UNASSIGNED: @entity356 is one of the most common @entity5 in the world. A large number of differentially expressed genes have been identified as being associated with @entity356 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, @entity2513 samples to identify gene interconnection changes during @entity5 progression, based on which we inferred normal, @entity652 , and @entity189 -specific gene regulation networks by using linear regression model. It was observed that @entity5 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 @entity5 genes and drug targets are significantly higher ranked. The top 4% normal vs. @entity652 DRGs (36 genes) and top 6% @entity652 vs. @entity189 DRGs (56 genes) proved to be worthy of further investigation to explore their association with @entity356 . Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. @entity652 and @entity652 vs. @entity189 comparisons, 15 have been reported to be @entity356 or @entity5 related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of @entity43289 , @entity2260 and their signaling pathways in @entity356 . 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 @entity5 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 @entity5 progression and other phenotypic changes.

Differential network analysis reveals dysfunctional regulatory networks in XXXX .

@entity43289 :: ('2627', 'Gene') :: ['GATA6'] @entity5 :: ('MESH:D009369', 'Disease') :: ['cancers', 'cancer'] @entity2260 :: ('2104', 'Gene') :: ['ESRRG'] @entity189 :: ('MESH:D002277', 'Disease') :: ['carcinoma'] @entity652 :: ('MESH:D000236', 'Disease') :: ['adenoma'] @entity356 :: ('MESH:D013274', 'Disease') :: ['gastric cancer', 'Gastric Carcinoma', 'gastric carcinogenesis'] @entity2513 :: ('MESH:C563365', 'Disease') :: ['adenoma and carcinoma']
@entity356 :: (MESH:D013274,Disease) :: ['gastric carcinogenesis']
