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Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

ABSTRACT: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. The Tff1 knockout (KO) mouse model develops gastric lesions that include low-grade dysplasia (LGD), high-grade dysplasia (HGD), and adenocarcinomas. In this study, we used Affymetrix microarrays gene expression platforms for analysis of molecular signatures in the mouse stomach [Tff1-KO (LGD) and Tff1 wild-type (normal)] and human gastric cancer tissues and their adjacent normal tissue samples. Combined integrated bioinformatics analysis of mouse and human datasets indicated that 172 genes were consistently deregulated in both human gastric cancer samples and Tff1-KO LGD lesions (P < .05). Using Ingenuity pathway analysis, these genes mapped to important transcription networks that include MYC, STAT3, β-catenin, RELA, NFATC2, HIF1A, and ETS1 in both human and mouse. Further analysis demonstrated activation of FOXM1 and inhibition of TP53 transcription networks in human gastric cancers but not in Tff1-KO LGD lesions. Using real-time RT-PCR, we validated the deregulated expression of several genes (VCAM1, BGN, CLDN2, COL1A1, COL1A2, COL3A1, EpCAM, IFITM1, MMP9, MMP12, MMP14, PDGFRB, PLAU, and TIMP1) that map to altered transcription networks in both mouse and human gastric neoplasia. Our study demonstrates significant similarities in deregulated transcription networks in human gastric cancer and gastric tumorigenesis in the Tff1-KO mouse model. The data also suggest that activation of MYC, STAT3, RELA, and β-catenin transcription networks could be an early molecular step in gastric carcinogenesis.

SUBMITTER: Chen Z

PROVIDER: S-EPMC5524517 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

Chen Zheng Z Soutto Mohammed M Rahman Bushra B Fazili Muhammad W MW Peng DunFa D Blanca Piazuelo Maria M Chen Heidi H Kay Washington M M Shyr Yu Y El-Rifai Wael W

Genes, chromosomes & cancer 20170404 7

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. The Tff1 knockout (KO) mouse model develops gastric lesions that include low-grade dysplasia (LGD), high-grade dysplasia (HGD), and adenocarcinomas. In this study, we used Affymetrix microarrays gene expression platforms for analysis of molecular signatures in the mouse stomach [Tff1-KO (LGD) and Tff1 wild-type (normal)] and human gastric cancer tissues and their adjacent normal tissue samples. Combined integrated bioinfo ...[more]

PMID: 28281307

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Project description:Background & aimsmicroRNAs (miRNAs) are small noncoding RNAs that bind to the 3' untranslated regions of mRNAs to promote their degradation or block their translation. Mice with disruption of the trefoil factor 1 gene (Tff1) develop gastric neoplasms. We studied these mice to identify conserved miRNA networks involved in gastric carcinogenesis.MethodsWe performed next-generation miRNA sequencing analysis of normal gastric tissues (based on histology) from patients without evidence of gastric neoplasm (n = 64) and from TFF1-knockout mice (n = 22). We validated our findings using 270 normal gastric tissues (including 61 samples from patients without evidence of neoplastic lesions) and 234 gastric tumor tissues from 3 separate cohorts of patients and from mice. We performed molecular and functional assays using cell lines (MKN28, MKN45, STKM2, and AGS cells), gastric organoids, and mice with xenograft tumors.ResultsWe identified 117 miRNAs that were significantly deregulated in mouse and human gastric tumor tissues compared with nontumor tissues. We validated changes in levels of 6 miRNAs by quantitative real-time polymerase chain reaction analyses of neoplastic gastric tissues from mice (n = 39) and 3 independent patient cohorts (n = 332 patients total). We found levels of MIR135B-5p, MIR196B-5p, and MIR92A-5p to be increased in tumor tissues, whereas levels of MIR143-3p, MIR204-5p, and MIR133-3p were decreased in tumor tissues. Levels of MIR143-3p were reduced not only in gastric cancer tissues but also in normal tissues adjacent to tumors in humans and low-grade dysplasia in mice. Transgenic expression of MIR143-3p in gastric cancer cell lines reduced their proliferation and restored their sensitivity to cisplatin. AGS cells with stable transgenic expression of MIR143-3p grew more slowly as xenograft tumors in mice than control AGS cells; tumor growth from AGS cells that expressed MIR143-3p, but not control cells, was sensitive to cisplatin. We identified and validated bromodomain containing 2 (BRD2) as a direct target of MIR143-3p; increased levels of BRD2 in gastric tumors was associated with shorter survival times for patients.ConclusionsIn an analysis of miRNA profiles of gastric tumors from mice and human patients, we identified a conserved signature associated with the early stages of gastric tumorigenesis. Strategies to restore MIR143-3p or inhibit BRD2 might be developed for treatment of gastric cancer.

