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A bioinformatic analysis study of m(7)G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma

BACKGROUND: Glioblastoma is one of the most common brain cancers in adults, and is characterized by recurrence and little curative effect. An effective treatment for glioblastoma patients remains elusive worldwide. 7-methylguanosine (m7G) is a common RNA modification, and its role in tumors has beco...

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Autores principales: Wu, Xinrui, Li, Chuanyu, Wang, Zhisu, Zhang, Yundi, Liu, Shifan, Chen, Siqi, Chen, Shuai, Liu, Wangrui, Liu, Xiaoman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251941/
https://www.ncbi.nlm.nih.gov/pubmed/35788194
http://dx.doi.org/10.1186/s12885-022-09791-y
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author Wu, Xinrui
Li, Chuanyu
Wang, Zhisu
Zhang, Yundi
Liu, Shifan
Chen, Siqi
Chen, Shuai
Liu, Wangrui
Liu, Xiaoman
author_facet Wu, Xinrui
Li, Chuanyu
Wang, Zhisu
Zhang, Yundi
Liu, Shifan
Chen, Siqi
Chen, Shuai
Liu, Wangrui
Liu, Xiaoman
author_sort Wu, Xinrui
collection PubMed
description BACKGROUND: Glioblastoma is one of the most common brain cancers in adults, and is characterized by recurrence and little curative effect. An effective treatment for glioblastoma patients remains elusive worldwide. 7-methylguanosine (m7G) is a common RNA modification, and its role in tumors has become a research hotspot. METHODS: By searching for differentially expressed genes related to m7G, we generated a prognostic signature via cluster analysis and established classification criteria of high and low risk scores. The effectiveness of classification was validated using the Non-negative matrix factorization (NMF) algorithm, and repeatedly verified using training and test groups. The dimension reduction method was used to clearly show the difference and clinical significance of the data. All analyses were performed via R (version 4.1.2). RESULTS: According to the signature that included four genes (TMOD2, CACNG2, PLOD3, and TMSB10), glioblastoma patients were divided into high and low risk score groups. The survival rates between the two groups were significantly different, and the predictive abilities for 1-, 3-, and 5-year survivals were effective. We further established a Nomogram model to further examine the signature,as well as other clinical factors, with remaining significant results. Our signature can act as an independent prognostic factor related to immune-related processes in glioblastoma. CONCLUSIONS: Our research addresses the gap in knowledge in the m7G and glioblastoma research fields. The establishment of a prognostic signature and the extended analysis of the tumor microenvironment, immune correlation, and tumor mutation burden further suggest the important role of m7G in the development and development of this disease. This work will provide support for future research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09791-y.
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spelling pubmed-92519412022-07-05 A bioinformatic analysis study of m(7)G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma Wu, Xinrui Li, Chuanyu Wang, Zhisu Zhang, Yundi Liu, Shifan Chen, Siqi Chen, Shuai Liu, Wangrui Liu, Xiaoman BMC Cancer Research BACKGROUND: Glioblastoma is one of the most common brain cancers in adults, and is characterized by recurrence and little curative effect. An effective treatment for glioblastoma patients remains elusive worldwide. 7-methylguanosine (m7G) is a common RNA modification, and its role in tumors has become a research hotspot. METHODS: By searching for differentially expressed genes related to m7G, we generated a prognostic signature via cluster analysis and established classification criteria of high and low risk scores. The effectiveness of classification was validated using the Non-negative matrix factorization (NMF) algorithm, and repeatedly verified using training and test groups. The dimension reduction method was used to clearly show the difference and clinical significance of the data. All analyses were performed via R (version 4.1.2). RESULTS: According to the signature that included four genes (TMOD2, CACNG2, PLOD3, and TMSB10), glioblastoma patients were divided into high and low risk score groups. The survival rates between the two groups were significantly different, and the predictive abilities for 1-, 3-, and 5-year survivals were effective. We further established a Nomogram model to further examine the signature,as well as other clinical factors, with remaining significant results. Our signature can act as an independent prognostic factor related to immune-related processes in glioblastoma. CONCLUSIONS: Our research addresses the gap in knowledge in the m7G and glioblastoma research fields. The establishment of a prognostic signature and the extended analysis of the tumor microenvironment, immune correlation, and tumor mutation burden further suggest the important role of m7G in the development and development of this disease. This work will provide support for future research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09791-y. BioMed Central 2022-07-04 /pmc/articles/PMC9251941/ /pubmed/35788194 http://dx.doi.org/10.1186/s12885-022-09791-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wu, Xinrui
Li, Chuanyu
Wang, Zhisu
Zhang, Yundi
Liu, Shifan
Chen, Siqi
Chen, Shuai
Liu, Wangrui
Liu, Xiaoman
A bioinformatic analysis study of m(7)G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma
title A bioinformatic analysis study of m(7)G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma
title_full A bioinformatic analysis study of m(7)G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma
title_fullStr A bioinformatic analysis study of m(7)G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma
title_full_unstemmed A bioinformatic analysis study of m(7)G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma
title_short A bioinformatic analysis study of m(7)G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma
title_sort bioinformatic analysis study of m(7)g regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251941/
https://www.ncbi.nlm.nih.gov/pubmed/35788194
http://dx.doi.org/10.1186/s12885-022-09791-y
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