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Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683591/ https://www.ncbi.nlm.nih.gov/pubmed/37934565 http://dx.doi.org/10.18632/aging.205179 |
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author | Xiao, Zhenyong Li, Jinwei Liang, Cong Liu, Yamei Zhang, Yuxiu Zhang, Yuxia Liu, Quan Yan, Xianlei |
author_facet | Xiao, Zhenyong Li, Jinwei Liang, Cong Liu, Yamei Zhang, Yuxiu Zhang, Yuxia Liu, Quan Yan, Xianlei |
author_sort | Xiao, Zhenyong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10683591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Impact Journals |
record_format | MEDLINE/PubMed |
spelling | pubmed-106835912023-11-30 Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma Xiao, Zhenyong Li, Jinwei Liang, Cong Liu, Yamei Zhang, Yuxiu Zhang, Yuxia Liu, Quan Yan, Xianlei Aging (Albany NY) Research Paper 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. Impact Journals 2023-11-06 /pmc/articles/PMC10683591/ /pubmed/37934565 http://dx.doi.org/10.18632/aging.205179 Text en Copyright: © 2023 Xiao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Xiao, Zhenyong Li, Jinwei Liang, Cong Liu, Yamei Zhang, Yuxiu Zhang, Yuxia Liu, Quan Yan, Xianlei Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma |
title | Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma |
title_full | Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma |
title_fullStr | Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma |
title_full_unstemmed | Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma |
title_short | Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma |
title_sort | identification of m5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683591/ https://www.ncbi.nlm.nih.gov/pubmed/37934565 http://dx.doi.org/10.18632/aging.205179 |
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