Project description:The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-level investigation of genotype-phenotype relationships in a wide range of systems. Since mouse is an important model organism for biomedical research on human disease, it is of great interest to identify similarities and differences in the functional roles of human and mouse orthologous genes. Here, we develop a novel network comparison approach which we demonstrate by comparing two gene-expression data sets from a large number of human and mouse tissues. The method uses weighted topological overlap alongside the recently developed network-decomposition method of s-core analysis, which is suitable for making gene-centrality rankings for weighted networks. The aim is to identify globally central genes separately in the human and mouse networks. By comparing the ranked gene lists, we identify genes that display conserved or diverged centrality-characteristics across the networks. This framework only assumes a single threshold value that is chosen from a statistical analysis, and it may be applied to arbitrary network structures and edge-weight distributions, also outside the context of biology. When conducting the comparative network analysis, both within and across the two species, we find a clear pattern of enrichment of transcription factors, for the homeobox domain in particular, among the globally central genes. We also perform gene-ontology term enrichment analysis and look at disease-related genes for the separate networks as well as the network comparisons. We find that gene ontology terms related to regulation and development are generally enriched across the networks. In particular, the genes FOXE3, RHO, RUNX2, ALX3 and RARA, which are disease genes in either human or mouse, are on the top-10 list of globally central genes in the human and mouse networks.

Project description:Gastric cancer (GC) has a high incidence worldwide, and when detected, the majority of patients have already progressed to advanced stages. Long non-coding RNAs (lncRNAs) have a wide range of biological functions and affect tumor occurrence and development. However, the potential role of lncRNAs in GC diagnosis remains unclear. We selected five high-quality samples from each group of chronic non-atrophic gastritis, gastric mucosal intraepithelial neoplasia, and GC tissues for analysis. RNA-seq was used to screen the differentially expressed lncRNAs, and we identified 666 differentially expressed lncRNAs between the chronic non-atrophic gastritis and GC groups, 13 differentially expressed lncRNAs between the gastric mucosal intraepithelial neoplasia and GC groups, and 507 differentially expressed lncRNAs between the chronic non-atrophic gastritis and gastric mucosal intraepithelial neoplasia groups. We also identified six lncRNAs (lncRNA H19, LINC00895, lnc-SRGAP2C-16, lnc-HLA-C-2, lnc-APOC1-1, and lnc-B3GALT2-1) which not only differentially expressed between the chronic non-atrophic gastritis and GC groups, but also differentially expressed between the gastric mucosal intraepithelial neoplasia and GC groups. Furthermore, RT-qPCR was used to verify the differentially co-expressed lncRNAs. LncSEA was used to conduct a functional analysis of differentially expressed lncRNAs. We also predicted the target mRNAs of the differentially expressed lncRNAs through bioinformatics analysis and analyzed targeting correlations between three differentially co-expressed lncRNAs and mRNAs (lncRNA H19, LINC00895, and lnc-SRGAP2C-16). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used to explore the functions of target mRNAs of differentially expressed lncRNAs. In conclusion, our study provides a novel perspective on the potential functions of differentially expressed lncRNAs in GC occurrence and development, indicating that the differentially expressed lncRNAs might be new biomarkers for early GC diagnosis.

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Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

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Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

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