Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer.
OBJECTIVE
Cigarette smoking is the most demonstrated risk factor for the development of lung cancer, while the related genetic mechanisms are still unclear.


METHODS
The preprocessed microarray expression dataset was downloaded from Gene Expression Omnibus database. Samples were classified according to the disease state, stage and smoking state. A new computational strategy was applied for the identification and biological interpretation of new candidate genes in lung cancer and smoking by coupling a network-based approach with gene set enrichment analysis.


METHODS
Network analysis was performed by pair-wise comparison according to the disease states (tumor or normal), smoking states (current smokers or nonsmokers or former smokers), or the disease stage (stages I-IV). The most activated metabolic pathways were identified by gene set enrichment analysis.


RESULTS
Panels of top ranked gene candidates in smoking or cancer development were identified, including genes involved in cell proliferation and drug metabolism like cytochrome P450 and WW domain containing transcription regulator 1. Semaphorin 5A and protein phosphatase 1F are the common genes represented as major hubs in both the smoking and cancer related network. Six pathways, e.g. cell cycle, DNA replication, RNA transport, protein processing in endoplasmic reticulum, vascular smooth muscle contraction and endocytosis were commonly involved in smoking and lung cancer when comparing the top ten selected pathways.


CONCLUSIONS
New approach of bioinformatics for biomarker identification and validation can probe into deep genetic relationships between cigarette smoking and lung cancer. Our studies indicate that disease-specific network biomarkers, interaction between genes/proteins, or cross-talking of pathways provide more specific values for the development of precision therapies for lung.
Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. fangxiaocong@gmail.com
Xiaocong
X
Fang
Michael
M
Netzer
Christian
C
Baumgartner
Chunxue
C
Bai
Xiangdong
X
Wang
D014408:Biomarkers, Tumor / Q000235:genetics; D030541:Databases, Genetic; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D008175:Lung Neoplasms / Q000209:etiology / Q000235:genetics* / Q000473:pathology; D008297:Male; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012907:Smoking / Q000009:adverse effects / Q000235:genetics*
D014408:Biomarkers, Tumor