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Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas

BACKGROUND: Lower-grade gliomas (LGGs) patients presented seizure-free have a worse survival than those presented with seizures. However, the current knowledge on its potential value in LGGs remains scarce. PURPOSE: This study aimed to identify a novel gene signature associated with seizures-free fo...

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Autores principales: Li, Jinxing, Huan, Jing, Yang, Fu, Chen, Haixin, Wang, Mingguang, Heng, Xueyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570923/
https://www.ncbi.nlm.nih.gov/pubmed/34754221
http://dx.doi.org/10.2147/IJGM.S329745
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author Li, Jinxing
Huan, Jing
Yang, Fu
Chen, Haixin
Wang, Mingguang
Heng, Xueyuan
author_facet Li, Jinxing
Huan, Jing
Yang, Fu
Chen, Haixin
Wang, Mingguang
Heng, Xueyuan
author_sort Li, Jinxing
collection PubMed
description BACKGROUND: Lower-grade gliomas (LGGs) patients presented seizure-free have a worse survival than those presented with seizures. However, the current knowledge on its potential value in LGGs remains scarce. PURPOSE: This study aimed to identify a novel gene signature associated with seizures-free for predicting poor prognosis for LGGs patients. MATERIALS AND METHODS: The RNA expression and clinical information of LGGs patients were downloaded from the Cancer Genome Atlas database. Differentially expressed genes (DEGs) were screened out between LGGs patients presented seizures-free and seizures. The novel gene signature was constructed by Lasso and multivariate regression analyses for predicting prognosis in LGGs. Its prognostic value was assessed and validated by Kaplan–Meier analyses and receiver operating characteristic (ROC) curves. Multivariate regression analysis was applied to identify the independent prognostic value of the gene signature. Furthermore, bioinformatics analysis was performed to elucidate the molecular mechanisms. RESULTS: A total of 253 DEGs were screened out between LGG patients presented with seizures and free of seizures. A 5-gene signature (HIST1H4F, HORMAD2, LILRA3, PRSS33, and TBX20 genes) was constructed from these 253 DEGs. Kaplan–Meier analyses and ROC curves assessed and validated the good performance of the 5-gene signature in differentiating and predicting prognosis of high- and low-risk patients. Multivariate regression analysis determined the independent prognostic value of the 5-gene signature. According to bioinformatics analysis, DEGs were mainly enriched in biological processes related to positive regulation of transcription from RNA polymerase II promoter, G-protein coupled receptor signaling pathway, and pathways of cytokine–cytokine receptor interaction, chemokine signaling pathway. CONCLUSION: Our findings suggested that the 5-gene signature might serve as a potential prognostic biomarker and provide guidance for the personalized LGGs management.
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spelling pubmed-85709232021-11-08 Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas Li, Jinxing Huan, Jing Yang, Fu Chen, Haixin Wang, Mingguang Heng, Xueyuan Int J Gen Med Original Research BACKGROUND: Lower-grade gliomas (LGGs) patients presented seizure-free have a worse survival than those presented with seizures. However, the current knowledge on its potential value in LGGs remains scarce. PURPOSE: This study aimed to identify a novel gene signature associated with seizures-free for predicting poor prognosis for LGGs patients. MATERIALS AND METHODS: The RNA expression and clinical information of LGGs patients were downloaded from the Cancer Genome Atlas database. Differentially expressed genes (DEGs) were screened out between LGGs patients presented seizures-free and seizures. The novel gene signature was constructed by Lasso and multivariate regression analyses for predicting prognosis in LGGs. Its prognostic value was assessed and validated by Kaplan–Meier analyses and receiver operating characteristic (ROC) curves. Multivariate regression analysis was applied to identify the independent prognostic value of the gene signature. Furthermore, bioinformatics analysis was performed to elucidate the molecular mechanisms. RESULTS: A total of 253 DEGs were screened out between LGG patients presented with seizures and free of seizures. A 5-gene signature (HIST1H4F, HORMAD2, LILRA3, PRSS33, and TBX20 genes) was constructed from these 253 DEGs. Kaplan–Meier analyses and ROC curves assessed and validated the good performance of the 5-gene signature in differentiating and predicting prognosis of high- and low-risk patients. Multivariate regression analysis determined the independent prognostic value of the 5-gene signature. According to bioinformatics analysis, DEGs were mainly enriched in biological processes related to positive regulation of transcription from RNA polymerase II promoter, G-protein coupled receptor signaling pathway, and pathways of cytokine–cytokine receptor interaction, chemokine signaling pathway. CONCLUSION: Our findings suggested that the 5-gene signature might serve as a potential prognostic biomarker and provide guidance for the personalized LGGs management. Dove 2021-10-29 /pmc/articles/PMC8570923/ /pubmed/34754221 http://dx.doi.org/10.2147/IJGM.S329745 Text en © 2021 Li et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Li, Jinxing
Huan, Jing
Yang, Fu
Chen, Haixin
Wang, Mingguang
Heng, Xueyuan
Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas
title Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas
title_full Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas
title_fullStr Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas
title_full_unstemmed Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas
title_short Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas
title_sort identification and validation of a seizure-free-related gene signature for predicting poor prognosis in lower-grade gliomas
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570923/
https://www.ncbi.nlm.nih.gov/pubmed/34754221
http://dx.doi.org/10.2147/IJGM.S329745
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