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Table of Contents
ORIGINAL ARTICLE
Year : 2022  |  Volume : 5  |  Issue : 1  |  Page : 20-28

A novel nomogram based on DNA damage response-related gene expression in patients with O-6-methylguanine-DNA methyltransferase unmethylated glioblastoma receiving temozolomide chemotherapy: A population-based analysis


1 The Cancer Center of the Fifth Affiliated Hospital of Sun Yat-sen University; Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
2 Department of Pediatrics, Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
3 Department of Radiation Oncology, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong Province, China

Date of Submission24-Jan-2022
Date of Decision12-Feb-2022
Date of Acceptance24-Feb-2022
Date of Web Publication30-Mar-2022

Correspondence Address:
Dr. Zhigang Liu
The Cancer Center of the Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong Province
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/glioma.glioma_3_22

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  Abstract 


Background and Aim: Glioblastoma (GBM) is the most lethal primary brain tumor. Patients with unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) promoter have higher MGMT expression, are less sensitive to temozolomide (TMZ), and are linked to poor prognosis. The aim of this study was to identify patients from this population with a better prognosis, explore the molecular mechanism, and provide a theoretical basis for the formulation of treatment strategies. Materials and Methods: Prognostic genes involved in the DNA damage response (DDR) pathway were screened, and the risk score of each GBM patient undergoing TMZ chemotherapy from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database was calculated. A comprehensive prognostic nomogram model was constructed by combining the risk score and other clinical features. Results: Two DDR-related genes (replication factor C subunit 2 [RFC2] and methyl-CpG binding domain 4, DNA glycosylase [MBD4]) were identified as having a prognostic value in GBM patients with unmethylated MGMT promoter. Patients were classified into high- and low-risk groups using the risk score based on the expression of these two genes. The median overall survival of patients in the low-risk group was significantly longer than that recorded in the high-risk group in the TCGA cohort (15.95 vs. 11.90 months, respectively, P = 0.027) and CGGA cohort (25.90 vs. 11.87 months, respectively, P = 0.0082). The expression of those two genes was confirmed in tissue samples, and the risk scoring model showed that their prognostic value was independent of other clinical characteristics (P = 0.032), such as age. A final nomogram model was constructed, and its good performance was validated (concordance-index = 0.6656). Conclusions: A comprehensive prognostic model for patients with MGMT unmethylated GBM receiving TMZ chemotherapy was constructed using RFC2 and MBD4 gene expression, age, sex, and isocitrate dehydrogenase. The model showed good performance.

Keywords: Bioinformatics, DNA damage response-related genes, glioblastoma, O-6-methylguanine-DNA methyltransferase, nomogram, overall survival, prognostic model, temozolomide chemotherapy


How to cite this article:
Wang R, Peng Y, Wei W, Zhou Y, Li X, Xia Y, Liu Z. A novel nomogram based on DNA damage response-related gene expression in patients with O-6-methylguanine-DNA methyltransferase unmethylated glioblastoma receiving temozolomide chemotherapy: A population-based analysis. Glioma 2022;5:20-8

How to cite this URL:
Wang R, Peng Y, Wei W, Zhou Y, Li X, Xia Y, Liu Z. A novel nomogram based on DNA damage response-related gene expression in patients with O-6-methylguanine-DNA methyltransferase unmethylated glioblastoma receiving temozolomide chemotherapy: A population-based analysis. Glioma [serial online] 2022 [cited 2022 May 16];5:20-8. Available from: http://www.jglioma.com/text.asp?2022/5/1/20/341377




  Introduction Top


Glioblastoma (GBM) is the most common intracranial primary malignant tumor and associated with poor prognosis.[1] The current standard of care for GBM, termed “The Stupp Protocol,” is postoperative concurrent treatment with temozolomide (TMZ) and radiotherapy, followed by adjuvant TMZ chemotherapy.[2],[3] Despite the use of aggressive surgical resection, radiotherapy, and chemotherapy, patients with GBM remain linked to a poor prognosis, with a median overall survival (OS) of 14.6–16.7 months and a 5-year survival rate of 10%.[4] In 2016, the introduction of molecular subtypes revolutionized the World Health Organization classification of central nervous system (CNS) tumors.[5] GBM has been defined as isocitrate dehydrogenase (IDH) wild-type and IDH mutant. In the molecular era, it has been shown that other characteristics (i.e., IDH1/2 mutation, 1p/19q co-deletion, H3K27M mutation, and loss of ATRX) are involved in the classification of gliomas.[6] Therefore, a better understanding of the characteristics of GBM at the molecular level is particularly important for the development of precision therapy.

