Grade-Specific Influences of MGMT and TERT Genes on the Prognosis of Glioma

Grade-Specific Influences of MGMT and TERT Genes on the Prognosis of 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 Research Article Grade-Specific Influences of MGMT and TERT Genes on the Prognosis of Glioma Jianglong Yu, Abudula Aiaha, Yabo Sun, Yongji Zhu, Xiangtao Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7539957/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 Objective: To investigate the combined prognostic effects of MGMT expression/promoter methylation and TERT expression in gliomas across different grades (II, III, and IV) and evaluate their clinical implications. Methods: This study analyzed 454 low-grade glioma (LGG; grades II/III) and 162 glioblastoma (GBM; grade IV) samples from The Cancer Genome Atlas (TCGA) to evaluate the prognostic roles of MGMT (expression and promoter methylation) and TERT (expression) across glioma grades. Survival analyses were performed using R packages survivalROC and survminer, with Kaplan-Meier curves generated to visualize survival differences between risk groups stratified by MGMT and TERT status. Log-rank tests were applied to statistically compare survival outcomes. A combined risk score integrating MGMT methylation/expression and TERT expression was developed to assess their synergistic prognostic effects. Statistical significance was defined as p < 0.05. Result: Analysis revealed grade-specific prognostic associations for MGMT and TERT: in grade II gliomas, neither MGMT (expression/methylation) nor TERT expression, nor their combined score, showed significant survival correlations. In grade III tumors, lower TERT expression was associated with prolonged survival (p < 0.05), while MGMT and the combined score remained non-prognostic. Notably, in grade IV glioblastomas (GBM), both MGMT methylation/expression and the combined MGMT-TERT score significantly predicted survival outcomes (p < 0.01), with higher combined scores indicating poorer prognosis. These findings highlight divergent roles of MGMT and TERT across glioma grades, emphasizing their synergistic prognostic relevance in high-grade disease. Conclusion: MGMT and TERT exert distinct prognostic impacts across glioma grades: Grade II gliomas showed no survival association with MGMT (expression/methylation), TERT expression, or their combined score, while Grade III tumors revealed prolonged survival linked to lower TERT expression (p < 0.05), with MGMT and the combined score remaining non-prognostic. In contrast, Grade IV glioblastomas (GBM) demonstrated significant survival correlations for both MGMT (methylation/expression) and the MGMT-TERT combined score (p < 0.01), where higher combined scores predicted poorer outcomes. These findings highlight the grade-specific roles of MGMT and TERT in glioma progression, underscoring their potential utility in personalized clinical decision-making for high-grade gliomas. Glioma MGMT TERT Kaplan-Meier curve Cox regression Prognostic prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Glioma, including glioblastoma, is the most common malignant tumor of the central nervous system, characterized by high morbidity, recurrence, and mortality. It accounts for approximately 40% of all intracranial tumors [ 1 , 2 ]. WHO classifies gliomas into four grades, namely grade I, II, III, and IV gliomas, of which grade II and III gliomas are called Lower Grade Glioma (LGG) [ 3 ]. The classification revised by WHO in 2016 prioritizes molecular features in pathological determinations, but the diagnosis, treatment, and prognosis of brain tumors depend not only on phenotype, but also on genotype [ 4 ]. O6-methylguanine-DNA-methyltransferase (MGMT) has a reparative role in maintaining genomic stability in cells, and MGMT promoter methylation has long been recognized as an important factor in LGG therapy [ 5 , 6 ]. Recent studies have shown that MGMT promoter methylation not only affects treatment response, but also significantly correlates with overall patient survival. A large-scale meta-analysis showed that glioma patients with MGMT promoter methylation had a median survival prolongation of 6.4 months compared to unmethylated patients [ 7 ]. More scholars are also exploring compositions of MGMT promoter methylation with other molecular markers to better predict treatment response and prognosis. One study found that the compositions of MGMT promoter methylation with IDH mutations could more accurately predict the prognosis of glioma patients [ 8 ]. TERT plays an important role in the proliferation of malignant cells due to telomerase activation [ 9 ], TERT C228T and C250T are the most common mutations [ 10 ]. TERT promoter mutations are frequently detected as a genetic event in 60–75% of glioblastomas (GBM) and are associated with poor prognosis [ 11 ]. Recent studies have revealed novel mechanisms of TERT promoter mutations in glioma development and progression. One study found that TERT promoter mutations promote tumor growth by activating the GABPB1L transcription factor [ 12 ]. A prospective study showed that detection of TERT promoter mutations in circulating tumor DNA in the blood of glioma patients allowed early diagnosis of tumor recurrence [ 13 ]. Katja [ 14 ] published their latest findings in the journal CELL revealing that in samples without MGMT expression, their promoter methylation was significantly higher than in samples with MGMT expression; patients with MGMT promoter methylation had significantly higher OS than unmethylated patients. Therefore, this indirect conclusion indicates that expression is associated with OS and that low or no expression of MGMT favors the improved OS of patients. This finding provides important clues for the prognostic assessment of gliomas. However, it is noteworthy that, to date, the scientific community has not yet explored in depth how the combined effects of two key genes, MGMT and TERT, work together in the pathological process of gliomas and the specific mechanisms of their respective roles in the overall survival of patients with low-grade versus high-grade gliomas. Most articles analysed the prognostic effects of MGMT and TERT only in patients with GBM [ 15 , 16 ]. In view of this research gap, our study aims to deeply analyze the combined effects of MGMT and TERT genes and their specific mechanisms of action in different pathological processes. We expect to provide a more powerful scientific basis for the development of precision medicine strategies for gliomas. Materials and methods Data collection and pre-processing This study utilizes data from The Cancer Genome Atlas (TCGA) database, which offers comprehensive cancer genomics information, including gene expression profiles, mutations, and copy number variations. We screened and downloaded the gene expression data of low-grade glioma (LGG) and glioblastoma (GBM) from the TCGA database. The specific steps are as follows: Data screening: first, the original sample sets of LGG and GBM were retrieved from the TCGA database, which were 515 LGG samples and 617 GBM samples, respectively. Subsequently, these