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Background: The mechanisms of Gastric cancer (GC) initiation and progression are complicated, at least partly owing to the dynamic changes of gene regulation during carcinogenesis. Thus, investigations on the changes in regulatory networks can improve the understanding of cancer development and provide novel insights into the molecular mechanisms of cancer.
Methods: Differential co-expression analysis (DCEA), differential gene regulation network (GRN) modeling and differential regulation analysis (DRA) were integrated to detect differential transcriptional regulation events between gastric normal mucosa and cancer samples based on GSE54129 dataset. Cytological experiments and IHC staining assays were used to validate the dynamic changes of CREB1 regulated targets in different stages.
Results: A total of 1955 differentially regulated genes (DRGs) were identified and prioritized in a quantitative way. Among the top 1% DRGs, 14 out of 19 genes have been reported to be GC relevant. The four transcription factors (TFs) among the top 1% DRGs, including CREB1, BPTF, GATA6 and CEBPA, were regarded as crucial TFs relevant to GC progression. The differentially regulated links (DRLs) around the four crucial TFs were then prioritized to generate testable hypotheses on the differential regulation mechanisms of gastric carcinogenesis. To validate the dynamic alterations of gene regulation patterns of crucial TFs during GC progression, we took CREB1 as an example to screen its differentially regulated targets by using cytological and IHC staining assays. Eventually, TCEAL2 and MBNL1 were proved to be differentially regulated by CREB1 during tumorigenesis of gastric cancer.
Conclusions: By combining differential networking information and molecular cell experiments verification, testable hypotheses on the regulation mechanisms of GC around the core TFs and their top ranked DRLs were generated. Since TCEAL2 and MBNL1 have been reported to be potential therapeutic targets in SCLC and breast cancer respectively, their translation values in GC are worthy of further investigation.
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http://dx.doi.org/10.1186/s10020-022-00468-7 | DOI Listing |
Plant Physiol
December 2024
School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.
MYB family transcription factors (TFs) play crucial roles in plant development, metabolism, and responses to various stresses. However, whether MYB TFs are involved in regulating fatty acid biosynthesis in seeds remains largely elusive. Here, we demonstrated that transgenic Arabidopsis (Arabidopsis thaliana) plants overexpressing MYB73 exhibit altered FATTY ACID ELONGATION1 (FAE1) expression, seed oil content, and seed fatty acid composition.
View Article and Find Full Text PDFPest Manag Sci
December 2024
School of Life Sciences, Chongqing University, Chongqing, China.
Background: The entomopathogenic fungus (EPF) Metarhizium acridum, a typical filamentous fungus, has been utilized for the biological control of migratory locusts (Locusta migratoria manilensis). Fungal-specific transcription factors (TFs) play a crucial role in governing various cellular processes in fungi, although TFs with only the Fungal_trans domain remain poorly understood.
Results: In this study, we identified a unique fungal-specific TF in M.
Front Plant Sci
December 2024
Xinjiang Production and Construction Corps, Shihezi University, Shihezi, China.
KAR (Karrikin), a novel plant growth regulator, can be recognized specifically by plants and can activate resistance responses. MdKAI2 is the natural receptor of KARs in apple. Here, we report the identification of osmotic stress resistance in via the method of genetic transformation.
View Article and Find Full Text PDFMethods Mol Biol
December 2024
Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
In this chapter, we illustrate the utilization of network analysis and mechanistic modeling, two potent branches of systems biology, to simplify the representation of intricate biological processes such as cell signaling, gene regulation, and metabolic pathways. Specifically, we demonstrate the application of a well-established method to generate a microRNA-transcription factor-gene regulatory feed-forward loop network extracted from the GEO dataset GSE163877. Furthermore, we outline a method for constructing a deterministic model using the LSODA method based on the sub-network.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!