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.

Beclin‑1 expression is associated with prognosis in a Bcl‑2‑dependent manner in non‑small cell lung cancer

“…As a specific marker of autophagy, Beclin-1 has been the focus of previous research. Recent studies have reported that Beclin-1 is downregulated in glioblastoma, liver cancer, bladder cancer and breast cancer (15)(16)(17)(18), while it is upregulated in colon cancer (19). The present study revealed that Beclin-1 expression was downregulated in NSCLC tissues, consistent with a study by Zheng et al (20), which demonstrated that the regulation of Beclin-1 may serve a role in the development of this type of cancer.…”

Integrative analysis of Paneth cell proteomic and transcriptomic data from intestinal organoids reveals functional processes dependent on autophagy

“…Similarly, various other studies have profiled the global proteomic changes in response to artificial impairment of autophagy by knocking out critical autophagy genes (Avin-Wittenberg et al, 2015;Mathew et al, 2014). Studies combining different -omic readouts have also been performed in various contexts to understand the role of autophagy in various processes and phenotypes (Chen et al, 2017;Kramer et al, 2017;Masclaux-Daubresse et al, 2014;Stingele et al, 2012). However, the studies aforementioned do not provide an explanation as to how the proteome level alterations are indeed dependent on autophagy from a mechanistic point of view.…”

What We Learned From Big Data for Autophagy Research

“…They used the Least Absolute Shrinkage and Selection Operator (LASSO) method which is designed for -omics data integration. By bringing together three different data types namely mRNA expression (DNA microarray), DNA methylation (Illumina Methylation Assay), and copy number alteration (GenomeWideSNP_6 array) data provided by The Cancer Genome Atlas database (TGCA— https://cancergenome.nih.gov/ ), they identified the regulators of BECN1, a core autophagy component, which has an inhibitory effect on tumor formation (Chen et al, 2017 ).…”