This study presents a novel network methodology to identify prognostic gene signatures. Implication networks based on prediction logic are used to construct genome-wide coexpression networks for different disease states. From the differential components associated with specific disease states, candidate genes that are co-expressed with major disease signal hallmarks are selected. From these candidate genes, top genes that are the most predictive of clinical outcome are identified using univariate Cox model and Relief algorithm. Using this approach, a 13-gene lung cancer prognosis signature was identified, which generated significant prognostic stratifications (log-rank P < 0.05) in Director’s Challenge Study (n=442).
Keywords: Prognostic gene signature, lung cancer, implication networks, gene co-expression networks, signalling pathways