NET-related genes predict prognosis and are correlated with the immune microenvironment in osteosarcoma

NET-related genes predict prognosis and are correlated with the immune microenvironment in 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 NET-related genes predict prognosis and are correlated with the immune microenvironment in osteosarcoma Dawei Chu, Rui Huang, Jian dang Shi, Ruiqing Xu, Daihao Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5332874/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 Background Osteosarcoma is the most common primary bone tumor. It has a high rate of early metastasis, and its treatment is one of the most challenging topics in the bone tumor field. Recent studies have shown that neutrophil extracellular traps play an important role in tumor metastasis and may provide new horizons for exploring metastasis in osteosarcoma. Methods OS data were downloaded from the TARGET database and Gene Expression Omnibus datasets. Univariate Cox regression was conducted to assess NETRGs. Patients were subsequently categorized into high- and low-risk groups on the basis of risk score values derived from multivariate Cox analysis, and prognostic models were established. The immune infiltration of relevant genes and drug sensitivity of key genes were also analyzed. Results A total of 15 NET-related genes associated with osteosarcoma metastases were identified. Among them, a total of 4 genes were related to prognosis, namely, MAPK1, CFH, ATG7 and DDIT4, and a prognostic model based on these 4 genes was established. The prognosis was worse in the high-risk group, whose areas under the ROC curves (AUCs) were 0.857, 0.779, and 0.689 at 1, 3, and 5 years, respectively. The key genes were subsequently found to be associated with the infiltration of 20 types of immune cells. Finally, the small-molecule drug toxin c 10, an approximately 6700 mw protein, may target key genes. Finally, ATG7 was validated at the histological level by combining the results of the validation group dataset analysis. Conclusions A risk model based on 4 NETRDEGs is a reliable prognostic predictor for OS patients, and ATG7 may serve as a new diagnostic and therapeutic target. Osteosarcoma Neutrophil extracellular traps Key genes Therapeutic targets ATG7 Metastases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Osteosarcoma is a malignant primary bone tumor that is prevalent in children and adolescents [ 1 – 3 ] and is most common in the distal femur, proximal tibia, and humeral metaphyseal locations [ 4 ] . It is characterized by the malignant proliferation of prismatic mesenchymal stromal cells that directly produce osteoid or immature bone tissue [ 5 , 6 ] . It is highly malignant with a high rate of early metastasis, and its high propensity for metastasis is a major cause of poor prognosis [ 7 , 8 ] . Simultaneous pulmonary metastases have been reported to occur in approximately 15–20% of patients at the time of initial diagnosis [ 9 – 11 ] . Once metastasis occurs in patients with osteosarcoma, the prognosis is extremely poor. Moreover, treatment is of limited importance, with an overall 5-year survival rate of 20%-30% [ 12 , 13 ] , which is much lower than that of patients without metastasis. The survival rate of osteosarcoma patients has not improved over the past 30 years, primarily due to the intractability of osteosarcoma metastases in patients [ 14 , 15 ] . Therefore, an in-depth study of the molecules involved in the invasion and metastasis of osteosarcoma cells is urgently needed. Neutrophil extracellular traps (NETs), NET-like substances released by neutrophils during their immune action against pathogens that can capture and kill microorganisms, were discovered by Volker Brinkmann's research team in as early as 2004 [ 16 ] . NETs consist mainly of intracellular DNA, histones and granule proteins, such as myeloperoxidase and neutrophil elastase [ 17 ] . They are closely associated with the onset and progression of diseases such as infections, sepsis, autoimmune disorders and diabetes [ 18 – 20 ] . In recent years, studies involving the interactive functions of neutrophils and the tumor microenvironment have revealed that NETs are involved in the entire invasion‒metastasis cascade of a variety of tumors [ 21 , 22 ] . In breast cancer, NET DNA can interact with CCDC25 on tumor cell membranes to activate the ILK-β-Parvin pathway and promote liver metastasis [ 23 ] . Amyloid-β produced by cancer-associated fibroblasts (CAFs) promotes the generation of NETs by facilitating the production of ROS in neutrophils. NETs promote the hepatic metastasis of pancreatic tumors by enhancing the migration of hepatic stellate cells [ 24 , 25 ] . In addition, NETs contain programmed cell death ligand 1 (PD-L1), which promotes tumor metastasis by binding to programmed cell death protein 1 (PD-1) on the surface of T cells to inhibit T-cell function, leading to T-cell dysfunction and metabolic failure [ 26 ] . These findings provide new directions for understanding the mechanism of osteosarcoma metastasis and possible future treatments. Therefore, exploring the molecular mechanisms of NET-related genes in the development and metastasis of osteosarcoma is highly important for the early diagnosis and clinical treatment of osteosarcoma patients. Here, we utilized bioinformatics approaches to explore the role of NET-related genes in osteosarcoma metastasis (Fig. 1 ). First, we identified MAPK1, CFH, ATG7, and DDIT4 as independent prognostic factors in osteosarcoma patients via Cox regression analysis. In addition, a nomogram graph was established. Analysis of immune infiltration and drug sensitivity of key genes was performed for relevant genes. Finally, ATG7 was validated at the histological level based on the results of the validation group dataset analysis. These results suggest that ATG7 may be a reliable diagnostic and therapeutic target for patients with metastatic osteosarcoma. 2. Data and Methods 2.1 Materials. The expression profile data of TARGET-OS in osteosarcoma patients were downloaded from UCSC Xena ( https://xena.ucsc.edu/ ), and a total of 84 samples were obtained after patient samples with no expression data or survival data were removed. The expression profiling dataset GSE21257 containing metastasis group data was downloaded from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) (Table 1 ). Combined with the Gene Cards database [ 27 ] and published literature [ 28 , 29 ] , 258 neutrophil extracellular traps-related genes (NETRGs) were obtained. 2.2 Methods 2.2.1 Data collection and processing The R software “limma” package was used for data correction to ensure the comparability of the data. The samples were divided into 2 groups (the unmetastases group and the metastases group), and the genes whose |logFC| was > 0 and P value was < 0.05 were considered differentially expressed genes (DEGs). The intersections of the DEGs and NETRGs were then plotted as Venn diagrams and differential ordering plots. 2.2.2 GSEA and GO analysis GSEA was performed via the “cluster Profiler” package and the “c2.cp.v7.2.symbols.gmt” gene set from the Molecular Signatures Database (MSigDB). The parameters were set as follows: the number of seeds was 2020, the number of calculations was 1000, the number of genes contained in each gene set was at least 10, and the maximum number of genes contained was 500. Subsequently, Gene Ontology (GO) functional enrichment analysis was carried out on 15 NETRDEGs. 2.2.3 Construction and validation of the nomogram model Univariate Cox analysis of NETRDEGs was performed using the “survival” package, and genes with P < 0.05 were used as the key genes in our subsequent study. Patients were categorized into high- and low-risk groups based on the median risk score via multivariate regression analysis. A nomogram was constructed using the R software package “rms”, and a decision curve was constructed using the R package “gg DCA” to evaluate the accuracy of the prediction results. $$\:\text{R}\text{i}\text{s}\text{k}\text{S}\text{c}\text{o}\text{r}\text{e}=\sum\:_{\text{i}}\text{C}\text{o}\text{e}\text{f}\text{f}\text{i}\text{c}\text{i}\text{e}\text{n}\text{t}\left({\text{g}\text{e}\text{n}\text{e}}_{\text{i}}\right)\text{*}\text{m}\text{R}\text{N}\text{A}\text{E}\text{x}\text{p}\text{r}\text{e}\text{s}\text{s}\text{i}\text{o}\text{n}\left({\text{g}\text{e}\text{n}\text{e}}_{\text{i}}\right)$$ In this formula, the coefficient represents the risk factor, and mRNA expression represents the expression value of the gene. The functional similarity between key genes was calculated using the R package “GO Sem Sim”. The “p ROC” package was used to plot the ROC curves of the key genes. 2.2.4 Immune infiltration and drug sensitivity analysis Enrichment scores for the level of infiltration of each immune cell type with other stromal cells were calculated using the R software packages “GSVA” and “MCP Counter”. The correlation between immune infiltrating cells was determined via Spearman's correlation analysis, and P < 0.05 was considered statistically significant. The Cell Miner database ( https://discover.nci.nih.gov/cellminer/home.do ) was searched and based on the expression of the key genes with the drug data in the Cell Miner database. Drug sensitivity analysis of key genes was performed using the “pRRophetic” package. 