A novel nomogram predicting short-term overall survival of patients with glioma

A novel nomogram predicting short-term overall survival of patients with glioma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A novel nomogram predicting short-term overall survival of patients with glioma Xing-jie Yang, Jian-hua Xi, Nai-ying Sun, Jin Bao, Qiang Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3892766/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 Gliomas are the most common malignant tumors in the central nervous system. This study aimed to create a tumor survival prediction model to predict short-term overall survival in patients with glioma. In this study, the mRNAseq_325 dataset was downloaded from the Chinese Glioma Genome Atlas database as the training group, and the mRNAseq_693 dataset was downloaded as the validation group. LASSO‐COX algorithm was applied to shrink predictive factor size and build a risk score. The calibration curves and C‐Index were assessed to evaluate the nomogram's performance. This study found that the risk score, built by the LASSO‐COX algorithm, was significantly associated with overall survival in gliomas, and the nomogram, combining the risk score and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups. In conclusion, an individualized prediction model was established for predicting the short-term overall survival of glioma patients, which can provide valuable insights into identifying individuals at high risk and highlight the potential in facilitating early interventions and accurate treatment for patients with limited survival prognosis. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Cancer/Cns cancer Glioma Prognosis Nomogram Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The annual incidence rate of gliomas is 3~6.4/100,000, accounting for nearly 80% of all malignant tumors in the central nervous system 1,2 . Glial tumors in the brain were first described by Rudolph Virchow in 1865 3 . Gliomas are tumors that arise from glial or precursor cells and include astrocytoma (including glioblastoma), oligodendroglioma, ependymoma, oligoastrocytoma, malignant glioma, not otherwise specified, and a few rare histologies 4 . Among them, glioblastoma, as the highest-grade (WHO IV) malignant tumor, exhibits strong invasiveness and low survival rates 5,6 . Poor prognosis gliomas present unique challenges for clinicians and patients in terms of tumor monitoring. The traditional therapeutic approach for gliomas includes surgical resection, followed by radiation and chemotherapy, as part of a multimodal treatment strategy 7,8 . With the continuous progress in research, multiple new diagnostic and treatment strategies have been proposed for the diagnosis and treatment of gliomas, such as colorimetric techniques, electrochemical biosensors, optical coherence tomography, reflectometric interference spectroscopy, gene circuits, Fcgamma chimeric receptor T cells immunotherapy 9 . However, it cannot be denied that limited progress has been made in improving the treatment and prognosis of gliomas, despite conducting extensive research 10 . Prognostic models are being developed to predict the survival of glioma patients. However, accurately forecasting the prognosis of individual patients remains challenging, despite the increasing number of prognostic models available 11 . Nomograms, presenting the results of predictive models in a printed format, could be widely used in clinical practice 12 . The nomograms have significant clinical significance as they provide a visual and personalized tool for predicting prognosis and making treatment decisions. They allow for the integration of multiple variables, provide a more accurate estimate of individual patient outcomes compared to traditional prognostic models, and empower clinicians in risk assessment, patient counseling, and treatment planning, ultimately improving patient care and outcomes. To our knowledge, there are rarely specific nomograms established focusing on early death in glioma. The objective of this study was to explore the biological features of early-death gliomas patients and create a prognostic model that incorporates clinical risk factors to accurately predict the survival rate at different time points, providing a treatment-guidance tool for individualized postoperative care. Methods Patient selection We obtained transcriptome sequencing data from the Chinese Glioma Genome Atlas (CGGA) databases ( https://www.cgga.org.cn ) 13 . Extracted data included: primary/recurrent/secondary (PRS) status, WHO grade, gender, age, radiotherapy status, chemotherapy status, IDH mutation status, 1p/19q codeletion status, O 6 -methylguanine-DNA-methyltransferase gene promoter (MGMTp) methylation status, follow-up time and survival. The primary endpoint of the study was overall survival (OS), defined as the time from diagnosis to death from glioma or time to last follow-up. LASSO-COX dimension reduction analysis A total of 161 patients, including those who had a survival time of less than 360 days and had died, as well as those who had a survival time of more than 1800 days and were still alive, were selected from the training group. These patients were used for LASSO-COX dimension reduction analysis. The analysis was performed by glmnet and survival packages in R. The λ value corresponding to the minimum partial likelihood deviance was selected as the optimal λ in our study. Nomogram construction A nomogram analysis was performed using the rms package in R to predict the 1-, 3-, and 5-year survival and recurrent rate of glioma patients. The analysis consisted of a scoring system in the upper part and a prediction system in the lower part. The total points, which were calculated by summing the points of each factor, provided an accurate prediction of the outcomes. The accuracy of the survival prediction was assessed in a validation group by using calibration curves and C-Index values. Statistical analysis Statistical analyses were executed using R ( https://www.r-project.org/ , v4.2.3), SPSS software (IBM, v19.0, Chicago, IL), and GraphPad Prism (v8.0, La Jolla, CA). For comparison of count data between groups, chi-square or Fisher's exact test is used. Continuous variables were compared using the t-test or the one-way ANOVA test. In the training group, univariate and multivariate analyses were performed by Cox proportional hazards regression models to identify independent prognostic factors associated with glioma. For all statistical methods, p < 0.05 was considered as significant difference. Results Patient characteristics This resulted in a total of 325 samples (mRNAseq_325) and 693 samples (mRNAseq_693), respectively (Fig. 1 ). After excluding samples without survival data, the final datasets comprised 313 samples in the training group and 657 samples in the validation group, respectively. Clinical information of patients was summarized in Table 1 . Table 1 Clinical information of patients. Characteristics Training group (n = 313) Validation group (n = 657) Age (years) Mean 42.2 43.4 Standard Deviation 11.98 12.52 PRS status Primary 222 404 Recurrent/Secondary 87 253 Not Available 4 0 WHO Grade WHO II 98 172 WHO III 74 248 WHO IV 137 237 Not Available 4 0 Gender Male 197 374 Female 116 283 Radiotherapy status No 62 131 Yes 241 501 Not Available 10 25 Chemotherapy status No 110 156 Yes 190 480 Not Available 13 21 IDH mutation status Wildtype 145 276 Mutant 167 333 Not Available 1 48 1p/19q Codeletion Status Non-Codeletion 243 