Insilico-based identification of survival-associated lncRNAs, mRNAs and, miRNAs in breast cancer

Abstract

Abstract Aim: We aimed to analyze a unique competing endogenous RNA (ceRNA) network encompassing possible pathogenic interaction among miRNA–target gene and lncRNAs related to breast cancer prognosis. Background: Breast cancer is responsible for 30% of all new female cancers each year. Bioinformatics serve us to find new biomarkers and facilitate future experimental research. Objective: In this article we propose a novel regulatory ceRNA network and a list of 22 genes, lncRNAs and four miRNAs related to breast cancer prognosis. Methods: Differentially expressed lncRNAs, mRNAs, and miRNAs were collected based on Gene Expression Omnibus datasets. DEGs were validated based on TCGA. Functional analysis and pathway activity were also done. Results: A total of 696 mRNAs, 48 lncRNAs and, 43 miRNAs were identified to have significant differential expression in cancerous breast tissue than normal breast tissue samples. Functional analysis showed significant pathway enrichments in cancer, and we found that 13 individual genes, lncRNAs, and miRNAs, CDC6, ERBB2, EZR, HELLS, MAPK13, MCM2, MMP1, SLC7A5, TINCR, TRIP13, hsa-miR-376a, hsa-miR-21, hsa-miR-454 were significantly predictive of poor overall survival and AKAP12, CXCL12, FGF2, IRS2, LINC00342, LINC01140, MEG3, MIR250HG, NAV3, NDRG2, NEAT1, TGFBR3 and, hsa-miR-29c were associated with favorable overall survival. Conclusions: We successfully made a unique ce-network, providing new clues to understand the regulatory functions of non-coding RNAs (miRNAs and lncRNAs) in the pathogenesis and prognosis of breast cancer and will facilitate further experimental studies to develop new biomarkers in the diagnosis, prognosis and, therapy of breast cancer.