In Silico Analysis of Oxidative Stress Gene Expression Profile and Biomarker Detection

Abstract

The normal cell is survived and functions at a proper redox state, so it is important to maintain the cellular redox state by having a balance between ROS biosynthesis and its degradation. Cancer cell with more cellular activity imposes more metabolic burden upon the cell, which ultimately produces more reactive oxygen species and results in an oxidative stress. The exerted level of ROS interacts with proteins involved in signaling cascade and alters their functions and cellular metabolism. However, the increased ROS levels damage DNA, lipids, and proteins. ROS can serve as a double-edged sword; it has both oncogenic and tumor suppressive effects. Thus, there should be a proper balance between ROS production and ROS scavenging mechanisms in normal cell. In response to ROS, cancer cells adapt themselves by increasing the production of antioxidant enzymes that regulate ROS level which is still higher than normal cell. Targeting this antioxidant defense system in cancer cell can be a good therapeutic option against cancer cells. Bioinformatics analyses of gene expression profiles from cancer genome data can help to enrich ROS-induced gene ontology, gene, and protein networks to identify the ROS response gene pools and biomarker in pre-cancer diagnosis and identification of specific drug targets in different cancer types. Here, we will discuss a brief introduction and role of all these factors in cancer and use them as example to explain the role of bioinformatics tools and techniques to help decode gene linkages and specific biomarker which could be useful to aid in cancer therapy.KeywordsReactive oxygen speciesCancer cellBiomarkerBioinformatics analysismiRNAmiRNA detection tool