Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients

Zhao, Zheng; Zhang, Kenan; Wang, Qiangwei; Li, Guanzhang; Zeng, Fan; Zhang, Ying; Wu, Fan; Chai, Rui‐Chao; Wang, Zheng; Zhang, Chuanbao; Zhang, Wei; Bao, Zhaoshi; Jiang, Tao

doi:10.1016/j.gpb.2020.10.005

Cited by 602 publications

(455 citation statements)

References 31 publications

(36 reference statements)

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“…WHO grade II and III gliomas were classified as lower-grade gliomas. For the CGGA dataset, the process of clinical specimen collection and sequencing was described in detail in our previous work (Zhao et al, 2021). Specifically, the RNA-seq data was processed as recommended by TCGA.…”

Section: Patients and Datasetsmentioning

confidence: 99%

T-Cell Exhaustion Status Under High and Low Levels of Hypoxia-Inducible Factor 1α Expression in Glioma

Liu

Zhang

et al. 2021

Front. Pharmacol.

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Background: Hypoxia-inducible factor 1α (HIF1A), the principal regulator of hypoxia, is involved in the suppression of antitumor immunity. We aimed to describe the T-cell exhaustion status of gliomas under different levels of HIF1A expression.Methods: In this study, 692 patients, whose data were collected from the Chinese Glioma Genome Atlas (CGGA) database, and 669 patients, whose data were collected from The Cancer Genome Atlas database, were enrolled. We further screened the data of a cohort of paired primary and recurrent patients from the CGGA dataset (n = 50). The abundance of immune cells was calculated using the transcriptome data. The association between HIF1A and T-cell exhaustion-related genes and immune cells was investigated.Results: According to the median value of HIF1A expression, gliomas were classified into low-HIF1A-expression and high-HIF1A-expression groups. The expression levels of PDL1 (CD274), FOXO1, and PRDM1 in the high-HIF1A-expression group were significantly higher in both glioblastoma (GBM) and lower-grade glioma. The abundance of exhausted T cells and B cells was significantly higher in the high-HIF1A-expression group, while that of macrophage, monocyte, and natural killer cell was significantly higher in the low-HIF1A-expression group in both GBM and lower-grade glioma. After tumor recurrence, the expression of HIF1A significantly increased, and the correlation between HIF1A expression levels and exhausted T cells and induced regulatory T cells became stronger.Conclusion: In diffuse gliomas, the levels of T-cell exhaustion-associated genes and the abundance of immune cells were elevated under high HIF1A expression. Reversing hypoxia may improve the efficacy of immunotherapy.

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Section: Patients and Datasetsmentioning

confidence: 99%

T-Cell Exhaustion Status Under High and Low Levels of Hypoxia-Inducible Factor 1α Expression in Glioma

Liu

Zhang

et al. 2021

Front. Pharmacol.

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“…Two independent RNA-seq datasets (mRNAseq_325 and mRNAseq_693) and paired clinical information were obtained from Chinese Glioma Genome Atlas (CGGA) database 1 ( Zhao et al, 2021 ). In the two datasets, only samples with definite WHO classification were included for survival and grade expression pattern analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Two independent RNA-seq datasets (mRNAseq_325 and mRNAseq_693) and paired clinical information were obtained from Chinese Glioma Genome Atlas (CGGA) database 1 (Zhao et al, 2021…”

Section: Rna-sequencing Datamentioning

confidence: 99%

Mutation and Copy Number Alterations Analysis of KIF23 in Glioma

et al. 2021

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In glioma, kinesin family member 23 (KIF23) is up-regulated and plays a vital role in oncogenesis. However, the mechanism underlying KIF23 overexpression in malignant glioma remains to be elucidated. This study aims to find potential causes of KIF23 high expression at genome level. To clarify this issue, we obtained point mutation and copy number alterations (CNAs) of KIF23 in 319 gliomas using whole-exome sequencing. Only two glioma samples with missense mutations in KIF23 coding region were identified, while 7 patients were detected with amplification of KIF23. Additional analysis showed that KIF23 amplification was significantly associated with higher expression of KIF23. Gene ontology analysis indicated that higher copy number of KIF23 was associated TNF-α signaling pathway and mitotic cell circle checkpoint, which probably caused by subsequent upregulated expression of KIF23. Moreover, pan-cancer analysis showed that gaining of copy number was significantly associated with higher expression of KIF23, consolidating our findings in glioma. Thus, it was deduced that elevated KIF23 expression in glioma tended to be caused by DNA copy number amplification, instead of mutation.

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“…Only the interactions of miRNAs and mRNAs with opposite expression were included for subsequent analysis. Pearson’s correlation analysis was performed in CGGA 17 (Chinese Glioma Genome Atlas) datasets ( http://www.cgga.org.cn/ ) to validate miRNA–mRNA regulation.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas

Wang

Cui

Cai

et al. 2021

CMAR

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Purpose Low-grade glioma is the most common type of primary intracranial tumour, and the overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past few decades. Therefore, it is crucial to understand the precise molecular mechanisms involved in the carcinogenesis of LGG. Methods To investigate the regulatory mechanisms of mRNA–miRNA networks related to LGG, in the present study, a comprehensive analysis of the genomic landscape between low-grade gliomas and normal brain tissues from the GEO and TCGA datasets was first conducted to identify differentially expressed genes (DEGs) and differentially expressed miRNAs in LGG. Following a series of analyses, including WGCNA, GO and KEGG analyses, PPI and key model analyses, and survival analysis of the DEGs with clinical phenotypes, the potential key genes were screened and identified, and the related miRNA–mRNA networks were subsequently constructed through miRWalk 3.0. Finally, the potential miRNA–mRNA networks were further validated in CGGA (Chinese Glioma Genome Atlas) datasets and clinical specimens by qRT-PCR. Results In our results, six hub genes, MELK, NCAPG, KIF4A, NUSAP1, CEP55, and TOP2A, were ultimately identified. Two regulatory pathways, miR-495-3p-TOP2A and miR-1224-3p-MELK, that regulate the pathogenesis of LGG were ultimately identified. Furthermore, the expression of miR-495-3p-TOP2A and miR-1224-3p-MELK in solid tissues was validated by qRT-PCR. Conclusion Our study identified hub genes and related miRNA–mRNA regulatory pathways that contribute to the carcinogenesis of LGG, which may help us reveal the mechanisms underlying the development of LGG.

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Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients

Cited by 602 publications

References 31 publications

T-Cell Exhaustion Status Under High and Low Levels of Hypoxia-Inducible Factor 1α Expression in Glioma

T-Cell Exhaustion Status Under High and Low Levels of Hypoxia-Inducible Factor 1α Expression in Glioma

Mutation and Copy Number Alterations Analysis of KIF23 in Glioma

Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas

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