454 Codeletion 62 137 Not Available 8 66 MGMTp Methylation Status Un-Methylated 143 218 Methylated 152 304 Not Available 18 135 PRS: primary/recurrent/secondary; IDH: isocitrate dehydrogenase; MGMTp: O 6 -methylguanine-DNA-methyltransferase gene promoter. Candidate genes through LASSO-COX dimension reduction analysis A risk score was constructed by LASSO-COX dimension reduction analysis (Fig. 2 ). Finally, 8 candidate genes and corresponding lambda values (EN1: 0.038344874, HOXD13: 0.012374376, HOXD9: 0.016517464, IGF2BP3: 0.022116275, IGFBP2: 0.00000127, NNMT: 0.000523001, PRLHR: -0.030555542, and RP1-293L6.1: -0.278458515) were obtained based on OS of glioma patients. The risk score of each patient was calculated as follows: Risk score = expr EN1 × λ EN1 + expr HOXD13 × λ HOXD13 + expr HOXD9 × λ HOXD9 + expr IGF2BP3 × λ IGF2BP3 + expr IGFBP2 × λ IGFBP2 + expr NNMT × λ NNMT + expr PRLHR × λ PRLHR + expr RP1−293L6.1 × λ RP1−293L6.1 . where expr gene was the expression level of the gene and λ gene was the corresponding lambda value. Relationship between risk score and clinical-pathologic characteristics the prognosis of patients with glioma Patients in different risk score groups showed distinct patterns of clinical-pathologic characteristics. In the training group, tumor PRS status, WHO Grade, age, radiotherapy status, chemotherapy status, IDH mutation status, 1p/19q codeletion status and MGMTp methylation status showed asymmetric distributions in different risk score groups (Fig. 3 a,c-j). In the validation group, tumor PRS status, WHO Grade, gender, age, chemotherapy status, IDH mutation status, 1p/19q codeletion status and MGMTp methylation status showed distinct patterns in different risk score groups (Fig. 3 b,k-r). In the training and validation database, there were no significant correlations between the risk score and gender or radiotherapy status, respectively ( p > 0.05). The risk score showed superior predictive values for OS in both training and validation databases (Fig. 3 s,u). In addition, the receiver operating characteristic (ROC) analysis revealed that the area under the curve (AUC) was 0.834 for the training group and 0.768 for the validation group (Fig. 3 t,v). The individualized prediction model showed robust predictive accuracy An individualized prediction model was developed to facilitate the clinical application of the prognostic prediction model. The model was constructed based on several independent predictive factors (Figure S1 ), including risk score, PRS status, WHO grade, age, chemotherapy status, and 1p/19q codeletion status. With this individualized prediction model, the survival rates of glioma patients at 1-, 3-, and 5-years can be estimated accurately (Fig. 4 a). The C-index values of the nomogram in the training and validation groups were 0.803 (95% CI 0.775 ~ 0.830) and 0.795 (95% CI 0.773 ~ 0.817), respectively, indicating good prediction accuracy. Additionally, calibration curve analysis demonstrated that the predicted survival rates from the nomogram corresponded well with the actual survival rates, indicating superior predictive performance (Fig. 4 b). Discussion Given the high malignancy of glioma, it is crucial for clinicians to make precise and individualized prognostic assessments based on a comprehensive set of clinicopathological features. Traditional staging systems, such as the WHO grade, tend to incorporate only a limited number of pathological attributes, but overlook other critical clinical and treatment-related data, which in turn restricts their predictive power. Therefore, there exists an imperative need to develop a prediction system that can more accurately forecast the prognosis of glioma patients by integrating these diverse factors. A nomogram serves as a graphical statistical tool that facilitates the calculation of probable outcomes through the incorporation of multiple variables into a single model. It has gained widespread acceptance due to its ability to holistically consider a range of elements including both clinical pathology and patient demographics characteristics 14 . This is the driving force behind our focus on constructing a nomogram specifically designed for predicting OS in glioma patients. By leveraging the advantages of the CGGA database, we developed a prediction model based on patients with shorter survival time (less than 360 days) and longer survival time (greater than 1800 days). The LASSO-COX dimension reduction analysis was utilized to select an optimum prognostic signature comprising the most representative gene markers for identifying the 8-gene signature in short-term survival glioma patients. Subsequently, a risk score based nomogram was devised to forecast the probability of early mortality in individuals diagnosed with glioma. The nomogram, incorporating the risk score, PRS status, WHO grade, age, chemotherapy status and 1p/19q codeletion status, effectively predicts patients at a high risk of early death. According to the nomogram, the risk score is the most significant factor affecting patient prognosis. The risk score incorporates 8 genes, including EN1, HOXD13, HOXD9, IGF2BP3, IGFBP2, NNMT, PRLHR, and RP1-293L6.1, which have been proven to impact the prognosis of glioma patients 15 – 22 . The second important factor is age. Multiple studies have emphasized that age serves as an independent and critical prognostic factor in glioma; Even when the histopathological diagnosis is identical, differing patient ages can significantly impact glioma prognosis. Thus, it is proposed that individualized treatment modalities be based on meticulous age considerations 23 . The WHO grade for gliomas is a critically important classification, where grade IV (such as glioblastoma) have the poorest prognosis, and grade II tumors generally fare better than grade III 24 , 25 . The WHO grade classification is currently one of the most significant means for predicting the prognosis in glioma patients. The likelihood of recurrence of malignant tumors will have a significant impact on the prognosis of patients. Due to the infiltrative nature of glioma, recurrence is typically inevitable for the tumor, particularly with glioblastoma, where the rate of recurrence approximates nearly 100% 26–28 . This invasiveness plays a critical role in tumor progression 16 . Codeletion of 1p/19q has also been demonstrated to be a prognostic marker, patients harboring 1p/19q codeletion exhibit notably longer survival compared to those without the alteration, furthermore, the demonstration of the 1p/19q status as the main factor associated with sensitivity to PCV (procarbazine, lomustine, and vincristine) chemotherapy has gained increased clinical significance 29 , 30 . Chemotherapy plays a significant role in glioma prognosis, aligning with prior research findings 31 . This study has some limitations. Firstly, both the training group and the validation group included only Chinese patients. It is necessary to validate using data from other countries or regions. Secondly, the included data did not have IDH mutant status, while IDH mutation has been proven to be an important factor affecting the prognosis of glioma patients 32 . In addition, some latest treatment methods were not included. With the continuous progress in glioma research, these new methods and technologies may have impact on the prognosis of glioma patients. Lastly, the calculation of risk score will increase the economic burden on patients, so it needs to be replaced with more cost-effective methods. In