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. 2024 Feb 11;15(7):1916-1928.
doi: 10.7150/jca.91546. eCollection 2024.

Identification of a lncRNA/circRNA-miRNA-mRNA network in Nasopharyngeal Carcinoma by deep sequencing and bioinformatics analysis

Shilei Liu  1   2 , Xiaoxiao Li  1   3 , Qingming Xie  1   2 , Sai Zhang  1   2 , Xujun Liang  1   2 , Shisheng Li  4 , Pengfei Zhang  1   2
Affiliations

Identification of a lncRNA/circRNA-miRNA-mRNA network in Nasopharyngeal Carcinoma by deep sequencing and bioinformatics analysis

Shilei Liu et al. J Cancer. .

Abstract

Background: Accumulating evidence indicates that non-coding RNAs (ncRNA), including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), can function as competitive endogenous RNAs (ceRNAs) by binding to microRNAs (miRNAs) and regulating host gene expression at the transcriptional or post-transcriptional level. Dysregulation in ceRNA network regulation has been implicated in the occurrence and development of cancer. However, the lncRNA/circRNA-miRNA-mRNA regulatory network is still lacking in nasopharyngeal carcinoma (NPC). Methods: Differentially expressed genes (DEGs) were obtained from our previous sequencing data and Gene Expression Omnibus (GEO). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were used to explore the biological functions of these common DEGs. Through a series of bioinformatic analyses, the lncRNA/circRNA-miRNA-mRNA network was established. In additional, the external data GSE102349 was used to test the prognostic value of the hub mRNAs through the Kaplan-Meier method. Results: We successfully constructed a lncRNA/circRNA-miRNA-mRNA network in NPC, consisting of 16 lncRNAs, 6 miRNAs, 3 circRNAs and 10 mRNAs and found that three genes (TOP2A, ZWINT, TTK) were significantly associated with overall survival time (OS) in patients. Conclusion: The regulatory network revealed in this study may help comprehensively elucidate the ceRNA mechanisms driving NPC, and provide novel candidate biomarkers for evaluating the prognosis of NPC.

Keywords: bioinformatics; competitive endogenous RNA; deep sequencing; nasopharyngeal carcinoma; non-coding RNAs.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Flowchart of this study. DEGs: differentially expressed genes; PPI: protein-protein interaction; miRNAs: microRNAs; lncRNAs: long non-coding RNAs; circRNAs: circular RNAs; ceRNA: competitive endogenous RNA.
Figure 2
Figure 2
Identification of differentially expressed genes. (A, B) heatmap and volcano plot of Sequencing data. (C, D) heatmap and volcano plot of GSE61218. (E) Venn Diagram of DEGs. DEGs: differentially expressed genes.
Figure 3
Figure 3
Functional enrichment analysis of common differentially expressed genes. (A, B) The results of GO enrichment analysis of the common DEGs, including BP, CC and MF. (C, D) The results of KEGG pathways analysis of the common DEGs. DEGs: differentially expressed genes, GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes, BP: biologial process, CC:cellular component, MF: molecular function.
Figure 4
Figure 4
Construction of PPI network and identified hub mRNAs in the network. (A) PPI network of common DEGs. (B) hub module in the network.
Figure 5
Figure 5
Expression level analysis of hub mRNAs. The red presented tumor, while the gray indicated normal. (A) AURKB: (B) CCNA2: (C) CCNB2: (D) CDK1: (E) NDC80: (F) PBK: (G) PTTG1: (H) TOP2A: (I) TTK: (J) ZWINT.
Figure 6
Figure 6
The results of ceRNA network prediction and construction of ceRNA network. (A) Venn diagram of common lncRNAs. (B) Venn diagram of common miRNAs. (C) Venn diagram of common circRNAs. (D) The ceRNA network. As shown in Figure D, diamond represents lncRNAs, college represents mRNAs, triangle represents miRNAs, and inverted triangle represents circRNAs. ceRNA: competitive endogenous RNA; lncRNAs: long non-coding RNAs; miRNA: microRNAs; circRNAs: circular RNAs.
Figure 7
Figure 7
The survival curve of hub mRNAs. (A) AURKB: (B) CCNA2: (C) CCNB2: (D) NDC80: (E) PBK: (F) PTTG1: (G) TTK: (H) ZWINT: (I) TOP2A (J) CDK1.
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