O-6-methylguanine-DNA methyltransferase (MGMT) is an enzyme involved in DNA damage response (DDR). Methylation in the CpG island promoter region is the key factor for the silencing of the MGMT gene;[7] the frequency of methylation of the MGMT promoter in gliomas is common, reaching 50%–75%.[8] Evidence showed that MGMT is an important prognostic factor and predictor of efficacy in GBM, regardless of the IDH status.[9],[10],[11],[12] Patients with unmethylated MGMT promoter were associated with resistance to TMZ chemotherapy and poor response to treatment.[13],[14],[15] The treatment of patients with unmethylated MGMT promoter remains clinically controversial, and there is no standard therapeutic protocol.[16] Further identification of TMZ-sensitive patients from the population with unmethylated MGMT promoter may expand the proportion of patients who can benefit from chemotherapy with this agent and assist in better implementing individualized treatment.

DDR includes genes related to base excision repair (BER), Fanconi's anemia (FA), homologous recombination repair (HRR), mismatch repair (MMR), nucleotide excision repair (NER), nonhomologous end ligation (NHEJ), translesion DNA synthesis (TLS), and checkpoint factor (CPF).[17] DDR pathways, such as BER, FA, and MMR, play important roles in the development of resistance to TMZ. MGMT is one of the targets or mechanisms responsible for TMZ resistance.[18] However, other DDR pathways may function to overcome resistance to TMZ in patients with unmethylated MGMT.[19] The present study was designed to identify DDR genes with prognostic value in GBM patients with unmethylated MGMT promoter treated with TMZ. The objective was to establish a comprehensive prognostic nomogram model for the guidance of clinical decision-making.


  Materials and Methods Top


Data sources and processing

In this study, 241 GBM cases [according to the fourth edition WHO Classification of CNS, [Supplementary Table 1]] from The Cancer Genome Atlas (TCGA) were collected. Cases with complete data on survival, treatment, and MGMT promoter methylation status were included in the analysis of OS and progression-free survival (PFS) based on the MGMT promoter methylation status. Similarly, GBM cases from the TCGA and Chinese Glioma Genome Atlas (CGGA) were collected. Cases with complete data on survival, treatment, mRNA expression, and MGMT promoter methylation status were screened. In the TCGA and CGGA cohorts, Inclusion criteria were MGMT unmethylation, prior TMZ chemotherapy, and complete survival follow-up data. 45 and 77 patients were eligible for subsequent analysis, respectively. The gene expression data and clinicopathological information of patients with GBM in TCGA and CGGA can be obtained from the TCGA (https://xena.ucsc.edu) and CGGA (http://www.cgga.org.cn) data portals, respectively. Because the data were obtained from TCGA and CGGA, approval for our study by the ethics committee was not necessary.



DNA damage response-related genes

Genes related to DDR were determined according to previous studies.[17] There are 113 channels divided into eight categories, namely BER, FA, HRR, MMR, NER, NHEJ, TLS, and CPF [[Supplementary Table 2] for specific genes].



Establishment of a prognostic model

A scoring model was established using 45 patients with GBM from TCGA, and the relationship between the 113 DDR-related genes and prognosis was analyzed by univariate and multivariate Cox regression. After multivariate Cox regression, two genes were screened to be associated with prognosis. The regression coefficients of these two genes are shown in [Supplementary Table 3]. The risk score for the identified genes was calculated according to the following formula: Risk score = Regression coefficients × gene (gene expression levels). A gene-based survival risk assessment model was established using the multivariate Cox regression coefficient. Next, according to the median score value, patients were classified into high-and low-risk groups. Kaplan–Meier plots and Log-rank tests were used to estimate and compare the OS of patients between the two risk groups; P < 0.05 were set as the cutoff value. A time-dependent receiver operating characteristic (ROC) curve and area under the curve (AUC) were applied to evaluate the predictive accuracy of the risk model and the selected genes.