2.2.5 Immunohistochemical analysis Osteosarcoma tissue microarrays (Changsha Yaxiang Biotechnologies, Changsha, China) were used for these experiments. The chips were subjected to a dewaxing process and antigen repair, followed by serum blocking to block nonspecific binding. For primary antibody incubation, the samples were rinsed three times with phosphate buffer for 3 min each. Then, the ATG7 antibody (OriGene Technologies, Wuxi, China) was diluted at a ratio of 1:1000, the primary antibody was added dropwise, and the samples were incubated overnight at 4°C in a refrigerator. For secondary antibody incubation, the samples were washed with phosphate buffer three times for 3 min each, horseradish peroxidase-labeled secondary antibody was added dropwise, the samples were incubated at room temperature for 2 h, and the samples were rinsed with phosphate buffer three times for 3 min each. DAB staining solution was added dropwise for coloring, and the color development was observed under a microscope. The samples were quickly washed after coloring. The samples were restained by incubating them with hematoxylin for 1min, followed by rinsing for 5 min and drying at room temperature before sealing them with a coverslip. The next day, images were obtained with a tissue microarray scanner. 2.2.6 Statistical analysis R software version 4.1.2 was used for analysis, and R language-related packages (“limma,” “cluster Profiler,” “p ROC,” “rms,” “survival,” “GO Sem Sim,” “gg DCA,” “GSVA,” etc.) were used to process data. Differences in survival were analyzed via the Kaplan‒Meier method and are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). P < 0.05 was considered a statistically significant difference. The statistical significance is shown as follows: P value < 0.05 ( * ) and P value < 0.01 ( ** ). 3. Results 3.1 Standardization of the dataset The dataset GSE21257 (Fig. 2 A-B) was standardized such that the trend of expression among different samples converged, and a box line plot was drawn for the distribution of data before and after standardization. 3.2 Screening results of NETRDEGs In the dataset, 1068 genes satisfied the threshold of |logFC| > 0 and P value < 0.05, and 602 and 466 genes exhibited high and low expression in the metastasis group, respectively (Fig. 3 A). Taking the intersection of all DEGs and NETRGs, a total of 15 NETRDEGs were obtained, including IL1RL1, AZU1, NFIL3, DDIT4, ENO1, KRT10, ATG7, MAPK1, PIK3CG, DECR1, IL36G, CFH, SELL, SFTPD, and COLEC11 (Fig. 3 B-C). 3.3 GSEA and GO analysis To analyze the biological functions of the 15 NETRDEGs, we first performed GO analysis of the NETRDEGs (Fig. 4 A-D). These genes were found to be involved in autophagy in the nucleus, the regulation of the cellular response to hypoxia, the response to hypoxia, and the circadian rhythm in biological processes (BP). The CC terms were significantly associated with the secretory granule lumen, cytoplasmic vesicle lumen, vesicle lumen, and collagen trimer. The enriched MFs of the DE-FRGs were as follows: oligosaccharide binding, heparan sulfate proteoglycan binding, heparin binding, and proteoglycan binding. GSEA revealed that the DEGs were significantly enriched in the glycolysis pathway, autophagy pathway, IL7 pathway, Wnt signaling pathway, and PI3K/Akt signaling pathway (Fig. 4 E-J). 3.4 Construction of a prognostic model and establishment of a nomogram To obtain a prognostic model for NET-related genes, we screened for NETRGs via univariate Cox analysis in conjunction with survival outcomes and survival times and constructed a forest plot (Fig. 5 A). We then included these key genes in a multivariate Cox analysis to obtain the risk score value and grouped the samples of the dataset into high- and low-risk groups according to the median value of the risk score (cutoff value = -0.050965805) and found that the prognosis was worse in the high-risk group (Fig. 5 C-D). The prognostic model can be expressed as follows: risk score=MAPK1*(-0.350932209)་CFH*(།0.540468911)་ATG7*(།0.765106538)་DDIT4*0.132203877. We then performed a nomogram analysis to determine the prognostic ability of the key genes (Fig. 5 B). The nomogram yields a score for each item, and the total score and corresponding survival rate can be obtained after adding the scores of all the items. The results showed that the utility of the expression of the CFH gene in the model was significantly greater than that of the other genes. Moreover, the AUCs of the 1-, 3- and 5-year ROCs were 0.857, 0.779 and 0.689, respectively (Fig. 5 E). In addition, we performed 1-, 3-, and 5-year prognostic calibration analyses and plotted calibration curves for the prognostic model (Fig. 5 F-H). We found that the model predicted patient survival in general agreement with actual patient survival. We then used decision curve analysis to assess the magnitude of the clinical utility of the constructed models at 1, 3, and 5 years (Fig. 5 I-K), which revealed that the 5-year prognostic model had the best clinical utility. 3.5 Prognostic analysis of key genes in the training group To assess the relationships between the four key genes and prognosis, we plotted prognostic Kaplan‒Meier survival curves in the TARGET-OS dataset for each of the key genes (Fig. 6 A‒D), which revealed that all four genes significantly correlated with survival: MAPK ( P = 0.035), CFH ( P = 0.029), ATG7 ( P = 0.004), and DDIT4 ( P = 0.043). In addition, gene correlation analysis was performed based on the complete expression matrix of key genes, and correlation heatmaps were drawn (Fig. 6 F). The results revealed a positive correlation between the genes ATG7 and MAPK1 and between CFH and ATG7. We subsequently performed functional similarity analysis of the key genes and then visualized the results of the functional similarity analysis among the key genes via a box-and-line plot (Fig. 7 G), which revealed that ATG7 was the most similar gene to the other three genes in terms of function. Next, the ROC curves of the key genes were plotted (Fig. 6 H-K). The ROC curves revealed that the differences in the expression of the CFH gene (AUC = 0.711) in the dataset presented comparable accuracy across subgroups. 3.6 Analysis of immune cell infiltration To explore immune cell infiltration, the correlation between the infiltration abundance of 28 immune cells was calculated via the ssGSEA algorithm. The results of the correlation heatmap (Fig. 7 A) revealed a positive correlation between the infiltration abundance of immune cells that activated CD8 + T cells and macrophages and between effector memory CD8 + T cells and immature B cells, macrophages and MDSCs. Subsequently, we analyzed the relationships between the key genes and the infiltration abundance of 28 immune cells via the ssGSEA algorithm, and the key genes CFH, ATG7, and DDIT4 were correlated with 20 of these immune cells (Fig. 7 B). Among them, positive correlations were identified between CFH and immune cells, central memory CD4 T cells, natural killer cells, as well as between ATG7 and immune cells and killer cells. To ensure the accuracy of the above algorithm, we also calculated the correlation between key genes and immune cell infiltration abundance via the MCP Counter algorithm (Fig. 7 C), which revealed that the key genes were related to 10 types of immune cells. Among them, positive correlations were identified between ATG7 and endothelial and monocyte lineage cells as well as between CFH and monocytic lineage cells; DDIT4 negatively correlated with NK cells. 3.7 Drug sensitivity analysis To obtain small-molecule drugs that target key genes, we used data from the cancer drug database Cell Miner, including the mRNA expression profiles of key genes and drug activities. Using the pRRophetic algorithm, a ridge regression model was constructed based on the expression and gene expression profiles of the key genes in the TARGET-OS dataset, and the sensitivities of the key genes to common anticancer drugs were predicted by the IC50 values (Fig. 8 ). The results show that key genes can be found in the database Cell Miner for a variety of drugs with interaction relationships. Among them, ATG7, kinetin riboside, MAPK1, and CFH positively correlated with the small molecule sri1215. Negative correlations were identified between ATG7 and the small-molecule drug protein toxin c10-mwapprox.6700, between CFH and benzethonium chloride, and between MAPK1 and zimelidine hydrochloride. 3.8 Immunohistochemical analysis To explore the expression of key genes in different sequencing datasets, the differences in the high- and low-expression key genes among different subgroups in the GEO dataset were analyzed (Fig. 9 A). ATG7 expression was significantly lower in the metastasis group than in the metastasis-free group in the GSE21257 dataset, which was consistent with the analysis of the TARGET dataset. This difference may be a common phenomenon in metastatic patients. However, the expression levels of MAPK1, CFH and DDIT4 did not significantly differ between groups in the GSE21257 dataset, but the trend was consistent with the results in the training set. Low expression of ATG7 is likely a common genetic variant in all patients with osteosarcoma metastases. To assess the potential of ATG7 as a biomarker and therapeutic target for osteosarcoma metastases, we analyzed the expression of ATG7 in tissue microarrays using immunohistochemistry. ATG7 was expressed at low levels at the histological level, which was consistent with the results of our bioinformatics analysis. (Fig. 9 B). 