Network Elastic Net for Identifying Smoking specific gene expression for lung cancer

TL;DR

This paper introduces the network elastic net, a method that clusters lung cancer patients based on smoking behavior and gene expression to identify biomarkers and predict cancer stage without additional tests.

Contribution

It proposes the network elastic net, a novel approach that combines clustering and regression on graphs to analyze gene expression and smoking data for lung cancer prognosis.

Findings

Effective clustering of patients based on smoking and gene expression.

Identification of smoking-specific gene expression biomarkers.

Clusters correlate with cancer stages.

Abstract

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.