samples were strictly screened for missing values to remove samples with a large number of missing values in the gene expression data to ensure the reliability of the analysis results. Finally, we retained 454 cases of LGG samples and 162 cases of GBM samples for subsequent analysis. Gene expression data extraction: for the screened sample set, we extracted expression data of two core genes, MGMT and TERT. These data, in the form of RNA-seq or microarrays, were normalized by transcripts per million (TPM) [17] to eliminate technical differences and batch effects. Survival analysis Single-gene survival curve analysis: using the survival package and survminer package in R, we performed single-gene survival curve analysis for MGMT and TERT genes respectively. The median expression value of each gene was used as a cut-off point to categorize patients into high-risk group (expression above the median) and low-risk group (expression below the median). Survival curves were plotted using the Kaplan-Meier method, which can visualize the distribution of overall survival of patients in different risk groups. Log-rank test was applied to assess the statistical significance of the difference in overall survival between the high-risk group and the low-risk group, and a P value of less than 0.05 was used as the criterion for a significant difference. Multi-gene survival curve analysis: two genes were combined for survival analysis, and the grouping threshold was the combined calculated risk score, which was calculated as follows: the regression coefficient of each gene was calculated by Cox regression, and then multiplied by the grouping threshold of single-gene survival analysis (the median of the expression expressed as TPM) respectively, and then summed up to obtain. The median of the combined risk score was then used as a new cut-off point to categorize patients into high- and low-risk groups. Survival curves were plotted using the Kaplan-Meier method as well, and the statistical significance of the difference in overall survival between the different risk groups was assessed by Log-rank test. ， where x is the Cox regression coefficient for each gene, E is the expression level of each gene in TPM (Transcripts Per Million) [18, 19]. Statistical analysis and visualization Throughout the analysis process, we focused on the statistical significance and visual presentation of the results. All statistical analyses were performed using the R language and ensured that the results were reproducible and transparent. Results Results of independent and combined survival analysis of MGMT and TERT genes by TCGA-LGG dataset In the independent and combined survival analyses of the MGMT and TERT genes using the TCGA-LGG dataset, we presented the results of the survival analyses in Fig. 1 , using TPM (transcripts per million) as a measure of expression. The results showed no statistically significant differences in OS for high and low risk subgroups, either for a single gene or a compositions of the two genes. TCGA-LGG dataset subdivided into grade II versus grade III, independent and combined survival analysis results for MGMT and TERT To explore the potential impact of tumor grade on survival prognosis we subdivided the patients in the TCGA-LGG dataset into grade II versus grade III subgroups based on neoplasm histologic grade and performed survival analyses respectively(Figs. 2 and 3 ). Figure 2 shows that no significant differences in OS were observed between the high and low risk groups within each grade, regardless of whether a single-gene or two-gene strategy was used. However, Fig. 3 reveals that the TERT gene had significantly lower OS in its high-risk group than in the low-risk group in grade III patients (P < 0.05), whereas the MGMT and dual-gene compositions failed to demonstrate a similar effect. The calculation procedure for the two-gene combined survival analysis was to calculate the regression coefficients of each gene by Cox regression, and then multiply them by the respective TPMs respectively, and then sum them up to obtain. From the results of the TCGA-LGG level III Cox regression analysis in Table 1 , it can be seen that the expression of TERT had a significant effect on survival time, and MGMT was not significant. Table 1 Multifactor Cox regression model for TCGA-LGG Level III data genes coef exp(coef) se(coef) z Pr(>|z|) Signif MGMT 0.05958 1.06139 0.03655 1.630 0.103 TERT 0.55502 1.74198 0.10489 5.291 1.21e-07 *** Note: coef denotes regression coefficient; exp(coef) is an exponent of the coefficient and represents the risk ratio; se(coef) is the standard error of the coefficient; z is the value of z after the coefficient is divided by the standard error; Pr(>|z|) is the p-value corresponding to the z-value and indicates the result of the test of the significance of the coefficient; Signif. codes: *** indicates P < 0.001; ** indicates P < 0.01; * indicates P < 0.05; '.' denotes P < 0.1. The mean value of MGMT expression was 4.563784 greater than the mean value of TERT 0.308367, while the Cox regression coefficient of MGMT was about 1/10 of that of TERT, so the calculation of the combined risk score may have leveled out the contributions of MGMT and TERT, resulting in the final results showing no significant difference. Results of Independent and Combined Survival Analysis of MGM and TERT Genes on TCGA-GBM Dataset The results in Fig. 4 focus on the TCGA-GBM dataset, in which the OS difference did not reach significance in the TERT single-gene analysis, whereas the results of both the MGMT single-gene and the combined two-gene analysis showed significance, i.e., high expression of MGMT significantly inhibited the prolongation of OS in the patients (P < 0.05), which is in line with the results in Fig. 4 (A). From the results of Cox regression analysis of TCGA-GBM data in Table 2 , it can be seen that the expression of MGMT had a significant effect on survival time, and TERT was not significant. Table 2 Multifactor Cox regression model for TCGA-GBM data genes coef exp(coef) se(coef) z Pr(>|z|) Signif MGMT 0.06850 1.07091 0.02819 2.43 0.0151 * TERT 0.02256 1.02282 0.05503 0.41 0.6818 Note: coef denotes regression coefficient; exp(coef) is an exponent of the coefficient and represents the risk ratio; se(coef) is the standard error of the coefficient; z is the value of z after the coefficient is divided by the standard error; Pr(>|z|) is the p-value corresponding to the z-value and indicates the result of the test of the significance of the coefficient; Signif. codes: *** indicates P < 0.001; ** indicates P < 0.01; * indicates P < 0.05; '.' denotes P < 0.1. The mean value of MGMT expression was 6.146292 greater than the mean value of TERT 0.7416461, and the Cox regression coefficient of MGMT was about three times higher than that of TERT, so the contribution of TERT was small in the calculation of the combined risk score, and the final result was dominated by MGMT, which showed a significant difference. Discussion Glioma is one of the most common primary brain tumors in adults and children, also known as