A bioinformatics analysis of acute pancreatitis based on gene expression microarray and drug screening
10.3969/j.issn.1001-5256.2020.03.033
- VernacularTitle:急性胰腺炎基因表达谱芯片的生物信息学分析及药物筛选
- Author:
Xiaopeng DONG
1
;
Lijuan WANG
;
Chunlin ZHAO
Author Information
1. School of Clinical Medicine, Gansu University of Chinese Medicine, Lanzhou 730000, China
- Publication Type:Research Article
- Keywords:
pancreatitis, acute necrotizing;
gene microarray;
gene expression;
computational biology
- From:
Journal of Clinical Hepatology
2020;36(3):636-640
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo screen out differentially expressed genes (DEGs) and related candidate therapeutic drugs for acute pancreatitis (AP) using the bioinformatics method. MethodsHigh-throughput microarray datasets (GSE109227 and GSE65146) associated with AP in mice were downloaded from gene expression omnibus, and GEO2R was used to screen out DEGs. Database for Annotation, Visualization and Integrated Discovery was used to perform gene ontology and pathway enrichment analysis. Protein-protein interaction (PPI) was established in String database and visualized by Cytoscape, and then subnetwork modules and hub genes were screened out. The microRNAs associated with the hub genes were predicted and candidate drugs were screened out using Comparative Toxicogenomics Database (CTD). ResultsA total of 130 upregulated and 16 downregulated DEGs were screened out in the high-throughput microarray datasets GSE109227 and GSE65146. DEGs were mainly involved in the biological processes such as inflammatory response, neutrophil chemotaxis, tumor necrosis factor-mediated cellular response, and positive regulation of gene expression, and they were also involved in the signaling pathways of extracellular matrix-receptor interaction, regulation of actin cytoskeleton, leukocyte transendothelial migration, and focal adhesion. A total of 12 hub genes and 6 subnetwork modules were screened out in the PPI network. The microRNAs including miR-199a-5p and miR-1-3p might regulate the post-transcriptional regulation of key genes. Genistein, resveratrol, and quercetin which were screened out from CTD database could reduce the expression of key genes. ConclusionRelated genes screened out by the bioinformatics method may play an important role in the development of AP and can be used as the basis for drug screening.
