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Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma.

ABSTRACT: Objective: The goal of our study is to identify a competing endogenous RNA (ceRNA) network using dysregulated RNAs between HCC tumors and the adjacent normal liver tissues from The Cancer Genome Atlas (TCGA) datasets, and to investigate underlying prognostic indicators in hepatocellular carcinoma (HCC) patients. Methods: All of the RNA- and miRNA-sequencing datasets of HCC were obtained from TCGA, and dysregulated RNAs between HCC tumors and the adjacent normal liver tissues were investigated by DESeq and edgeR algorithm. Survival analysis was used to confirm underlying prognostic indicators. Results: In the present study, we constructed a ceRNA network based on 16 differentially expressed genes (DEGs), 7 differentially expressed microRNAs and 34 differentially expressed long non-coding RNAs (DELs). Among these dysregulated RNAs, three DELs (AP002478.1, HTR2A-AS1, and ERVMER61-1) and six DEGs (enhancer of zeste homolog 2 [EZH2], kinesin family member 23 [KIF23], chromobox 2 [CBX2], centrosomal protein 55 [CEP55], cell division cycle 25A [CDC25A], and claspin [CLSPN]) were used for construct a prognostic signature for HCC overall survival (OS), and performed well in HCC OS (adjusted P<0.0001, adjusted hazard ratio = 2.761, 95% confidence interval = 1.838-4.147). Comprehensive survival analysis demonstrated that this prognostic signature may be act as an independent prognostic indicator of HCC OS. Functional assessment of these dysregulated DEGs in the ceRNA network and gene set enrichment of this prognostic signature suggest that both were enriched in the biological processes and pathways of the cell cycle, cell division and cell proliferation. Conclusions: Our current study constructed a ceRNA network for HCC, and developed a prognostic signature that may act as an independent indicator for HCC OS.

SUBMITTER: Liao X

PROVIDER: S-EPMC6603367 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma.

Liao Xiwen X Wang Xiangkun X Huang Ketuan K Han Chuangye C Deng Jianlong J Yu Tingdong T Yang Chengkun C Huang Rui R Liu Xiaoguang X Yu Long L Zhu Guangzhi G Su Hao H Qin Wei W Zeng Xianmin X Han Bowen B Han Quanfa Q Liu Zhengqian Z Zhou Xin X Gong Yizhen Y Liu Zhengtao Z Huang Jianlv J Winkler Cheryl A CA O'Brien Stephen J SJ Ye Xinping X Peng Tao T

Journal of Cancer 20190602 14

<b>Objective</b>: The goal of our study is to identify a competing endogenous RNA (ceRNA) network using dysregulated RNAs between HCC tumors and the adjacent normal liver tissues from The Cancer Genome Atlas (TCGA) datasets, and to investigate underlying prognostic indicators in hepatocellular carcinoma (HCC) patients. <b>Methods</b>: All of the RNA- and miRNA-sequencing datasets of HCC were obtained from TCGA, and dysregulated RNAs between HCC tumors and the adjacent normal liver tissues were i ...[more]

PMID: 31289599

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Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Recently, competing endogenous RNAs (ceRNA) have revealed a significant role in the progression of HCC. Herein, we aimed to construct a ceRNA network to identify potential biomarkers and illustrate its correlation with immune infiltration in HCC.MethodsRNA sequencing data and clinical traits of HCC patients were downloaded from TCGA. The limma R package was used to identify differentially expressed (DE) RNAs. The predicted prognostic model was established using univariate and multivariate Cox regression. A K-M curve, TISIDB and GEPIA website were utilized for survival analysis. Functional annotation was determined using Enrichr and Reactome. Protein-to-protein network analysis was implemented using SRTNG and Cytoscape. Hub gene expression was validated by quantitative polymerase chain reaction, Oncomine and the Hunan Protein Atlas database. Immune infiltration was analyzed by TIMMER, and Drugbank was exploited to identify bioactive compounds.ResultsThe predicted model that was established revealed significant efficacy with 3- and 5-years of the area under ROC at 0.804 and 0.744, respectively. Eleven DEmiRNAs were screened out by a K-M survival analysis. Then, we constructed a ceRNA network, including 56 DElncRNAs, 6 DEmiRNAs, and 28 DEmRNAs. The 28 DEmRNAs were enriched in cancer-related pathways, for example, the TNF signaling pathway. Moreover, six hub genes, CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1, were all overexpressed in HCC tissues and independently correlated with survival rate. Furthermore, expression of hub genes was related to immune cell infiltration in HCC, including B cells, CD8+ T cells, CD4+ T cells, monocytes, macrophages, neutrophils, and dendritic cells.ConclusionThe findings from this study demonstrate that CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1 are closely associated with prognosis and immune infiltration, representing potential therapeutic targets or prognostic biomarkers in HCC.

