Dataset Information

Identification of Autophagy-Related Genes in the Progression from Non-Alcoholic Fatty Liver to Non-Alcoholic Steatohepatitis.

ABSTRACT:

Background

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Autophagy plays a vital role in NAFLD development and progression. We aimed to establish a novel autophagy-related gene (ARG) signature as a therapeutic target in NAFLD patients based on high-throughput sequencing data.

Methods

ARGs obtained from the HAMdb and high-sequencing data obtained from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed ARGs (DEARGs) between normal and NASH tissues. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore potential biological and pathological functions of DEARGs. The protein-protein interaction (PPI) network of the DEARGs was established through the STRING website, and visualized by Cytoscape. In addition, hub genes were validated by an independent dataset GSE89632. Finally, we performed Gene Set Variation Analysis (GSVA) pathway-related analysis to identify the pivotal signaling pathways and genes for the progression of non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH).

Results

A total of 76 DEARGs were identified in the GSE126848 dataset, of which 45 genes were upregulated and 31 genes were downregulated. GO analysis showed that the biological functions of DEARGs focused primarily on autophagy, cellular response to external stimulus, fibroblast proliferation, late endosome, and ubiquitin protein ligase binding. KEGG pathway analysis showed that these DEARGs were mainly involved in the apoptosis, PI3K-Akt signaling pathway, and estrogen signaling pathway. Among DEGs, 9 most closely related genes were identified from the PPI network. Furthermore, NOS3, IGF1, VAMP8, FOS, and HMOX1 were verified in the GSE89632 dataset. At last, the MAPK signal pathway was identified as important pathway, and JUN was identified as a key gene involved in the progression from NAFL to NASH.

Conclusion

This study may provide credible molecular biomarkers in terms of screening and diagnosis for NAFLD. Meanwhile, it also serves as a basis for exploring the molecular mechanisms underlying the progression of NAFL to NASH.

SUBMITTER: Ma M

PROVIDER: S-EPMC8275104 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStudies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear.MethodsThe NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks.ResultsThere were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1.ConclusionThe study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.