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

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 coexpression 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 o...

• Type: Journal
• Year: 2010
• Where: BIBM
• Authors: Ying-Wooi Wan, Changchang Xiao, Nancy Lan Guo
• Keywords: BIBM 2010, Bioinformatics, Gene Co-expression Networks, Lung Cancer, Lung Cancer Survival