Indexed on: 19 May '18Published on: 19 May '18Published in: Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
To seek survival-related genes in glioblastoma and establish a survival-gene signature for predicting prognoses of glioblastoma using public databases. Methods: Three independent glioma databases (GEO GSE53733, CGGA, TCGA) with whole genome expression data were included for analysis. Survival-related genes were obtained by comparing the long-term (>36 months) and short-term (<12 months) survivors in the database GSE53733. CGGA was used as the training set to develop the signature and TCGA was used as the validation set. Cox regression analysis and linear risk score assessment were conducted to look for prognostic signatures with survival-related genes. Principal components analysis, gene set enrichment analysis (GSEA), gene ontology (GO) and protein-protein interaction (PPI) analysis were performed to explore distinct expression profiles between risk grouped glioblastoma. Results: We totally found 211 survival-related genes and developed a signature with 17 survival-related genes for prognosis of glioblastoma. Based on this signature, the low-risk group had longer survival time while the high-risk group had shorter survival time. Additionally, the expression profiles between the high-risk and low-risk glioblastoma were different. Functional annotations revealed that the genes enriched in the high-risk glioblastoma were involved in immune systems and processes of extracellular matrix (ECM). Conclusion: The novel survival-gene signature can predict high-risk glioblastoma with shorter survival time, enhance immunosuppressive features, and increased invasion preferences.