Glioma is one of the most common primary brain tumors with poor prognosis. Although radiotherapy is an important treatment method for gliomas, the efficacy is still limited by the high occurrence of radioresistance and the underlying molecular mechanism is unclear. Here, we performed a data mining work based on four glioma expression datasets. These datasets were classified into training set and validation set. Radiotherapy-induced differential expressed genes and prognosis-associated genes were screened using different classifiers. The Kaplan-Meier curves along with the two-sided Log Rank (Mantel-Cox) test were used to evaluate overall survival. We found the gene expression profiles of gliomas between those patients received radiotherapy and those patients without received radiotherapy were quite different. A 20-gene signature was identified, which was associated with radiotherapy.Furthermore, a novel 5-gene signature (HOXC10, LOC101928747, CYB561D2, RPL36A and RPS4XP2) as an independent predictor of glioma patients' prognosis was further derived from the 20-gene signature. These findings provided a new insight into the molecular mechanism of radioresistance in gliomas. The 5-gene signature might represent therapeutic target for gliomas.
Keywords: gene signature; glioma; prognosis.