4. Discussion Despite advances in the diagnosis and treatment of osteosarcoma, distant metastasis has become a bottleneck in improving the survival of osteosarcoma patients, which severely restricts their long-term survival [ 30 ] . In fact, metastasis is a multifactorial and multistep process in which tumor cells undergo three stages: acquisition of in situ invasive ability, escape from the immune surveillance system during the circulatory process, and colonization of the premetastatic microenvironment; then, the surviving tumor cells grow in distal organs far from the site of origin, resulting in multiorgan failure [ 31 ] . Recent studies have demonstrated that the extracellular trap network released by neutrophils during their physiological function is involved in the three stages of the metastatic process to varying degrees, including the establishment of the premetastatic microenvironment, epithelial-to-mesenchymal transition, the colonization of circulating tumor cells, and the growth of tumor cells in micrometastatic lesions [ 17 ] . However, the role of NETs in the pathogenesis of OS remains poorly understood, prompting us to explore the possibility of using NET-related genes as OS biomarkers. Here, we functionally analyzed osteosarcoma NET-related genes via bioinformatics methods. A new prognostic risk model associated with osteosarcoma NETs was also identified, and the correlations of the associated genes with the immune microenvironment and small-molecule drugs were also analyzed. In this study, we initially identified 1068 osteosarcoma metastasis-associated genes and 258 NET-associated genes, resulting in a crossover gene set of 15 genes. We subsequently screened the genes in the crossover gene set via univariate Cox regression to identify four prognosis-associated genes, among which the expression of MAPK1, CFH, and ATG7 was downregulated in the metastasis group, whereas the expression of DDIT4 was upregulated in this group. These changes were significantly correlated with the prognosis of OS patients. Notably, via Cox regression analysis, we constructed a prognostic model consisting of four genes, namely, MAPK1, CFH, ATG7 and DDIT4: with risk score = MAPK1 × (-0.350932209) + CFH × (-0.540468911) + ATG7 × (-0.765106538) + DDIT4 × 0.132203877. Patients with higher risk scores were found to have a worse prognosis. ROC analysis revealed that this model had good 1-, 3- and 5-year survival AUCs, which indicates that this model is reliable in predicting OS prognosis. In addition, the combination of data on the mRNA expression profiles of key genes and drug activity revealed that drug small molecules, such as protein toxin c 10 - mw approx. 6700, may serve as drugs to target corresponding to key genes. Recent developments in the field of immunotherapy have facilitated in-depth studies of the osteosarcoma tumor microenvironment, where immune cells within the TME play a key role in osteosarcoma genesis and influence the therapeutic response and clinical outcomes [ 32 ] . Previous studies have demonstrated that many neutrophils in the tumor microenvironment are affected by CXCR1- and CXCR2-activating ligands produced by tumor cells, which induce the production of NETs to shield immune cells (CD8 + T cells and NK cells) from exposure to tumor cells, thereby preventing tumor cells from being killed by immune cells and facilitating tumor metastasis [ 19 , 33 ] . Therefore, to further clarify the driving role of immune cells in osteosarcoma metastasis, we explored the infiltration of NET-related genes by various immune cells. We found that these four genes were significantly associated with the infiltration of 20 types of immune cells, including T cells and NK cells [ 34 ] . These findings confirm that key genes play important roles in tumor immunity and provide new ideas for osteosarcoma immunotherapy. MAPK1, also known as extracellular signal-regulated kinase (ERK2), is an important component of the MAP kinase signal transduction pathway. It plays an important role in regulating cell proliferation, differentiation, apoptosis, migration and other activities [ 35 ] . Studies have shown that aberrant activation of ERK2 in the MAPK pathway is an important cause of a variety of cancers, such as oral cancer [ 36 ] and hepatocellular carcinoma [ 37 ] , in which the hyperactivation of ERK2 can be detected. In addition, a study revealed that this protein, which is a moonlighting protein, also has a transcriptional repressive effect independent of kinase activity. Specifically, IFNγ signaling leads to ERK overactivation in melanoma cells, followed by the generation of an overstress response that leads to cell death. Moreover, the overexpression of either ERK1 or ERK2 leads to cell death in human melanoma cell lines [ 38 ] . In our study, MAPK1 was expressed at low levels in the training set, but this difference was not significant in the validation set GSE21257. We speculate that the reason for this difference may be related to differences in the site of metastasis and the heterogeneity of the tumor, resulting in different molecular biological alterations; however, this hypothesis needs to be verified in larger studies. The relationship between cancer and autophagy is complex and is characterized by the fact that the pro- and anticancer properties of autophagy are mutually transformative under specific circumstances [ 39 – 42 ] . As an important autophagy effector enzyme, ATG7 can regulate immunity, cell death, and protein secretion together with other autophagy-associated proteins and independently regulate the cell cycle and apoptosis [ 43 ] . ATG7 multifunctionality is reportedly associated with oncogenic or pro-oncogenic properties in different tumors. Studies have reported that ATG7 deficiency in mice leads to hepatocellular carcinoma by activating the Yap metabolic pathway [ 44 ] . In another study, elevated ATG7 expression was associated with bladder cancer [ 45 ] and lung cancer [ 46 ] , and high levels of ATG7 expression were associated with poor prognosis in breast cancer patients [ 47 ] . Other studies have shown that whether ATG7 promotes or suppresses tumors also seems to depend on the status of the tumor suppressor P53 [ 48 , 49 ] . Our findings suggest that ATG7 may suppress metastasis, and its association with the status of P53 has not been reported in the field of osteosarcoma and warrants further investigation. Although the complex link between ATG7 and osteosarcoma remains puzzling, alterations in autophagy are increasingly associated with tumors, and targeting and regulating ATG7 may constitute a promising therapeutic approach. As a recently discovered innate immune checkpoint [ 50 ] , it mediated immunosuppression enhances the ability of tumor cells to avoid immune recognition and generate an immunosuppressive tumor microenvironment to evade the complement system against tumor cells [ 51 ] . However, some studies have reported different results: CFH can exert anticancer effects on specific types of cancers by inhibiting cancer-related inflammation [ 52 , 53 ] , and CFH can exert antimetastatic effects by inhibiting excessive angiogenesis in tumor tissues [ 54 , 55 ] . In the present study, we found that CFH expression was lower in the metastatic group than in the nonmetastatic group, which may have a role in inhibiting OS metastasis rather than promoting it. DNA damage-inducible transcript 4 (DDIT4) is a tumor-associated protein that is highly expressed under stress conditions, such as chemotherapy, heat shock, energy depletion, hypoxia and DNA damage. It is involved not only in tumor survival, antitumor resistance and antiapoptotic processes but also in tumor metastatic behaviors, such as proliferation and invasion [ 56 ] . Recent analyses of DDIT4 in several cancer types have shown that high expression of this gene is associated with poor prognosis in several hematological and solid tumors, such as acute myeloid leukemia [ 57 ] , breast cancer [ 58 ] and lung cancer [ 59 ] . In terms of mechanism, DDIT4 is involved in the mTORC1, p53, HIF, autophagy and oxygen sensing signaling pathways through intermolecular interactions with multiple pathway proteins. It is directly involved in the activation of several important pathways and has a driving role in tumor progression and metastasis [ 60 ] . This finding is consistent with our findings and can be used as a new therapeutic strategy to provide a research basis. To date, this is the first study of NET-related genes and OS metastasis. After being stimulated by cytokines (LPS, PMA, IL-8, C5a, etc.) produced by the primary tumor, neutrophils form a network structure consisting of DNA, histones and granule proteins, such as myeloperoxidase and neutrophil elastase, which are involved in enhancing local invasion of the tumor, increasing vascular permeability, facilitating immune escape and colonization, and promoting tumor metastasis. This particular mechanism may lead to new ideas for the treatment of OS. However, our study is subject to several shortcomings. First, the data used in our study were not our own but were obtained from public databases, and whether the sequencing data in the databases can reflect the genetic alterations in all patients remains to be demonstrated. Second, due to the lack of clinical samples from osteosarcoma patients, the key genes could not be quantitatively analyzed by RT‒qPCR and WB experiments. Third, the specific mechanisms of these DEGs with respect to the OS immune microenvironment and drug-related small molecules have not been further investigated. More prospective studies are needed if the value of NETs in OS metastasis is to be further confirmed. 5. Conclusion In conclusion, we developed a prognostic model based on four NETRDEGs, namely, MAPK1, CFH, ATG7 and DDIT4. ROC curves and nomogram plots were used to assess the accuracy of the model, which demonstrated that our prognostic model could reliably predict OS outcome. In addition, our study revealed that NETRDEGs can affect immune cells in the TME and further influence the development of OS, which provides new clues for exploring immunotherapeutic approaches for OS patients. These findings may lead to new therapeutic targets for the diagnosis and treatment of metastasis in OS patients, and more relevant studies are needed to further validate the link between NETs and osteosarcoma metastases. This study provides a basis for exploring the molecular mechanisms, diagnosis and treatment of osteosarcoma metastases. Table 1 Osteosarcoma dataset information TARGET-OS GSE21257 Platform TARGET GPL10295 Species Homo sapiens Homo sapiens Tissue Osteosarcoma tumor tissues Osteosarcoma tumor tissues Samples in Unmetastases group 63 19 Samples in metastases group 21 34 OS: osteosarcoma. GEO: Gene Expression Omnibus. Abbreviations NET neutrophil extracellular traps OS osteosarcoma NETRGs neutrophil extracellular traps related genes NETRDEGs neutrophil extracellular traps related differentially expressed genes. Declarations Funding This research was funded by the Key Program of Ningxia Hui Autonomous Region Natural 353 Science Foundation of China, grant number (No. 2024A1398). Availability of data and materials All basic data can be found in articles, supplementary documents or designated websites. Ethics approval and consent to participate All studies’ procedures have been approved by China Ethics Committee and performed in accordance with the ethical standards. Authors' contributions Jiandang Shi was involved in the conception and design of this study. Dawei Chu and Rui Huang collected the data and performed the bioinformatics analyses. Rui Huang and Dawei Chu prepared the figures and interpreted the data. Ruiqing Xu and Daihao Wei drafted the manuscript. Jiandang Shi and Dawei Chu revised the manuscript. All the authors read and approved the final manuscript. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We thank the UCSC Xena website (https://xena.ucsc.edu/) and GEO website (https://www.ncbi.nlm.nih.gov/geo/) for providing the sequencing data. 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Autophagy promotes mammalian survival by suppressing oxidative stress and p53. Genes Dev. 2020;34(9–10):688–700. Saxena R, Gottlin EB, Campa MJ, et al. Complement factor H: a novel innate immune checkpoint in cancer immunotherapy. Front Cell Dev Biol. 2024;12:1302490. Parente R, Clark SJ, Inforzato A, Day AJ. Complement factor H in host defense and immune evasion. Cell Mol Life Sci. 2017;74(9):1605–24. Bonavita E, Gentile S, Rubino M, et al. PTX3 is an extrinsic oncosuppressor regulating complement-dependent inflammation in cancer. Cell. 2015;160(4):700–14. Corrales L, Ajona D, Rafail S, et al. Anaphylatoxin C5a creates a favorable microenvironment for lung cancer progression. J Immunol. 2012;189(9):4674–83. Liu J, Hoh J. Loss of Complement Factor H in Plasma Increases Endothelial Cell Migration. J Cancer. 2017;8(12):2184–90. Martin M, Leffler J, Smoląg KI, et al. Factor H uptake regulates intracellular C3 activation during apoptosis and decreases the inflammatory potential of nucleosomes. Cell Death Differ. 2016;23(5):903–11. Ding F, Gao F, Zhang S, Lv X, Chen Y, Liu Q. A review of the mechanism of DDIT4 serve as a mitochondrial related protein in tumor regulation. Sci Prog. 2021;104(1):36850421997273. Cheng Z, Dai Y, Pang Y, et al. Up-regulation of DDIT4 predicts poor prognosis in acute myeloid leukaemia. J Cell Mol Med. 2020;24(1):1067–75. Pinto JA, Araujo J, Cardenas NK, et al. A prognostic signature based on three-genes expression in triple-negative breast tumours with residual disease. NPJ Genom Med. 2016;1:15015. Jin HO, Hong SE, Kim JY, et al. Induction of HSP27 and HSP70 by constitutive overexpression of Redd1 confers resistance of lung cancer cells to ionizing radiation. Oncol Rep. 2019;41(5):3119–26. Tirado-Hurtado I, Fajardo W, Pinto JA. DNA Damage Inducible Transcript 4 Gene: The Switch of the Metabolism as Potential Target in Cancer. Front Oncol. 2018;8:106. Additional Declarations No competing interests reported. Supplementary Files GeneCardsSearchResults.csv NETreelatedgenesDownloadfrompublishedartical.xlsx tableS1.NETRGs.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5332874","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375452460,"identity":"3d00c660-8f9b-4b93-98d9-9335fe3f76b0","order_by":0,"name":"Dawei Chu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYFCCBMYHCRU2PPzsjY0PPxCphdngw5k0Ocmew83GEkRqYZOc2XbY2OBGepsADzEa+NtzDKR52JgTG24+bGOQYLCT020goEXizBsDYx4etsTG2YltDwoYko3NDhDQYiCRuyGZR4InsVk6sd1AguFA4jZitBzmMZBIbJM82CbBQ6SWjY0zEoCOk2AkUovEmfefGT4cSJADuc1YwoAIv/C3p6X/SPz3n8f++PGHDz9U2MkR1ILuTtKUj4JRMApGwSjAAQCAe0RTeBhByAAAAABJRU5ErkJggg==","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Chu","suffix":""},{"id":375452461,"identity":"82b0504d-b129-462b-8cec-d37d053cf660","order_by":1,"name":"Rui Huang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Huang","suffix":""},{"id":375452462,"identity":"1ec9bffb-1c27-4e70-816a-487fa41bd7a0","order_by":2,"name":"Jian dang Shi","email":"","orcid":"","institution":"Ningxia Medical University General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"dang","lastName":"Shi","suffix":""},{"id":375452463,"identity":"236d2fff-780a-40b7-ae21-7dcd149e9412","order_by":3,"name":"Ruiqing Xu","email":"","orcid":"","institution":"Xi'an Honghui Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruiqing","middleName":"","lastName":"Xu","suffix":""},{"id":375452464,"identity":"1f6be764-f458-4c3e-8c89-7cbd009be70d","order_by":4,"name":"Daihao Wei","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daihao","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-10-25 13:53:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5332874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5332874/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69429649,"identity":"25910f54-d302-4406-ad1e-28f70f1079ef","added_by":"auto","created_at":"2024-11-20 09:21:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69703,"visible":true,"origin":"","legend":"\u003cp\u003eResearch flowchart\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/8b6c33a95f5855eecfd8976a.png"},{"id":69430557,"identity":"e9a05d2e-bce3-40a6-a172-e1bf613be01c","added_by":"auto","created_at":"2024-11-20 09:29:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7027766,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of the GSE21257 dataset before and after correction. (Red represents the metastases group, blue represents the unmetastases group)\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/062f5c8f2597ecceca4a4b00.jpg"},{"id":69429656,"identity":"5b3447af-24f2-4a07-814a-0a2d3843054b","added_by":"auto","created_at":"2024-11-20 09:21:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4305111,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of TARGET-OS differential genes in the dataset\u003c/p\u003e\n\u003cp\u003e(A). Volcano plots of differentially expressed genes. (B-C). Venn diagrams and differential ordering plots of intersecting genes\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/fa21ce2dad612fd501501763.jpg"},{"id":69430771,"identity":"0f3accaa-9491-4ba8-8870-f3a38d03944d","added_by":"auto","created_at":"2024-11-20 09:37:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":522908,"visible":true,"origin":"","legend":"\u003cp\u003eGO analysis of NETRDEGs (A-D) and GSEA of DEGs (E-J).\u003c/p\u003e\n\u003cp\u003eA-D. GO analysis of NETRDEGs. (A) Bubble chart, (B) column chart, (C) ring network chart, and (D) chord chart. E‒J. Genes associated with the glycolysis pathway (F), the autophagy pathway (G), the IL7 pathway (H), the Wnt signaling pathway (I), and the PI3K/Akt signaling pathway (J) were significantly enriched.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/08169c5a3cc46d8430a9812f.jpg"},{"id":69430561,"identity":"37c711a0-ec3a-4089-9daa-bd7a57245780","added_by":"auto","created_at":"2024-11-20 09:29:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3403185,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the Cox regression model\u003c/p\u003e\n\u003cp\u003e(A). Forest plots for univariate Cox regression; (B). Nomogram integrating the risk score and clinical characteristics; (C-E). Distribution, Kaplan–Meier plot, and time-dependent ROC curve of the risk model. (F-H). 1-year (F), 3-year (G), and 5-year (H) survival calibration plots of the nomogram; (I-K). 1-year (I), 3-year (J), and 5-year (K) survival DCA plots of the nomogram. (DCA): decision curve analysis.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/af3b2250c801989cda98107f.jpg"},{"id":69429660,"identity":"e8e54f95-41fc-4b0f-9953-ee916e2045c3","added_by":"auto","created_at":"2024-11-20 09:21:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":10852320,"visible":true,"origin":"","legend":"\u003cp\u003eK‒M curves and correlation analysis for the TARGET-OS dataset\u003c/p\u003e\n\u003cp\u003e(A-D). K‒M curves for prognostic analysis of the genes MAPK1, CFH, ATG7, and DDIT4. (E). Group comparison plot of key genes among different subgroups in the TARGET-OS dataset. (F). Correlation heatmap of key genes in the TARGET-OS dataset. (G). Functional similarity analysis of key genes. (H-K). ROC curve analysis of key genes in the TARGET-OS dataset.\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/3f9c6294374622707f47d5c8.jpg"},{"id":69431934,"identity":"635e591b-1dc3-4784-9597-5aab7acaec97","added_by":"auto","created_at":"2024-11-20 09:45:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":72239,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis\u003c/p\u003e\n\u003cp\u003e(A). Correlation analysis of the infiltration abundances of 28 immune cell types calculated via the ssGSEA algorithm. (B). The results of the ssGSEA algorithm. (C). The results of the MCP Counter algorithm.\u003c/p\u003e","description":"","filename":"Figure7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/0e7879bd00cf00879af0d4e3.jpg"},{"id":69429658,"identity":"77bc009f-d98e-49e5-b7df-6a779af8cd7b","added_by":"auto","created_at":"2024-11-20 09:21:24","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1844373,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis (dark brown represents antagonists, and blue‒gray represents agonists).\u003c/p\u003e","description":"","filename":"Figure8.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/81b7833c768a8576067f4714.jpg"},{"id":69430559,"identity":"5263d0ec-9633-435b-bf0f-1ecb49df04e2","added_by":"auto","created_at":"2024-11-20 09:29:24","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":44936,"visible":true,"origin":"","legend":"\u003cp\u003eExpression validation of key genes in the GSE21257 dataset\u003c/p\u003e\n\u003cp\u003e(A-B). Differential expression of key genes between different subgroups in GSE14359 and GSE21257. (C). Immunohistochemistry of ATG7 in the metastatic and nonmetastatic groups.