Construction of a circRNA-Related ceRNA Prognostic Regulatory Network in Breast Cancer

Abstract

Purpose: Accumulating evidence has indicated that circRNAs are closely involved in tumorigenesis and progression of human cancers. However, the molecular mechanism underlying function of circRNAs in breast cancer has not been thoroughly elucidated. Currently, we aimed to characterize the circRNA-related competing endogenous RNA (ceRNA) regulatory network in breast cancer and to construct prognostic model.

Materials and methods: First, we constructed circRNA expression profiles for paired breast cancer in a Chinese population using a human circRNA microarray. Expression profiles of circRNAs, miRNAs, and mRNAs were retrieved from our circRNA dataset, the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We applied the limma and edgeR packages to identify differentially expressed RNAs. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules of mRNAs. Next, a ceRNA network was established based on circRNA-miRNA and miRNA-mRNA intersections. Both Cox regression analysis and ROC curve analysis were performed to generate prognostic model. Additionally, we performed Gene Set Enrichment Analysis (GSEA) on prognostic signatures.

Results: Total of 59 circRNAs, 98 miRNAs and 3966 mRNAs were identified as differentially expressed RNAs. We first identified 38 miRNA-mRNA pairs and 38 circRNA-miRNA pairs to construct the circRNA-miRNA-mRNA regulatory network and then generated a prognostic model based on 7 signatures (MMD, SLC29A4, CREB5, FOS, ANKRD29, MYOCD, and PIGR), and patients with high-risk scores presented poor prognosis. Several cancer-related pathways were enriched, including the TGF-β pathway, the focal adhesion pathway, and the JAK-STAT signaling pathway, and 20 prognostic ceRNA regulatory networks were subsequently identified.

Conclusion: In all, we screened a series of dysregulated circRNAs, miRNAs, and mRNAs, and constructed circRNA-related ceRNA network in breast cancer. Our findings may help to deepen the understanding of circRNA-related regulatory mechanisms. Moreover, we generated a prognostic model that provided new insight into postoperative management for breast cancer.

Keywords: TCGA; breast cancer; ceRNA; circRNA; mRNA; miRNA; ncRNA.

Figure 1

Flowchart of circRNA-related ceRNA regulatory network analysis in breast cancer.

Figure 2

Differentially expressed genes in breast cancer. (A) Heatmap and volcano of DEcircRNAs identified from previous circRNA datasets and GEO databases using the limma package (|log2(fold-change) >1| and FDR < 0.05); (B) Heatmap and volcano of DEmiRNAs identified from TCGA database using the edgeR package (|log2(fold-change) >2| and FDR < 0.05); (C) Heatmap and volcano of DEmRNAs identified from TCGA database using the edgeR package (|log2(fold-change) > 2| and FDR < 0.05).

Figure 3

Weighted gene correlation network analysis (WGCNA) of mRNAs in breast cancer. (A) Identification of a coexpression module of DEmRNAs in breast cancer. (B) The correlation between the DEmRNA module and clinical traits. (C) Scatter plot of module eigengenes in the turquoise modules. P < 0.05. Abbreviation: DEmRNAs, differentially expressed mRNAs.

Figure 4

CircRNA-miRNA-mRNA ceRNA regulatory network in breast cancer. (A) The ceRNA regulatory network in breast cancer. (B) The prognostic ceRNA regulatory network in breast cancer.

Figure 5

Construction of 7 signature-based prognostic models. (A) The ROC curve to evaluate the risk score. (B) Kaplan–Meier analysis for patients with high and low risk scores for breast cancer. (C) Univariate Cox regression analysis. (D) Multivariable Cox regression analysis. P < 0.05. Abbreviation: ROC, receiver operating characteristic curve.

Figure 6

GSEA analysis of these 7 signatures in breast cancer. (A) GSEA analysis of MMD; (B) GSEA analysis of SLC29A4; (C) GSEA analysis of CREB5; (D) GSEA analysis of FOS; (E) GSEA analysis of ANKRD29; (F) GSEA analysis of MYOCD; (G) GSEA analysis of PIGR. (FDR < 0.05).