Integrative Analysis of DNA Methylation and Gene Expression Identify a Three-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Abstract

Background/Aims: In the current study, we performed an integrated analysis of genome-wide methylation and gene expression data to find novel prognostic genes for lower-grade gliomas (LGGs). Methods: First, TCGA methylation data were used to identify prognostic genes associated with promoter methylation. Second, candidate genes that were stably regulated by promoter methylation were explored. Third, Cox proportional hazards regression analysis was used to generate a prognostic signature, and the signature genes were used to construct a survival risk score system. Results: Three genes (EMP3, GSX2 and EMILIN3) were selected as signature genes. These three signature genes were used to construct a survival risk score system. The high-risk group exhibited significantly worse overall survival (OS) and relapse-free survival (RFS) as compared to the low-risk group in the TCGA dataset. The association of the three-gene prognostic signature with patient’ survival was then validated using the CGGA dataset. Moreover, Kaplan-Meier plots showed that the three-gene prognostic signature risk remarkably stratified grade II and grade III patients in terms of both OS and RFS in the TCGA cohort. There was also a significant difference between the low- and high-risk groups in IDH wild-type glioma patients, indicating that the three-gene signature may be able to help in predicting prognosis for patients with IDH wild-type gliomas. Conclusion: We identified and validated a three-gene (EMP3, GSX2 and EMILIN3) prognostic signature in LGGs by integrating multidimensional genomic data from the TCGA and CGGA datasets, which may help in fine-tuning the current histology-based tumors classification system and providing better stratification for future clinical trials.