created_at":"2024-06-26 14:48:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1125523,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of IITZ-01 on OS cell function. (A) Cell viability (%) of CCK8 experiments after treatment with different doses of IITZ-01 in HOS and (B) MG-63 cells. (C) Representative migration assays were imaged or counted (D) in HOS and MG-63 cells after treatment with different doses of IITZ-01. (E) Representative invasion assays were imaged or counted (F) in HOS and MG-63 cells treated with different doses of IITZ-01. *** p \u0026lt;0.001, * * p \u0026lt;0.01, * p \u0026lt;0.05, ns p\u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"Figure94.png","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/9b672bebd14d180c2f1abd22.png"},{"id":69983382,"identity":"f9effb87-8914-4c7c-abfa-40b6ab1e0069","added_by":"auto","created_at":"2024-11-27 08:09:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4199623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/0182c39b-4a62-439b-9bcf-66f5ed501ca5.pdf"},{"id":59120689,"identity":"17df4d79-d7fd-4e8d-a4a4-2188a2781bf4","added_by":"auto","created_at":"2024-06-26 14:48:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":590772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig 1. \u003c/strong\u003eOriginal western blotting. (A) The expression levels of SERPINE2 in HFOB, MG63, HOS, 143B, SAOS2, U2OS, and 1059D cells were determined via western blotting. (B) The expression level of SERPINE2 in HOS cells and (C) MG-63 cells treated without IITZ-01 was determined by western blotting.\u003c/p\u003e","description":"","filename":"SupplementaryInformation1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4495593/v1/136a2a9d800ff779e9a4bc8e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Highly Invasive Cell-Related Gene Signature for Predicting the Prognosis and Treatment of Osteosarcoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteosarcoma (OS) is a highly prevalent bone tumor derived from primitive mesenchymal cells; OS most commonly occurs among children and adolescents, accounting for more than 60% of all malignancies among children (Belayneh, Fourman, Bhogal, \u0026amp; Weiss, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kansara, Teng, Smyth, \u0026amp; Thomas, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mutsaers \u0026amp; Walkley, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). OS occurs primarily in fast-growing bones \u0026ndash; most often in children and young adults \u0026ndash; near the end of the leg or arm, such as the femur, tibia, and humerus, the most common locations (Isakoff, Bielack, Meltzer, \u0026amp; Gorlick, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Local invasion and rapid metastasis to the lungs are the main features of OS, and metastatic OS often recurs with poor prognosis (Dean, Shen, Hornicek, \u0026amp; Duan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although the combination of extensive surgical resection and multiagent chemotherapy slightly improves OS compared with surgical resection alone, there currently seems to be no substantial increase in survival for patients without metastasis, and there has been little improvement in survival for patients with metastasis in the past two decades (Pan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In patients with localized OS, the 5-year survival rate is 80%, and in patients with metastatic OS, the 5-year survival rate does not exceed 30% (Gaspar et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The development of molecular biology approaches has considerably enhanced our understanding of the genetic etiology of OS (Gill \u0026amp; Gorlick, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite advancements in medical research, the outlook for patients diagnosed with OS is still not favorable. As a consequence, finding novel molecular markers is essential for correctly predicting the prognosis of OS and improving treatment outcomes.\u003c/p\u003e \u003cp\u003eTumor heterogeneity involves multiple aspects of tumor prognosis, including drug resistance, recurrence, and metastasis (Schiavone, Garnier, Heymann, \u0026amp; Heymann, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Like many other malignancies, OS has been shown to be characterized by extensive tumor heterogeneity, which has a significant impact on treatment and prognosis (Mohseny et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conventional transcriptome analysis of mixed cell populations has shown that the resolution of detecting specific cell types is insufficient to accurately assess the complexity of tumor heterogeneity in OS. Recently, single-cell RNA sequencing (scRNA-seq) has become a valuable tool in tumor research. By using optimized next-generation sequencing technology to define the overall gene expression profile of a single cell, it is easy to analyze the heterogeneity hidden before the cell population; identify cell lineages, new cell subsets, and regulatory networks between genes and cell-specific biological characteristics; and provide new insights for complex biological systems (Dong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In particular, scRNA-seq studies have revealed a high degree of intratumor heterogeneity, differential expression of multiple genes in multiple cell types, and cellular cross-talk with the tumor microenvironment (TME), which cannot be determined by traditional bulk sequencing datasets (K. Sun et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, there have been few reports on the heterogeneity of OS tumors through scRNA-seq.\u003c/p\u003e \u003cp\u003eHerein, we conducted a dimensionality reduction clustering analysis of scRNA-seq data from the GEO database, identified seven OS cell subtypes, and identified marker genes for each subtype. Through functional analysis, we found a close connection between highly invasive OS cells and OS patient prognosis. Next, we obtained OS clinical case samples and HIS-OS-related gene expression data from the database to further explore the associations of HIS-OS-related genes with OS patient survival. Based on our risk profile, we identified seven HIS-OS-related genes that independently predicted OS patient outcomes. Next, we assessed immune-infiltrating cells to determine whether they can predict immunological checkpoint activity and susceptibility to immunotherapy. In addition, we investigated the pathways and biological processes that are regulated by genes associated with HIS-OS. Finally, we investigated the potential therapeutic target SERPINE2 by analyzing the expression levels and survival rates of the prognostic model genes and further verified the inhibitory effect of IITZ-01, an inhibitor of SERPINE2, on the migration and invasion of OS cells via virtual screening and cell function experiments. These findings will aid in predicting the prognosis of OS and facilitate the development of targeted therapy for OS.