Genome‐wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma. Issue 2 (11th December 2021)

• Record Type: Journal Article
• Authors: Guo, Wenna; Ma, Shanshan; Zhang, Yanting; Liu, Hongtao; Li, Ya; Xu, Ji‐Tian; Yang, Bo; Guan, Fangxia
• Is Part Of: Journal of cellular and molecular medicine. Volume 26:Issue 2(2022)
• Page Start: 449
• Page End: 461
• Publication Date: 2021-12-11
• Subjects: DNA methylation -- immunotherapy -- lower grade glioma -- prognostic biomarker -- regression analysis -- risk stratification; Cytology; Medicine; Molecular Biology; Cytologie -- Périodiques; Médecine -- Périodiques; Biologie moléculaire -- Périodiques; Cytology -- Periodicals; Medicine -- Periodicals; Molecular biology -- Periodicals; 611.01805
• DOI: 10.1111/jcmm.17101
• Languages: English
• ISSNs: 1582-1838

Abstract

Abstract: Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.