Title :
Network-based identification of smoking-associated gene signature for lung cancer
Author :
Wan, Ying-Wooi ; Xiao, Changchang ; Guo, Nancy Lan
Author_Institution :
Mary Babb Randolph Cancer Center, West Virginia Univ., Morgantown, WV, USA
Abstract :
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.
Keywords :
bioinformatics; cancer; genetics; lung; medical diagnostic computing; Pathway Studio; STRING 8; gene co-expression networks; gene co-expression patterns; lung cancer survival; network-based identification; smoking-associated gene signature; Bioinformatics; Cancer; Diseases; Genomics; Lungs; Prediction algorithms; Training;
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-8306-8
Electronic_ISBN :
978-1-4244-8307-5
DOI :
10.1109/BIBM.2010.5706613