conclusion, this study provides clinicians with a practical tool specifically designed for assessing survival rates in glioma patients. The tool is designed to be concise and easy to use, suitable for various clinical scenarios. By integrating multiple influencing factors, the tool can generate highly accurate predictions of survival probabilities, thereby enhancing overall management and treatment outcomes for glioma patients. Declarations Ethics approval and consent to participate. Approval was waived by the local ethics committee, as CGGA data is publicly available and de-identified. Data availability The data that support the findings of this study are openly available in Chinese Glioma Genome Atlas (http://www.cgga.org.cn/). Acknowledgements We acknowledged the contributions of the Chinese Glioma Genome Atlas (CGGA) for creating and updating and Dr. Huisheng Liu for his guidance in bioinformatics analysis. Author contributions This study was designed by X.J.Y. and Q.M. Data extraction, statistical analysis and interpretation of data were performed by X.J.Y., J.H.X., N.Y.S., and J.B. All authors were involved in drafting and revision of the manuscript and approved the final version to be published. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to M.Q. References Zhao, S., Zhang, M., Zhang, Q., Wu, J. & Dai, H. Anlotinib alone or in combination with bevacizumab in the treatment of recurrent high-grade glioma: a prospective single-arm, open-label phase II trial. BMC Cancer 24, 6. https://doi.org/10.1186/s12885-023-11776-4 (2024). Li, R. et al . DTX2 promotes glioma development via regulation of HLTF. Biol. Direct 19, 2. https://doi.org/10.1186/s13062-023-00447-w (2024). Donabedian, P. et al . Glioblastoma with a primitive neuroectodermal component: two cases with implications for glioblastoma cell-of-origin. Clin. Imag. 73, 139–145. https://doi.org/10.1016/j.clinimag.2020.10.041 (2021). Ostrom, Q. T. et al . CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009–2013. Neuro-Oncology 18, v1-v75. https://doi.org/10.1093/neuonc/now207 (2016). Farooq, M. et al . A systematic review of nanomedicine in glioblastoma treatment: clinical efficacy, safety, and future directions. Brain Sci. 13, 1727. https://doi.org/10.3390/brainsci13121727 (2023). Dai, W. et al . Overcoming therapeutic resistance in oncolytic herpes virotherapy by targeting IGF2BP3-induced NETosis in malignant glioma. Nat. Commun. 15, 131. https://doi.org/10.1038/s41467-023-44576-2 (2024). Toader, C. et al . Low-grade gliomas: histological subtypes, molecular mechanisms, and treatment strategies. Brain Sci. 13, 1700. https://doi.org/10.3390/brainsci13121700 (2023). Yang, Y. C. et al . ROS regulation in gliomas: implications for treatment strategies. Front. Immunol. 14, 1259797. https://doi.org/10.3389/fimmu.2023.1259797 (2023). Thenuwara, G., Curtin, J. & Tian, F. Advances in diagnostic tools and therapeutic approaches for gliomas: A comprehensive review. Sensors 23, 9842. https://doi.org/10.3390/s23249842 (2023). Wang, G., Wang, J., Niu, C., Zhao, Y. & Wu, P. Neutrophils: New Critical Regulators of Glioma. Front. Immunol. 13, 927233. https://doi.org/10.3389/fimmu.2022.927233 (2022). Tewarie, I. A. et al . Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential. Neurosurg. Rev. 44, 2047–2057. https://doi.org/10.1007/s10143-020-01430-z (2021). Li, G. et al . A novel DNA repair-related nomogram predicts survival in low-grade gliomas. CNS Neurosci. Ther. 27, 186–195. https://doi.org/10.1111/cns.13464 (2021). Zhao, Z. et al . Chinese Glioma Genome Atlas (CGGA): a comprehensive resource with functional genomic data from Chinese glioma patients. Genom. Proteom. Bioinform. 19, 1–12. https://doi.org/10.1016/j.gpb.2020.10.005 (2021). Zhuang, Y., Yu, H., Chen, C. J., Jian, J. L. & Wu, X. Y. Establishment and validation of a prognostic nomogram for patients with colorectal neuroendocrine carcinoma. Asian J. Surg. 47, 433–442. https://doi.org/10.1016/j.asjsur.2023.09.099 (2024). Chang, J. et al . EN1 regulates cell growth and proliferation in human glioma cells via Hedgehog signaling. Int. J. Mol. Sci. 23, 1123. https://doi.org/10.3390/ijms23031123 (2022). Zhang, J. et al . A novel miR-7156-3p-HOXD13 axis modulates glioma progression by regulating tumor cell stemness. Int. J. Biol. Sci. 16, 3200–3209. https://doi.org/10.7150/ijbs.51293 (2020). Tabuse, M. et al . Functional analysis of HOXD9 in human gliomas and glioma cancer stem cells. Mol. Cancer 10, 1–14. https://doi.org/10.1186/1476-4598-10-60 (2011). Zheng, X. et al . N 6 -methyladenosine reader IGF2BP3 as a prognostic Biomarker contribute to malignant progression of glioma. Transl. Cancer Res. 12, 992–1005. https://doi.org/10.21037/tcr-23-449 (2023). Luo, Q. et al . IGFBP2 from a novel copper metabolism-associated biomarker promoted glioma progression and response to immunotherapy. Front. Immunol. 14, 1282734. https://doi.org/10.3389/fimmu.2023.1282734 (2023). Sun, W. et al . Overexpression of NNMT in glioma aggravates tumor cell progression: an emerging therapeutic target. Cancers 14, 3538. https://doi.org/10.3390/cancers14143538 (2022). Liu, Y., Xiang, J., Peng, G. & Shen, C. PRLHR immune genes associated with tumor mutation burden can be used as prognostic markers in patients with gliomas. Front. Oncol. 12, 620190. https://doi.org/10.3389/fonc.2022.620190 (2022). Feng, Q., Qian, C. & Fan, S. A hypoxia-related long non-coding RNAs signature associated with prognosis in lower-grade glioma. Front. Oncol. 11, 771512. https://doi.org/10.3389/fonc.2021.771512 (2021). Jia, Z. et al . Exploring the relationship between age and prognosis in glioma: Rethinking current age stratification. BMC Neurol. 22, 350. https://doi.org/10.1186/s12883-022-02879-9 (2022). Wrensch, M., Wiencke, J., Molinaro, A. & Taylor, J. Genetic and molecular epidemiology of adult diffuse glioma. Nat. Rev. Neurol. 15, 405–417. https://doi.org/10.1038/s41582-019-0220-2 (2019). Gittleman, H., Sloan, A. E. & Barnholtz-Sloan, J. S. An independently validated survival nomogram for lower-grade glioma. Neuro-Oncology 22, 665–674. https://doi.org/10.1093/neuonc/noz191 (2020). Wei, R. et al . Online calculator to predict early mortality in patient with surgically treated recurrent lower-grade glioma. BMC Cancer 22, 114. https://doi.org/10.1186/s12885-022-09225-9 (2022). Zhang, L., Jiang, Y., Zhang, G. & Wei, S. The diversity and dynamics of tumor-associated macrophages in recurrent glioblastoma. Front. Immunol. 14, 1238233. https://doi.org/10.3389/fimmu.2023.1238233 (2023). Liau, L. M. et al . Association of autologous tumor lysate-loaded dendritic cell vaccination with extension of survival among patients with newly diagnosed and recurrent glioblastoma: a phase 3 prospective externally controlled cohort trial. JAMA Oncol. 9, 112–121. https://doi.org/10.1001/jamaoncol.2022.5370 (2023). Lv, L. et al . Effects of 1p/19q codeletion on immune phenotype in low grade glioma. Front. Cell. Neurosci. 15, 704344. https://doi.org/10.3389/fncel.2021.704344 (2021). Van Den Bent, M. Thirty years of progress in the management of low-grade gliomas. Rev. Neurol. 179, 425–429. https://doi.org/10.1016/j.neurol.2023.03.001 (2023). Van Den Bent, M. J. et al . Adjuvant and concurrent temozolomide for 1p/19q non-co-deleted anaplastic glioma (CATNON; EORTC study 26053 – 22054): second interim analysis of a randomised, open-label, phase 3 study. Lancet Oncol. 