Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning
Purpose: TNF family members (TFMs) play a crucial role in different types of cancers, with TNF Receptor Superfamily Member 19 (TNFRSF19) standing out as a particularly important member in this category. Further research is necessary to investigate the potential impact of TFMs on prognosis prediction...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024044761 |
| _version_ | 1797245008147906560 |
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| author | Youwei Guo Quanwei Zhou Min Wei Jianfeng Fan He Huang |
| author_facet | Youwei Guo Quanwei Zhou Min Wei Jianfeng Fan He Huang |
| author_sort | Youwei Guo |
| collection | DOAJ |
| description | Purpose: TNF family members (TFMs) play a crucial role in different types of cancers, with TNF Receptor Superfamily Member 19 (TNFRSF19) standing out as a particularly important member in this category. Further research is necessary to investigate the potential impact of TFMs on prognosis prediction and to elucidate the function and potential therapeutic targets linked to TNFRSF19 expression in gliomas. Methods: Three databases provided the data on gene expression and clinical information. Fourteen prognostic members were found through univariate Cox analysis and subsequently utilized to construct TFMs-based model in LASSO and multivariate Cox analyses. TFMs-based subtypes based on the expression profile were identified using an unsupervised clustering method. Machine learning algorithm identified key genes linked to prognostic model and subtype. A sequence of immune infiltrations was evaluated using the ssGSEA and ESTIMATE algorithms. Immunohistochemistry was used to examine the patterns of expression and the clinical significance of TNFRSF19. Results: Our development of a prognostic model and subtypes based on the TNF family was successful, resulting in accurate predictions of prognosis. The findings indicate that TNFRSF19 exhibited strong performance. Upregulation of TNFRSF19 was correlated with malignant phenotypes and poor prognosis, which was confirmed through immunohistochemistry. TNFRSF19 played a role in reshaping the immunosuppressive microenvironment in gliomas, and multiple drug-targeted TNFRSF19 molecules were identified. Conclusions: The TMF-based prognostic model and subtype can facilitate treatment decisions for glioma. TNFRSF19 is an outstanding representative of a predictor of prognosis and immunotherapy effect in gliomas. |
| first_indexed | 2024-04-24T19:20:04Z |
| format | Article |
| id | doaj.art-cc0899ae4a1c43cab771af742e999ba3 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T19:20:04Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-cc0899ae4a1c43cab771af742e999ba32024-03-26T04:26:52ZengElsevierHeliyon2405-84402024-04-01107e28445Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learningYouwei Guo0Quanwei Zhou1Min Wei2Jianfeng Fan3He Huang4Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan Province, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaThe National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, ChinaNational Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan Province, ChinaDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan Province, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan Province, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; Corresponding author. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan Province, China.Purpose: TNF family members (TFMs) play a crucial role in different types of cancers, with TNF Receptor Superfamily Member 19 (TNFRSF19) standing out as a particularly important member in this category. Further research is necessary to investigate the potential impact of TFMs on prognosis prediction and to elucidate the function and potential therapeutic targets linked to TNFRSF19 expression in gliomas. Methods: Three databases provided the data on gene expression and clinical information. Fourteen prognostic members were found through univariate Cox analysis and subsequently utilized to construct TFMs-based model in LASSO and multivariate Cox analyses. TFMs-based subtypes based on the expression profile were identified using an unsupervised clustering method. Machine learning algorithm identified key genes linked to prognostic model and subtype. A sequence of immune infiltrations was evaluated using the ssGSEA and ESTIMATE algorithms. Immunohistochemistry was used to examine the patterns of expression and the clinical significance of TNFRSF19. Results: Our development of a prognostic model and subtypes based on the TNF family was successful, resulting in accurate predictions of prognosis. The findings indicate that TNFRSF19 exhibited strong performance. Upregulation of TNFRSF19 was correlated with malignant phenotypes and poor prognosis, which was confirmed through immunohistochemistry. TNFRSF19 played a role in reshaping the immunosuppressive microenvironment in gliomas, and multiple drug-targeted TNFRSF19 molecules were identified. Conclusions: The TMF-based prognostic model and subtype can facilitate treatment decisions for glioma. TNFRSF19 is an outstanding representative of a predictor of prognosis and immunotherapy effect in gliomas.http://www.sciencedirect.com/science/article/pii/S2405844024044761GliomaTNFImmune cellSubtypePrognosisTNFRSF19 |
| spellingShingle | Youwei Guo Quanwei Zhou Min Wei Jianfeng Fan He Huang Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning Heliyon Glioma TNF Immune cell Subtype Prognosis TNFRSF19 |
| title | Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning |
| title_full | Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning |
| title_fullStr | Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning |
| title_full_unstemmed | Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning |
| title_short | Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning |
| title_sort | association of tnfrsf19 with a tnf family based prognostic model and subtypes in gliomas using machine learning |
| topic | Glioma TNF Immune cell Subtype Prognosis TNFRSF19 |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024044761 |
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