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. 2023 Jan 6:9:1021195.
doi: 10.3389/fsurg.2022.1021195. eCollection 2022.

Construction and analysis of a survival-associated competing endogenous RNA network in breast cancer

Gang Chen  1 , Yalun Li  1 , Jianqiao Cao  1 , Yuanping Dai  2 , Yizi Cong  1 , Guangdong Qiao  1
Affiliations

Construction and analysis of a survival-associated competing endogenous RNA network in breast cancer

Gang Chen et al. Front Surg. .

Abstract

Background: Recently, increasing studies have shown that non-coding RNAs are closely associated with the progression and metastasis of cancer by participating in competing endogenous RNA (ceRNA) networks. However, the role of survival-associated ceRNAs in breast cancer (BC) remains unknown.

Methods: The Gene Expression Omnibus database and The Cancer Genome Atlas BRCA_dataset were used to identify differentially expressed RNAs. Furthermore, circRNA-miRNA interactions were predicted based on CircInteractome, while miRNA-mRNA interactions were predicted based on TargetScan, miRDB, and miRTarBase. The survival-associated ceRNA networks were constructed based on the predicted circRNA-miRNA and miRNA-mRNA pairs. Finally, the mechanism of miRNA-mRNA pairs was determined. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of survival-related mRNAs were performed using the hypergeometric distribution formula in R software.The prognosis of hub genes was confirmed using gene set enrichment analysis.

Results: Based on the DE-circRNAs of the top 10 initial candidates, 162 DE-miRNAsand 34 DE-miRNAs associated with significant overall survival were obtained. The miRNA target genes were then identified using online tools and verified using the Cancer Genome Atlas (TCGA) database. Overall, 46 survival-associated DE-mRNAs were obtained. The results of GO and KEGG pathway enrichment analyses implied that up-regulated survival-related DE-mRNAs were mostly enriched in the "regulation of cell cycle" and "chromatin" pathways, while down-regulated survival-related DE-mRNAs were mostly enriched in "negative regulation of neurotrophin TRK receptor signaling" and "interleukin-6 receptor complex" pathways. Finally, the survival-associated circRNA-miRNA-mRNA ceRNA network was constructed using 34 miRNAs, 46 mRNAs, and 10 circRNAs. Based on the PPI network, two ceRNA axes were identified. These ceRNA axescould be considered biomarkers for BC.GSEA results revealed that the hub genes were correlated with "VANTVEER_BREAST_CANCER_POOR_PROGNOSIS", and the hub genes were verified using BC patients' tissues.

Conclusions: In this study, we constructed a circRNA-mediated ceRNA network related to BC. This network provides new insight into discovering potential biomarkers for diagnosing and treating BC.

Keywords: GEO; bioinformatics; breast cancer; ceRNAs; prognosis biomarker.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of the ceRNA network analysis.
Figure 2
Figure 2
Differentially expressed circRNAs in BC patients compared with the Normal group. (A,B) Heatmap of the differentiallyexpressed circRNAs in BC based on GSE101123 and GSE101124. (C,D) Volcano map for all circRNAs in GSE101123 and GSE101124. (E,F) Overlap in DE circRNAs between GSE101123 and GSE101124.
Figure 3
Figure 3
Identification of differentially expressed miRNAs. (A) Heatmap of the DE miRNAs from the GEOmicroarray in GSE97811. (B) Volcano map for all miRNAs in GSE97811. (C,D) Overlap between circRNA-related target miRNAs predicted bythe online tool and DEmiRNAs in GSE97811.
Figure 4
Figure 4
The ceRNA network of circRNA-miRNA-mRNA in BC. Circles represent circRNAs, triangles indicate miRNAs, and “V” indicates mRNAs. The red and green nodesindicate upregulation anddownregulation, respectively.Blue represents the hub nodes.
Figure 5
Figure 5
Go and KEGG enrichment analysis of up-regulated survival-associated mRNAs. (A–C) GO analysis of up-regulated mRNAs. (D) KEGG analysis of up-regulated mRNAs.
Figure 6
Figure 6
Go and KEGG enrichment analysis of down-regulated survival-associated mRNAs. (A–C) GO analysis of up-regulated mRNAs. (D) KEGG analysis of up-regulated mRNAs.
Figure 7
Figure 7
Enrichment plots from GSEA. (A) CCNB1 was positively related tothe poor prognosis of BC. (B) CCNB1 genes validation in nine BC patient's tissues.
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