Abstract
BACKGROUND: Studies have shown that long noncoding RNAs (lncRNAs) make up the major proportion of the ceRNA network and can regulate gene expression by competitively binding to miRNAs. This reveals the existence of an RNA-miRNA regulatory pathway and is of great biological significance. CeRNAs, as competitive endogenous RNAs, have revealed a new mechanism of interaction between RNAs. Until now, the role of lncRNA-mediated ceRNAs in breast cancer and their regulatory mechanisms have been elucidated to some extent. PURPOSE: In this study, comprehensive analysis of large-scale invasive breast cancer samples in TCGA were conducted to further explore the developmental mechanism of invasive breast cancer and the potential predictive markers for invasive breast cancer prognosis in the ceRNA network. METHODS: Abnormal expression profiles of invasive breast cancer associated mRNAs, lncRNAs and miRNAs were obtained from the TCGA database. Through further alignment and prediction of target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network was constructed for invasive breast cancer. Through the overall survival analysis, Identification prognostic bio-markers for invasive breast cancer patients. In addition, we used Cytoscape plug-in BinGo for the different mRNA performance functional cluster analysis. RESULTS: Differential analysis revealed that 1059 lncRNAs, 86 miRNAs, and 2138 mRNAs were significantly different in invasive breast cancer samples versus normal samples. Then we construct an abnormal lncRNA-miRNA-mRNA ceRNA network for invasive breast cancer, consisting of 90 DElncRNAs, 18 DEmiRNAs and 26 DEmRNAs.Further, 4 out of 90 lncRNAs, 3 out of 26 mRNAs, and 2 out of 18 miRNAs were useful as prognostic biomarkers for invasive breast cancer patients (P value < 0.05). It is worth noting that based on the ceRNA network, we found that the LINC00466-Hsa-mir-204- NTRK2 LINC00466-hsa-mir-204-NTRK2 axis was present in 9 RNAs associated with the prognosis of invasive breast cancer. CONCLUSION: This study provides an effective bioinformatics basis for further understanding of the molecular mechanism of invasive breast cancerand for predicting outcomes, which can guide the use of invasive breast cancerdrugs and subsequent related research.