Huize Wang1,2, Shiyan Li3, Hongxin Liu3, Shiyu Bian4, Wanjiang Huang5, Chengzhong Xing2, Yin Wang2,3. 1. Department of Nursing, First Affiliated Hospital of China Medical University 155# North Nanjing Street, Shenyang 110001, Liaoning, China. 2. Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University 155# North Nanjing Street, Heping District, Shenyang 110001, Liaoning Province, China. 3. Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University Shenyang 110122, Liaoning Province, China. 4. China Medical University - The Queen's University of Belfast Joint College, China Medical University Shenyang 110122, Liaoning Province, China. 5. No. 10 Middle School Xiangyang 441021, Hubei Province, China.
Abstract
BACKGROUND: Glioblastoma (GBM) is an aggressive brain tumor and the mechanisms of progression are very complex. Accelerated aging is a driving factor of GBM. However, there has not been thorough research about the mechanisms of GBM progression based on aging acceleration. METHODS: The aging predictor was modeled based on normal brain samples. Then an aging acceleration background network was constructed to explore GBM mechanisms. RESULTS: The accelerated aging-related mechanisms provided an innovative way to study GBM, wherein integrative analysis of somatic mutations and differential expression revealed key pathologic characteristics. Moreover, the influence of the immune system, the nervous system and other critical factors on GBM were identified. The survival analysis also disclosed crucial GBM markers. CONCLUSION: An integrative analysis of multi-omics data based on aging acceleration identified new driving factors for GBM. IJCEP
BACKGROUND:Glioblastoma (GBM) is an aggressive brain tumor and the mechanisms of progression are very complex. Accelerated aging is a driving factor of GBM. However, there has not been thorough research about the mechanisms of GBM progression based on aging acceleration. METHODS: The aging predictor was modeled based on normal brain samples. Then an aging acceleration background network was constructed to explore GBM mechanisms. RESULTS: The accelerated aging-related mechanisms provided an innovative way to study GBM, wherein integrative analysis of somatic mutations and differential expression revealed key pathologic characteristics. Moreover, the influence of the immune system, the nervous system and other critical factors on GBM were identified. The survival analysis also disclosed crucial GBM markers. CONCLUSION: An integrative analysis of multi-omics data based on aging acceleration identified new driving factors for GBM. IJCEP
Authors: Mark A Catherwood; David Gonzalez; David Donaldson; Ruth Clifford; Ken Mills; Patrick Thornton Journal: J Clin Pathol Date: 2019-02-02 Impact factor: 3.411
Authors: Philip N Dannhauser; Stéphane M Camus; Kazuho Sakamoto; L Amanda Sadacca; Jorge A Torres; Marine D Camus; Kit Briant; Stéphane Vassilopoulos; Alice Rothnie; Corinne J Smith; Frances M Brodsky Journal: J Biol Chem Date: 2017-11-02 Impact factor: 5.157