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Lung cancer is the second most common cancer in the world. The aim of this study is to identify biomarkers for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles from GEO database were analyzed by GEO2R to identify Differentially Expressed Genes (DEGs) and further analyzed using Cytoscape. The data was divided into two categories: non-treatment and treatment groups. A total of 407 DEGs (254 upregulated and 153 downregulated) and 259 DEGs (124 upregulated and 135 downregulated) were isolated for non-treatment and treatment studies respectively. The significant Gene Ontologies and pathways enriched with DEGS were identified using Cytoscape apps, BiNGO and ReactomeFIPlugIn, respectively. Hub genes based on network parameters - Degree, Closeness and Betweenness - were isolated using CytoHubba. In conclusion, DEGs identified in this study may play an important role in early diagnosis or as biomarkers of lung cancer. 

Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer.