Dataset Information

Diagnosis, prognosis and bioinformatics analysis of lncRNAs in hepatocellular carcinoma.

ABSTRACT: This study aims to comprehensively analyze the diagnosis and prognosis of lncRNAs in hepatocellular carcinoma (HCC). From the Gene Expression Omnibus database, we screened out and analyzed the differently expressed lncRNAs and related mRNAs using bioinformatics methods. The expressions of lncRNAs were validated in tumor tissues, cell lines and the Cancer Genome Atlas database. At the same time, we also conducted an exploratory analysis on the diagnostic and prognostic ability of lncRNAs in HCC. In this study, we found that most of the targeted mRNAs promote the biological process of cell division, cellular component of nucleoplasm, molecular function of protein binding, which were significantly associated with 12 KEGG pathways. LncRNA CRNDE and LINC01419 also had significant diagnostic value in HCC. In particular, the sensitivity, specificity and area under the curve of CRNDE were 71.0%, 87.1% and 0.701 (95% CI: 0.543-0.860), respectively. In addition, the high expression of CRNDE and GBAP1 predicated poor prognosis, while the high expression of LINC01093 suggested the opposite outcome. Through the comprehensive analysis of lncRNAs, it provided an important reference for the early diagnosis, prognosis evaluation, pathogenesis and targeted therapy of HCC.

SUBMITTER: Dai M

PROVIDER: S-EPMC5707062 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Diagnosis, prognosis and bioinformatics analysis of lncRNAs in hepatocellular carcinoma.

Dai Meiyu M Chen Siyuan S Wei Xiaomou X Zhu Xuan X Lan Fang F Dai Shengming S Qin Xue X

Oncotarget 20170928 56

This study aims to comprehensively analyze the diagnosis and prognosis of lncRNAs in hepatocellular carcinoma (HCC). From the Gene Expression Omnibus database, we screened out and analyzed the differently expressed lncRNAs and related mRNAs using bioinformatics methods. The expressions of lncRNAs were validated in tumor tissues, cell lines and the Cancer Genome Atlas database. At the same time, we also conducted an exploratory analysis on the diagnostic and prognostic ability of lncRNAs in HCC. ...[more]

PMID: 29221168

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Project description:BackgroundThe number of patients with hepatocellular carcinoma (HCC) is showing a growing trend all over the world. The metabolic microenvironment has been shown to play a key role in the pathogenesis of HCC in recent studies. The expression of metabolites and metabolic processes in tumor cells can be regulated by gene regulation mediated by long non-coding RNAs (lncRNAs), the abnormal expression of which is closely related to tumor occurrence and metastasis. However, the fundamental mechanism of applying metabolism-related lncRNAs to predicting HCC is still unclear.MethodsWith the complete RNA sequence data and clinical data obtained from The Cancer Genome Atlas database and metabolism-related genes downloaded from the Kyoto Encyclopedia of Genes and Genomes database, with false discovery rate < 0.001, log fold change > 1.5 selected as the screening criteria for lncRNA, the relationship between the expression level of metabolism-related LncRNAs (MRLs) and the overall survival rate was determined by the Univariate Cox regression analyses with the establishment of the metabolic prognosis model by the application of Multivariate Cox regression analyses, revealing the different biological processes and signaling pathways in both high-risk groups and low-risk groups by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis, leading the expression of lncRNA to be assessed by the reverse transcription-polymerase chain reaction results.ResultsThe overall survival rate of HCC patients is significantly correlated with signature of 5-MRLs. The prognosis characteristics of lncRNA reveal the relatively high death rate of patients in the high-risk groups, with the predicted signals by functional and pathway enrichment analysis related to biosynthesis, metabolic process, and metabolic pathway, with the prognostic characteristics of 5-MRLs by the combined analysis showing that it is an independent factor of HCC superior to the traditional clinical indicators in predicting the prognosis. A trend of high-expression was shown in MRLs in tumors by reverse transcription-polymerase chain reaction.ConclusionThe new 5-MRLs as potential biomarkers provide more powerful prognostic information for HCC patients. In the future clinical treatment of HCC, it will provide doctors with more methods.

