Lianwang Li1, Chuanbao Zhang2, Zheng Wang2, Yuhao Guo1, Yinyan Wang2, Xing Fan3,4, Tao Jiang5,6,7. 1. Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070, People's Republic of China. 2. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China. 3. Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070, People's Republic of China. xingkongyaoxiang@163.com. 4. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China. xingkongyaoxiang@163.com. 5. Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070, People's Republic of China. taojiang1964@163.com. 6. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China. taojiang1964@163.com. 7. Research Units of Accurate Diagnosis and Treatment of Brain Tumors and Translational Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China. taojiang1964@163.com.
Abstract
BACKGROUND: Glioma-related epilepsy (GRE) is a common symptom in patients with diffuse gliomas. However, the underlying mechanisms of GRE remain unclear. The current study aimed to investigate the underlying epileptogenic mechanisms of GRE through RNA sequencing analysis. METHODS: Demographic, RNA sequencing, and follow-up data of 643 patients were reviewed. Patients were divided into test and validation groups (223 and 420 patients, respectively) by different time periods for RNA sequencing. The differentially expressed genes (DEGs) associated with preoperative GRE were identified using R software. Functional enrichment analysis was subsequently performed, and tissue-infiltrating immune cells were also estimated. Weighted correlation network analysis (WGCNA) was conducted to further identify key modules exhibiting the highest correlation with preoperative GRE. Overlapping genes between the DEG set and key gene set identified by WGCNA were selected and verified in the validation cohort. The protein-protein interaction (PPI) network analysis was then constructed to identify hub genes for preoperative GRE. RESULTS: A total of 219 DEGs were identified, among which 112 were upregulated and 107 downregulated in patients with GRE. Functional enrichment analysis revealed that upregulated DEGs were related to ion channel activity, while downregulated genes were related to immunity. Forty-two genes were further selected from overlapping DEGs and the key gene set. Among these genes, 31 genes showed significant differences in the validation cohort. Finally, the PPI network analysis identified six genes, including SCN3B, KCNIP2, KCNJ11, VEGFA, MMP9, and ANXA2, as hub genes for GRE. CONCLUSION: The current study revealed that ion channel activity and immunity dysfunction in diffuse glioma patients contributed to the occurrence of GRE, and SCN3B might be a shared therapeutic target for both diffuse gliomas and GRE. These findings could improve the understanding of the mechanisms of GRE and promote individualized medications for glioma management.
BACKGROUND: Glioma-related epilepsy (GRE) is a common symptom in patients with diffuse gliomas. However, the underlying mechanisms of GRE remain unclear. The current study aimed to investigate the underlying epileptogenic mechanisms of GRE through RNA sequencing analysis. METHODS: Demographic, RNA sequencing, and follow-up data of 643 patients were reviewed. Patients were divided into test and validation groups (223 and 420 patients, respectively) by different time periods for RNA sequencing. The differentially expressed genes (DEGs) associated with preoperative GRE were identified using R software. Functional enrichment analysis was subsequently performed, and tissue-infiltrating immune cells were also estimated. Weighted correlation network analysis (WGCNA) was conducted to further identify key modules exhibiting the highest correlation with preoperative GRE. Overlapping genes between the DEG set and key gene set identified by WGCNA were selected and verified in the validation cohort. The protein-protein interaction (PPI) network analysis was then constructed to identify hub genes for preoperative GRE. RESULTS: A total of 219 DEGs were identified, among which 112 were upregulated and 107 downregulated in patients with GRE. Functional enrichment analysis revealed that upregulated DEGs were related to ion channel activity, while downregulated genes were related to immunity. Forty-two genes were further selected from overlapping DEGs and the key gene set. Among these genes, 31 genes showed significant differences in the validation cohort. Finally, the PPI network analysis identified six genes, including SCN3B, KCNIP2, KCNJ11, VEGFA, MMP9, and ANXA2, as hub genes for GRE. CONCLUSION: The current study revealed that ion channel activity and immunity dysfunction in diffuse glioma patients contributed to the occurrence of GRE, and SCN3B might be a shared therapeutic target for both diffuse gliomas and GRE. These findings could improve the understanding of the mechanisms of GRE and promote individualized medications for glioma management.
Authors: Michael C Dewan; Reid C Thompson; Steven N Kalkanis; Fred G Barker; Constantinos G Hadjipanayis Journal: J Neurosurg Date: 2016-06-24 Impact factor: 5.115
Authors: José Luis Castañeda-Cabral; Adacrid Colunga-Durán; Mónica E Ureña-Guerrero; Carlos Beas-Zárate; Maria de Los Angeles Nuñez-Lumbreras; Sandra Orozco-Suárez; Mario Alonso-Vanegas; Rosalinda Guevara-Guzmán; Maria A Deli; María Guadalupe Valle-Dorado; Vicente Sánchez-Valle; Luisa Rocha Journal: Microvasc Res Date: 2020-08-13 Impact factor: 3.514