Hongwang Song1, Xiaojun Fu2, Chenxing Wu3, Shouwei Li3. 1. Department of Emergency Medicine, Shengjing Hospital of China Medical University, Shenyang, China. shw20150808@sina.com. 2. Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Xiangshanyikesong 50#, HaiDian District, Beijing, 100093, China. fuxiaojun880205@163.com. 3. Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Xiangshanyikesong 50#, HaiDian District, Beijing, 100093, China.
Abstract
BACKGROUND: Glioblastoma multiforme (GBM) is the most malignant tumor in human brain, with highly heterogeneity among different patients. Age could function as an incidence and prognosis risk factor for many tumors. METHOD: A series of bioinformatic experiments were conducted to evaluate the differences of incidence, differential expressed genes, enriched pathways with the data from Surveillance, Epidemiology, and End Results (SEER) program, the cancer genome atlas (TCGA) and Chinese glioma genome atlas (CGGA) project. RESULTS: We discovered in our present study that distinct difference of incidence and prognosis of different aged GBM patients. By a series of bioinformatic method, we found that the tumor associated fibroblasts (TAFs) was the most crucial tumor microenvironment (TME) component that led to this phenomenon. Epithelial-mesenchymal transition (EMT) could be the mechanism by which TAFs regulate the progression of GBM. CONCLUSION: We have proposed a close correlation between age and GBM incidence and prognosis, and propose the underlying mechanism behind this correlation by mining different databases, which laid the foundation for future research.
BACKGROUND: Glioblastoma multiforme (GBM) is the most malignant tumor in human brain, with highly heterogeneity among different patients. Age could function as an incidence and prognosis risk factor for many tumors. METHOD: A series of bioinformatic experiments were conducted to evaluate the differences of incidence, differential expressed genes, enriched pathways with the data from Surveillance, Epidemiology, and End Results (SEER) program, the cancer genome atlas (TCGA) and Chinese glioma genome atlas (CGGA) project. RESULTS: We discovered in our present study that distinct difference of incidence and prognosis of different aged GBM patients. By a series of bioinformatic method, we found that the tumor associated fibroblasts (TAFs) was the most crucial tumor microenvironment (TME) component that led to this phenomenon. Epithelial-mesenchymal transition (EMT) could be the mechanism by which TAFs regulate the progression of GBM. CONCLUSION: We have proposed a close correlation between age and GBM incidence and prognosis, and propose the underlying mechanism behind this correlation by mining different databases, which laid the foundation for future research.
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