Analysis of the Genetic Relationship between Atherosclerosis and Non-Alcoholic Fatty Liver Disease through Biological Interaction Networks.
Francisco Andújar-VeraMaría Ferrer-MillánCristina García-FontanaBeatriz García-FontanaSheila González-SalvatierraRaquel Sanabria-de la TorreLuis Martínez-HerediaBlanca Riquelme GallegoManuel Munoz-TorresPublished in: International journal of molecular sciences (2023)
Non-alcoholic fatty liver disease (NAFLD) seems to have some molecular links with atherosclerosis (ATH); however, the molecular pathways which connect both pathologies remain unexplored to date. The identification of common factors is of great interest to explore some therapeutic strategies to improve the outcomes for those affected patients. Differentially expressed genes (DEGs) for NAFLD and ATH were extracted from the GSE89632 and GSE100927 datasets, and common up- and downregulated DEGs were identified. Subsequently, a protein-protein interaction (PPI) network based on the common DEGs was performed. Functional modules were identified, and the hub genes were extracted. Then, a Gene Ontology (GO) and pathway analysis of common DEGs was performed. DEGs analysis in NAFLD and ATH showed 21 genes that were regulated similarly in both pathologies. The common DEGs with high centrality scores were ADAMTS1 and CEBPA which appeared to be down- and up-regulated in both disorders, respectively. For the analysis of functional modules, two modules were identified. The first one was oriented to post-translational protein modification, where ADAMTS1 and ADAMTS4 were identified, and the second one mainly related to the immune response, where CSF3 was identified. These factors could be key proteins with an important role in the NAFLD/ATH axis.
Keyphrases
- protein protein
- genome wide
- bioinformatics analysis
- genome wide identification
- small molecule
- immune response
- network analysis
- end stage renal disease
- cardiovascular disease
- transcription factor
- newly diagnosed
- ejection fraction
- chronic kidney disease
- genome wide analysis
- metabolic syndrome
- dendritic cells
- insulin resistance