Bioinformatics analysis of differentially expressed miRNA-related mRNAs and their prognostic value in breast carcinoma.

Breast carcinoma is one of the most common types of malignant neoplasms, and is associated with high rates of morbidity and mortality. Altered gene expression is critical in the development of breast cancer. To identify the important differentially expressed genes and microRNAs in breast carcinoma, mRNA (GSE26910, GSE42568, and GSE89116) and microRNA (GSE35412) microarray datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed microRNA expression data were extracted with GEO2R online software. The DAVID online database was used to perform a function and pathway enrichment analysis of the key identified differentially expressed genes. A protein-protein interaction (PPI) network was constructed using the STRING online database, and visualized in Cytoscape software. The effect of the expression level of the key identified genes on overall survival (OS) time was analyzed by using the Kaplan-Meier Plotter online database. Furthermore, the online miRNA databases TargetScan, microT-CDS, and TarBase were used to identify the target genes of the differentially expressed miRNAs. A total of 254 differentially expressed genes were identified, which were enriched in cell adhesion, polysaccharide binding, extracellular region part and ECM-receptor interactions. The PPI network contained 250 nodes and 375 edges. Five differentially expressed genes were found to be significantly negatively correlated with the differentially expressed miRNAs, which were potentially also target genes for miRNAs. Four of the five genes, including AKAP12, SOPB, TCF7L2, COL12A1 and TXNIP were downregulated, and were associated with the OS of patients with breast carcinoma. In addition, a total of 130 differentially expressed miRNAs were identified. In conclusion, these results constitute a novel model for miRNA-mRNA differential expression patterns, and further studies may provide potential targets for diagnosing and understanding the mechanisms of breast carcinoma.