Abstract
Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.
Highlights
Glioblastoma, known as glioblastoma multiforme (GBM), is the most common primary malignant cancer involving the central nervous system (CNS), characterized by aggressive invasiveness and an infiltrative growth pattern (Lapointe et al, 2018; Lee et al, 2018)
A total of 210 Glioblastoma multiforme (GBM) patients from the Gene Expression Omnibus (GEO) database make up an independent validation set
The results showed that the six-mRNA signature and methylation of the O6methylguanine DNA-methyltransferase (MGMT) methylated status contributed most to overall survival (OS) in 1, 2, and 3 years, followed by the CpG Island Methylator Phenotype (CIMP) methylated status, patient age, and the isocitrate dehydrogenase 1 (IDH1) mutation status in six-mRNAbased nomograms
Summary
Glioblastoma, known as glioblastoma multiforme (GBM), is the most common primary malignant cancer involving the central nervous system (CNS), characterized by aggressive invasiveness and an infiltrative growth pattern (Lapointe et al, 2018; Lee et al, 2018). The 2007 World Health Organization (WHO) classification system has been used for glioma classification over the past decade (Fuller and Scheithauer, 2007) This histological classification and grading system was largely based on visual criteria alone and fails to accurately evaluate the clinical outcomes of GBM patients. MGMT promoter region was recognized to have a predictive value for the efficacy of TMZ-based chemotherapy, and mutations in the genes encoding for iso-citrate dehydrogenases 1/2 (IDH1/2) could predict a relatively long-term survival of GBM patients (Feyissa et al, 2019; Radke et al, 2019). Due to the biological heterogeneity of GBM, individual biomarkers alone are unable to predict the therapy sensitivity and survival of GBM patients In this regard, there is an urgent need to identify more effective tumor biomarkers for risk stratification and prognosis prediction
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