Identification of Hub Genes Associated With Non-alcoholic Steatohepatitis Using Integrated Bioinformatics Analysis

Abstract

Background and aims: As a major cause of liver disease worldwide, non-alcoholic fatty liver disease (NAFLD) comprises non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH). Due to the high prevalence and poor prognosis of NASH, it is critical to understand its mechanisms. However, the etiology and mechanisms remain largely unknown. In addition, the gold standard for the diagnosis of NASH is liver biopsy, which is an invasive procedure. Therefore, there is a pressing need to develop noninvasive tests for NASH diagnosis. The goal of the study is to discover key genes involved in NASH development and investigate their value as noninvasive biomarkers.Methods: The Gene Expression Omnibus (GEO) database was used to obtain two datasets encompassing NASH patients and healthy controls. We used weighted gene co-expression network analysis (WGCNA) and differential expression analysis in order to investigate the association between gene sets and clinical features, as well as to discover co-expression modules. A protein-protein interaction (PPI) network was created to extract hub genes. The results were validated using another publicly available dataset and mice treated with a high-fat diet (HFD) and carbon tetrachloride (CCl4).Results: A total of 24 differentially co-expressed genes were selected by WGCNA and differential expression analysis. KEGG analysis indicated most of them were enriched in the focal adhesion pathway. GO analysis showed these genes were mainly enriched in circadian rhythm, aging, angiogenesis and response to drug (biological process), endoplasmic reticulum lumen (cellular component), and protein binding (molecular function). As a result, eight genes (JUN, SERPINE1, GINS2, TYMS, HMMR, IGFBP2, BIRC3, TNFRSF12A) were identified as hub genes. Finally, three genes were found significantly changed in both the validation dataset and the mouse model.Conclusion: Our research discovered genes that have the potential to mediate the process of NASH and might be useful diagnostic biomarkers for the disorder.

Exploring Key Genes with Diagnostic Value for Nonalcoholic Steatohepatitis Based on Bioinformatics Analysis

“…17−20 Importantly, recent studies highlighted GINS2 as the hub gene involved in NASH. 9,21,22 GINS2-overexpressed HCC patients showed poorer overall survival. 23 It has been documented that decreased RFC3 can limit the multiplication of cancer cells.…”

“…GINS2, a member of the GINS family, exerts a crucial role in DNA duplication, and its overexpression causes unfavorable outcomes in diverse tumors, such as non-small-cell lung cancer (NSCLC), breast cancer, HCC, and cervical cancer. − Importantly, recent studies highlighted GINS2 as the hub gene involved in NASH. ,, GINS2-overexpressed HCC patients showed poorer overall survival …”

Comparative Proteomic Analysis of Liver Tissues and Serum in db/db Mice

“…Moreover, they found that LIF attenuated liver steatosis via binding to LIFR and activating the STAT3 pathway, which provided a rationale for LIF–LIFR to be a potential therapeutic target for NAFLD treatment. Insulin-like growth factor-binding protein 2 (IGFBP2) is one of six proteins that bind to insulin-like growth factor (IGF) and exert influence on regulating glucose and lipid metabolism [ 80 ]. Stanley et al [ 81 ] discovered a negative association between the hepatic IGFBP2 mRNA levels and the grades of liver steatosis in NAFLD patients.…”

“…In addition, Fahlbusch et al [ 82 ] and Yang et al [ 83 ] reported that circulating IGFBP2 levels were lower among obese NAFLD patients compared with that of healthy controls whereas weight loss restored the plasma IGFBP2 level accompanied by a downregulation of fatty liver contents [ 82 ]. A recent study has also indicated that hepatic IGFBP2 mRNA levels were lower in NASH patients compared with healthy subjects [ 80 ]. These results suggest IGFBP2 is a potential novel non-invasive biomarker for NAFLD.…”

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

“…High-dimensional data from various sources should be integrated to develop computational methods for large datasets while reducing the cost of omics analysis. Bioinformatics analysis is increasingly applied to diffuse liver diseases, and such studies may provide useful information to explore the potential diagnosis, prognosis, and candidate drug target biomarkers of these diseases [5,6].…”