Identification of a novel lncRNA-miRNA-mRNA competing endogenous RNA network associated with prognosis of breast cancer

Recently, the effects of competing endogenous RNA (ceRNA) on molecular biological mechanism of cancer have aroused great interest. In this study, long noncoding RNA-microRNA-messenger RNA (lncRNA-miRNA-mRNA) ceRNA network was screened and constructed based on the Cancer Genome Atlas (TCGA) database, and its efficacy in predicting the prognosis of breast cancer patients was evaluated. The RNA-sequencing, miRNA-sequencing, and corresponding clinical information were downloaded from the TCGA database, and differentially expressed genes were screened after data searching. The similarity between two groups of genes was analyzed by weighted correlation network analysis (WGCNA). Next, the interaction among lncRNA, miRNA, and mRNA was predicted followed construction of the lncRNA-miRNA-mRNA ceRNA network. Finally, univariate and multivariate Cox regression analysis was used to screen prognostic factors to construct prognostic risk model. Receiver operating characteristic (ROC) curve was used to evaluate the efficacy of this model in predicting the prognosis of breast cancer patients. In total 5056 differentially expressed lncRNAs, 712 differentially expressed miRNAs, and 9878 differentially expressed mRNAs were identified in breast cancer tissues. WGCNA predicted that 823 lncRNAs and 1813 mRNAs were closely related to breast cancer. The lncRNA-miRNA-mRNA ceRNA network involved in breast cancer was constructed based on 27 lncRNA, 14 miRNAs, and 4 mRNAs. ZC3H12B, HRH1, TMEM132C, and PAG were the possible independent risk factors for the prognosis of breast cancer patients with the area under the signal characteristic curve under ROC curve of 0.609. This study suggested that the prognosis risk model based on ZC3H12B, HRH1, TMEM132C, and PAG1 accurately predicted the prognosis of breast cancer patients.

Automated summary

This study constructed a lncRNA-miRNA-mRNA ceRNA network for breast cancer based on the TCGA database and identified potential prognostic risk factors, providing a new approach for prognosis prediction in breast cancer patients.