Urinary Biomarkers for Disease Detection

Abstract

The current healthcare system relies largely on a passive approach toward disease detection, which typically involves patients presenting a 'chief complaint' linked to a particular set of symptoms for diagnosis. Since all degenerative diseases occur slowly and initiate as changes in the regulation of individual cells within our organs and tissues, it is inevitable that with the current approach to medical care we are bound to discover some illnesses at a point in time when the damage is irreversible and meaningful treatments are no longer available. There exist organ-specific sets (or panels) of nucleic acids, such as microRNAs (miRNAs), which regulate and help to ensure the proper function of each of our organs and tissues. Thus, dynamic readout of their relative abundance can serve as a means to facilitate real-time health monitoring. With the advent and mass utilization of next-generation sequencing (NGS), such a proactive approach is currently feasible. Because of the computational complexity of customized analyses of 'big data', dedicated efforts to extract reliable information from longitudinal datasets is key to successful early detection of disease. Here, we present our preliminary results for the analysis of healthy donor samples and drug-naive lung cancer patients.