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003escRNA-seq data of human OS samples were obtained from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The testing cohort consisted of 53 clinical case samples from the GSE21257 dataset in the GEO database. The clinical case samples from 85 OS patients in the training cohort were obtained from the TARGET database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/programs/target/data-matrix\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/programs/target/data-matrix\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GTEx (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtexportal.org/\u003c/span\u003e\u003cspan address=\"https://gtexportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to analyze normal tissue samples (n\u0026thinsp;=\u0026thinsp;396).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSubtypes of OS cells were annotated\u003c/h2\u003e \u003cp\u003eThe tumor tissue scRNA-seq data were categorized into different cell populations, after which the OS cells and data were extracted for additional bioinformatics studies. Based on the characteristics of the cells, including intercellular heterogeneity and genetic similarity, seven cell subtypes were identified from the single-cell sequencing data of OS cells. These seven cell subtypes were subsequently annotated using the R programming language (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.R-project.org/\u003c/span\u003e\u003cspan address=\"https://cran.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Gene Ontology (GO) enrichment analysis, and collection of gene signatures from published literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of OS cell subtypes to be studied and screening for genes\u003c/h2\u003e \u003cp\u003eThe overall survival of patients in the different risk categories within each cell subtype was analyzed using R. The outcomes were evaluated using Kaplan‒Meier (K‒M) survival curves. The cell subtype that exhibited both the highest overall survival and the greatest variability in survival time (HIS-OS) was chosen. The next step was to apply screening criteria to search for genes with log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 among all highly invasive OS cells; ultimately, 25 genes were identified as significantly expressed genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the risk prediction model\u003c/h2\u003e \u003cp\u003eTo determine the clinical importance of the HIS-OS genes in predicting patient prognosis, differential expression analysis and univariate Cox regression analysis were carried out on 25 marker genes to compare them between clinical patient samples. Then, to further narrow the number of marker genes, a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied as a selection standard, and seven genes significantly associated with survival were identified.\u003c/p\u003e \u003cp\u003eThe expression of seven HIS-OS-related genes composed the final signature, which significantly affected the prognostic model. Multivariate regression analysis was performed to assess the relative importance of the prognostic model's candidate genes in estimating OS patient risk scores. OS patient risk scores were computed as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$Riskscore={\\sum }_{x=1}^{n}coef\\left(x\\right)\\times exp\\left(x\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCoef(x) and exp(x) denote gene coefficients and gene x expression levels, respectively. The median risk score was utilized as the cutoff to categorize OS patients into high-risk subgroups and low-risk subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eThe survival times of the patients in the several groups were studied (using the \"survival\" package, R software) and are shown using K‒M curves. The prognostic accuracy of the model was assessed using ROC analysis, and the outcomes were represented as ROC curves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis\u003c/h2\u003e \u003cp\u003eThe statistics were computed by executing GSEA software (4.1.0), which was used to identify signaling pathways in both risk groups. An investigation was conducted using GO and KEGG analyses to examine the cellular functions linked to seven genes that are considered risk factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune microenvironment and immune checkpoint molecules related to OS in the two risk groups\u003c/h2\u003e \u003cp\u003eWe used the ESTIMATE algorithm to assess the immunological status of two risk subgroups in the TME, which has three dimensions: immune, stromal, and ESTIMATE scores. These three results were used to determine tumor purity. Subsequently, the same findings were verified in the test set, with comparable outcomes. The single sample gene set enrichment analysis (ssGSEA) algorithm was used to assess the different infiltration levels of 24 immune cells in the two risk groups. The ggpubr R package was used to analyze immune checkpoint molecules, and the findings are shown utilizing a boxplot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of the effectiveness of immunotherapy\u003c/h2\u003e \u003cp\u003eThe tumor immune dysfunction and exclusion (TIDE) algorithm and subclass mapping in the TARGET dataset were applied to evaluate the clinical checkpoint effects on CTLA4 and PD-1 immune checkpoints in the high- and low-risk subgroups. In addition, we obtained the IPS of OS patients from the Cancer-immune Group Atlas (TCIA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcia.athome\u003c/span\u003e\u003cspan address=\"https://tcia.athome\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The degree of immunogenicity was measured using immunophenotype scores, which included four separate immunophenotype scores: effector cells, immunosuppressive cells, major histocompatibility complex (MHC) molecules, and immune checkpoints. IPS scores that are significantly elevated are associated with heightened immunogenicity during this phase, which was assessed by evaluating gene expression in relevant cell types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis and graph production operations were performed with GraphPad Prism 8 software. RStudio software was used to conduct both univariate and multivariate Cox regression analyses. The reliability of the risk model was evaluated using K‒M curves and ROC curves. The chi-square test was used to evaluate differences in clinical features among samples with varying median risk ratings. To assess correlations, we utilized Pearson's correlation analysis and Student's t test. Statistical significance was defined as a two-tailed p-value less than 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVirtual screening and molecular docking\u003c/h2\u003e \u003cp\u003eThe UniProtKB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provided the crystal structure of SERPINE2 with the PDB ID 4DY0. The correctness of the docking results was ensured by using AutoDockTools-1.5.7. The binding site for the 4DY0 receptor protein was found by reviewing the literature, and the docking box center was subsequently determined by AutoDockTools-1.5.7. There are 3764 compounds in the databases, including small molecule targets, FDA, Chinese drug library, etc. The 3D structures of the compounds were downloaded from the PubChem website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.aov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.aov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Structure-based molecular docking using AutoDockvina resulted in 50 compounds with high docking scores.\u003c/p\u003e \u003cp\u003eAfter testing the 50 compounds, llTZ-01 was found to have the best effect. Then, we used AutoDockvina for molecular docking and visualized the results using PyMOL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eThe proteins were then separated via electrophoresis via the SDS‒PAGE method and deposited onto polyvinylidene fluoride (PVDF) membranes. The primary antibodies were treated with the membrane overnight at 4\u0026deg;C. After removing the primary antibodies, the blots were incubated for 1 hour at room temperature with the secondary antibodies. For protein detection, we employed an Odyssey fluorescence scanner (ChemiDoc XRS Bio-Rad USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCell Counting Kit-8 (CCK8) Assay\u003c/h2\u003e \u003cp\u003eA CCK8 kit was used to track the proliferation of OS cells. In a 96-well plate, 2\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells were incubated for a fixed period of time, after which 10 \u0026micro;l of CCK8 reagent was subsequently added. We measured the absorbance of each well at 450 nm after 2 hours of incubation. Finally, we estimated cell activity using the manufacturer's recommendations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMigration and Invasion Assays\u003c/h2\u003e \u003cp\u003eWe decided to use 24-well plates that Corning had given for our experiment. We resuspended 2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells in pure media and placed them in the upper compartment for migration studies. A total of 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells were added to the substrate-coated top layer (356234; BD Biocoat) for invasion studies. Subsequently, each culture well received 600 \u0026micro;l of 30% fetal bovine serum added as media. For both the 48-hour invasion experiment and the 24-hour migration experiment, cells were continuously grown. Next, we used 4% paraformaldehyde to fix the cells for 30 minutes. The next step involved staining sections with 0.5% crystal violet dye for 15 minutes. After the cells in the upper chamber were removed with a swab, images were obtained using an inverted microscope.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and annotation of OS cell subtypes\u003c/h2\u003e \u003cp\u003eA diagram (Fig.\u0026nbsp;1A) was constructed to depict the research flow. OS cells were separated into seven clusters in accordance with the METHODS protocol for further bioinformatics investigation. Following an analysis of previous research, we propose to define OS cell subtypes as follows: HIS-OS (highly invasive OS), HSM-OS (homeostatic OS), TPS-OS (tumor-promoting protein-forming OS), PA-OS (proangiogenic OS), IR-OS (immunoreactive OS), ST-OS (stressed OS), and ECM-OS (extracellular matrix OS). These definitions are based on the signature genes (Fig.\u0026nbsp;1B, C), enriched pathways, and predictive functions (Fig.\u0026nbsp;1D).\u003c/p\u003e \u003cp\u003eK‒M curves were generated to display the overall survival rate of patients in various risk categories within each OS cell subtype (Fig.\u0026nbsp;2G). The clinical patients with mutations in HIS-OS-related genes exhibited significant differences in survival time. These findings prompted us to further investigate the HIS-OS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDeveloping and verifying the predictive model for genes associated with HIS-OS\u003c/h2\u003e \u003cp\u003eWe screened genes from all genes expressed in HIS-OS using the average log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 as the cutoff. A comprehensive inventory of 25 significant genes present in HIS-OS was subsequently generated, and the adjusted p values of each gene were shown to be statistically significant. A heatmap (Fig.\u0026nbsp;2A) was subsequently created to visualize the differences in the expression of the genes and the amounts to which the 25 genes were expressed in the normal and OS samples. The screening criterion was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and 25 genes closely associated with survival were selected with statistically significant p values in the TARGET set. A bubble plot (Fig.\u0026nbsp;2B) further demonstrated the connections between 25 genes closely linked to survival. The univariate Cox regression analysis of 25 HIS-OS-related genes is shown in Fig.\u0026nbsp;2C. The best-performing marker gene composition model included one with a nonzero regression coefficient. In addition, we used the Least absolute shrinkage and selection operator (LASSO) algorithm to calculate the optimal number of genes to limit the complexity of the prognostic models while preventing overfitting (Fig.\u0026nbsp;2D, E). As a final signature, seven HIS-OS-associated genes that significantly impact the prognostic model were selected.