Prognostic value of a nine-gene signature in glioma patients based on mRNA expression profiling

Abstract

Introduction: Gliomas are the most common primary brain tumors in adults and a significant cause of cancer-related mortality. A 9-gene signature was identified as a novel prognostic model reflecting survival situation obviously in gliomas.

Aims: To identify an mRNA expression signature to improve outcome prediction for patients with different glioma grades.

Results: We used whole-genome mRNA expression microarray data of 220 glioma samples of all grades from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn) as a discovery set and data from Rembrandt and GSE16011 for validation sets. Data from every single grade were analyzed by the Kaplan-Meier method with a two-sided log-rank test. Univariate Cox regression and linear risk score formula were applied to derive a gene signature with better prognostic performance. We found that patients who had high risk score according to the signature had poor overall survival compared with patients who had low risk score. Highly expressed genes in the high-risk group were analyzed by gene ontology (GO) and gene set variation analysis (GSVA). As a result, the reason for the divisibility of gliomas was likely due to cell life processes and adhesion.

Conclusion: This 9-gene-signature prediction model provided a more accurate predictor of prognosis that denoted patients with high risk score have poor outcome. Moreover, these risk models based on defined molecular profiles showed the considerable prospect in personalized cancer management.

Keywords: Biomarker; Gliomas; Prognosis; Risk score; mRNA.

Figure 1

These Kaplan–Meier estimates of overall survival in patients with each grade glioma were constructed by the signature. P‐values are indicated for the high‐risk and low‐risk groups stratified according to the signature risk score in the Chinese Glioma Genome Atlas (CGGA) data (A, B, and C), the GSE16011 data (D, E, and F), and the Rambrandt data (G, H, and I). H, high‐risk group; L, low‐risk group.

Figure 2

The expression difference of 9‐gene signature in Chinese Glioma Genome Atlas (CGGA) dataset. A single spot was the gene expression value of an individual patient. Lines in the middle were the mean expression value.

Figure 3

Analysis of the signature risk score is illustrated for patients from grade II to IV (A, B, and C), including (Top) signature risk score distribution, (Middle) patient survival duration, and (Bottom) clinical and molecular information.

Figure 4

Gene set variation analysis samples from Chinese Glioma Genome Atlas (CGGA). The risk score (upper) was calculated with the formula described above and ranked from left to right. Gene set enrichment scores (lower) of cell cycle, development, apoptosis, adhere, and transcription were analyzed by gene set variation analysis (GSVA) package of R. Patients with high risk score tended to have a lower expression of anticell development (II), cell apoptosis and adhesion (III), transcription (IV), and higher expression of cell development (II), cell cycle (III), the first cell cycle denotes “phase of cell cycle”, and the second one means “mitotic prometaphase” (IV).