Abstract
BackgroundGastric Carcinoma is one of the most lethal cancer around the world, and is also the most common cancers in Eastern Asia. A lot of differentially expressed genes have been detected as being associated with Gastric Carcinoma (GC) progression, however, little is known about the underlying dysfunctional regulation mechanisms. To address this problem, we previously developed a differential networking approach that is characterized by involving differential coexpression analysis (DCEA), stage-specific gene regulatory network (GRN) modelling and differential regulation networking (DRN) analysis.ResultIn order to implement differential networking meta-analysis, we developed a novel framework which integrated the following steps. Considering the complexity and diversity of gastric carcinogenesis, we first collected three datasets (GSE54129, GSE24375 and TCGA-STAD) for Chinese, Korean and American, and aimed to investigate the common dysregulation mechanisms of gastric carcinogenesis across racial groups. Then, we constructed conditional GRNs for gastric cancer corresponding to normal and carcinoma, and prioritized differentially regulated genes (DRGs) and gene links (DRLs) from three datasets separately by using our previously developed differential networking method. Based on our integrated differential regulation information from three datasets and prior knowledge (e.g., transcription factor (TF)-target regulatory relationships and known signaling pathways), we eventually generated testable hypotheses on the regulation mechanisms of two genes, XBP1 and GIF, out of 16 common cross-racial DRGs in gastric carcinogenesis.ConclusionThe current cross-racial integrative study from the viewpoint of differential regulation networking provided useful clues for understanding the common dysfunctional regulation mechanisms of gastric cancer progression and discovering new universal drug targets or biomarkers for gastric cancer.
Highlights
Gastric Carcinoma is one of the most lethal cancer around the world, and is the most common cancers in Eastern Asia
It was noticed that the above studies always collected Gastric Carcinoma (GC) samples from a certain racial group such as Chinese [7, 12, 16], Korean [8], and American [14, 17], respectively, while the common dysregulation mechanisms of gastric carcinogenesis across racial groups has been paid little attention due to lack of integration research based on crossracial GC datasets
Construction of gene regulatory network (GRN) and identification of Differentially regulated gene (DRG) and Differentially regulated gene link (DRL) Based on the expression data of the selected Differential co-expression gene (DCG) sets, we built paired conditional GRNs (Fig. 2) respectively corresponding to Chinese (GSE54129), Korean (GSE24375) and American (TCGA-stomach adenocarcinoma (STAD)) by using the method described in the section of Materials and methods
Summary
Gastric Carcinoma is one of the most lethal cancer around the world, and is the most common cancers in Eastern Asia. A lot of differentially expressed genes have been detected as being associated with Gastric Carcinoma (GC) progression, little is known about the underlying dysfunctional regulation mechanisms. To address this problem, we previously developed a differential networking approach that is characterized by involving differential coexpression analysis (DCEA), stage-specific gene regulatory network (GRN) modelling and differential regulation networking (DRN) analysis. We designed and implemented a differential co-expression analysis (DCEA) approach called DCGL to recognize differential co-expression genes (DCGs) and links (DCLs) in a link-based quantitative way [27,28,29] Based on this methodology, we further developed a differential regulation networking (DRN) framework [30, 31], which built conditional gene regulatory network (GRN) or combinatorial GRN (cGRN) and prioritized differentially regulated genes (DRGs) and links (DRLs). Our DRN strategy proves to substantially reduce the computational burden and leads to insightful comments on selecting subject related genes and their differential regulation mechanisms underlying phenotypic changes
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