\u003c/p\u003e \u003cp\u003eNext, we computed patient risk scores using gene expression and correlation coefficients. Based on the median risk score, all samples were classified into high- and low-risk subgroups. The high-risk group had a considerably higher number of clinical characteristics associated with malignancy, such as tumor metastasis, immune score, primary tumor site, and survival (Fig.\u0026nbsp;2F). K‒M curves demonstrated that the overall survival rate was lower for individuals who were categorized as high risk. (Fig.\u0026nbsp;2E). In addition, the ROC curve was used to calculate the AUC for 1, 3, and 5 years to assess the prognostic model's predictive capacity. The AUC values were 0.828, 0.796, and 0.815 for 1, 3, and 5 years, respectively, indicating that the model has effective predictive ability (Fig.\u0026nbsp;2H). We also calculated survival curves and ROC curves for the test cohort (Fig.\u0026nbsp;2I, J), and the findings matched those of the training cohort.\u003c/p\u003e \u003cp\u003eThe heatmap shows the expression of seven genes associated with HIS-OS across the two risk subgroups (Fig.\u0026nbsp;3A). For both risk groupings, the risk score and survival time distribution are presented in Fig.\u0026nbsp;3B, C. Metastasis and the risk score were found to be high-risk variables for OS in univariate Cox regression analysis (Fig.\u0026nbsp;3D). Relatively comparable results were also observed in the multivariate Cox regression analysis (Fig.\u0026nbsp;3E). These findings suggest that the model we designed can better predict the prognosis of OS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune microenvironment with risk signature\u003c/h2\u003e \u003cp\u003eAccording to news reports, the TME of OS is closely related to its development and prognosis. Therefore, we evaluated the immune status and TME characteristics of patients in two risk subgroups using the ESTIMATE algorithm. Box plots revealed lower immune, stromal, and ESTIMATE scores as well as higher tumor purity in the high-risk subgroup than in the low-risk subgroup (Fig.\u0026nbsp;4A-D). Two risk subgroups were created from the clinical case samples in the GEO dataset GSE21257 based on the prognostic model that was constructed with the training cohort. We found that the high-risk group had greater tumor purity and lower immune scores, stromal scores, and ESTIMATE scores than did the low-risk group. (Fig.\u0026nbsp;4E-H).\u003c/p\u003e \u003cp\u003eMoreover, we applied ssGSEA to investigate the immune cell infiltration and abundance relationships of the two risk subgroups. These diagrams (Fig.\u0026nbsp;4I, J) demonstrated that the infiltration of iDCs, aDCs, CD8\u0026thinsp;+\u0026thinsp;T cells, cytotoxic T cells, neutrophils, macrophages, and T cells in the high-risk category was considerably lower than that in the low-risk category. To investigate the degree of immunogenicity between the two risk groupings, IPS analysis was also used. In the high-risk subgroup, MHC molecular and effector cells were lower, whereas immune checkpoint and immunosuppressive cell (ICC) scores were simultaneously increased, and there was no significant difference in immunophenoscore (IPS) (Fig.\u0026nbsp;4K-O). These findings imply that immune infiltration is directly related to the prognosis of OS and is an important factor in OS progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eForecasting the Efficacy of Immunotherapy and Anticancer Medications\u003c/h2\u003e \u003cp\u003eThe applicability of the risk prognosis model was determined by distinguishing individuals who had various responses to immune checkpoint blockade treatment (Fig.\u0026nbsp;5A). According to the results, the low-risk cohort might have a more positive response to anti-CTLA4 and anti-PD1 treatments (Fig.\u0026nbsp;5B, C). Currently, chemotherapeutic agents are commonly used to treat OS, so we aimed to investigate the responsiveness of these two risk subgroups to commonly used chemotherapeutic agents. We calculated IC50 values for each sample in the TARGET dataset and found that the high-risk group exhibited potentially greater responsiveness to commonly used chemotherapeutic medicines (BIRB.0796, OSI.906) (Fig.\u0026nbsp;5D-L).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis of prognostic model-related genes\u003c/h2\u003e \u003cp\u003eFor the purpose of further exploring potential changes in the functional characterization of the seven genes associated with HIS-OS, KEGG, GO, and GSEA analyses were carried out for both risk subgroups. GSEA revealed a significant negative association between the high-risk group and the following tumor hallmarks, as opposed to the low-risk group: IL-6/JAK/STAT3 signaling, inflammatory response, interferon-gamma (IFN-γ) response, and coagulation (Fig.\u0026nbsp;6A-D). GO enrichment studies revealed that high-risk subgroups were associated with biological processes such as ECM organization, extracellular structure organization, external encapsulating structure organization, and ossification (Fig.\u0026nbsp;6E). In addition, the results of the KEGG analysis support these pathways, including the PI3K-Akt signaling pathway, ribosome, focal adhesion and others (Fig.\u0026nbsp;6F). All of these pathways are strongly related to malignant processes such as tumor development, proliferation, metastasis, immunosuppression, and drug resistance. These findings reveal the relationship between prognostic model-related genes and biological processes, thus shedding light on the reasons for the poorer prognosis of OS patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of HIS-OS-related genes with patient prognosis and OS\u003c/h2\u003e \u003cp\u003eTo gain a deeper understanding of the functions of these seven HIS-OS-related genes in OS, we investigated the expression of individual genes and survival rates in the normal (396 samples) and tumor (88 samples) groups. The findings demonstrated that the expression levels of IFI44L, IFITM, and SERPINE2 were considerably greater in the OS samples (Fig.\u0026nbsp;7A-C) than in the normal samples. Among them, the group with elevated SERPINE2 expression had a considerably decreased survival rate compared to the group with low expression (Fig.\u0026nbsp;7D-F). In conclusion, among the three highly expressed genes (IFI44L, IFITM3, and SERPINE2), SERPINE2 independently promoted malignant progression and led to a worse prognosis among OS patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExpression of SERPINE2 in OS cells was significantly inhibited by IITZ-01.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo explore inhibitors targeting SERPINE2, we first used Western blotting to investigate SERPINE2 expression in tumor cells and observed that SERPINE2 was highly expressed in MG63 and HOS cells. (Fig.\u0026nbsp;8A). Next, we obtained 50 compounds with high docking scores to the target gene SERPINE2 from an FDA library containing 3764 compounds (including small molecule targets). To further clarify the inhibitory effects of the 50 compounds on HOS cells, we confirmed that IITZ-01 had a significant inhibitory effect on HOS cells by high-throughput screening technology (Fig.\u0026nbsp;8B). Next, 3D plots of the interaction between SERPINE2 and IITZ-01 were constructed (Fig.\u0026nbsp;8C) were obtained by molecular docking using AutoDockvina and PyMOL. The blue molecule is the receptor 4DY0, the green molecule is the ligand IITZ-01, and the rose is the amino acid residue of 4DY0 docking with IITZ-01. Coordinate files of receptors and ligands uploaded to ProteinPlus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proteins.plus/\u003c/span\u003e\u003cspan address=\"https://proteins.plus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) yielded a 2D map of the interaction between SERPINE2 and IITZ-01 (Fig.\u0026nbsp;8D). Finally, the protein expression levels were determined using Western blotting. IITZ-01 was shown to inhibit the expression of the SERPINE2 protein in HOS and MG-63 cells (Fig.\u0026nbsp;8E-F).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eIITZ-01 inhibits the proliferation, migration, and invasion of OS cells\u003c/h2\u003e \u003cp\u003eTo more fully investigate the role of IITZ-01 in OS, we analyzed cell viability by performing a CCK-8 assay and found that IITZ-01 decreased survival of HOS and MG-63 cells compared to negative control cells (Fig.\u0026nbsp;9A, B). Finally, cell migration experiments showed that IITZ-01 inhibited the migration of MG-63 and 143B cells (Fig.\u0026nbsp;9C-D). In addition, cell invasion experiments showed that IITZ-01 impeded the invasion of MG-63 and 143B cells (Fig.\u0026nbsp;9E-F). The above results showed that IITZ-01 inhibited the proliferation, migration, and invasion of OS cells.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe most common primary malignant tumor in orthopedics is OS. It is characterized by a strong inclination toward local aggressiveness and early metastasis, which leads to a poor prognosis for patients with OS (Luetke, Meyers, Lewis, \u0026amp; Juergens, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In recent years, predictive models based on multiple functional genomic methods for OS have become increasingly common in forecasting the prognosis of OS patients. Wang et al. analyzed the expression levels of cupping-associated long noncoding RNAs (lncRNAs) in OS and constructed a prognostic model for cupping-associated lncRNAs (X. Wang, Xie, \u0026amp; Lin, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Yu et al. analyzed the gene expression levels of CD8\u0026thinsp;+\u0026thinsp;lymphocytes in OS, identified six genes related to OS prognosis, and further constructed a prognostic model (Yu Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, few studies have directly targeted OS tumor cells, and the cell type composition, dynamics, and characteristics of OS tumor foci are largely unknown. Consequently, we speculated that the detection of OS tumor cell-related genes is important for the prognosis of OS patients. Furthermore, these discoveries might help to identify prognostic biomarkers for OS tumors and develop more accurate therapeutic regimens and potential targeted drugs. Therefore, we constructed a risk model using HIS-OS-related genes in OS cell subsets to predict the prognosis of OS patients.\u003c/p\u003e \u003cp\u003eWithin this research investigation, we established a bioinformatics prognostic model and validated its accuracy using HIS-OS genes. Seven HIS-OS-related genes were incorporated into a risk map to determine whether they could accurately predict OS prognosis. First, an alternative prognostic model comprising seven HIS-OS-associated genes was constructed by analyzing single-cell sequencing data of OS as well as differential gene expression analysis. These results proved that the risk profile is capable of accurately predicting an OS patient's prognosis. As a result, in the future clinical treatment of OS patients, the risk score can be calculated from our developed risk model, and the prognosis can be inferred from the calculated risk score. Furthermore, we examined the connection between the risk profile and the TME. Moreover, we investigated possible therapeutic targets and corresponding treatment drugs for this target. In conclusion, our HIS-OS-related gene-based prognostic model provides a valuable reference for evaluating the prognosis and treatment of OS.\u003c/p\u003e \u003cp\u003eTumor purity is the proportion of tumor cells in a mixture and is closely related to the prognosis of tumor patients (Y. Mao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). OS patients with increased tumor purity as well as decreased stromal scores, ESTIMATE scores, and immune scores tend to present a higher degree of malignancy, which often results in a negative prognosis (Yoshihara et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Consistent with our study, the high-risk group had high tumor purity and lower ESTIMATE, immune, and stromal scores. Tumor-infiltrating immune cells (TIICs) in OS are considered to have a substantial influence on tumor advancement and prognosis, among other factors (Ying Chen, Zhao, \u0026amp; Wang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; C. Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Z. Zhang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To further understand the impact of TIIC on the prognosis of OS, we compared the abundance of 24 TIICs in the two risk groups. The immature dendritic cells (iDCs) and activated dendritic cells (aDCs) are the immature and activated dendritic cell (DC) subsets, respectively. Moreover, iDCs strongly phagocytose tumor antigens and differentiate into aDCs when they ingest antigens or are stimulated by certain factors (Gardner \u0026amp; Ruffell, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). aDCs recognize and process immune signals, transport tumor antigens, and present them to T cells, which activate the antitumor function of T cells (Wculek et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). CD8\u0026thinsp;+\u0026thinsp;T cells and cytotoxic cells are considered to have important antitumor effects. They are considered to be the main T cells that exert antitumor effects; these cells can be activated by tumor antigens presented by aDCs and macrophages and thus recognize and kill tumor cells, exerting antitumor effects and thus significantly prolonging patient survival (Y. Sun et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thommen \u0026amp; Schumacher, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our findings were consistent with those of previous studies showing that aDCs, iDCs, CD8\u0026thinsp;+\u0026thinsp;T cells, cytotoxic cells, T cells, and macrophages were significantly downregulated in the high-risk group compared with the low-risk group. However, macrophages are M2-type macrophages in most malignant tumors and promote tumor metastasis due to their proangiogenic and other effects (Huang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, macrophages in highly differentiated OSs are composed of a mixture of both M1-type and M2-type cells. The greater the number of M1-type patients was compared to that of M2-type patients, the lower the risk of metastasis and the longer the lifespan expectancy of OS patients (Zhao, Zhang, Zhang, Ma, \u0026amp; Feng, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, we suggest that infiltrating macrophages in the low-risk group were likely dominated by M1-type macrophages.\u003c/p\u003e \u003cp\u003eThe IPS is an excellent biomarker for identifying responders to immunotherapy and consists of four main factors: immunosuppressive cells, effector cells, MHC molecules, and immune checkpoints (Charoentong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Increased IPS scores are associated with increased immunogenicity (Hajiran et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, IPS was found to be higher in both risk groups, without any notable difference between them, suggesting that OS is a highly immunogenic tumor that could benefit from immunotherapy.\u003c/p\u003e \u003cp\u003eImmune checkpoint molecules are often found at higher levels in the TME of a variety of malignant tumors (Arum et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Toor, Sasidharan Nair, Decock, \u0026amp; Elkord, 2020), and immune checkpoint inhibitor treatment has demonstrated encouraging clinical outcomes (Meftahpour, Aghebati-Maleki, Fotouhi, Safarzadeh, \u0026amp; Aghebati-Maleki, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, little information about this therapeutic strategy has been published for OS. Therefore, we compared the expression levels of 38 immune checkpoint molecules in the two risk groups. CD80 and CD86 are mostly found on the plasma membrane of antigen-presenting cells, namely, DCs and monocytes/macrophages, and their important function is to bind to and costimulate signals with CD28 receptor proteins on the surface of T cells, which in turn activate the proliferation and differentiation of T cells and contribute to antitumor immunity (Bolandi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Esensten, Helou, Chopra, Weiss, \u0026amp; Bluestone, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fleischer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). CTLA4 is expressed at higher levels on activated T cells and interacts with the ligands CD80 and CD86 to provide coinhibitory signals that suppress T-cell activation and multiplication (Engelhardt, Sullivan, \u0026amp; Allison, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Krummel \u0026amp; Allison, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Multiple studies in the field of cancer have demonstrated that elevated levels of CD80, CD86, and CD28 are associated with the suppression of OS cell proliferation and metastasis (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consistent with these findings, the expression levels of CD80, CD86, and CD28 were notably elevated in our study compared with those in the low-risk group. However, the low-risk group exhibited significant overexpression of CTLA4, which we suggested was associated with secondary upregulation of CTLA4 on the T-cell surface due to T-cell activation.\u003c/p\u003e \u003cp\u003eTIDE is highly regarded for its usefulness in identifying the effectiveness of immune checkpoint inhibitors, and numerous reports have suggested that CTLA-4 and PD-1 inhibitors are essential for treating many intermediate malignancies. Additionally, combining medications that target CTLA-4 and PD-1 receptors may have additive advantages when used with anticancer immunotherapies (Rotte, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). New research has indicated that CTLA-4 and PD-1 could have functions in the treatment of OS, but it is still uncertain how individuals with varying prognostic risks respond to this kind of immunotherapy (S.-D. Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, the TIDE score was chosen for this study to evaluate the effectiveness of PD-1 and CTLA-4 immunotherapy in both the high-risk and low-risk patients. The findings of our study indicate that individuals with a low risk of death exhibit positive responses to immune checkpoint therapy and may benefit from immunotherapy. The high-risk group had modest levels of immunosuppressive checkpoint expression, which remained unresponsive to immune checkpoint therapy. This finding is consistent with our conclusions and speculations above.