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Identification of prognosis-related gene features in low-grade glioma based on ssGSEA

Low-grade gliomas (LGG) are commonly seen in clinical practice, and the prognosis is often poor. Therefore, the determination of immune-related risk scores and immune-related targets for predicting prognoses in patients with LGG is crucial. A single-sample gene set enrichment analysis (ssGSEA) was p...

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Autores principales: He, Yuanzhi, Lin, Zhangping, Tan, Sanyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795048/
https://www.ncbi.nlm.nih.gov/pubmed/36591509
http://dx.doi.org/10.3389/fonc.2022.1056623
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author He, Yuanzhi
Lin, Zhangping
Tan, Sanyang
author_facet He, Yuanzhi
Lin, Zhangping
Tan, Sanyang
author_sort He, Yuanzhi
collection PubMed
description Low-grade gliomas (LGG) are commonly seen in clinical practice, and the prognosis is often poor. Therefore, the determination of immune-related risk scores and immune-related targets for predicting prognoses in patients with LGG is crucial. A single-sample gene set enrichment analysis (ssGSEA) was performed on 22 immune gene sets to calculate immune-based prognostic scores. The prognostic value of the 22 immune cells for predicting overall survival (OS) was assessed using the least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox analyses. Subsequently, we constructed a validated effector T-cell risk score (TCRS) to identify the immune subtypes and inflammatory immune features of LGG patients. We divided an LGG patient into a high-risk–score group and a low-risk–score group based on the optimal cutoff value. Kaplan–Meier survival curve showed that patients in the low-risk–score group had higher OS. We then identified the differentially expressed genes (DEGs) between the high-risk–score group and low-risk-score group and obtained 799 upregulated genes and 348 downregulated genes. The analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) show that DEGs were mainly concentrated in immune-related processes. In order to further explore the immune-related genes related to prognosis, we constructed a protein–protein interaction (PPI) network using Cytoscape and then identified the 50 most crucial genes. Subsequently, nine DEGs were found to be significantly associated with OS based on univariate and multivariate Cox analyses. It was further confirmed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were independent prognostic factors for LGG through batch survival analysis and a nomogram prediction model. In addition, we used an RT-qPCR assay to validate the bioinformatics results. The results showed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were highly expressed in LGG. Our study can provide a reference value for the prediction of prognosis in LGG patients and may help in the clinical development of effective therapeutic agents.
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spelling pubmed-97950482022-12-29 Identification of prognosis-related gene features in low-grade glioma based on ssGSEA He, Yuanzhi Lin, Zhangping Tan, Sanyang Front Oncol Oncology Low-grade gliomas (LGG) are commonly seen in clinical practice, and the prognosis is often poor. Therefore, the determination of immune-related risk scores and immune-related targets for predicting prognoses in patients with LGG is crucial. A single-sample gene set enrichment analysis (ssGSEA) was performed on 22 immune gene sets to calculate immune-based prognostic scores. The prognostic value of the 22 immune cells for predicting overall survival (OS) was assessed using the least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox analyses. Subsequently, we constructed a validated effector T-cell risk score (TCRS) to identify the immune subtypes and inflammatory immune features of LGG patients. We divided an LGG patient into a high-risk–score group and a low-risk–score group based on the optimal cutoff value. Kaplan–Meier survival curve showed that patients in the low-risk–score group had higher OS. We then identified the differentially expressed genes (DEGs) between the high-risk–score group and low-risk-score group and obtained 799 upregulated genes and 348 downregulated genes. The analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) show that DEGs were mainly concentrated in immune-related processes. In order to further explore the immune-related genes related to prognosis, we constructed a protein–protein interaction (PPI) network using Cytoscape and then identified the 50 most crucial genes. Subsequently, nine DEGs were found to be significantly associated with OS based on univariate and multivariate Cox analyses. It was further confirmed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were independent prognostic factors for LGG through batch survival analysis and a nomogram prediction model. In addition, we used an RT-qPCR assay to validate the bioinformatics results. The results showed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were highly expressed in LGG. Our study can provide a reference value for the prediction of prognosis in LGG patients and may help in the clinical development of effective therapeutic agents. Frontiers Media S.A. 2022-12-14 /pmc/articles/PMC9795048/ /pubmed/36591509 http://dx.doi.org/10.3389/fonc.2022.1056623 Text en Copyright © 2022 He, Lin and Tan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
He, Yuanzhi
Lin, Zhangping
Tan, Sanyang
Identification of prognosis-related gene features in low-grade glioma based on ssGSEA
title Identification of prognosis-related gene features in low-grade glioma based on ssGSEA
title_full Identification of prognosis-related gene features in low-grade glioma based on ssGSEA
title_fullStr Identification of prognosis-related gene features in low-grade glioma based on ssGSEA
title_full_unstemmed Identification of prognosis-related gene features in low-grade glioma based on ssGSEA
title_short Identification of prognosis-related gene features in low-grade glioma based on ssGSEA
title_sort identification of prognosis-related gene features in low-grade glioma based on ssgsea
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795048/
https://www.ncbi.nlm.nih.gov/pubmed/36591509
http://dx.doi.org/10.3389/fonc.2022.1056623
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