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Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis.

ABSTRACT: Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analysis. We first applied DCEA to a gene expression dataset of gastric normal mucosa, adenoma and carcinoma samples to identify gene interconnection changes during cancer progression, based on which we inferred normal, adenoma, and carcinoma-specific gene regulation networks by using linear regression model. It was observed that cancer genes and drug targets were enriched in each network. To investigate the dynamic changes of gene regulation during carcinogenesis, we then designed two quantitative methods to prioritize differentially regulated genes (DRGs) and gene pairs or links (DRLs) between adjacent stages. It was found that known cancer genes and drug targets are significantly higher ranked. The top 4% normal vs. adenoma DRGs (36 genes) and top 6% adenoma vs. carcinoma DRGs (56 genes) proved to be worthy of further investigation to explore their association with gastric cancer. Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. adenoma and adenoma vs. carcinoma comparisons, 15 have been reported to be gastric cancer or cancer related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of GATA6, ESRRG and their signaling pathways in gastric carcinogenesis. Compared with established approaches which build genome-scale GRNs, or sub-networks around differentially expressed genes, the present one proved to be better at enriching cancer genes and drug targets, and prioritizing disease-related genes on the dataset we considered. We propose this extendable differential networking framework as a promising way to gain insights into gene regulatory mechanisms underlying cancer progression and other phenotypic changes.

SUBMITTER: Cao MS

PROVIDER: S-EPMC4633893 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis.

Cao Mu-Shui MS Liu Bing-Ya BY Dai Wen-Tao WT Zhou Wei-Xin WX Li Yi-Xue YX Li Yuan-Yuan YY

American journal of cancer research 20150815 9

Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analy ...[more]

PMID: 26609471

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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:In the conventional analysis of complex diseases, the control and case samples are assumed to be of great purity. However, due to the heterogeneity of disease samples, many disease genes are even not always consistently up-/down-regulated, leading to be under-estimated. This problem will seriously influence effective personalized diagnosis or treatment. The expression variance and expression covariance can address such a problem in a network manner. But, these analyses always require multiple samples rather than one sample, which is generally not available in clinical practice for each individual. To extract the common and specific network characteristics for individual patients in this paper, a novel differential network model, e.g. personalized dysfunctional gene network, is proposed to integrate those genes with different features, such as genes with the differential gene expression (DEG), genes with the differential expression variance (DEVG) and gene-pairs with the differential expression covariance (DECG) simultaneously, to construct personalized dysfunctional networks. This model uses a new statistic-like measurement on differential information, i.e., a differential score (DEVC), to reconstruct the differential expression network between groups of normal and diseased samples; and further quantitatively evaluate different feature genes in the patient-specific network for each individual. This DEVC-based differential expression network (DEVC-net) has been applied to the study of complex diseases for prostate cancer and diabetes. (1) Characterizing the global expression change between normal and diseased samples, the differential gene networks of those diseases were found to have a new bi-coloured topological structure, where their non hub-centred sub-networks are mainly composed of genes/proteins controlling various biological processes. (2) The differential expression variance/covariance rather than differential expression is new informative sources, and can be used to identify genes or gene-pairs with discriminative power, which are ignored by traditional methods. (3) More importantly, DEVC-net is effective to measure the expression state or activity of different feature genes and their network or modules in one sample for an individual. All of these results support that DEVC-net indeed has a clear advantage to effectively extract discriminatively interpretable features of gene/protein network of one sample (i.e. personalized dysfunctional network) even when disease samples are heterogeneous, and thus can provide new features like gene-pairs, in addition to the conventional individual genes, to the analysis of the personalized diagnosis and prognosis, and a better understanding on the underlying biological mechanisms.

Project description:Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. In pioneer studies, disease similarity was commonly estimated according to clinical manifestation. Subsequently, scientists started to investigate disease similarity based on gene-phenotype knowledge, which were inevitably biased to well-studied diseases. In recent years, estimating disease similarity according to transcriptomic behavior significantly enhances the probability of finding novel disease relationships, while the currently available studies usually mine expression data through differential expression analysis that has been considered to have little chance of unraveling dysfunctional regulatory relationships, the causal pathogenesis of diseases.We developed a computational approach to measure human disease similarity based on expression data. Differential coexpression analysis, instead of differential expression analysis, was employed to calculate differential coexpression level of every gene for each disease, which was then summarized to the pathway level. Disease similarity was eventually calculated as the partial correlation coefficients of pathways' differential coexpression values between any two diseases. The significance of disease relationships were evaluated by permutation test.Based on mRNA expression data and a differential coexpression analysis based method, we built a human disease network involving 1326 significant Disease-Disease links among 108 diseases. Compared with disease relationships captured by differential expression analysis based method, our disease links shared known disease genes and drugs more significantly. Some novel disease relationships were discovered, for example, Obesity and cancer, Obesity and Psoriasis, lung adenocarcinoma and S. pneumonia, which had been commonly regarded as unrelated to each other, but recently found to share similar molecular mechanisms. Additionally, it was found that both the type of disease and the type of affected tissue influenced the degree of disease similarity. A sub-network including Allergic asthma, Type 2 diabetes and Chronic kidney disease was extracted to demonstrate the exploration of their common pathogenesis.The present study produces a global view of human diseasome for the first time from the viewpoint of regulation mechanisms, which therefore could provide insightful clues to etiology and pathogenesis, and help to perform drug repositioning and design novel therapeutic interventions.

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Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis.

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Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis.

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