Image_2_Development and Validation of a Novel DNA Methylation-Driven Gene Based Molecular Classification and Predictive Model for Overall Survival and.TIF (213.17 kB)
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Image_2_Development and Validation of a Novel DNA Methylation-Driven Gene Based Molecular Classification and Predictive Model for Overall Survival and Immunotherapy Response in Patients With Glioblastoma: A Multiomic Analysis.TIF

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posted on 03.09.2020, 04:20 by Zihao Wang, Lu Gao, Xiaopeng Guo, Wei Lian, Kan Deng, Bing Xing
Purpose

Glioblastoma (GBM) is the most common primary malignant tumor of the central nervous system, with a 5-year overall survival (OS) rate of only 5.6%. This study aimed to develop a novel DNA methylation-driven gene (MDG)-based molecular classification and risk model for individualized prognosis prediction for GBM patients.

Methods

The DNA methylation profiles (458 samples) and gene expression profiles (376 samples) of patients were enrolled to identify MDGs using the MethylMix algorithm. Unsupervised consensus clustering was performed to develop the MDG-based molecular classification. By performing the univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis, a MDG-based prognostic model was developed and validated. Then, Bisulfite Amplicon Sequencing (BSAS) and quantitative real-time polymerase chain reaction (qPCR) were performed to verify the methylation and expressions of MDGs in GBM cell lines.

Results

A total of 199 MDGs were identified, the expression patterns of which enabled TCGA and CGGA GBM patients to be divided into 2 clusters by unsupervised consensus clustering. Cluster 1 patients commonly exhibited a poor prognosis, were older in age, and were more sensitive to immunotherapies. Then, six MDGs (ANKRD10, BMP2, LOXL1, RPL39L, TMEM52, and VILL) were further selected to construct the prognostic risk score model, which was validated in the CGGA cohort. Kaplan-Meier survival analysis demonstrated that high-risk patients had significantly poorer OS than low-risk patients (logrank P = 3.338 × 10-6). Then, a prognostic nomogram was constructed and validated. Calibration plots, receiver operating characteristic curves, and decision curve analysis indicated excellent predictive performance for the nomogram in both the TCGA training and CGGA validation cohorts. Finally, in vitro BSAS and qPCR analysis validated that the expressions of the MDGs were negatively regulated by methylations of target genes, especially promoter region methylation.

Conclusion

The MDG-based prognostic model could serve as a promising prognostic indicator and potential therapeutic target to facilitate individualized survival prediction and better treatment options for GBM patients.

History

References