\u003c/p\u003e","description":"","filename":"Figure9.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/e57d61b40b8cc6e99f6e0117.jpg"},{"id":70937546,"identity":"5ea4aadc-ae65-4901-b6b8-cf706ac06a82","added_by":"auto","created_at":"2024-12-09 11:17:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28748123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/f8abf3bb-a428-4ed2-9498-b3a90d078f42.pdf"},{"id":69430556,"identity":"c337a648-86a7-40bd-acc7-48262739eabe","added_by":"auto","created_at":"2024-11-20 09:29:24","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29843,"visible":true,"origin":"","legend":"","description":"","filename":"GeneCardsSearchResults.csv","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/1c0c25b838f2535ca98bb2ff.csv"},{"id":69429650,"identity":"f0481867-5ede-4dd9-8405-599cbbe440ef","added_by":"auto","created_at":"2024-11-20 09:21:24","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14560,"visible":true,"origin":"","legend":"","description":"","filename":"NETreelatedgenesDownloadfrompublishedartical.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/8f24a87c7da12010217465f0.xlsx"},{"id":69429652,"identity":"3777217e-ada9-4bc3-bf76-72f92c0241f3","added_by":"auto","created_at":"2024-11-20 09:21:24","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16414,"visible":true,"origin":"","legend":"","description":"","filename":"tableS1.NETRGs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5332874/v1/754be21342dc178c7c69fd44.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"NET-related genes predict prognosis and are correlated with the immune microenvironment in osteosarcoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOsteosarcoma is a malignant primary bone tumor that is prevalent in children and adolescents\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e and is most common in the distal femur, proximal tibia, and humeral metaphyseal locations\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. It is characterized by the malignant proliferation of prismatic mesenchymal stromal cells that directly produce osteoid or immature bone tissue\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. It is highly malignant with a high rate of early metastasis, and its high propensity for metastasis is a major cause of poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Simultaneous pulmonary metastases have been reported to occur in approximately 15\u0026ndash;20% of patients at the time of initial diagnosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Once metastasis occurs in patients with osteosarcoma, the prognosis is extremely poor. Moreover, treatment is of limited importance, with an overall 5-year survival rate of 20%-30%\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, which is much lower than that of patients without metastasis. The survival rate of osteosarcoma patients has not improved over the past 30 years, primarily due to the intractability of osteosarcoma metastases in patients\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Therefore, an in-depth study of the molecules involved in the invasion and metastasis of osteosarcoma cells is urgently needed.\u003c/p\u003e \u003cp\u003eNeutrophil extracellular traps (NETs), NET-like substances released by neutrophils during their immune action against pathogens that can capture and kill microorganisms, were discovered by Volker Brinkmann's research team in as early as 2004\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. NETs consist mainly of intracellular DNA, histones and granule proteins, such as myeloperoxidase and neutrophil elastase\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. They are closely associated with the onset and progression of diseases such as infections, sepsis, autoimmune disorders and diabetes\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In recent years, studies involving the interactive functions of neutrophils and the tumor microenvironment have revealed that NETs are involved in the entire invasion‒metastasis cascade of a variety of tumors\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In breast cancer, NET DNA can interact with CCDC25 on tumor cell membranes to activate the ILK-β-Parvin pathway and promote liver metastasis\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Amyloid-β produced by cancer-associated fibroblasts (CAFs) promotes the generation of NETs by facilitating the production of ROS in neutrophils. NETs promote the hepatic metastasis of pancreatic tumors by enhancing the migration of hepatic stellate cells\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In addition, NETs contain programmed cell death ligand 1 (PD-L1), which promotes tumor metastasis by binding to programmed cell death protein 1 (PD-1) on the surface of T cells to inhibit T-cell function, leading to T-cell dysfunction and metabolic failure\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. These findings provide new directions for understanding the mechanism of osteosarcoma metastasis and possible future treatments. Therefore, exploring the molecular mechanisms of NET-related genes in the development and metastasis of osteosarcoma is highly important for the early diagnosis and clinical treatment of osteosarcoma patients.\u003c/p\u003e \u003cp\u003eHere, we utilized bioinformatics approaches to explore the role of NET-related genes in osteosarcoma metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). First, we identified MAPK1, CFH, ATG7, and DDIT4 as independent prognostic factors in osteosarcoma patients via Cox regression analysis. In addition, a nomogram graph was established. Analysis of immune infiltration and drug sensitivity of key genes was performed for relevant genes. Finally, ATG7 was validated at the histological level based on the results of the validation group dataset analysis. These results suggest that ATG7 may be a reliable diagnostic and therapeutic target for patients with metastatic osteosarcoma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials.\u003c/h2\u003e \u003cp\u003eThe expression profile data of TARGET-OS in osteosarcoma patients were downloaded from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and a total of 84 samples were obtained after patient samples with no expression data or survival data were removed. The expression profiling dataset GSE21257 containing metastasis group data was downloaded 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) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Combined with the Gene Cards database\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e and published literature\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, 258 neutrophil extracellular traps-related genes (NETRGs) were obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Data collection and processing\u003c/h2\u003e \u003cp\u003eThe R software \u0026ldquo;limma\u0026rdquo; package was used for data correction to ensure the comparability of the data. The samples were divided into 2 groups (the unmetastases group and the metastases group), and the genes whose |logFC| was \u0026gt;\u0026thinsp;0 and \u003cem\u003eP\u003c/em\u003e value was \u0026lt;\u0026thinsp;0.05 were considered differentially expressed genes (DEGs). The intersections of the DEGs and NETRGs were then plotted as Venn diagrams and differential ordering plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 GSEA and GO analysis\u003c/h2\u003e \u003cp\u003eGSEA was performed via the \u0026ldquo;cluster Profiler\u0026rdquo; package and the \u0026ldquo;c2.cp.v7.2.symbols.gmt\u0026rdquo; gene set from the Molecular Signatures Database (MSigDB). The parameters were set as follows: the number of seeds was 2020, the number of calculations was 1000, the number of genes contained in each gene set was at least 10, and the maximum number of genes contained was 500. Subsequently, Gene Ontology (GO) functional enrichment analysis was carried out on 15 NETRDEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Construction and validation of the nomogram model\u003c/h2\u003e \u003cp\u003eUnivariate Cox analysis of NETRDEGs was performed using the \u0026ldquo;survival\u0026rdquo; package, and genes with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used as the key genes in our subsequent study. Patients were categorized into high- and low-risk groups based on the median risk score via multivariate regression analysis. A nomogram was constructed using the R software package \u0026ldquo;rms\u0026rdquo;, and a decision curve was constructed using the R package \u0026ldquo;gg DCA\u0026rdquo; to evaluate the accuracy of the prediction results.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{i}\\text{s}\\text{k}\\text{S}\\text{c}\\text{o}\\text{r}\\text{e}=\\sum\\:_{\\text{i}}\\text{C}\\text{o}\\text{e}\\text{f}\\text{f}\\text{i}\\text{c}\\text{i}\\text{e}\\text{n}\\text{t}\\left({\\text{g}\\text{e}\\text{n}\\text{e}}_{\\text{i}}\\right)\\text{*}\\text{m}\\text{R}\\text{N}\\text{A}\\text{E}\\text{x}\\text{p}\\text{r}\\text{e}\\text{s}\\text{s}\\text{i}\\text{o}\\text{n}\\left({\\text{g}\\text{e}\\text{n}\\text{e}}_{\\text{i}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this formula, the coefficient represents the risk factor, and mRNA expression represents the expression value of the gene.