Quantitative estimation of intracellular oxidative stress in human tissues.

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.