Abstract
Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes and are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers.
Highlights
Over the last two decades, gene networks (GNs) have become an essential tool in the field of biomedicine [1]
In this work we present a study of human lung carcinoma gene expression samples corresponding to smoker patients by means of an ensemble co-expression algorithm
The results presented showed that their approach can identify genes that are significantly different from those using different alternatives
Summary
Over the last two decades, gene networks (GNs) have become an essential tool in the field of biomedicine [1]. Such GNs are usually presented as a graph comprising nodes and rods, where nodes represent genes (or gene products) and rods represent interactions among genes [1,2]. These rods may include a numeric value or weight which refers to the strength of these relationships. According to the different works in the literature [1,6], GN inference algorithms lie under four main categories: co-expression, boolean networks, differential equation-based and Bayesian networks
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