Research Paper Volume 15, Issue 21 pp 12275—12295

Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma

Zhenyong Xiao1, *, , Jinwei Li1,2, *, , Cong Liang3, *, , Yamei Liu1, , Yuxiu Zhang1, , Yuxia Zhang1, , Quan Liu1, , Xianlei Yan1,2, ,

  • 1 Department of Neurosurgery, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou 545000, Guangxi, China
  • 2 Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610000, Sichuan, China
  • 3 Department of Pharmacy, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou 545000, Guangxi, China
* Co first author

Received: June 16, 2023       Accepted: October 2, 2023       Published: November 6, 2023
How to Cite

Copyright: © 2023 Xiao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Glioma is a common intracranial tumor and is generally associated with poor prognosis. Recently, numerous studies illustrated the importance of 5-methylcytosine (m5C) RNA modification to tumorigenesis. However, the prognostic value and immune correlation of m5C in glioma remain unclear. We obtained RNA expression and clinical information from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) datasets to analyze. Nonnegative matrix factorization (NMF) was used to classify patients into two subgroups and compare these patients in survival and clinicopathological characteristics. CIBERSORT and single-sample gene-set algorithm (ssGSEA) methods were used to investigate the relationship between m5C and the immune environment. The Weighted correlation network analysis (WGCNA) and univariate Cox proportional hazard model (CoxPH) were used to construct a m5C-related signature. Most of m5C RNA methylation regulators presented differential expression and prognostic values. There were obvious relationships between immune infiltration cells and m5C regulators, especially NSUN7. In the m5C-related module from WGCNA, we found SEPT3, CHI3L1, PLBD1, PHYHIPL, SAMD8, RAP1B, B3GNT5, RER1, PTPN7, SLC39A1, and MXI1 were prognostic factors for glioma, and they were used to construct the signature. The great significance of m5C-related signature in predicting the survival of patients with glioma was confirmed in the validation sets and CGGA cohort.


CGGA: Chinese Glioma Genome Atlas; m5C: 5-methylcytosine m5C; NMF: Nonnegative matrix factorization; ssGSEA: single-sample gene-set algorithm; WGCNA: Weighted correlation network analysis; CoxPH: Cox proportional hazard model; LGG: lower-grade glioma; GBM: glioblastoma.