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

Abstract

Gliomas are the most common and malignant intracranial tumors in adults. Recent studies have revealed the significance of functional genomics for glioma pathophysiological studies and treatments. However, access to comprehensive genomic data and analytical platforms is often limited. Here, we developed the Chinese Glioma Genome Atlas (CGGA), a user-friendly data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples from Chinese cohort. Currently, open access is provided to whole-exome sequencing data (286 samples), mRNA sequencing (1018 samples) and microarray data (301 samples), DNA methylation microarray data (159 samples), and microRNA microarray data (198 samples), and to detailed clinical information (age, gender, chemoradiotherapy status, WHO grade, histological type, critical molecular pathological information, and survival data). In addition, we have developed several tools for users to analyze the mutation profiles, mRNA/microRNA expression, and DNA methylation profiles, and to perform survival and gene correlation analyses of specific glioma subtypes. This database removes the barriers for researchers, providing rapid and convenient access to high-quality functional genomic data resources for biological studies and clinical applications. CGGA is available at http://www.cgga.org.cn.

Keywords: Chinese Glioma Genome Atlas; Chinese cohort; Database; Functional genomics; Glioma.

Figure 1

Schematic of CGGA illustrating thedata processing and display approaches

Figure 2

Main contents of CGGA database and the functionality of WEseq analysis A. CGGA contains whole-exome sequencing, mRNA and microRNA expression, DNA methylation data, clinical data, and several analysis modules. B. The web image in the WEseq analysis page to search the OncoPrint and prognostic value of target genes. C. The mutation profile in all glioma samples included (in the ‘WEseq_286’ dataset). D. Survival analysis of primary LGG patients with IDH1 mutation. Left: the plot for overall survival of primary LGG patients with wildtype or mutant IDH1 (in the ‘WEseq_286’ dataset); middle: the data used to generate the plot; right: the R code used to generate the plot. LGG, lower-grade glioma; GBM, glioblastoma; A, astrocytoma; O, oligodendroglioma; OA, oligo‐astrocytoma; AOA, anaplastic oligo-astrocytoma; AA: anaplastic astrocytoma; rGBM: recurrent glioblastoma; rAA, recurrent anaplastic astrocytoma; rA, recurrent astrocytoma; AO, anaplastic oligodendroglioma; rAO, recurrent anaplastic oligodendroglioma; rO, recurrent oligodendroglioma; rAOA, recurrent anaplastic oligo‐astrocytoma.

Figure 3

Examples of CGGA RNA-seq analysis A. The screenshot of the RNA-seq analysis page to search the distribution, correlated genes, and prognostic value of target genes. B. The ADAMTSL4 gene expression distribution in primary gliomas based on the 2016 WHO grading system (in the ‘mRNAseq_325’ dataset). C. The correlation of gene expression between ADAMTSL4 and CD274 (in the ‘mRNAseq_325’ dataset). D. The overall survival of glioma patients with low and high expression of ADAMTSL4 (in the ‘mRNAseq_325’ dataset).