Bioinformatics screening and analysis of key differentially expressed genes characteristics in nonalcoholic fatty liver disease.
10.3760/cma.j.cn501113-20210525-00251
- VernacularTitle:生物信息学筛选和分析非酒精性脂肪性肝病特征差异表达关键基因
- Author:
Jie Xia DING
1
;
Wen Bao HUANG
1
;
Xiao Xian JIANG
1
;
Li Dan ZHANG
1
;
Hong FANG
1
;
Jie JIN
1
Author Information
1. Department of Infectious Diseases, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
- Publication Type:Journal Article
- MeSH:
Computational Biology/methods*;
Gene Expression Profiling/methods*;
Gene Regulatory Networks;
Humans;
Non-alcoholic Fatty Liver Disease/genetics*;
Protein Interaction Maps/genetics*
- From:
Chinese Journal of Hepatology
2022;30(3):297-303
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To screen and analyze the key differentially expressed genes characteristics in nonalcoholic fatty liver disease (NAFLD) with bioinformatics method. Methods: NAFLD-related expression matrix GSE89632 was downloaded from the GEO database. Limma package was used to screen differentially expressed genes (DEGs) in healthy, steatosis (SS), and nonalcoholic steatohepatitis (NASH) samples. WGCNA was used to analyze the output gene module. The intersection of module genes and differential genes was used to determine the differential genes characteristic, and then GO function and KEGG signaling pathway enrichment analysis were performed. The protein-protein interaction network (PPI) was constructed using the online website STRING and Cytoscape software, and the key (Hub) genes were screened. Finally, R software was used to analyze the receiver operating characteristic curve (ROC) of the Hub gene. Results: 92 differentially expressed genes characteristic were obtained through screening, which were mainly enriched in inflammatory response-related functions of "lipopolysaccharide response and molecular response of bacterial origin", as well as cancer signaling pathways of "proteoglycan in cancer" and "T-cell leukemia virus infection-related". 10 hub genes (FOS, CXCL8, SERPINE1, CYR61, THBS1, FOSL1, CCL2, MYC, SOCS3 and ATF3) had good diagnostic value. Conclusion: The differentially expressed hub genes among the 10 NAFLD disease-related characteristics obtained with bioinformatics analysis may become a diagnostic and prognostic marker and potential therapeutic target for NAFLD. However, further basic and clinical studies are needed to validate.
