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Copyright (c) 2025 Huijun Li, Bin Luo, Yibadaiti Tulufu, Xiong Wang, Daoyuan Yue
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.A super-enhancer-related gene signature predicts prognosis and immune microenvironment features in glioma
Corresponding Author(s) : Xiong Wang
Cellular and Molecular Biology,
Vol. 71 No. 6: Issue 6
Abstract
Glioma is the most frequent malignant tumor in the brain. Super-enhancer (SE) is a class of transcriptional activator, which drives gene expression. SE-related genes (SERGs) affect occurrence and development of several tumors. We explored the predictive role of SERGs in the prognosis and immune features of glioma. A total of 1557 glioma patients were collected from four data sets, including The Cancer Genomic Atlas (TCGA, n = 691), the Chinese Glioma Genomic Atlas (CGGA) array (n = 286), the CGGA sequencing (n = 316), and GSE16011 (n = 264) from Gene Expression Omnibus (GEO) database. SERGs were selected from SEdb (http://www.licpathway.net/sedb), a comprehensive human SE database. Survival analysis and visualization were performed using the R packages survival (v3.3-1) and survminer (v0.4.9). Immune subtype classification was conducted with the ImmuneSubtypeClassifier (v0.1.0) R package. A nomogram was generated using the rms (v6.7-1) package. A risk score model based on 13 super-enhancer-related genes (SERGs) was constructed, demonstrating that patients in the low-risk group had significantly better prognosis. The SERGs signature significantly correlated with age, molecular and immune subtypes, IDH mutation, MTMG promoter methylation, 1p19q co-deletion, and expression of immune checkpoint genes in glioma patients. The SERGs signature could predict the prognosis and immune features of glioma, and SERGs might serve as novel immunotherapy options for glioma.
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