Screening and Regulatory Network Analysis of Core MiRNAs in Gastric Cancer Based on Bioinformatics / 胃肠病学

ABSTRACT

Background: MicroRNAs (miRNAs) are negative regulators of gene expression in various eukaryotes and play roles in RNA silencing and post-transcriptional regulation. Gene expression is affected by miRNAs dysregulation in almost all types of malignancies. Aims: To explore the core miRNAs regulatory network of gastric cancer, and to provide a theoretical basis for the analysis of molecular mechanism of miRNAs in the development of gastric cancer. Methods: MiRNAs and mRNA expression profile microarray were used to screen the differentially expressed miRNAs and mRNA. MiRWalk 2.0 was used to predict miRNAs-mRNA interactions, cross-matching with genes were selected by expression profile microarray to define the core differentially expressed miRNAs; and miRNAs-mRNA regulatory network was constructed. GO analysis and KEGG analysis were performed to analyze the targeted gene. Results: Twenty-one up-regulated miRNAs and 36 down-regulated miRNAs in gastric cancer were screened by expression profile microarray. After cross-matching, 1 042 low-expressed genes and 711 high-expressed genes were found, and 10 core miRNAs-mRNA regulatory networks were finally defined. The differentially expressed genes regulated by the core miRNAs were mainly involved in tumor-related pathways, and the high-expressed genes were mainly enriched in 9 signaling pathways such as ECM-receptor interaction, while the low-expressed genes were mainly enriched in 5 signaling pathways such as neuroactive ligand-receptor interaction. GO analysis showed that the up-regulated genes involved 315 functions and the down-regulated genes involved 88 functions, of which extracellular matrix organization was the most relevant. Conclusions: MiRNAs-mRNA regulatory network analysis based on bioinformatics provides a new perspective for gastric cancer research, which helps to systematically elucidate the molecular mechanism of miRNAs in the development of gastric cancer. It could provide a theoretical basis for the screening of biomarkers and the precise target selection for drug treatment of gastric cancer.