Immunohistochemistry

The immunohistochemical image of immunoreactivity of replication factor C subunit 2 (RFC2) and methyl-CpG binding domain 4 (MBD4) in GBM and brain tissues was taken from the human protein atlas (https://www. proteinatlas. org/).[20]

Gene set variation analysis

The gene set enrichment analysis package (GSEABase[21]) was used to calculate the enrichment status in Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www. kegg. jp) terms pertaining to the TCGA samples. Spearman Correlation analysis was performed using the risk scores and KEGG terms, and items with P < 0.05 and high correlation coefficients were selected.[21]

Protein interaction

The STRING database (https://cn.string-db.org/) was used to analyze protein-protein interactions, including RFC2, MBD4 and DDR-related genes.[22]

Drug sensitivity analysis

The response of each patient with GBM to chemotherapy was predicted using data from the public pharmacogenomic database Genomics of Drug Sensitivity in Cancer (www.cancerrxgene.org). Prediction of drug sensitivity (IC50) was conducted using the R package “pRRophetic.”[23]

Statistical analysis

All statistical analyses were performed using the R software. Significant quantitative differences between groups were determined using a two-tailed t test. The Chi-squared test was used to analyze correlations of the classified data. Differences in OS and PFS were calculated using the Kaplan–Meier method, and Univariate and multivariate Cox regression analysis was performed using the survival package in R. Spearman correlation was used to determine significant differences. The Gene set variation analysis package was used to calculate the enrichment status in KEGG. The R package survival ROC was used to plot and visualize the ROC curves for the calculation of the AUC.[24] All figures and statistical analyses were performed based on the R language for Windows, version 4.1.2 (http://www.r-project.org). The P < 0.05 denoted statistically significant differences.


  Results Top


Glioblastoma patients with unmethylated O-6-methylguanine-DNA methyltransferase receiving temozolomide had poorer prognosis than those with methylated O-6-methylguanine-DNA methyltransferase

We first verified the difference in prognosis between the groups of GBM patients with methylated and unmethylated MGMT using the TCGA database. The results showed that the median OS of patients with methylated and unmethylated MGMT was 20.60 months and 14.30 months, respectively (P = 0.00023), confirming the prognostic significance of this gene [Figure 1]A. The median PFS was also longer for patients with methylated MGMT versus unmethylated MGMT, and the difference was statistically significant (P = 0.01) [Figure 1]B. The flowchart of this study is illustrated in [Figure 1]C.
Figure 1: Glioblastoma patients with unmethylated O-6-methylguanine-DNA methyltransferase receiving temozolomide had a poorer prognosis than those with methylated O-6-methylguanine-DNA methyltransferase. (A) The median OS of Glioblastoma patients with methylated and unmethylated O-6-methylguanine-DNA methyltransferase from The Cancer Genome Atlas database (20.60 vs. 14.30 months, respectively, P = 0.00023). (B) The median PFS of Glioblastoma patients with methylated and unmethylated O-6-methylguanine-DNA methyltransferase from The Cancer Genome Atlas database (8.48 vs. 7.36 months, respectively, P = 0.01). (C) The flowchart of the study design. CGGA: Chinese Glioma Genome Atlas, DDR: DNA damage response, GBM: Glioblastoma, MGMT: O-6-methylguanine-DNA methyltransferase, OS: Overall survival, PFS: Progression-free survival, PPI: Protein-protein interaction, TCGA: The Cancer Genome Atlas, TMZ: Temozolomide

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A risk-scoring model with independent prognostic value for glioblastoma patients with unmethylated O-6-methylguanine-DNA methyltransferase undergoing temozolomide chemotherapy

