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A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma

BACKGROUND: Glioma is the most common central nervous system (CNS) cancer with a short survival period and a poor prognosis. The S100 family gene, comprising 25 members, relates to diverse biological processes of human malignancies. Nonetheless, the significance of S100 genes in predicting the progn...

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Autores principales: Hu, Yifang, Song, Jiahang, Wang, Zhen, Kan, Jingbao, Ge, Yaoqi, Wang, Dan, Zhang, Rihua, Zhang, Wensong, Liu, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548170/
https://www.ncbi.nlm.nih.gov/pubmed/34712325
http://dx.doi.org/10.1155/2021/3586589
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author Hu, Yifang
Song, Jiahang
Wang, Zhen
Kan, Jingbao
Ge, Yaoqi
Wang, Dan
Zhang, Rihua
Zhang, Wensong
Liu, Yun
author_facet Hu, Yifang
Song, Jiahang
Wang, Zhen
Kan, Jingbao
Ge, Yaoqi
Wang, Dan
Zhang, Rihua
Zhang, Wensong
Liu, Yun
author_sort Hu, Yifang
collection PubMed
description BACKGROUND: Glioma is the most common central nervous system (CNS) cancer with a short survival period and a poor prognosis. The S100 family gene, comprising 25 members, relates to diverse biological processes of human malignancies. Nonetheless, the significance of S100 genes in predicting the prognosis of glioma remains largely unclear. We aimed to build an S100 family-based signature for glioma prognosis. METHODS: We downloaded 665 and 313 glioma patients, respectively, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database with RNAseq data and clinical information. This study established a prognostic signature based on the S100 family genes through multivariate COX and LASSO regression. The Kaplan–Meier curve was plotted to compare overall survival (OS) among groups, whereas Receiver Operating Characteristic (ROC) analysis was performed to evaluate model accuracy. A representative gene S100B was further verified by in vitro experiments. RESULTS: An S100 family-based signature comprising 5 genes was constructed to predict the glioma that stratified TCGA-derived cases as a low- or high-risk group, whereas the significance of prognosis was verified based on CGGA-derived cases. Kaplan–Meier analysis revealed that the high-risk group was associated with the dismal prognosis. Furthermore, the S100 family-based signature was proved to be closely related to immune microenvironment. In vitro analysis showed S100B gene in the signature promoted glioblastoma (GBM) cell proliferation and migration. CONCLUSIONS: We constructed and verified a novel S100 family-based signature associated with tumor immune microenvironment (TIME), which may shed novel light on the glioma diagnosis and treatment.
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spelling pubmed-85481702021-10-27 A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma Hu, Yifang Song, Jiahang Wang, Zhen Kan, Jingbao Ge, Yaoqi Wang, Dan Zhang, Rihua Zhang, Wensong Liu, Yun J Oncol Research Article BACKGROUND: Glioma is the most common central nervous system (CNS) cancer with a short survival period and a poor prognosis. The S100 family gene, comprising 25 members, relates to diverse biological processes of human malignancies. Nonetheless, the significance of S100 genes in predicting the prognosis of glioma remains largely unclear. We aimed to build an S100 family-based signature for glioma prognosis. METHODS: We downloaded 665 and 313 glioma patients, respectively, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database with RNAseq data and clinical information. This study established a prognostic signature based on the S100 family genes through multivariate COX and LASSO regression. The Kaplan–Meier curve was plotted to compare overall survival (OS) among groups, whereas Receiver Operating Characteristic (ROC) analysis was performed to evaluate model accuracy. A representative gene S100B was further verified by in vitro experiments. RESULTS: An S100 family-based signature comprising 5 genes was constructed to predict the glioma that stratified TCGA-derived cases as a low- or high-risk group, whereas the significance of prognosis was verified based on CGGA-derived cases. Kaplan–Meier analysis revealed that the high-risk group was associated with the dismal prognosis. Furthermore, the S100 family-based signature was proved to be closely related to immune microenvironment. In vitro analysis showed S100B gene in the signature promoted glioblastoma (GBM) cell proliferation and migration. CONCLUSIONS: We constructed and verified a novel S100 family-based signature associated with tumor immune microenvironment (TIME), which may shed novel light on the glioma diagnosis and treatment. Hindawi 2021-09-29 /pmc/articles/PMC8548170/ /pubmed/34712325 http://dx.doi.org/10.1155/2021/3586589 Text en Copyright © 2021 Yifang Hu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hu, Yifang
Song, Jiahang
Wang, Zhen
Kan, Jingbao
Ge, Yaoqi
Wang, Dan
Zhang, Rihua
Zhang, Wensong
Liu, Yun
A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma
title A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma
title_full A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma
title_fullStr A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma
title_full_unstemmed A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma
title_short A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma
title_sort novel s100 family-based signature associated with prognosis and immune microenvironment in glioma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548170/
https://www.ncbi.nlm.nih.gov/pubmed/34712325
http://dx.doi.org/10.1155/2021/3586589
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