glioblastoma and neuroglioma, and is one of the most common types of primary intracranial tumors, with three high and one low characteristics, i.e., high morbidity, high recurrence, high morbidity and mortality, and low cure rate [ 20 ]. Gliomas can occur at any age and are most common in people aged 60–80 years. There have been efforts to decipher the molecular biomarkers of gliomas and their prognostic significance, and to apply these findings in clinical practice [ 9 , 21 ] The most common biomarkers for gliomas are TERT promoter mutations. Among them, TERT promoter mutation and MGMT methylation status are the most important markers [ 8 , 11 , 13 ]. In our study, while deeply dissecting the data on low-grade gliomas (LGG) in the TCGA database, we unexpectedly found that examining the expression of the MGMT or TERT genes in isolation did not directly predict the survival prognosis of the patients, which is in contrast to the study by Katja [ 14 ] study published in CELL, who noted a significant association between the methylation status of MGMT and the level of MGMT protein expression and patient overall survival (OS). However, in our analysis, this direct correlation was not evident in LGG. Therefore, we further divided the patients in the TCGA-LGG dataset into grade II and grade III according to neoplasm_histologic_grade for survival analysis, respectively. The results showed that in patients with more malignant grade III gliomas, the difference in OS between patients with high-risk scores and those with low-risk scores was significant when the expression of the TERT gene was used as the basis for grouping. Similar survival differences were not observed when the expression of the MGMT gene or a compositions of both genes (double gene) was examined in the same grading context. This result highlights the possible significant differences in gene expression patterns and their prognostic value in the context of different tumor grades. Therefore, future studies need to consider tumor heterogeneity and grading factors more carefully in order to develop a more precise and personalized prognostic assessment system. Turning to the results of the data analysis of high-grade gliomas (GBM), the expression level of the MGMT gene emerged as a key factor in distinguishing high-risk from low-risk scoring patients, which demonstrated a significant difference in survival prognosis. This finding is in high agreement with Katja [ 14 ] who noted that high MGMT expression significantly attenuated patients' overall survival (OS), thus reinforcing the centrality of MGMT in the prognostic assessment of GBM. Our study not only reconfirms the strong association between MGMT expression and survival time, but also further corroborates that the significant correlation between expression and survival time may indeed be profoundly influenced by the key variable of disease grade. Furthermore in the results of the GBM dataset analysis, the results of the combined two-gene analysis showed that there was also a significant difference in OS between patients with high and low risk scores, a result that is consistent with the study by Eckel-Passow et al. that a single molecular marker may not be sufficient to accurately predict the prognosis of glioma patients, highlighting the importance of combining multiple molecular markers [ 22 ]. By comprehensively considering changes in the expression of multiple key genes, the prognosis of GBM patients can be more comprehensively and accurately assessed. As a slow-growing, long-duration, low-grade malignant lesion, the progression of LGG from asymptomatic to symptomatic may span many years or even decades, and therefore, it may be difficult to adequately demonstrate a significant difference in OS over the time span of the present study, especially given the relatively long median survival of patients with grade II gliomas (approximately 5 to 10 years) [ 23 ]. In contrast, GBM is a highly malignant and aggressive tumor with a poor prognosis, characterized by a short median survival and a 5-year survival rate of just 5% [ 24 ]. This makes the impact of gene expression on overall survival (OS) more pronounced and detectable in GBM patients. Thus, our study highlights the complexity and specificity of the relationship between gene expression and clinical prognosis in different disease contexts, providing valuable insights for future precision medicine strategies. Conclusion In conclusion, TERT may be associated with overall survival in patients with grade III gliomas, while MGMT and the combined effects of both genes may be linked to overall survival in grade IV gliomas. The results of our findings results highlight the possible significant differences in gene expression patterns and their prognostic value in the context of different tumor grades, providing valuable insights into clinical prognosis and precision medicine strategies. Next, we will continue to validate in clinical trials. Declarations Acknowledgments The authors are grateful for the reviewer’s valuable comments that improved the manuscript. Funding information Open Project of the State Key Laboratory for the Cause and Control of High Incidence in Central Asia jointly built by the province and ministry (Project Number: SKL-HIDCA-2022-JZ9); Open project number of Xinjiang Key Laboratory of Neurological Disease Research (Project Number: XJDX1711-2224). Authors contribution Jianglong Yu: Manuscript draft preparation. Abudula Aiaha and Yabo Sun: Data cleaning and analysis. Yongji Zhu: Manuscript revision. Xiangtao Liu: Study design of the research. All authors have read and approved the final manuscript. Conflicts of Interest The authors declare no conflict of interest. Ethical Declaration: This study did not involve human or animal experiments; therefore, ethical approval was not required. Data Availability Statement The datasets generated during analyzed the current study are available from the corresponding author on reasonable request. References Tian K, Tian QY. Research progress and application of celastrol in glioma. Chin J Mod Appl Pharm. 2023;40:1881-8. Huang LP. Correlation Study of Ki-67, CD34, IDH1 Gene Expression and Clinical Characteristics of Glioma Based on MR Brain Functional Imaging [Master thesis]. Nanchang: Nanchang University; 2023. Wang CL. Correlation Study Between MR Spectroscopy-Related Metabolites and Genotyping and Prognosis of Human Brain Gliomas [Master thesis]. Luzhou: Southwest Medical University; 2023. van den Bent MJ, Weller M, Wen PY, et al. A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics. Neuro Oncol. 2017;19:614-24. Xu S, Wang Z, Ye J, et al. Identification of Iron Metabolism-Related Genes as Prognostic Indicators for Lower-Grade Glioma. Front Oncol. 2021;11:729103. Paech D, Windschuh J, Oberhollenzer J, et al. Assessing the predictability of IDH mutation and MGMT methylation status in glioma patients using relaxation-compensated multipool CEST MRI at 7.0 T. Neuro Oncol. 