Fan Fan1,2,3, Hao Zhang1, Ziyu Dai1, Yakun Zhang4, Zhiwei Xia5, Hui Cao6, Kui Yang1,3, Shui Hu4, Yong Guo1, Fengqin Ding7, Quan Cheng8,9, Nan Zhang10. 1. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China. 2. Center for Medical Genetics & Hunan Provincial Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China. 3. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China. 4. One-third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang, 150000, People's Republic of China. 5. Department of Neurology, Hunan Aerospace Hospital, Changsha, China. 6. Department of Psychiatry, The Second People's Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China. 7. Department of Clinical Laboratory Medicine, People's Hospital of Ningxia Hui Autonomous Region, First Affiliated Hospital of Northwest Minzu University, Yinchuan, Ningxia, China. 8. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China. chengquan@csu.edu.cn. 9. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China. chengquan@csu.edu.cn. 10. One-third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang, 150000, People's Republic of China. awekevin@onethird-lab.com.
Abstract
PURPOSE: Glioblastoma (GBM) is the most common and deadly brain tumor. We aimed to reveal potential prognostic GBM marker genes, elaborate their functions, and build an effective a prognostic model for GBM patients. METHODS: Through data mining of The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we screened for significantly differentially expressed genes (DEGs) to calculate risk scores for individual patients. Published data of somatic mutation and copy number variation profiles were analyzed for distinct genomic alterations associated with risk scores. In addition, single-cell sequencing was used to explore the biological functions of the identified prognostic marker genes. By combining risk scores and other clinical features, we built a comprehensive prognostic GBM model. RESULTS: Seven DEGs (CLEC5A, HOXC6, HOXA5, CCL2, GPRASP1, BSCL2 and PTX3) were identified as being prognostic for GBM. Expression of these genes was confirmed in different GBM cell lines using real-time PCR. Risk scores calculated from the seven DEGs revealed prognostic value irrespective of other clinical factors, including IDH mutation status, and were negatively correlated with TP53 expression. The prognostic genes were found to be associated with tumor proliferation and progression based on pseudo-time analysis in neoplastic cells. A final prognostic model was developed and validated with a good performance, especially in geriatric GBM patients. CONCLUSIONS: Using genetic profiles, age, IDH mutation status, and chemotherapy and radiotherapy, we constructed a comprehensive prognostic model for GBM patients. The model has a good performance, especially in geriatric GBM patients.
PURPOSE: Glioblastoma (GBM) is the most common and deadly brain tumor. We aimed to reveal potential prognostic GBM marker genes, elaborate their functions, and build an effective a prognostic model for GBM patients. METHODS: Through data mining of The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we screened for significantly differentially expressed genes (DEGs) to calculate risk scores for individual patients. Published data of somatic mutation and copy number variation profiles were analyzed for distinct genomic alterations associated with risk scores. In addition, single-cell sequencing was used to explore the biological functions of the identified prognostic marker genes. By combining risk scores and other clinical features, we built a comprehensive prognostic GBM model. RESULTS: Seven DEGs (CLEC5A, HOXC6, HOXA5, CCL2, GPRASP1, BSCL2 and PTX3) were identified as being prognostic for GBM. Expression of these genes was confirmed in different GBM cell lines using real-time PCR. Risk scores calculated from the seven DEGs revealed prognostic value irrespective of other clinical factors, including IDH mutation status, and were negatively correlated with TP53 expression. The prognostic genes were found to be associated with tumor proliferation and progression based on pseudo-time analysis in neoplastic cells. A final prognostic model was developed and validated with a good performance, especially in geriatric GBM patients. CONCLUSIONS: Using genetic profiles, age, IDH mutation status, and chemotherapy and radiotherapy, we constructed a comprehensive prognostic model for GBM patients. The model has a good performance, especially in geriatric GBM patients.