Li Xiaozhi, Meng Yutong
Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China.
Department of Stomatology, Shengjing Hospital of China Medical University, Shenyang, China.
Front Cell Dev Biol. 2021 Apr 9;9:658856. doi: 10.3389/fcell.2021.658856. eCollection 2021.
SUMOylation is one of the post-translational modifications. The relationship between the expression of SUMOylation regulators and the prognosis of glioblastoma is not quite clear.
The single nucleotide variant data, the transcriptome data, and survival information were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and cBioportal database. Wilcoxon test was used to analyze differentially expressed genes between glioblastoma and normal brain tissues. Gene set enrichment analysis was conducted to find the possible functions. One risk scoring model was built by the least absolute shrinkage and selection operator Cox regression. Kaplain-Meier survival curves and receiver operating characteristic curves were applied to evaluate the effectiveness of the model in predicting the prognosis of glioblastoma.
Single-nucleotide variant mutations were found in SENP7, SENP3, SENP5, PIAS3, RANBP2, USPL1, SENP1, PIAS2, SENP2, and PIAS1. Moreover, UBE2I, UBA2, PIAS3, and SENP1 were highly expressed in glioblastoma, whereas PIAS1, RANBP2, SENP5, and SENP2 were downregulated in glioblastoma. Functional enrichment analysis showed that the SUMOylation regulators of glioblastoma might involve cell cycle, DNA replication, and other functions. A prognostic model of glioblastoma was constructed based on SUMOylation regulator-related molecules (ATF7IP, CCNB1IP1, and LBH). Kaplain-Meier survival curves and receiver operating characteristic curves showed that the model had a strong ability to predict the overall survival of glioblastoma.
This study analyzed the expression of 15 SUMOylation regulators in glioblastoma. The risk assessment model was constructed based on the SUMOylation regulator-related genes, which had a strong predictive ability for the overall survival of patients with glioblastoma. It might provide targets for the study of the relationship between SUMOylation and glioblastoma.
小泛素样修饰(SUMOylation)是一种翻译后修饰。SUMOylation调节因子的表达与胶质母细胞瘤预后之间的关系尚不完全清楚。
从癌症基因组图谱(The Cancer Genome Atlas)、基因表达综合数据库(Gene Expression Omnibus)和cBioportal数据库获取单核苷酸变异数据、转录组数据和生存信息。采用Wilcoxon检验分析胶质母细胞瘤与正常脑组织之间的差异表达基因。进行基因集富集分析以发现可能的功能。通过最小绝对收缩和选择算子Cox回归建立一个风险评分模型。应用Kaplan-Meier生存曲线和受试者工作特征曲线评估该模型预测胶质母细胞瘤预后的有效性。
在SENP7、SENP3、SENP5、PIAS3、RANBP2、USPL1、SENP1、PIAS2、SENP2和PIAS1中发现单核苷酸变异突变。此外,UBE2I、UBA2、PIAS3和SENP1在胶质母细胞瘤中高表达,而PIAS1、RANBP2、SENP5和SENP2在胶质母细胞瘤中下调。功能富集分析表明,胶质母细胞瘤的SUMOylation调节因子可能涉及细胞周期、DNA复制等功能。基于SUMOylation调节因子相关分子(ATF7IP、CCNB1IP1和LBH)构建了胶质母细胞瘤的预后模型。Kaplan-Meier生存曲线和受试者工作特征曲线表明,该模型具有很强的预测胶质母细胞瘤总生存的能力。
本研究分析了15种SUMOylation调节因子在胶质母细胞瘤中的表达。基于SUMOylation调节因子相关基因构建了风险评估模型,该模型对胶质母细胞瘤患者的总生存具有很强的预测能力。它可能为研究SUMOylation与胶质母细胞瘤之间的关系提供靶点。