22, 813–823. https://doi.org/10.1016/S1470-2045(21)00090-5 (2021). Barthel, F. P. et al . Longitudinal molecular trajectories of diffuse glioma in adults. Nature 576, 112–120. https://doi.org/10.1038/s41586-019-1775-1 (2019). Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf 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-3892766","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":271053140,"identity":"6c4db864-3845-43da-a19e-bc13cd0a9a65","order_by":0,"name":"Xing-jie Yang","email":"","orcid":"","institution":"Sunshine Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xing-jie","middleName":"","lastName":"Yang","suffix":""},{"id":271053141,"identity":"f8f94984-2357-4e21-9a0e-b5147ef034f3","order_by":1,"name":"Jian-hua Xi","email":"","orcid":"","institution":"Sunshine Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian-hua","middleName":"","lastName":"Xi","suffix":""},{"id":271053142,"identity":"4d27a779-88d0-493f-aef1-fab9094c4763","order_by":2,"name":"Nai-ying Sun","email":"","orcid":"","institution":"Sunshine Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nai-ying","middleName":"","lastName":"Sun","suffix":""},{"id":271053143,"identity":"25084c2d-9863-45b1-bac2-5ea938f1263b","order_by":3,"name":"Jin Bao","email":"","orcid":"","institution":"Fangzi People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Bao","suffix":""},{"id":271053144,"identity":"7164d70e-eba5-487f-828c-c97c0951d5c0","order_by":4,"name":"Qiang Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACfvnDBwwS/0nIsbE3EKlFcgZbQsEDNhtjPp4DRGoxuMGj8PEBW1riPIkEYrXc7mHckMBzmLFN8vHGGww1NtGEHXbn7GGDBInDzGzSacUWDMfSchsIaeE7kJdmkGBwmI1NOsdMgrHhMGEtDAdyzH8kJBzmYZM8Q6QWgRs5BgYJB9Ik2CR4iNQi2XMswSCxwcaAjQfolwRi/MLP3nzA8GeDRP389sMbb3yosSHCL0jAgOioQdJCqo5RMApGwSgYGQAABuJAcZs7ImMAAAAASUVORK5CYII=","orcid":"","institution":"Sunshine Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-01-24 03:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3892766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3892766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50814149,"identity":"d205b95d-04f8-4281-85b3-4ce6436f72df","added_by":"auto","created_at":"2024-02-07 19:36:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58930,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient selection.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3892766/v1/434d5982466f833dcbc8e7a9.png"},{"id":50814154,"identity":"5c379844-eaf3-48cc-9537-9c6b309c1081","added_by":"auto","created_at":"2024-02-07 19:36:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73530,"visible":true,"origin":"","legend":"\u003cp\u003eScreening the most representative 8 genes by LASSO-COX analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3892766/v1/59fd4fa006cc35cdcd7cf724.png"},{"id":50814153,"identity":"34446400-9c7d-4723-8351-edfb8f1af20d","added_by":"auto","created_at":"2024-02-07 19:36:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1072376,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between risk score and clinical-pathologic characteristics and survival in glioma patients. (a,b) The heatmap shows the clinical-pathologic factors of each glioma in the training and validation groups, sorted by ascending order of risk scores. (c-r) The boxplots show the distribution of risk scores among different clinical-pathologic characteristics of gliomas in the training and validation groups. (s,t) In the training group, patients were divided into high-risk and low-risk groups based on the median risk score, and Kaplan-Meier curves were plotted to show that patients in the high-risk group had shorter overall survival compared to those in the low-risk group. (u,v) In the validation group, patients were divided into high-risk and low-risk groups based on the median risk score, and Kaplan-Meier curves were plotted to show that patients in the high-risk group had shorter overall survival compared to those in the low-risk group. (PRS: primary/recurrent/secondary; IDH: isocitrate dehydrogenase; MGMTp: O6-methylguanine-DNA-methyltransferase gene promoter; AUC: area under the curve)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3892766/v1/d5fb69f5c6880a7fbca90f14.png"},{"id":50814150,"identity":"ed150559-8aea-4b20-b379-49f9cb9069bd","added_by":"auto","created_at":"2024-02-07 19:36:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":223806,"visible":true,"origin":"","legend":"\u003cp\u003eThe individualized prediction models for OS in glioma patients. (a) The nomogram can accurately predict the survival probabilities of glioma patients at 1-, 3- and 5-year. (b) The calibration plot shows the comparison between predicted and actual OS for 1-, 3- and 5-year survival probabilities in the training and validation groups. (PRS: primary/recurrent/secondary; OS: overall survival)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3892766/v1/6c0054390c530fada2120757.png"},{"id":66583992,"identity":"513f0035-e951-42a1-8140-289f15b29c4d","added_by":"auto","created_at":"2024-10-14 14:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1791846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3892766/v1/2240a803-ac3f-4fe6-9806-ef2d9366f30e.pdf"},{"id":50814152,"identity":"8d46eea2-4df7-4845-bbba-984f3f477605","added_by":"auto","created_at":"2024-02-07 19:36:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":170774,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3892766/v1/0ccb56f271b0a252f3943eec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel nomogram predicting short-term overall survival of patients with glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe annual incidence rate of gliomas is 3~6.4/100,000, accounting for nearly 80% of all malignant tumors in the central nervous system\u003csup\u003e1,2\u003c/sup\u003e. Glial tumors in the brain were first described by Rudolph Virchow in 1865\u003csup\u003e3\u003c/sup\u003e. Gliomas are tumors that arise from glial or precursor cells and include astrocytoma (including glioblastoma), oligodendroglioma, ependymoma, oligoastrocytoma, malignant glioma, not otherwise specified, and a few rare histologies\u003csup\u003e4\u003c/sup\u003e. Among them, glioblastoma, as the highest-grade (WHO IV) malignant tumor, exhibits strong invasiveness and low survival rates\u003csup\u003e5,6\u003c/sup\u003e. Poor prognosis gliomas present unique challenges for clinicians and patients in terms of tumor monitoring.\u003c/p\u003e\n\u003cp\u003eThe traditional therapeutic approach for gliomas includes surgical resection, followed by radiation and chemotherapy, as part of a multimodal treatment strategy\u003csup\u003e7,8\u003c/sup\u003e. With the continuous progress in research, multiple new diagnostic and treatment strategies have been proposed for the diagnosis and treatment of gliomas, such as colorimetric techniques, electrochemical biosensors, optical coherence tomography, reflectometric interference spectroscopy, gene circuits, Fcgamma\u0026nbsp;chimeric receptor T cells immunotherapy\u003csup\u003e9\u003c/sup\u003e. However, it cannot be denied that limited progress has been made in improving the treatment and prognosis of gliomas, despite conducting extensive research\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Prognostic models are being developed to predict the survival of glioma patients. However, accurately forecasting the prognosis of individual patients remains challenging, despite the increasing number of prognostic models available\u003csup\u003e11\u003c/sup\u003e. Nomograms, presenting the results of predictive models in a printed format, could be widely used in clinical practice\u003csup\u003e12\u003c/sup\u003e. The