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Network-based identification of smoking-associated gene signature for lung cancer

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3 Author(s)
Ying-Wooi Wan ; Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 ; Changchang Xiao ; Nancy Lan Guo

This study presents a novel computational approach to identifying a smoking-associated gene signature. The methodology contains the following steps: 1) identifying genes significantly associated with lung cancer survival, 2) selecting genes which are differentially expressed in smoker versus non-smoker groups from the survival genes, 3) from these candidate genes, constructing gene co-expression networks based on prediction logic for smokers and non-smokers, 4) identifying smoking-mediated differential components, i.e., the unique gene co-expression patterns specific to each group, and 5) from the differential components, identifying genes directly co-expressed with major lung cancer hallmarks. The identified 7-gene signature could separate lung cancer patients into two risk groups with distinct postoperative survival (log-rank P <; 0.05, Kaplan-Meier analysis) in four independent cohorts (n=427). It also has implications in the diagnosis of lung cancer (accuracy = 74%) in a cohort of smokers (n=164). Computationally derived co-expression patterns were validated with Pathway Studio and STRING 8.

Published in:

Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on

Date of Conference:

18-21 Dec. 2010