Univariate [Figure 2]A and multivariate [Figure 2]B analyses revealed that of the 113 genes analyzed, two DDR genes (i.e., RFC2 and MBD4) were associated with the prognosis of GBM patients with unmethylated MGMT undergoing TMZ chemotherapy. The risk score, survival time, and expression of RFC2 and MBD4 of each patient are shown in [Figure 2]C. Univariate and multivariate analyses revealed that the risk score was an independent prognostic factor for GBM [Figure 2]D and [Figure 2]E.
Figure 2: Screening of RFC2 and methyl-CpG binding domain 4 and construction of a risk scoring model. (A and B) Univariate (A) and multivariate analyses (B) of 113 DNA damage response genes which were associated with the prognosis of Glioblastoma patients with unmethylated O-6-methylguanine-DNA methyltransferase undergoing temozolomide chemotherapy. (C) The risk score, survival time, and expression of RFC2 and methyl-CpG binding domain 4 of each patient. (D and E) Univariate (D) and multivariate (E) analyses of the risk score with other clinical features of Glioblastoma. CI: Confidence interval, DDR: DNA damage response, GBM: Glioblastoma, MBD4: Methyl-CpG binding domain 4, DNA Glycosylase, MGMT: O-6-methylguanine-DNA methyltransferase, RFC2: Replication factor C subunit 2, TMZ: Temozolomide

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The present findings showed that the risk scoring model could be used to identify the subset of patients with unmethylated MGMT and a better prognosis. The median OS was significantly shorter in the high-risk group versus the low-risk group (11.90 vs. 15.95 months, respectively, P = 0.027) [Figure 3]A and [Supplementary Table 4]. The ROC curve demonstrated that the AUC value of 12 months was 0.66 [Figure 3]B. The mRNA expression levels of RFC2 and MBD4 were lower in the low-risk group versus the high-risk group, and the difference was statistically significant [Figure 3]C. We further verified our model using data from the CGGA database. The low-risk group showed a significant advantage in median OS compared with the high-risk group (25.90 vs. 11.87 months, respectively, P = 0.0082) [Figure 3]D and [Supplementary Table 5], with an AUC value of 0.51 (12-month) and 0.62 (24-month) [Figure 3]E. The expression of RFC2 and MBD4 was also significantly different in the high-and low-risk groups [Figure 3]F. The KEGG pathway analysis displayed significant differences in DDR-and metabolism-related pathways between the two groups [Figure 3]G.
Figure 3: The risk scoring model exhibit independent prognostic value for Glioblastoma patients with unmethylated O-6-methylguanine-DNA methyltransferase undergoing temozolomide chemotherapy. (A) The median OS of patients in the high- and low-risk groups from The Cancer Genome Atlas database (11.90 vs. 15.95 months, respectively, P = 0.027). (B) ROC curve of the classification model using data from The Cancer Genome Atlas database. (C) The mRNA expression of RFC2 and methyl-CpG binding domain 4 in the high- and low-risk groups using data from The Cancer Genome Atlas database. (D) The median OS of the high- and low-risk groups from the CCGA database (11.87 vs. 25.90 months, respectively, P = 0.0082). (E) ROC curve of the classification model using data from the CCGA database. (F) The mRNA expression of RFC2 and methyl-CpG binding domain 4 in the high- and low-risk groups using data from the CCGA database. (G) KEGG pathway enrichment analysis of differential pathways in the high- and low-risk groups. CGGA: Chinese Glioma Genome Atlas, GBM: Glioblastoma, KEGG: Kyoto Encyclopedia of Genes and Genomes, MBD4: Methyl-CpG binding domain 4, DNA glycosylase, MGMT: O-6-methylguanine-DNA methyltransferase, OS: Overall survival, RFC2: Replication factor C subunit 2, ROC: Receiver operating characteristic, TCGA: The Cancer Genome Atlas, TMZ: Temozolomide

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Replication factor C subunit 2 and methyl-CpG binding domain 4 played important roles in glioblastoma

