Dataset Information

Identification and integrated analysis of hepatocellular carcinoma-related circular RNA signature.

ABSTRACT: Background:Circular RNAs (circRNAs), a novel type of non-coding RNA, play a vital role in the pathogenesis and development of cancer. CircRNAs signatures may be useful as prognostic and predictive factors as well as clinical tools for evaluating disease status and prognosis. This study was carried out to explore novel circRNA signatures in hepatocellular carcinoma (HCC). Methods:The expression profiles of circRNAs were retrieved from the Gene Expression Omnibus (GEO). The expression profiles of miRNAs and mRNAs were obtained from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The results of the microarray were validated by quantitative real-time RCR (qPCR). Based on circRNA-miRNA pairs and miRNA-mRNA pairs, a competitive endogenous RNA (ceRNA) network was constructed. Functional analysis was performed via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA). Furthermore, survival analysis was carried out using the Kaplan-Meier curve and the log-rank test. Results:Differentially expressed circRNAs in HCC from GEO databases (GSE94508 and GSE97332) were screened and analyzed using the bioinformatics method. We detected a total of 26 differentially expressed circRNAs by qPCR and then selected 6 circRNAs to construct the circRNA-miRNA-mRNA networks. Through prognostic analysis, 3 target hub genes (AURKA, KIF5B, and RHOA) of circRNAs were discovered. Moreover, GSEA and GSVA were used to reveal the functions of AURKA, KIF5B, and RHOA in HCC. Conclusions:We identified three hub genes, and our results suggest that the circHMGCS1/miR-581/AURKA, circHMGCS1/miR-892a/KIF5B, and circTMCO3/miR-577/RHOA axes may play a vital role in HCC progression.

SUBMITTER: Wang F

PROVIDER: S-EPMC7186732 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Identification and integrated analysis of hepatocellular carcinoma-related circular RNA signature.

Wang Feiran F Xu Xiaodong X Zhang Nannan N Chen Zhong Z

Annals of translational medicine 20200301 6

<h4>Background</h4>Circular RNAs (circRNAs), a novel type of non-coding RNA, play a vital role in the pathogenesis and development of cancer. CircRNAs signatures may be useful as prognostic and predictive factors as well as clinical tools for evaluating disease status and prognosis. This study was carried out to explore novel circRNA signatures in hepatocellular carcinoma (HCC).<h4>Methods</h4>The expression profiles of circRNAs were retrieved from the Gene Expression Omnibus (GEO). The expressi ...[more]

PMID: 32355738

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Project description:Hepatocellular carcinoma (HCC) is a common type of liver cancer and one of the highly lethal diseases worldwide. Hypoxia plays an important role in the development and prognosis of HCC. This study aimed to construct a new hypoxia-related prognosis signature and investigate its potential ceRNA axes in HCC. RNA profiles and hypoxia genes were downloaded, respectively, from the Cancer Genome Atlas hepatocellular carcinoma database and Gene Set Enrichment Analysis website. Cox regression analyses were performed to select the prognostic genes and construct the risk model. The ENCORI database was applied to build the lncRNA-miRNA-mRNA prognosis-related network. The TIMER and CellMiner databases were employed to analyze the association of gene expression in ceRNA with immune infiltration and drug sensitivity, respectively. Finally, the co-expression analysis was carried out to construct the potential lncRNA/miRNA/mRNA regulatory axes. We obtained a prognostic signature including eight hypoxia genes (ENO2, KDELR3, PFKP, SLC2A1, PGF, PPFIA4, SAP30, and TKTL1) and further established a hypoxia-related prognostic ceRNA network including 17 lncRNAs, six miRNAs, and seven mRNAs for hepatocellular carcinoma. Then, the analysis of immune infiltration and drug sensitivity showed that gene expression in the ceRNA network was significantly correlated with the infiltration abundance of multiple immune cells, the expression level of immune checkpoints, and drug sensitivity. Finally, we identified three ceRNA regulatory axes (SNHG1/miR-101-3p/PPFIA4, SNHG1/miR-101-3p/SAP30, and SNHG1/miR-101-3p/TKTL1) associated with the progression of HCC under hypoxia. Here, we constructed a prognosis gene signature and a ceRNA network related to hypoxia for hepatocellular carcinoma. Among the ceRNA network, six highly expressed lncRNAs (AC005540.1, AC012146.1, AC073529.1, AC090772.3, AC138150.2, AL390728.6) and one highly expressed mRNA (PPFIA4) were the potential biomarkers of hepatocellular carcinoma which we firstly reported. The three predicted hypoxia-related regulatory axes may play a vital role in the progression of hepatocellular carcinoma.

