eISSN: 1896-9151
ISSN: 1734-1922
Archives of Medical Science
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SCImago Journal & Country Rank
2/2020
vol. 16
 
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abstract:
Basic research

Identification of key pathways and genes in nonalcoholic fatty liver disease using bioinformatics analysis

Jingqi Liu
,
Bogeng Lin
,
Zhiqing Chen
,
Manxiang Deng
,
Ye Wang
,
Jisu Wang
,
Luling Chen
,
Zhenyu Zhang
,
Xueling Xiao
,
Chunlin Chen
,
Yang Song

Arch Med Sci 2020; 16 (2): 374–385
Online publish date: 2020/03/02
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Introduction
Nonalcoholic fatty liver disease (NAFLD) is one of the most common types of liver disease in the world. However, the molecular mechanisms regulating the development of NAFLD have remained unclear.

Material and methods
In the present study, we analyzed two public datasets (GSE48452 and GSE89632) to identify differentially expressed mRNAs in the progression of NAFLD. Next, we performed bioinformatics analysis to explore key pathways underlying NAFLD development.

Results
Gene Ontology (GO) analysis showed that differentially expressed genes (DEGs) were mainly involved in regulating a series of metabolism-related pathways (including proteolysis and lipid metabolism), cell proliferation and adhesion, the inflammatory response, and the immune response. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that DEGs in NAFLD were mainly enriched in the insulin signaling pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway, and p53 signaling pathway. We also constructed protein-protein interaction (PPI) networks for these DEGs. Interestingly, we observed that key hub nodes in PPI networks were also associated with the progression of hepatocellular carcinoma (HCC).

Conclusions
Taken together, our analysis revealed that a series of pathways, such as metabolism and PPAR signaling pathways, were involved in NAFLD development. Moreover, we observed that many DEGs in NAFLD were also dysregulated in HCC. Although further validation is still needed, we believe this study could provide useful information to explore the potential candidate biomarkers for diagnosis, prognosis, and drug targets of NAFLD.

keywords:

differentially expressed genes, protein-protein interaction network, nonalcoholic fatty liver disease, expression profiling, bioinformatics analysis

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