\u003c/p\u003e \u003cp\u003eThe functional similarity between key genes was calculated using the R package \u0026ldquo;GO Sem Sim\u0026rdquo;. The \u0026ldquo;p ROC\u0026rdquo; package was used to plot the ROC curves of the key genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Immune infiltration and drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eEnrichment scores for the level of infiltration of each immune cell type with other stromal cells were calculated using the R software packages \u0026ldquo;GSVA\u0026rdquo; and \u0026ldquo;MCP Counter\u0026rdquo;. The correlation between immune infiltrating cells was determined via Spearman's correlation analysis, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The Cell Miner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer/home.do\u003c/span\u003e\u003cspan address=\"https://discover.nci.nih.gov/cellminer/home.do\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was searched and based on the expression of the key genes with the drug data in the Cell Miner database. Drug sensitivity analysis of key genes was performed using the \u0026ldquo;pRRophetic\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Immunohistochemical analysis\u003c/h2\u003e \u003cp\u003eOsteosarcoma tissue microarrays (Changsha Yaxiang Biotechnologies, Changsha, China) were used for these experiments. The chips were subjected to a dewaxing process and antigen repair, followed by serum blocking to block nonspecific binding. For primary antibody incubation, the samples were rinsed three times with phosphate buffer for 3 min each. Then, the ATG7 antibody (OriGene Technologies, Wuxi, China) was diluted at a ratio of 1:1000, the primary antibody was added dropwise, and the samples were incubated overnight at 4\u0026deg;C in a refrigerator. For secondary antibody incubation, the samples were washed with phosphate buffer three times for 3 min each, horseradish peroxidase-labeled secondary antibody was added dropwise, the samples were incubated at room temperature for 2 h, and the samples were rinsed with phosphate buffer three times for 3 min each. DAB staining solution was added dropwise for coloring, and the color development was observed under a microscope. The samples were quickly washed after coloring. The samples were restained by incubating them with hematoxylin for 1min, followed by rinsing for 5 min and drying at room temperature before sealing them with a coverslip. The next day, images were obtained with a tissue microarray scanner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eR software version 4.1.2 was used for analysis, and R language-related packages (\u0026ldquo;limma,\u0026rdquo; \u0026ldquo;cluster Profiler,\u0026rdquo; \u0026ldquo;p ROC,\u0026rdquo; \u0026ldquo;rms,\u0026rdquo; \u0026ldquo;survival,\u0026rdquo; \u0026ldquo;GO Sem Sim,\u0026rdquo; \u0026ldquo;gg DCA,\u0026rdquo; \u0026ldquo;GSVA,\u0026rdquo; etc.) were used to process data. Differences in survival were analyzed via the Kaplan‒Meier method and are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered a statistically significant difference. The statistical significance is shown as follows: \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (\u003csup\u003e*\u003c/sup\u003e) and \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (\u003csup\u003e**\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Standardization of the dataset\u003c/h2\u003e \u003cp\u003eThe dataset GSE21257 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B) was standardized such that the trend of expression among different samples converged, and a box line plot was drawn for the distribution of data before and after standardization.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Screening results of NETRDEGs\u003c/h2\u003e \u003cp\u003eIn the dataset, 1068 genes satisfied the threshold of |logFC| \u0026gt; 0 and \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and 602 and 466 genes exhibited high and low expression in the metastasis group, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Taking the intersection of all DEGs and NETRGs, a total of 15 NETRDEGs were obtained, including IL1RL1, AZU1, NFIL3, DDIT4, ENO1, KRT10, ATG7, MAPK1, PIK3CG, DECR1, IL36G, CFH, SELL, SFTPD, and COLEC11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 GSEA and GO analysis\u003c/h2\u003e \u003cp\u003eTo analyze the biological functions of the 15 NETRDEGs, we first performed GO analysis of the NETRDEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D). These genes were found to be involved in autophagy in the nucleus, the regulation of the cellular response to hypoxia, the response to hypoxia, and the circadian rhythm in biological processes (BP). The CC terms were significantly associated with the secretory granule lumen, cytoplasmic vesicle lumen, vesicle lumen, and collagen trimer. The enriched MFs of the DE-FRGs were as follows: oligosaccharide binding, heparan sulfate proteoglycan binding, heparin binding, and proteoglycan binding. GSEA revealed that the DEGs were significantly enriched in the glycolysis pathway, autophagy pathway, IL7 pathway, Wnt signaling pathway, and PI3K/Akt signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-J).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Construction of a prognostic model and establishment of a nomogram\u003c/h2\u003e \u003cp\u003eTo obtain a prognostic model for NET-related genes, we screened for NETRGs via univariate Cox analysis in conjunction with survival outcomes and survival times and constructed a forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We then included these key genes in a multivariate Cox analysis to obtain the risk score value and grouped the samples of the dataset into high- and low-risk groups according to the median value of the risk score (cutoff value = -0.050965805) and found that the prognosis was worse in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D). The prognostic model can be expressed as follows: risk score=MAPK1*(-0.350932209)་CFH*(།0.540468911)་ATG7*(།0.765106538)་DDIT4*0.132203877. We then performed a nomogram analysis to determine the prognostic ability of the key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The nomogram yields a score for each item, and the total score and corresponding survival rate can be obtained after adding the scores of all the items. The results showed that the utility of the expression of the CFH gene in the model was significantly greater than that of the other genes. Moreover, the AUCs of the 1-, 3- and 5-year ROCs were 0.857, 0.779 and 0.689, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In addition, we performed 1-, 3-, and 5-year prognostic calibration analyses and plotted calibration curves for the prognostic model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-H). We found that the model predicted patient survival in general agreement with actual patient survival. We then used decision curve analysis to assess the magnitude of the clinical utility of the constructed models at 1, 3, and 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI-K), which revealed that the 5-year prognostic model had the best clinical utility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Prognostic analysis of key genes in the training group\u003c/h2\u003e \u003cp\u003eTo assess the relationships between the four key genes and prognosis, we plotted prognostic Kaplan‒Meier survival curves in the TARGET-OS dataset for each of the key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA‒D), which revealed that all four genes significantly correlated with survival: MAPK (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), CFH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), ATG7 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and DDIT4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043). In addition, gene correlation analysis was performed based on the complete expression matrix of key genes, and correlation heatmaps were drawn (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). The results revealed a positive correlation between the genes ATG7 and MAPK1 and between CFH and ATG7. We subsequently performed functional similarity analysis of the key genes and then visualized the results of the functional similarity analysis among the key genes via a box-and-line plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG), which revealed that ATG7 was the most similar gene to the other three genes in terms of function. Next, the ROC curves of the key genes were plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH-K). The ROC curves revealed that the differences in the expression of the CFH gene (AUC\u0026thinsp;=\u0026thinsp;0.711) in the dataset presented comparable accuracy across subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Analysis of immune cell infiltration\u003c/h2\u003e \u003cp\u003eTo explore immune cell infiltration, the correlation between the infiltration abundance of 28 immune cells was calculated via the ssGSEA algorithm. The results of the correlation heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) revealed a positive correlation between the infiltration abundance of immune cells that activated CD8\u0026thinsp;+\u0026thinsp;T cells and macrophages and between effector memory CD8\u0026thinsp;+\u0026thinsp;T cells and immature B cells, macrophages and MDSCs. Subsequently, we analyzed the relationships between the key genes and the infiltration abundance of 28 immune cells via the ssGSEA algorithm, and the key genes CFH, ATG7, and DDIT4 were correlated with 20 of these immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Among them, positive correlations were identified between CFH and immune cells, central memory CD4 T cells, natural killer cells, as well as between ATG7 and immune cells and killer cells. To ensure the accuracy of the above algorithm, we also calculated the correlation between key genes and immune cell infiltration abundance via the MCP Counter algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), which revealed that the key genes were related to 10 types of immune cells. Among them, positive correlations were identified between ATG7 and endothelial and monocyte lineage cells as well as between CFH and monocytic lineage cells; DDIT4 negatively correlated with NK cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eTo obtain small-molecule drugs that target key genes, we used data from the cancer drug database Cell Miner, including the mRNA expression profiles of key genes and drug activities. Using the pRRophetic algorithm, a ridge regression model was constructed based on the expression and gene expression profiles of the key genes in the TARGET-OS dataset, and the sensitivities of the key genes to common anticancer drugs were predicted by the IC50 values (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The results show that key genes can be found in the database Cell Miner for a variety of drugs with interaction relationships. Among them, ATG7, kinetin riboside, MAPK1, and CFH positively correlated with the small molecule sri1215. Negative correlations were identified between ATG7 and the small-molecule drug protein toxin c10-mwapprox.6700, between CFH and benzethonium chloride, and between MAPK1 and zimelidine hydrochloride.