Abstract P4-07-07: Analysis of miRNAs and proteins relations in breast cancer
Authors: Ciruelos EM , Gámez-Pozo A , Nanni P , Berges-Soria J , Navarro H , Lopez-Vacas R , Arevalilllo J , Castañeda C , Grossmann J , Pascual T , de Velasco G , Diaz-Almiron M , Madero R , Main P , Espinosa E , Fresno JA 
Published: August 2014

Introduction / Objectives

MicroRNAs (miRNAs) constitute a new class of small noncoding RNAs that control post-transcriptionally the expression of gene products, either modulating directly protein translation, or regulating the stability of messenger RNA. There is increasing evidence of the role that miRNAs play in regulating breast cancer gene expression. However, there is little knowledge about the function and targets of miRNAs, and how they regulate complete processes or pathways.

The main objective of this study was to unravel biological processes and signaling pathways regulated by miRNAs in breast cancer comparing their expression with the expression of the proteins they regulate. New statistical approaches were conducted in order to associate miRNA and protein quantification results with breast cancer subtype and to evaluate the association of miRNAs and protein expression patterns.

Materials and Methods

MicroRNA and protein expression were obtained from 79 breast cancer FFPE samples (16 TNBCs and 63 Luminal tumors). RNA was extracted from FFPE samples using RecoverAll (Ambion). MicroRNA expression was analyzed by RT-qPCR using TaqMan Arrays (Applied Biosystems). We selected for subsequent analysis those miRNAs with significant correlation between FF and FFPE samples. Protein extracts from FFPE samples were prepared in 2% SDS buffer using a protocol based on heat-induced antigen retrieval (Gámez-Pozo A et al. Mol Biosyst. 2011; 7: 2368-74). Protein abundance was calculated on the basis of normalized spectral protein intensity (LFQ intensity) using MaxQuant.

Probabilistic graphical models are being applied successfully to represent complex biological systems as networks. In this phase of the study, we have chosen an appropriate methodology in the analysis of high dimensional data selecting a forest which minimizes the BIC criterion. This procedure extends the Chow and Liu's approach to the Gaussian case. The software used for implementation is based on the R library gRapHD.

Results

We measured the expression of 90 miRNAs in 79 breast cancer samples using RT-qPCR. We identified and quantified more than 3000 protein groups. We selected for subsequent analyses more than 1000 quantifiable proteins, defined as those identified at least in 75% of the samples in at least one type of sample with more than two unique peptides. Then, we analyzed the relations between miRNA and protein expressions.

We identified miRNAs strongly related with processes considered as hallmarks of cancer, such as cellular adhesion, using probabilistic graphical models.

Conclusions

The integration of miRNA and protein expression patterns may be useful to describe how miRNAs regulate biological processes and signaling pathways in breast cancer. There is a need of new statistical approaches to evaluate these relations and to obtain meaningful information from such complex and massive data.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-07-07.