关键词: 胶质母细胞瘤, 数据挖掘, 差异表达基因, 预后相关基因

Abstract: Glioblastoma (GBM) is the most aggressive human brain tumor. Since currently available drugs are of limited efficacy, the recurrence rate is high and the prognosis is poor for GBM. There is an urgent need to develop a new anti-GBM drugs in clinical practice. The purpose of this study was to screen genes that are closely related to the prognosis of GBM and to provide clues for finding new drug targets. In the study, the GEO2R tool was used to initially screen differentially expressed genes from 269 tumor tissues and 61 normal tissues in the GEO database. Cluster Profiler databases was then used to perform the gene function enrichment analysis, STRING and Cytoscape further resulted a total of 37 differentially expressed genes. The expression of these genes were then verified by GEPIA interaction analysis in GBM tumor tissues. To further explore the relationship between these differentially expressed genes and the prognosis of patients with GBM, GEPIA was used to deeply mine the patient prognosis-related data in the TCGA database. Finally, three genes, named PTTG1, RRM2, and E2F7, were found to be significantly negatively correlated with the patient's median survival. Conclusively, the screened genes highly related to patient prognosis not only offer a reference for assessing patient prognosis, but also provide potential targets for the development of new anti-GBM drugs.

Key words: glioblastoma, data mining, differentially expressed genes, prognosis-related genes