Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis

Abstract

IntroductionNon-alcoholic steatohepatitis (NASH), an advanced subtype of non-alcoholic fatty liver disease (NAFLD), has becoming the most important aetiology for end-stage liver disease, such as cirrhosis and hepatocellular carcinoma. This study were designed to explore novel genes associated with NASH.MethodsHere, five independent Gene Expression Omnibus (GEO) datasets were combined into a single cohort and analyzed using network biology approaches.Results11 modules identified by weighted gene co-expression network analysis (WGCNA) showed significant association with the status of NASH. Further characterization of four gene modules of interest demonstrated that molecular pathology of NASH involves the upregulation of hub genes related to immune response, cholesterol and lipid metabolic process, extracellular matrix organization, and the downregulation of hub genes related to cellular amino acid catabolic, respectively. After DEGs enrichment analysis and module preservation analysis, the Turquoise module associated with immune response displayed a remarkably correlation with NASH status. Hub genes with high degree of connectivity in the module, including CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, PLEK, FCER1G, HLA-DRA and SRGN were further verified in clinical samples and mouse model of NASH. Moreover, single-cell RNA-seq analysis showed that those key genes were expressed by distinct immune cells such as microphages, natural killer, dendritic, T and B cells. Finally, the potential transcription factors of Turquoise module were characterized, including NFKB1, STAT3, RFX5, ILF3, ELF1, SPI1, ETS1 and CEBPA, the expression of which increased with NASH progression.DiscussionIn conclusion, our integrative analysis will contribute to the understanding of NASH and may enable the development of potential biomarkers for NASH therapy.

Machine learning-based integration identifies the ferroptosis hub genes in nonalcoholic steatohepatitis

“…The model was compared with previously published NASH-related biomarkers. In total, 43 NASH diagnostic genes were identified in nine previous studies [ 24 – 32 ]. Notably, FeRS also exhibited good predictive performance (Fig.…”

Integrative analysis identifies oxidative stress biomarkers in non-alcoholic fatty liver disease via machine learning and weighted gene co-expression network analysis

“…Transcription factors such as SPI1, ETS1, and CEBPA have been identified as promising targets for the prevention and treatment of NASH ( 64 ). These transcription factors are integral components of a complex regulatory network involving TF-miRNA-mRNA interactions, highlighting the sophisticated molecular interplay underlying NASH pathogenesis.…”

Bioinformatics based analysis of the underlying comorbidity mechanisms of non-alcoholic steatohepatitis and primary biliary cholangitis

“…Our study found that CEBPA/B is key comorbidities transcription factor for NASH and PBC, which is consistent with other studies. Zhang et al [19]found that CEBPA is one of the key transcription factors in NASH, and increases with the progression of NASH. Frietze et al [20]found that CEBPB participates in autophagy activated by DDX58, alleviates in ammation and apoptosis caused by lipid toxicity, and delays the progression of NASH.…”