2018;20:1661-71. Killela PJ, Reitman ZJ, Jiao Y, et al. TERT promoter mutations occur frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc Natl Acad Sci U S A. 2013;110:6021-6. Qiu X, Chen Y, Bao Z, et al. Chemoradiotherapy with temozolomide vs. radiotherapy alone in patients with IDH wild-type and TERT promoter mutation WHO grade II/III gliomas: A prospective randomized study. Radiother Oncol. 2022;167:1-6. Gabler L, Lötsch D, Kirchhofer D, et al. TERT expression is susceptible to BRAF and ETS-factor inhibition in BRAF(V600E)/TERT promoter double-mutated glioma. Acta Neuropathol Commun. 2019;7:128. Shboul ZA, Chen J, I KM. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features. Sci Rep. 2020;10:3711. Chen CH, Lin YJ, Lin YY, et al. Glioblastoma Primary Cells Retain the Most Copy Number Alterations That Predict Poor Survival in Glioma Patients. Front Oncol. 2021;11:621432. Hu WM, Yang YZ, Zhang TZ, et al. LGALS3 Is a Poor Prognostic Factor in Diffusely Infiltrating Gliomas and Is Closely Correlated With CD163+ Tumor-Associated Macrophages. Front Med (Lausanne). 2020;7:182. Zhang ZT. 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An N, Yang X, Cheng S, et al. Developmental genes significantly afflicted by aberrant promoter methylation and somatic mutation predict overall survival of late-stage colorectal cancer. Sci Rep. 2015;5:18616. Chen EG, Wang P, Lou H, et al. A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients. Oncotarget. 2018;9:6862-71. Li J, Liu QY, Jiang LQ, et al. Retrospective Study on the Methylation Status of MGMT in Gliomas and Its Clinical Significance. Chin J Cancer. 2023;33:740-50. Wu HZ, Kang JM. Analysis of Prognostic Factors in Patients with Infratentorial Diffuse Glioma. J Tianjin Med Univ. 2023;29:532-5. Eckel-Passow JE, Lachance DH, Molinaro AM, et al. Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors. N Engl J Med. 2015;372:2499-508. Guo Q, Tan Y. Effects of Radiotherapy on Cognitive Function in Patients with Low-Grade Brain Glioma and Prevention Strategies. Chin J Radiol Med Prot. 2015;35:397-400. Śledzińska P, Bebyn MG, Furtak J, et al. Prognostic and Predictive Biomarkers in Gliomas. Int J Mol Sci. 2021;22:10373. Additional Declarations No competing interests reported. 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-7539957","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512943026,"identity":"5446a3f0-94b5-4745-9d45-6b60dc6985e8","order_by":0,"name":"Jianglong Yu","email":"","orcid":"","institution":"Neurosurgery Department of the Second Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianglong","middleName":"","lastName":"Yu","suffix":""},{"id":512943027,"identity":"649a19dc-e9f3-4324-91db-880943328d3a","order_by":1,"name":"Abudula Aiaha","email":"","orcid":"","institution":"Neurosurgery Department of the Second Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Abudula","middleName":"","lastName":"Aiaha","suffix":""},{"id":512943030,"identity":"6cc68da3-9fb6-4067-b09e-834bb74348b3","order_by":2,"name":"Yabo Sun","email":"","orcid":"","institution":"Neurosurgery Department of the Second Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yabo","middleName":"","lastName":"Sun","suffix":""},{"id":512943031,"identity":"e8a103bc-055f-4274-8a8e-4985782f109f","order_by":3,"name":"Yongji Zhu","email":"","orcid":"","institution":"Neurosurgery Department of the Second Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongji","middleName":"","lastName":"Zhu","suffix":""},{"id":512943033,"identity":"a35dbc46-cd31-4efe-b6bc-f34e72d37c75","order_by":4,"name":"Xiangtao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACPmYwJcFj395ApBY2sJYEGzkDngPEagGTCWnGBhIJxGph5zFg5v1xOHG75OONNxhqbKKJcBiPAeOMhMOJO2enFVswHEvLbSBGC8MHoJaG2zlmEowNh4nUkgDScvMMKVo+gLx/g4doLWwFjDPSbOQke4B+SSDGL/z8hzcw89hI8PCzH95440ONDWEtQMD+A8ogPmrgwECCVB2jYBSMglEwMgAAhXg1API0nYQAAAAASUVORK5CYII=","orcid":"","institution":"Gannan Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiangtao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-09-05 02:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7539957/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7539957/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91107269,"identity":"56b31e04-1275-442a-9611-e9f365f51c2d","added_by":"auto","created_at":"2025-09-11 15:46:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95699,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves of the TCGA-LGG dataset (expression expressed as TPM number), none of (A) MGMT; (B) TERT; (C) MGMT+TERT, showed significant differences.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7539957/v1/8563587901825e9f071a1a87.png"},{"id":91107271,"identity":"90e27636-6f44-4fd2-bf02-8068418fe9c2","added_by":"auto","created_at":"2025-09-11 15:46:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101993,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves of grade II in the TCGA-LGG dataset (expression expressed as TPM number), (A) MGMT; (B) TERT; (C) MGMT+TERT, none of which showed significant differences.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7539957/v1/2e5676e34329dfc40c25e3df.png"},{"id":91107270,"identity":"9e66b17f-2bac-41fc-884e-038c4ab86ca3","added_by":"auto","created_at":"2025-09-11 15:46:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90397,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves of TCGA-LGG dataset grade III (expression expressed as TPM number) (A) MGMT; (C) MGMT+TERT, neither of them shows significant difference, (B) TERT; there is significant difference.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7539957/v1/241d04707cd0f946ccd6359c.png"},{"id":91107273,"identity":"d33932b8-5e53-43d7-b5cf-ca62380f319d","added_by":"auto","created_at":"2025-09-11 15:46:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94999,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves of the TCGA-GBM dataset (expression expressed as TPM number), (A) MGMT; (C) MGMT+TERT, both show significant differences, (B) TERT not significant differences.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7539957/v1/395d0ac60e9ff4dac639afbb.png"},{"id":91775487,"identity":"9212fe2c-44d3-4fa5-8016-e0b8b987d9dc","added_by":"auto","created_at":"2025-09-20 18:46:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":832022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7539957/v1/0158f72d-ffe4-4d3e-9e5b-ec6f48a8ee1b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Grade-Specific Influences of MGMT and TERT Genes on the Prognosis of Glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioma, including glioblastoma, is the most common malignant tumor of the central nervous system, characterized by high morbidity, recurrence, and mortality. It accounts for approximately 40% of all intracranial tumors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. WHO classifies gliomas into four grades, namely grade I, II, III, and IV gliomas, of which grade II and III gliomas are called Lower Grade Glioma (LGG) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The classification revised by WHO in 2016 prioritizes molecular features in pathological determinations, but the diagnosis, treatment, and prognosis of brain tumors depend not only on phenotype, but also