Abstract
BACKGROUND. Aberrant expression of RNA processing genes may drive the alterative RNA profile in lower-grade gliomas (LGGs). Thus, we aimed to further stratify LGGs based on the expression of RNA processing genes. METHODS. This study included 446 LGGs from The Cancer Genome Atlas (training set) and 171 LGGs from the Chinese Glioma Genome Atlas (validation set). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was conducted to develop a risk signature. The receiver operating characteristic curves and Kaplan-Meier curves were used to study the prognostic value of the risk signature. RESULTS. Among the tested 784 RNA processing genes, 276 were significantly correlated with the overall survival of LGGs. Further LASSO Cox regression identified a 19-gene risk signature, whose risk score was also an independent prognosis factor (P < 0.0001, multiplex Cox regression) in the validation data set. The signature had better prognostic value than the traditional factors “age,” “grade,” and “WHO 2016 classification” for 3- and 5-year survival both data sets (AUCs >85%). Importantly, the risk signature could further stratify the survival of LGGs in specific subgroups of WHO 2016 classification. Furthermore, alternative splicing events for genes such as EGFR and FGFR were found to be associated with the risk score. mRNA expression levels for genes, which participated in cell proliferation and other processes, were significantly correlated to the risk score. CONCLUSIONS. Our results highlight the role of RNA processing genes for further stratifying the survival of patients with LGGs and provide insight into the alternative splicing events underlying this role. FUNDING. The National Natural Science Foundation of China (81773208, 81402052), the Beijing Nova Program (Z16110004916082), and the National Key Research and Development Plan (2016YFC0902500).
Authors
Rui-Chao Chai, Yi-Ming Li, Ke-Nan Zhang, Yu-Zhou Chang, Yu-Qing Liu, Zheng Zhao, Zhi-Liang Wang, Yuan-Hao Chang, Guan-Zhang Li, Kuan-Yu Wang, Fan Wu, Yong-Zhi Wang
Figure 8
GSEA analysis of genes correlated with the risk scores.
