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Abstract
Background Gastric 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. Result In 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. Conclusion The 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. Electronic supplementary material The online version of this article (10.1186/s12918-018-0564-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wentao Dai
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China.,Shanghai Engineering Research Center of Pharmaceutical Translation & Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China
| | - Quanxue Li
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China.,School of biotechnology, East China University of Science and Technology, Shanghai, 200237, China
| | - Bing-Ya Liu
- Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China
| | - Yi-Xue Li
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China. .,School of biotechnology, East China University of Science and Technology, Shanghai, 200237, China. .,Shanghai Engineering Research Center of Pharmaceutical Translation & Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China. .,Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Yuan-Yuan Li
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China. .,School of biotechnology, East China University of Science and Technology, Shanghai, 200237, China. .,Shanghai Engineering Research Center of Pharmaceutical Translation & Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China. .,Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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