on genotype [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eO6-methylguanine-DNA-methyltransferase (MGMT) has a reparative role in maintaining genomic stability in cells, and MGMT promoter methylation has long been recognized as an important factor in LGG therapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies have shown that MGMT promoter methylation not only affects treatment response, but also significantly correlates with overall patient survival. A large-scale meta-analysis showed that glioma patients with MGMT promoter methylation had a median survival prolongation of 6.4 months compared to unmethylated patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. More scholars are also exploring compositions of MGMT promoter methylation with other molecular markers to better predict treatment response and prognosis. One study found that the compositions of MGMT promoter methylation with IDH mutations could more accurately predict the prognosis of glioma patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTERT plays an important role in the proliferation of malignant cells due to telomerase activation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], TERT C228T and C250T are the most common mutations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. TERT promoter mutations are frequently detected as a genetic event in 60\u0026ndash;75% of glioblastomas (GBM) and are associated with poor prognosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent studies have revealed novel mechanisms of TERT promoter mutations in glioma development and progression. One study found that TERT promoter mutations promote tumor growth by activating the GABPB1L transcription factor [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A prospective study showed that detection of TERT promoter mutations in circulating tumor DNA in the blood of glioma patients allowed early diagnosis of tumor recurrence [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKatja [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] published their latest findings in the journal CELL revealing that in samples without MGMT expression, their promoter methylation was significantly higher than in samples with MGMT expression; patients with MGMT promoter methylation had significantly higher OS than unmethylated patients. Therefore, this indirect conclusion indicates that expression is associated with OS and that low or no expression of MGMT favors the improved OS of patients. This finding provides important clues for the prognostic assessment of gliomas. However, it is noteworthy that, to date, the scientific community has not yet explored in depth how the combined effects of two key genes, MGMT and TERT, work together in the pathological process of gliomas and the specific mechanisms of their respective roles in the overall survival of patients with low-grade versus high-grade gliomas. Most articles analysed the prognostic effects of MGMT and TERT only in patients with GBM [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In view of this research gap, our study aims to deeply analyze the combined effects of MGMT and TERT genes and their specific mechanisms of action in different pathological processes. We expect to provide a more powerful scientific basis for the development of precision medicine strategies for gliomas.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData collection and pre-processing\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilizes data from The Cancer Genome Atlas (TCGA) database, which offers comprehensive cancer genomics information, including gene expression profiles, mutations, and copy number variations. We screened and downloaded the gene expression data of low-grade glioma (LGG) and glioblastoma (GBM) from the TCGA database. The specific steps are as follows:\u003c/p\u003e\n\u003cp\u003eData screening: first, the original sample sets of LGG and GBM were retrieved from the TCGA database, which were 515 LGG samples and 617 GBM samples, respectively. Subsequently, these samples were strictly screened for missing values to remove samples with a large number of missing values in the gene expression data to ensure the reliability of the analysis results. Finally, we retained 454 cases of LGG samples and 162 cases of GBM samples for subsequent analysis.\u003c/p\u003e\n\u003cp\u003eGene expression data extraction: for the screened sample set, we extracted expression data of two core genes, MGMT and TERT. These data, in the form of RNA-seq or microarrays, were normalized by transcripts per million (TPM) [17] to eliminate technical differences and batch effects.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSurvival analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSingle-gene survival curve analysis: using the survival package and survminer package in R, we performed single-gene survival curve analysis for MGMT and TERT genes respectively. The median expression value of each gene was used as a cut-off point to categorize patients into high-risk group (expression above the median) and low-risk group (expression below the median). Survival curves were plotted using the Kaplan-Meier method, which can visualize the distribution of overall survival of patients in different risk groups. Log-rank test was applied to assess the statistical significance of the difference in overall survival between the high-risk group and the low-risk group, and a P value of less than 0.05 was used as the criterion for a significant difference.\u003c/p\u003e\n\u003cp\u003eMulti-gene survival curve analysis: two genes were combined for survival analysis, and the grouping threshold was the combined calculated risk score, which was calculated as follows: the regression coefficient of each gene was calculated by Cox regression, and then multiplied by the grouping threshold of single-gene survival analysis (the median of the expression expressed as TPM) respectively, and then summed up to obtain. The median of the combined risk score was then used as a new cut-off point to categorize patients into high- and low-risk groups. Survival curves were plotted using the Kaplan-Meier method as well, and the statistical significance of the difference in overall survival between the different risk groups was assessed by Log-rank test.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"264\" height=\"24\" src=\"data:image/wmf;base64,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\" alt=\"image\"\u003e，\u003c/p\u003e\n\u003cp\u003ewhere x is the Cox regression coefficient for each gene, E is the expression level of each gene in TPM (Transcripts Per Million) [18, 19].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStatistical analysis and visualization\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the analysis process, we focused on the statistical significance and visual presentation of the results. All statistical analyses were performed using the R language and ensured that the results were reproducible and transparent.