Abstract
Analyzing gene network structure is an important way to discover and understand some unknown relevant functions and regulatory mechanisms of organism at the molecular level. In this work, mutual information networks and Boolean logic networks are constructed using the methods of reverse modeling based on gene expression profiles in lung tissues with and without cancer. The comparison of these network structures shows that average degree, the proportion of non-isolated nodes, average betweenness and average coreness can distinguish the networks corresponding to the lung tissues with and without cancer. According to the difference of degree, betweenness and coreness of each gene in these networks, nine structural key genes are obtained. Seven of them which are related to lung cancer are supported by literatures. The remaining two genes AKT1 and RBL may have important roles in the formation, development and metastasis of lung cancer. Furthermore, the contrast of these logic networks suggests that the distributions of logic types are obviously different. The structural differences can help us to understand the mechanism of formation and development of lung cancer.
Highlights
Lung cancer is one of the most common and lethal malignancy carcinomas, and its formation, development and metastasis is an extremely complex polygene regulatory process [1]
Chen et al [7] identified sixteen genes correlated with survival among patients with nonsmall-cell lung cancer (NSCLC) by analyzing microarray data and risk scores and found five-gene (DUSP6, MMD, STAT1, ERBB3, and LCK) signature is closely associated with relapse-free and overall survival among patients with NSCLC
Based on the gene expression data of carcinoma-related genes expressed in normal tissues and diseased tissues with AC and SCLC, we construct the corresponding mutual information networks
Summary
Lung cancer is one of the most common and lethal malignancy carcinomas, and its formation, development and metastasis is an extremely complex polygene regulatory process [1]. Finding the genes critical to the formation and development of a disease from potentially disease-related genes is of significance to the diagnosis and cure of the disease and drug design This is an important component in the research of bioinformatics [2]. In this work, according to the viewpoint that the structures of matters can determine their functions, we can catch disease-risk genes through analyzing the differential structure of gene network in lung tissues with and without cancer. Mutual information networks of carcinoma-related genes are constructed using their expression profiles under the contexts of normal and two types of lung cancer (adenocarcinoma and small cell lung cancer, abbreviated as AC and SCLC respectively). It provides some enlightenment roles to study the intrinsic mechanism of lung cancer
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