\u003c/p\u003e \u003cp\u003eAccording to our functional analysis, GSEA revealed that genes associated with biological processes such as the inflammatory response and IFN-γ response were enriched predominantly in patients at low risk for OS. The inflammatory response has dual functions in tumor progression and can either promote or inhibit tumor progression (Greten \u0026amp; Grivennikov, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Philip, Rowley, \u0026amp; Schreiber, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Current research suggests that the induction of acute inflammation promotes immune cell maturation and antigen presentation, leading to an antitumor immune response (H. Zhao et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). IFN-γ has numerous antitumor effects, including promoting inflammatory responses, modulating antigen presentation, inhibiting angiogenesis, and promoting tumor dormancy and apoptosis to inhibit tumor growth (Burke \u0026amp; Young, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mauldin et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, it has been shown that enhanced IFN-γ secretion could reduce the development of tumorigenic M2 macrophages, thereby inhibiting the growth of OS (Kang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). GO enrichment analysis revealed that these genes were involved mostly in biological processes such as ECM organization, external encapsulating structure organization, and ossification. Several investigations have indicated that the tumor-associated ECM is involved in promoting tumor cell growth, invasion, metastasis, and angiogenesis and that it resists cell death and drug diffusion (Najafi, Farhood, \u0026amp; Mortezaee, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Theocharis, Skandalis, Gialeli, \u0026amp; Karamanos, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The collective influence of these EMC-associated biological processes may serve as a significant catalyst for OS cell migration. In addition, KEGG analysis revealed that these genes were enriched in pathways including the PI3k-Akt signaling pathway, The ribosome pathway, and the focal adhesion pathway. The PI3K-Akt signaling pathway is an intracellular signaling pathway that responds to extracellular signals to promote metabolism, proliferation, cell survival, growth, and angiogenesis (Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ribosome synthesis is increased in cancer cells as a reaction to increased protein synthesis and maintenance of unrestricted growth, and several articles have revealed that the ribosome pathway is related to the development of tumors and a negative prognosis (El Khoury \u0026amp; Nasr, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Figueiredo \u0026amp; McCarthy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). High expression of the focal adhesion pathway has been shown to be closely associated with tumor metastasis, and inhibition of related genes improves the survival of tumor patients (Legerstee \u0026amp; Houtsmuller, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lu, Linares, Xu, \u0026amp; Rui, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The above evidence demonstrated that pathways enriched for HIS-OS-related genes included those that promote tumor proliferation, migration, and invasion, ultimately resulting in a worse prognosis among patients with OS.\u003c/p\u003e \u003cp\u003eWe examined the gene expression levels in both the tumor and normal groups and the association between gene expression and survival prognosis to further determine the role of the above prognosis-related genes in OS. Analysis of the findings revealed a substantial increase in SERPINE2 expression in the tumor group, and these patients had a poorer prognosis. SERPINE2 encodes a serpin protein that belongs to a family of proteins that inhibit serine proteases and are effective against trypsin, thrombin, and plasma proteases (Buchholz et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In other malignancies, the role of SERPINE2 in inhibiting serine proteases and promoting tumor progression has been widely reported, especially in cases of tumor metastasis, such as breast, gastric, and lung adenocarcinomas (Dokuni et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fayard et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; K. Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Experimental investigations have demonstrated that SERPINE2 is significantly upregulated in OS tissues, particularly in stage II-III patients with metastases and tumor nodal metastases (M. Mao \u0026amp; Wang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). High expression of SERPINE2 in OS stimulates tumor cell proliferation, promotes drug resistance, and leads to poor survival through regulation of CDK4 and cell cycle protein D. The aforementioned information indicates that SERPINE2 might serve as a promising target for therapeutic intervention in patients with OS.\u003c/p\u003e \u003cp\u003eThe PDB structure of SERPINE2 was shown to dock well with that of IITZ-01 in virtual screening and molecular docking simulations. We further conducted CCK-8 and migration invasion tests to confirm the inhibitory impact of IITZ-01 on OS cells. Specifically, we observed significant inhibition of OS cell proliferation, migration, and invasion. However, the exact underlying mechanism requires further investigation. Furthermore, future research will need animal trials to confirm the inhibitory impact of IITZ-01 on OS in living organisms. In conclusion, our findings suggest that SERPINE2 might serve as a promising therapeutic target for OS and that IITZ-01, an inhibitor of SERPINE2, shows promise as a therapeutic drug for OS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present research, we constructed a new model that enables us to predict the prognosis of OS patients based on seven HIS-OS prognosis-related genes screened, and the results verified that the model has good predictive capability. Moreover, the immune characteristics of OS patients in the two groups exhibited significant differences. The functional analysis showed that the HIS-OS prognostic genes might be related to malignant processes such as OS migration and invasion. In addition, we identified a potential therapeutic target, SERPINE2, and a drug that inhibits SERPINE2, IITZ-01, which may inhibit tumor function by inhibiting the SERPINE2 protein. In conclusion, our study can provide guidance regarding the prediction of the prognosis of OS as well as the clinical treatment of this disease.