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eResults of independent and combined survival analysis of MGMT and TERT genes by TCGA-LGG dataset\u003c/p\u003e\u003cp\u003eIn the independent and combined survival analyses of the MGMT and TERT genes using the TCGA-LGG dataset, we presented the results of the survival analyses in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, using TPM (transcripts per million) as a measure of expression. The results showed no statistically significant differences in OS for high and low risk subgroups, either for a single gene or a compositions of the two genes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTCGA-LGG dataset subdivided into grade II versus grade III, independent and combined survival analysis results for MGMT and TERT\u003c/p\u003e\u003cp\u003eTo explore the potential impact of tumor grade on survival prognosis we subdivided the patients in the TCGA-LGG dataset into grade II versus grade III subgroups based on neoplasm histologic grade and performed survival analyses respectively(Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that no significant differences in OS were observed between the high and low risk groups within each grade, regardless of whether a single-gene or two-gene strategy was used. However, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveals that the TERT gene had significantly lower OS in its high-risk group than in the low-risk group in grade III patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas the MGMT and dual-gene compositions failed to demonstrate a similar effect.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe calculation procedure for the two-gene combined survival analysis was to calculate the regression coefficients of each gene by Cox regression, and then multiply them by the respective TPMs respectively, and then sum them up to obtain. From the results of the TCGA-LGG level III Cox regression analysis in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it can be seen that the expression of TERT had a significant effect on survival time, and MGMT was not significant.\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\u003eMultifactor Cox regression model for TCGA-LGG Level III data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003egenes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eexp(coef)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese(coef)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSignif\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMGMT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.06139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTERT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.74198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: coef denotes regression coefficient;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eexp(coef) is an exponent of the coefficient and represents the risk ratio;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ese(coef) is the standard error of the coefficient;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ez is the value of z after the coefficient is divided by the standard error;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ePr(\u0026gt;|z|) is the p-value corresponding to the z-value and indicates the result of the test of the significance of the coefficient;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignif. codes: *** indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ** indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; '.' denotes P\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mean value of MGMT expression was 4.563784 greater than the mean value of TERT 0.308367, while the Cox regression coefficient of MGMT was about 1/10 of that of TERT, so the calculation of the combined risk score may have leveled out the contributions of MGMT and TERT, resulting in the final results showing no significant difference.\u003c/p\u003e\u003cp\u003eResults of Independent and Combined Survival Analysis of MGM and TERT Genes on TCGA-GBM Dataset\u003c/p\u003e\u003cp\u003eThe results in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e focus on the TCGA-GBM dataset, in which the OS difference did not reach significance in the TERT single-gene analysis, whereas the results of both the MGMT single-gene and the combined two-gene analysis showed significance, i.e., high expression of MGMT significantly inhibited the prolongation of OS in the patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which is in line with the results in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(A).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom the results of Cox regression analysis of TCGA-GBM data in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be seen that the expression of MGMT had a significant effect on survival time, and TERT was not significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultifactor Cox regression model for TCGA-GBM data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003egenes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eexp(coef)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese(coef)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSignif\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMGMT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTERT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.02282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: coef denotes regression coefficient;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eexp(coef) is an exponent of the coefficient and represents the risk ratio;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ese(coef) is the standard error of the coefficient;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ez is the value of z after the coefficient is divided by the standard error;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ePr(\u0026gt;|z|) is the p-value corresponding to the z-value and indicates the result of the test of the significance of the coefficient;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignif. codes: *** indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ** indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; '.' denotes P\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mean value of MGMT expression was 6.146292 greater than the mean value of TERT 0.7416461, and the Cox regression coefficient of MGMT was about three times higher than that of TERT, so the contribution of TERT was small in the calculation of the combined risk score, and the final result was dominated by MGMT, which showed a significant difference.