Hindawi BioMed Research International Volume 2019, Article ID 3526407, 13 pages https://doi.org/10.1155/2019/3526407 Research Article A Simple Competing Endogenous RNA Network Identifies Novel mRNA, miRNA, and lncRNA Markers in Human Cholangiocarcinoma Cheng Zhang and Chunlin Ge Department of Pancreatic and Biliary Surgery, e First Hospital of China Medical University, Shenyang, Liaoning , China Correspondence should be addressed to Chunlin Ge; [email protected] Received 10 December 2018; Revised 22 February 2019; Accepted 23 February 2019; Published 24 March 2019 Academic Editor: Udayan Apte Copyright © 2019 Cheng Zhang and Chunlin Ge. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 insufcient. Te present study aimed to uncover the underlying regulatory mechanism on CCA and fnd novel biomarkers for the disease prognosis. Method. Te 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. Afer pretreatment, diferentially expressed RNAs (DERNAs) were identifed and further interrogated for their correlations with clinical information. Prognostic RNAs were selected using univariate Cox regression. Ten, a ceRNA network was constructed based on these RNAs. Results. We identifed a total of fve prognostic DEmiRNAs, 63 DElncRNAs, and 90 DEmRNAs between CCA and matched normal tissues. Integrating the relationship between the diferent 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 identifed 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. 1. Introduction increased by 39% [5]. Te increased incidence and mortality have both contributed to the rising interest in this cancer. Cholangiocarcinoma (CCA) is an epithelial cell malignancy In 2011, Leonardo Salmena and colleagues frst proposed arising from diferent locations within the biliary tree. the famous competitive endogenous RNA (ceRNA) hypoth- According to its anatomical location, CCA is classifed into esis, positing that RNAs can engage in crosstalk with one intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA) another and manipulate biological functions independently subtypes [1]. Surgical treatment is the preferred option for of protein translation [7]. lncRNAs are endogenous noncod- all subtypes [2]; however, most patients with CCA present ing RNAs longer than 200 nucleotides and constitute the with unresectable or metastatic disease. As one of the dead- largest portion of the human noncoding transcriptome [8]. liest cancers, patient prognoses remain poor afer systemic Recent studies have revealed that lncRNAs can act as ceRNAs chemotherapy [3]. More than 90% of patients die within fve to compete with mRNAs for binding to miRNAs like sponges, years,andthemajorityofpatientssurvivelessthan12months and thereby regulate the expression level of genes [9, 10]. afer diagnosis [4]. Additionally, the incidence of CCA seems Moreover, this pattern has been extensively demonstrated to to be increasing in many western countries [2], and because play important roles in carcinogenesis and in the develop- of this increase, cumulative CCA mortality rates have also ment of numerous cancers, including bladder cancer, thyroid 2 BioMed Research International Table 1: Clinicopathological characteristics of 33 CCA patients. Characteristic Subtype No. of cases overall survival (Days) Age (years) < 65 16(48.5%) 942.4 ≥65 17(51.5%) 729.2 Gender Male 14(42.4%) 695.5 Female 19(57.6%) 932.8 Pathologic stage Stage I 18(54.5%) 904.9 Stage II 8(24.2%) 686.9 Stage III 1(3.0%) 1077.0 Stage IV 6(18.2%) 768.8 Pathologic T T1 18(54.5%) 904.9 T2 10(30.3%) 669.0 T3 5(15.2%) 899.0 Pathologic N NO 25(75.8%) 920.8 N1 5(15.2%) 642.2 NX 3(9.1%) 414.7 Pathologic M M0 27(81.8%) 861.6 M1 4(12.1%) 826.0 MX 2(6.1%) 453.0 Vital status Alive 17(51.5%) 991.1 Dead 16(48.5%) 664.0 cancer, and gastric cancer [11–13]. However, compared with there is some merit to our research in terms of a deeper other malignancies, the ceRNA research in CCA remains understanding of CCA. sparse. Sun et al. discovered that KCNQ1OT1 can act as a In the present study, with the aim of identifying the poten- ceRNA to improve CCA progression by regulating the miR- tial ceRNA interactions that contribute to patient survival, 140-5p/SOX4 axis [14]. In addition, lncRNA H19, HULC, and we constructed a ceRNA regulatory network that included TUG1 