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Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis

BACKGROUND: Glioblastoma multiforme (GBM) is a highly, malignant tumor of the primary central nervous system. Patients diagnosed with this type of tumor have a poor prognosis. Lymphocyte activation plays important roles in the development of cancers and its therapeutic treatments. OBJECTIVE: We soug...

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Autores principales: Lan, Yujia, Zhao, Erjie, Zhang, Xinxin, Zhu, Xiaojing, Wan, Linyun, A, Suru, Ping, Yanyan, Wang, Yihan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401751/
https://www.ncbi.nlm.nih.gov/pubmed/34527446
http://dx.doi.org/10.7717/peerj.12070
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author Lan, Yujia
Zhao, Erjie
Zhang, Xinxin
Zhu, Xiaojing
Wan, Linyun
A, Suru
Ping, Yanyan
Wang, Yihan
author_facet Lan, Yujia
Zhao, Erjie
Zhang, Xinxin
Zhu, Xiaojing
Wan, Linyun
A, Suru
Ping, Yanyan
Wang, Yihan
author_sort Lan, Yujia
collection PubMed
description BACKGROUND: Glioblastoma multiforme (GBM) is a highly, malignant tumor of the primary central nervous system. Patients diagnosed with this type of tumor have a poor prognosis. Lymphocyte activation plays important roles in the development of cancers and its therapeutic treatments. OBJECTIVE: We sought to identify an efficient lymphocyte activation-associated gene signature that could predict the progression and prognosis of GBM. METHODS: We used univariate Cox proportional hazards regression and stepwise regression algorithm to develop a lymphocyte activation-associated gene signature in the training dataset (TCGA, n = 525). Then, the signature was validated in two datasets, including GSE16011 (n = 150) and GSE13041 (n = 191) using the Kaplan Meier method. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. RESULTS: We identified a lymphocyte activation-associated gene signature (TCF3, IGFBP2, TYRO3 and NOD2) in the training dataset and classified the patients into high-risk and low-risk groups with significant differences in overall survival (median survival 15.33 months vs 12.57 months, HR = 1.55, 95% CI [1.28–1.87], log-rank test P < 0.001). This signature showed similar prognostic values in the other two datasets. Further, univariate and multivariate Cox proportional hazards regression models analysis indicated that the signature was an independent prognostic factor for GBM patients. Moreover, we determined that there were differences in lymphocyte activity between the high- and low-risk groups of GBM patients among all datasets. Furthermore, the lymphocyte activation-associated gene signature could significantly predict the survival of patients with certain features, including IDH-wildtype patients and patients undergoing radiotherapy. In addition, the signature may also improve the prognostic power of age. CONCLUSIONS: In summary, our results suggested that the lymphocyte activation-associated gene signature is a promising factor for the survival of patients, which is helpful for the prognosis of GBM patients.
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spelling pubmed-84017512021-09-14 Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis Lan, Yujia Zhao, Erjie Zhang, Xinxin Zhu, Xiaojing Wan, Linyun A, Suru Ping, Yanyan Wang, Yihan PeerJ Bioinformatics BACKGROUND: Glioblastoma multiforme (GBM) is a highly, malignant tumor of the primary central nervous system. Patients diagnosed with this type of tumor have a poor prognosis. Lymphocyte activation plays important roles in the development of cancers and its therapeutic treatments. OBJECTIVE: We sought to identify an efficient lymphocyte activation-associated gene signature that could predict the progression and prognosis of GBM. METHODS: We used univariate Cox proportional hazards regression and stepwise regression algorithm to develop a lymphocyte activation-associated gene signature in the training dataset (TCGA, n = 525). Then, the signature was validated in two datasets, including GSE16011 (n = 150) and GSE13041 (n = 191) using the Kaplan Meier method. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. RESULTS: We identified a lymphocyte activation-associated gene signature (TCF3, IGFBP2, TYRO3 and NOD2) in the training dataset and classified the patients into high-risk and low-risk groups with significant differences in overall survival (median survival 15.33 months vs 12.57 months, HR = 1.55, 95% CI [1.28–1.87], log-rank test P < 0.001). This signature showed similar prognostic values in the other two datasets. Further, univariate and multivariate Cox proportional hazards regression models analysis indicated that the signature was an independent prognostic factor for GBM patients. Moreover, we determined that there were differences in lymphocyte activity between the high- and low-risk groups of GBM patients among all datasets. Furthermore, the lymphocyte activation-associated gene signature could significantly predict the survival of patients with certain features, including IDH-wildtype patients and patients undergoing radiotherapy. In addition, the signature may also improve the prognostic power of age. CONCLUSIONS: In summary, our results suggested that the lymphocyte activation-associated gene signature is a promising factor for the survival of patients, which is helpful for the prognosis of GBM patients. PeerJ Inc. 2021-08-25 /pmc/articles/PMC8401751/ /pubmed/34527446 http://dx.doi.org/10.7717/peerj.12070 Text en ©2021 Lan 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/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Lan, Yujia
Zhao, Erjie
Zhang, Xinxin
Zhu, Xiaojing
Wan, Linyun
A, Suru
Ping, Yanyan
Wang, Yihan
Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis
title Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis
title_full Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis
title_fullStr Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis
title_full_unstemmed Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis
title_short Prognostic impact of a lymphocyte activation-associated gene signature in GBM based on transcriptome analysis
title_sort prognostic impact of a lymphocyte activation-associated gene signature in gbm based on transcriptome analysis
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401751/
https://www.ncbi.nlm.nih.gov/pubmed/34527446
http://dx.doi.org/10.7717/peerj.12070
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