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlioma is one of the most common primary brain tumors in adults and children, also known as glioblastoma and neuroglioma, and is one of the most common types of primary intracranial tumors, with three high and one low characteristics, i.e., high morbidity, high recurrence, high morbidity and mortality, and low cure rate [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Gliomas can occur at any age and are most common in people aged 60\u0026ndash;80 years. There have been efforts to decipher the molecular biomarkers of gliomas and their prognostic significance, and to apply these findings in clinical practice [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The most common biomarkers for gliomas are TERT promoter mutations. Among them, TERT promoter mutation and MGMT methylation status are the most important markers [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, while deeply dissecting the data on low-grade gliomas (LGG) in the TCGA database, we unexpectedly found that examining the expression of the MGMT or TERT genes in isolation did not directly predict the survival prognosis of the patients, which is in contrast to the study by Katja [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] study published in CELL, who noted a significant association between the methylation status of MGMT and the level of MGMT protein expression and patient overall survival (OS). However, in our analysis, this direct correlation was not evident in LGG. Therefore, we further divided the patients in the TCGA-LGG dataset into grade II and grade III according to neoplasm_histologic_grade for survival analysis, respectively. The results showed that in patients with more malignant grade III gliomas, the difference in OS between patients with high-risk scores and those with low-risk scores was significant when the expression of the TERT gene was used as the basis for grouping. Similar survival differences were not observed when the expression of the MGMT gene or a compositions of both genes (double gene) was examined in the same grading context. This result highlights the possible significant differences in gene expression patterns and their prognostic value in the context of different tumor grades. Therefore, future studies need to consider tumor heterogeneity and grading factors more carefully in order to develop a more precise and personalized prognostic assessment system.\u003c/p\u003e\u003cp\u003eTurning to the results of the data analysis of high-grade gliomas (GBM), the expression level of the MGMT gene emerged as a key factor in distinguishing high-risk from low-risk scoring patients, which demonstrated a significant difference in survival prognosis. This finding is in high agreement with Katja [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] who noted that high MGMT expression significantly attenuated patients' overall survival (OS), thus reinforcing the centrality of MGMT in the prognostic assessment of GBM. Our study not only reconfirms the strong association between MGMT expression and survival time, but also further corroborates that the significant correlation between expression and survival time may indeed be profoundly influenced by the key variable of disease grade. Furthermore in the results of the GBM dataset analysis, the results of the combined two-gene analysis showed that there was also a significant difference in OS between patients with high and low risk scores, a result that is consistent with the study by Eckel-Passow et al. that a single molecular marker may not be sufficient to accurately predict the prognosis of glioma patients, highlighting the importance of combining multiple molecular markers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. By comprehensively considering changes in the expression of multiple key genes, the prognosis of GBM patients can be more comprehensively and accurately assessed.\u003c/p\u003e\u003cp\u003eAs a slow-growing, long-duration, low-grade malignant lesion, the progression of LGG from asymptomatic to symptomatic may span many years or even decades, and therefore, it may be difficult to adequately demonstrate a significant difference in OS over the time span of the present study, especially given the relatively long median survival of patients with grade II gliomas (approximately 5 to 10 years) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, GBM is a highly malignant and aggressive tumor with a poor prognosis, characterized by a short median survival and a 5-year survival rate of just 5% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This makes the impact of gene expression on overall survival (OS) more pronounced and detectable in GBM patients. Thus, our study highlights the complexity and specificity of the relationship between gene expression and clinical prognosis in different disease contexts, providing valuable insights for future precision medicine strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, TERT may be associated with overall survival in patients with grade III gliomas, while MGMT and the combined effects of both genes may be linked to overall survival in grade IV gliomas. The results of our findings results highlight the possible significant differences in gene expression patterns and their prognostic value in the context of different tumor grades, providing valuable insights into clinical prognosis and precision medicine strategies. Next, we will continue to validate in clinical trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful for the reviewer\u0026rsquo;s valuable comments that improved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Project of the State Key Laboratory for the Cause and Control of High Incidence in Central Asia jointly built by the province and ministry (Project Number: SKL-HIDCA-2022-JZ9); Open project number of Xinjiang Key Laboratory of Neurological Disease Research (Project Number: XJDX1711-2224).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJianglong Yu: Manuscript draft preparation. Abudula Aiaha and Yabo Sun: Data cleaning and analysis. Yongji Zhu: Manuscript revision. Xiangtao Liu: Study design of the research. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Declaration: \u003c/strong\u003eThis study did not involve human or animal experiments; therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during analyzed the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTian K, Tian QY. Research progress and application of celastrol in glioma. Chin J Mod Appl Pharm. 2023;40:1881-8.\u003c/li\u003e\n\u003cli\u003eHuang LP. Correlation Study of Ki-67, CD34, IDH1 Gene Expression and Clinical Characteristics of Glioma Based on MR Brain Functional Imaging [Master thesis]. Nanchang: Nanchang University; 2023.\u003c/li\u003e\n\u003cli\u003eWang CL. Correlation Study Between MR Spectroscopy-Related Metabolites and Genotyping and Prognosis of Human Brain Gliomas [Master thesis]. Luzhou: Southwest Medical University; 2023.\u003c/li\u003e\n\u003cli\u003evan den Bent MJ, Weller M, Wen PY, et al. A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics. Neuro Oncol. 2017;19:614-24.\u003c/li\u003e\n\u003cli\u003eXu S, Wang Z, Ye J, et al. Identification of Iron Metabolism-Related Genes as Prognostic Indicators for Lower-Grade Glioma. Front Oncol. 2021;11:729103.\u003c/li\u003e\n\u003cli\u003ePaech D, Windschuh J, Oberhollenzer J, et al. Assessing the predictability of IDH mutation and MGMT methylation status in glioma patients using relaxation-compensated multipool CEST MRI at 7.0 T. Neuro Oncol. 2018;20:1661-71.