The expression of RFC2 and MBD4 genes was confirmed in tissue samples. There were 3 normal tissue samples and 7 tumor tissue samples for analysis of RFC2 and MBD4 expression. Both genes exhibited significantly higher expression in tumor tissues versus normal tissues. Representative immunohistochemistry images are shown here [Figure 4]A and [Figure 4]B. Moreover, the expression levels of RFC2 and MBD4 were correlated with the prognosis of GBM; patients with high expression of RFC2 or MBD4 had a worse prognosis than those with low expression [Figure 4]C and [Figure 4]D. The results suggested that RFC2 and MBD4 may play important roles in the occurrence and development of GBM. Signaling pathway analysis showed that RFC2 and MBD4 mainly interacted with other DDR genes, such as other RFC families, MutL homolog 1 (MLH1), RAD17, proliferating cell nuclear antigen [Figure 4]E.
Figure 4: The role of RFC2 and methyl-CpG binding domain 4 in Glioblastoma. (A and B) The expression of RFC2 (A) and methyl-CpG binding domain 4 (B) in tumor and normal tissue samples (immunohistochemical staining). (C and D) Kaplan–Meier analysis of RFC2 (C) and methyl-CpG binding domain 4 (D) in Glioblastoma. (E) The protein interaction analysis of RFC2 and methyl-CpG binding domain 4. ATAD5: ATPase family AAA domain-containing 5, CHTF18: Chromosome transmission fidelity factor 18, DSCC1: DNA replication and sister chromatid cohesion 1, GBM: Glioblastoma, MBD4: Methyl-CpG binding domain 4, DNA glycosylase, MLH1: Mutl homolog 1, PCNA: Proliferating cell nuclear antigen, RAD17: RAD17 checkpoint clamp loader component, RFC2: Replication factor C subunit 2

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A nomogram model predicting overall survival in patients with O-6-methylguanine-DNA methyltransferase unmethylated glioblastoma receiving temozolomide chemotherapy

Finally, we established a nomogram model by combining the risk score and other clinical features, such as age, sex, and IDH mutation status. Each feature corresponded to a score; the 1-year survival rate was calculated by adding all feature scores to yield the final score [Figure 5]A. The model performed well in terms of predictive accuracy [Figure 5]B.
Figure 5: A nomogram model for predicting the overall survival of Glioblastoma patients with unmethylated O-6-methylguanine-DNA methyltransferase receiving temozolomide chemotherapy. (A) The nomogram model combining the risk score and other clinical features, such as age, sex, and IDH mutation status. (B) The predictive accuracy of the nomogram model. GBM: Glioblastoma, IDH: Isocitrate dehydrogenase, MGMT: O-6-methylguanine-DNA methyltransferase, TMZ: Temozolomide

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  Discussion Top


GBM patients with unmethylated MGMT undergoing TMZ chemotherapy exhibit poor response to treatment and prognosis.[8],[25] Analysis of data collected from TCGA database demonstrated that DDR genes RFC2 and MBD4 were correlated with the prognosis of those patients. The risk scoring model revealed that the expression levels of RFC2 and MBD4 were lower in the low-risk group versus the high-risk group. These findings indicated that the expression levels of the two genes were correlated with the efficacy and prognosis of TMZ chemotherapy. RFC2 and MBD4 genes may be involved in the development of resistance to TMZ. The results were verified in a patient cohort from the CGGA. In addition, the model performed well in terms of predictive accuracy.

The present study obtained better results in the CGGA cohort, with the low-risk group showing a more significant advantage in median OS versus the high-risk group. These observations may be related to the relatively larger size of the CGGA cohort. Thus, studies involving larger patient cohorts are warranted to verify the role of RFC2 and MBD4 in distinguishing the efficacy and prognosis of TMZ chemotherapy in patients with unmethylated MGMT. In univariate and multivariate analyses, there was no statistically significant difference between the IDH mutation status and prognosis in this specific GBM population, which was limited by the small sample size. Previous studies have confirmed that both age and IDH mutation status were associated with the prognosis of GBM; hence, we included these parameters in the nomogram analysis.[26],[27]

