  Survival month for non-small lung cancer patients depend upon which stage of
lung cancer is present. Our aim is to identify smoking specific gene expression
biomarkers in the prognosis of lung cancer patients. In this paper, we
introduce the network elastic net, a generalization of network lasso that
allows for simultaneous clustering and regression on graphs. In Network elastic
net, we consider similar patients based on smoking cigarettes per year to form
the network. We then further find the suitable cluster among patients based on
coefficients of genes having different survival month structures and showed the
efficacy of the clusters using stage enrichment. This can be used to identify
the stage of cancer using gene expression and smoking behavior of patients
without doing any tests.
