Research Article: Mining TCGA database for genes of prognostic value in glioblastoma microenvironment

Date Published: April 16, 2018

Publisher: Impact Journals

Author(s): Di Jia, Shenglan Li, Dali Li, Haipeng Xue, Dan Yang, Ying Liu.

http://doi.org/10.18632/aging.101415

Abstract

Glioblastoma (GBM) is one of the most deadly brain tumors. The convenient access to The Cancer Genome Atlas (TCGA) database allows for large-scale global gene expression profiling and database mining for potential correlation between genes and overall survival of a variety of malignancies including GBM. Previous reports have shown that tumor microenvironment cells and the extent of infiltrating immune and stromal cells in tumors contribute significantly to prognosis. Immune scores and stromal scores calculated based on the ESTIMATE algorithm could facilitate the quantification of the immune and stromal components in a tumor. To better understand the effects of genes involved in immune and stromal cells on prognosis, we categorized GBM cases in the TCGA database according to their immune/stromal scores into high and low score groups, and identified differentially expressed genes whose expression was significantly associated with prognosis in GBM patients. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. Finally, we validated these genes in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Thus, we obtained a list of tumor microenvironment-related genes that predict poor outcomes in GBM patients.

Partial Text

Glioblastoma multiforme (GBM) is one of the most fatal brain tumors with a mean survival rate of 35.7% at one Year, 4.7% at five years, and median overall survival (OS) of 14.6 months [1,2]. To better understand the impacts of genetic composition of tumor on clinical prognosis, comprehensive genome-wide gene expression collections such as The Cancer Genome Atlas (TCGA) have been established to categorize and discover genomic abnormalities in large cohorts across the world [3,4]. In the TCGA database, according to global gene expression profiles, GBM was initially classified into four subtypes: proneural, neural, classical, and mesenchymal [5]. Of these subtypes, the neural subtype is no longer recognized as a major one due to its lack of tumor-intrinsic characteristics based on several recent reports [6-8]. In 2016, the updated World Health Organization (WHO) classification integrated molecular parameters with histology and divided GBM into three subtypes: (1) IDH-wildtype, (2) IDH-mutant, and (3) NOS (not otherwise specified) [9]. With these progresses, gene expression profiling has been increasingly incorporated with and accepted by clinical diagnostic criteria.

In the current work, we attempted to identify tumor microenvironment related genes that contribute to GBM overall survival in the TCGA database. In particular, by comparing global gene expression in a large number of cases with high vs. low immune scores, we extracted 258 genes involved in extracellular matrix and immune response. Importantly, we were able to validate 44 genes in GBM patients from CGGA, a separate GBM database (Figure 7).

 

Source:

http://doi.org/10.18632/aging.101415

 

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