TMZ is the fundamental chemotherapeutic agent in the treatment of GBM because it can penetrate the blood-brain barrier.[18] Therefore, overcoming resistance to TMZ is the main concern in the treatment of GBM. Drug-resistant cancer cells recover from the DNA lesions induced by TMZ mainly through alternative repair pathways.[28] When unmethylated, MGMT is activated and can induce resistance to TMZ by blocking the TMZ-induced alkylation of nucleotides.[18] At present, it is considered the most significant factor associated with resistance to TMZ. MMR, BER, and double-strand break repair by the FA pathway also contribute to the development of resistance to TMZ. Functional loss of MMR genes (e.g., MLH1, PMS2, MutS homolog 2 [MSH2], MSH3, and MSH6) and BER genes (e.g., poly (ADP-ribose) polymerase [PARP]) can lead to TMZ resistance.[19],[29],[30] Inhibition of MGMT expression or other DNA repair genes can overcome resistance to TMZ.[31],[32],[33],[34] Our results suggested that GBM patients with unmethylated MGMT and low expression of RFC2 and MBD4 may be sensitive to treatment with TMZ. In addition, pathway analysis suggested that RFC2 and MBD4 were related to MLH1 and other DDR genes. Therefore, the elucidation of this mechanism warrants further investigation in the future.

Currently, studies have investigated the combination of TMZ with PARP inhibitors to treat GBM patients with unmethylated MGMT in an attempt to overcome TMZ resistance.[35],[36] The present study found that the high expression of RFC2 and MBD4 may contribute to TMZ resistance, providing a new direction for the use of combination treatment (including TMZ) in GBM patients with unmethylated MGMT.

This study uses Cox regression model to screen key independent variables for modeling. This method is also used in many literatures. Of course, there are other methods such as the orthogonal partial least squares discrimination analysis and 1.5-fold expression change criterion methods.[37] The difference between the two methods is that the Cox regression model removes the less influential independent variables and retains the key factors for model building. However, orthogonal partial least squares discrimination analysis does not remove independent variables that have little influence, but only removes independent variables that have nothing to do with the dependent variable.

Limitations

The disadvantage of this study is the small sample size, which reduces the accuracy of the model. The ROC curve results show that the model performance is not good enough. This may be related to the number of samples. We currently only have a small number of samples to build and validate the model. With a larger sample size, this result might have been greatly improved.


  Conclusions Top


RFC2 and MBD4 are correlated with the prognosis of GBM patients with unmethylated MGMT promoter. A nomogram model based on this observation may be able to accurately predict the 1-year OS rate.

Acknowledgments

Nil.

Financial support and sponsorship

The present study was funded by the Natural Science Foundation of Guangdong Province, China (Grant No. 2019A1515010274 and 2021A1515010416, to ZL), the National Natural Science Foundation of China (Grant No. 81572500 and 81201982, to ZL), The Fundamental Research Funds for the Central Universities (Grant No. 19ykzd07, to ZL), Science and Technology Projects of Zhuhai (Grant No. ZH2202200013HJL, ZL), and The Science Development Program of Guangzhou (No.201707020001, to YX).

Institutional review board statement

Because the data were obtained from TCGA and CGGA, approval for our study by the ethics committee was not necessary.

Declaration of patient consent

Not applicable.

Conflicts of interest

The authors have no conflicts of interest relating to this work.

Supplementary Material

Supplementary material is available at Glioma online (http://www.jglioma.com/).



