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LncRNA ROR1‑AS1 high expression and its prognostic significance in liver cancer.

ABSTRACT: Hepatocellular carcinoma (HCC) is a common disease of the digestive system with no curative treatments. Long noncoding RNA tyrosine protein kinase transmembrane receptor 1 antisense RNA 1 (lncRNA ROR1‑AS1) is an lncRNA whose functions have been predicted in human diseases; however, its important role in cancer has been probed only in mantle cell lymphoma, not in HCC. Therefore, the present study aimed to elucidate the prognostic significance of lncRNA ROR1‑AS1 in HCC. The Cancer Genome Atlas Liver Hepatocellular Carcinoma was used to analyze the expression of ROR1‑AS1 in liver cancer. χ2 tests were performed to evaluate associations between clinical characteristics and ROR1‑AS1 expression. The role of ROR1‑AS1 in HCC prognosis was assessed using Kaplan‑Meier curves and proportional hazards model (Cox) analysis. Gene set enrichment analysis was performed by using a Gene Expression Omnibus dataset. At the same time, Multi Experiment Matrix was used to predict genes that may be co‑expressed with ROR1‑AS1. The Database for Annotation, Visualization and Integrated Discovery and KO‑Based Annotation System were used to analyze the most closely associated cytological behaviors and pathways in HCC. Then, the genes in the three databases were integrated to screen mRNAs, microRNAs and lncRNAs that had co‑expression relationships with ROR1‑AS1. Cytoscape, Search Tool for the Retrieval of Interacting Genes/Proteins and Molecular Evolutionary Genetics Analysis were used to map potential regulatory networks and developmental relationships associated with ROR1‑AS1. Finally, 12 genes most closely associated with ROR1‑AS1 were identified, and their relationship was described using a Circos plot. The results showed that ROR1‑AS1 was upregulated in HCC, and its expression was related to clinical stage, T stage and N stage. Furthermore, Kaplan‑Meier curves and Cox analysis indicated that high expression of ROR1‑AS1 was associated with poor prognosis, and that ROR1‑AS1 was an independent risk factor for HCC. Co‑expression data suggested that there may be a large regulatory network of 45 genes with indirect associations with ROR1‑AS1, a small regulatory network of 15 genes with direct or indirect regulatory relationships, and a special regulatory network containing 12 genes directly associated with ROR1‑AS1. The present findings indicated that high expression of ROR1‑AS1 suggests poor prognosis in patients with HCC.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC6908930 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Hepatocellular carcinoma (HCC) is a common digestive system disease with no curative treatment. Zinc finger protein 385D antisense RNA 2 (ZNF385D‑AS2) is a long non‑coding RNA (lncRNA) that has been predicted to function in human diseases, including several types of cancer. Yet, it has not been investigated in relation to liver cancer. Thus, the present study was designed with an aim to elucidate the prognostic significance of lncRNA ZNF385D‑AS2 in HCC. The Cancer Genome Atlas‑Liver Hepatocellular Carcinoma (TCGA‑LIHC) collection of data was utilized to analyze the expression of lncRNA ZNF385D‑AS2 in liver cancer. Then Chi‑square tests were used to evaluate the correlation between clinical characteristics and lncRNA ZNF385D‑AS2 expression. The significance of lncRNA ZNF385D‑AS2 in patient prognosis was evaluated using Kaplan‑Meier curves and Cox analysis. Concomitantly, Gene Set Enrichment Analysis (GSEA) was performed to analyze the most closely related cytological behavior. Finally, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID) and KOBAS software and data from the Gene Expression Omnibus (GEO) database to analyze the possible competing endogenous RNA (ceRNA) network pattern as well as the co‑expression network in liver cancer. Based on the results, analysis of RNA‑Seq gene expression data for 303 patients with primary tumors revealed low expression of ZNF385D‑AS2 in liver cancer. Low expression of ZNF385D‑AS2 was found to be significantly associated with sex (P=0.050), T stage (P=0.049), M stage (P=0.040), N stage (P<0.001) and clinical stage (P=0.037). Patients with ZNF385D‑AS2 low‑expression liver cancers had a shorter median overall survival compared with the patients with ZNF385D‑AS2 high‑expression liver cancers (P=0.0079). Cox analysis identified ZNF385D‑AS2 low‑expression as an independent prognostic variable (AUC=0.594) for overall survival in liver cancer patients. Co‑expression and ceRNA predictive analysis data suggested that there may be a regulatory signaling axis between ZNF385D‑AS2 and miR‑96 and miR‑182. In conclusion, our results suggests that low expression of ZNF385D‑AS2 is predictive of a poor prognosis of liver cancer patients.

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.

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LncRNA ROR1‑AS1 high expression and its prognostic significance in liver cancer.

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