nomograms have significant clinical significance as they provide a visual and personalized tool for predicting prognosis and making treatment decisions. They allow for the integration of multiple variables, provide a more accurate estimate of individual patient outcomes compared to traditional prognostic models, and empower clinicians in risk assessment, patient counseling, and treatment planning, ultimately improving patient care and outcomes. To our knowledge, there are rarely specific nomograms established focusing on early death in glioma. The objective of this study was to explore the biological features of early-death gliomas patients and create a prognostic model that incorporates clinical risk factors to accurately predict the survival rate at different time points, providing a treatment-guidance tool for individualized postoperative care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eWe obtained transcriptome sequencing data from the Chinese Glioma Genome Atlas (CGGA) databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cgga.org.cn\u003c/span\u003e\u003cspan address=\"https://www.cgga.org.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e13\u003c/sup\u003e. Extracted data included: primary/recurrent/secondary (PRS) status, WHO grade, gender, age, radiotherapy status, chemotherapy status, IDH mutation status, 1p/19q codeletion status, O\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e-methylguanine-DNA-methyltransferase gene promoter (MGMTp) methylation status, follow-up time and survival. The primary endpoint of the study was overall survival (OS), defined as the time from diagnosis to death from glioma or time to last follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLASSO-COX dimension reduction analysis\u003c/h2\u003e \u003cp\u003eA total of 161 patients, including those who had a survival time of less than 360 days and had died, as well as those who had a survival time of more than 1800 days and were still alive, were selected from the training group. These patients were used for LASSO-COX dimension reduction analysis. The analysis was performed by \u003cem\u003eglmnet\u003c/em\u003e and \u003cem\u003esurvival\u003c/em\u003e packages in R. The λ value corresponding to the minimum partial likelihood deviance was selected as the optimal λ in our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eNomogram construction\u003c/h2\u003e \u003cp\u003eA nomogram analysis was performed using the \u003cem\u003erms\u003c/em\u003e package in R to predict the 1-, 3-, and 5-year survival and recurrent rate of glioma patients. The analysis consisted of a scoring system in the upper part and a prediction system in the lower part. The total points, which were calculated by summing the points of each factor, provided an accurate prediction of the outcomes. The accuracy of the survival prediction was assessed in a validation group by using calibration curves and C-Index values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were executed using R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, v4.2.3), SPSS software (IBM, v19.0, Chicago, IL), and GraphPad Prism (v8.0, La Jolla, CA). For comparison of count data between groups, chi-square or Fisher's exact test is used. Continuous variables were compared using the t-test or the one-way ANOVA test. In the training group, univariate and multivariate analyses were performed by Cox proportional hazards regression models to identify independent prognostic factors associated with glioma. For all statistical methods, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eThis resulted in a total of 325 samples (mRNAseq_325) and 693 samples (mRNAseq_693), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After excluding samples without survival data, the final datasets comprised 313 samples in the training group and 657 samples in the validation group, respectively. Clinical information of patients was summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \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\u003eClinical information of patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTraining group (n\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eValidation group (n\u0026thinsp;=\u0026thinsp;657)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePRS status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRecurrent/Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot Available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWHO Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWHO II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWHO III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWHO IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot Available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRadiotherapy status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot Available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChemotherapy status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot Available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIDH mutation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWildtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot Available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1p/19q Codeletion Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNon-Codeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCodeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot Available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMGMTp Methylation Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUn-Methylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot Available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePRS: primary/recurrent/secondary; IDH: isocitrate dehydrogenase; MGMTp: O\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e-methylguanine-DNA-methyltransferase gene promoter.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCandidate genes through LASSO-COX dimension reduction analysis\u003c/h2\u003e \u003cp\u003eA risk score was constructed by LASSO-COX dimension reduction analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, 8 candidate genes and corresponding lambda values (EN1: 0.038344874, HOXD13: 0.012374376, HOXD9: 0.016517464, IGF2BP3: 0.022116275, IGFBP2: 0.00000127, NNMT: 0.000523001, PRLHR: -0.030555542, and RP1-293L6.1: -0.278458515) were obtained based on OS of glioma patients. The risk score of each patient was calculated as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRisk score\u0026thinsp;=\u0026thinsp;expr\u003csub\u003eEN1\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eEN1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eHOXD13\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eHOXD13\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eHOXD9\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eHOXD9\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eIGF2BP3\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eIGF2BP3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eIGFBP2\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eIGFBP2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eNNMT\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eNNMT\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003ePRLHR\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003ePRLHR\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eRP1\u0026minus;293L6.1\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eRP1\u0026minus;293L6.1\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003ewhere