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

作者全名："Zhao, Songyun; Ji, Wei; Shen, Yifan; Fan, Yuansheng; Huang, Hui; Huang, Jin; Lai, Guichuan; Yuan, Kemiao; Cheng, Chao"

作者地址："[Zhao, Songyun; Ji, Wei; Shen, Yifan; Fan, Yuansheng; Huang, Hui; Huang, Jin; Cheng, Chao] Nanjing Med Univ, Wuxi Peoples Hosp, Dept Neurosurg, 299 Qing Yang Rd, Wuxi 214023, Jiangsu, Peoples R China; [Lai, Guichuan] Chongqing Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, Yixue Rd, Chongqing 400016, Peoples R China; [Yuan, Kemiao] Tradit Chinese Med Hosp Wuxi, Dept Oncol, 8 West Zhongnan Rd, Wuxi 214071, Peoples R China"

通信作者："Cheng, C (通讯作者)，Nanjing Med Univ, Wuxi Peoples Hosp, Dept Neurosurg, 299 Qing Yang Rd, Wuxi 214023, Jiangsu, Peoples R China.; Yuan, KM (通讯作者)，Tradit Chinese Med Hosp Wuxi, Dept Oncol, 8 West Zhongnan Rd, Wuxi 214071, Peoples R China."

来源：BMC CANCER

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000893589100005

JCR分区：Q2

影响因子：3.8

年份：2022

卷号：22

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：GBM; scRNA-seq; Risk score; Prognostic model; Immune cell infiltration

摘要："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."

基金机构："Wuxi Taihu Lake Talent Plan; General project of the Wuxi Commission of Health [2020THRC-DJ-SNW]; Youth project of Wuxi commission of Health [2020ZHYB16, MS201933, T202120]; [Q202133]"

基金资助正文："This work was supported by the Wuxi Taihu Lake Talent Plan, Supports for Leading Talents in Medical and Health Profession (2020THRC-DJ-SNW ), the General project of the Wuxi Commission of Health (2020ZHYB16, MS201933, T202120), and the Youth project of Wuxi commission of Health (Q202133)."