\u003c/li\u003e\n\u003cli\u003eKillela PJ, Reitman ZJ, Jiao Y, et al. TERT promoter mutations occur frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc Natl Acad Sci U S A. 2013;110:6021-6.\u003c/li\u003e\n\u003cli\u003eQiu X, Chen Y, Bao Z, et al. Chemoradiotherapy with temozolomide vs. radiotherapy alone in patients with IDH wild-type and TERT promoter mutation WHO grade II/III gliomas: A prospective randomized study. Radiother Oncol. 2022;167:1-6.\u003c/li\u003e\n\u003cli\u003eGabler L, L\u0026ouml;tsch D, Kirchhofer D, et al. TERT expression is susceptible to BRAF and ETS-factor inhibition in BRAF(V600E)/TERT promoter double-mutated glioma. Acta Neuropathol Commun. 2019;7:128.\u003c/li\u003e\n\u003cli\u003eShboul ZA, Chen J, I KM. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features. Sci Rep. 2020;10:3711.\u003c/li\u003e\n\u003cli\u003eChen CH, Lin YJ, Lin YY, et al. Glioblastoma Primary Cells Retain the Most Copy Number Alterations That Predict Poor Survival in Glioma Patients. Front Oncol. 2021;11:621432.\u003c/li\u003e\n\u003cli\u003eHu WM, Yang YZ, Zhang TZ, et al. LGALS3 Is a Poor Prognostic Factor in Diffusely Infiltrating Gliomas and Is Closely Correlated With CD163+ Tumor-Associated Macrophages. Front Med (Lausanne). 2020;7:182.\u003c/li\u003e\n\u003cli\u003eZhang ZT. Application of Machine Learning Based on MR Radiomics in Predicting Short-term Efficacy of Standard Postoperative Radiotherapy and Chemotherapy for Brain Glioma [PhD thesis]. Nanchang: Nanchang University; 2023.\u003c/li\u003e\n\u003cli\u003eZappe K, P\u0026uuml;hringer K, Pflug S, et al. Association between MGMT Enhancer Methylation and MGMT Promoter Methylation, MGMT Protein Expression, and Overall Survival in Glioblastoma. Cells. 2023;12:1639.\u003c/li\u003e\n\u003cli\u003ePurkait S, Mallick S, Sharma V, et al. Prognostic Stratification of GBMs Using Combinatorial Assessment of IDH1 Mutation, MGMT Promoter Methylation, and TERT Mutation Status: Experience from a Tertiary Care Center in India. Transl Oncol. 2016;9:371-6.\u003c/li\u003e\n\u003cli\u003eTunthanathip T, Sangkhathat S, Tanvejsilp P, et al. Prognostic Impact of the Combination of MGMT Methylation and TERT Promoter Mutation in Glioblastoma. J Neurosci Rural Pract. 2021;12:694-703.\u003c/li\u003e\n\u003cli\u003eZhao S, Ye Z, Stanton R. Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols. RNA. 2020;26:903-9.\u003c/li\u003e\n\u003cli\u003eAn N, Yang X, Cheng S, et al. Developmental genes significantly afflicted by aberrant promoter methylation and somatic mutation predict overall survival of late-stage colorectal cancer. Sci Rep. 2015;5:18616.\u003c/li\u003e\n\u003cli\u003eChen EG, Wang P, Lou H, et al. A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients. Oncotarget. 2018;9:6862-71.\u003c/li\u003e\n\u003cli\u003eLi J, Liu QY, Jiang LQ, et al. Retrospective Study on the Methylation Status of MGMT in Gliomas and Its Clinical Significance. Chin J Cancer. 2023;33:740-50.\u003c/li\u003e\n\u003cli\u003eWu HZ, Kang JM. Analysis of Prognostic Factors in Patients with Infratentorial Diffuse Glioma. J Tianjin Med Univ. 2023;29:532-5.\u003c/li\u003e\n\u003cli\u003eEckel-Passow JE, Lachance DH, Molinaro AM, et al. Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors. N Engl J Med. 2015;372:2499-508.\u003c/li\u003e\n\u003cli\u003eGuo Q, Tan Y. Effects of Radiotherapy on Cognitive Function in Patients with Low-Grade Brain Glioma and Prevention Strategies. Chin J Radiol Med Prot. 2015;35:397-400.\u003c/li\u003e\n\u003cli\u003eŚledzińska P, Bebyn MG, Furtak J, et al. Prognostic and Predictive Biomarkers in Gliomas. Int J Mol Sci. 2021;22:10373.\u003c/li\u003e\n\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, MGMT, TERT, Kaplan-Meier curve, Cox regression, Prognostic prediction","lastPublishedDoi":"10.21203/rs.3.rs-7539957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7539957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003eTo investigate the combined prognostic effects of MGMT expression/promoter methylation and TERT expression in gliomas across different grades (II, III, and IV) and evaluate their clinical implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis study analyzed 454 low-grade glioma (LGG; grades II/III) and 162 glioblastoma (GBM; grade IV) samples from The Cancer Genome Atlas (TCGA) to evaluate the prognostic roles of MGMT (expression and promoter methylation) and TERT (expression) across glioma grades. Survival analyses were performed using R packages survivalROC and survminer, with Kaplan-Meier curves generated to visualize survival differences between risk groups stratified by MGMT and TERT status. Log-rank tests were applied to statistically compare survival outcomes. A combined risk score integrating MGMT methylation/expression and TERT expression was developed to assess their synergistic prognostic effects. Statistical significance was defined as p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eAnalysis revealed grade-specific prognostic associations for MGMT and TERT: in grade II gliomas, neither MGMT (expression/methylation) nor TERT expression, nor their combined score, showed significant survival correlations. In grade III tumors, lower TERT expression was associated with prolonged survival (p \u0026lt; 0.05), while MGMT and the combined score remained non-prognostic. Notably, in grade IV glioblastomas (GBM), both MGMT methylation/expression and the combined MGMT-TERT score significantly predicted survival outcomes (p \u0026lt; 0.01), with higher combined scores indicating poorer prognosis. These findings highlight divergent roles of MGMT and TERT across glioma grades, emphasizing their synergistic prognostic relevance in high-grade disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMGMT and TERT exert distinct prognostic impacts across glioma grades: Grade II gliomas showed no survival association with MGMT (expression/methylation), TERT expression, or their combined score, while Grade III tumors revealed prolonged survival linked to lower TERT expression (p \u0026lt; 0.05), with MGMT and the combined score remaining non-prognostic. In contrast, Grade IV glioblastomas (GBM) demonstrated significant survival correlations for both MGMT (methylation/expression) and the MGMT-TERT combined score (p \u0026lt; 0.01), where higher combined scores predicted poorer outcomes. These findings highlight the grade-specific roles of MGMT and TERT in glioma progression, underscoring their potential utility in personalized clinical decision-making for high-grade gliomas.\u003c/p\u003e","manuscriptTitle":"Grade-Specific Influences of MGMT and TERT Genes on the Prognosis of Glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 15:46:49","doi":"10.21203/rs.3.rs-7539957/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":"b072bfa4-fc50-4c18-a792-2b3cfa7d1163","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-30T11:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 15:46:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7539957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7539957","identity":"rs-7539957","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}