Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses

Abstract

Objectives: In the present study, we aimed to determine the candidate genes that may function as biomarkers to further distinguish patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM), which are heterogeneous with respect to clinical outcomes. Materials and Methods: We selected 41 candidate genes associated with overall survival (OS) using univariate Cox regression from IDH-wildtype GBM patients based on RNA sequencing (RNAseq) expression data from the Chinese Glioma Genome Atlas (CGGA, n = 105) and The Cancer Genome Atlas (TCGA, n = 139) cohorts. Next, a sevengene-based risk signature was formulated according to Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm in the CGGA RNAseq database as a training set, while another 525 IDH-wildtype GBM patient TCGA datasets, consisting of RNA sequencing and microarray data, were used for validation. Patient survival in the low-and high-risk groups was calculated using Kaplan-Meier survival curve analysis and the log-rank test. Uni-and multivariate Cox regression analysis was used to assess the prognosis value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were performed for the functional analysis of the seven-gene-based risk signature. Results: We developed a seven-gene-based signature, which allocated each patient to a risk group (low or high). Patients in the high-risk group had dramatically shorter overall survival than their low-risk counterparts in three independent cohorts. Univariate and multivariate analysis showed that the seven-gene signature remained an independent prognostic factor. Moreover, the seven-gene risk signature exhibited a striking prognostic validity, with AUC of 78.4 and 73.9%, which was higher than for traditional "age" (53.7%, 62.4%) and "GBM sub-type" (57.7%, 52.9%) in the CGGA-and TCGA-RNAseq databases, respectively. Subsequent bioinformatics analysis predicted that the sevengene signature was involved in the inflammatory response, immune response, cell adhesion, and apoptotic process. Conclusions: Our findings indicate that the seven-gene signature could be a potential prognostic biomarker. This study refined the current classification system of IDH-wildtype GBM and may provide a novel perspective for the research and individual therapy of IDH-wildtype GBM.

The Influence of Gene Aberrations on Survival in Resected IDH Wildtype Glioblastoma Patients: A Single-Institution Study

“…Despite considerable efforts to clarify the molecular basis of GBM, the functional roles of known key genes and molecular markers are still unclear [ 16 , 17 , 18 ]. Previous studies have sought to identify distinct molecular signatures of diffuse gliomas and thereby reveal clinically relevant or functionally distinct GBM subclasses, but clinical applications for such signatures remain largely elusive [ 19 , 20 , 21 , 22 , 23 ]. Another problem is that genetic abnormalities have been defined rather ambiguously in many previous studies.…”

Clinical implication of cellular vaccine in glioma: current advances and future prospects

“…For example, given the spectrum of aggressive phenotypes, tumor marker-based classification, such as IDH mutation, 1p/19q co-deletion, and TERT promoter mutations, predict favorable prognosis in gliomas [ 126 – 128 ]. In IDH-wild type GBM, a gene-based signature could be a potential prognostic biomarker [ 129 ]. Thus, based on molecular markers, educated and advisable vaccine use for a cellular vaccine may be critical for safety and promotable anti-tumor effects in glioma treatment.…”

Analyses of metastasis-associated genes in IDH wild-type glioma

“…Studies on the analyses of RNA sequencing and prediction of the prognosis of IDH wild-type glioblastoma have been reported [ 28 ]. Since metastasis is generally considered as a pivotal factor of prognosis of glioma, the innovation of this paper is to identify the molecular characteristics of metastatic glioma cells by analyzing single-cell sequencing of glioma, and to use high-throughput sequencing for prognostic validation.…”