expr\u003csub\u003egene\u003c/sub\u003e was the expression level of the gene and λ\u003csub\u003egene\u003c/sub\u003e was the corresponding lambda value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between risk score and clinical-pathologic characteristics the prognosis of patients with glioma\u003c/h2\u003e \u003cp\u003ePatients in different risk score groups showed distinct patterns of clinical-pathologic characteristics. In the training group, tumor PRS status, WHO Grade, age, radiotherapy status, chemotherapy status, IDH mutation status, 1p/19q codeletion status and MGMTp methylation status showed asymmetric distributions in different risk score groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea,c-j). In the validation group, tumor PRS status, WHO Grade, gender, age, chemotherapy status, IDH mutation status, 1p/19q codeletion status and MGMTp methylation status showed distinct patterns in different risk score groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb,k-r). In the training and validation database, there were no significant correlations between the risk score and gender or radiotherapy status, respectively (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). The risk score showed superior predictive values for OS in both training and validation databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003es,u). In addition, the receiver operating characteristic (ROC) analysis revealed that the area under the curve (AUC) was 0.834 for the training group and 0.768 for the validation group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003et,v).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe individualized prediction model showed robust predictive accuracy\u003c/h2\u003e \u003cp\u003eAn individualized prediction model was developed to facilitate the clinical application of the prognostic prediction model. The model was constructed based on several independent predictive factors (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), including risk score, PRS status, WHO grade, age, chemotherapy status, and 1p/19q codeletion status. With this individualized prediction model, the survival rates of glioma patients at 1-, 3-, and 5-years can be estimated accurately (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The C-index values of the nomogram in the training and validation groups were 0.803 (95% CI 0.775\u0026thinsp;~\u0026thinsp;0.830) and 0.795 (95% CI 0.773\u0026thinsp;~\u0026thinsp;0.817), respectively, indicating good prediction accuracy. Additionally, calibration curve analysis demonstrated that the predicted survival rates from the nomogram corresponded well with the actual survival rates, indicating superior predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGiven the high malignancy of glioma, it is crucial for clinicians to make precise and individualized prognostic assessments based on a comprehensive set of clinicopathological features. Traditional staging systems, such as the WHO grade, tend to incorporate only a limited number of pathological attributes, but overlook other critical clinical and treatment-related data, which in turn restricts their predictive power. Therefore, there exists an imperative need to develop a prediction system that can more accurately forecast the prognosis of glioma patients by integrating these diverse factors. A nomogram serves as a graphical statistical tool that facilitates the calculation of probable outcomes through the incorporation of multiple variables into a single model. It has gained widespread acceptance due to its ability to holistically consider a range of elements including both clinical pathology and patient demographics characteristics\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This is the driving force behind our focus on constructing a nomogram specifically designed for predicting OS in glioma patients.\u003c/p\u003e \u003cp\u003eBy leveraging the advantages of the CGGA database, we developed a prediction model based on patients with shorter survival time (less than 360 days) and longer survival time (greater than 1800 days). The LASSO-COX dimension reduction analysis was utilized to select an optimum prognostic signature comprising the most representative gene markers for identifying the 8-gene signature in short-term survival glioma patients. Subsequently, a risk score based nomogram was devised to forecast the probability of early mortality in individuals diagnosed with glioma. The nomogram, incorporating the risk score, PRS status, WHO grade, age, chemotherapy status and 1p/19q codeletion status, effectively predicts patients at a high risk of early death.\u003c/p\u003e \u003cp\u003eAccording to the nomogram, the risk score is the most significant factor affecting patient prognosis. The risk score incorporates 8 genes, including EN1, HOXD13, HOXD9, IGF2BP3, IGFBP2, NNMT, PRLHR, and RP1-293L6.1, which have been proven to impact the prognosis of glioma patients\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The second important factor is age. Multiple studies have emphasized that age serves as an independent and critical prognostic factor in glioma; Even when the histopathological diagnosis is identical, differing patient ages can significantly impact glioma prognosis. Thus, it is proposed that individualized treatment modalities be based on meticulous age considerations\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe WHO grade for gliomas is a critically important classification, where grade IV (such as glioblastoma) have the poorest prognosis, and grade II tumors generally fare better than grade III\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The WHO grade classification is currently one of the most significant means for predicting the prognosis in glioma patients. The likelihood of recurrence of malignant tumors will have a significant impact on the prognosis of patients. Due to the infiltrative nature of glioma, recurrence is typically inevitable for the tumor, particularly with glioblastoma, where the rate of recurrence approximates nearly 100%\u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. This invasiveness plays a critical role in tumor progression\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Codeletion of 1p/19q has also been demonstrated to be a prognostic marker, patients harboring 1p/19q codeletion exhibit notably longer survival compared to those without the alteration, furthermore, the demonstration of the 1p/19q status as the main factor associated with sensitivity to PCV (procarbazine, lomustine, and vincristine) chemotherapy has gained increased clinical significance\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Chemotherapy plays a significant role in glioma prognosis, aligning with prior research findings\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has some limitations. Firstly, both the training group and the validation group included only Chinese patients. It is necessary to validate using data from other countries or regions. Secondly, the included data did not have IDH mutant status, while IDH mutation has been proven to be an important factor affecting the prognosis of glioma patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In addition, some latest treatment methods were not included. With the continuous progress in glioma research, these new methods and technologies may have impact on the prognosis of glioma patients. Lastly, the calculation of risk score will increase the economic burden on patients, so it needs to be replaced with more cost-effective methods.