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 (TCGA, training set) and 171 LGGs from the Chinese Glioma Genome Atlas (CGGA, validation set). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was conducted to develop a risk-signature. The receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to study the prognosis value of the risk-signature. RESULTS: Among the tested 784 RNA processing genes, 276 were significantly correlated with the OS of LGGs. Further LASSO Cox regression identified a 19-gene risk-signature, whose risk score was also an independently prognosis factor (P<0.0001, multiplex Cox regression) in the validation dataset. The signature had better prognostic value than the traditional factors "age", "grade" and "WHO 2016 classification" for 3- and 5-year survival both two datasets (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.
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 (TCGA, training set) and 171 LGGs from the Chinese Glioma Genome Atlas (CGGA, validation set). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was conducted to develop a risk-signature. The receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to study the prognosis value of the risk-signature. RESULTS: Among the tested 784 RNA processing genes, 276 were significantly correlated with the OS of LGGs. Further LASSO Cox regression identified a 19-gene risk-signature, whose risk score was also an independently prognosis factor (P<0.0001, multiplex Cox regression) in the validation dataset. The signature had better prognostic value than the traditional factors "age", "grade" and "WHO 2016 classification" for 3- and 5-year survival both two datasets (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.
Authors: Mehmet Baysan; Kevin Woolard; Margaret C Cam; Wei Zhang; Hua Song; Svetlana Kotliarova; Demosthenes Balamatsias; Amanda Linkous; Susie Ahn; Jennifer Walling; Galina I Belova; Howard A Fine Journal: Int J Cancer Date: 2017-08-21 Impact factor: 7.396
Authors: Quinn T Ostrom; Haley Gittleman; Jordan Xu; Courtney Kromer; Yingli Wolinsky; Carol Kruchko; Jill S Barnholtz-Sloan Journal: Neuro Oncol Date: 2016-10-01 Impact factor: 12.300
Authors: Niklas Thon; Mathias Kunz; Lena Lemke; Nathalie L Jansen; Sabina Eigenbrod; Simone Kreth; Jürgen Lutz; Rupert Egensperger; Armin Giese; Jochen Herms; Michael Weller; Hans Kretzschmar; Jörg-Christian Tonn; Christian la Fougère; Friedrich-Wilhelm Kreth Journal: Int J Cancer Date: 2014-11-03 Impact factor: 7.396
Authors: Mahmoud S Alghamri; Rohit Thalla; Ruthvik P Avvari; Ali Dabaja; Ayman Taher; Lili Zhao; Peter J Ulintz; Maria G Castro; Pedro R Lowenstein Journal: Neurooncol Adv Date: 2020-03-27