 
  References Top

1.
Miller KD, Ostrom QT, Kruchko C, Patil N, Tihan T, Cioffi G, et al. Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin 2021;71:381-406.  Back to cited text no. 1
    
2.
Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 2009;10:459-66.  Back to cited text no. 2
    
3.
Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005;352:987-96.  Back to cited text no. 3
    
4.
Stupp R, Taillibert S, Kanner AA, Kesari S, Steinberg DM, Toms SA, et al. Maintenance therapy with tumor-treating fields plus temozolomide vs. temozolomide alone for glioblastoma: A Randomized Clinical Trial. JAMA 2015;314:2535-43.  Back to cited text no. 4
    
5.
Chen R, Smith-Cohn M, Cohen AL, Colman H. Glioma subclassifications and their clinical significance. Neurotherapeutics 2017;14:284-97.  Back to cited text no. 5
    
6.
Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro Oncol 2021;23:1231-51.  Back to cited text no. 6
    
7.
Esteller M. CpG island hypermethylation and tumor suppressor genes: A booming present, a brighter future. Oncogene 2002;21:5427-40.  Back to cited text no. 7
    
8.
Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 2005;352:997-1003.  Back to cited text no. 8
    
9.
Gomes I, Moreno DA, Dos Reis MB, da Silva LS, Leal LF, Gonçalves GM, et al. Low MGMT digital expression is associated with a better outcome of IDH1 wildtype glioblastomas treated with temozolomide. J Neurooncol 2021;151:135-44.  Back to cited text no. 9
    
10.
Chai R, Li G, Liu Y, Zhang K, Zhao Z, Wu F, et al. Predictive value of MGMT promoter methylation on the survival of TMZ treated IDH-mutant glioblastoma. Cancer Biol Med 2021;18:272-82.  Back to cited text no. 10
    
11.
Brandner S, McAleenan A, Kelly C, Spiga F, Cheng HY, Dawson S, et al. MGMT promoter methylation testing to predict overall survival in people with glioblastoma treated with temozolomide: A comprehensive meta-analysis based on a Cochrane Systematic Review. Neuro Oncol 2021;23:1457-69.  Back to cited text no. 11
    
12.
Butler M, Pongor L, Su YT, Xi L, Raffeld M, Quezado M, et al. MGMT status as a clinical biomarker in glioblastoma. Trends Cancer 2020;6:380-91.  Back to cited text no. 12
    
13.
Oldrini B, Vaquero-Siguero N, Mu Q, Kroon P, Zhang Y, Galán-Ganga M, et al. MGMT genomic rearrangements contribute to chemotherapy resistance in gliomas. Nat Commun 2020;11:3883.  Back to cited text no. 13
    
14.
Villalva C, Cortes U, Wager M, Tourani JM, Rivet P, Marquant C, et al. O6-Methylguanine-methyltransferase (MGMT) promoter methylation status in glioma stem-like cells is correlated to temozolomide sensitivity under differentiation-promoting conditions. Int J Mol Sci 2012;13:6983-94.  Back to cited text no. 14
    
15.
van Nifterik KA, van den Berg J, van der Meide WF, Ameziane N, Wedekind LE, Steenbergen RD, et al. Absence of the MGMT protein as well as methylation of the MGMT promoter predict the sensitivity for temozolomide. Br J Cancer 2010;103:29-35.  Back to cited text no. 15
    
16.
Liu D, Yang T, Ma W, Wang Y. Clinical strategies to manage adult glioblastoma patients without MGMT hypermethylation. J Cancer 2022;13:354-63.  Back to cited text no. 16
    
17.
Wang Z, Zhao J, Wang G, Zhang F, Zhang Z, Zhang F, et al. Comutations in DNA damage response pathways serve as potential biomarkers for immune checkpoint blockade. Cancer Res 2018;78:6486-96.  Back to cited text no. 17
    
18.
Lang F, Liu Y, Chou FJ, Yang C. Genotoxic therapy and resistance mechanism in gliomas. Pharmacol Ther 2021;228:107922.  Back to cited text no. 18
    
19.
Tomar MS, Kumar A, Srivastava C, Shrivastava A. Elucidating the mechanisms of Temozolomide resistance in gliomas and the strategies to overcome the resistance. Biochim Biophys Acta Rev Cancer 2021;1876:188616.  Back to cited text no. 19
    
20.
Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science 2015;347:1260419.  Back to cited text no. 20
    
21.
Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013;14:7.  Back to cited text no. 21
    
22.
Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021;49:D605-12.  Back to cited text no. 22
    