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Immunohistochemical analysis\u003c/h2\u003e \u003cp\u003eTo explore the expression of key genes in different sequencing datasets, the differences in the high- and low-expression key genes among different subgroups in the GEO dataset were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). ATG7 expression was significantly lower in the metastasis group than in the metastasis-free group in the GSE21257 dataset, which was consistent with the analysis of the TARGET dataset. This difference may be a common phenomenon in metastatic patients. However, the expression levels of MAPK1, CFH and DDIT4 did not significantly differ between groups in the GSE21257 dataset, but the trend was consistent with the results in the training set. Low expression of ATG7 is likely a common genetic variant in all patients with osteosarcoma metastases. To assess the potential of ATG7 as a biomarker and therapeutic target for osteosarcoma metastases, we analyzed the expression of ATG7 in tissue microarrays using immunohistochemistry. ATG7 was expressed at low levels at the histological level, which was consistent with the results of our bioinformatics analysis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDespite advances in the diagnosis and treatment of osteosarcoma, distant metastasis has become a bottleneck in improving the survival of osteosarcoma patients, which severely restricts their long-term survival\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In fact, metastasis is a multifactorial and multistep process in which tumor cells undergo three stages: acquisition of in situ invasive ability, escape from the immune surveillance system during the circulatory process, and colonization of the premetastatic microenvironment; then, the surviving tumor cells grow in distal organs far from the site of origin, resulting in multiorgan failure\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Recent studies have demonstrated that the extracellular trap network released by neutrophils during their physiological function is involved in the three stages of the metastatic process to varying degrees, including the establishment of the premetastatic microenvironment, epithelial-to-mesenchymal transition, the colonization of circulating tumor cells, and the growth of tumor cells in micrometastatic lesions\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. However, the role of NETs in the pathogenesis of OS remains poorly understood, prompting us to explore the possibility of using NET-related genes as OS biomarkers.\u003c/p\u003e \u003cp\u003eHere, we functionally analyzed osteosarcoma NET-related genes via bioinformatics methods. A new prognostic risk model associated with osteosarcoma NETs was also identified, and the correlations of the associated genes with the immune microenvironment and small-molecule drugs were also analyzed. In this study, we initially identified 1068 osteosarcoma metastasis-associated genes and 258 NET-associated genes, resulting in a crossover gene set of 15 genes. We subsequently screened the genes in the crossover gene set via univariate Cox regression to identify four prognosis-associated genes, among which the expression of MAPK1, CFH, and ATG7 was downregulated in the metastasis group, whereas the expression of DDIT4 was upregulated in this group. These changes were significantly correlated with the prognosis of OS patients. Notably, via Cox regression analysis, we constructed a prognostic model consisting of four genes, namely, MAPK1, CFH, ATG7 and DDIT4: with risk score\u0026thinsp;=\u0026thinsp;MAPK1 \u0026times; (-0.350932209)\u0026thinsp;+\u0026thinsp;CFH \u0026times; (-0.540468911)\u0026thinsp;+\u0026thinsp;ATG7 \u0026times; (-0.765106538)\u0026thinsp;+\u0026thinsp;DDIT4 \u0026times; 0.132203877. Patients with higher risk scores were found to have a worse prognosis. ROC analysis revealed that this model had good 1-, 3- and 5-year survival AUCs, which indicates that this model is reliable in predicting OS prognosis. In addition, the combination of data on the mRNA expression profiles of key genes and drug activity revealed that drug small molecules, such as protein toxin c 10 - mw approx. 6700, may serve as drugs to target corresponding to key genes.\u003c/p\u003e \u003cp\u003eRecent developments in the field of immunotherapy have facilitated in-depth studies of the osteosarcoma tumor microenvironment, where immune cells within the TME play a key role in osteosarcoma genesis and influence the therapeutic response and clinical outcomes\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Previous studies have demonstrated that many neutrophils in the tumor microenvironment are affected by CXCR1- and CXCR2-activating ligands produced by tumor cells, which induce the production of NETs to shield immune cells (CD8\u0026thinsp;+\u0026thinsp;T cells and NK cells) from exposure to tumor cells, thereby preventing tumor cells from being killed by immune cells and facilitating tumor metastasis\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Therefore, to further clarify the driving role of immune cells in osteosarcoma metastasis, we explored the infiltration of NET-related genes by various immune cells. We found that these four genes were significantly associated with the infiltration of 20 types of immune cells, including T cells and NK cells\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. These findings confirm that key genes play important roles in tumor immunity and provide new ideas for osteosarcoma immunotherapy.\u003c/p\u003e \u003cp\u003eMAPK1, also known as extracellular signal-regulated kinase (ERK2), is an important component of the MAP kinase signal transduction pathway. It plays an important role in regulating cell proliferation, differentiation, apoptosis, migration and other activities\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that aberrant activation of ERK2 in the MAPK pathway is an important cause of a variety of cancers, such as oral cancer\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e and hepatocellular carcinoma\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, in which the hyperactivation of ERK2 can be detected. In addition, a study revealed that this protein, which is a moonlighting protein, also has a transcriptional repressive effect independent of kinase activity. Specifically, IFNγ signaling leads to ERK overactivation in melanoma cells, followed by the generation of an overstress response that leads to cell death. Moreover, the overexpression of either ERK1 or ERK2 leads to cell death in human melanoma cell lines\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. In our study, MAPK1 was expressed at low levels in the training set, but this difference was not significant in the validation set GSE21257. We speculate that the reason for this difference may be related to differences in the site of metastasis and the heterogeneity of the tumor, resulting in different molecular biological alterations; however, this hypothesis needs to be verified in larger studies.\u003c/p\u003e \u003cp\u003eThe relationship between cancer and autophagy is complex and is characterized by the fact that the pro- and anticancer properties of autophagy are mutually transformative under specific circumstances\u003csup\u003e[\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. As an important autophagy effector enzyme, ATG7 can regulate immunity, cell death, and protein secretion together with other autophagy-associated proteins and independently regulate the cell cycle and apoptosis\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. ATG7 multifunctionality is reportedly associated with oncogenic or pro-oncogenic properties in different tumors. Studies have reported that ATG7 deficiency in mice leads to hepatocellular carcinoma by activating the Yap metabolic pathway\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. In another study, elevated ATG7 expression was associated with bladder cancer\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e and lung cancer\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, and high levels of ATG7 expression were associated with poor prognosis in breast cancer patients\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Other studies have shown that whether ATG7 promotes or suppresses tumors also seems to depend on the status of the tumor suppressor P53\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Our findings suggest that ATG7 may suppress metastasis, and its association with the status of P53 has not been reported in the field of osteosarcoma and warrants further investigation. Although the complex link between ATG7 and osteosarcoma remains puzzling, alterations in autophagy are increasingly associated with tumors, and targeting and regulating ATG7 may constitute a promising therapeutic approach.