Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients

Wei-Zhen Gao,Yu-Hua Yin,Tian-Qi Xu,Feng Jia,Lie-Mei Guo

doi:10.1186/s12967-018-1744-8

Abstract

BackgroundGlioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radiotherapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment. Diverse clinicopathological and molecular features are major obstacles to accurate predict survival and evaluate the efficacy of chemotherapy or radiotherapy. Reliable prognostic biomarkers are urgently needed for postoperative GBM patients.MethodsThe protein coding genes (PCGs) and long non-coding RNA (lncRNA) gene expression profiles of 233 GBM postoperative patients were obtained from The Cancer Genome Atlas (TCGA), TANRIC and Gene Expression Omnibus (GEO) database. We randomly divided the TCGA set into a training (n = 76) and a test set (n = 77) and used GSE7696 (n = 80) as an independent validation set. Survival analysis and the random survival forest algorithm were performed to screen survival associated signature.ResultsSix PCGs (EIF2AK3, EPRS, GALE, GUCY2C, MTHFD2, RNF212) and five lncRNAs (CTD-2140B24.6, LINC02015, AC068888.1, CERNA1, LINC00618) were screened out by a risk score model and formed a PCG-lncRNA signature for its predictive power was strongest (AUC = 0.78 in the training dataset). The PCG-lncRNA signature could divide patients into high- risk or low-risk group with significantly different survival (median 7.47 vs. 18.27 months, log-rank test P < 0.001) in the training dataset. Similar result was observed in the test dataset (median 11.40 vs. 16.80 months, log-rank test P = 0.001) and the independent set (median 8.93 vs. 16.22 months, log-rank test P = 0.007). Multivariable Cox regression analysis verified that it was an independent prognostic factor for the postsurgical patients with GBM. Compared with IDH mutation status, O-(6)-methylguanine DNA methyltransferase promoter methylation status and age, the signature was proved to have a superior predictive power. And stratified analysis found that the signature could further separated postoperative GBM patients who received TMZ-chemoradiation into high- and low-risk groups in TCGA and GEO dataset.ConclusionsThe PCG-lncRNA signature was a novel prognostic marker to predict survival and TMZ-chemoradiation response in GBM patients after surgery.

Highlights

Glioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radio‐ therapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment
After screened the data downloaded from the The Cancer Genome Atlas (TCGA), TANRIC and Gene Expression Omnibus (GEO) database, we identified 233 eligible patients diagnosed with Glioblastoma multiforme (GBM) concurrently including protein coding gene (PCG) and long non-coding RNA (lncRNA) expression profiles and corresponding clinical data
We obtained a total of 14,607 PCGs and 6613 lncRNAs from the 233 GBM patients

Summary

Introduction

Glioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radio‐ therapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment. Glioblastoma multiforme (GBM) is regarded as the most common malignant brain tumor in adults, accounting for 47.1% of all malignant brain tumors [1], and the median survival time of untreated patients with GBM is only 3 months [2]. Maximal surgical resection, is considered as the first-line treatment for GBM patients relieving clinical symptoms, extending survival time and providing tissue to pathological diagnosis [3]. A large-scale randomized phase III trial, initiated by the European Organization for Research and Treatment of Cancer and National Cancer Institute of Canada Clinical Trials Group, found that the 2-year survival rate of GBM patients was improved to 26.5% by radiotherapy plus temozolomide from 10.4% by radiotherapy alone [4]. Prediction of response to chemotherapy drugs or radiation and prediction of prognosis are crucial for post-surgical GBM patients

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