A systems biology approach to predict novel genes associated with complex diseases: Application to colorectal cancer

Shivashankar H Nagaraj and Antonio Reverter


Cancer has enormous complexity at the molecular level, with multiple genes, proteins and pathways and regulatory interconnections being affected. Motivated by this complexity, we propose a systems biology approach that formally integrates the available genetic, transcriptomic, epigenetic and molecular knowledge on cancer biology. After classifying genes into cancer-associated and non-cancer-associated, we compile a set of functional attributes highly relevant to cancer biology including protein kinases, secreted proteins, transcription factors, and tissue specificity. Cancer-associated genes are used to extract "common cancer fingerprints" through these molecular attributes, and a Boolean logic is implemented allowing the rational integration of expression data and functional attributes. This Boolean logic gives rise to a guilt-by-association classifier that generates an inventory of novel cancer-associated genes. Finally, novel cancer-associated genes are interlaced with the known cancer-related genes in a weighted network circuitry aimed at identifying highly conserved gene interactions that impact cancer outcome. We demonstrate the effectiveness of this approach using colorectal cancer as a prototype and identify several novel candidate genes classified according to their functional attributes. We argue that this is a holistic approach that faithfully mimics cancer characteristics, efficiently predicts novel candidates and has universal applicability to complex diseases.

Figure 3


The network file used to generate The Always Conserved network (Figure 3) is available here for download and can be opened using cytoscape.