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides clinicians with a practical tool specifically designed for assessing survival rates in glioma patients. The tool is designed to be concise and easy to use, suitable for various clinical scenarios. By integrating multiple influencing factors, the tool can generate highly accurate predictions of survival probabilities, thereby enhancing overall management and treatment outcomes for glioma patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e Approval was waived by the local ethics committee, as CGGA data is publicly available and de-identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in Chinese Glioma Genome Atlas (http://www.cgga.org.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledged the contributions of the Chinese Glioma Genome Atlas (CGGA) for creating and updating and Dr. Huisheng Liu for his guidance in bioinformatics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was designed by X.J.Y. and Q.M. Data extraction, statistical analysis and interpretation of data were performed by X.J.Y., J.H.X., N.Y.S., and J.B. All authors were involved in drafting and revision of the manuscript and approved the final version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to M.Q.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhao, S., Zhang, M., Zhang, Q., Wu, J. \u0026amp; Dai, H. Anlotinib alone or in combination with bevacizumab in the treatment of recurrent high-grade glioma: a prospective single-arm, open-label phase II trial. BMC Cancer 24, 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12885-023-11776-4\u003c/span\u003e\u003cspan address=\"10.1186/s12885-023-11776-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, R. \u003cem\u003eet al\u003c/em\u003e. DTX2 promotes glioma development via regulation of HLTF. Biol. Direct 19, 2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13062-023-00447-w\u003c/span\u003e\u003cspan address=\"10.1186/s13062-023-00447-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonabedian, P. \u003cem\u003eet al\u003c/em\u003e. Glioblastoma with a primitive neuroectodermal component: two cases with implications for glioblastoma cell-of-origin. Clin. Imag. 73, 139\u0026ndash;145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clinimag.2020.10.041\u003c/span\u003e\u003cspan address=\"10.1016/j.clinimag.2020.10.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOstrom, Q. T. \u003cem\u003eet al\u003c/em\u003e. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009\u0026ndash;2013. \u003cem\u003eNeuro-Oncology\u003c/em\u003e 18, v1-v75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/neuonc/now207\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/now207\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarooq, M. \u003cem\u003eet al\u003c/em\u003e. A systematic review of nanomedicine in glioblastoma treatment: clinical efficacy, safety, and future directions. Brain Sci. 13, 1727. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/brainsci13121727\u003c/span\u003e\u003cspan address=\"10.3390/brainsci13121727\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai, W. \u003cem\u003eet al\u003c/em\u003e. Overcoming therapeutic resistance in oncolytic herpes virotherapy by targeting IGF2BP3-induced NETosis in malignant glioma. Nat. Commun. 15, 131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-023-44576-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-44576-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToader, C. \u003cem\u003eet al\u003c/em\u003e. Low-grade gliomas: histological subtypes, molecular mechanisms, and treatment strategies. Brain Sci. 13, 1700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/brainsci13121700\u003c/span\u003e\u003cspan address=\"10.3390/brainsci13121700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, Y. C. \u003cem\u003eet al\u003c/em\u003e. ROS regulation in gliomas: implications for treatment strategies. Front. Immunol. 14, 1259797. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2023.1259797\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1259797\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThenuwara, G., Curtin, J. \u0026amp; Tian, F. Advances in diagnostic tools and therapeutic approaches for gliomas: A comprehensive review. Sensors 23, 9842. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s23249842\u003c/span\u003e\u003cspan address=\"10.3390/s23249842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, G., Wang, J., Niu, C., Zhao, Y. \u0026amp; Wu, P. Neutrophils: New Critical Regulators of Glioma. Front. Immunol. 13, 927233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2022.927233\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.927233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTewarie, I. A. \u003cem\u003eet al\u003c/em\u003e. Survival prediction of glioblastoma patients\u0026mdash;are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential. Neurosurg. Rev. 44, 2047\u0026ndash;2057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10143-020-01430-z\u003c/span\u003e\u003cspan address=\"10.1007/s10143-020-01430-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, G. \u003cem\u003eet al\u003c/em\u003e. A novel DNA repair-related nomogram predicts survival in low-grade gliomas. CNS Neurosci. Ther. 27, 186\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cns.13464\u003c/span\u003e\u003cspan address=\"10.1111/cns.13464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Z. \u003cem\u003eet al\u003c/em\u003e. Chinese Glioma Genome Atlas (CGGA): a comprehensive resource with functional genomic data from Chinese glioma patients. Genom. Proteom. Bioinform. 19, 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gpb.2020.10.005\u003c/span\u003e\u003cspan address=\"10.1016/j.gpb.2020.10.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuang, Y., Yu, H., Chen, C. J., Jian, J. L. \u0026amp; Wu, X. Y. Establishment and validation of a prognostic nomogram for patients with colorectal neuroendocrine carcinoma. Asian J. Surg. 47, 433\u0026ndash;442. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.asjsur.2023.09.099\u003c/span\u003e\u003cspan address=\"10.1016/j.asjsur.2023.09.099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, J. \u003cem\u003eet al\u003c/em\u003e. EN1 regulates cell growth and proliferation in human glioma cells via Hedgehog signaling. Int. J. Mol. Sci. 23, 1123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms23031123\u003c/span\u003e\u003cspan address=\"10.3390/ijms23031123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, J. \u003cem\u003eet al\u003c/em\u003e. A novel miR-7156-3p-HOXD13 axis modulates glioma progression by regulating tumor cell stemness. Int. J. Biol. Sci. 16, 3200\u0026ndash;3209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/ijbs.51293\u003c/span\u003e\u003cspan address=\"10.7150/ijbs.51293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabuse, M. \u003cem\u003eet al\u003c/em\u003e. Functional analysis of HOXD9 in human gliomas and glioma cancer stem cells. Mol. Cancer 10, 1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1476-4598-10-60\u003c/span\u003e\u003cspan address=\"10.1186/1476-4598-10-60\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, X. \u003cem\u003eet al\u003c/em\u003e. N\u003csup\u003e6\u003c/sup\u003e-methyladenosine reader IGF2BP3 as a prognostic Biomarker contribute to malignant progression of glioma. Transl. Cancer Res. 12, 992\u0026ndash;1005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/tcr-23-449\u003c/span\u003e\u003cspan address=\"10.21037/tcr-23-449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, Q. \u003cem\u003eet al\u003c/em\u003e. IGFBP2 from a novel copper metabolism-associated biomarker promoted glioma progression and response to immunotherapy. Front. Immunol. 