Integrated network analysis for studying human lung squamous cell carcinoma

We propose an integrated network-based approach to analyse the correlation network arising from largescale gene expression data. Our approach represents a novel perspective to the problem of node classification by assigning roles to nodes on the basis of network topology together with the gene expression data. Our rationale is that the biological roles of nodes should be identifiable looking at local interactions of each node with respect to the global connectivity of the network. We applied our methodology to the RNA and microRNAsequencing data of human lung squamous cell carcinoma in order to identify a small pool of genes that might have a key role in lung cancer development and progression.