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The aim of the present study was to identify the candidate genes induced by trichostatin A (TSA) in BGC-823 gastric cancer (GC) cells and to explore the possible inhibition mechanism of TSA in GC. Gene expression data were obtained through chip detection, and differentially expressed genes (DEGs) between GC cells treated with TSA and untreated GC cells (control group) were identified. Gene ontology analysis of the DEGs was performed using the database for annotation, visualization and integrated discovery. Then sub-pathway enrichment analysis was performed and a microRNA (miRNA) regulatory network was constructed. We selected 76 DEGs, among which 43 were downregulated genes and 33 were upregulated genes. By sub-pathway enrichment analysis of the DEGs, the propanoate metabolism pathway was selected as the sub-pathway. By constructing a miRNA regulatory network, we identified that DKK1 and KLF13 were the top hub nodes. The propanoate metabolism pathway and the genes DKK1 and KLF13 may play significant roles in the inhibition of GC induced by TSA. These genes may be potential therapeutic targets for GC. However, further experiments are still required to confirm our results.

The aim of the present study was to investigate the PAQR3 gene expression and its methylation level in colorectal cancer tissues, as well as the association with colorectal cancer clinical data. In total, 54 cases of colorectal cancer tissue samples and normal adjacent tissue samples were collected between June, 2013 and July, 2014. RT-PCR and western blot analysis were used to detect the mRNA and protein levels of PAQR3 in colorectal samples, respectively. MSP was used to detect the methylation level of PAQR3 gene in colorectal samples, which was compared with colorectal data. The results showed that a decreased expression level of PAQR3 mRNA in colorectal cancer tissues and the expression reduction rate was 57.4% (31/54). Similarly, the expression level of PAQR3 protein was reduced in cancer tissues, and the reduction rate was 46.3% (25/54), while the protein expression reduction rate in cancer adjacent tissue was 5.6% (3/54), and the difference was statistically significant (P

Background Rectal cancer is an important contributor to cancer mortality. Objective The objective of this paper is to identify key genes across three phenotypes (fungating, polypoid and polypoid & small-ulcer) of rectal cancer based on multiple differential expression networks (DENs). Methods Differential interactions and non-differential interactions were evaluated according to Spearman correlation coefficient (SCC) algorithm, and were selected to construct DENs. Topological analysis was performed for exploring hub genes in largest components of DENs. Key genes were denoted as intersections between nodes of DENs and rectal cancer associated genes from Genecards. Finally, we utilized hub genes to classify phenotypes of rectal cancer on the basis of support vector machines (SVM) methodology. Results We obtained 19 hub genes and total 12 common key genes of three largest components of DENs, and EGFR was the common element. The SVM results revealed that hub genes could classify phenotypes, and validated feasibility of DEN methods. Conclusions We have successfully identified significant genes (such as EGFR and UBC) across fungating, polypoid and polypoid & small-ulcer phenotype of rectal cancer. They might be potential biomarkers for classification, detection and therapy of this cancer.