Ferroptosis-related NFE2L2 and NOX4 Genes are Potential Risk Prognostic Biomarkers and Correlated with Immunogenic Features in Glioma.
Ferroptosis-related NFE2L2 and NOX4 Genes are Potential Risk Prognostic Biomarkers and Correlated with Immunogenic Features in Glioma.
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铁死亡相关的 NFE2L2 和 NOX4 基因是潜在的风险预后生物标志物，并与胶质瘤的免疫原性特征相关
DOI：
10.1007/s12013-022-01124-x
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发表时间：
2023-03
影响因子：
2.6
通讯作者：
Li X
中科院分区：
文献类型：
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作者：
Lin L;Li X;Zhu S;Long Q;Hu Y;Zhang L;Liu Z;Li B;Li X
铁死亡是一种新发现的程序性细胞死亡方式，可能与胶质瘤的预后或诊断因素存在潜在关联。在本研究中，通过FerrDb数据库筛选出5个与胶质瘤相关的基因，并借助TCGA、GEPIA、TIMER等数据库，分析了这些基因与胶质瘤生存及预后之间的关系。生存曲线和预后分析表明，NFE2L2和NOX4的过表达分别与胶质瘤较差的预后显著相关。随后，基于GSCA探究了这两个基因的表达与肿瘤浸润免疫细胞水平之间的关联，同时基于TISIDB数据库研究了NFE2L2和NOX4的免疫特性。在胶质瘤，尤其是胶质母细胞瘤（GBM）中，基因表达与免疫浸润之间存在紧密联系，这种联系在巨噬细胞、调节性T细胞（nTreg）和辅助性T细胞2（Th2）中尤为明显，这些细胞在肿瘤微环境（TME）中发挥免疫抑制功能。总之，这些结果表明，NFE2L2和NOX4可能是胶质瘤的风险预后生物标志物，且在肿瘤发生过程中与免疫浸润和肿瘤免疫密切相关。
Ferroptosis is a newfound mode of regulated cell death that may have potential to associate with prognostic or diagnostic factors in glioma. In this research, 5 genes related to glioma were screened through the FerrDb database, and we analyzed the combination between genes and glioma of survival and prognosis via TCGA, GEPIA, TIMER, and other databases. Survival curve and prognostic analysis showed that the overexpression of NFE2L2 and NOX4, respectively, has a remarkable link with a worse prognosis in glioma. Then, the association between the expression of the two genes and tumor-infiltrating immune cells level was explored based on the GSCA, and the immunity of NFE2L2 and NOX4 based on the TISIDB database was also investigated. In glioma, especially GBM, there is a strong association between gene expression and immune infiltration, even in macrophages, nTreg, and Th2 cells, which play immunosuppressive functions in TME. In conclusion, these results indicate that NFE2L2 and NOX4 could be risk prognosis biomarkers in glioma, and they bound up with immune infiltration and tumor immunity in tumorigenesis.
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ACSL4 通过塑造细胞脂质成分来决定铁死亡敏感性。
影响因子：
14.8
作者：
Doll S;Proneth B;Tyurina YY;Panzilius E;Kobayashi S;Ingold I;Irmler M;Beckers J;Aichler M;Walch A;Prokisch H;Trümbach D;Mao G;Qu F;Bayir H;Füllekrug J;Scheel CH;Wurst W;Schick JA;Kagan VE;Angeli JP;Conrad M
通讯作者：
Conrad M
影响因子：
14.8
作者：
Kagan VE;Mao G;Qu F;Angeli JP;Doll S;Croix CS;Dar HH;Liu B;Tyurin VA;Ritov VB;Kapralov AA;Amoscato AA;Jiang J;Anthonymuthu T;Mohammadyani D;Yang Q;Proneth B;Klein-Seetharaman J;Watkins S;Bahar I;Greenberger J;Mallampalli RK;Stockwell BR;Tyurina YY;Conrad M;Bayır H
通讯作者：
Bayır H
影响因子：
64.5
作者：
Dixon SJ;Lemberg KM;Lamprecht MR;Skouta R;Zaitsev EM;Gleason CE;Patel DN;Bauer AJ;Cantley AM;Yang WS;Morrison B 3rd;Stockwell BR
通讯作者：
Stockwell BR
影响因子：
11.4
作者：
Park MW;Cha HW;Kim J;Kim JH;Yang H;Yoon S;Boonpraman N;Yi SS;Yoo ID;Moon JS
通讯作者：
Moon JS
影响因子：
82.9
作者：
通讯作者：
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