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  • Title: Gene expression profiling and bioinformatics analysis of gastric carcinoma.
    Author: Liu N, Liu X, Zhou N, Wu Q, Zhou L, Li Q.
    Journal: Exp Mol Pathol; 2014 Jun; 96(3):361-6. PubMed ID: 24589858.
    Abstract:
    Gastric cancer remains one of the major health problems worldwide, and it is one of the most common cancers and the leading cause of cancer-related deaths in China. This study was to analyze the expression profiles of genes in gastric carcinoma, and predict potential regulating factors. The gene expression profile data GSE13911 was downloaded from Gene Expression Omnibus and the differentially expressed genes (DEGs) were identified by t-test. Gene modules were constructed using hierarchical clustering in R based on average linkage and Pearson's correlation coefficient and functional analysis for these genes were performed with DAVID. Genes in each module with Pearson's correlation coefficient >0.3 were obtained to construct co-expression network. Protein-protein interactions (PPIs) were identified by comparing protein-protein interaction (PPI) network with co-expression networks. In addition, the potential regulatory microRNAs and the transcription factors for each module were screened out. In this study, six modules associated with protein degradation, cell cycle, protein trafficking and immunoreaction were identified. COPS5 (COP9 Subunit 5) was the core protein in the largest PPI network of module 1. The transcription factors MYC and MAZ (Myc-associated zinc-finger protein) were enriched in module 1. A total of 9 microRNA-target bi-clusters were identified and module 1 enriched 20 genes targeting to miR-17-92 gene cluster(miR-17/20ab)and miR-106b-25 gene cluster (miR-106b/93). In conclusion, we constructed 6 gene modules and screened out some genes, transcriptional factors and microRNAs that may be used as potential molecular biomarkers for gastric carcinoma.
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