Métodos computacionais para a análise de dados de expressão gênica provenientes de uma análise de microarray utilizada para teste farmacológico

Abstract

The advent of the Human Genome Project (HGP), completed in October 2003, propelled the development of techniques for obtaining and analyzing biological data. The need to manage the vast amount of digital genome data was a decisive factor in the growth of a multidisciplinary area of knowledge, Computational Biology. In the two decades following the completion of the HGP, genomes of different organisms were obtained. Regarding mammals, projects such as the 1000 Genomes Project and the Cancer Genome Atlas (TCGA) illustrated the advancement of knowledge in the analysis of complex data. Among the newest techniques, we highlight Microarrays. They provide a significant amount of data in a single experiment, allowing the comparison of complete genomes. The analysis of Microarray data is relatively complex and requires protocols that make this analysis simpler, producing more understandable information. The present study involves the use of computational methods to analyze gene expression data obtained from a Microarray experiment used for pharmacological testing related to breast cancer. To process the raw data, obtained from a spreadsheet containing more than 3216 genes resulting from a Microarray analysis, a script was developed to facilitate the extraction of information from this data and subsequent selection of genes of interest. The program allowed the search for genes involved in the processes of cell death (apoptosis, necrosis, and autophagy), which are determining factors in the success analysis of the tested drug. To categorize the genes involved in the apoptotic, necrotic, and autophagic death cascade, heatmaps were constructed from fold-change values (difference in gene expression for values before and after treatment of cancerous cells with the mesoionic compound), using k-means clustering and hierarchical clustering techniques provided in the Heatmapper program. Results of the study include the development of a script in the R program that resulted in the separation of 20 genes involved in the apoptotic death cascade, six involved in the autophagic death, and seven involved in the necrotic death cascade, as well as the development of three heatmaps, contributing to the biological analysis of data, in addition to making Microarray data processing more accessible.