Liu Liyang, Cui Haining, Xu Ying
College of Physics, Jilin University, Changchun, China.
Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, The University of Georgia, Athens, GA, United States.
Front Genet. 2020 May 19;11:494. doi: 10.3389/fgene.2020.00494. eCollection 2020.
Quantitative assessment of the intracellular oxidative stress level is a very important problem since it is the basis for elucidation of the fundamental causes of metabolic changes in diseased human cells, particularly cancer. However, the problem proves to be very challenging to solve because of the complex nature of the problem. Here a computational method is presented for predicting the quantitative level of the intracellular oxidative stress in cancer tissue cells. The basic premise of the predictor is that the genomic mutation level is strongly associated with the intracellular oxidative stress level. Based on this, a statistical analysis is conducted to identify a set of enzyme-encoding genes, whose combined expression levels can well explain the mutation rates in individual cancer tissues in the TCGA database. We have assessed the validity of the predictor by assessing it against genes that are known to have anti-oxidative functions for specific types of oxidative stressors. Then the applications of the predictor are conducted to illustrate its utility.
细胞内氧化应激水平的定量评估是一个非常重要的问题，因为它是阐明患病人类细胞，特别是癌细胞代谢变化根本原因的基础。然而，由于该问题的复杂性，事实证明解决起来极具挑战性。本文提出了一种计算方法，用于预测癌组织细胞内氧化应激的定量水平。该预测器的基本前提是基因组突变水平与细胞内氧化应激水平密切相关。基于此，进行统计分析以识别一组酶编码基因，其组合表达水平能够很好地解释TCGA数据库中各个癌组织的突变率。我们通过将该预测器与已知对特定类型氧化应激源具有抗氧化功能的基因进行对比，评估了其有效性。然后进行了该预测器的应用以说明其效用。
