Characterizing basal-like triple negative breast cancer using gene expression analysis: A data mining approach

• DOI: 10.1016/j.eswa.2020.113253
• Publication Date: 2020-01-30T04:22:20Z
• Authors: Amir Hassan Zadeh; Qamar Alsabi; Jaime E. Ramirez-...; Nasim Nosoudi
• Keywords: Triple-negative breast cancer; Progesterone receptor

Triple‐negative breast cancer (TNBC) constitutes approximately 20%–25% of all breast cancer cases with poor prognosis. It is unresponsive to targeted hormonal therapies, which limits treatment options to nonselective chemotherapeutic agents. Recently, gene expression analysis has shown promise to characterize triple negative breast cancers (TNBC). Although TNBCs have many basal‐like breast cancer characteristics, the relationship between clinically defined triple‐negative breast cancer and the gene expression profile of basal‐like breast cancer (BLBC) is not fully examined. The purpose of this study is to assemble publicly‐available TNBC gene expression datasets generated on Affymetrix gene chips and define a set of genes, or gene signature, that can classify BLBC and Non‐BLBC subtypes under TNBC type. We compiled over 3,500 breast cancer gene expression profiles from several individual publicly available datasets and extracted Affymetrix gene expression data for 580 TNBC cases. Several popular data mining along with variable reduction and feature selection techniques were applied to the resultant data sets to build predictive models to understand molecular characteristics and mechanisms associated with BLBCs and classify them more accurately according to important features extracted from Microarray data analysis of BLBC and Non‐BLBC cases. Our result can lead to proper identification and diagnosis of BLBCs, which can potentially direct clinical implications by dictating the most effective therapy.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.