Whole-genome mRNA expression profiling identifies functional and prognostic signatures in patients with mesenchymal glioblastoma multiforme

Abstract

Background: The Cancer Genome Atlas (TCGA) has divided patients with glioblastoma multiforme (GBM) into four subtypes based on mRNA expression microarray. The mesenchymal subtype, with a larger proportion, is considered a more lethal one. Clinical outcome prediction is required to better guide more personalized treatment for these patients.

Aims: The objective of this study was to identify a mRNA expression signature to improve outcome prediction for patients with mesenchymal GBM.

Results: For signature identification and validation, we downloaded mRNA expression microarray data from TCGA as training set and data from Rembrandt and GSE16011 as validation set. Cox regression and risk-score analysis were used to develop the 4 signatures, which were function and prognosis associated as revealed by Gene Ontology (GO) analysis and Gene Set Variation Analysis (GSVA). Patients who had high-risk scores according to the signatures had poor overall survival compared with patients who had low-risk scores.

Conclusions: The signatures were identified as risk predictors that patients who had a high-risk score tended to have unfavorable outcome, demonstrating their potential for personalizing cancer management.

Keywords: Biomarker; Glioblastoma; Mesenchymal; Prognosis; Risk score.

Figure 1

These Kaplan–Meier estimates of overall survival in patients with glioblastoma multiforme constructed by the gene signatures. (A) 61 genes signature; (B) 17 genes signature; (C) 8 genes signature; (D) 5 genes signature. P‐values were indicated for the high‐risk and low‐risk groups stratified according to the median risk score in the Cancer Genome Atlas (TCGA) data. H, high‐risk group; L, low‐risk group.

Figure 2

Analysis of the four sets of signature risk score was illustrated for patients in the training set, including (Top) gene signature risk‐score distribution and (Bottom) patient survival duration. (A) 61‐gene signature; (B) 17‐gene signature; (C) eight‐gene signature; (D) five‐gene signature. The blue vertical lines in the middle of each graph represented the gene signature cutoff (median risk score), each dot represented a single patient.

Figure 3

Kaplan–Meier estimates of overall survival in patients with mesenchymal glioblastoma multiforme (GBMs) illustrated the risk‐score analysis using the four separate gene signatures. Except for the 17‐gene signature in GSE16011, which got marginal P‐value, patients could be divided into two groups with significantly different prognosis. H, high‐risk group; L, low‐risk group. Cutoff value, (A–C, G) median risk score; (D–F, H), patient proportion, 29:38, 59:32, 39:28, 42:25 (low‐risk vs. high‐risk).

Figure 4

Gene Set Variation Analysis of the Cancer Genome Atlas (TCGA) samples with risk score of 61‐gene signature. The risk score (upper panel) was calculated with the formula described above and ranked from left to right. Gene set enrichment scores (lower panel) of adhesion and transcription were analyzed by Gene Set Variation Analysis (GSVA) package of R. Patients with higher risk score tended to have a lower expression of adhesion‐associated genes and higher expression of transcription‐associated ones.