Random walk ranking guided by disease association networks for lung cancer biomarker discovery

The identification of candidate molecular entities involved in a specific disease has been a primary focus of cancer study on biomarker discovery. Prioritizing proteins from a disease-specific protein-protein interaction (PPI) network has become an efficient computational strategy for cancer biomarker discovery. Although some successful methods, such as random walk ranking (RWR) algorithm, can exploit global network topology to prioritize proteins, this network-based computational strategy still needs more comprehensive prior knowledge, like genome-wide association study (GWAS), to improve its discovering capability. In this paper, we first analyzed genome-wide association loci for human diseases, and built disease association networks (DAN), whose associations were defined by two diseases sharing common genetic variants. Then we assigned each node in a human PPI network a disease-specific weight, based on knowledge from the DANs and text mining. Finally, we presented a seed-weighted random walk ranking (SW-RWR) method to prioritize biomarkers in the global human PPI network. We used a lung cancer case study to show that our ranking strategy has better accuracy and sensitivity in discovering potential clinically-useful; biomarkers than a similar network-based ranking method. This result suggests that close association among different diseases could play an important role in biomarker discovery.