BackgroundDespite new treatment options for hepatocellular carcinomas (HCC) recently, 5-year survival remains poor, ranging from 50 to 70%, which may attribute to the lack of early diagnostic biomarkers. Thus, developing new biomarkers for early diagnosis of HCC, is extremely urgent, aiming to decrease HCC-related deaths.MethodsIn the study, we conducted a comprehensive characterization of gene expression data of HCC based on a bioinformatics method. The results were confirmed by real time polymerase chain reaction (RT-PCR) and TCGA database to prove the credibility of this integrated analysis.ResultsAfter integrating analysis of seven HCC gene expression datasets, 1167 differential expressed genes (DEGs) were identified. These genes mainly participated in the process of cell cycle, oocyte meiosis, and oocyte maturation mediated by progesterone. The results of experiments and TCGA database validation in 10 genes was in full accordance with findings in integrated analysis, indicating the high credibility of our integrated analysis of different gene expression datasets. ASPM, CCT3, and NEK2 was showed to be significantly associated with overall survival of HCC patients in TCGA database.ConclusionThis method of integrated analysis may be a useful tool to minish the heterogeneity of individual microarray, hopefully outputs more accurate HCC transcriptome profiles based on large sample size, and explores some potential biomarkers and therapy targets for HCC.Electronic supplementary materialThe online version of this article (doi:10.1186/s13000-016-0596-x) contains supplementary material, which is available to authorized users.

Abstract. Gastric cancer (GC) is often diagnosed in the advanced stages and is associated with a poor prognosis. Obtaining an in depth understanding of the molecular mechanisms of GC has lagged behind compared with other cancers. This study aimed to identify candidate biomarkers for GC. An integrated analysis of microarray datasets was performed to identify differentially expressed genes (DEGs) between GC and normal tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed to identify the functions of the DEGs. Furthermore, a protein-protein interaction (PPI) network of the DEGs was constructed. The expression levels of the DEGs were validated in human GC tissues using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A set of 689 DEGs were identified in GC tissues, as compared with normal tissues, including 202 upregulated DEGs and 487 downregulated DEGs. The KEGG pathway analysis suggested that various pathways may play important roles in the pathology of GC, including pathways related to protein digestion and absorption, extracellular matrix-receptor interaction, and the metabolism of xenobiotics by cytochrome P450. The PPI network analysis indicated that the significant hub proteins consisted of SPP1, TOP2A and ARPC1B. RT-qPCR validation indicated that the expression levels of the top 10 most significantly dysexpressed genes were consistent with the illustration of the integrated analysis. The present study yielded a reference list of reliable DEGs, which represents a robust pool of candidates for further evaluation of GC pathogenesis and treatment.

BackgroundThis study investigated the effect of transcriptional gene silencing (TGS) of the heparanase gene on hepatoma SMCC-7721 cells.MethodsSiRNAs targeting the promoter region and coding region of the heparanase gene were designed and synthesized. Then the siRNAs were transfected into hepatoma SMCC-7721 cells by nuclear transfection or cytoplasmic transfection. The expression of heparanase was detected by RT-PCR and Western blotting 48 h, 72 h and 96 h post-transfection. In addition, wound healing and invasion assays were performed to estimate the effect of TGS of the heparanase gene on the migration and invasion of hepatoma SMCC-7721 cells.ResultsProtein and mRNA expression of the heparanase gene were interfered with by TGS or post-transcriptional gene silencing (PTGS) 48 h after transfection. At 72 h post-transfection, the expression of the PTGS group of genes had recovered unlike the TGS group. At 96 h post-transfection, the expression of the heparanase gene had recovered in both the TGS group and PTGS group. Invasion and wound healing assays showed that both TGS and PTGS of the heparanase gene could inhibit invasion and migration of hepatoma SMCC-7721 cells, especially the TGS group.ConclusionsTGS can effectively interfere with the heparanase gene to reduce the invasion and migration of hepatoma SMCC-7721 cells.