4.5 Article

Long non-coding RNAs and genes contributing to the generation of cancer stem cells in hepatocellular carcinoma identified by RNA sequencing analysis

Journal

ONCOLOGY REPORTS
Volume 36, Issue 5, Pages 2619-2624

Publisher

SPANDIDOS PUBL LTD
DOI: 10.3892/or.2016.5120

Keywords

cancer stem cells; RNA sequencing; Gene Expression Omnibus database; lncRNA

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Cancer stem cells (CSCs) play important roles in cancer initiation, progression and metastasis. The aim of the present study was to identify the potential targets that may contribute to the generation of hepatocellular carcinoma stem cells (HCSCs) from hepatocellular carcinoma (HCC) cells. The RNA sequencing (RNA-Seq) dataset GSE70537 was downloaded from the Gene Expression Omnibus (GEO) database. Raw RNA sequences were mapped to the GRCh37/hg19 genome based on TopHat and assembled through Cufflinks. Cuffdiff of Cufflinks was used for the screening of differentially expressed genes (DEGs) in the two types of HCSCs (Hep3B-C and Huh7-C) compared with the two types of HCC cells (Hep3B and Huh7) which were satisfied by the criteria of vertical bar log2(RPKMHCSC/RPKMHCC)vertical bar >1 and p<0.05. In addition, based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID), we screened the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways which were enriched in the DEGs. For the DEGs with consistent differential expression in the two lists of DEGs, the LncRNA2Target database was used for the identification of long non-coding RNA (lncRNA)-gene pairs. A total of 218 and 591 DEGs were identified for the Hep3B-C and Huh7-C samples, respectively, and 22 overlaps were obtained. Biological processes and pathways related to steroid biosynthesis/metabolism or other substance transport were found to be enriched in the two lists of DEGs. Among the 22 overlaps, 16 were found to be consistently differentially expressed in the two HCSC samples, and the lncRNA-gene regulatory network of these genes was constructed. Moreover, several potential biomarkers that may play important roles in the transformation of HCSCs were identified in the regulation network. Through the bioinformatics analysis of the RNA-Seq dataset, several novel targets that were associated with the progression of HCC were obtained, and these targets may be valuable for the treatment and prognosis of HCC.

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