\u003c/p\u003e \u003cp\u003eAs a recently discovered innate immune checkpoint\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, it mediated immunosuppression enhances the ability of tumor cells to avoid immune recognition and generate an immunosuppressive tumor microenvironment to evade the complement system against tumor cells\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. However, some studies have reported different results: CFH can exert anticancer effects on specific types of cancers by inhibiting cancer-related inflammation\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e, and CFH can exert antimetastatic effects by inhibiting excessive angiogenesis in tumor tissues\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. In the present study, we found that CFH expression was lower in the metastatic group than in the nonmetastatic group, which may have a role in inhibiting OS metastasis rather than promoting it.\u003c/p\u003e \u003cp\u003eDNA damage-inducible transcript 4 (DDIT4) is a tumor-associated protein that is highly expressed under stress conditions, such as chemotherapy, heat shock, energy depletion, hypoxia and DNA damage. It is involved not only in tumor survival, antitumor resistance and antiapoptotic processes but also in tumor metastatic behaviors, such as proliferation and invasion\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Recent analyses of DDIT4 in several cancer types have shown that high expression of this gene is associated with poor prognosis in several hematological and solid tumors, such as acute myeloid leukemia\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e, breast cancer\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e and lung cancer\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. In terms of mechanism, DDIT4 is involved in the mTORC1, p53, HIF, autophagy and oxygen sensing signaling pathways through intermolecular interactions with multiple pathway proteins. It is directly involved in the activation of several important pathways and has a driving role in tumor progression and metastasis\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. This finding is consistent with our findings and can be used as a new therapeutic strategy to provide a research basis.\u003c/p\u003e \u003cp\u003eTo date, this is the first study of NET-related genes and OS metastasis. After being stimulated by cytokines (LPS, PMA, IL-8, C5a, etc.) produced by the primary tumor, neutrophils form a network structure consisting of DNA, histones and granule proteins, such as myeloperoxidase and neutrophil elastase, which are involved in enhancing local invasion of the tumor, increasing vascular permeability, facilitating immune escape and colonization, and promoting tumor metastasis. This particular mechanism may lead to new ideas for the treatment of OS. However, our study is subject to several shortcomings. First, the data used in our study were not our own but were obtained from public databases, and whether the sequencing data in the databases can reflect the genetic alterations in all patients remains to be demonstrated. Second, due to the lack of clinical samples from osteosarcoma patients, the key genes could not be quantitatively analyzed by RT‒qPCR and WB experiments. Third, the specific mechanisms of these DEGs with respect to the OS immune microenvironment and drug-related small molecules have not been further investigated. More prospective studies are needed if the value of NETs in OS metastasis is to be further confirmed.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, we developed a prognostic model based on four NETRDEGs, namely, MAPK1, CFH, ATG7 and DDIT4. ROC curves and nomogram plots were used to assess the accuracy of the model, which demonstrated that our prognostic model could reliably predict OS outcome. In addition, our study revealed that NETRDEGs can affect immune cells in the TME and further influence the development of OS, which provides new clues for exploring immunotherapeutic approaches for OS patients.\u003c/p\u003e \u003cp\u003eThese findings may lead to new therapeutic targets for the diagnosis and treatment of metastasis in OS patients, and more relevant studies are needed to further validate the link between NETs and osteosarcoma metastases. This study provides a basis for exploring the molecular mechanisms, diagnosis and treatment of osteosarcoma metastases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOsteosarcoma dataset information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTARGET-OS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE21257\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTARGET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL10295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHomo sapiens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHomo sapiens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsteosarcoma tumor tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOsteosarcoma tumor tissues\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples in Unmetastases group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples in metastases group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOS: osteosarcoma. GEO: Gene Expression Omnibus.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil extracellular traps\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eosteosarcoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNETRGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil extracellular traps related genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNETRDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil extracellular traps related differentially expressed genes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Key Program of Ningxia Hui Autonomous Region Natural 353 Science Foundation of China, grant number (No. 2024A1398).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll basic data can be found in articles, supplementary documents or designated websites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll studies\u0026rsquo; procedures have been approved by China Ethics Committee and performed in accordance with the ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiandang Shi was involved in the conception and design of this study. Dawei Chu and Rui Huang collected the data and performed the bioinformatics analyses. Rui Huang and Dawei Chu prepared the figures and interpreted the data. Ruiqing Xu and Daihao Wei drafted the manuscript. Jiandang Shi and Dawei Chu revised the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the UCSC Xena website (https://xena.ucsc.edu/) and GEO website (https://www.ncbi.nlm.nih.gov/geo/) for providing the sequencing data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePanez-Toro I, Mu\u0026ntilde;oz-Garc\u0026iacute;a J, Vargas-Franco JW, et al. Advances in Osteosarcoma. Curr Osteoporos Rep. 2023;21(4):330\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown HK, Schiavone K, Gouin F, Heymann MF, Heymann D. 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Front Oncol. 2018;8:106.\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, Neutrophil extracellular traps, Key genes, Therapeutic targets, ATG7, Metastases","lastPublishedDoi":"10.21203/rs.3.rs-5332874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5332874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteosarcoma is the most common primary bone tumor. It has a high rate of early metastasis, and its treatment is one of the most challenging topics in the bone tumor field. Recent studies have shown that neutrophil extracellular traps play an important role in tumor metastasis and may provide new horizons for exploring metastasis in osteosarcoma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOS data were downloaded from the TARGET database and Gene Expression Omnibus datasets. Univariate Cox regression was conducted to assess NETRGs. Patients were subsequently categorized into high- and low-risk groups on the basis of risk score values derived from multivariate Cox analysis, and prognostic models were established. The immune infiltration of relevant genes and drug sensitivity of key genes were also analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 15 NET-related genes associated with osteosarcoma metastases were identified. Among them, a total of 4 genes were related to prognosis, namely, MAPK1, CFH, ATG7 and DDIT4, and a prognostic model based on these 4 genes was established. The prognosis was worse in the high-risk group, whose areas under the ROC curves (AUCs) were 0.857, 0.779, and 0.689 at 1, 3, and 5 years, respectively. The key genes were subsequently found to be associated with the infiltration of 20 types of immune cells. Finally, the small-molecule drug toxin c 10, an approximately 6700 mw protein, may target key genes. Finally, ATG7 was validated at the histological level by combining the results of the validation group dataset analysis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA risk model based on 4 NETRDEGs is a reliable prognostic predictor for OS patients, and ATG7 may serve as a new diagnostic and therapeutic target.\u003c/p\u003e","manuscriptTitle":"NET-related genes predict prognosis and are correlated with the immune microenvironment in osteosarcoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 09:21:19","doi":"10.21203/rs.3.rs-5332874/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":"c5909325-9df1-49b2-ba1f-0d52c3a54381","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-08T11:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 09:21:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5332874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5332874","identity":"rs-5332874","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}