Liwei Sun1, Bing Li2, Bin Wang3, Jinduo Li3, Jing Li3. 1. Department of Intervention, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Disease, Tianjin Neurosurgical Institute, Tianjin, People's Republic of China. 2. Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin, People's Republic of China. 3. Department of Intervention, Tianjin Huanhu Hospital, Tianjin, People's Republic of China.
Abstract
Purpose: Ferroptosis is closely associated with tumors. The purpose of this study was to investigate the correlation between ferroptosis and prognosis of low grade glioma (LGG) via construction and verification of a risk model. Patients and Methods: The data of LGG were downloaded from public databases. Through LASSO analysis of characteristic genes, a gene signature was constructed. Patients into were divided two groups based on risk score. Subsequently, survival, clinical phenotype, functional enrichment, immune cell infiltration and somatic mutation analysis were performed. In addition, whether ferroptosis-related genes (FRGs) signature can predict the patient's response to anti-PD-1/PD-L1 immunotherapy was also investigated. Results: FRGs signature had strong prognostic assessment ability, and high risk score was associated with poor overall survival (OS) of LGG. The high risk score group had higher degree of immune cell infiltration, stronger stromal activity, higher immune score, and high expression of immune checkpoint. In low risk score group anti-PD-1/PD-L1 immunotherapy has significant therapeutic advantages and clinical response. Genes and frequency of somatic mutations and clinical phenotypes in the high and low risk score groups were significantly different. Conclusion: A prognostic model based on 7 FRGs can be used to predict the prognosis and immunotherapeutic response of LGG.
Purpose: Ferroptosis is closely associated with tumors. The purpose of this study was to investigate the correlation between ferroptosis and prognosis of low grade glioma (LGG) via construction and verification of a risk model. Patients and Methods: The data of LGG were downloaded from public databases. Through LASSO analysis of characteristic genes, a gene signature was constructed. Patients into were divided two groups based on risk score. Subsequently, survival, clinical phenotype, functional enrichment, immune cell infiltration and somatic mutation analysis were performed. In addition, whether ferroptosis-related genes (FRGs) signature can predict the patient's response to anti-PD-1/PD-L1 immunotherapy was also investigated. Results: FRGs signature had strong prognostic assessment ability, and high risk score was associated with poor overall survival (OS) of LGG. The high risk score group had higher degree of immune cell infiltration, stronger stromal activity, higher immune score, and high expression of immune checkpoint. In low risk score group anti-PD-1/PD-L1 immunotherapy has significant therapeutic advantages and clinical response. Genes and frequency of somatic mutations and clinical phenotypes in the high and low risk score groups were significantly different. Conclusion: A prognostic model based on 7 FRGs can be used to predict the prognosis and immunotherapeutic response of LGG.
Authors: Diana Miao; Claire A Margolis; Wenhua Gao; Martin H Voss; Wei Li; Dylan J Martini; Craig Norton; Dominick Bossé; Stephanie M Wankowicz; Dana Cullen; Christine Horak; Megan Wind-Rotolo; Adam Tracy; Marios Giannakis; Frank Stephen Hodi; Charles G Drake; Mark W Ball; Mohamad E Allaf; Alexandra Snyder; Matthew D Hellmann; Thai Ho; Robert J Motzer; Sabina Signoretti; William G Kaelin; Toni K Choueiri; Eliezer M Van Allen Journal: Science Date: 2018-01-04 Impact factor: 47.728
Authors: Quinn T Ostrom; Luc Bauchet; Faith G Davis; Isabelle Deltour; James L Fisher; Chelsea Eastman Langer; Melike Pekmezci; Judith A Schwartzbaum; Michelle C Turner; Kyle M Walsh; Margaret R Wrensch; Jill S Barnholtz-Sloan Journal: Neuro Oncol Date: 2014-07 Impact factor: 12.300
Authors: Guoxin Zhang; Zhen Dong; Briana C Prager; Leo Jk Kim; Qiulian Wu; Ryan C Gimple; Xiuxing Wang; Shideng Bao; Petra Hamerlik; Jeremy N Rich Journal: JCI Insight Date: 2019-04-04
Authors: Nicola V L Serão; Kristin R Delfino; Bruce R Southey; Jonathan E Beever; Sandra L Rodriguez-Zas Journal: BMC Med Genomics Date: 2011-06-07 Impact factor: 3.063
Authors: Margriet IJzerman-Korevaar; Tom J Snijders; Alexander de Graeff; Saskia C C M Teunissen; Filip Y F de Vos Journal: J Neurooncol Date: 2018-10-30 Impact factor: 4.130