14, 1282734. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2023.1282734\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1282734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, W. \u003cem\u003eet al\u003c/em\u003e. Overexpression of NNMT in glioma aggravates tumor cell progression: an emerging therapeutic target. Cancers 14, 3538. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers14143538\u003c/span\u003e\u003cspan address=\"10.3390/cancers14143538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Y., Xiang, J., Peng, G. \u0026amp; Shen, C. PRLHR immune genes associated with tumor mutation burden can be used as prognostic markers in patients with gliomas. Front. Oncol. 12, 620190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2022.620190\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.620190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng, Q., Qian, C. \u0026amp; Fan, S. A hypoxia-related long non-coding RNAs signature associated with prognosis in lower-grade glioma. Front. Oncol. 11, 771512. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2021.771512\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2021.771512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia, Z. \u003cem\u003eet al\u003c/em\u003e. Exploring the relationship between age and prognosis in glioma: Rethinking current age stratification. BMC Neurol. 22, 350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12883-022-02879-9\u003c/span\u003e\u003cspan address=\"10.1186/s12883-022-02879-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWrensch, M., Wiencke, J., Molinaro, A. \u0026amp; Taylor, J. Genetic and molecular epidemiology of adult diffuse glioma. Nat. Rev. Neurol. 15, 405\u0026ndash;417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41582-019-0220-2\u003c/span\u003e\u003cspan address=\"10.1038/s41582-019-0220-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGittleman, H., Sloan, A. E. \u0026amp; Barnholtz-Sloan, J. S. An independently validated survival nomogram for lower-grade glioma. Neuro-Oncology 22, 665\u0026ndash;674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/neuonc/noz191\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/noz191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei, R. \u003cem\u003eet al\u003c/em\u003e. Online calculator to predict early mortality in patient with surgically treated recurrent lower-grade glioma. BMC Cancer 22, 114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12885-022-09225-9\u003c/span\u003e\u003cspan address=\"10.1186/s12885-022-09225-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, L., Jiang, Y., Zhang, G. \u0026amp; Wei, S. The diversity and dynamics of tumor-associated macrophages in recurrent glioblastoma. Front. Immunol. 14, 1238233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2023.1238233\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1238233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiau, L. M. \u003cem\u003eet al\u003c/em\u003e. Association of autologous tumor lysate-loaded dendritic cell vaccination with extension of survival among patients with newly diagnosed and recurrent glioblastoma: a phase 3 prospective externally controlled cohort trial. JAMA Oncol. 9, 112\u0026ndash;121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamaoncol.2022.5370\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2022.5370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv, L. \u003cem\u003eet al\u003c/em\u003e. Effects of 1p/19q codeletion on immune phenotype in low grade glioma. Front. Cell. Neurosci. 15, 704344. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fncel.2021.704344\u003c/span\u003e\u003cspan address=\"10.3389/fncel.2021.704344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Den Bent, M. Thirty years of progress in the management of low-grade gliomas. Rev. Neurol. 179, 425\u0026ndash;429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurol.2023.03.001\u003c/span\u003e\u003cspan address=\"10.1016/j.neurol.2023.03.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Den Bent, M. J. \u003cem\u003eet al\u003c/em\u003e. Adjuvant and concurrent temozolomide for 1p/19q non-co-deleted anaplastic glioma (CATNON; EORTC study 26053\u0026thinsp;\u0026ndash;\u0026thinsp;22054): second interim analysis of a randomised, open-label, phase 3 study. Lancet Oncol. 22, 813\u0026ndash;823. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1470-2045(21)00090-5\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(21)00090-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarthel, F. P. \u003cem\u003eet al\u003c/em\u003e. Longitudinal molecular trajectories of diffuse glioma in adults. Nature 576, 112\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-019-1775-1\u003c/span\u003e\u003cspan address=\"10.1038/s41586-019-1775-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\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":"Glioma, Prognosis, Nomogram, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-3892766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3892766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGliomas are the most common malignant tumors in the central nervous system. This study aimed to create a tumor survival prediction model to predict short-term overall survival in patients with glioma. In this study, the mRNAseq_325 dataset was downloaded from the Chinese Glioma Genome Atlas database as the training group, and the mRNAseq_693 dataset was downloaded as the validation group. LASSO‐COX algorithm was applied to shrink predictive factor size and build a risk score. The calibration curves and C‐Index were assessed to evaluate the nomogram's performance. This study found that the risk score, built by the LASSO‐COX algorithm, was significantly associated with overall survival in gliomas, and the nomogram, combining the risk score and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups. In conclusion, an individualized prediction model was established for predicting the short-term overall survival of glioma patients, which can provide valuable insights into identifying individuals at high risk and highlight the potential in facilitating early interventions and accurate treatment for patients with limited survival prognosis.\u003c/p\u003e","manuscriptTitle":"A novel nomogram predicting short-term overall survival of patients with glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-07 19:36:25","doi":"10.21203/rs.3.rs-3892766/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":"f6bda0d5-842f-491d-a1ad-aa19a6118a12","owner":[],"postedDate":"February 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28563914,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":28563915,"name":"Biological sciences/Cancer/Cns cancer"}],"tags":[],"updatedAt":"2024-10-14T13:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-07 19:36:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3892766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3892766","identity":"rs-3892766","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}