Dataset Information

LncRNA UCHL1-AS1 prevents cell mobility of hepatocellular carcinoma: a study based on in vitro and bioinformatics.

ABSTRACT: We set out to investigate biological functions and potential molecular mechanisms of long non-coding RNA (lncRNA) in hepatocellular carcinoma (HCC). HCC cell line Bel-7404 was cultured and transfected with antisense to the ubiquitin carboxyl-terminal hydrolase L1 (UCHL1-AS1). Viability and mobility were detected by MTT and wound healing assays. Additionally, enrichment analysis and functional networks of UCHL1-AS1 related genes in HCC were performed. Results showed that high level UCHL1-AS1 could effectively inhibit HCC cell migration. However, there was no significant correlation between overexpressed UCHL1-AS1 and HCC proliferation. Meanwhile, BMP4, CALM3, and HRAS were selected from 204 genes that related to UCHL1-AS1. All of these hub genes play critical roles in HCC occurrence and development. Thus, underlying molecular mechanisms among hub genes and UCHL1-AS1 in HCC might be valuable for prognosis and treatment.

SUBMITTER: Zhang R

PROVIDER: S-EPMC6958276 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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LncRNA UCHL1-AS1 prevents cell mobility of hepatocellular carcinoma: a study based on <i>in vitro</i> and bioinformatics.

Zhang Rui R Wei Yichen Y Zhu Li'ou L Huang Lanshan L Wei Yan Y Chen Gang G Dang Yiwu Y Feng Zhenbo Z

International journal of clinical and experimental pathology 20180501 5

We set out to investigate biological functions and potential molecular mechanisms of long non-coding RNA (lncRNA) in hepatocellular carcinoma (HCC). HCC cell line Bel-7404 was cultured and transfected with antisense to the ubiquitin carboxyl-terminal hydrolase L1 (UCHL1-AS1). Viability and mobility were detected by MTT and wound healing assays. Additionally, enrichment analysis and functional networks of UCHL1-AS1 related genes in HCC were performed. Results showed that high level UCHL1-AS1 coul ...[more]

PMID: 31938339

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Project description:Background: Knowledge about the prognostic role of long noncoding RNA (lncRNA) in colorectal cancer (CRC) is limited. Therefore, we constructed a lncRNA-related prognostic model based on data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Materials and Methods: CRC transcriptome and clinical data were downloaded from the GSE20916 dataset and the TCGA database, respectively. R software was used for data processing and analysis. The differential lncRNA expression within the two datasets was first screened, and then intersections were measured. Cox regression and the Kaplan-Meier method were used to evaluate the effects of various factors on prognosis. The area under the curve (AUC) of the receiver operating characteristic curve and a nomogram based on multivariate Cox analysis were used to estimate the prognostic value of the lncRNA-related model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to elucidate the significantly involved biological functions and pathways. Results: A total of 11 lncRNAs were crossed. The univariate Cox analysis screened out two lncRNAs, which were analyzed in the multivariate Cox analysis. A nomogram based on the two lncRNAs and other clinicopathological risk factors was constructed. The AUC of the nomogram was 0.56 at 3 years and 0.71 at 5 years. The 3-year nomogram model was compared with the ideal model, which showed that some indices of the 3-year model were consistent with the ideal model, suggesting that our model was highly accurate. The GO and KEGG enrichment analyses showed that positive regulation of secretion by cells, positive regulation of secretion, positive regulation of exocytosis, endocytosis, and the calcium signaling pathway were differentially enriched in the two-lncRNA-associated phenotype. Conclusions: A two-lncRNA prognostic model of CRC was constructed by bioinformatics analysis. The model had moderate prediction accuracy. LncRNA BBOX1-AS1 and lncRNA FOXP4-AS1 were identified as prognostic biomarkers.

Project description:Liver hepatocellular carcinoma (LIHC) is one of the major causes of cancer-related death worldwide with increasing incidences, however there are very few studies about the underlying mechanisms and pathways in the development of LIHC. We obtained LIHC samples from The Cancer Genome Atlas (TCGA) to screen differentially expressed mRNAs, lncRNAs, miRNAs and driver mutations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses and protein-protein interaction (PPI) network were performed. Moreover, we constructed a competing endogenous lncRNAs-miRNAs-mRNAs network. Finally, cox proportional hazards regression analysis was used to identify important prognostic differentially expressed genes. Total of 1284 mRNAs, 123 lncRNAs, 47 miRNAs were identified within different tissues of LIHC patients. GO analysis indicated that upregulated and downregulated differentially expressed mRNAs (DEmRNAs) were mainly associated with cell division, DNA replication, mitotic sister chromatid segregation and complement activation respectively. Meanwhile, KEGG terms revealed that upregulated and downregulated DEmRNAs were primarily involved in DNA replication, Metabolic pathways, cell cycle and Metabolic pathways, chemical carcinogenesis, retinol metabolism pathway respectively. Among the DERNAs, 542 lncRNAs-miRNAs-mRNAs pairs were predicted to construct a ceRNA regulatory network including 35 DElncRNAs, 26 DEmiRNAs and 112 DEmRNAs. In the Kaplan-Meier analysis, total of 43 mRNAs, 14 lncRNAs and 3 miRNAs were screened out to be significantly correlated with overall survival of LIHC. The mutation signatures were analyzed and its correlation with immune infiltrates were evaluated using the TIMER in LIHC. Among the mutation genes, TTN mutation is often associated with poor immune infiltration and a worse prognosis in LIHC. This work conducted a novel lncRNAs-miRNAs-mRNAs network and mutation signatures for finding potential molecular mechanisms underlying the development of LIHC. The biomarkers also can be used for predicting prognosis of LIHC.

Dataset Information

LncRNA UCHL1-AS1 prevents cell mobility of hepatocellular carcinoma: a study based on in vitro and bioinformatics.

Publications

LncRNA UCHL1-AS1 prevents cell mobility of hepatocellular carcinoma: a study based on <i>in vitro</i> and bioinformatics.

Similar Datasets

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