Cancer Systems Biology Center, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China.
School of Artificial Intelligence, Jilin University, Changchun, China.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac206.
Oxidative stress is known to be involved in and possibly a key driver of the development of numerous chronic diseases, including cancer. It is highly desired to have a capability to reliably estimate the level of intracellular oxidative stress as it can help to identify functional changes and disease phenotypes associated with such a stress, but the problem proves to be very challenging. We present a novel computational model for quantitatively estimating the level of oxidative stress in tissues and cells based on their transcriptomic data. The model consists of (i) three sets of marker genes found to be associated with the production of oxidizing molecules, the activated antioxidation programs and the intracellular stress attributed to oxidation, respectively; (ii) three polynomial functions defined over the expression levels of the three gene sets are developed aimed to capture the total oxidizing power, the activated antioxidation capacity and the oxidative stress level, respectively, with their detailed parameters estimated by solving an optimization problem and (iii) the optimization problem is so formulated to capture the relevant known insights such as the oxidative stress level generally goes up from normal to chronic diseases and then to cancer tissues. Systematic assessments on independent datasets indicate that the trained predictor is highly reliable and numerous insights are made based on its application results to samples in the TCGA, GTEx and GEO databases.
氧化应激被认为是许多慢性疾病（包括癌症）发展的一个关键因素。人们非常希望能够可靠地估计细胞内氧化应激的水平，因为这有助于识别与这种应激相关的功能变化和疾病表型，但事实证明，这个问题极具挑战性。我们提出了一种新的计算模型，用于根据组织和细胞的转录组数据定量估计氧化应激水平。该模型由三部分组成：（i）三组分别与氧化分子产生、抗氧化活性程序激活以及氧化引起的细胞内应激相关的标记基因；（ii）定义了三个多项式函数，用于分别捕捉总氧化能力、激活的抗氧化能力和氧化应激水平，其详细参数通过求解优化问题来估计；（iii）优化问题的制定旨在捕捉相关的已知见解，例如氧化应激水平通常从正常组织到慢性疾病组织再到癌症组织逐渐升高。对独立数据集的系统评估表明，经过训练的预测器具有高度的可靠性，并且根据其在 TCGA、GTEx 和 GEO 数据库中的样本的应用结果得出了许多见解。
