Integrating multiple omics data for the discovery of potential Beclin-1 interactions in breast cancer

Abstract

Breast cancer has been reported as one of the most frequently diagnosed malignant diseases and the leading cause of cancer death in women all around the world. Furthermore, this complicated cancer is divided into multiple subtypes which present different clinical symptoms and need correspondingly directed therapy. We took BECN1, a core gene in autophagy performing a tumor inhibitory effect, as a starting point. The study in this paper aims to identify genes related to breast cancer and its multiple subtypes by integrating multiple omics data using the least absolute shrinkage and selection operator (LASSO), which is a statistical method that can integrate more than two types of omics data. All the data is obtained from The Cancer Genome Atlas (TCGA) platform which stores clinical and molecular tumor data. The model constructed is based on three kinds of data including mRNA-gene expression with a dependent variable level, DNA methylation and copy number alterations as independent variables. Finally, we propose four subnets of four subtypes of breast cancer, and consider as a result of microarray analysis that AFF3 is associated with BECN1 in breast cancer, and may be a potential therapeutic target. This finding may provide some potential targeted therapeutics for the four different subtypes of breast cancer at the genetic level. In conclusion, finding out the major role Beclin-1 plays in breast cancer subtypes is of great value. The results obtained are instructive for further research and may provide excellent results in clinical applications, as well as testing in animal experiments, and may also indicate a new method to perform bioinformatics analysis.