A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma
Abstract 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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-022-09791-y |
| _version_ | 1818488536284790784 |
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| author | Xinrui Wu Chuanyu Li Zhisu Wang Yundi Zhang Shifan Liu Siqi Chen Shuai Chen Wangrui Liu Xiaoman Liu |
| author_facet | Xinrui Wu Chuanyu Li Zhisu Wang Yundi Zhang Shifan Liu Siqi Chen Shuai Chen Wangrui Liu Xiaoman Liu |
| author_sort | Xinrui Wu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-10T16:52:18Z |
| format | Article |
| id | doaj.art-6056de8f1da740eb9bb22335ac7be76e |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-12-10T16:52:18Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-6056de8f1da740eb9bb22335ac7be76e2022-12-22T01:40:51ZengBMCBMC Cancer1471-24072022-07-0122111710.1186/s12885-022-09791-yA bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastomaXinrui Wu0Chuanyu Li1Zhisu Wang2Yundi Zhang3Shifan Liu4Siqi Chen5Shuai Chen6Wangrui Liu7Xiaoman Liu8Department of oncology and chemotherapy, Affiliated Hospital of Nantong UniversityDepartment of Neurosurgery, Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of Clinical Medicine, Medical School of Nantong UniversityNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of Medical imaging, Medical School of Nantong UniversityDepartment of Medical imaging, Medical School of Nantong UniversityDepartment of measurement and control technology and instruments, School of mechanical engineering, Nantong UniversityDepartment of Neurosurgery, Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of oncology and chemotherapy, Affiliated Hospital of Nantong UniversityAbstract 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.https://doi.org/10.1186/s12885-022-09791-ym7G methylationImmune infiltrationSignatureBiomarkersCancerHealth informatics |
| spellingShingle | Xinrui Wu Chuanyu Li Zhisu Wang Yundi Zhang Shifan Liu Siqi Chen Shuai Chen Wangrui Liu Xiaoman Liu A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma BMC Cancer m7G methylation Immune infiltration Signature Biomarkers Cancer Health informatics |
| title | A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma |
| title_full | A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma |
| title_fullStr | A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma |
| title_full_unstemmed | A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma |
| title_short | A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma |
| title_sort | bioinformatic analysis study of m7g regulator mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma |
| topic | m7G methylation Immune infiltration Signature Biomarkers Cancer Health informatics |
| url | https://doi.org/10.1186/s12885-022-09791-y |
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