have also been shown to promote CCA progression mRNAsthatweredysregulatedinCCAascomparedtonor- as ceRNAs [15, 16]. However, the essential biomarkers and mal tissues. Te expression and clinical data were acquired mechanisms for the development and progression of CCA from the Cancer Genome Atlas (TCGA), a publicly funded remain unclear. projectthataimstocatalogueanddiscovermajorcancer- With the rapid development of gene profle and next causing genomic alterations to create a comprehensive “atlas” generation sequencing technology, bioinformatics analysis of cancer genomic profles [23]. All RNAs participating in the can provide more valuable information for new research construction of the network are demonstrated to be dysreg- [17]. However, like most bioinformatics studies on CCA, ulated and prognosis-related. Because of this strict standard, Huang and Zhong’s groups have only identifed diferentially the network appears to be simple; however, compared with expressed genes (DEGs) between CCA and normal tissues networks containing hundreds of molecules, it may provide and constructed a protein–protein interaction (PPI) network more reliable clues for ongoing basic research. [18, 19]. An analysis of lncRNA and miRNA functions and their interactions is absent. Additionally, bioinformatics studies related to the ceRNA network in CCA are rare. At 2. Materials and Methods present, only a few articles [20–22] can be found. In contrast to all the aforementioned studies, all of the molecules that we .. Acquisition of Clinical Characteristics and RNA-Seq selected in the construction of our network were proven to Data. Te clinical and mRNA expression data of 48 be diferentially expressed and prognosis-related. In addition, CCA patients were obtained from the TCGA database we determined the relationships between RNAs from the (https://portal.gdc.cancer.gov/). Exclusion criteria were set as expression data in the CCA samples rather than in RNA follows: (i) histologic diagnosis excluding CCA; (ii) incom- interaction databases. Te method of functional enrichment plete RNA expression data for analysis; and (iii) overall analysis was also diferent. We built a GO terms network survival time less than 30 days. Ultimately, 33 patients were to further visualize the biological process and molecular included in survival analysis. Te clinical and pathological function of crucial genes. Ten, the KEGG, Reactome and characteristics of these patients are summarized in Table 1. WikiPathways were used to enrich the related mRNAs path- Ten, lncRNA, miRNA, and mRNA expression fles that ways as opposed to the sole use of KEGG, which can help included 36 CCA samples and nine adjacent normal samples to obtain a more comprehensive analytic result. Terefore, were also downloaded from the TCGA portal. BioMed Research International 3 0.0 33.09953 1620.667 TCGA–ZH–A8Y8–01 TCGA–ZH–A8Y4–01 TCGA–W5–AA2I–01 TCGA–W5–AA2Z–01 TCGA–4G–AAZO–01 TCGA–W5–AA34–01 TCGA–W5–AA2O–01 TCGA–ZH–A8Y2–01 TCGA–W5–AA2W–01 TCGA–W6–AAOS–01 TCGA–3X–AAVB–01 TCGA–W5–AA31–01 TCGA–3X–AAV9–01 TCGA–ZU–A8S4–01 TCGA–ZH–A8Y1–01 TCGA–YR–A95A–01 TCGA–ZD–A8I3–01 TCGA–W5–AA2U–01 TCGA–ZH–A8Y5–01 TCGA–3X–AAVA–01 TCGA–W5–AA2G–01 TCGA–W5–AA38–01 TCGA–WD–A7RX–01 TCGA–W5–AA2X–01 TCGA–W5–AA2T–01 TCGA–W5–AA30–01 TCGA–3X–AAVE–01 TCGA–W5–AA36–01 TCGA–4G–AAZT–01 TCGA–W5–AA2Q–01 TCGA–W5–AA33–01 TCGA–W5–AA2R–01 TCGA–ZH–A8Y6–01 TCGA–3X–AAVC–01 TCGA–W5–AA39–01 TCGA–W5–AA2H–01 TCGA–W5–AA2X–11 TCGA–W5–AA2Q–11 TCGA–ZU–A8S4–11 TCGA–W5–AA2I–11 TCGA–W5–AA34–11 TCGA–W5–AA2R–11 TCGA–W5–AA31–11 TCGA–W5–AA30–11 TCGA–W5–AA2U–11 418143.12 209071.56 2668004.5 1334002.2 0.0 0.0 hsa–mir–21 hsa–mir–148a hsa–mir–192 hsa–mir–122 hsa–mir–101–2 hsa–mir–101–1 hsa–mir–194–2 hsa–mir–194–1 hsa–let–7c hsa–mir–455 hsa–mir–99a hsa–mir–378a hsa–mir–429 hsa–mir–135b hsa–mir–92b hsa–mir–203b hsa–mir–216a hsa–mir–216b hsa–mir–34c hsa–mir–378c hsa–mir–1468 hsa–mir–511 hsa–mir–1295b hsa–mir–6715a hsa–mir–1258 hsa–mir–551b hsa–mir–618 hsa–mir–383 hsa–mir–490 hsa–mir–5589 hsa–mir–96 hsa–mir–181d hsa–mir–708 hsa–mir–505 hsa–mir–483 hsa–mir–4662a hsa–mir–486–1 hsa–mir–486–2 hsa–mir–144 hsa–mir–139 hsa–mir–885 hsa–mir–675 hsa–mir–451a hsa–mir–200b hsa–mir–200a hsa–mir–183 hsa–mir–182 Figure 1: Expressions of diferentially expressed miRNAs in diferent samples.