1500 Prediction and characterisation of biomarker network for benzene exposure

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Abstract

Introduction

Benzene is identified as a carcinogen. Long-term exposure to benzene causes haematological alterations, including an increased risk of acute myeloid leukaemia. However, the molecular mechanisms of Benzene systems effects remain poorly understood. Hence, a better understanding of the molecular mechanisms involved in this condition is a priority. Here, we employed a joint the integration of molecular networks, a gene–gene interaction database, biological processes analysis and functional annotation analysis to explore system effects for prioritising candidate genes to biomarkers to evaluate benzene exposure.

Methods

We selected 96 genes targets with altered expression in occupational exposed to benzene (2009 to 2014). The analysis was performed using the multiple association network integration algorithm for predicting gene function, which identifies known gene-gene interactions among a genes list and provides additional genes. Topological properties of network were calculated by MCODE, BINGO and Centiscape,

Results

A network of 114 genes and 2415 interactions were obtained. After topological analysis, a minor network composed by 16 nodes was identified, which is composed by most relevant nodes of major network. In this sub-network, KLF6, KLF4 and JUN are the most interconnected nodes, they being been considered a putative biomarker in which the exclusion of one node could produce a strong perturbation in the signalling network.

Conclusion

The biological interaction network method presented probabilities of interactions between genes, demonstrating the potential of the use and application of the multiple association network integration algorithms for predicting gene function and for the observation of multiple genes in the system, using theoretical data to building clusters for identification of possible genes as biomarker.

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