Network Elastic Net for Identifying Smoking specific gene expression for lung cancer
This work addresses the need for non-invasive cancer stage identification in lung cancer patients using gene expression and smoking data, though it appears incremental as it builds on existing network lasso methods.
The authors tackled the problem of identifying smoking-specific gene expression biomarkers for lung cancer prognosis by developing the network elastic net, a method that clusters patients based on smoking behavior and gene coefficients, and demonstrated its efficacy through stage enrichment analysis.
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.