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eADCs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Activated dendritic cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Area under the curve\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCCK8 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;Cell Counting Kit‑8\u003c/p\u003e\n\u003cp\u003eDC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp;Dendritic cell\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;Differentially expressed genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eECM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Extracellular matrix\u003c/p\u003e\n\u003cp\u003eECM-OS \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp;Extracellular matrix OS\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eHIS-OS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Highly invasive OS\u003c/p\u003e\n\u003cp\u003eHSM-OS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Homeostatic OS\u003c/p\u003e\n\u003cp\u003eICC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Immunosuppressive cell\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eiDCs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immature dendritic cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIFN-\u0026gamma;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interferon-gamma\u003c/p\u003e\n\u003cp\u003eIPS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immunophenoscore\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIR-OS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immunoreactive OS\u003c/p\u003e\n\u003cp\u003eK‒M \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kaplan‒Meier\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLASSO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eLncRNAs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Long noncoding RNAs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMHC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Major histocompatibility complex \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Osteosarcoma\u003c/p\u003e\n\u003cp\u003ePA-OS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Proangiogenic OS\u003c/p\u003e\n\u003cp\u003ePVDF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Polyvinylidene fluoride\u0026nbsp;\u003c/p\u003e\n\u003cp\u003escRNA-seq \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003essGSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Single sample gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eST-OS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Stressed OS\u003c/p\u003e\n\u003cp\u003eTCIA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The Cancer-immune Group Atlas\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTIDE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tumor immune dysfunction and exclusion\u003c/p\u003e\n\u003cp\u003eTIICs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tumor-infiltrating immune cells\u003c/p\u003e\n\u003cp\u003eTME \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eTPS-OS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tumor-promoting protein-forming OS\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eAll data included in this study are available upon request by contact with the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by Clinical Research and Cultivation Program of the Second Hospital of Anhui Medical University (2020LCZD05); Translational Medicine Research Foundation of the Second Hospital of Anhui Medical University (2022ZHYJ13); Key Projects of Natural Science Research in Colleges and Universities in Anhui Province (2022AH040102); Research Foundation of Anhui Institute of Translational Medicine (No.2022zhyx-C49).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEB, DT, ZL, and MW conceived the study. MW, CY, JL, HX, and YW performed the experiments. EB, ZL, MW, DT and CY analyzed and interpreted the results. EB, ZL, and MW wrote the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe author thanks TARGET network and GEO network for their contributions.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll data included in this study are available upon request by contact with the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArum C-J, Anderssen E, Viset T, Kodama Y, Lundgren S, Chen D, Zhao C-M (2010) Cancer immunoediting from immunosurveillance to tumor escape in microvillus-formed niche: A study of syngeneic orthotopic rat bladder cancer model in comparison with human bladder cancer. 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Nat Commun 11(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-020-20059-6\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-20059-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"Osteosarcoma, scRNA-seq, Immune, Therapeutic target, Gene signature, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4495593/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4495593/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOsteosarcoma (OS) is a highly prevalent bone tumor derived from primitive mesenchymal cells that occurs mostly in adolescents and children. OS has a notable propensity for aggressive behavior and resistance to treatment. Additionally, accurately evaluating and predicting the prognosis of OS remains challenging. For this investigation, we utilized scRNA-seq data to identify seven subtypes of OS cells. Survival analysis of each OS cell subtype revealed that highly invasive OS (HIS-OS) had a poorer prognosis. Through differential expression analysis, an entire set of seven genes linked to HIS-OS was identified. Subsequently, these seven genes were employed to construct a predictive model using the LASSO approach. Based on the median risk score, the OS samples in the training set were categorized into high-risk and low-risk groups, and the high-risk group exhibited a significantly shorter survival time. The analysis of immunotherapy and anticancer treatment responsiveness indicated a negative correlation between HIS-OS-related gene signatures and immune checkpoints as well as chemotherapy sensitivity. In addition, functional analysis demonstrated high enrichment of these gene sets throughout the process of tumor invasion. Finally, SERPINE2 was identified as a therapeutically critical gene. Therefore, we subsequently selected an inhibitor, IITZ-01, that targets SERPINE2, and we performed molecular docking simulations. Furthermore, we validated the inhibitory effect of IITZ-01 on OS at the cellular level. The results suggest that HIS-OS-related genes are important for prognostic stratification and therapeutic strategies for OS.\u003c/p\u003e","manuscriptTitle":"A Novel Highly Invasive Cell-Related Gene Signature for Predicting the Prognosis and Treatment of Osteosarcoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 14:48:16","doi":"10.21203/rs.3.rs-4495593/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":"fcbb6441-b796-4fab-9e00-be4bd5ffd560","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-27T08:09:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-26 14:48:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4495593","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4495593","identity":"rs-4495593","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}