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. 2019 Mar 24:2019:3526407.
doi: 10.1155/2019/3526407. eCollection 2019.

A Simple Competing Endogenous RNA Network Identifies Novel mRNA, miRNA, and lncRNA Markers in Human Cholangiocarcinoma

Cheng Zhang  1 , Chunlin Ge  1
Affiliations

A Simple Competing Endogenous RNA Network Identifies Novel mRNA, miRNA, and lncRNA Markers in Human Cholangiocarcinoma

Cheng Zhang et al. Biomed Res Int. .

Abstract

Background: Cholangiocarcinoma (CCA) is the second most common malignant primary liver tumor and has shown an alarming increase in incidence over the last two decades. However, the mechanisms behind tumorigenesis and progression remain insufficient. The present study aimed to uncover the underlying regulatory mechanism on CCA and find novel biomarkers for the disease prognosis.

Method: The RNA-sequencing (RNA-seq) datasets of lncRNAs, miRNAs, and mRNAs in CCA as well as relevant clinical information were obtained from the Cancer Genome Atlas (TCGA) database. After pretreatment, differentially expressed RNAs (DERNAs) were identified and further interrogated for their correlations with clinical information. Prognostic RNAs were selected using univariate Cox regression. Then, a ceRNA network was constructed based on these RNAs.

Results: We identified a total of five prognostic DEmiRNAs, 63 DElncRNAs, and 90 DEmRNAs between CCA and matched normal tissues. Integrating the relationship between the different types of RNAs, an lncRNA-miRNA-mRNA network was established and included 28 molecules and 47 interactions. Screened prognostic RNAs involved in the ceRNA network included 3 miRNAs (hsa-mir-1295b, hsa-mir-33b, and hsa-mir-6715a), 7 lncRNAs (ENSG00000271133, ENSG00000233834, ENSG00000276791, ENSG00000241155, COL18A1-AS1, ENSG00000274737, and ENSG00000235052), and 18 mRNAs (ANO9, FUT4, MLLT3, ABCA3, FSCN2, GRID2IP, NCK2, MACC1, SLC35E4, ST14, SH2D3A, MOB3B, ACTL10, RAB36, ATP1B3, MST1R, SEMA6A, and SEL1L3).

Conclusions: Our study identified novel prognostic makers and predicted a previously unknown ceRNA regulatory network in CCA and may provide novel insight into a further understanding of lncRNA-mediated ceRNA regulatory mechanisms in CCA.

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Figures

Figure 1
Figure 1
Expressions of differentially expressed miRNAs in different samples. X-axis represents samples, Y-axis represents miRNAs, red stands for upregulation, and green stands for downregulation [6].
Figure 2
Figure 2
Expressions of differentially expressed top 50 lncRNAs in different samples. X-axis represents samples, Y-axis represents lncRNAs, red stands for upregulation, and green stands for downregulation [6].
Figure 3
Figure 3
Expressions of top 50 differentially expressed mRNAs in different samples. X-axis represents samples, Y-axis represents mRNAs, red stands for upregulation, and green stands for downregulation [6].
Figure 4
Figure 4
Kaplan-Meier curve analysis of DEmiRNAs, DElncRNAs, and DElncRNAs for the overall survival in CCA patients.
Figure 5
Figure 5
Gene ontology (GO) network of prognostic DEmRNAs. Yellow stands for GO terms and blue stands for mRNAs.
Figure 6
Figure 6
CeRNA regulatory network. Yellow represents miRNAs, blue represents mRNAs, and green represents lncRNAs.
See this image and copyright information in PMC

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