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Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing

BACKGROUND: This study aimed to use single-cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells (ECs) and construct a prognostic model for glioblastoma multiforme (GBM) patients in combination with traditional high-throughput RNA sequencing (bulk RNA-seq). METHODS: Bulk RNA-seq dat...

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Autores principales: Zhao, Songyun, Ji, Wei, Shen, Yifan, Fan, Yuansheng, Huang, Hui, Huang, Jin, Lai, Guichuan, Yuan, Kemiao, Cheng, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724299/
https://www.ncbi.nlm.nih.gov/pubmed/36474171
http://dx.doi.org/10.1186/s12885-022-10305-z
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author Zhao, Songyun
Ji, Wei
Shen, Yifan
Fan, Yuansheng
Huang, Hui
Huang, Jin
Lai, Guichuan
Yuan, Kemiao
Cheng, Chao
author_facet Zhao, Songyun
Ji, Wei
Shen, Yifan
Fan, Yuansheng
Huang, Hui
Huang, Jin
Lai, Guichuan
Yuan, Kemiao
Cheng, Chao
author_sort Zhao, Songyun
collection PubMed
description BACKGROUND: This study aimed to use single-cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells (ECs) and construct a prognostic model for glioblastoma multiforme (GBM) patients in combination with traditional high-throughput RNA sequencing (bulk RNA-seq). METHODS: Bulk RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases. 10x scRNA-seq data for GBM were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Key modules and differentially expressed genes (DEGs) were identified by weighted gene correlation network analysis (WGCNA). A non-negative matrix decomposition (NMF) algorithm was used to identify the different subtypes based on DEGs, and multivariate cox regression analysis to model the prognosis. Finally, differences in mutational landscape, immune cell abundance, immune checkpoint inhibitors (ICIs)-associated genes, immunotherapy effects, and enriched pathways were investigated between different risk groups. RESULTS: The analysis of scRNA-seq data from eight samples revealed 13 clusters and four cell types. After applying Fisher’s exact test, ECs were identified as the most important cell type. The NMF algorithm identified two clusters with different prognostic and immunological features based on DEGs. We finally built a prognostic model based on the expression levels of four key genes. Higher risk scores were significantly associated with poorer survival outcomes, low mutation rates in IDH genes, and upregulation of immune checkpoints such as PD-L1 and CD276. CONCLUSION: We built and validated a 4-gene signature for GBM using 10 scRNA-seq and bulk RNA-seq data in this work. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-10305-z.
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spelling pubmed-97242992022-12-07 Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing Zhao, Songyun Ji, Wei Shen, Yifan Fan, Yuansheng Huang, Hui Huang, Jin Lai, Guichuan Yuan, Kemiao Cheng, Chao BMC Cancer Research BACKGROUND: This study aimed to use single-cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells (ECs) and construct a prognostic model for glioblastoma multiforme (GBM) patients in combination with traditional high-throughput RNA sequencing (bulk RNA-seq). METHODS: Bulk RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases. 10x scRNA-seq data for GBM were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Key modules and differentially expressed genes (DEGs) were identified by weighted gene correlation network analysis (WGCNA). A non-negative matrix decomposition (NMF) algorithm was used to identify the different subtypes based on DEGs, and multivariate cox regression analysis to model the prognosis. Finally, differences in mutational landscape, immune cell abundance, immune checkpoint inhibitors (ICIs)-associated genes, immunotherapy effects, and enriched pathways were investigated between different risk groups. RESULTS: The analysis of scRNA-seq data from eight samples revealed 13 clusters and four cell types. After applying Fisher’s exact test, ECs were identified as the most important cell type. The NMF algorithm identified two clusters with different prognostic and immunological features based on DEGs. We finally built a prognostic model based on the expression levels of four key genes. Higher risk scores were significantly associated with poorer survival outcomes, low mutation rates in IDH genes, and upregulation of immune checkpoints such as PD-L1 and CD276. CONCLUSION: We built and validated a 4-gene signature for GBM using 10 scRNA-seq and bulk RNA-seq data in this work. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-10305-z. BioMed Central 2022-12-06 /pmc/articles/PMC9724299/ /pubmed/36474171 http://dx.doi.org/10.1186/s12885-022-10305-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhao, Songyun
Ji, Wei
Shen, Yifan
Fan, Yuansheng
Huang, Hui
Huang, Jin
Lai, Guichuan
Yuan, Kemiao
Cheng, Chao
Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing
title Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing
title_full Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing
title_fullStr Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing
title_full_unstemmed Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing
title_short Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing
title_sort expression of hub genes of endothelial cells in glioblastoma-a prognostic model for gbm patients integrating single-cell rna sequencing and bulk rna sequencing
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724299/
https://www.ncbi.nlm.nih.gov/pubmed/36474171
http://dx.doi.org/10.1186/s12885-022-10305-z
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