Project description:BackgroundCancer-associated metabolic reprogramming promotes cancer cell differentiation, growth, and influences the tumor immune microenvironment (TIME) to promote hepatocellular carcinoma (HCC) progression. However, the clinical significance of metabolism-related lncRNA remains largely unexplored.MethodsBased on The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) dataset, we identified characteristic prognostic long non-coding RNAs (lncRNAs) and construct metabolism-related lncRNA prognostic signature for HCC. Gender, age, grade, stage and TP53 status were used as covariates were used to assess the prognostic capacity of the characteristic lncRNA signature. Subsequently, the molecular and immune characteristics and drug sensitivity in metabolism-related lncRNA signature defined subgroups were analyzed.ResultsWe identified 34 metabolism-related lncRNAs significantly associated with the prognosis of HCC (P<0.05). Subsequently, we constructed a multigene signature based on 9 characteristics prognostic lncRNAs and classified HCC patients into high- and low-risk groups based on cutoff values. We found the lncRNA signature [hazard ratio (HR) =3.55 (2.44-5.15), P<0.001] to be significantly associated with survival. The receiver operating characteristic curve (ROC) curves area under the curve (AUC) values for 1-, 3-, and 5-year survival were 0.811, 0.773, and 0.753, respectively. In univariate and multivariate Cox regression analyses, prognostic characteristic lncRNAs were the most crucial prognostic factor besides the stage. The prognostic signature was subsequently validated in the test set. In addition, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analyses revealed potential biological features and signaling pathways associated with the prognostic signature. We constructed a nomogram including risk groups and clinical parameters (age, gender, grade, and stage). Calibration plots and decision curve analysis (DCA) showed that our nomogram had a good predictive performance. Finally, we found reduced expression of immune-activated cells in the high-risk group.ConclusionsThe metabolism-related lncRNA signature is a promising biomarker to distinguish the prognosis and an immune characteristic in HCC.

Project description:BackgroundRecent papers have described circular RNAs (circRNAs) playing important roles in the development and progression of colorectal cancer (CRC). However, the expression profiles of circRNAs and their functions in CRC have rarely been studied. The objective was to identify circRNAs involved in the carcinogenesis and progression of CRC and to explore potential molecular mechanisms as a competitive endogenous RNA (ceRNA). Moreover, we aimed to establish an immune-related gene signature for predicting the overall survival (OS) of CRC.MethodsThe expression patterns of circRNA, miRNA, mRNA, and clinicopathological data were collected from the GEO and TCGA databases. A ceRNA network would be established, and the functional enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed, and hub genes were identified using a cytohub plugin. Subsequently, an immune-related signature was developed based on mRNAs in the ceRNA network. In addition, OS-nomogram was constructed by combining an immune-related signature and clinicopathological characterization to predict the OS.ResultsWe established a circRNA-miRNA-mRNA ceRNA network. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the mRNAs were mainly enriched in neuroactive ligand-receptor interaction, Wnt signaling pathway, cell adhesion molecules (CAMs), and renin secretion. PPI network and module analysis identified 10 hub genes, and the circRNA-miRNA hub gene regulatory modules was established. After univariate and multivariate analysis, seven immune-related genes in the ceRNA network were used to construct the immune-related signature. Patients were divided into low-risk and high-risk groups, and there were significant differences in the OS. The ROC of the nomogram indicated the satisfactory accuracy and predictive power. Furthermore, we established a prognostic nomogram based on immune-related risk score and clinical characterization. The ROC and calibration curves revealed the accuracy of the nomogram. In addition, the high-risk score was positively correlated with six immune infiltrating cells (P < 0.05).ConclusionWe screened the key genes and established a circRNA-related ceRNA network involved in CRC, which will assist in understanding the molecular mechanisms underlying the carcinogenesis and progression. Moreover, our proposed immune-based signature may predict survival and reflect the immune status of CRC patients.

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Identification and integrated analysis of hepatocellular carcinoma-related circular RNA signature.

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Identification and integrated analysis of hepatocellular carcinoma-related circular RNA signature.

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