Abstract
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system. GBM causes poor clinical outcome and high mortality rate, mainly due to the lack of effective targeted therapy and prognostic biomarkers. Here, we developed a user-friendly Online Survival analysis web server for GlioBlastoMa, abbreviated OSgbm, to assess the prognostic value of candidate genes. Currently, OSgbm contains 684 samples with transcriptome profiles and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA). The survival analysis results can be graphically presented by Kaplan-Meier (KM) plot with Hazard ratio (HR) and log-rank p value. As demonstration, the prognostic value of 51 previously reported survival associated biomarkers, such as PROM1 (HR = 2.4120, p = 0.0071) and CXCR4 (HR = 1.5578, p < 0.001), were confirmed in OSgbm. In summary, OSgbm allows users to evaluate and develop prognostic biomarkers of GBM. The web server of OSgbm is available at http://bioinfo.henu.edu.cn/GBM/GBMList.jsp.
Highlights
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system (CNS) and causes a high mortality rate (Nikiforova and Hamilton, 2011; Stoyanov et al, 2018)
The Clinical Characteristics of GBM Datasets Used in OSgbm In OSgbm, we included a total of 684 unique GBM samples from seven datasets, including one The Cancer Genome Atlas (TCGA) cohort, four Gene Expression Omnibus (GEO) cohorts and two Chinese Glioma Genome Atlas (CGGA) cohorts
We developed a new web server, OSgbm, to help researchers to evaluate the prognostic value of a given gene for GBM patients
Summary
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system (CNS) and causes a high mortality rate (Nikiforova and Hamilton, 2011; Stoyanov et al, 2018). Many new therapies have improved the clinical outcome and more clinical trials have demonstrated the high efficacy in treating GBM, the survival rate of GBM patients is still low. Prognostic biomarkers have been showing great roles in cancer patient management and may guide targeted therapies. It is greatly needed to investigate prognostic biomarkers in GBM. Previous studies have reported some prognostic biomarkers in GBM, such as gene mutation of gene IDH and PTEN, and expression variation of gene CD133
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