Microarray-based Detection of Critical Overexpressed Genes in the Progression of Hepatic Fibrosis in Non-alcoholic Fatty Liver Disease: A Protein-protein Interaction Network Analysis.

Non-alcoholic fatty liver disease (NAFLD) is a prevalent cause of chronic liver disease and encompasses a broad spectrum of disorders, including simple steatosis, steatohepatitis, fibrosis, cirrhosis, and liver cancer. However, due to the global epidemic of NAFLD, where invasive liver biopsy is the gold standard for diagnosis, it is necessary to identify a more practical method for early NAFLD diagnosis with useful therapeutic targets; as such, molecular biomarkers could most readily serve these aims. To this end, we explored the hub genes and biological pathways in fibrosis progression in NAFLD patients. Raw data from microarray chips with GEO accession GSE49541 were downloaded from the Gene Expression Omnibus database, and the R package (Affy and Limma) was applied to investigate differentially expressed genes (DEGs) involved in the progress of low- (mild 0-1 fibrosis score) to high- (severe 3-4 fibrosis score) fibrosis stage NAFLD patients. Subsequently, significant DEGs with pathway enrichment were analyzed, including gene ontology (GO), KEGG and Wikipathway. In order to then explore critical genes, the protein-protein interaction network (PPI) was established and visualized using the STRING database, with further analysis undertaken using Cytoscape and Gephi software. Survival analysis was undertaken to determine the overall survival of the hub genes in the progression of NAFLD to hepatocellular carcinoma. A total of 311 significant genes were identified, with an expression of 278 being upregulated and 33 downregulated in the high vs. low group. Gene functional enrichment analysis of these significant genes demonstrated major involvement in extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, and the AGE-RAGE signaling pathway. The PPI network was constructed with 196 nodes and 572 edges with PPI enrichment using a p-value < 1.0 e-16. Based on this cut-off, we identified 12 genes with the highest score in four centralities: Degree, Betweenness, Closeness, and Eigenvector. Those twelve hub genes were CD34, THY1, CFTR, COL3A1, COL1A1, COL1A2, SPP1, THBS1, THBS2, LUM, VCAN, and VWF. Four of these hub genes, namely CD34, VWF, SPP1, and VCAN, showed significant association with the development of hepatocellular carcinoma. This PPI network analysis of DEGs identified critical hub genes involved in the progression of fibrosis and the biological pathways through which they exert their effects in NAFLD patients. Those 12 genes offer an excellent opportunity for further focused research to determine potential targets for therapeutic applications.

Mahmoudi A ，Butler AE ，De Vincentis A ，Jamialahmadi T ，Sahebkar A ... -  《-》

Bioinformatics analysis reveals novel core genes associated with nonalcoholic fatty liver disease and nonalcoholic steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) is the most frequent liver disease and associated with a wide spectrum of hepatic disorders ranging from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). NASH is projected to become the most common indication for liver transplantation, and the annual incidence rate of NASH-related HCC is 5.29 cases per 1000 person-years. Owing to the epidemics of NAFLD and the unclear mechanism of NAFLD progression, it is important to elucidate the underlying NAFLD mechanisms in detail. NASH is mainly caused by the development of NAFL Therefore, it is also of great significance to understand the mechanism of progression from NAFL to NASH. Gene expression chip data for NAFLD and NASH were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between NAFLD and normal controls (called DEGs for NAFLD), as well as between NASH and normal tissue (called DEGs for NASH-Normal), and between NASH and NAFL tissue (called DEGs for NASH-NAFL). For DEGs for the NAFLD group, key genes were identified by studying the form of intersection. Potential functions of DEGs for NASH were then analyzed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A protein-protein interaction network (PPI) was constructed using the STRING database. A total of 249 DEGs and one key gene for NAFLD were identified. For NASH-Normal, 514 DEGs and 11 hub genes were identified, three of which were closely related to the survival analysis of HCC, and potentially closely related to progression from NASH to HCC. One key gene for NASH-NAFL (AKR1B10) was identified. These genes appear to mediate the molecular mechanism underlying NAFLD and may be promising biomarkers for the presence of NASH.

Feng G ，Li XP ，Niu CY ，Liu ML ，Yan QQ ，Fan LP ，Li Y ，Zhang KL ，Gao J ，Qian MR ，He N ，Mi M ... -  《-》

[Analysis of hub genes and molecular mechanisms in non-alcoholic steatohepatitis based on the gene expression omnibus database].

Li LY ，Wu JX 《-》

The role of differentially expressed genes and immune cell infiltration in the progression of nonalcoholic steatohepatitis (NASH) to hepatocellular carcinoma (HCC): a new exploration based on bioinformatics analysis.

Liu Y ，Yu X ，Wang Y ，Wu J ，Feng B ，Li M ... -  《-》

Diagnostic role of SPP1 and collagen IV in a rat model of type 2 diabetes mellitus with MASLD.

Type 2 diabetes mellitus combined with metabolic dysfunction-associated steatotic liver disease (MASLD) leads to an increasing incidence of liver injury year by year, and patients are at a significantly higher risk of developing cirrhosis or even liver failure. No drugs have emerged to specifically treat this disease. The aim of this study is to investigate the mechanisms and causative hub genes of type 2 diabetes combined with MASLD. The data were obtained through the GEO platform for bioinformatics analysis and validated by in vitro experiments to find the causative targets of type 2 diabetes mellitus combined with MASLD, which will provide some theoretical basis for the development of future therapeutic drugs. GSE23343 and GSE49541 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) in type 2 diabetes mellitus combined with MASLD for functional enrichment analysis. And STRING database and Cytoscape software were used to construct Protein-Protein Interaction (PPI) and hub gene networks. And GO (gene ontology, GO) analysis and KEGG (Kyoto encyclopedia of genes and genomes, KEGG) enrichment analysis were performed on target genes. A total of 185 co-expressed DEGs were obtained by differential analysis, and 20 key genes involved in the development and progression of type 2 diabetes were finally screened. These 20 key genes were involved in 529 GO enrichment results and 20 KEGG enrichment results, and were mainly associated with ECM-receptor interaction, Focal adhesion, Human papillomavirus infection, PI3K-Akt signaling pathway, and the Toll-like receptor signaling pathway. A total of two target genes (SPP1, collagen IV) were found to be highly correlated with type 2 diabetes mellitus combined with MASLD. Real time PCR results showed that there was a significant difference in SPP1 and collagen IV mRNA expression among the three groups (P < 0.05). SPP1 and Collagen IV may be candidate biomarkers for type 2 diabetes mellitus combined with MASLD, as verified by bioinformatics screening and in vitro experiments. Our findings provide new targets for the treatment of type 2 diabetes combined with MASLD.

Xiao S ，Wang XB ，Yang Y ，Wang Q ... -  《Scientific Reports》