Identification of colorectal cancer candidate genes based on subnetwork extraction algorithm

Abstract

Colorectal cancer (CRC) is one of the most common malignancies that could threaten human health. As the molecular mechanism of CRC has not yet been completely uncovered, identifying related genes of this disease is an important area of CRC research that could provide new insights into gene function as well as potential targets for CRC treatment. Here we used a subnetwork extraction algorithm (Limited K-walks algorithm) to discover CRC related genes based on protein-protein interaction network. In particular, we computationally predicted two genes (UBC and SMAD4) as putative key genes of CRC. Therapy targeting on the functions of these two key genes may provide a promising therapeutic strategy for CRC treatment.