Differential expression and methylation profiles of heat shock proteins genes in breast cancer molecular subtypes

ZOPPINO F. C. M.; FANELLI M. A.; CASTRO G. N.; CAYADO-GUTIÉRREZ N.; GUERREO-GIMÉNEZ M. E.; CUELLO CARRION F. D.; CIOCCA D. R.

Bogotá

Congreso; Second Latin American Chapter Conference of Cell Stress Society International; 2016

Cell Stress Society International

Aim: To evaluate the differential expression levels of the Heat Shock Proteins (HSPs) genes and their methylation profiles in the different breast cancer (BC) molecular subtypes to identify HSPs coding genomic regions associated with the disease. Methods: Software exclusively supported in R was used to carry out the various analyzes. The data was extracted from the publicly available TCGA data set of mammary adenocarcinoma downloaded on May- 01-2015 (https://tcga-data.nci.nih.gov/tcga/). The "TCGA assambler" v.1.0.3 package was used to programmatically download level 3´s standardized and non-standardized gene expression levels from 1,097 tumor samples and 114 normal breast tissue data available in the RNASeqV2 platform. The level 3 methylation profile for each sample available in the JHU_USC__HumanMethylation450k platform was downloaded directly from the TCGA website on 10 October 2014 (81 normal tissue samples and 726 tumor samples). Given the expression levels of 50 different specific genes PAM50 classification was performed to classify the samples (LumA, LumB, Basal_Like, Her2, Normal_Like). To assess the differential gene expression (DGE) between normal tissue and tumor samples we implemented DESeq2 and EdgeR DGE analysis, in both cases log2 Fold change values were obtained associated with exact p-values and False Discovery Rate values (FDR). Methylation levels of each tumor subtype relative to normal breast tissue were compared. For this task the algorithm "bumphunter" was performed using the R "MINFI" package. Bumphunter provides a robust method for detecting differentially methylated loci by performing a probe-level regression and smoothing the coefficient of interest within clusters to identify bumps along the genome. A simple linear regression and a Pearson correlation of the methylation values of each CpG site opposed to the gene expression was performed to assess the overall impact of the CpG site methylation in gene expression. Results: We found important HSPs differential gene expression levels in BC, the magnitude of these variations were related with the BC molecular subtypes. Importantly we identify that almost all of the Chaperonines and the HSPCs were up regulated in BC and that this deregulation was not associated with changes in the methylation profiles of the genes. Another important finding of our study is that there was a small cluster of small heat shock proteins (HSPBs) that appeared strongly down regulated in BC and this could be due to gene methylation. Finally we could identify CRYAB, HSPB2 and HSPB6 as the most both methylated and down regulated HSPs genes in breast cancer.