23.
Geeleher P, Cox N, Huang RS. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014;9:e107468.  Back to cited text no. 23
    
24.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: An open-source package for R and S+to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77.  Back to cited text no. 24
    
25.
Cen L, Carlson BL, Pokorny JL, Mladek AC, Grogan PT, Schroeder MA, et al. Efficacy of protracted temozolomide dosing is limited in MGMT unmethylated GBM xenograft models. Neuro Oncol 2013;15:735-46.  Back to cited text no. 25
    
26.
Fan F, Zhang H, Dai Z, Zhang Y, Xia Z, Cao H, et al. A comprehensive prognostic signature for glioblastoma patients based on transcriptomics and single cell sequencing. Cell Oncol (Dordr) 2021;44:917-35.  Back to cited text no. 26
    
27.
Cai HQ, Liu AS, Zhang MJ, Liu HJ, Meng XL, Qian HP, et al. Identifying predictive gene expression and signature related to temozolomide sensitivity of glioblastomas. Front Oncol 2020;10:669.  Back to cited text no. 27
    
28.
Sarkaria JN, Kitange GJ, James CD, Plummer R, Calvert H, Weller M, et al. Mechanisms of chemoresistance to alkylating agents in malignant glioma. Clin Cancer Res 2008;14:2900-8.  Back to cited text no. 28
    
29.
Nguyen SA, Stechishin OD, Luchman HA, Lun XQ, Senger DL, Robbins SM, et al. Novel MSH6 mutations in treatment-naïve glioblastoma and anaplastic oligodendroglioma contribute to temozolomide resistance independently of MGMT promoter methylation. Clin Cancer Res 2014;20:4894-903.  Back to cited text no. 29
    
30.
Wu S, Li X, Gao F, de Groot JF, Koul D, Yung WK. PARP-mediated PARylation of MGMT is critical to promote repair of temozolomide-induced O6-methylguanine DNA damage in glioblastoma. Neuro Oncol 2021;23:920-31.  Back to cited text no. 30
    
31.
Ohba S, Yamashiro K, Hirose Y. Inhibition of DNA repair in combination with temozolomide or dianhydrogalactiol overcomes temozolomide-resistant glioma cells. Cancers (Basel) 2021;13:2570.  Back to cited text no. 31
    
32.
Yang B, Ma YB, Chu SH. Silencing SATB1 overcomes temozolomide resistance by downregulating MGMT expression and upregulating SLC22A18 expression in human glioblastoma cells. Cancer Gene Ther 2018;25:309-16.  Back to cited text no. 32
    
33.
Tsai CK, Huang LC, Wu YP, Kan IY, Hueng DY. SNAP reverses temozolomide resistance in human glioblastoma multiforme cells through down-regulation of MGMT. FASEB J 2019;33:14171-84.  Back to cited text no. 33
    
34.
Chen X, Zhang M, Gan H, Wang H, Lee JH, Fang D, et al. A novel enhancer regulates MGMT expression and promotes temozolomide resistance in glioblastoma. Nat Commun 2018;9:2949.  Back to cited text no. 34
    
35.
Zampieri LX, Sboarina M, Cacace A, Grasso D, Thabault L, Hamelin L, et al. Olaparib is a mitochondrial complex I inhibitor that kills temozolomide-resistant human glioblastoma cells. Int J Mol Sci 2021;22:11938.  Back to cited text no. 35
    
36.
Sim HW, McDonald KL, Lwin Z, Barnes EH, Rosenthal M, Foote MC, et al. A randomized phase II trial of veliparib, radiotherapy, and temozolomide in patients with unmethylated MGMT glioblastoma: The VERTU study. Neuro Oncol 2021;23:1736-49.  Back to cited text no. 36
    
37.
Cao W, Liu JN, Liu Z, Wang X, Han ZG, Ji T, et al. A three-lncRNA signature derived from the Atlas of ncRNA in cancer (TANRIC) database predicts the survival of patients with head and neck squamous cell carcinoma. Oral Oncol 2017;65:94-101.  Back to cited text no. 37
    


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