Identification of competitive endogenous RNAs network in breast cancer

Background MiRNAs can regulate gene expression directly or indirectly, and long noncoding RNAs as competing endogenous RNA (ceRNAs) can bind to miRNAs competitively and affect mRNA expression. The ceRNA network is still unclear in breast cancer. In this study, a ceRNA network was constructed, and new treatment and prognosis targets and biomarkers for breast cancer were explored. Methods A total of 1 096 cancer tissues and 112 adjacent normal tissues to cancer from the TCGA database were used to screen out significant differentially expressed mRNAs (DEMs), lncRNAs (DELs), and miRNAs (DEMis) to construct a ceRNA network. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to predict potential functions. Survival analysis was performed to predict which functions were significant for prognosis. Results From the analysis, 2 139 DEMs, 1 059 DELs, and 84 DEMis were obtained. Targeting predictions for DEMis‐DELs and DEMis‐DEMs can yield 26 DEMs, 90 DELs, and 18 DEMis. We performed GO enrichment analysis, and the results showed that the upregulated DEMs were involved in nucleosomes, extracellular regions, and nucleosome assembly, while the downregulated DEMs were mainly involved in Z disk, muscle contraction, and structural constituents of muscle. KEGG pathway analysis was performed on all DEMs, and the pathways were enriched in retinol metabolism, steroid hormone biosynthesis, and tyrosine metabolism. Through survival analysis of the ceRNA network, we identified four DEMs, two DELs, and two DEMis that were significant for poor prognosis. Conclusions This study suggested that constructing a ceRNA network and performing survival analysis on the network could screen out new significant treatment and prognosis targets and biomarkers. Identification of differentially expressed lncRNAs (DELs), mRNAs (DEMs), and miRNAs (DEMis) in breast cancer. Construction of competing endogenous RNA (ceRNA) regulatory network and screening new treatment and prognosis targets and biomarkers for breast cancer.