Project description:Hepatocellular carcinoma (HCC) is a malignant tumor with unsatisfactory prognosis. The abnormal genes expression is significantly associated with initiation and poor prognosis of HCC. The aim of the present study was to identify molecular biomarkers related to the initiation and development of HCC via bioinformatics analysis, so as to provide a certain molecular mechanism for individualized treatment of hepatocellular carcinoma.Three datasets (GSE101685, GSE112790, and GSE121248) from the GEO database were used for the bioinformatics analysis. Differentially expressed genes (DEGs) of HCC and normal liver samples were obtained using GEO2R online tools. Gene ontology term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were conducted via the Database for Annotation, Visualization, and Integrated Discovery online bioinformatics tool. The protein-protein interaction (PPI) network was constructed by the Search Tool for the Retrieval of Interacting Genes database and hub genes were visualized by Cytoscape. Survival analysis and RNA sequencing expression were conducted by UALCAN and Gene Expression Profiling Interactive Analysis.A total of 115 shared DEGs were identified, including 30 upregulated genes and 85 downregulated genes in HCC samples. P53 signaling pathway and cell cycle were the major enriched pathways for the upregulated DEGs whereas metabolism-related pathways were the major enriched pathways for the downregulated DEGs. The PPI network was established with 105 nodes and 249 edges and 3 significant modules were identified via molecular complex detection. Additionally, 17 candidate genes from these 3 modules were significantly correlated with HCC patient survival and 15 of 17 genes exhibited high expression level in HCC samples. Moreover, 4 hub genes (CCNB1, CDK1, RRM2, BUB1B) were identified for further reanalysis of KEGG pathway, and enriched in 2 pathways, the P53 signaling pathway and cell cycle pathway.Overexpression of CCNB1, CDK1, RRM2, and BUB1B in HCC samples was correlated with poor survival in HCC patients, which could be potential therapeutic targets for HCC.

Project description:Hepatocellular carcinoma (HCC) is a common malignant cancer with poor survival outcomes, and hepatitis B virus (HBV) infection is most likely to contribute to HCC. But the molecular mechanism remains obscure. Our study intended to identify the candidate potential hub genes associated with the carcinogenesis of HBV-related HCC (HBV-HCC), which may be helpful in developing novel tumor biomarkers for potential targeted therapies. Four transcriptome datasets (GSE84402, GSE25097, GSE94660, and GSE121248) were used to screen the 309 overlapping differentially expressed genes (DEGs), including 100 upregulated genes and 209 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to explore the biological function of DEGs. A PPI network based on the STRING database was constructed and visualized by the Cytoscape software, consisting of 209 nodes and 1676 edges. Then, we recognized 17 hub genes by CytoHubba plugin, which were further validated on additional three datasets (GSE14520, TCGA-LIHC, and ICGC-LIRI-JP). The diagnostic effectiveness of hub genes was assessed with receiver operating characteristic (ROC) analysis, and all hub genes displayed good performance in discriminating TNM stage I patient samples and normal tissue ones. For prognostic analysis, two prognostic key genes (TOP2A and KIF11) out of the 17 hub genes were screened and used to develop a prognostic signature, which showed good potential for overall survival (OS) stratification of HBV-HCC patients. Gene Set Enrichment Analysis (GSEA) was performed in order to better understand the function of this prognostic gene signature. Finally, the miRNA-mRNA regulatory relationships of all hub genes in human liver were predicted using miRNet. In conclusion, the current study gives further insight on the pathogenesis and carcinogenesis of HBV-HCC, and the identified DEGs provide a promising direction for improving the diagnostic, prognostic, and therapeutic outcomes of HBV-HCC.

Dataset Information

Diagnosis, prognosis and bioinformatics analysis of lncRNAs in hepatocellular carcinoma.

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Diagnosis, prognosis and bioinformatics analysis of lncRNAs in hepatocellular carcinoma.

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