Project description:BackgroundPancreatic cancer is an insidious and heterogeneous malignancy with poor prognosis that is often locally unresectable. Therefore, determining the underlying mechanisms and effective prognostic indicators of pancreatic cancer may help optimize clinical management. This study was conducted to develop a prognostic model for pancreatic cancer based on a competing endogenous RNA (ceRNA) network.MethodsWe obtained transcriptomic data and corresponding clinicopathological information of pancreatic cancer samples from The Cancer Genome Atlas (TCGA) database (training set). Based on the ceRNA interaction network, we screened candidate genes to build prediction models. Univariate Cox regression analysis was performed to screen for genes associated with prognosis, and least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive model. A receiver operating characteristic (ROC) curve was drawn, and the C-index was calculated to evaluate the accuracy of the prediction model. Furthermore, we downloaded transcriptomic data and related clinical information of pancreatic cancer samples from the Gene Expression Omnibus database (validation set) to evaluate the robustness of our prediction model.ResultsEight genes (ANLN, FHDC1, LY6D, SMAD6, ACKR4, RAB27B, AUNIP, and GPRIN3) were used to construct the prediction model, which was confirmed as an independent predictor for evaluating the prognosis of patients with pancreatic cancer through univariate and multivariate Cox regression analysis. By plotting the decision curve, we found that the risk score model is an independent predictor has the greatest impact on survival compared to pathological stage and targeted molecular therapy.ConclusionsAn eight-gene prediction model was constructed for effectively and independently predicting the prognosis of patients with pancreatic cancer. These eight genes identified show potential as diagnostic and therapeutic targets.

Project description:BackgroundHepatocellular carcinoma (HCC) is an extremely common malignant tumor with worldwide prevalence. The aim of this study was to identify potential prognostic genes and construct a competing endogenous RNA (ceRNA) regulatory network to explore the mechanisms underlying the development of HCC.MethodsIntegrated analysis was used to identify potential prognostic genes in HCC with R software based on the GSE14520, GSE17548, GSE19665, GSE29721, GSE60502, and the Cancer Genome Atlas databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enrichment analyses were performed to explore the molecular mechanisms of potential prognostic genes. Differentially expressed miRNAs (DEMs) and lncRNAs (DELs) were screened based on the Cancer Genome Atlas database. An lncRNA-miRNA-mRNA ceRNA regulatory network was constructed based on information about interactions derived from the miRcode, TargetScan, miRTarBase, and miRDB databases.ResultsA total of 152 potential prognostic genes were screened that were differentially expressed in HCC tissue and significantly associated with overall survival of HCC patients. There were 13 key potential prognostic genes in the ceRNA regulatory network: eleven upregulated genes (CCNB1, CEP55, CHEK1, EZH2, KPNA2, LRRC1, PBK, RRM2, SLC7A11, SUCO, and ZWINT) and two downregulated genes (ACSL1 and CDC37L1) whose expression might be regulated by eight DEMs and 61 DELs. Kaplan-Meier curve analysis showed that nine DELs (AL163952.1, AL359878.1, AP002478.1, C2orf48, C10orf91, CLLU1, CLRN1-AS1, ERVMER61-1, and WARS2-IT1) in the ceRNA regulatory network were significantly associated with HCC-patient prognoses.ConclusionThis study identified potential prognostic genes and constructed an lncRNA- miRNA-mRNA ceRNA regulatory network of HCC, which not only has important clinical significance for early diagnoses but also provides effective targets for HCC treatments and could provide new insights for HCC-interventional strategies.

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Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma.

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Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma.

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