Bioinformatics analysis of differentially expressed genes in non alcoholic fatty liver disease using next generation sequencing data

Abstract

Non alcoholic fatty liver disease (NAFLD) is the most common metabolic disease in humans, affecting the majority of individuals. In the current investigation, we aim to identify potential key genes linked with NAFLD through bioinformatics analyses of next generation sequencing (NGS) dataset. NGS dataset of GSE135251 from the Gene Expression Omnibus (GEO) database were retrieved. Differentially expressed genes (DEGs) were obtained by DESeq2 package. g:Profiler database was further used to identify the potential gene ontology (GO) and REACTOME pathways. Protein-protein interaction (PPI) network was constructed using the Hippie interactome database. miRNet and NetworkAnalyst databases were used to establish a miRNA-hub gene regulatory network and TF-hub gene regulatory network for the hub genes. Hub genes were verified based on receiver operating characteristic curve (ROC) analysis. Totally, 951 DEGs were identified including 476 up regulated genes and 475 down regulated genes screened in NAFLD and normal control. GO showed that DEGs were significantly enhanced for signaling and regulation of biological quality. REACTOME pathway analysis revealed that DEGs were enriched in signaling by interleukins and extracellular matrix organization. ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 were indicated as hub genes from PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, ROC analysis revealed that ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 might serve as diagnostic biomarkers in NAFLD. The current investigation provided insights into the molecular mechanism of NAFLD that might be useful in further investigations.

Differential methylation patterns in lean and obese non-alcoholic steatohepatitis-associated hepatocellular carcinoma

“…The corresponding genes are involved in physiological process including cell signaling pathways, catabolic processes, and protein translation [11,12]. Five hypermethylated regions were in genes previously associated with cancer (TRAP1, SLC38A3, CHRM1, EDN2, and PROX1), while six hypomethylated regions were in genes previously associated with cancer (NUMBL, ALDH1B1, FTCD, FASTKD2, FAM96A, and ARHGAP15) [20][21][22][23][24][25][26][27][28][29][30]. Five differentially methylated regions were in genes previously found to be associated with obesity and altered metabolic states (TRAP1, SLC38A3, PROX1, ALDH1B1, and FAM96A) [31][32][33][34][35].…”