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Abstract: Objectives: 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. Measurements: 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. Conclusion: 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. [Copyright &y& Elsevier]

Abstract: In this paper we present an approach to analyse the migration of stent grafts after endovascular aortic aneurysm repair. Stent graft migration and kinking or buckling of the graft can cause the perilous risk of rupture. Our approach allows describing the occurring movements quantitatively towards the aim to find common patterns related to different endograft devices or to dangerous changes in morphology. We proceed by defining the spinal canal as reference system and by investigating the movements of the graft relative to it. Here, we segment the spinal canal as well as the stent graft at different time points and we apply a point set registration algorithm in order to calculate the transformation to the spinal canal reference system and to determine the occurring motion of the stent. First results illustrate that essentially rigid motion occurs in case of thoracic aortic aneurysms. [Copyright &y& Elsevier]