Title:Monitoring Oxidative Stress Biomarkers in the Lipidome: Is There a Roadmap for “Human Inspection”?
Volume: 12
Issue: 6
Author(s): H. Jungnickel, J. Tentschert and A. Luch
Affiliation:
Keywords:
Biomarkers, cancer, isoprostanes, life-long biomarker evaluation, lipidomics, metabolomics, oxidative
stress, phenotype, medicine, DNA damage, cell demise, atherosclerosis, neurodegenerative diseases, cardiovascular diseases, chemo-attractants
Abstract: Oxidative stress is more and more recognized as the underlying motif for a broad variety of diseases
including cancer. Medicine faces the paramount task to develop better diagnostic tools and drug treatment
prediction models in the future to significantly enhance the quality of life. Special interest will focus on earlystage
disease biomarkers and biomarkers that could predict healing success at the earliest time point after the
treatment started. The accelerated formation of so-called reactive oxygen species (ROS) is becoming widely
regarded as the underlying process associated with many diseases like myocardial infarction, Alzheimer’s,
Parkinson’s and kidney disease, etc. Once generated within cells and tissues, ROS can react with a variety of
cellular metabolites like fatty acids, proteins or DNA. This review investigates the possibilities for various
oxidized metabolites as well as proteomics, genomics and bioimaging biomarkers to serve as early-stage
disease biomarkers or biomarkers for drug treatment success. We also assess the value of a step-by-step or
cascade biomarker approach as a new paradigm in medical diagnostics. Examples are given for possible
analytical methodology and tools as well as statistical methods that could be applied. Such an approach may
straighten the road toward new medical diagnostics and treatment regimes, which ultimately could lead to a
significantly enhanced medical service for patients suffering from chronic and debilitating or deadly diseases
including cancer. Examples from recent research are given to show the progress and possibilities for the
proposed model.
