Title: A novel network model identified a 13-gene lung cancer prognostic signature

Authors: Nancy Lan Guo, Ying-Wooi Wan, Swetha Bose, James Denvir, Michael L. Kashon, Michael E. Andrew

Addresses: Mary Babb Randolph Cancer Centre, Department of Community Medicine, West Virginia University, Morgantown, WV 26506 – 9300, USA. ' Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA. ' Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA. ' Department of Statistics, West Virginia University, Morgantown, WV 26506, USA. ' National Institute of Occupational Safety and Health, Biostatistics and Epidemiology, 1095 Willowdale Road, Morgantown, WV 26505, USA. ' National Institute of Occupational Safety and Health, Biostatistics and Epidemiology, 1095 Willowdale Road, Morgantown, WV 26505, USA

Abstract: 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 signatures; lung cancer; implication networks; gene co-expression networks; signalling pathways; disease states.

DOI: 10.1504/IJCBDD.2011.038655

International Journal of Computational Biology and Drug Design, 2011 Vol.4 No.1, pp.19 - 39

Received: 29 Oct 2010
Accepted: 16 Dec 2010
Published online: 24 Jan 2015 *

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